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Xin Zhang

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astro-ph.CO gr-qc hep-ph hep-th math.CO Discrete Mathematics Machine Learning cond-mat.mes-hall cond-mat.mtrl-sci Computer Vision quant-ph math.OC physics.optics Computation and Language math-ph math.MP physics.app-ph Information Theory math.IT cond-mat.stat-mech math.PR Methodology cond-mat.str-el Distributed, Parallel, and Cluster Computing physics.geo-ph q-fin.MF Artificial Intelligence Neural and Evolutionary Computing Applications astro-ph.HE astro-ph.IM Biological Physics math.AP math.NT physics.comp-ph astro-ph cond-mat.supr-con Cryptography and Security eess.AS eess.IV eess.SP eess.SY hep-ex math.DS math.GR Networking and Internet Architecture q-fin.PM Robotics Sound Systems and Control astro-ph.GA Biomolecules Computation cond-mat.quant-gas cs.CY Databases Graphics Human-Computer Interaction math.NA math.SP nucl-th physics.chem-ph physics.soc-ph Social and Information Networks Software Engineering

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preprint2026arXiv

A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization

In recent years, bilevel optimization (BLO) has attracted significant attention for its broad applications in machine learning. However, most existing works on BLO remain confined to the single-task setting and rely on the lower-level strong convexity assumption, which significantly restricts their applicability to modern machine learning problems of growing complexity. In this paper, we make the first attempt to extend BLO to the multi-task setting under a relaxed lower-level general convexity (LLGC) assumption. To this end, we reformulate the multi-task bilevel learning (MTBL) problem with LLGC into an equality constrained multi-objective optimization (ECMO) problem. However, ECMO itself is a new problem that has not yet been studied in the literature. To address this gap, we first establish a new Karush-Kuhn-Tucker (KKT)-based Pareto stationarity as the convergence criterion for ECMO algorithm design. Based on this foundation, we propose a weighted Chebyshev (WC)-penalty algorithm that achieves a finite-time convergence rate of $O(ST^{-\frac{1}{2})$ to KKT-based Pareto stationarity in both deterministic and stochastic settings, where $S$ denotes the number of objectives, and $T$ is the total iterations. Moreover, by varying the preference vector over the $S$-dimensional simplex, our WC-penalty method systematically explores the Pareto front. Finally, solutions to the ECMO problem translate directly into solutions for the original MTBL problem, thereby closing the loop between these two foundational optimization frameworks.

preprint2026arXiv

Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values

Autonomous agents have rapidly matured as task executors and seen widespread deployment via harnesses such as OpenClaw. Safety concerns have rightly drawn growing research attention, and beneath them lie the values silently steering agent behavior. Existing value benchmarks, however, remain confined to LLMs, leaving agent values largely uncharted. From intuitive, empirical, and theoretical vantage points, we show that an agent's values diverge from those of its underlying LLM, and the agentic modality further introduces dataset-, evaluation-, and system-level challenges absent from text-only protocols. We close this gap with Agent-ValueBench, the first benchmark dedicated to agent values. It features 394 executable environments across 16 domains, offering 4,335 value-conflict tasks that cover 28 value systems and 332 dimensions. Every instance is co-synthesized through our purpose-built end-to-end pipeline and curated per-instance by professional psychologists. Each task ships with two pole-aligned golden trajectories whose checkpoints anchor a trajectory-level rubric-based judge. Benchmarking 14 frontier proprietary and open-weights models across 4 mainstream harnesses, we uncover three concerted findings. Agent values first manifest as a Value Tide of cross-model homogeneity beneath interpretable counter-currents. This tide bends non-additively under harness pull, and yet more decisively under deliberate steering via embedded skills. Together these results signal that the agent-alignment lever is shifting from classical model alignment and prompt steering toward harness alignment and skill steering.

preprint2026arXiv

EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization

We present EnergyLens, an end-to-end framework for energy-aware large language model (LLM) inference optimization. As LLMs scale, predicting and reducing their energy footprint has become critical for sustainability and datacenter operations, yet existing approaches either require production-level code and expensive profiling or fail to accurately capture multi-GPU energy behavior. As a result, practitioners lack tools for deciding which optimizations to prioritize and for selecting among existing deployment configurations when exhaustive profiling is impractical. EnergyLens addresses this gap with an intuitive einsum-based interface that captures LLM specifications including fusion, parallelism, and compute-communication overlap, combined with load-imbalance-aware MoE modeling and an empirically driven communication energy model for multi-GPU settings. We validate EnergyLens on Llama3 and Qwen3-MoE across tensor-parallel and expert-parallel configurations, achieving mean absolute percentage errors (MAPEs) between 9.25% and 13.19% for multi-GPU prefill and decode energy, and 12.97% across SM allocations for Megatron-style overlap. Our energy-driven exploration reveals up to 1.47x and 52.9x energy variation across configurations in prefill and decode efficiency and motivates distributed serving. We further show that compute-communication overlap is difficult to optimize with intuition alone, but EnergyLens correctly identifies Pareto-optimal overlap configurations.

preprint2026arXiv

Gravitational wave standard sirens: A brief review of cosmological parameter estimation

Gravitational wave (GW) observations are expected to serve as a powerful and independent probe of the expansion history of the universe. By providing direct and calibration-free measurements of luminosity distances through waveform analysis, GWs provide a fundamentally different and potentially more robust approach to measuring cosmic-scale distances compared to traditional electromagnetic (EM) observations, which is known as the standard siren method. In this review, we present an overview of recent developments in GW standard siren cosmology, including up-to-date $H_0$ constraints, and prospects for constraining cosmological parameters using future GW detections. A central focus of this review is the unique ability of GW observations to break cosmological parameter degeneracies inherent in the EM observations. We also briefly highlight the impact of systematic uncertainties, such as detector calibration, weak lensing, peculiar velocities, and host-galaxy catalog completeness, and corresponding potential mitigation strategies, which currently limit the constraint precision of cosmological parameters. Looking forward, we highlight the importance of combining GW standard sirens with other emerging late-universe cosmological probes such as fast radio bursts, 21 cm intensity mapping, and strong gravitational lensing to forge a precise cosmological probe for exploring the late universe.

preprint2026arXiv

Implicit Graph, Explicit Retrieval: Towards Efficient and Interpretable Long-horizon Memory for Large Language Models

Long-horizon applications increasingly require large language models (LLMs) to answer queries when relevant evidence is sparse and dispersed across very long contexts. Existing memory systems largely follow two paradigms: explicit structured memories offer interpretability but often become brittle under long-context overload, while latent memory mechanisms are efficient and stable yet difficult to inspect. We propose LatentGraphMem, a memory framework that combines implicit graph memory with explicit subgraph retrieval. LatentGraphMem stores a graph-structured memory in latent space for stability and efficiency, and exposes a task-specific subgraph retrieval interface that returns a compact symbolic subgraph under a fixed budget for downstream reasoning and human inspection. During training, an explicit graph view is materialized to interface with a frozen reasoner for question-answering supervision. At inference time, retrieval is performed in latent space and only the retrieved subgraph is externalized. Experiments on long-horizon benchmarks across multiple model scales show that LatentGraphMem consistently outperforms representative explicit-graph and latent-memory baselines, while enabling parameter-efficient adaptation and flexible scaling to larger reasoners without introducing large symbolic artifacts.

preprint2026arXiv

Loupe: A Generalizable and Adaptive Framework for Image Forgery Detection

The proliferation of generative models has raised serious concerns about visual content forgery. Existing deepfake detection methods primarily target either image-level classification or pixel-wise localization. While some achieve high accuracy, they often suffer from limited generalization across manipulation types or rely on complex architectures. In this paper, we propose Loupe, a lightweight yet effective framework for joint deepfake detection and localization. Loupe integrates a patch-aware classifier and a segmentation module with conditional queries, allowing simultaneous global authenticity classification and fine-grained mask prediction. To enhance robustness against distribution shifts of test set, Loupe introduces a pseudo-label-guided test-time adaptation mechanism by leveraging patch-level predictions to supervise the segmentation head. Extensive experiments on the DDL dataset demonstrate that Loupe achieves state-of-the-art performance, securing the first place in the IJCAI 2025 Deepfake Detection and Localization Challenge with an overall score of 0.846. Our results validate the effectiveness of the proposed patch-level fusion and conditional query design in improving both classification accuracy and spatial localization under diverse forgery patterns. The code is available at https://github.com/Kamichanw/Loupe.

preprint2026arXiv

MiMo-V2-Flash Technical Report

We present MiMo-V2-Flash, a Mixture-of-Experts (MoE) model with 309B total parameters and 15B active parameters, designed for fast, strong reasoning and agentic capabilities. MiMo-V2-Flash adopts a hybrid attention architecture that interleaves Sliding Window Attention (SWA) with global attention, with a 128-token sliding window under a 5:1 hybrid ratio. The model is pre-trained on 27 trillion tokens with Multi-Token Prediction (MTP), employing a native 32k context length and subsequently extended to 256k. To efficiently scale post-training compute, MiMo-V2-Flash introduces a novel Multi-Teacher On-Policy Distillation (MOPD) paradigm. In this framework, domain-specialized teachers (e.g., trained via large-scale reinforcement learning) provide dense and token-level reward, enabling the student model to perfectly master teacher expertise. MiMo-V2-Flash rivals top-tier open-weight models such as DeepSeek-V3.2 and Kimi-K2, despite using only 1/2 and 1/3 of their total parameters, respectively. During inference, by repurposing MTP as a draft model for speculative decoding, MiMo-V2-Flash achieves up to 3.6 acceptance length and 2.6x decoding speedup with three MTP layers. We open-source both the model weights and the three-layer MTP weights to foster open research and community collaboration.

preprint2026arXiv

Multi-messenger standard-siren cosmology for third-generation gravitational-wave detectors: forecasts considering observations of gamma-ray bursts and kilonovae

In the third-generation (3G) gravitational-wave (GW) detector era, GW multi-messenger observations for binary neutron star merger events can exert great impacts on exploring the cosmic expansion history. Extending the previous work, we explore the potential of 3G GW standard siren observations in cosmological parameter estimation by considering their associated electromagnetic (EM) counterparts, including $γ$-ray burst (GRB) coincidence observations by the Gravitational wave high-energy Electromagnetic Counterpart All-sky Monitor and GW-triggered target-of-opportunity observations of kilonovae by different optical survey projects. During an assumed 10-year observation, we predict that the number of detectable GW-kilonova events is $\sim 4900$ with redshifts below $\sim 0.4$ under GW network and Large Synoptic Survey Telescope in the $i$ band, which is three times more than that of GW-GRB detections. For the cosmological analysis, we find that with the inclusion of GW-kilonova detections, the constraints on cosmological parameters from GW-EM detections are significantly improved compared to those from GW-GRB detections. In particular, GW-EM detections can tightly constrain the Hubble constant with a precision ranging from $0.076\%$ to $0.034\%$. Moreover, GW multi-messenger observations could effectively break the cosmological parameter degeneracies generated by the mainstream EM observations, CMB+BAO+SN (CBS). The combination of CBS and GW-EM can tightly constrain the equation of state parameters of dark energy $w$ in the $w$CDM model and $w_0$ in the $w_0w_a$CDM model with precisions of $0.72\%$ and $0.99\%$, respectively, meeting the standard of precision cosmology. In conclusion, GW multi-messenger observations could play a crucial role in helping solve the Hubble tension and probing the fundamental nature of dark energy.

preprint2026arXiv

On Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated Learning

The rapid development of low-altitude economy has driven the proliferation of Unmanned Aerial Vehicle (UAV) applications, including logistics, inspection, and emergency response. However, transmitting high-volume image data from UAVs to ground stations faces significant challenges due to limited bandwidth and stringent privacy requirements. To address these issues, a Semantic Communication (SC) framework based on Federated Learning (FL) is proposed for efficient and privacy-preserving image transmission. A Swin Transformer-based Semantic Communication (STSC) architecture is designed to extract multi-scale semantic features under constrained bandwidth conditions. Dedicated communication and computing nodes are deployed on UAVs to enhance real-time coverage and flexibility. Meanwhile, a FL mechanism enables global model training across distributed devices without sharing raw data, thus preserving user privacy. Simulation experiments conducted on the CIFAR-10 dataset demonstrate that the proposed STSC framework achieves at least 5.7 dB improvement in Peak Signal-to-Noise Ratio (PSNR) compared to DeepJSCC baselines, while also showing superior convergence and generalization performance. The framework effectively integrates UAV-assisted deployment with SC and privacy protection, offering a practical solution for bandwidth-constrained image transmission in low-altitude networks.

preprint2026arXiv

On regional treatment effect assessment using robust MAP priors

Bayesian dynamic borrowing has become an increasingly important tool for evaluating the consistency of regional treatment effects which is a key requirement for local regulatory approval of a new drug. It helps increase the precision of regional treatment effect estimate when regional and global data are similar, while guarding against potential bias when they differ. In practice, the two-component mixture prior, of which one mixture component utilizes the power prior to incorporate external data, is widely used. It allows convenient prior specification, analytical posterior computation, and fast evaluation of operating characteristics. Though the robust meta-analytical-predictive (MAP) prior is broadly used with multiple external data sources, it remains underutilized for regional treatment effect assessment (typically only one external data source is available) due to its inherit complexity in prior specification and posterior computation. In this article, we illustrate the applicability of the robust MAP prior in the regional treatment effect assessment by developing a closed-form approximation for its posterior distribution while leveraging its relationship with the power prior. The proposed methodology substantially reduces the computational burden of identifying prior parameters for desired operating characteristics. Moreover, we have demonstrated that the MAP prior is an attractive choice to construct the informative component of the mixture prior compared to the power prior. The advantage can be explained through a Bayesian hypothesis testing perspective. Using a real-world example, we illustrate how our proposed method enables efficient and transparent development of a Bayesian dynamic borrowing design to show regional consistency.

preprint2026arXiv

RCDN: Real-Centered Detection Network for Robust Face Forgery Identification

Image forgery has become a critical threat with the rapid proliferation of AI-based generation tools, which make it increasingly easy to synthesize realistic but fraudulent facial content. Existing detection methods achieve near-perfect performance when training and testing are conducted within the same domain, yet their effectiveness deteriorates substantially in crossdomain scenarios. This limitation is problematic, as new forgery techniques continuously emerge and detectors must remain reliable against unseen manipulations. To address this challenge, we propose the Real-Centered Detection Network (RCDN), a frequency spatial convolutional neural networks(CNN) framework with an Xception backbone that anchors its representation space around authentic facial images. Instead of modeling the diverse and evolving patterns of forgeries, RCDN emphasizes the consistency of real images, leveraging a dual-branch architecture and a real centered loss design to enhance robustness under distribution shifts. Extensive experiments on the DiFF dataset, focusing on three representative forgery types (FE, I2I, T2I), demonstrate that RCDN achieves both state-of-the-art in-domain accuracy and significantly stronger cross-domain generalization. Notably, RCDN reduces the generalization gap compared to leading baselines and achieves the highest cross/in-domain stability ratio, highlighting its potential as a practical solution for defending against evolving and unseen image forgery techniques.

preprint2026arXiv

SGR: A Stepwise Reasoning Framework for LLMs with External Subgraph Generation

Large Language Models (LLMs) have demonstrated strong capabilities across diverse NLP applications, such as translation, text generation, and question answering. Nevertheless, they remain limited in complex settings that demand deep reasoning and logical inference. Since these models are trained on large-scale text corpora, their generation process may still introduce irrelevant, noisy, or factually inconsistent content. To mitigate this problem, we introduce SGR, a stepwise framework that enhances LLM reasoning through external subgraph generation. SGR builds query-specific subgraphs from external knowledge bases and uses their semantic structure to support multi-step inference. By grounding intermediate reasoning steps in structured external knowledge, the framework helps the model concentrate on relevant entities, relations, and supporting evidence. In particular, SGR first constructs a subgraph tailored to the input question. It then guides the model to reason progressively over the generated structure and combines multiple reasoning trajectories to obtain the final prediction. Experimental results across several benchmark datasets show that SGR achieves consistent improvements over competitive baselines, highlighting its value for improving both reasoning accuracy and factual reliability.

preprint2026arXiv

Strong Evidence for Dark Sector Interactions

Recent DESI baryon acoustic oscillation data reveal deviations from $Λ$CDM cosmology, conventionally attributed to dynamical dark energy (DE). We demonstrate that these deviations are equally, if not better, explained by interactions between dark matter and dark energy (IDE), without requiring a time-varying DE equation of state. Using a unified framework, we analyze two IDE models -- coupled quintessence and coupled fluid -- against the latest CMB (Planck, ACT, SPT), DESI BAO, and SN (including DES-Dovekie recalibrated) data. Both IDE scenarios show robust evidence for non-vanishing interactions at the 3-5$σ$ level, with marginalized constraints significantly deviating from the $Λ$CDM limit. This preference persists even under DES-Dovekie SN recalibration, which weakens dynamical DE evidence. Crucially, for the same number of free parameters, IDE models provide fits to low- and high-redshift data that match or exceed the performance of the CPL dynamical DE parametrization. Our results establish IDE as a physically motivated alternative to dynamical DE, highlighting the necessity of future cosmological perturbation measurements (e.g., weak lensing, galaxy clustering) to distinguish between these paradigms.

preprint2026arXiv

Visual Latents Know More Than They Say: Unsilencing Latent Reasoning in MLLMs

Continuous latent-space reasoning offers a compact alternative to textual chain-of-thought for multimodal models, enabling high-dimensional visual evidence to be integrated without explicit reasoning tokens. However, we identify a previously overlooked optimization pathology in existing latent visual reasoning methods: although visual latents become semantically enriched during training, their contribution to final answer prediction is systematically suppressed. Within the shared parameter space, the autoregressive objective favors shortcut reliance on direct visual input, driving latent tokens toward transition-like states rather than informative reasoning content. We term this phenomenon Silenced Visual Latents. To address it, we disentangle the two conflicting objectives by directly optimizing the latent reasoning at inference time, keeping backbone parameters frozen. In Stage I, visual latents are warmed up via query-guided contrastive latent--visual alignment, improving semantic quality while preventing latent collapse. In Stage II, the latent reasoning is further optimized via a confidence-progression reward, which incentivizes predicted token distributions along the latent span to become progressively more concentrated, routing predictions through the latent reasoning rather than bypassing it. Experiments across eight benchmarks and four model backbones show that inference-time latent optimization, without any parameter updates, effectively unleashes the suppressed reasoning capacity of visual latents.

preprint2026arXiv

WorldArena 2.0: Extending Embodied World Model Benchmarking on Modality, Functionality and Platform

World models have emerged as a central paradigm for embodied intelligence, enabling agents to predict action-conditioned future and reason about environmental dynamics. However, existing embodied world model benchmarks are still largely confined to vision-only prediction, offline embodied applications, and simulator-based evaluation, making them insufficient for assessing increasingly comprehensive world models. In this work, we introduce WorldArena 2.0, an expanded benchmark that systematically broadens embodied world model evaluation along three dimensions: modality, functionality, and platform. Along the modality dimension, WorldArena 2.0 extends evaluation from vision-only to visuotactile modalities, enabling assessment of multimodal perception and prediction. Along the functionality dimension, it extends beyond policy evaluation and planning to assess world models as interactive RL environments for policy optimization. Along the platform dimension, it moves beyond simulator-only evaluation to a diverse suite of simulated and real-world robotic settings across multiple embodiments. Under a standardized protocol, WorldArena 2.0 comprehensively evaluates perceptual quality, interactive utility, and cross-platform performance, providing a comprehensive testbed for tracking progress toward embodied world models. The benchmark is available at: https://world-arena.ai.

preprint2026arXiv

WorldVLN: Autoregressive World Action Model for Aerial Vision-Language Navigation

Aerial vision-language navigation (VLN) requires agents to follow natural-language instructions through closed-loop perception and action in 3D environments. We argue that aerial VLN can be formulated as a prediction-driven world-action problem: the agent should anticipate latent world evolution and act according to the predicted consequences. To this end, we propose WorldVLN, the first autoregressive world action model for aerial VLN. Unlike full-sequence video-generation world models that generate an entire visual clip, WorldVLN adapts a latent autoregressive video backbone to predict short-horizon world-state transitions and directly decodes them into executable waypoint actions. After each action segment is executed, newly received observations are encoded back into the autoregressive context, enabling closed-loop world-action prediction. We further introduce a two-stage training framework that first grounds the video prior in instruction-conditioned navigation dynamics and then develops Action-aware GRPO, the first reinforcement learning method tailored to autoregressive WAMs, to optimize waypoint decisions through their downstream rollout consequences. On public outdoor and indoor benchmarks, WorldVLN consistently outperforms existing Vision-Language-Action baselines with 12\%+ success-rate gains and larger advantages on challenging cases. It further transfers zero-shot to real drone deployment, suggesting that the proposed WorldVLN offers a promising route for spatial action tasks. Demos and code are available at https://embodiedcity.github.io/WorldVLN/.

preprint2025arXiv

Introduction to the Chinese Space Station Survey Telescope (CSST)

The Chinese Space Station Survey Telescope (CSST) is an upcoming Stage-IV sky survey telescope, distinguished by its large field of view (FoV), high image quality, and multi-band observation capabilities. It can simultaneously conduct precise measurements of the Universe by performing multi-color photometric imaging and slitless spectroscopic surveys. The CSST is equipped with five scientific instruments, i.e. Multi-band Imaging and Slitless Spectroscopy Survey Camera (SC), Multi-Channel Imager (MCI), Integral Field Spectrograph (IFS), Cool Planet Imaging Coronagraph (CPI-C), and THz Spectrometer (TS). Using these instruments, CSST is expected to make significant contributions and discoveries across various astronomical fields, including cosmology, galaxies and active galactic nuclei (AGN), the Milky Way and nearby galaxies, stars, exoplanets, Solar System objects, astrometry, and transients and variable sources. This review aims to provide a comprehensive overview of the CSST instruments, observational capabilities, data products, and scientific potential.

preprint2025arXiv

MiMo-Audio: Audio Language Models are Few-Shot Learners

Existing audio language models typically rely on task-specific fine-tuning to accomplish particular audio tasks. In contrast, humans are able to generalize to new audio tasks with only a few examples or simple instructions. GPT-3 has shown that scaling next-token prediction pretraining enables strong generalization capabilities in text, and we believe this paradigm is equally applicable to the audio domain. By scaling MiMo-Audio's pretraining data to over one hundred million of hours, we observe the emergence of few-shot learning capabilities across a diverse set of audio tasks. We develop a systematic evaluation of these capabilities and find that MiMo-Audio-7B-Base achieves SOTA performance on both speech intelligence and audio understanding benchmarks among open-source models. Beyond standard metrics, MiMo-Audio-7B-Base generalizes to tasks absent from its training data, such as voice conversion, style transfer, and speech editing. MiMo-Audio-7B-Base also demonstrates powerful speech continuation capabilities, capable of generating highly realistic talk shows, recitations, livestreaming and debates. At the post-training stage, we curate a diverse instruction-tuning corpus and introduce thinking mechanisms into both audio understanding and generation. MiMo-Audio-7B-Instruct achieves open-source SOTA on audio understanding benchmarks (MMSU, MMAU, MMAR, MMAU-Pro), spoken dialogue benchmarks (Big Bench Audio, MultiChallenge Audio) and instruct-TTS evaluations, approaching or surpassing closed-source models. Model checkpoints and full evaluation suite are available at https://github.com/XiaomiMiMo/MiMo-Audio.

preprint2025arXiv

RelServe: Fast LLM Inference Serving on Relational Data

The use of Large Language Models (LLMs) for querying relational data has given rise to relQuery, a workload pattern that applies templated LLM calls to structured tables. As relQuery services become more widely adopted in applications such as AI-powered spreadsheets, fast response times under concurrent query loads are increasingly important. Unfortunately, current LLM engines face severe latency bottlenecks from Head-of-Line (HoL) blocking across three comparable inference phases: waiting, core running, and tail running. Existing static priority scheduling methods only address HoL blocking during the waiting phase, leaving two critical problems unsolved. First, the absence of a priority update mechanism causes inaccurate prioritization and continued HoL blocking during core execution. Second, suboptimal prefill-decode batching exacerbates HoL blocking in tail execution and worsens latency trade-offs between running and waiting relQueries. To address these problems, we propose RelServe, an optimized LLM engine for low-latency relQuery serving. RelServe features two core innovations: a Dynamic Priority Updater that continuously adjusts priorities while minimizing overhead via statistical approximations, and an Adaptive Batch Arranger that quantitatively evaluates candidate prefill and decode batches to minimize projected average latency. Extensive experiments on four real-world datasets using LLMs ranging from 13B to 70B parameters show that RelServe reduces average serving latency by up to 3.1x compared to vLLM.

preprint2025arXiv

TeleWorld: Towards Dynamic Multimodal Synthesis with a 4D World Model

World models aim to endow AI systems with the ability to represent, generate, and interact with dynamic environments in a coherent and temporally consistent manner. While recent video generation models have demonstrated impressive visual quality, they remain limited in real-time interaction, long-horizon consistency, and persistent memory of dynamic scenes, hindering their evolution into practical world models. In this report, we present TeleWorld, a real-time multimodal 4D world modeling framework that unifies video generation, dynamic scene reconstruction, and long-term world memory within a closed-loop system. TeleWorld introduces a novel generation-reconstruction-guidance paradigm, where generated video streams are continuously reconstructed into a dynamic 4D spatio-temporal representation, which in turn guides subsequent generation to maintain spatial, temporal, and physical consistency. To support long-horizon generation with low latency, we employ an autoregressive diffusion-based video model enhanced with Macro-from-Micro Planning (MMPL)--a hierarchical planning method that reduces error accumulation from frame-level to segment-level-alongside efficient Distribution Matching Distillation (DMD), enabling real-time synthesis under practical computational budgets. Our approach achieves seamless integration of dynamic object modeling and static scene representation within a unified 4D framework, advancing world models toward practical, interactive, and computationally accessible systems. Extensive experiments demonstrate that TeleWorld achieves strong performance in both static and dynamic world understanding, long-term consistency, and real-time generation efficiency, positioning it as a practical step toward interactive, memory-enabled world models for multimodal generation and embodied intelligence.

preprint2024arXiv

Efficient Sparse Least Absolute Deviation Regression with Differential Privacy

In recent years, privacy-preserving machine learning algorithms have attracted increasing attention because of their important applications in many scientific fields. However, in the literature, most privacy-preserving algorithms demand learning objectives to be strongly convex and Lipschitz smooth, which thus cannot cover a wide class of robust loss functions (e.g., quantile/least absolute loss). In this work, we aim to develop a fast privacy-preserving learning solution for a sparse robust regression problem. Our learning loss consists of a robust least absolute loss and an $\ell_1$ sparse penalty term. To fast solve the non-smooth loss under a given privacy budget, we develop a Fast Robust And Privacy-Preserving Estimation (FRAPPE) algorithm for least absolute deviation regression. Our algorithm achieves a fast estimation by reformulating the sparse LAD problem as a penalized least square estimation problem and adopts a three-stage noise injection to guarantee the $(ε,δ)$-differential privacy. We show that our algorithm can achieve better privacy and statistical accuracy trade-off compared with the state-of-the-art privacy-preserving regression algorithms. In the end, we conduct experiments to verify the efficiency of our proposed FRAPPE algorithm.

preprint2024arXiv

HEAP: Unsupervised Object Discovery and Localization with Contrastive Grouping

Unsupervised object discovery and localization aims to detect or segment objects in an image without any supervision. Recent efforts have demonstrated a notable potential to identify salient foreground objects by utilizing self-supervised transformer features. However, their scopes only build upon patch-level features within an image, neglecting region/image-level and cross-image relationships at a broader scale. Moreover, these methods cannot differentiate various semantics from multiple instances. To address these problems, we introduce Hierarchical mErging framework via contrAstive grouPing (HEAP). Specifically, a novel lightweight head with cross-attention mechanism is designed to adaptively group intra-image patches into semantically coherent regions based on correlation among self-supervised features. Further, to ensure the distinguishability among various regions, we introduce a region-level contrastive clustering loss to pull closer similar regions across images. Also, an image-level contrastive loss is present to push foreground and background representations apart, with which foreground objects and background are accordingly discovered. HEAP facilitates efficient hierarchical image decomposition, which contributes to more accurate object discovery while also enabling differentiation among objects of various classes. Extensive experimental results on semantic segmentation retrieval, unsupervised object discovery, and saliency detection tasks demonstrate that HEAP achieves state-of-the-art performance.

preprint2024arXiv

SpeechAgents: Human-Communication Simulation with Multi-Modal Multi-Agent Systems

Human communication is a complex and diverse process that not only involves multiple factors such as language, commonsense, and cultural backgrounds but also requires the participation of multimodal information, such as speech. Large Language Model (LLM)-based multi-agent systems have demonstrated promising performance in simulating human society. Can we leverage LLM-based multi-agent systems to simulate human communication? However, current LLM-based multi-agent systems mainly rely on text as the primary medium. In this paper, we propose SpeechAgents, a multi-modal LLM based multi-agent system designed for simulating human communication. SpeechAgents utilizes multi-modal LLM as the control center for individual agent and employes multi-modal signals as the medium for exchanged messages among agents. Additionally, we propose Multi-Agent Tuning to enhance the multi-agent capabilities of LLM without compromising general abilities. To strengthen and evaluate the effectiveness of human communication simulation, we build the Human-Communication Simulation Benchmark. Experimental results demonstrate that SpeechAgents can simulate human communication dialogues with consistent content, authentic rhythm, and rich emotions and demonstrate excellent scalability even with up to 25 agents, which can apply to tasks such as drama creation and audio novels generation. Code and models will be open-sourced at https://github. com/0nutation/SpeechAgents

preprint2024arXiv

The Taiji-TianQin-LISA network: Precisely measuring the Hubble constant using both bright and dark sirens

In the coming decades, the space-based gravitational-wave (GW) detectors such as Taiji, TianQin, and LISA are expected to form a network capable of detecting millihertz GWs emitted by the mergers of massive black hole binaries (MBHBs). In this work, we investigate the potential of GW standard sirens from the Taiji-TianQin-LISA network in constraining cosmological parameters. For the optimistic scenario in which electromagnetic (EM) counterparts can be detected, we predict the number of detectable bright sirens based on three different MBHB population models, i.e., pop III, Q3d, and Q3nod. Our results show that the Taiji-TianQin-LISA network alone could achieve a constraint precision of $0.9\%$ for the Hubble constant, meeting the standard of precision cosmology. Moreover, the Taiji-TianQin-LISA network could effectively break the cosmological parameter degeneracies generated by the CMB data, particularly in the dynamical dark energy models. When combined with the CMB data, the joint CMB+Taiji-TianQin-LISA data offer $σ(w)=0.036$ in the $w$CDM model, which is close to the latest constraint result obtained from the CMB+SN data. We also consider a conservative scenario in which EM counterparts are not available. Due to the precise sky localizations of MBHBs by the Taiji-TianQin-LISA network, the constraint precision of the Hubble constant is expected to reach $1.2\%$. In conclusion, the GW standard sirens from the Taiji-TianQin-LISA network will play a critical role in helping solve the Hubble tension and shedding light on the nature of dark energy.

preprint2024arXiv

Understanding LLMs: A Comprehensive Overview from Training to Inference

The introduction of ChatGPT has led to a significant increase in the utilization of Large Language Models (LLMs) for addressing downstream tasks. There's an increasing focus on cost-efficient training and deployment within this context. Low-cost training and deployment of LLMs represent the future development trend. This paper reviews the evolution of large language model training techniques and inference deployment technologies aligned with this emerging trend. The discussion on training includes various aspects, including data preprocessing, training architecture, pre-training tasks, parallel training, and relevant content related to model fine-tuning. On the inference side, the paper covers topics such as model compression, parallel computation, memory scheduling, and structural optimization. It also explores LLMs' utilization and provides insights into their future development.

preprint2023arXiv

Asymptotic Mutual Information Analysis for Double-scattering MIMO Channels: A New Approach by Gaussian Tools

The asymptotic mutual information (MI) analysis for multiple-input multiple-output (MIMO) systems over double-scattering channels has achieved engaging results, but the convergence rates of the mean, variance, and the distribution of the MI are not yet available in the literature. In this paper, by utilizing the large random matrix theory (RMT), we give a central limit theory (CLT) for the MI and derive the closed-form approximation for the mean and the variance by a new approach -- Gaussian tools. The convergence rates of the mean, variance, and the characteristic function are proved to be O(1/N) for the first time, where N is the number of transmit antennas. Furthermore, the impact of the number of effective scatterers on the mean and variance was investigated in the moderate-to-high SNR regime with some interesting physical insights. The proposed evaluation framework can be utilized for the asymptotic performance analysis of other systems over double-scattering channels.

preprint2023arXiv

How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection

The introduction of ChatGPT has garnered widespread attention in both academic and industrial communities. ChatGPT is able to respond effectively to a wide range of human questions, providing fluent and comprehensive answers that significantly surpass previous public chatbots in terms of security and usefulness. On one hand, people are curious about how ChatGPT is able to achieve such strength and how far it is from human experts. On the other hand, people are starting to worry about the potential negative impacts that large language models (LLMs) like ChatGPT could have on society, such as fake news, plagiarism, and social security issues. In this work, we collected tens of thousands of comparison responses from both human experts and ChatGPT, with questions ranging from open-domain, financial, medical, legal, and psychological areas. We call the collected dataset the Human ChatGPT Comparison Corpus (HC3). Based on the HC3 dataset, we study the characteristics of ChatGPT's responses, the differences and gaps from human experts, and future directions for LLMs. We conducted comprehensive human evaluations and linguistic analyses of ChatGPT-generated content compared with that of humans, where many interesting results are revealed. After that, we conduct extensive experiments on how to effectively detect whether a certain text is generated by ChatGPT or humans. We build three different detection systems, explore several key factors that influence their effectiveness, and evaluate them in different scenarios. The dataset, code, and models are all publicly available at https://github.com/Hello-SimpleAI/chatgpt-comparison-detection.

preprint2022arXiv

A forecast of using fast radio burst observations to constrain holographic dark energy

Recently, about five hundred fast radio bursts (FRBs) detected by CHIME/FRB Project have been reported. The vast amounts of data would make FRBs a promising low-redshift cosmological probe in the forthcoming years, and thus the issue of how many FRBs are needed for precise cosmological parameter estimation in different dark energy models should be detailedly investigated. Different from the usually considered $w(z)$-parameterized models in the literature, in this work we investigate the holographic dark energy (HDE) model and the Ricci dark energy (RDE) model, which originate from the holographic principle of quantum gravity, using the simulated localized FRB data as a cosmological probe for the first time. We show that the Hubble constant $H_0$ can be constrained to about 2% precision in the HDE model with the Macquart relation of FRB by using 10000 accurately-localized FRBs combined with the current CMB data, which is similar to the precision of the SH0ES value. Using 10000 localized FRBs combined with the CMB data can achieve about 6% constraint on the dark-energy parameter $c$ in the HDE model, which is tighter than the current BAO data combined with CMB. We also study the combination of the FRB data and another low-redshift cosmological probe, i.e. gravitational wave (GW) standard siren data, with the purpose of measuring cosmological parameters independent of CMB. Although the parameter degeneracies inherent in FRB and in GW are rather different, we find that more than 10000 FRBs are demanded to effectively improve the constraints in the holographic dark energy models.

preprint2022arXiv

A Hybrid Parallelization Approach for Distributed and Scalable Deep Learning

Recently, Deep Neural Networks (DNNs) have recorded great success in handling medical and other complex classification tasks. However, as the sizes of a DNN model and the available dataset increase, the training process becomes more complex and computationally intensive, which usually takes a longer time to complete. In this work, we have proposed a generic full end-to-end hybrid parallelization approach combining both model and data parallelism for efficiently distributed and scalable training of DNN models. We have also proposed a Genetic Algorithm based heuristic resources allocation mechanism (GABRA) for optimal distribution of partitions on the available GPUs for computing performance optimization. We have applied our proposed approach to a real use case based on 3D Residual Attention Deep Neural Network (3D-ResAttNet) for efficient Alzheimer Disease (AD) diagnosis on multiple GPUs. The experimental evaluation shows that the proposed approach is efficient and scalable, which achieves almost linear speedup with little or no differences in accuracy performance when compared with the existing non-parallel DNN models.

preprint2022arXiv

A new way to explore cosmological tensions using gravitational waves and strong gravitational lensing

In recent years, a crisis in the standard cosmology has been caused by inconsistencies in the measurements of some key cosmological parameters, Hubble constant $H_0$ and cosmic curvature parameter $Ω_K$ for example. It is necessary to remeasure them with the cosmological model-independent methods. In this paper, based on the distance sum rule, we present such a way to constrain $H_0$ and $Ω_K$ simultaneously in the late universe from strong gravitational lensing time delay (SGLTD) data and gravitational wave (GW) standard siren data simulated from the future observation of the Einstein Telescope (ET). Based on the currently 6 observed SGLTD data, we find that the constraint precision of $H_0$ from the combined 100 GW events can be comparable with the measurement from SH0ES collaboration. As the number of GW events increases to 700, the constraint precision of $H_0$ will exceed that of the \textit{Planck} 2018 results. Considering 1000 GW events as the conservative estimation of ET in ten-year observation, we obtain $H_0=73.69\pm 0.36 \mathrm{~km~s^{-1}~Mpc^{-1}}$ with a 0.5\% uncertainty and $Ω_K=0.076^{+0.068}_{-0.087}$. In addition, we simulate 55 SGL systems with 6.6\% uncertainty for the measurement of time-delay distance. By combining with 1000 GWs, we infer that $H_0=73.65\pm0.35 \mathrm{~km~s^{-1}~Mpc^{-1}}$ and $Ω_K=0.008\pm0.048$. Our results suggest that this approach can play an important role in exploring cosmological tensions.

preprint2022arXiv

A PDE approach for regret bounds under partial monitoring

In this paper, we study a learning problem in which a forecaster only observes partial information. By properly rescaling the problem, we heuristically derive a limiting PDE on Wasserstein space which characterizes the asymptotic behavior of the regret of the forecaster. Using a verification type argument, we show that the problem of obtaining regret bounds and efficient algorithms can be tackled by finding appropriate smooth sub/supersolutions of this parabolic PDE.

preprint2022arXiv

Alternating current conductivity and superconducting properties of the holographic effective theory

We construct a holographic effective superconducting theory by considering a special gauge-axion higher derivative term. The gauge-axion coupling results in the transport behavior similar to the vortex response in the dual boundary field theory leading to non-Drude behavior of alternating current (AC) conductivity at the weak momentum dissipation. With the momentum dissipation increasing, a dip exhibits in the AC conductivity at low frequency. It is thought to be the result of a combination of the strong momentum dissipation and the gauge-axion coupling. In the superconducting phase, this gauge-axion coupling also plays a key role leading to a more evident gap at the low frequency conductivity. In addition, we also study the combined effects of the strength of momentum dissipation and various couplings among the gauge field, axion fields and the complex scalar field.

preprint2022arXiv

An introduction to variational inference in Geophysical inverse problems

In a variety of scientific applications we wish to characterize a physical system using measurements or observations. This often requires us to solve an inverse problem, which usually has non-unique solutions so uncertainty must be quantified in order to define the family of all possible solutions. Bayesian inference provides a powerful theoretical framework which defines the set of solutions to inverse problems, and variational inference is a method to solve Bayesian inference problems using optimization while still producing fully probabilistic solutions. This chapter provides an introduction to variational inference, and reviews its applications to a range of geophysical problems, including petrophysical inversion, travel time tomography and full-waveform inversion. We demonstrate that variational inference is an efficient and scalable method which can be deployed in many practical scenarios.

preprint2022arXiv

Anarchic Federated Learning

Present-day federated learning (FL) systems deployed over edge networks consists of a large number of workers with high degrees of heterogeneity in data and/or computing capabilities, which call for flexible worker participation in terms of timing, effort, data heterogeneity, etc. To satisfy the need for flexible worker participation, we consider a new FL paradigm called "Anarchic Federated Learning" (AFL) in this paper. In stark contrast to conventional FL models, each worker in AFL has the freedom to choose i) when to participate in FL, and ii) the number of local steps to perform in each round based on its current situation (e.g., battery level, communication channels, privacy concerns). However, such chaotic worker behaviors in AFL impose many new open questions in algorithm design. In particular, it remains unclear whether one could develop convergent AFL training algorithms, and if yes, under what conditions and how fast the achievable convergence speed is. Toward this end, we propose two Anarchic Federated Averaging (AFA) algorithms with two-sided learning rates for both cross-device and cross-silo settings, which are named AFA-CD and AFA-CS, respectively. Somewhat surprisingly, we show that, under mild anarchic assumptions, both AFL algorithms achieve the best known convergence rate as the state-of-the-art algorithms for conventional FL. Moreover, they retain the highly desirable {\em linear speedup effect} with respect of both the number of workers and local steps in the new AFL paradigm. We validate the proposed algorithms with extensive experiments on real-world datasets.

preprint2022arXiv

Approaching a Minimal Topological Electronic Structure in Antiferromagnetic Topological Insulator MnBi2Te4 via Surface Modification

The topological electronic structure plays a central role in the non-trivial physical properties in topological quantum materials. A minimal, hydrogen-atom-like topological electronic structure is desired for researches. In this work, we demonstrate an effort towards the realization of such a system in the intrinsic magnetic topological insulator MnBi2Te4, by manipulating the topological surface state (TSS) via surface modification. Using high resolution laser- and synchrotron-based angle-resolved photoemission spectroscopy (ARPES), we found the TSS in MnBi2Te4 is heavily hybridized with a trivial Rashba-type surface state (RSS), which could be efficiently removed by the in situ surface potassium (K) dosing. By employing multiple experimental methods to characterize K dosed surface, we attribute such a modification to the electrochemical reactions of K clusters on the surface. Our work not only gives a clear band assignment in MnBi2Te4, but also provides possible new routes in accentuating the topological behavior in the magnetic topological quantum materials.

preprint2022arXiv

Ballistic Transport Enhanced Heat Convection at Nanoscale Hotspots

Along with device miniaturization, severe heat accumulation at unexpected nanoscale hotspots attracts wide attentions and urges efficient thermal management. Heat convection is one of the important heat dissipating paths but its mechanism at nanoscale hotspots is still unclear. Here shows the first experimental investigation of the convective heat transfer coefficient at size-controllable nanoscale hotspots. A specially designed structure of a single layer graphene supported by gold nanorods (AuNRs) is proposed, in which the AuNRs generate plasmonic heating sources of the order of hundreds of nanometers under laser irradiation and the graphene layer works as a temperature probe in Raman thermometry. The determined convective heat transfer coefficient is found to be about three orders of magnitude higher than that of nature convection, when the simultaneous interfacial heat conduction and radiation are carefully evaluated. Heat convection thus accounts to more than half of the total energy transferred across the graphene/AuNRs interface. Both the plasmonic heating induced nanoscale hotspots and ballistic convection of gas molecules contribute to the enhanced heat convection. This work reveals the importance of heat convection at nanoscale hotspots to the accurate thermal design of miniaturized electronics, and further offers a new way to evaluate the convective heat transfer coefficient at nanoscale hotspots.

preprint2022arXiv

Can depth-adaptive BERT perform better on binary classification tasks

In light of the success of transferring language models into NLP tasks, we ask whether the full BERT model is always the best and does it exist a simple but effective method to find the winning ticket in state-of-the-art deep neural networks without complex calculations. We construct a series of BERT-based models with different size and compare their predictions on 8 binary classification tasks. The results show there truly exist smaller sub-networks performing better than the full model. Then we present a further study and propose a simple method to shrink BERT appropriately before fine-tuning. Some extended experiments indicate that our method could save time and storage overhead extraordinarily with little even no accuracy loss.

preprint2022arXiv

Complementary Vanadium Dioxide Metamaterials with Enhanced Modulation Amplitude at Terahertz Frequencies

One route to create tunable metamaterials is through integration with "on-demand" dynamic quantum materials, such as vanadium dioxide (VO2). This enables new modalities to create high performance devices for historically challenging applications. Indeed, dynamic materials have often been integrated with metamaterials to imbue artificial structures with some degree of tunability. Conversely, metamaterials can be used to enhance and extend the natural tuning range of dynamic materials. Utilizing a complementary split ring resonator array deposited on a VO2 film, we demonstrate enhanced terahertz transmission modulation upon traversing the insulator-to-metal transition (IMT) at ~340 K. Our complementary metamaterial increases the modulation amplitude of the original VO2 film from 42% to 68.3% at 0.47 THz upon crossing the IMT, corresponding to an enhancement of 62.4%. Moreover, temperature dependent transmission measurements reveal a redshift of the resonant frequency arising from a giant increase of the permittivity of the VO2 film. Maxwell-Garnett effective medium theory was employed to explain the permittivity change upon transitioning through the IMT. Our results highlight that symbiotic integration of metamaterial arrays with quantum materials provides a powerful approach to engineer emergent functionality.

preprint2022arXiv

Constraints on interacting dark energy models from time-delay cosmography with seven lensed quasars

Measurements of time-delay cosmography of lensed quasars can provide an independent probe to explore the expansion history of the late-time Universe. In this paper, we employ the time-delay cosmography measurements from seven lenses (here abbreviated as the TD data) to constrain interacting dark energy (IDE) models. We mainly focus on the scenario of vacuum energy (with $w=-1$) interacting with cold dark matter, and consider four typical cases of the interaction form $Q$. When the TD data alone are employed, we find that the IDE models with $Q\propto ρ_{\rm de}$ seem to have an advantage in relieving the $H_{0}$ tension between the cosmic microwave background (CMB) and TD data. When the TD data are added to the CMB$+$BAO$+$SN$+H_0$ data, we find that: (i) the coupling parameter $β$ in all the considered IDE models is positive within 1$σ$ range, implying a mild preference for the case of cold dark matter decaying into dark energy; (ii) the IDE model with $Q = βH_{0} ρ_{\rm c}$ slightly relieves the $S_8$ tension, but the other considered IDE models further aggravate this tension; (iii) the Akaike information criteria of the IDE models with $Q \propto ρ_{\rm c}$ are lower than that of the $Λ$CDM model, indicating that these IDE models are more preferred by the current mainstream data. We conclude that the considered IDE models have their own different advantages when the TD data are employed, and none of them can achieve good scores in all aspects.

preprint2022arXiv

Contrastive-mixup learning for improved speaker verification

This paper proposes a novel formulation of prototypical loss with mixup for speaker verification. Mixup is a simple yet efficient data augmentation technique that fabricates a weighted combination of random data point and label pairs for deep neural network training. Mixup has attracted increasing attention due to its ability to improve robustness and generalization of deep neural networks. Although mixup has shown success in diverse domains, most applications have centered around closed-set classification tasks. In this work, we propose contrastive-mixup, a novel augmentation strategy that learns distinguishing representations based on a distance metric. During training, mixup operations generate convex interpolations of both inputs and virtual labels. Moreover, we have reformulated the prototypical loss function such that mixup is enabled on metric learning objectives. To demonstrate its generalization given limited training data, we conduct experiments by varying the number of available utterances from each speaker in the VoxCeleb database. Experimental results show that applying contrastive-mixup outperforms the existing baseline, reducing error rate by 16% relatively, especially when the number of training utterances per speaker is limited.

preprint2022arXiv

Cosmological Parameter Estimation Using Current and Future Observations of Strong Gravitational Lensing

Remarkable development of cosmology is benefited from the increasingly improved measurements of cosmic distances including absolute distances and relative distances. In recent years, however, the emerged cosmological tensions motivate us to explore the independent and precise late-universe probes. The two observational effects of strong gravitational lensing (SGL), the velocity dispersions of lens galaxies and the time delays between multiple images, can provide measurements of relative and absolute distances respectively, and their combination is possible to break the degeneracies between cosmological parameters and enable tight constraints on cosmological parameters. In this paper, we combine the observed 130 SGL systems with velocity-dispersion measurements and 7 SGL systems with time-delay measurements to constrain dark-energy cosmological models. It is found that the combination of the two effects does not significantly break the degeneracies between cosmological parameters as expected. However, with the simulations of 8000 SGL systems with well-measured velocity dispersions and 55 SGL systems with well-measured time delays based on the forthcoming LSST survey, we find that the combination of two effects can significantly break the parameter degeneracies, and make the constraint precision of cosmological parameters meet the standard of precision cosmology. We conclude that the observations of SGL will become a useful late-universe probe for precisely measuring cosmological parameters.

preprint2022arXiv

Cosmological search for sterile neutrinos after Planck 2018

Sterile neutrinos can affect the evolution of the universe, and thus using the cosmological observations can search for sterile neutrinos. In this work, we use the cosmic microwave background (CMB) anisotropy data from the Planck 2018 release, combined with the latest baryon acoustic oscillation (BAO), type Ia supernova (SN), and Hubble constant ($H_0$) data, to constrain the cosmological models with considering sterile neutrinos. In order to test the influences of the properties of dark energy on the {results} of searching for sterile neutrinos, in addition to the $Λ$ cold dark matter ($Λ$CDM) model, we also consider the $w$CDM model and the holographic dark energy (HDE) model. We find that the existence of sterile neutrinos {is not preferred} when the $H_0$ local measurement is not included in the data combination. When the $H_0$ measurement is included in the joint constraints, it is found that $ΔN_{\rm eff}>0$ is {favored} at about 2.7$σ$ level for the $Λ$CDM model and at about 1-1.7$σ$ level for the $w$CDM model. However, $m_{ν,{\rm{sterile}}}^{\rm{eff}}$ still cannot be well constrained and only upper limits can be given. In addition, we find that the HDE model is definitely ruled out by the current data. We also discuss the issue of the Hubble tension, and we conclude that involving sterile neutrinos in the cosmological models cannot truly resolve the Hubble tension.

preprint2022arXiv

Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey

Currently, deep learning-based visual inspection has been highly successful with the help of supervised learning methods. However, in real industrial scenarios, the scarcity of defect samples, the cost of annotation, and the lack of a priori knowledge of defects may render supervised-based methods ineffective. In recent years, unsupervised anomaly localization algorithms have become more widely used in industrial inspection tasks. This paper aims to help researchers in this field by comprehensively surveying recent achievements in unsupervised anomaly localization in industrial images using deep learning. The survey reviews more than 120 significant publications covering different aspects of anomaly localization, mainly covering various concepts, challenges, taxonomies, benchmark datasets, and quantitative performance comparisons of the methods reviewed. In reviewing the achievements to date, this paper provides detailed predictions and analysis of several future research directions. This review provides detailed technical information for researchers interested in industrial anomaly localization and who wish to apply it to the localization of anomalies in other fields.

preprint2022arXiv

Defective incidence coloring of graphs

We define the $d$-defective incidence chromatic number of a graph, generalizing the notion of incidence chromatic number, and determine it for some classes of graphs including trees, complete bipartite graphs, complete graphs, and outerplanar graphs. Fast algorithms for constructing the optimal $d$-defective incidence colorings of those graphs are presented.

preprint2022arXiv

Distributional Robust Portfolio Construction based on Investor Aversion

In behavioral finance, aversion affects investors' judgment of future uncertainty when profit and loss occur. Considering investors' aversion to loss and risk, and the ambiguous uncertainty characterizing asset returns, we construct a distributional robust portfolio model (DRP) under the condition that the distribution of risky asset returns is unknown. Specifically, our objective is to find an optimal portfolio of assets that maximizes the worst-case utility level on the Wasserstein ball, which is centered on the empirical distribution of sample returns and the radius of the ball quantifies the investor's ambiguity level. The model is also formulated as a mixed-integer quadratic programming problem with cardinality constraints. In addition, we propose a hybrid algorithm to improve the efficiency of the solution and make it more suitable for large-scale problems. The distributional robust portfolio model considering aversion is empirically tested for superior performance in asset allocation, and we also compare common asset allocation strategies to further enhance the credibility of the portfolio.

preprint2022arXiv

Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domains

Domain generalization (DG) is a difficult transfer learning problem aiming to learn a generalizable model for unseen domains. Recent foundation models (FMs) are robust to many distribution shifts and, therefore, should substantially improve the performance of DG. In this work, we study generic ways to adopt CLIP, a Visual-Language Foundation Model, for DG problems in image classification. While ERM greatly improves the accuracy with bigger backbones and training datasets using standard DG benchmarks, fine-tuning FMs is not practical in many real-world situations. We propose Domain Prompt Learning (DPL) as a novel approach for domain inference in the form of conditional prompt generation. DPL achieved a significant accuracy improvement with only training a lightweight prompt generator (a three-layer MLP), whose parameter is of equivalent scale to the classification projector in the previous DG literature. Combining \dplshort~with CLIP provides surprising performance, raising the accuracy of zero-shot CLIP from 73.7% to 79.3% on several standard datasets, namely PACS, VLCS, OfficeHome, and TerraIncognita. We hope the simplicity and success of our approach lead to broader adoption and analysis of foundation models in the domain generalization field. Our code is available at https://github.com/shogi880/DPLCLIP.

preprint2022arXiv

Dynamic Cournot-Nash Equilibrium: The Non-Potential Case

We consider a large population dynamic game in discrete time where players are characterized by time-evolving types. It is a natural assumption that the players' actions cannot anticipate future values of their types. Such games go under the name of dynamic Cournot-Nash equilibria, and were first studied by Acciaio et al., as a time/information dependent version of the games devised by Blanchet and Carlier for the static situation, under an extra assumption that the game is of potential type. The latter means that the game can be reduced to the resolution of an auxiliary variational problem. In the present work we study dynamic Cournot-Nash equilibria in their natural generality, namely going beyond the potential case. As a first result, we derive existence and uniqueness of equilibria under suitable assumptions. Second, we study the convergence of the natural fixed-point iterations scheme in the quadratic case. Finally we illustrate the previously mentioned results in a toy model of optimal liquidation with price impact, which is a game of non-potential kind.

preprint2022arXiv

Effects of Small-Scale Absorption Systems on Neutral Islands during the Late Epoch of Reionization

The reionization process is expected to be prolonged by the small-scale absorbers (SSAs) of ionizing photons, which have been seen as Lyman-limit systems in quasar absorption line observations. We use a set of semi-numerical simulations to investigate the effects of absorption systems on the reionization process, especially their impacts on the neutral islands during the late Epoch of Reionization (EoR). Three models are studied, i.e. the extreme case of no-SSA model with a high level of ionizing background, the moderate-SSA model with a relatively high level of ionizing background, and the dense-SSA model with a low level of ionizing background. We find that while the characteristic scale of neutral regions decreases during the early and middle stages of reionization, it stays nearly unchanged at about 10 comoving Mpc during the late stage for the no-SSA and moderate-SSA models. However, in the case of weak ionizing background in the dense-SSA model, the characteristic island scale shows obvious evolution, as large islands break into many small ones that are slowly ionized. The evolutionary behavior of neutral islands during the late EoR thus provides a novel way to constrain the abundance of SSAs. We discuss the 21-cm observation with the upcoming Square Kilometre Array. The different models can be distinguished by either the 21-cm imaging or the 21-cm power spectrum measurements.

preprint2022arXiv

Eliminating Primary Beam Effect in Foreground Subtraction of Neutral Hydrogen Intensity Mapping Survey with Deep Learning

In the neutral hydrogen (HI) intensity mapping (IM) survey, the foreground contamination on the cosmological signals is extremely severe, and the systematic effects caused by radio telescopes themselves further aggravate the difficulties in subtracting foreground. In this work, we investigate whether the deep learning method, concretely the 3D U-Net algorithm here, can play a crucial role in foreground subtraction when considering the systematic effect caused by the telescope's primary beam. We consider two beam models, i.e., the Gaussian beam model as a simple case and the Cosine beam model as a sophisticated case. The traditional principal component analysis (PCA) method is employed as a comparison and, more importantly, as the preprocessing step for the U-Net method to reduce the sky map dynamic range. We find that in the case of the Gaussian beam, the PCA method can effectively clean the foreground. However, the PCA method cannot handle the systematic effect induced by the Cosine beam, and the additional U-Net method can improve the result significantly. In order to show how well the PCA and U-Net methods can recover the HI signals, we also derive the HI angular power spectra, as well as the HI 2D power spectra, after performing the foreground subtractions. It is found that, in the case of Gaussian beam, the concordance with the original HI map using U-Net is better than that using PCA by $27.4\%$, and in the case of Cosine beam, the concordance using U-Net is better than that using PCA by $144.8\%$. Therefore, the U-Net based foreground subtraction can efficiently eliminate the telescope primary beam effect and shed new light on recovering the HI power spectrum for future HI IM experiments.

preprint2022arXiv

Envelopes and principal component regression

Envelope methods offer targeted dimension reduction for various models. The overarching goal is to improve efficiency in multivariate parameter estimation by projecting the data onto a lower-dimensional subspace known as the envelope. Envelope approaches have advantages in analyzing data with highly correlated variables, but their iterative Grassmannian optimization algorithms do not scale very well with ultra high-dimensional data. While the connections between envelopes and partial least squares in multivariate linear regression have promoted recent progress in high-dimensional studies of envelopes, we propose a more straightforward way of envelope modeling from a novel principal components regression perspective. The proposed procedure, Non-Iterative Envelope Component Estimation (NIECE), has excellent computational advantages over the iterative Grassmannian optimization alternatives in high dimensions. We develop a unified NIECE theory that bridges the gap between envelope methods and principal components in regression. The new theoretical insights also shed light on the envelope subspace estimation error as a function of eigenvalue gaps of two symmetric positive definite matrices used in envelope modeling. We apply the new theory and algorithm to several envelope models, including response and predictor reduction in multivariate linear models, logistic regression, and Cox proportional hazard model. Simulations and illustrative data analysis show the potential for NIECE to improve standard methods in linear and generalized linear models significantly.

preprint2022arXiv

Fast and Robust Sparsity Learning over Networks: A Decentralized Surrogate Median Regression Approach

Decentralized sparsity learning has attracted a significant amount of attention recently due to its rapidly growing applications. To obtain the robust and sparse estimators, a natural idea is to adopt the non-smooth median loss combined with a $\ell_1$ sparsity regularizer. However, most of the existing methods suffer from slow convergence performance caused by the {\em double} non-smooth objective. To accelerate the computation, in this paper, we proposed a decentralized surrogate median regression (deSMR) method for efficiently solving the decentralized sparsity learning problem. We show that our proposed algorithm enjoys a linear convergence rate with a simple implementation. We also investigate the statistical guarantee, and it shows that our proposed estimator achieves a near-oracle convergence rate without any restriction on the number of network nodes. Moreover, we establish the theoretical results for sparse support recovery. Thorough numerical experiments and real data study are provided to demonstrate the effectiveness of our method.

preprint2022arXiv

Galaxy-Scale Test of General Relativity with Strong Gravitational Lensing

Although general relativity (GR) has been precisely tested at the solar system scale, precise tests at a galactic or cosmological scale are still relatively insufficient. Here, in order to test GR at the galactic scale, we use the newly compiled galaxy-scale strong gravitational lensing (SGL) sample to constrain the parameter $γ_{PPN}$ in the parametrized post-Newtonian (PPN) formalism. We employ the Pantheon sample of type Ia supernovae observation to calibrate the distances in the SGL systems using the Gaussian Process method, which avoids the logical problem caused by assuming a cosmological model within GR to determine the distances in the SGL sample. Furthermore, we consider three typical lens models in this work to investigate the influences of the lens mass distributions on the fitting results. We find that the choice of the lens models has a significant impact on the constraints on the PPN parameter $γ_{PPN}$. We use the Bayesian information criterion as an evaluation tool to make a comparison for the fitting results of the three lens models, and we find that the most reliable lens model gives the result of $γ_{PPN}=1.065^{+0.064}_{-0.074}$, which is in good agreement with the prediction of $γ_{PPN}=1$ by GR. As far as we know, our 6.4% constraint result is the best result so far among the recent works using the SGL method.

preprint2022arXiv

Gate-Tunable Spin-Orbit Coupling in a Germanium Hole Double Quantum Dot

Hole spins confined in semiconductor quantum dot systems have gained considerable interest for their strong spin-orbit interactions (SOIs) and relatively weak hyperfine interactions. Here we experimentally demonstrate a tunable SOI in a double quantum dot in a Germanium (Ge) hut wire (HW), which could help enable fast all-electric spin manipulations while suppressing unwanted decoherence. Specifically, we measure the transport spectra in the Pauli spin blockade regime in the double quantum dot device.By adjusting the interdot tunnel coupling, we obtain an electric field tuned spin-orbit length lso = 2.0 - 48.9 nm. This tunability of the SOI could pave the way toward the realization of high-fidelity qubits in Ge HW systems.

preprint2022arXiv

Graph Contrastive Learning with Personalized Augmentation

Graph contrastive learning (GCL) has emerged as an effective tool for learning unsupervised representations of graphs. The key idea is to maximize the agreement between two augmented views of each graph via data augmentation. Existing GCL models mainly focus on applying \textit{identical augmentation strategies} for all graphs within a given scenario. However, real-world graphs are often not monomorphic but abstractions of diverse natures. Even within the same scenario (e.g., macromolecules and online communities), different graphs might need diverse augmentations to perform effective GCL. Thus, blindly augmenting all graphs without considering their individual characteristics may undermine the performance of GCL arts.To deal with this, we propose the first principled framework, termed as \textit{G}raph contrastive learning with \textit{P}ersonalized \textit{A}ugmentation (GPA), to advance conventional GCL by allowing each graph to choose its own suitable augmentation operations.In essence, GPA infers tailored augmentation strategies for each graph based on its topology and node attributes via a learnable augmentation selector, which is a plug-and-play module and can be effectively trained with downstream GCL models end-to-end. Extensive experiments across 11 benchmark graphs from different types and domains demonstrate the superiority of GPA against state-of-the-art competitors.Moreover, by visualizing the learned augmentation distributions across different types of datasets, we show that GPA can effectively identify the most suitable augmentations for each graph based on its characteristics.

preprint2022arXiv

Gravitational lensing by a black hole in effective loop quantum gravity

It is well known that general relativity is an effective theory of gravity at low energy scale, and actually quantum effects cannot be ignored in the strong-field regime. As a strong gravitational object, black hole plays a key role in testing the quantum effects of gravity in the strong-field regime. In this paper, we focus on black hole in effective loop quantum gravity and investigate what the influences are of the quantum effects on the weak and strong bending angles of light rays. We find that this black hole could be a Schwarzschild black hole, a regular black hole, a one-way traversable wormhole, or a two-way traversable wormhole for the different values of the quantum parameter, and the strong bending angle for this compact object exhibits two different divergent behaviors, i.e., the logarithmic divergence and non-logarithmic divergence. There are a series of relativistic images on both sides of the optical axis. Only the outermost one can be resolved as a single image, and all others are packed together at the limiting angular position. It is interesting to note that the angular separation between the outermost relativistic image and the others initially increases and then decreases as the quantum parameter increases, indicating that there is a maximum in the angular separation. The maximum is reached after the black hole becomes a wormhole, which can be taken as a signal for the formation of the wormhole. Moreover, the limiting angular position decreases as the quantum parameter increases but a little bounce occurs after the formation of the wormhole, and the relative magnification in magnitudes first decreases and then increases as the quantum parameter increases.

preprint2022arXiv

Gravitational wave constraints on Lorentz and parity violations in gravity: high-order spatial derivative cases

High-order spatial derivatives are of crucial importance for constructing the low energy effective action of a Lorentz or parity violating theory of quantum gravity. One example is the Hořava-Lifshitz gravity, in one has to consider at least the sixth-order spatial derivatives in the gravitational action, in order to make the theory power-counting renormalizable. In this paper, we consider the Lorentz and parity violating effects on the propagation of GWs due to the fifth and sixth-order spatial derivatives respectively. For this purpose we calculate the corresponding Lorentz and parity violating waveforms of GWs produced by the coalescence of compact binaries. By using these modified waveforms, we perform the full Bayesian inference with the help of the open source software \texttt{Bilby} on the selected GW events of binary black hole (BBH) and binary neutron stars (BNS) merges in the LIGO-Virgo catalogs GWTC-1 and GWTC-2. Overall we do not find any significant evidence of Lorentz and parity violation due to the fifth and sixth-order spatial derivatives and thus place lower bounds on the energy scales $M_{\rm LV} > 2.4 \times 10^{-16} \; {\rm GeV}$ for Lorentz violation and $M_{\rm PV} > 1.0 \times 10^{-14} \; {\rm GeV}$ for parity violation at 90\% confidence level. Both constraints represent the first constraints on the fifth- and sixth-order spatial derivative terms respectively in the framework of spatial covariant gravity by using the observational data of GWs.

preprint2022arXiv

Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations

Recent works of opinion expression identification (OEI) rely heavily on the quality and scale of the manually-constructed training corpus, which could be extremely difficult to satisfy. Crowdsourcing is one practical solution for this problem, aiming to create a large-scale but quality-unguaranteed corpus. In this work, we investigate Chinese OEI with extremely-noisy crowdsourcing annotations, constructing a dataset at a very low cost. Following zhang et al. (2021), we train the annotator-adapter model by regarding all annotations as gold-standard in terms of crowd annotators, and test the model by using a synthetic expert, which is a mixture of all annotators. As this annotator-mixture for testing is never modeled explicitly in the training phase, we propose to generate synthetic training samples by a pertinent mixup strategy to make the training and testing highly consistent. The simulation experiments on our constructed dataset show that crowdsourcing is highly promising for OEI, and our proposed annotator-mixup can further enhance the crowdsourcing modeling.

preprint2022arXiv

Improving Tese Case Generation for Python Native Libraries Through Constraints on Input Data Structures

Modern Python projects execute computational functions using native libraries and give Python interfaces to boost execution speed; hence, testing these libraries becomes critical to the project's robustness. One challenge is that existing approaches use coverage to guide generation, but native libraries run as black boxes to Python code with no execution information. Another is that dynamic binary instrumentation reduces testing performance as it needs to monitor both native libraries and the Python virtual machine. To address these challenges, in this paper, we propose an automated test case generation approach that works at the Python code layer. Our insight is that many path conditions in native libraries are for processing input data structures through interacting with the VM. In our approach, we instrument the Python Interpreter to monitor the interactions between native libraries and VM, derive constraints on the structures, and then use the constraints to guide test case generation. We implement our approach in a tool named PyCing and apply it to six widely-used Python projects. The experimental results reveal that with the structure constraint guidance, PyCing can cover more execution paths than existing test cases and state-of-the-art tools. Also, with the checkers in the testing framework Pytest, PyCing can identify segmentation faults in 10 Python interfaces and memory leaks in 9. Our instrumentation strategy also has an acceptable influence on testing efficiency.

preprint2022arXiv

Invariant subspaces and explicit Bethe vectors in the integrable open spin $1/2$ $\XYZ$ chain

We derive a criterion under which splitting of all eigenstates of an open $\XYZ$ Hamiltonian with boundary fields into two invariant subspaces, spanned by chiral shock states, occurs. The splitting is governed by an integer number, which has the geometrical meaning of the maximal number of kinks in the basis states. We describe the generic structure of the respective Bethe vectors. We obtain explicit expressions for Bethe vectors, in the absence of Bethe roots, and those generated by one Bethe root, and investigate the \multiplet. We also describe in detail an elliptic analogue of the spin-helix state, appearing in both the periodic and the open $\XYZ$ model, and derive the eigenstate condition. The elliptic analogue of the spin-helix state is characterized by a quasi-periodic modulation of the magnetization profile, governed by Jacobi elliptic functions.

preprint2022arXiv

K-core in percolated dense graph sequences

We determine the size of $k$-core in a large class of dense graph sequences. Let $G_n$ be a sequence of undirected, $n$-vertex graphs with edge weights $\{a^n_{i,j}\}_{i,j \in [n]}$ that converges to a kernel $W:[0,1]^2\to [0,+\infty)$ in the cut metric. Keeping an edge $(i,j)$ of $G_n$ with probability $\min \{ {a^n_{i,j}}/{n},1 \}$ independently, we obtain a sequence of random graphs $G_n(\frac{1}{n})$. Denote by $\mathcal{A}$ the property of a branching process that the initial particle has at least $k$ children, each of which has at least $k-1$ children, each of which has at least $k-1$ children, and so on. Using branching process and the theory of dense graph limits, under mild assumptions we obtain the size of $k$-core of random graphs $G_n(\frac{1}{n})$, \begin{align*} \text{size of $k$-core of } G_n\left(\frac{1}{n}\right) =n \mathbb{P}_{X^W}\left(\mathcal{A}\right) +o_p(n). \end{align*} Our result can also be used to obtain the threshold of appearance of a $k$-core of order $n$.

preprint2022arXiv

Linear quadratic leader-follower stochastic differential games for mean-field switching diffusions

In this paper, we consider a linear quadratic (LQ) leader-follower stochastic differential game for regime switching diffusions with mean-field interactions. One of the salient features of this paper is that conditional mean-field terms are included in the state equation and cost functionals. Based on stochastic maximum principles (SMPs), the follower's problem and the leader's problem are solved sequentially and an open-loop Stackelberg equilibrium is obtained. Further, with the help of the so-called four-step scheme, the corresponding Hamiltonian systems for the two players are decoupled and then the open-loop Stackelberg equilibrium admits a state feedback representation if some new-type Riccati equations are solvable.

preprint2022arXiv

Local collective dynamics at equilibrium BCC crystal-melt interfaces

We present a classical molecular-dynamics study of the collective dynamical properties of the coexisting liquid phase at equilibrium body-centered cubic (BCC) Fe crystal-melt interfaces. For the three interfacial orientations (100), (110), and (111), the collective dynamics are characterized through the calculation of the intermediate scattering functions, dynamical structure factors and density relaxation times in a sequential local region of interest. An anisotropic speed up of the collective dynamics in all three BCC crystal-melt interfacial orientations is observed. This trend differs significantly different from the previously observed slowing down of the local collective dynamics at the liquid-vapor interface [Acta Mater 2020;198:281]. Examining the interfacial density relaxation times, we revisit the validity of the recently developed time-dependent Ginzburg-Landau (TDGL) theory for the solidification crystal-melt interface kinetic coefficients, resulting in excellent agreement with both the magnitude and the kinetic anisotropy of the CMI kinetic coefficients measured from the non-equilibrium MD simulations

preprint2022arXiv

NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data

Federated learning (FL) has received a surge of interest in recent years thanks to its benefits in data privacy protection, efficient communication, and parallel data processing. Also, with appropriate algorithmic designs, one could achieve the desirable linear speedup for convergence effect in FL. However, most existing works on FL are limited to systems with i.i.d. data and centralized parameter servers and results on decentralized FL with heterogeneous datasets remains limited. Moreover, whether or not the linear speedup for convergence is achievable under fully decentralized FL with data heterogeneity remains an open question. In this paper, we address these challenges by proposing a new algorithm, called NET-FLEET, for fully decentralized FL systems with data heterogeneity. The key idea of our algorithm is to enhance the local update scheme in FL (originally intended for communication efficiency) by incorporating a recursive gradient correction technique to handle heterogeneous datasets. We show that, under appropriate parameter settings, the proposed NET-FLEET algorithm achieves a linear speedup for convergence. We further conduct extensive numerical experiments to evaluate the performance of the proposed NET-FLEET algorithm and verify our theoretical findings.

preprint2022arXiv

Next-to-tribimaximal mixing against CP violation and baryon asymmetry signs

Four next-to-tribimaximal (NTBM) mixing patterns are widely considered as the feasible candidates for the observed leptonic mixing structure. With the recent measurements from T2K, as well as intensive global fits, the interval of $\sinδ_{ CP} > 0$ for the Dirac CP-violating phase is persistently small in the normal ordering while $\sinδ_{ CP} < 0$ is successively obtained up to $3σ$ level in the inverted ordering. In this paper, we advocate the fitting results of Dirac CP-violating phase as a constraint, and show that, it can basically rule out half of the regions allowed by the constraint from three mixing angles only. In addition, given the small $\sinδ_{ CP} > 0$ interval, we find that a unique NTBM pattern can be selected out of the four candidates within $3σ$ uncertainty of the latest NuFIT 5.1, when the patterns are exposed to a Yukawa texture-independent leptogenesis for the baryon asymmetry of the Universe. For the surviving pattern, we find an interesting correlation between the Dirac CP-violating phase and the octant of atmospheric angle $θ_{23}$ , where $θ_{23}>45^\circ$ can be predicted once $5π/6 < δ_{CP} <π$ in the normal ordering can be confirmed in the future measurements.

preprint2022arXiv

On Length Sets of Subarithmetic Hyperbolic Manifolds

We formulate the Asymptotic Length-Saturation Conjecture on the length sets of closed geodesics on hyperbolic manifolds whose fundamental groups are subarithmetic, that is, contained in an arithmetic group. We prove the first instance of the conjecture for punctured, Zariski dense covers of the modular surface. The tools involved include the Orbital Circle Method, expansion and counting in congruence towers of thin groups, estimates for exponential sums, bilinear forms, and quadratic L-series.

preprint2022arXiv

Out-of-distribution Detection with Boundary Aware Learning

There is an increasing need to determine whether inputs are out-of-distribution (\emph{OOD}) for safely deploying machine learning models in the open world scenario. Typical neural classifiers are based on the closed world assumption, where the training data and the test data are drawn \emph{i.i.d.} from the same distribution, and as a result, give over-confident predictions even faced with \emph{OOD} inputs. For tackling this problem, previous studies either use real outliers for training or generate synthetic \emph{OOD} data under strong assumptions, which are either costly or intractable to generalize. In this paper, we propose boundary aware learning (\textbf{BAL}), a novel framework that can learn the distribution of \emph{OOD} features adaptively. The key idea of BAL is to generate \emph{OOD} features from trivial to hard progressively with a generator, meanwhile, a discriminator is trained for distinguishing these synthetic \emph{OOD} features and in-distribution (\emph{ID}) features. Benefiting from the adversarial training scheme, the discriminator can well separate \emph{ID} and \emph{OOD} features, allowing more robust \emph{OOD} detection. The proposed BAL achieves \emph{state-of-the-art} performance on classification benchmarks, reducing up to 13.9\% FPR95 compared with previous methods.

preprint2022arXiv

Outage and Finite-SNR DMT Analysis for IRS-aided MIMO Systems: How Large IRSs Need to Be?

Intelligent reflecting surfaces (IRSs) are promising enablers for high-capacity wireless communication systems by constructing favorable channels between the transmitter and receiver. However, general, accurate, and tractable outage analysis for IRS-aided multiple-input-multiple-output (MIMO) systems is not available in the literature. In this paper, we first characterize the mutual information (MI) of IRS-aided MIMO systems by capitalizing on large random matrix theory (RMT). Based on this result, a closed-form approximation for the outage probability is derived and a gradient-based algorithm is proposed to minimize the outage probability with statistical channel state information (CSI). We also investigate the diversity-multiplexing tradeoff (DMT) with the finite signal-to-noise ratio (SNR). Based on these theoretical results, we further study the impact of the IRS size on system performance. In the high SNR regime, we provide closed-form expressions for the ergodic mutual information (EMI) and outage probability as a function of the IRS size, which analytically reveal that the benefit of increasing the IRS size saturates quickly. Simulation results validate the accuracy of the theoretical analysis and confirm the increasing cost for deploying larger IRSs to improve system performance. For example, for an IRS-aided MIMO system with 20 antennas at both the transmitter and receiver, we need to double the size of the IRS to increase the throughout from 90% to 95% of its maximum value.

preprint2022arXiv

Persistent exchange splitting in a chiral helimagnet Cr1/3NbS2

Using high-resolution angle-resolved photoemission spectroscopy (ARPES) and ab-initio calculation, we systematically investigate the electronic structure of the chiral helimagnet Cr1/3NbS2 and its temperature evolution. The comparison with NbS2 suggests that the electronic structure of Cr1/3NbS2 is strongly modified by the intercalation of Cr atoms. Our ab-initio calculation, consistent with experimental result, suggests strong hybridization between Nb- and Cr-derived states near the Fermi level. In the chiral helimagnetic state (below the Curie temperature Tc), we observe exchange splitting of the energy bands crossing EF, which follows the temperature evolution of the magnetic moment, suggesting an important role of the conduction electrons in the long-range magnetic ordering. Interestingly, the exchange splitting persists far above Tc with negligible temperature dependence, in drastic contrast to the itinerant ferromagnetism described by the Stoner model, indicating the existence of short-range magnetic order. Our results provide important insights into the microscopic mechanism of the chiral helimagnetic ordering in Cr1/3NbS2.

preprint2022arXiv

PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification

Multi-label image classification aims to predict all possible labels in an image. It is usually formulated as a partial-label learning problem, given the fact that it could be expensive in practice to annotate all labels in every training image. Existing works on partial-label learning focus on the case where each training image is annotated with only a subset of its labels. A special case is to annotate only one positive label in each training image. To further relieve the annotation burden and enhance the performance of the classifier, this paper proposes a new partial-label setting in which only a subset of the training images are labeled, each with only one positive label, while the rest of the training images remain unlabeled. To handle this new setting, we propose an end-to-end deep network, PLMCL (Partial Label Momentum Curriculum Learning), that can learn to produce confident pseudo labels for both partially-labeled and unlabeled training images. The novel momentum-based law updates soft pseudo labels on each training image with the consideration of the updating velocity of pseudo labels, which help avoid trapping to low-confidence local minimum, especially at the early stage of training in lack of both observed labels and confidence on pseudo labels. In addition, we present a confidence-aware scheduler to adaptively perform easy-to-hard learning for different labels. Extensive experiments demonstrate that our proposed PLMCL outperforms many state-of-the-art multi-label classification methods under various partial-label settings on three different datasets.

preprint2022arXiv

Propagation of Chaos of Forward-Backward Stochastic Differential Equations with Graphon Interactions

In this paper, we study graphon mean field games using a system of forward-backward stochastic differential equations. We establish the existence and uniqueness of solutions under two different assumptions and prove the stability with respect to the interacting graphons which are necessary to show propagation of chaos results. As an application of propagation of chaos, we prove the convergence of n-player game Nash equilibrium for a general model, which is new in the theory of graphon mean field games.

preprint2022arXiv

Secrecy Analysis for IRS-aided Wiretap MIMO Communications: Fundamental Limits and System Design

In order to meet the demands of future innovative applications, many efforts have been made to exceed the limits predicted by Shannon's Theory. Besides the investigation of beyond-Shannon metrics such as security, latency, and semantics, another direction is to jointly design the transceiver and the environment by utilizing the intelligent reflecting surface (IRS). In this paper, we consider the analysis and design of IRS-aided multiple-input multiple-output (MIMO) secure communications, which has attracted much research attention but still in its infancy. For example, despite their importance, the fundamental limits of IRS-aided wiretap MIMO communications are not yet available in the literature. In this paper, we will investigate these fundamental limits by determining the ergodic secrecy rate (ESR) and secrecy outage probability (SOP). For that purpose, the central limit theorem (CLT) for the joint distributions of the mutual information (MI) statistics over the IRS-aided MIMO secure communication channel is derived by utilizing the random matrix theory (RMT). The derived CLT is then used to obtain the closed form expressions for the ESR and SOP, which are also extended to the scenario with multiple multi-antenna eavesdroppers. Based on the theoretical results, algorithms for maximizing the artificial noise (AN) aided ESR and minimizing the SOP are proposed. Numerical results validate the accuracy of the theoretical results and effectiveness of the proposed optimization algorithms.

preprint2022arXiv

Shadow and weak deflection angle of a black hole in nonlocal gravity

Black hole shadow and gravitational lensing play important roles in testing gravitational theories in the strong field regime. As the first-order modifications from quantum gravity, the nonlocality can be manifested by black hole shadow and gravitational lensing. For example, the cut-off parameter introduced by nonlocality will affect the shape and size of the black hole shadow, and also affect the deflection angle of light rays. In this paper, we mainly investigate the effects of the nonlocality on the black hole shadow and the gravitational lensing for two types of rotating black holes in nonlocal gravity. It is found that the size of the black hole shadow decreases with the cut-off parameter since the nonlocality weakens the gravitational constant, and the shape of the shadow gets more deformed with the increase of the cut-off parameter. However, if the rotation parameter is small, the shape of the shadow is almost a circle even though the cut-off parameter approaches its maximum. The energy emission rate in both models is also studied. The results show that there is a peak for each curve and the peak decreases and shifts to the low frequency with the increase of the cut-off parameter. Besides, we also explore the shadow of both types of black holes surrounded by a nonmagnetized pressureless plasma which satisfies the separability condition. It is found that the plasma has a frequency-dependent dispersive effect on the size and shape of the black hole shadow. For the gravitational lensing, we find that the cut-off parameter of model A makes a positive contribution to the deflection angle, which can be compared with the contribution of the rotation parameter, while the cut-off parameter of model B makes a negative contribution which can be ignored. These results may be helpful for probing nonlocal gravity in future observations.

preprint2022arXiv

Stochastic Linear Quadratic Optimal Control Problems with Random Coefficients and Markovian Regime Switching System

This paper thoroughly investigates stochastic linear-quadratic optimal control problems with the Markovian regime switching system, where the coefficients of the state equation and the weighting matrices of the cost functional are random. We prove the solvability of the stochastic Riccati equation under the uniform convexity condition and obtain the closed-loop representation of the open-loop optimal control using the unique solvability of the corresponding stochastic Riccati equation. Moreover, by applying Itô's formula with jumps, we get a representation of the cost functional on a Hilbert space, characterized as the adapted solutions of some forward-backward stochastic differential equations. We show that the necessary condition of the open-loop optimal control is the convexity of the cost functional, and the sufficient condition of the open-loop optimal control is the uniform convexity of the cost functional. In addition, we study the properties of the stochastic value flow of the stochastic linear-quadratic optimal control problem. Finally, as an application, we present a continuous-time mean-variance portfolio selection problem and prove its unique solvability.

preprint2022arXiv

Surface wave dispersion inversion using an energy likelihood function

Seismic surface wave dispersion inversion is used widely to study the subsurface structure of the Earth. The dispersion property is usually measured by using frequency-phase velocity (f-c) analysis and by picking phase velocities from the obtained f-c spectrum. However, because of potential contamination the f-c spectrum often has multimodalities at each frequency for each mode. These introduce uncertainty and errors in the picked phase velocities, and consequently the obtained shear velocity structure is biased. To overcome this issue, in this study we introduce a new method which directly uses the spectrum as data. We achieve this by solving the inverse problem in a Bayesian framework and define a new likelihood function, the energy likelihood function, which uses the spectrum energy to define data fit. We apply the new method to a land dataset recorded by a dense receiver array, and compare the results to those obtained using the traditional method. The results show that the new method produces more accurate results since they better match independent data from refraction tomography. This real-data application also shows that it can be applied efficiently since it removes the need to pick phase velocities, and with relatively little adjustment to current practice since it uses standard f-c panels to define the likelihood. We therefore recommend using the energy likelihood function rather than explicitly picking phase velocities in surface wave dispersion inversion.

preprint2022arXiv

SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for Distributed Learning in Computing Clusters

To increase the training speed of distributed learning, recent years have witnessed a significant amount of interest in developing both synchronous and asynchronous distributed stochastic variance-reduced optimization methods. However, all existing synchronous and asynchronous distributed training algorithms suffer from various limitations in either convergence speed or implementation complexity. This motivates us to propose an algorithm called STNTHESIS (semi-asynchronous path-integrated stochastic gradient search), which leverages the special structure of the variance-reduction framework to overcome the limitations of both synchronous and asynchronous distributed learning algorithms while retaining their salient features. We consider two implementations of STNTHESIS under distributed and shared memory architectures. We show that our STNTHESIS algorithms have $O(\sqrt{N}ε^{-2}(Δ+1)+N)$ and $O(\sqrt{N}ε^{-2}(Δ+1) d+N)$ computational complexities for achieving an $ε$-stationary point in non-convex learning under distributed and shared memory architectures, respectively, where N denotes the total number of training samples and $Δ$ represents the maximum delay of the workers. Moreover, we investigate the generalization performance of \algname by establishing algorithmic stability bounds for quadratic strongly convex and non-convex optimization. We further conduct extensive numerical experiments to verify our theoretical findings

preprint2022arXiv

The self-supervised spectral-spatial attention-based transformer network for automated, accurate prediction of crop nitrogen status from UAV imagery

Nitrogen (N) fertilizer is routinely applied by farmers to increase crop yields. At present, farmers often over-apply N fertilizer in some locations or at certain times because they do not have high-resolution crop N status data. N-use efficiency can be low, with the remaining N lost to the environment, resulting in higher production costs and environmental pollution. Accurate and timely estimation of N status in crops is crucial to improving cropping systems' economic and environmental sustainability. Destructive approaches based on plant tissue analysis are time consuming and impractical over large fields. Recent advances in remote sensing and deep learning have shown promise in addressing the aforementioned challenges in a non-destructive way. In this work, we propose a novel deep learning framework: a self-supervised spectral-spatial attention-based vision transformer (SSVT). The proposed SSVT introduces a Spectral Attention Block (SAB) and a Spatial Interaction Block (SIB), which allows for simultaneous learning of both spatial and spectral features from UAV digital aerial imagery, for accurate N status prediction in wheat fields. Moreover, the proposed framework introduces local-to-global self-supervised learning to help train the model from unlabelled data. The proposed SSVT has been compared with five state-of-the-art models including: ResNet, RegNet, EfficientNet, EfficientNetV2 and the original vision transformer on both testing and independent datasets. The proposed approach achieved high accuracy (0.96) with good generalizability and reproducibility for wheat N status estimation.

preprint2022arXiv

Ultrafast coherent control of a hole spin qubit in a germanium quantum dot

Operation speed and coherence time are two core measures for the viability of a qubit. Strong spin-orbit interaction (SOI) and relatively weak hyperfine interaction make holes in germanium (Ge) intriguing candidates for spin qubits with rapid, all-electrical coherent control. Here we report ultrafast single-spin manipulation in a hole-based double quantum dot in a germanium hut wire (GHW). Mediated by the strong SOI, a Rabi frequency exceeding 540 MHz is observed at a magnetic field of 100 mT, setting a record for ultrafast spin qubit control in semiconductor systems. We demonstrate that the strong SOI of heavy holes (HHs) in our GHW, characterized by a very short spin-orbit length of 1.5 nm, enables the rapid gate operations we accomplish. Our results demonstrate the potential of ultrafast coherent control of hole spin qubits to meet the requirement of DiVincenzo's criteria for a scalable quantum information processor.

preprint2022arXiv

Ultrafast modulation of the molten metal surface tension under femtosecond laser irradiation

We predict ultrafast modulation of the pure molten metal surface stress fields under the irradiation of the single femtosecond laser pulse through the two-temperature model molecular-dynamics simulations. High-resolution and precision calculations are used to resolve the ultrafast laser-induced anisotropic relaxations of the pressure components on the time-scale comparable to the intrinsic liquid density relaxation time. The magnitudes of the dynamic surface tensions are found being modulated sharply within picoseconds after the irradiation, due to the development of the nanometer scale non-hydrostatic regime behind the exterior atomic layer of the liquid surfaces. The reported novel regulation mechanism of the liquid surface stress field and the dynamic surface tension hints at levitating the manipulation of liquid surfaces, such as ultrafast steering the surface directional transport and patterning.

preprint2021arXiv

A two-dimensional electron gas based on a 5s oxide with high room-temperature mobility and strain sensitivity

The coupling of optical and electronic degrees of freedom together with quantum confinement in low-dimensional electron systems is particularly interesting for achieving exotic functionalities in strongly correlated oxide electronics. Recently, high room-temperature mobility has been achieved for a large bandgap transparent oxide - BaSnO$_3$ upon extrinsic La or Sb doping, which has excited significant research attention. In this work, we report the observation of room-temperature ferromagnetism in BaSnO$_3$ thin films and the realization of a two-dimensional electron gas (2DEG) on the surface of transparent BaSnO$_3$ via oxygen vacancy creation, which exhibits a high carrier density of $\sim 7.72*10^{14} /{\rm cm}^2$ and a high room-temperature mobility of ~18 cm$^2$/V/s. Such a 2DEG is rather sensitive to strain and a less than 0.1% in-plane biaxial compressive strain leads to a giant resistance enhancement of 350% (more than 540 kOhm/Square) at room temperature. Thus, this work creates a new path to exploring the physics of low-dimensional oxide electronics and devices applicable at room temperature.

preprint2021arXiv

Axion and dark photon limits from Crab Nebula high energy gamma-rays

The observation of cosmic sub-PeV gamma-rays from the Crab Nebula opens up the possibility of testing cosmic ray photon transparency at the multi-hundred TeV scale. Assuming no deviation from a source gamma-ray emission due to accelerated electron inverse-Compton scattering, higher event energies can extend constraints on the effects of new physics; We consider oscillation between gamma-rays and axions/dark photons, plus attenuation effects from gamma-ray absorption in the case of dark photon dark matter. Combining the recent AS$γ$ and HAWC sub-PeV data with earlier MAGIC and HEGRA data, axion-like particles are most constrained in the $10^{-7}-10^{-6}$ eV mass range, where the coupling $g_{aγ}$ is constrained to be below ${1.8}\times 10^{-10}$ GeV$^{-1}$. In comparison, gamma ray flux attenuation due to oscillation with a dark photon leads to a very weak constraint on the mixing parameter; $ε\lesssim$ 0.2 for dark photon mass between $10^{-7}$ and $10^{-6}$ eV. Direct scattering from dark photon dark matter limits $ε\lesssim$ 0.01 for masses between $6$ and $400$ eV.

preprint2021arXiv

Boundary driven $XYZ$ chain: Exact inhomogeneous triangular matrix product ansatz

We construct an explicit matrix product ansatz for the steady state of a boundary driven $XY\!Z$ spin-$\tfrac{1}{2}$ chain for arbitrary local polarizing channels at the chain's ends. The ansatz, where the Lax operators are written explicitly in terms of infinite-dimensional bidiagonal (triangular) site-dependent matrices, becomes exact either in the (Zeno) limit of infinite dissipation strength, or thermodynamic limit of infinite chain length. The solution is based on an extension of the newly discovered family of separable eigenstates of the model.

preprint2021arXiv

Chiral coordinate Bethe ansatz for phantom eigenstates in the open XXZ spin-$\frac12$ chain

We construct the coordinate Bethe ansatz for all eigenstates of the open spin-$\frac12$ XXZ chain that fulfill the phantom roots criterion (PRC). Under the PRC, the Hilbert space splits into two invariant subspaces and there are two sets of homogeneous Bethe ansatz equations (BAE) to characterize the subspaces in each case. We propose two sets of vectors with chiral shocks to span the invariant subspaces and expand the corresponding eigenstates. All the vectors are factorized and have symmetrical and simple structures. Using several simple cases as examples, we present the core elements of our generalized coordinate Bethe ansatz method. The eigenstates are expanded in our generating set and show clear chirality and certain symmetry properties. The bulk scattering matrices, the reflection matrices on the two boundaries and the BAE are obtained, which demonstrates the agreement with other approaches. Some hypotheses are formulated for the generalization of our approach.

preprint2021arXiv

Designing 24-hour Electrical Power Generator: Thermoradiative Device for Harvesting Energy from Sun and Outer Space

Energy harvesting from sun and outer space using thermoradiative devices (TRD), despite being promising renewable energy sources, are limited only to daytime and nighttime period, respectively. A system with 24-hour continuous energy generation remains an open question thus far. Here, we propose a TRD-based power generator that harvests solar energy via concentrated solar irradiation during daytime and via thermal infrared emission towards the outer space at nighttime, thus achieving the much sought-after 24-hour electrical power generation. We develop a rigorous thermodynamical model to investigate the performance characteristics, parametric optimum design, and the role of various energy loss mechanisms. Our model predicts that the TRD-based system yields a peak efficiency of 12.6\% at daytime and a maximum power density of 10.8 Wm$^{-2}$ at nighttime, thus significantly outperforming the state-of-art record-setting thermoelectric generator. These findings reveal the potential of TRD towards 24-hour electricity generation and future renewable energy technology.

preprint2021arXiv

Designing a Concentrated High-Efficiency Thermionic Solar Cell Enabled by Graphene Collector

We propose a concentrated thermionic emission solar cell design, which demonstrates a high solar-to-electricity energy conversion efficiency larger than 10\% under 600 sun, by harnessing the exceptional electrical, thermal and radiative properties of the graphene as a collector electrode. By constructing an analytical model that explicitly takes into account the non-Richardson behavior of the thermionic emission current from graphene, space charge effect in vacuum gap, and the various irreversible energy losses within the subcomponents, we perform a detailed characterization on the conversion efficiency limit and electrical power output characteristics of the proposed system. We systematically model and compare the energy conversion efficiency of various configurations of graphene-graphene and graphene-diamond and diamond-diamond thermionic emitter, and show that utilizing diamond films as an emitter and graphene as a collector offers the highest maximum efficiency, thus revealing the important role of graphene in achieving high-performance thermionic emission solar cell. A maximum efficiency of 12.8\% under 800 sun has been revealed, which is significantly higher than several existing solid-state solar cell designs, such as the solar-driven thermoelectric and thermophotovoltaic converters. Our work thus opens up new avenues to advance the efficiency limit of thermionic solar energy conversion and the development of next-generation novel-nanomaterial-based solar energy harvesting technology.

preprint2021arXiv

Dynamic list coloring of 1-planar graphs

A graph is $k$-planar if it can be drawn in the plane so that each edge is crossed at most $k$ times. Typically, the class of 1-planar graphs is among the most investigated graph families within the so-called "beyond planar graphs". A dynamic $\ell$-list coloring of a graph is a proper coloring so that each vertex receives a color from a list of $\ell$ distinct candidate colors assigned to it, and meanwhile, there are at least two colors appearing in the neighborhood of every vertex of degree at least two. In this paper, we prove that each 1-planar graph has a dynamic $11$-list coloring. Moreover, we show a relationship between the dynamic coloring of 1-planar graphs and the proper coloring of 2-planar graphs, which states that the dynamic (list) chromatic number of the class of 1-planar graphs is at least the (list) chromatic number of the class of 2-planar graphs.

preprint2021arXiv

Epitaxial Integration of a Perpendicularly Magnetized Ferrimagnetic Metal on a Ferroelectric oxide for Electric-Field Control

Ferrimagnets, which contain the advantages of both ferromagnets (detectable moments) and antiferromagnets (ultrafast spin dynamics), have recently attracted great attention. Here we report the optimization of epitaxial growth of a tetragonal perpendicularly magnetized ferrimagnet Mn2Ga on MgO. Electrical transport, magnetic properties and the anomalous Hall effect (AHE) were systematically studied. Furthermore, we successfully integrated high-quality epitaxial ferrimagnetic Mn2Ga thin films onto ferroelectric PMN-PT single crystals with a MgO buffer layer. It was found that the AHE of such a ferrimagnet can be effectively modulated by a small electric field over a large temperature range in a nonvolatile manner. This work thus demonstrates the great potential of ferrimagnets for developing high-density and low-power spintronic devices.

preprint2021arXiv

Genetic Algorithm based hyper-parameters optimization for transfer Convolutional Neural Network

Hyperparameter optimization is a challenging problem in developing deep neural networks. Decision of transfer layers and trainable layers is a major task for design of the transfer convolutional neural networks (CNN). Conventional transfer CNN models are usually manually designed based on intuition. In this paper, a genetic algorithm is applied to select trainable layers of the transfer model. The filter criterion is constructed by accuracy and the counts of the trainable layers. The results show that the method is competent in this task. The system will converge with a precision of 97% in the classification of Cats and Dogs datasets, in no more than 15 generations. Moreover, backward inference according the results of the genetic algorithm shows that our method can capture the gradient features in network layers, which plays a part on understanding of the transfer AI models.

preprint2021arXiv

Interrogating probabilistic inversion results for subsurface structural information

The goal of a scientific investigation is to find answers to specific questions. In geosciences this is typically achieved by solving an inference or inverse problem and interpreting the solution. However, the answer obtained is often biased because the solution to an inverse problem is nonunique and human interpretation is a biased process. Interrogation theory provides a systematic way to find optimal answers by considering their full uncertainty estimates, and by designing an objective function that defines desirable qualities in the answer. In this study we demonstrate interrogation theory by quantifying the size of a particular subsurface structure. The results show that interrogation theory provides an accurate estimate of the true answer, which cannot be obtained by direct, subjective interpretation of the solution mean and standard deviation. This demonstrates the value of interrogation theory. It also shows that fully nonlinear uncertainty assessments may be critical in order to address real-world scientific problems, which goes some way towards justifying their computational expense.

preprint2021arXiv

IRS-aided MIMO Systems over Double-scattering Channels: Impact of Channel Rank Deficiency

Intelligent reflecting surfaces (IRSs) are promising enablers for next-generation wireless communications due to their reconfigurability and high energy efficiency in improving poor propagation condition of channels, e.g., limited scattering environment. However, most existing works assumed full-rank channels requiring rich scatters, which may not be available in practice. To analyze the impact of rank-deficient channels and mitigate the ensued performance loss, we consider a large-scale IRS-aided MIMO system with statistical channel state information (CSI), where the double-scattering channel is adopted to model rank deficiency. By leveraging random matrix theory (RMT), we first derive a deterministic approximation (DA) of the ergodic rate with low computational complexity and prove the existence and uniqueness of the DA parameters. Then, we propose an alternating optimization algorithm for maximizing the DA with respect to phase shifts and signal covariance matrices. Numerical results will show that the DA is tight and our proposed method can effectively mitigate the performance loss induced by channel rank deficiency.

preprint2021arXiv

Prediction against a limited adversary

We study the problem of prediction with expert advice with adversarial corruption where the adversary can at most corrupt one expert. Using tools from viscosity theory, we characterize the long-time behavior of the value function of the game between the forecaster and the adversary. We provide lower and upper bounds for the growth rate of regret without relying on a comparison result. We show that depending on the description of regret, the limiting behavior of the game can significantly differ.

preprint2021arXiv

Prospects for measuring dark energy with 21 cm intensity mapping experiments

Using the 21 cm intensity mapping (IM) technique can efficiently perform large-scale neutral hydrogen surveys, and this method has great potential for measuring dark-energy parameters. Some 21 cm IM experiments aiming at measuring dark energy in the redshift range of $0<z<3$ have been proposed and performed, in which the typical ones using single-dish mode include e.g., BINGO, FAST, and SKA1-MID, and those using interferometric mode include e.g., HIRAX, CHIME, and Tianlai. In this work, we make a forecast for these typical 21 cm IM experiments on their capability of measuring parameters of dark energy. We find that the interferometers have great advantages in constraining cosmological parameters. In particular, the Tianlai cylinder array alone can achieve the standard of precision cosmology for the $Λ$CDM model (i.e., the precision of parameters is better than 1%). However, for constraining dynamical dark energy, we find that SKA1-MID performs very well. We show that the simulated 21 cm IM data can break the parameter degeneracies inherent in the CMB data, and CMB+SKA1-MID offers $σ(w)=0.013$ in the $w$CDM model, and $σ(w_0)=0.080$ and $σ(w_a)=0.25$ in the CPL model. Compared with CMB+BAO+SN, Tianlai can provide tighter constraints in $Λ$CDM and $w$CDM, but looser constraints (tighter than CMB+BAO) in CPL, and the combination CMB+BAO+SN+Tianlai gives $σ(w)=0.013$, $σ(w_0)=0.055$, and $σ(w_a)=0.13$. In addition, it is found that the synergy of FAST ($0<z<0.35$)+SKA1-MID ($0.35<z<0.77$)+Tianlai ($0.77<z<2.55$) offers a very promising survey strategy. Finally, we find that the residual foreground contamination amplitude has a considerable impact on constraint results. We show that in the future 21 cm IM experiments will provide a powerful probe for exploring the nature of dark energy.

preprint2021arXiv

Spectroscopic and Photometric Redshift Estimation by Neural Networks For the China Space Station Optical Survey (CSS-OS)

The estimation of spectroscopic and photometric redshifts (spec-z and photo-z) is crucial for future cosmological surveys. It can directly affect several powerful measurements of the Universe, e.g. weak lensing and galaxy clustering. In this work, we explore the accuracies of spec-z and photo-z that can be obtained in the China Space Station Optical Surveys (CSS-OS), which is a next-generation space survey, using neural networks. The 1-dimensional Convolutional Neural Networks (1-d CNN) and Multi-Layer Perceptron (MLP, one of the simplest forms of Artificial Neural Network) are employed to derive the spec-z and photo-z, respectively. The mock spectral and photometric data used for training and testing the networks are generated based on the COSMOS catalog. The networks have been trained with noisy data by creating Gaussian random realizations to reduce the statistical effects, resulting in similar redshift accuracy for both high-SNR (signal to noise ratio) and low-SNR data. The probability distribution functions (PDFs) of the predicted redshifts are also derived via Gaussian random realizations of the testing data, and then the best-fit redshifts and 1-sigma errors also can be obtained. We find that our networks can provide excellent redshift estimates with accuracies ~0.001 and 0.01 on spec-z and photo-z, respectively. Compared to existing photo-z codes, our MLP has similar accuracy but is more efficient in the training process. The fractions of catastrophic redshifts or outliers can be dramatically suppressed comparing to the ordinary template-fitting method. This indicates that the neural network method is feasible and powerful for spec-z and photo-z estimations in future cosmological surveys.

preprint2021arXiv

The $L_p$-$L_q$ decay estimate for the multidimensional compressible flow with free surface in the exterior domain

The aim of this paper is to develop the general $L_p$ theory for the barotropic compressible Navier-Stokes equations with the free boundary condition in the exterior domain in $\mathbb{R}^N$ ($N\geq 3$). By the spectral analysis, we obtain the classical $L_p$-$L_q$ decay estimate for the linearized model problem (with variable coefficients) in view of the partial Lagrangian transformation.

preprint2020arXiv

A Computer Vision Aided Beamforming Scheme with EM Exposure Control in Outdoor LOS Scenarios

Without any radiation control measures, a large-scale mmWave antenna array at close range may lead to a large amount of electromagnetic exposure of human. In this paper, with the aid of pose detection in computer vision, a beamforming scheme using a novel exposure avoidance method is proposed in outdoor line of sight scenarios. Instead of reducing transmitted power, the proposed method can protect the vulnerable parts of human body from electromagnetic exposure during transmission by deviating the transmission beams from vulnerable parts. Besides, a finer beam management granularity is adopted to better balance the trade-off between exposure reduction and communication quality loss, because finer beams can provide more adjustability for finding the beam that reduces exposure without excessively reducing the link quality. The proposed exposure avoidance method is validated in simulations, and the results show that the finer beam management granularity can guarantee communication quality while reducing the electromagnetic exposure.

preprint2020arXiv

A Computer Vision Based Beamforming Scheme for Millimeter Wave Communication in LOS Scenarios

A novel location-aware beamforming scheme for millimeter wave communication is proposed for line of sight (LOS) and low mobility scenarios, in which computer vision is introduced to derive the required position or spatial angular information from the image or video captured by camera(s) co-located with mmWave antenna array at base stations. A wireless coverage model is built to investigate the coverage performance and influence of positioning accuracy achieved by convolutional neural network (CNN) for image processing. In addition, videos could be intentionally blurred, or even low-resolution videos could be directly applied, to protect users' privacy with acceptable positioning precision, lower computation complexity and lower camera cost. It is proved by simulations that the beamforming scheme is practicable and the mainstream CNN we employed is sufficient in both aspects of beam directivity accuracy and processing speed in frame per second.

preprint2020arXiv

A new cosmological probe using super-massive black hole shadows

We study the prospects of using the low-redshift and high-redshift black hole shadows as new cosmological standard rulers for measuring cosmological parameters. We show that, using the low-redshift observation of the black hole shadow of M87$^\star$, the Hubble constant can be independently determined with a precision of about $13\%$ as $H_0=70\pm 9$ km s$^{-1}$ Mpc$^{-1}$. The high-redshift observations of super-massive black hole shadows may accurately determine a combination of parameters $H_0$ and ${Ω_{\rm m}}$, and we show by a simple simulation that combining them with the type Ia supernovae observations would give precise measurements of the cosmological parameters.

preprint2020arXiv

A novel method of measuring cosmological distances using broad-line regions of quasars

The absolute distance scale measurements in cosmology have always been an important mission. In particular, in recent years, the Hubble constant tension between the measurements from early and late universe has become a new crisis for cosmology, which calls for new, independent absolute cosmological distance measurements. Recently, a result of measuring the parallax distance to 3C 273 through spectroastrometry and reverberation mapping was reported. We comment on this novel method in this News & Views paper.

preprint2020arXiv

A Novel Semi-Supervised Data-Driven Method for Chiller Fault Diagnosis with Unlabeled Data

In practical chiller systems, applying efficient fault diagnosis techniques can significantly reduce energy consumption and improve energy efficiency of buildings. The success of the existing methods for fault diagnosis of chillers relies on the condition that sufficient labeled data are available for training. However, label acquisition is laborious and costly in practice. Usually, the number of labeled data is limited and most data available are unlabeled. The existing methods cannot exploit the information contained in unlabeled data, which significantly limits the improvement of fault diagnosis performance in chiller systems. To make effective use of unlabeled data to further improve fault diagnosis performance and reduce the dependency on labeled data, we proposed a novel semi-supervised data-driven fault diagnosis method for chiller systems based on the semi-generative adversarial network, which incorporates both unlabeled and labeled data into learning process. The semi-generative adversarial network can learn the information of data distribution from unlabeled data and this information can help to significantly improve the diagnostic performance. Experimental results demonstrate the effectiveness of the proposed method. Under the scenario that there are only 80 labeled samples and 16000 unlabeled samples, the proposed method can improve the diagnostic accuracy to 84%, while the supervised baseline methods only reach the accuracy of 65% at most. Besides, the minimal required number of labeled samples can be reduced by about 60% with the proposed method when there are enough unlabeled samples.

preprint2020arXiv

A study of the effect of the illumination model on the generation of synthetic training datasets

The use of computer generated images to train Deep Neural Networks is a viable alternative to real images when the latter are scarce or expensive. In this paper, we study how the illumination model used by the rendering software affects the quality of the generated images. We created eight training sets, each one with a different illumination model, and tested them on three different network architectures, ResNet, U-Net and a combined architecture developed by us. The test set consisted of photos of 3D printed objects produced from the same CAD models used to generate the training set. The effect of the other parameters of the rendering process, such as textures and camera position, was randomized. Our results show that the effect of the illumination model is important, comparable in significance to the network architecture. We also show that both light probes capturing natural environmental light, and modelled lighting environments, can give good results. In the case of light probes, we identified as two significant factors affecting performance the similarity between the light probe and the test environment, as well as the light probe's resolution. Regarding modelled lighting environment, similarity with the test environment was again identified as a significant factor.

preprint2020arXiv

Absence of Superconductivity in Nd$_{0.8}$Sr$_{0.2}$NiO$_x$ Thin Films without Chemical Reduction

The recently reported superconductivity 9-15 K in Nd0.8Sr0.2NiO2/SrTiO3 heterostructures that were fabricated by a soft-chemical topotactic reduction approach based on precursor Nd0.8Sr0.2NiO3 thin films deposited on SrTiO3 substrates, has excited an immediate surge of research interest. To explore an alternative physical path instead of chemical reduction for realizing superconductivity in this compound, using pulsed laser deposition, we systematically fabricated 63 Nd0.8Sr0.2NiOx (NSNO) thin films at a wide range of oxygen partial pressures on various different oxide substrates. Transport measurements did not find any signature of superconductivity in all the 63 thin-film samples. With reducing the oxygen content in the NSNO films by lowering the deposition oxygen pressure, the NSNO films are getting more resistive and finally become insulating. Furthermore, we tried to cap a 20-nm-thick amorphous LaAlO3 layer on a Nd0.8Sr0.2NiO3 thin film deposited at a high oxygen pressure of 150 mTorr to create oxygen vacancies on its surface and did not succeed in higher conductivity either. Our experimental results together with the recent report on the absence of superconductivity in synthesized bulk Nd0.8Sr0.2NiO2 crystals suggest that the chemical reduction approach could be unique for yielding superconductivity in NSNO/SrTiO3 heterostructures. However, SrTiO3 substrates could be reduced to generate oxygen vacancies during the chemical reduction process as well, which may thus partially contribute to conductivity.

preprint2020arXiv

Blockchain for Future Smart Grid: A Comprehensive Survey

The concept of smart grid has been introduced as a new vision of the conventional power grid to figure out an efficient way of integrating green and renewable energy technologies. In this way, Internet-connected smart grid, also called energy Internet, is also emerging as an innovative approach to ensure the energy from anywhere at any time. The ultimate goal of these developments is to build a sustainable society. However, integrating and coordinating a large number of growing connections can be a challenging issue for the traditional centralized grid system. Consequently, the smart grid is undergoing a transformation to the decentralized topology from its centralized form. On the other hand, blockchain has some excellent features which make it a promising application for smart grid paradigm. In this paper, we aim to provide a comprehensive survey on application of blockchain in smart grid. As such, we identify the significant security challenges of smart grid scenarios that can be addressed by blockchain. Then, we present a number of blockchain-based recent research works presented in different literatures addressing security issues in the area of smart grid. We also summarize several related practical projects, trials, and products that have been emerged recently. Finally, we discuss essential research challenges and future directions of applying blockchain to smart grid security issues.

preprint2020arXiv

Cascading Failures in Complex Networks

Cascading failure is a potentially devastating process that spreads on real-world complex networks and can impact the integrity of wide-ranging infrastructures, natural systems, and societal cohesiveness. One of the essential features that create complex network vulnerability to failure propagation is the dependency among their components, exposing entire systems to significant risks from destabilizing hazards such as human attacks, natural disasters or internal breakdowns. Developing realistic models for cascading failures as well as strategies to halt and mitigate the failure propagation can point to new approaches to restoring and strengthening real-world networks. In this review, we summarize recent progress on models developed based on physics and complex network science to understand the mechanisms, dynamics and overall impact of cascading failures. We present models for cascading failures in single networks and interdependent networks and explain how different dynamic propagation mechanisms can lead to an abrupt collapse and a rich dynamic behavior. Finally, we close the review with novel emerging strategies for containing cascades of failures and discuss open questions that remain to be addressed.

preprint2020arXiv

Complexity of tree-coloring interval graphs equitably

An equitable tree-$k$-coloring of a graph is a vertex $k$-coloring such that each color class induces a forest and the size of any two color classes differ by at most one. In this work, we show that every interval graph $G$ has an equitable tree-$k$-coloring for any integer $k\geq \lceil(Δ(G)+1)/2\rceil$, solving a conjecture of Wu, Zhang and Li (2013) for interval graphs, and furthermore, give a linear-time algorithm for determining whether a proper interval graph admits an equitable tree-$k$-coloring for a given integer $k$. For disjoint union of split graphs, or $K_{1,r}$-free interval graphs with $r\geq 4$, we prove that it is $W[1]$-hard to decide whether there is an equitable tree-$k$-coloring when parameterized by number of colors, or by treewidth, number of colors and maximum degree, respectively.

preprint2020arXiv

Constraints on active and sterile neutrinos in an interacting dark energy cosmology

We investigate the impacts of dark energy on constraining massive (active/sterile) neutrinos in interacting dark energy (IDE) models by using the current observations. We employ two typical IDE models, the interacting $w$ cold dark matter (I$w$CDM) model and the interacting holographic dark energy (IHDE) model, to make an analysis. To avoid large-scale instability, we use the parameterized post-Friedmann approach to calculate the cosmological perturbations in the IDE models. The cosmological observational data used in this work include the Planck cosmic microwave background (CMB) anisotropies data, the baryon acoustic oscillation data, the type Ia supernovae data, the direct measurement of the Hubble constant, the weak lensing data, the redshift-space distortion data, and the CMB lensing data. We find that the dark energy properties could influence the constraint limits of active neutrino mass and sterile neutrino parameters in the IDE models. We also find that the dark energy properties could influence the constraints on the coupling strength parameter $β$, and a positive coupling constant, $β>0$, can be detected at the $2.5σ$ statistical significance for the IHDE+$ν_s$ model by using the all-data combination. In addition, we also discuss the "Hubble tension" issue in these scenarios. We find that the $H_0$ tension can be effectively relieved by considering massive sterile neutrinos, and in particular in the IHDE+$ν_s$ model the $H_0$ tension can be reduced to be at the $1.28σ$ level.

preprint2020arXiv

Cosmological model-independent constraints on spatial curvature from strong gravitational lensing and type Ia supernova observations

Applying the distance sum rule in strong gravitational lensing (SGL) and type Ia supernova (SN Ia) observations, one can provide an interesting cosmological model-independent method to determine the cosmic curvature parameter $Ω_k$. In this paper, with the newly compiled data sets including 161 galactic-scale SGL systems and 1048 SN Ia data, we place constraints on $Ω_k$ within the framework of three types of lens models extensively used in SGL studies. Moreover, to investigate the effect of different mass lens samples on the results, we divide the SGL sample into three sub-samples based on the center velocity dispersion of intervening galaxies. In the singular isothermal sphere (SIS) and extended power-law lens models, a flat universe is supported with the uncertainty about 0.2, while a closed universe is preferred in the power-law lens model. We find that the choice of lens models and the classification of SGL data actually can influence the constraints on $Ω_k$ significantly.

preprint2020arXiv

Cosmological parameter measurement and neutral hydrogen 21 cm sky survey with the Square Kilometre Array

In order to precisely measure the cosmological parameters and answer the fundamental questions in cosmology, it is necessary to develop new, powerful cosmological probes, in addition to the proposed next-generation optical survey projects. The neutral hydrogen 21 cm sky surveys will provide a promising tool to study the late-universe evolution, helping shed light on the nature of dark energy. The Square Kilometre Array is the largest radio telescope in the world to be constructed in the near future, and it will push the 21 cm cosmology into a new era and greatly promote the development of cosmology in the forthcoming decades.

preprint2020arXiv

Designing Few-layer Graphene Schottky Contact Solar Cell: Theoretical Efficiency Limits and Parametric Optimization

We theoretically study the efficiency limits and performance characteristics of few-layer graphene-semiconductor solar cells (FGSCs) based on a Schottky contact device structure. We model and compare the energy conversion efficiency of various configurations by explicitly considering the non-Richardson thermionic emission across few-layer graphene/semiconductor Schottky heterostructures. The calculations reveal that ABA-stacked trilayer graphene-silicon solar cell exhibits a maximal conversion efficiency exceeding 28\% due to a lower reversed saturation current when compared to that of the ABC-stacking configuration. The thermal coefficients of PCE for ABA and ABC stacking FGSCs are -0.064\%/K and -0.049\%/K, respectively. Our work offers insights for optimal designs of graphene-based solar cells, thus paving a route towards the design of high-performance FGSC for future nanoscale energy converters.

preprint2020arXiv

Expected LHAASO sensitivity to decaying dark matter signatures from dwarf galaxies gamma-ray emission

As a next-generation complex extensive air shower array with a large field of view, the large high altitude air shower observatory (LHAASO) is very sensitive to the very high energy gamma-rays from $\sim$ 300 GeV to 1 PeV, and may thus serve as an important probe for the heavy dark matter (DM) particles. In this study, we make a forecast for the LHAASO sensitivities to the gamma-ray signatures resulting from DM decay in dwarf spheroidal satellite galaxies (dSphs) within the LHAASO field of view. Both individual and combined limits for 19 dSphs incorporating the uncertainties of the DM density profile are explored. Owing to the large effective area and strong capability of the photon-proton discrimination, we find that LHASSSO is sensitive to the signatures from decaying DM particles above $\mathcal{O}(1)$ TeV. The LHAASO sensitivity to the DM decay lifetime reaches $\mathcal{O} (10^{26}) \sim \mathcal{O} (10^{28})$ s for several decay channels at the DM mass scale from 1 TeV to 100 TeV.

preprint2020arXiv

Forecast for cosmological parameter estimation with gravitational-wave standard siren observation from the Cosmic Explorer

The third-generation ground-based gravitational-wave (GW) detector, Cosmic Explorer (CE), is scheduled to start its observation in the 2030s. In this paper, we make a forecast for cosmological parameter estimation with gravitational-wave standard siren observation from the CE. We use the simulated GW standard siren data of CE to constrain the $Λ$CDM, $w$CDM and CPL models. We combine the simulated GW data with the current cosmological electromagnetic observations including the latest cosmic microwave background anisotropies data from Planck, the optical baryon acoustic oscillation measurements, and the type Ia supernovae observation (Pantheon compilation) to do the analysis. We find that the future standard siren observation from CE will improve the cosmological parameter estimation to a great extent, since the future GW standard siren data can well break the degeneracies generated by the optical observations between various cosmological parameters. We also find that the CE's constraining capability on the cosmological parameters is slightly better than that of the same-type GW detector, the Einstein Telescope. In addition, the synergy between the GW standard siren observation from CE and the 21 cm emission observation from SKA is also discussed.

preprint2020arXiv

Forecast for weighing neutrinos in cosmology with SKA

We investigate what role the SKA neutral hydrogen (HI) intensity mapping (IM) sky survey observation will play in weighing neutrinos in cosmology. We use the simulated data of the baryon acoustic oscillation (BAO) measurements from the HI IM survey based on SKA1 and SKA2 to do the analysis. For the current observations, we use the Planck 2015 cosmic microwave background (CMB) anisotropies observation, the optical BAO measurements, the type Ia supernovae (SN) observation (Pantheon compilation), and the latest $H_0$ measurement. We consider three mass ordering cases for massive neutrinos, i.e., the normal hierarchy (NH), inverted hierarchy (IH), and degenerate hierarchy (DH) cases. It is found that the SKA observation can significantly improve the constraints on $Ω_{\rm m}$ and $H_0$. Compared to the current observation, the SKA1 data can improve the constraints on $Ω_{\rm m}$ by about 33%, and on $H_0$ by about 36%; the SKA2 data can improve the constraints on $Ω_{\rm m}$ by about 58%, and on $H_0$ by about 66%. It is also found that the SKA observation can only slightly improve the constraints on $\sum m_ν$. Compared to the current observation, the SKA1 data can improve the constraints on $\sum m_ν$ by about 4%, 3%, and 10%, for the NH, IH, and DH cases, respectively; the SKA2 data can improve the constraints on $\sum m_ν$ by about 7%, 7%, and 16%, for the NH, IH, and DH cases, respectively.

preprint2020arXiv

Giant anisotropy of spin relaxation and spin-valley mixing in a silicon quantum dot

In silicon quantum dots (QDs), at a certain magnetic field commonly referred to as the "hot spot", the electron spin relaxation rate (T_1^(-1)) can be drastically enhanced due to strong spin-valley mixing. Here, we experimentally find that with a valley splitting of 78.2 ${\pm}$ 1.6 $μ$eV, this hot spot in spin relaxation can be suppressed by more than 2 orders of magnitude when the in-plane magnetic field is oriented at an optimal angle, about 9° from the [100] sample plane. This directional anisotropy exhibits a sinusoidal modulation with a 180° periodicity. We explain the magnitude and phase of this modulation using a model that accounts for both spin-valley mixing and intravalley spin-orbit mixing. The generality of this phenomenon is also confirmed by tuning the electric field and the valley splitting up to 268.2 ${\pm}$ 0.7 $μ$eV.

preprint2020arXiv

HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation

Accurate segmentation of the prostate is a key step in external beam radiation therapy treatments. In this paper, we tackle the challenging task of prostate segmentation in CT images by a two-stage network with 1) the first stage to fast localize, and 2) the second stage to accurately segment the prostate. To precisely segment the prostate in the second stage, we formulate prostate segmentation into a multi-task learning framework, which includes a main task to segment the prostate, and an auxiliary task to delineate the prostate boundary. Here, the second task is applied to provide additional guidance of unclear prostate boundary in CT images. Besides, the conventional multi-task deep networks typically share most of the parameters (i.e., feature representations) across all tasks, which may limit their data fitting ability, as the specificities of different tasks are inevitably ignored. By contrast, we solve them by a hierarchically-fused U-Net structure, namely HF-UNet. The HF-UNet has two complementary branches for two tasks, with the novel proposed attention-based task consistency learning block to communicate at each level between the two decoding branches. Therefore, HF-UNet endows the ability to learn hierarchically the shared representations for different tasks, and preserve the specificities of learned representations for different tasks simultaneously. We did extensive evaluations of the proposed method on a large planning CT image dataset, including images acquired from 339 patients. The experimental results show HF-UNet outperforms the conventional multi-task network architectures and the state-of-the-art methods.

preprint2020arXiv

Higher derivatives driven symmetry breaking in holographic superconductors

In this paper, we construct a novel holographic superconductor from higher derivative (HD) gravity involving a coupling between the complex scalar field and the Weyl tensor. This HD coupling term provides a near horizon effective mass squared, which can violates IR Breitenlohner-Freedman (BF) bound by tuning the HD coupling and induces the instability of black brane such that the superconducting phase transition happens. We also study the properties of the condensation and the conductivity in the probe limit. We find that a wider extension of the superconducting energy gap ranging from 4.6 to 10.5 may provide a novel platform to model and interpret the phenomena in the real materials of high temperature superconductor.

preprint2020arXiv

Improving mobility of silicon metal-oxide-semiconductor devices for quantum dots by high vacuum activation annealing

To improve mobility of fabricated silicon metal-oxide-semiconductor (MOS) quantum devices, forming gas annealing is a common method used to mitigate the effects of disorder at the Si/SiO2 interface. However, the importance of activation annealing is usually ignored. Here, we show that a high vacuum environment for implantation activation is beneficial for improving mobility compared to nitrogen atmosphere. Low-temperature transport measurements of Hall bars show that peak mobility can be improved by a factor of two, reaching 1.5 m^2/(Vs) using high vacuum annealing during implantation activation. Moreover, the charge stability diagram of a single quantum dot is mapped, with no visible disturbance caused by disorder, suggesting possibility of fabricating high-quality quantum dots on commercial wafers. Our results may provide valuable insights into device optimization in silicon-based quantum computing.

preprint2020arXiv

Inflation model selection revisited after a 1.91% measurement of the Hubble constant

The recent local measurement of the Hubble constant based on the distance-ladder method has reached a $1.91\%$ precision, but this result is in tension with the early-universe measurements at the more than 4$σ$ level, bringing a crisis to the contemporary cosmology. In addition to the end-to-end test of the $Λ$CDM model in the late universe, it is also of great interest to see how the local $H_0$ measurement affects the determination of the primordial power spectra, and further to test the influences for the inflation model selection. Here, we constrain the primordial power spectra of scalar and tensor perturbations by using a series of observational data, including the Planck 2015 cosmic microwave background (CMB) temperature and polarization power spectra data, the Planck 2018 lensing power spectrum data, the BICEP2/Keck Array CMB B-mode data, and also the prior of optical depth $τ=0.054\pm0.007$, as well as the late-universe measurements (baryon acoustic oscillations and type Ia supernovae). In particular, we use the latest $1.91\%$ measurement of the Hubble constant, $H_{0}=74.03\pm1.42$ km s$^{-1}$ Mpc$^{-1}$, in this cosmological test. We find that considering the latest local $H_{0}$ measurement in the data combination will lead to a larger fit value of $n_{\rm s}$. With the addition of the latest local measurement of $H_{0}$, it is found that the natural inflation model is totally excluded at the $2σ$ level, the Starobinsky $R^{2}$ inflation model is marginally favored at around the $2σ$ level, and the spontaneously broken SUSY inflation model is the most favored model.

preprint2020arXiv

Malicious Experts versus the multiplicative weights algorithm in online prediction

We consider a prediction problem with two experts and a forecaster. We assume that one of the experts is honest and makes correct prediction with probability $μ$ at each round. The other one is malicious, who knows true outcomes at each round and makes predictions in order to maximize the loss of the forecaster. Assuming the forecaster adopts the classical multiplicative weights algorithm, we find upper and lower bounds for the value function of the malicious expert. Our results imply that the multiplicative weights algorithm cannot resist the corruption of malicious experts. We also show that an adaptive multiplicative weights algorithm is asymptotically optimal for the forecaster, and hence more resistant to the corruption of malicious experts.

preprint2020arXiv

Mitochondria in higher plants possess H2 evolving activity which is closely related to complex I

Hydrogenase occupy a central place in the energy metabolism of anaerobic bacteria. Although the structure of mitochondrial complex I is similar to that of hydrogenase, whether it has hydrogen metabolic activity remain unclear. Here, we show that a H2 evolving activity exists in higher plants mitochondria and is closely related to complex I, especially around ubiquinone binding site. The H2 production could be inhibited by rotenone and ubiquinone. Hypoxia could simultaneously promote H2 evolution and succinate accumulation. Redox properties of quinone pool, adjusted by NADH or succinate according to oxygen concentration, acts as a valve to control the flow of protons and electrons and the production of H2. The coupling of H2 evolving activity of mitochondrial complex I with metabolic regulation reveals a more effective redox homeostasis regulation mechanism. Considering the ubiquity of mitochondria in eukaryotes, H2 metabolism might be the innate function of higher organisms. This may serve to explain, at least in part, the broad physiological effects of H2.

preprint2020arXiv

Noncollinear Spintronics and Electric-Field Control: A Review

Our world is composed of various materials with different structures, where spin structures have been playing a pivotal role in spintronic devices of the contemporary information technology. Apart from conventional collinear spin materials such as collinear ferromagnets and collinear antiferromagnetically coupled materials, noncollinear spintronic materials have emerged as hot spots of research attention owing to exotic physical phenomena. In this Review, we firstly introduce two types noncollinear spin structures, i.e., the chiral spin structure that yields real-space Berry phases and the coplanar noncollinear spin structure that could generate momentum-space Berry phases, and then move to relevant novel physical phenomena including topological Hall effect, anomalous Hall effect, multiferroic, Weyl fermions, spin-polarized current, and spin Hall effect without spin-orbit coupling in these noncollinear spin systems. Afterwards, we summarize and elaborate the electric-field control of the noncollinear spin structure and related physical effects, which could enable ultralow power spintronic devices in future. In the final outlook part, we emphasize the importance and possible routes for experimentally detecting the intriguing theoretically predicted spin-polarized current, verifying the spin Hall effect in the absence of spin-orbit coupling and exploring the anisotropic magnetoresistance and domain-wall-related magnetoresistance effects for noncollinear antiferromagnetic materials.

preprint2020arXiv

On non-uniqueness in mean field games

We analyze an $N+1$-player game and the corresponding mean field game with state space $\{0,1\}$. The transition rate of $j$-th player is the sum of his control $α^j$ plus a minimum jumping rate $η$. Instead of working under monotonicity conditions, here we consider an anti-monotone running cost. We show that the mean field game equation may have multiple solutions if $η< \frac{1}{2}$. We also prove that that although multiple solutions exist, only the one coming from the entropy solution is charged (when $η=0$), and therefore resolve a conjecture of ArXiv: 1903.05788.

preprint2020arXiv

PolSAR Image Classification Based on Dilated Convolution and Pixel-Refining Parallel Mapping network in the Complex Domain

Efficient and accurate polarimetric synthetic aperture radar (PolSAR) image classification with a limited number of prior labels is always full of challenges. For general supervised deep learning classification algorithms, the pixel-by-pixel algorithm achieves precise yet inefficient classification with a small number of labeled pixels, whereas the pixel mapping algorithm achieves efficient yet edge-rough classification with more prior labels required. To take efficiency, accuracy and prior labels into account, we propose a novel pixel-refining parallel mapping network in the complex domain named CRPM-Net and the corresponding training algorithm for PolSAR image classification. CRPM-Net consists of two parallel sub-networks: a) A transfer dilated convolution mapping network in the complex domain (C-Dilated CNN) activated by a complex cross-convolution neural network (Cs-CNN), which is aiming at precise localization, high efficiency and the full use of phase information; b) A complex domain encoder-decoder network connected parallelly with C-Dilated CNN, which is to extract more contextual semantic features. Finally, we design a two-step algorithm to train the Cs-CNN and CRPM-Net with a small number of labeled pixels for higher accuracy by refining misclassified labeled pixels. We verify the proposed method on AIRSAR and E-SAR datasets. The experimental results demonstrate that CRPM-Net achieves the best classification results and substantially outperforms some latest state-of-the-art approaches in both efficiency and accuracy for PolSAR image classification. The source code and trained models for CRPM-Net is available at: https://github.com/PROoshio/CRPM-Net.

preprint2020arXiv

Private and Communication-Efficient Edge Learning: A Sparse Differential Gaussian-Masking Distributed SGD Approach

With rise of machine learning (ML) and the proliferation of smart mobile devices, recent years have witnessed a surge of interest in performing ML in wireless edge networks. In this paper, we consider the problem of jointly improving data privacy and communication efficiency of distributed edge learning, both of which are critical performance metrics in wireless edge network computing. Toward this end, we propose a new decentralized stochastic gradient method with sparse differential Gaussian-masked stochastic gradients (SDM-DSGD) for non-convex distributed edge learning. Our main contributions are three-fold: i) We theoretically establish the privacy and communication efficiency performance guarantee of our SDM-DSGD method, which outperforms all existing works; ii) We show that SDM-DSGD improves the fundamental training-privacy trade-off by {\em two orders of magnitude} compared with the state-of-the-art. iii) We reveal theoretical insights and offer practical design guidelines for the interactions between privacy preservation and communication efficiency, two conflicting performance goals. We conduct extensive experiments with a variety of learning models on MNIST and CIFAR-10 datasets to verify our theoretical findings. Collectively, our results contribute to the theory and algorithm design for distributed edge learning.

preprint2020arXiv

Prospect for constraining holographic dark energy with gravitational wave standard sirens from the Einstein Telescope

We study the holographic dark energy (HDE) model by using the future gravitational wave (GW) standard siren data observed from the Einstein Telescope (ET) in this work. We simulate 1000 GW standard siren data based on a 10-year observation of the ET to make this analysis. We find that all the cosmological parameters in the HDE model can be tremendously improved by including the GW standard siren data in the cosmological fit. The GW data combined with the current cosmic microwave background anisotropies, baryon acoustic oscillations, and type Ia supernovae data will measure the cosmological parameters $Ω_{\rm m}$, $H_0$, and $c$ in the HDE model to be at the accuracies of 1.28%, 0.59%, and 3.69%, respectively. A comparison with the cosmological constant model and the constant-$w$ dark energy model shows that, compared to the standard model, the parameter degeneracies will be broken more thoroughly in a dynamical dark energy model. We find that the GW data alone can provide a fairly good measurement for $H_0$, but for other cosmological parameters the GW data alone can only provide rather weak measurements. However, due to the fact that the parameter degeneracies can be broken by the GW data, the standard sirens can play an essential role in improving the parameter estimation.

preprint2020arXiv

Prospects for improving cosmological parameter estimation with gravitational-wave standard sirens from Taiji

Taiji, a space-based gravitational-wave observatory, consists of three satellites forming an equilateral triangle with arm length of $3\times 10^6$ km, orbiting around the Sun. Taiji is able to observe the gravitational-wave standard siren events of massive black hole binary (MBHB) merger, which is helpful in probing the expansion of the universe. In this paper, we preliminarily forecast the capability of Taiji for improving cosmological parameter estimation with the gravitational-wave standard siren data. We simulate five-year standard siren data based on three fiducial cosmological models and three models of MBHB's formation and growth. It is found that the standard siren data from Taiji can effectively break the cosmological parameter degeneracies generated by the cosmic microwave background (CMB) anisotropies data, especially for dynamical dark energy models. The constraints on cosmological parameters are significantly improved by the data combination CMB+Taiji, compared to the CMB data alone. Compared to the current optical cosmological observations, Taiji can still provide help in improving the cosmological parameter estimation to some extent. In addition, we consider an ideal scenario to investigate the potential of Taiji on constraining cosmological parameters. We conclude that the standard sirens of MBHB from Taiji will become a powerful cosmological probe in the future.

preprint2020arXiv

Quantifying the impacts of future gravitational-wave data on constraining interacting dark energy

In this work, we investigate the impacts of the future gravitational-wave (GW) standard siren observation by the Einstein Telescope (ET) on constraining the interacting dark energy (IDE) models. We simulate 1000 GW events in the redshift range of $0\lesssim z \lesssim 5$ based on the 10-year observation of the ET. We combine the simulated GW data with the current mainstream cosmological electromagnetic observations including the cosmic microwave background anisotropies, the baryon acoustic oscillations, and the type Ia supernovae to constrain the IDE models. We consider typical IDE models in the context of a perturbed universe. To avoid the large-scale instability problem for IDE models, we apply the extended parameterized post-Friedmann approach to calculate the cosmological perturbations. We find that the addition of the GW standard siren data could significantly improve the constraint accuracies for most of the cosmological parameters (e.g., $H_{0}$, $w$, and $Ω_{\rm m}$). For the coupling parameter $β$, the constraint errors could also be slightly improved when adding the GW data in the cosmological fit.

preprint2020arXiv

Real-time cosmology with SKA

In this work, we investigate what role the redshift drift data of Square Kilometre Array (SKA) will play in the cosmological parameter estimation in the future. To test the constraint capability of the redshift drift data of SKA-only, the $Λ$CDM model is chosen as a reference model. We find that using the SKA1 mock data, the $Λ$CDM model can be loosely constrained, while the model can be well constrained when the SKA2 mock data are used. When the mock data of SKA are combined with the data of the European Extremely Large Telescope (E-ELT), the constraints can be significantly improved almost as good as the data combination of the type Ia supernovae observation (SN), the cosmic microwave background observation (CMB), and the baryon acoustic oscillations observation (BAO). Furthermore, we explore the impact of the redshift drift data of SKA on the basis of SN+CMB+BAO+E-ELT in the $Λ$CDM model, the $w$CDM model, the CPL model, and the HDE model. We find that the redshift drift measurement of SKA could help to significantly improve the constraints on dark energy and could break the degeneracy between the cosmological parameters. Therefore, we conclude that redshift-drift observation of SKA would provide a good improvement in the cosmological parameter estimation in the future and has the enormous potential to be one of the most competitive cosmological probes in constraining dark energy.

preprint2020arXiv

Taming Convergence for Asynchronous Stochastic Gradient Descent with Unbounded Delay in Non-Convex Learning

Understanding the convergence performance of asynchronous stochastic gradient descent method (Async-SGD) has received increasing attention in recent years due to their foundational role in machine learning. To date, however, most of the existing works are restricted to either bounded gradient delays or convex settings. In this paper, we focus on Async-SGD and its variant Async-SGDI (which uses increasing batch size) for non-convex optimization problems with unbounded gradient delays. We prove $o(1/\sqrt{k})$ convergence rate for Async-SGD and $o(1/k)$ for Async-SGDI. Also, a unifying sufficient condition for Async-SGD's convergence is established, which includes two major gradient delay models in the literature as special cases and yields a new delay model not considered thus far.

preprint2020arXiv

The TianQin project: current progress on science and technology

TianQin is a planned space-based gravitational wave (GW) observatory consisting of three earth orbiting satellites with an orbital radius of about $10^5~{\rm km}$. The satellites will form a equilateral triangle constellation the plane of which is nearly perpendicular to the ecliptic plane. TianQin aims to detect GWs between $10^{-4}~{\rm Hz}$ and $1~{\rm Hz}$ that can be generated by a wide variety of important astrophysical and cosmological sources, including the inspiral of Galactic ultra-compact binaries, the inspiral of stellar-mass black hole binaries, extreme mass ratio inspirals, the merger of massive black hole binaries, and possibly the energetic processes in the very early universe or exotic sources such as cosmic strings. In order to start science operations around 2035, a roadmap called the 0123 plan is being used to bring the key technologies of TianQin to maturity, supported by the construction of a series of research facilities on the ground. Two major projects of the 0123 plan are being carried out. In this process, the team has created a new generation $17~{\rm cm}$ single-body hollow corner-cube retro-reflector which has been launched with the QueQiao satellite on 21 May 2018; a new laser ranging station equipped with a $1.2~{\rm m}$ telescope has been constructed and the station has successfully ranged to all the five retro-reflectors on the Moon; and the TianQin-1 experimental satellite has been launched on 20 December 2019 and the first round result shows that the satellite has exceeded all of its mission requirements.

preprint2020arXiv

Transport plans with domain constraints

This paper focuses on martingale optimal transport problems when the martingales are assumed to have bounded quadratic variation. First, we give a result that characterizes the existence of a probability measure satisfying some convex transport constraints in addition to having given initial and terminal marginals. Several applications are provided: martingale measures with volatility uncertainty, optimal transport with capacity constraints, and Skorokhod embedding with bounded times. Next, we extend this result to multi-marginal constraints. Finally, we consider an optimal transport problem with constraints and obtain its Kantorovich duality. A corollary of this result is a monotonicity principle which gives a geometric way of identifying the optimizer.

preprint2020arXiv

Unexpectedly strong diamagnetism and superparamagnetism of aromatic peptides due to self-assembling and cations

There is a considerable amount of work that shows the biomagnetism of organic components without ferromagnetic components at the molecular level, but it is of great challenge to cover the giant gap of biomagnetism between their experimental and theoretical results. Here, we show that the diamagnetism of an aromatic peptide, the AYFFF, is greatly enhanced for about 11 times by self-assembling, reaching two orders of magnitude higher than the mass susceptibility of pure water. Moreover, the AYFFF self-assemblies further mixed with ZnCl2 solution of sufficiently high concentrations display superparamagnetism, with the mass susceptibility reaching more than two orders of magnitude higher than the absolute value of pure water, which may approach the mass susceptibility of ferromagnetism. The aromatic rings in the peptide molecules and the cations are the keys to such a strong diamagnetism and superparamagnetism of aromatic peptides.

preprint2019arXiv

Dark energy versus modified gravity: Impacts on measuring neutrino mass

In this paper, we make a comparison for the impacts of smooth dynamical dark energy, modified gravity, and interacting dark energy on the cosmological constraints on the total mass of active neutrinos. For definiteness, we consider the $Λ$CDM model, the $w$CDM model, the $f(R)$ model, and two typical interacting vacuum energy models, i.e., the I$Λ$CDM1 model with $Q=βHρ_{\rm c}$ and the I$Λ$CDM2 model with $Q=βHρ_Λ$. In the cosmological fits, we use the Planck 2015 temperature and polarization data, in combination with other low-redshift observations including the baryon acoustic oscillations, the type Ia supernovae, the Hubble constant measurement, and the large-scale structure observations, such as the weak lensing as well as the redshift-space distortion. Besides, the Planck lensing measurement is also employed in this work. We find that, the $w$CDM model favors a higher upper limit on the neutrino mass compared to the $Λ$CDM model, while the upper limit in the $f(R)$ model is similar with that of $Λ$CDM model. For the interacting vacuum energy models, the I$Λ$CDM1 model favors a higher upper limit on neutrino mass, while the I$Λ$CDM2 model favors an identical neutrino mass with the case of $Λ$CDM.

preprint2019arXiv

On-line Search History-assisted Restart Strategy for Covariance Matrix Adaptation Evolution Strategy

Restart strategy helps the covariance matrix adaptation evolution strategy (CMA-ES) to increase the probability of finding the global optimum in optimization, while a single run CMA-ES is easy to be trapped in local optima. In this paper, the continuous non-revisiting genetic algorithm (cNrGA) is used to help CMA-ES to achieve multiple restarts from different sub-regions of the search space. The CMA-ES with on-line search history-assisted restart strategy (HR-CMA-ES) is proposed. The entire on-line search history of cNrGA is stored in a binary space partitioning (BSP) tree, which is effective for performing local search. The frequently sampled sub-region is reflected by a deep position in the BSP tree. When leaf nodes are located deeper than a threshold, the corresponding sub-region is considered a region of interest (ROI). In HR-CMA-ES, cNrGA is responsible for global exploration and suggesting ROI for CMA-ES to perform an exploitation within or around the ROI. CMA-ES restarts independently in each suggested ROI. The non-revisiting mechanism of cNrGA avoids to suggest the same ROI for a second time. Experimental results on the CEC 2013 and 2017 benchmark suites show that HR-CMA-ES performs better than both CMA-ES and cNrGA. A positive synergy is observed by the memetic cooperation of the two algorithms.

preprint2019arXiv

Probabilistic Neural Network Tomography across Grane field (North Sea) from Surface Wave Dispersion Data

Surface wave tomography uses measured dispersion properties of surface waves to infer the spatial distribution of subsurface properties such as shear-wave velocities. These properties can be estimated vertically below any geographical location at which surface wave dispersion data are available. As the inversion is significantly non-linear, Monte Carlo methods are often used to invert dispersion curves for shear-wave velocity profiles with depth to give a probabilistic solution. Such methods provide uncertainty information but are computationally expensive. Neural network based inversion provides a more efficient way to obtain probabilistic solutions when those solutions are required beneath many geographical locations. Unlike Monte Carlo methods, once a network has been trained it can be applied rapidly to perform any number of inversions. We train a class of neural networks called mixture density networks, to invert dispersion curves for shear-wave velocity models and their non-linearised uncertainty. Mixture density networks are able to produce fully probabilistic solutions in the form of weighted sums of multivariate analytic kernels such as Gaussians, and we show that including data uncertainties in the mixture density network gives more reliable mean velocity estimates when data contains significant noise. The networks were applied to data from the Grane field in the Norwegian North sea to produce shear-wave velocity maps at several depth levels. Post-training we obtained probabilistic velocity profiles with depth beneath 26,772 locations to produce a 3D velocity model in 21 seconds on a standard desktop computer. This method is therefore ideally suited for rapid, repeated 3D subsurface imaging and monitoring.

preprint2019arXiv

Probabilistic Verification of Fairness Properties via Concentration

As machine learning systems are increasingly used to make real world legal and financial decisions, it is of paramount importance that we develop algorithms to verify that these systems do not discriminate against minorities. We design a scalable algorithm for verifying fairness specifications. Our algorithm obtains strong correctness guarantees based on adaptive concentration inequalities; such inequalities enable our algorithm to adaptively take samples until it has enough data to make a decision. We implement our algorithm in a tool called VeriFair, and show that it scales to large machine learning models, including a deep recurrent neural network that is more than five orders of magnitude larger than the largest previously-verified neural network. While our technique only gives probabilistic guarantees due to the use of random samples, we show that we can choose the probability of error to be extremely small.

preprint2019arXiv

Seismic tomography using variational inference methods

Seismic tomography is a methodology to image the interior of solid or fluid media, and is often used to map properties in the subsurface of the Earth. In order to better interpret the resulting images it is important to assess imaging uncertainties. Since tomography is significantly nonlinear, Monte Carlo sampling methods are often used for this purpose, but they are generally computationally intractable for large datasets and high-dimensional parameter spaces. To extend uncertainty analysis to larger systems we use variational inference methods to conduct seismic tomography. In contrast to Monte Carlo sampling, variational methods solve the Bayesian inference problem as an optimization problem, yet still provide probabilistic results. In this study, we applied two variational methods, automatic differential variational inference (ADVI) and Stein variational gradient descent (SVGD), to 2D seismic tomography problems using both synthetic and real data and we compare the results to those from two different Monte Carlo sampling methods. The results show that variational inference methods can produce accurate approximations to the results of Monte Carlo sampling methods at significantly lower computational cost, provided that gradients of parameters with respect to data can be calculated efficiently. We expect that the methods can be applied fruitfully to many other types of geophysical inverse problems.

preprint2019arXiv

Semiconductor Quantum Computation

Semiconductors, a significant type of material in the information era, are becoming more and more powerful in the field of quantum information. In the last decades, semiconductor quantum computation was investigated thoroughly across the world and developed with a dramatically fast speed. The researches vary from initialization, control and readout of qubits, to the architecture of fault tolerant quantum computing. Here, we first introduce the basic ideas for quantum computing, and then discuss the developments of single- and two- qubit gate control in semiconductor. Till now, the qubit initialization, control and readout can be realized with relatively high fidelity and a programmable two-qubit quantum processor was even demonstrated. However, to further improve the qubit quality and scale it up, there are still some challenges to resolve such as the improvement of readout method, material development and scalable designs. We discuss these issues and introduce the forefronts of progress. Finally, considering the positive trend of the research on semiconductor quantum devices and recent theoretical work on the applications of quantum computation, we anticipate that semiconductor quantum computation may develop fast and will have a huge impact on our lives in the near future.

preprint2016arXiv

Bethe States of the integrable spin-s chain with generic open boundaries

Based on the inhomogeneous T-Q relation and the associated Bethe Ansatz equations obtained via the off-diagonal Bethe Ansatz, we construct the Bethe-type eigenstates of the SU(2)-invariant spin-s chain with generic non-diagonal boundaries by employing certain orthogonal basis of the Hilbert space.

preprint2016arXiv

Blocking Collapsed Gibbs Sampler for Latent Dirichlet Allocation Models

The latent Dirichlet allocation (LDA) model is a widely-used latent variable model in machine learning for text analysis. Inference for this model typically involves a single-site collapsed Gibbs sampling step for latent variables associated with observations. The efficiency of the sampling is critical to the success of the model in practical large scale applications. In this article, we introduce a blocking scheme to the collapsed Gibbs sampler for the LDA model which can, with a theoretical guarantee, improve chain mixing efficiency. We develop two procedures, an O(K)-step backward simulation and an O(log K)-step nested simulation, to directly sample the latent variables within each block. We demonstrate that the blocking scheme achieves substantial improvements in chain mixing compared to the state of the art single-site collapsed Gibbs sampler. We also show that when the number of topics is over hundreds, the nested-simulation blocking scheme can achieve a significant reduction in computation time compared to the single-site sampler.

preprint2016arXiv

Comparison of dark energy models after Planck 2015

We make a comparison for ten typical, popular dark energy models according to their capabilities of fitting the current observational data. The observational data we use in this work include the JLA sample of type Ia supernovae observation, the Planck 2015 distance priors of cosmic microwave background observation, the baryon acoustic oscillations measurements, and the direct measurement of the Hubble constant. Since the models have different numbers of parameters, in order to make a fair comparison, we employ the Akaike and Bayesian information criteria to assess the worth of the models. The analysis results show that, according to the capability of explaining observations, the cosmological constant model is still the best one among all the dark energy models. The generalized Chaplygin gas model, the constant $w$ model, and the $α$ dark energy model are worse than the cosmological constant model, but still are good models compared to others. The holographic dark energy model, the new generalized Chaplygin gas model, and the Chevalliear-Polarski-Linder model can still fit the current observations well, but from an economically feasible perspective, they are not so good. The new agegraphic dark energy model, the Dvali-Gabadadze-Porrati model, and the Ricci dark energy model are excluded by the current observations.

preprint2016arXiv

Constraining dark energy with Hubble parameter measurements: an analysis including future redshift-drift observations

Dark energy affects the Hubble expansion rate (namely, the expansion history) $H(z)$ by an integral over $w(z)$. However, the usual observables are the luminosity distances or the angular diameter distances, which measure the distance-redshift relation. Actually, dark energy affects the distances (and the growth factor) by a further integration over functions of $H(z)$. Thus, the direct measurements of the Hubble parameter $H(z)$ at different redshifts are of great importance for constraining the properties of dark energy. In this paper, we show how the typical dark energy models, for example, the $Λ$CDM, $w$CDM, CPL, and holographic dark energy (HDE) models, can be constrained by the current direct measurements of $H(z)$ (31 data in total, covering the redshift range of $z\in [0.07,2.34]$). In fact, the future redshift-drift observations (also referred to as the Sandage-Loeb test) can also directly measure $H(z)$ at higher redshifts, covering the range of $z\in [2,5]$. We thus discuss what role the redshift-drift observations can play in constraining dark energy with the Hubble parameter measurements. We show that the constraints on dark energy can be improved greatly with the $H(z)$ data from only a 10-year observation of redshift drift.

preprint2016arXiv

Determining layer number of two dimensional flakes of transition-metal dichalcogenides by the Raman intensity from substrate

Transition-metal dichalcogenide (TMD) semiconductors have been widely studied due to their distinctive electronic and optical properties. The property of TMD flakes is a function of its thickness, or layer number (N). How to determine N of ultrathin TMDs materials is of primary importance for fundamental study and practical applications. Raman mode intensity from substrates has been used to identify N of intrinsic and defective multilayer graphenes up to N=100. However, such analysis is not applicable for ultrathin TMD flakes due to the lack of a unified complex refractive index ($\tilde{n}$) from monolayer to bulk TMDs. Here, we discuss the N identification of TMD flakes on the SiO$_2$/Si substrate by the intensity ratio between the Si peak from 100-nm (or 89-nm) SiO$_2$/Si substrates underneath TMD flakes and that from bare SiO$_2$/Si substrates. We assume the real part of $\tilde{n}$ of TMD flakes as that of monolayer TMD and treat the imaginary part of $\tilde{n}$ as a fitting parameter to fit the experimental intensity ratio. An empirical $\tilde{n}$, namely, $\tilde{n}_{eff}$, of ultrathin MoS$_{2}$, WS$_{2}$ and WSe$_{2}$ flakes from monolayer to multilayer is obtained for typical laser excitations (2.54 eV, 2.34 eV, or 2.09 eV). The fitted $\tilde{n}_{eff}$ of MoS$_{2}$ has been used to identify N of MoS$_{2}$ flakes deposited on 302-nm SiO$_2$/Si substrate, which agrees well with that determined from their shear and layer-breathing modes. This technique by measuring Raman intensity from the substrate can be extended to identify N of ultrathin 2D flakes with N-dependent $\tilde{n}$ . For the application purpose, the intensity ratio excited by specific laser excitations has been provided for MoS$_{2}$, WS$_{2}$ and WSe$_{2}$ flakes and multilayer graphene flakes deposited on Si substrates covered by 80-110 nm or 280-310 nm SiO$_2$ layer.

preprint2016arXiv

End to End Learning for Self-Driving Cars

We trained a convolutional neural network (CNN) to map raw pixels from a single front-facing camera directly to steering commands. This end-to-end approach proved surprisingly powerful. With minimum training data from humans the system learns to drive in traffic on local roads with or without lane markings and on highways. It also operates in areas with unclear visual guidance such as in parking lots and on unpaved roads. The system automatically learns internal representations of the necessary processing steps such as detecting useful road features with only the human steering angle as the training signal. We never explicitly trained it to detect, for example, the outline of roads. Compared to explicit decomposition of the problem, such as lane marking detection, path planning, and control, our end-to-end system optimizes all processing steps simultaneously. We argue that this will eventually lead to better performance and smaller systems. Better performance will result because the internal components self-optimize to maximize overall system performance, instead of optimizing human-selected intermediate criteria, e.g., lane detection. Such criteria understandably are selected for ease of human interpretation which doesn't automatically guarantee maximum system performance. Smaller networks are possible because the system learns to solve the problem with the minimal number of processing steps. We used an NVIDIA DevBox and Torch 7 for training and an NVIDIA DRIVE(TM) PX self-driving car computer also running Torch 7 for determining where to drive. The system operates at 30 frames per second (FPS).

preprint2016arXiv

Global persistence of geometrical structures for the boussinesq equation with no diffusion

Here we investigate the so-called temperature patch problem for the incompressible Boussinesq system with partial viscosity, in the whole space $\mathbb{R}^N$ $(N \geq 2)$, where the initial temperature is the characteristic function of some simply connected domain with $C^{1, \varepsilon}$ H{ö}lder regularity. Although recent results in [1, 15] ensure that an initially $C^1$ patch persists through the evolution, whether higher regularity is preserved has remained an open question. In the present paper, we give a positive answer to that issue globally in time, in the 2-D case for large initial data and in the higher dimension case for small initial data.

preprint2016arXiv

Impacts of dark energy on weighing neutrinos after Planck 2015

We investigate how dark energy properties impact the cosmological limits on the total mass of active neutrinos. We consider two typical, simple dark energy models (that have only one more additional parameter than $Λ$CDM), i.e., the $w$CDM model and the holographic dark energy (HDE) model, as examples, to make an analysis. In the cosmological fits, we use the Planck 2015 temperature and polarization data, in combination with other low-redshift observations, including the baryon acoustic oscillations, type Ia supernovae, and Hubble constant measurement, as well as the Planck lensing measurements. We find that, once dynamical dark energy is considered, the degeneracy between $\sum m_ν$ and $H_0$ will be changed, i.e., in the $Λ$CDM model, $\sum m_ν$ is anti-correlated with $H_0$, but in the $w$CDM and HDE models, $\sum m_ν$ becomes positively correlated with $H_0$. Compared to $Λ$CDM, in the $w$CDM model the limit on $\sum m_ν$ becomes much looser, but in the HDE model the limit becomes much tighter. In the HDE model, we obtain $\sum m_ν<0.113$ eV ($95\%$ CL) with the combined data sets, which is perhaps the most stringent upper limit by far on neutrino mass. Thus, our result in the HDE model is nearly ready to diagnose the neutrino mass hierarchy with the current cosmological observations.

preprint2016arXiv

Impacts of dark energy on weighing neutrinos: mass hierarchies considered

Taking into account the mass splittings between three active neutrinos, we investigate impacts of dark energy on constraining the total neutrino mass $\sum m_ν$ by using recent cosmological observations. We consider two typical dark energy models, namely, the $w$CDM model and the holographic dark energy (HDE) model, which both have an additional free parameter compared with the $Λ$CDM model. We employ the Planck 2015 data of CMB temperature and polarization anisotropies, combined with low-redshift measurements on BAO distance scales, type Ia supernovae, Hubble constant, and Planck lensing. Compared to the $Λ$CDM model, our study shows that the upper limit on $\sum m_ν$ becomes much looser in the $w$CDM model while much tighter in the HDE model. In the HDE model, we obtain the $95\%$ CL upper limit $\sum m_ν<0.105~\textrm{eV}$ for three degenerate neutrinos. This might be the most stringent constraint on $\sum m_ν$ by far and is almost on the verge of diagnosing the neutrino mass hierachies in the HDE model. However, the difference of $χ^2$ is still not significant enough to distinguish the neutrino mass hierarchies, even though the minimal $χ^2$ of the normal hierarchy is slightly smaller than that of the inverted hierarchy.

preprint2016arXiv

Interface coupling in twisted multilayer graphene by resonant Raman spectroscopy of layer breathing modes

Raman spectroscopy is the prime non-destructive characterization tool for graphene and related layered materials. The shear (C) and layer breathing modes (LBMs) are due to relative motions of the planes, either perpendicular or parallel to their normal. This allows one to directly probe the interlayer interactions in multilayer samples. Graphene and other two-dimensional (2d) crystals can be combined to form various hybrids and heterostructures, creating materials on demand with properties determined by the interlayer interaction. This is the case even for a single material, where multilayer stacks with different relative orientation have different optical and electronic properties. In twisted multilayer graphene samples there is a significant enhancement of the C modes due to resonance with new optically allowed electronic transitions, determined by the relative orientation of the layers. Here we show that this applies also to the LBMs, that can be now directly measured at room temperature. We find that twisting does not affect LBMs, quite different from the case of the C modes. This implies that the periodicity mismatch between two twisted layers mostly affects shear interactions. Our work shows that Raman spectroscopy is an ideal tool to uncover the interface coupling of 2d hybrids and heterostructures.

preprint2016arXiv

Maximum Likelihood and Maximum A Posteriori Direction-of-Arrival Estimation in the Presence of SIRP Noise

The maximum likelihood (ML) and maximum a posteriori (MAP) estimation techniques are widely used to address the direction-of-arrival (DOA) estimation problems, an important topic in sensor array processing. Conventionally the ML estimators in the DOA estimation context assume the sensor noise to follow a Gaussian distribution. In real-life application, however, this assumption is sometimes not valid, and it is often more accurate to model the noise as a non-Gaussian process. In this paper we derive an iterative ML as well as an iterative MAP estimation algorithm for the DOA estimation problem under the spherically invariant random process noise assumption, one of the most popular non-Gaussian models, especially in the radar context. Numerical simulation results are provided to assess our proposed algorithms and to show their advantage in terms of performance over the conventional ML algorithm.

preprint2016arXiv

MIMO Radar Target Localization and Performance Evaluation under SIRP Clutter

Multiple-input multiple-output (MIMO) radar has become a thriving subject of research during the past decades. In the MIMO radar context, it is sometimes more accurate to model the radar clutter as a non-Gaussian process, more specifically, by using the spherically invariant random process (SIRP) model. In this paper, we focus on the estimation and performance analysis of the angular spacing between two targets for the MIMO radar under the SIRP clutter. First, we propose an iterative maximum likelihood as well as an iterative maximum a posteriori estimator, for the target's spacing parameter estimation in the SIRP clutter context. Then we derive and compare various Cramér-Rao-like bounds (CRLBs) for performance assessment. Finally, we address the problem of target resolvability by using the concept of angular resolution limit (ARL), and derive an analytical, closed-form expression of the ARL based on Smith's criterion, between two closely spaced targets in a MIMO radar context under SIRP clutter. For this aim we also obtain the non-matrix, closed-form expressions for each of the CRLBs. Finally, we provide numerical simulations to assess the performance of the proposed algorithms, the validity of the derived ARL expression, and to reveal the ARL's insightful properties.

preprint2016arXiv

On the persistence of Hölder regular patches of density for the inhomogeneous Navier-Stokes equations

In our recent work dedicated to the Boussinesq equations [Danchin and Zhang 2016], we established the persistence of solutions with piecewise constant temperature along interfaces with Hölder regularity. We here address the same problem for the inhomogeneous Navier-Stokes equations satisfied by a viscous incompressibleand inhomogeneous fluid. We establish that, indeed, in the slightly inhomogeneous case, patches of densities with $\mathcal{C}^{1, \varepsilon}$ regularity propagate for all time. As in [Danchin and Zhang 2016], our result follows from the conservation of Hölder regularityalong vector fields moving with the flow. The proof of that latter result is based on commutator estimates involving para-vector fields, and multiplier spaces. The overall analysis is more complicated than in [Danchin and Zhang 2016] however, since the coupling between the mass and velocity equations in the inhomogeneous Navier-Stokes equations is \emph{quasilinear} while it is linear for the Boussinesq equations.

preprint2016arXiv

Revisit of the interacting holographic dark energy model after Planck 2015

We investigate the observational constraints on the interacting holographic dark energy model. We consider five typical interacting models with the interaction terms $Q=3βHρ_{\rm{de}}$, $Q=3βHρ_{\rm{c}}$, $Q=3βH(ρ_{\rm{de}}+ρ_{\rm c})$, $Q=3βH\sqrt{ρ_{\rm{de}}ρ_{\rm c}}$, and $Q=3βH\frac{ρ_{\rm{de}}ρ_{c}}{ρ_{\rm{de}}+ρ_{\rm c}}$, respectively, where $β$ is a dimensionless coupling constant. The observational data we use in this paper include the JLA compilation of type Ia supernovae data, the Planck 2015 distance priors data of cosmic microwave background observation, the baryon acoustic oscillations measurements, and the Hubble constant direct measurement. We make a comparison for these five interacting holographic dark energy models by employing the information criteria, and we find that, within the framework of holographic dark energy, the $Q=3βH\frac{ρ_{\rm{de}}ρ_{\rm c}}{ρ_{\rm{ de}}+ρ_{\rm c}}$ model is most favored by current data, and the $Q=3βHρ_{\rm c}$ model is relatively not favored by current data. For the $Q=3βHρ_{\rm{de}}$ and $Q=3βH\frac{ρ_{\rm{de}}ρ_{\rm c}}{ρ_{\rm{ de}}+ρ_{\rm c}}$ models, a positive coupling $β$ can be detected at more than 2$σ$ significance.

preprint2016arXiv

Revisiting the one leptoquark solution to the $R(D^{(\ast)})$ anomalies and its phenomenological implications

It has been shown recently that the anomalies observed in $\bar B\to D^{(\ast)}τ\barν_τ$ and $\bar B\to \bar K\ell^+\ell^-$ decays could be resolved with just one scalar leptoquark. Fitting to the current data on $R(D^{(\ast)})$ along with acceptable $q^2$ distributions in $\bar B\to D^{(\ast)}τ\barν_τ$ decays, four best-fit solutions for the operator coefficients have been found. In this paper, we explore the possibilities of how to discriminate these four solutions. Firstly, we find that two of them are already excluded by the decay $B_c^-\to τ^-\barν_τ$, because the predicted decay widths have already overshot the total width $Γ_{B_c}$. It is then found that the remaining two solutions result in two effective Hamiltonians governing $b\to c τ\barν_τ$ transition, which differ by a sign and enhance the absolute value of the coefficient of $\bar c_Lγ_μb_L\,\bar τ_Lγ^μ{ν_τ}_L$ operator by about $12\%$. However, they give nearly the same predictions as in the SM for the $D^\ast$ and $τ$ longitudinal polarizations as well as the lepton forward-backward asymmetries in $\bar B\to D^{(\ast)}τ\barν_τ$ decays. For the other observables like $\mathcal B(B_c^-\to τ^-\barν_τ)$, $\mathcal B(B_c^- \to γτ^-\barν_τ)$, $R_{D^{(\ast)}}(q^2)$, ${\rm d}\mathcal B(\bar B\to D^{(\ast)}τ\barν_τ)/{\rm d}q^2$ and $\mathcal B(\bar B\to X_cτ\barν_τ)$, on the other hand, the two solutions give sizable enhancements relative to the SM predictions. With measurement of $B_c^-\to τ^-\barν_τ$ at LHCb and refined measurements of observables in $\bar B\to D^{(\ast)}τ\barν_τ$ at both LHCb and Belle-II, such a specific NP scenario could be further deciphered.

preprint2016arXiv

Scaling of quantum correlation and monogamy relation near a quantum phase transitions in two-dimensional XY spin system

The purpose of the paper is mainly to investigate the quantum critical behavior of two-dimensional XY spin system by calculating quantum correlation and monogamy relation through implementation of quantum renormalization group theory. Numerical analysis indicates that quantum correlation as well as quantum nonlocality can be used to efficiently detect the quantum critical property in two-dimensional XY spin system. The nonanalytic behavior of the first derivative of quantum correlation approaches infinity and the critical point is reached as the size of the model increases. Furthermore, we discuss the quantum correlation distribution in this model based on square of concurrence (SC) and square of quantum discord (SQD). The monogamous properties of SC and SQD are obtained for the present system. We finally reveal that the monogamy score can be used to capture the quantum critical point.

preprint2016arXiv

Small-time asymptotics for Gaussian self-similar stochastic volatility models

We consider the class of self-similar Gaussian stochastic volatility models, and compute the small-time (near-maturity) asymptotics for the corresponding asset price density, the call and put pricing functions, and the implied volatilities. Unlike the well-known model-free behavior for extreme-strike asymptotics, small-time behaviors of the above depend heavily on the model, and require a control of the asset price density which is uniform with respect to the asset price variable, in order to translate into results for call prices and implied volatilities. Away from the money, we express the asymptotics explicitly using the volatility process' self-similarity parameter $H$, its first Karhunen-Loeve eigenvalue at time 1, and the latter's multiplicity. Several model-free estimators for $H$ result. At the money, a separate study is required: the asymptotics for small time depend instead on the integrated variance's moments of orders 1/2 and 3/2, and the estimator for $H$ sees an affine adjustment, while remaining model-free.

preprint2016arXiv

Terahertz Saturable Absorption in Superconducting Metamaterials

We present a superconducting metamaterial saturable absorber at terahertz frequencies. The absorber consists of an array of split ring resonators (SRRs) etched from a 100nm YBaCu3O7 (YBCO) film. A polyimide spacer layer and gold ground plane are deposited above the SRRs, creating a reflecting perfect absorber. Increasing either the temperature or incident electric field (E) decreases the superconducting condensate density and corresponding kinetic inductance of the SRRs. This alters the impedance matching in the metamaterial, reducing the peak absorption. At low electric fields, the absorption was optimized near 80% at T=10K and decreased to 20% at T=70K. For E=40kV/cm and T=10K, the peak absorption was 70% decreasing to 40% at 200kV/cm, corresponding to a modulation of 43%.

preprint2016arXiv

Testing models of vacuum energy interacting with cold dark matter

We test the models of vacuum energy interacting with cold dark matter and try to probe the possible deviation from the $Λ$CDM model using current observations. We focus on two specific models, $Q=3βHρ_Λ$ and $Q=3βHρ_c$. The data combinations come from the Planck 2013 data, the baryon acoustic oscillations measurements, the type-Ia supernovae data, the Hubble constant measurement, the redshift space distortions data and the galaxy weak lensing data. For the $Q=3βHρ_c$ model, we find that it can be tightly constrained by all the data combinations, while for the $Q=3βHρ_Λ$ model, there still exist significant degeneracies between parameters. The tightest constraints for the coupling constant are $β=-0.026^{+0.036}_{-0.053}$ (for $Q=3βHρ_Λ$) and $β=-0.00045\pm0.00069$ (for $Q=3βHρ_c$) at the $1σ$ level. For all the fit results, we find that the null interaction $β=0$ is always consistent with data. Our work completes the discussion on the interacting dark energy model in the recent Planck 2015 papers. Considering this work together with the Planck 2015 results, it is believed that there is no evidence for the models beyond the standard $Λ$CDM model from the point of view of possible interaction.

preprint2016arXiv

The gap distribution of directions in some Schottky groups

We prove the existence and some properties of the limiting gap distribution functions for the directions of orbits of some infinite covolume subgroups of $Isom(\mathbb{H}^2)$ in the Poincaré disk.

preprint2016arXiv

Theoretical study on nuclear structure by the multiple Coulomb scattering and magnetic scattering of relativistic electrons

Electron scattering is an effective method to study the nuclear structure. For the odd-$A$ nuclei with proton holes in the outmost orbits, we investigate the contributions of proton holes to the nuclear quadrupole moments $Q$ and magnetic moments $μ$ by the multiple Coulomb scattering and magnetic scattering. The deformed nuclear charge densities are constructed by the relativistic mean-field (RMF) models. Comparing the theoretical Coulomb and magnetic form factors with the experimental data, the nuclear quadrupole moments $Q$ and nuclear magnetic moments $μ$ are investigated. From the electron scattering, the wave functions of the proton holes of odd-$A$ nuclei can be tested, which can also reflect the validity of the nuclear structure model.

preprint2016arXiv

Universal quantum correlation close to quantum critical phenomena

We study the ground state quantum correlation of Ising model in a transverse field (ITF) by implementing the quantum renormalization group (QRG) theory. It is shown that various quantum correlation measures and the Clauser-Horne-Shimony-Holt inequality will highlight the critical point related with quantum phase transitions, and demonstrate nonanalytic phenomena and scaling behavior when the size of the systems becomes large. Our results also indicate a universal behavior of the critical exponent of ITF under QRG theory that the critical exponent of different measures is identical, even when the quantities vary from entanglement measures to quantum correlation measures. This means that the two kinds of quantum correlation criterion including the entanglement-separability paradigm and the information-theoretic paradigm have some connections between them. These remarkable behaviors may have important implications on condensed matter physics because the critical exponent directly associates with the correlation length exponent.

preprint2016arXiv

Wireless transfer of power by a 35-GHz metamaterial split-ring resonator rectenna

Wireless transfer of power via high frequency microwave radiation using a miniature split ring resonator rectenna is reported. RF power is converted into DC power by integrating a rectification circuit with the split ring resonator. The near-field behavior of the rectenna is investigated with microwave radiation in the frequency range between 20-40 GHz with a maximum power level of 17 dBm. The observed resonance peaks match those predicted by simulation. Polarization studies show the expected maximum in signal when the electric field is polarized along the edge of the split ring resonator with the gap and minimum for perpendicular orientation. The efficiency of the rectenna is on the order of 1% for a frequency of 37.2 GHz. By using a cascading array of 9 split ring resonators the output power was increased by a factor of 20.

preprint2015arXiv

$B_{s,d}-\bar{B}_{s,d}$ mixings and $B_{s,d}\to\ell^+\ell^-$ decays within the Manohar-Wise model

In this paper, we perform a complete one-loop computation of the short-distance Wilson coefficients for $B_{s,d}-\bar{B}_{s,d}$ mixings and $B_{s,d}\to\ell^+\ell^-$ decays in the Manohar-Wise model, which extends the SM scalar sector by a colour-octet and weak-doublet scalar. Based on these calculations, combined constraints on the model parameters are derived from the current flavour data, including the $B_{s,d}-{\bar{B}}_{s,d}$ mixings, $B_{s,d}\toμ^+μ^-$, $B\to X_sγ$, $B\to K^{\ast}γ$, $B\toργ$, and $Z\to b \bar{b}$ decays. The future sensitivity to the model is also explored in the observables achievable with $50~{\rm fb}^{-1}$ of LHCb and $50~{\rm ab}^{-1}$ of Belle-II data. We find that the Manohar-Wise model could explain the current data, especially when the couplings of the charged colour-octet scalars to quarks are complex, with the resulting $χ^2_{\rm min}$ being significantly smaller than that of the SM. Finally, we investigate correlations among the two isospin asymmetries $Δ(K^*γ)$ and $Δ(ργ)$, as well as the averaged time-integrated branching ratios $\bar{\mathcal B}(B_{s,d}\to μ^+μ^-)$; some of them are found to be quite strong and could, therefore, provide further insights into the model, once more precise experimental measurements and theoretical predictions for these observables are available in the future.

preprint2015arXiv

A closer look at interacting dark energy with statefinder hierarchy and growth rate of structure

We investigate the interacting dark energy models by using the diagnostics of statefinder hierarchy and growth rate of structure. We wish to explore the deviations from $Λ$CDM and to differentiate possible degeneracies in the interacting dark energy models with the geometrical and structure growth diagnostics. We consider two interacting forms for the models, i.e., $Q_1=βHρ_c$ and $Q_2=βHρ_{de}$, with $β$ being the dimensionless coupling parameter. Our focus is the I$Λ$CDM model that is a one-parameter extension to $Λ$CDM by considering a direct coupling between the vacuum energy ($Λ$) and cold dark matter (CDM), with the only additional parameter $β$. But we begin with a more general case by considering the I$w$CDM model in which dark energy has a constant $w$ (equation-of-state parameter). For calculating the growth rate of structure, we employ the "parametrized post-Friedmann" theoretical framework for interacting dark energy to numerically obtain the $ε(z)$ values for the models. We show that in both geometrical and structural diagnostics the impact of $w$ is much stronger than that of $β$ in the I$w$CDM model. We thus wish to have a closer look at the I$Λ$CDM model by combining the geometrical and structural diagnostics. We find that the evolutionary trajectories in the $S^{(1)}_3$--$ε$ plane exhibit distinctive features and the departures from $Λ$CDM could be well evaluated, theoretically, indicating that the composite null diagnostic $\{S^{(1)}_3, ε\}$ is a promising tool for investigating the interacting dark energy models.

preprint2015arXiv

A Parallel Orbital-updating Based Optimization Method for Electronic Structure Calculations

In this paper, we propose a parallel optimization method for electronic structure calculations based on a single orbital-updating approximation. It is shown by our numerical experiments that the method is efficient and reliable for atomic and molecular systems of large scale over supercomputers.

preprint2015arXiv

Algebraic structure of the two-qubit quantum Rabi model and its solvability using Bogoliubov operators

We have found the algebraic structure of the two-qubit quantum Rabi model behind the possibility of its novel quasi-exact solutions with finite photon numbers by analyzing the Hamiltonian in the photon number space. The quasi-exact eigenstates with at most $1$ photon exist in the whole qubit-photon coupling regime with constant eigenenergy equal to single photon energy \hbarω, which can be clear demonstrated from the Hamiltonian structure. With similar method, we find these special "dark states"-like eigenstates commonly exist for the two-qubit Jaynes-Cummings model, with $E=N\hbarω$ (N=-1,0,1,...), and one of them is also the eigenstate of the two-qubit quantum Rabi model, which may provide some interesting application in a simper way. Besides, using Bogoliubov operators, we analytically retrieve the solution of the general two-qubit quantum Rabi model. In this more concise and physical way, without using Bargmann space, we clearly see how the eigenvalues of the infinite-dimensional two-qubit quantum Rabi Hamiltonian are determined by convergent power series, so that the solution can reach arbitrary accuracy reasonably because of the convergence property.

preprint2015arXiv

Bethe ansatz for an AdS/CFT open spin chain with non-diagonal boundaries

We consider the integrable open-chain transfer matrix corresponding to a Y=0 brane at one boundary, and a Y_theta=0 brane (rotated with the respect to the former by an angle theta) at the other boundary. We determine the exact eigenvalues of this transfer matrix in terms of solutions of a corresponding set of Bethe equations.

preprint2015arXiv

Bethe states of the XXZ spin-1/2 chain with arbitrary boundary fields

Based on the inhomogeneous T-Q relation constructed via the off-diagonal Bethe Ansatz, the Bethe-type eigenstates of the XXZ spin-1/2 chain with arbitrary boundary fields are constructed. It is found that by employing two sets of gauge transformations, proper generators and reference state for constructing Bethe vectors can be obtained respectively. Given an inhomogeneous T-Q relation for an eigenvalue, it is proven that the resulting Bethe state is an eigenstate of the transfer matrix, provided that the parameters of the generators satisfy the associated Bethe Ansatz equations.

preprint2015arXiv

Critical behaviors and universality classes of percolation phase transitions on two-dimensional square lattice

We have investigated both site and bond percolation on two dimensional lattice under the random rule and the product rule respectively. With the random rule, sites or bonds are added randomly into the lattice. From two candidates picked randomly, the site or bond with the smaller size product of two connected clusters is added when the product rule is taken. Not only the size of the largest cluster but also its size jump are studied to characterize the universality class of percolation. The finite-size scaling forms of giant cluster size and size jump are proposed and used to determine the critical exponents of percolation from Monte Carlo data. It is found that the critical exponents of both size and size jump in random site percolation are equal to that in random bond percolation. With the random rule, site and bond percolation belong to the same universality class. We obtain the critical exponents of the site percolation under the product rule, which are different from that of both random percolation and the bond percolation under the product rule. The universality class of site percolation differs different from that of bond percolation when the product rule is used.

preprint2015arXiv

Dynamic Structure Embedded Online Multiple-Output Regression for Stream Data

Online multiple-output regression is an important machine learning technique for modeling, predicting, and compressing multi-dimensional correlated data streams. In this paper, we propose a novel online multiple-output regression method, called MORES, for stream data. MORES can \emph{dynamically} learn the structure of the coefficients change in each update step to facilitate the model's continuous refinement. We observe that limited expressive ability of the regression model, especially in the preliminary stage of online update, often leads to the variables in the residual errors being dependent. In light of this point, MORES intends to \emph{dynamically} learn and leverage the structure of the residual errors to improve the prediction accuracy. Moreover, we define three statistical variables to \emph{exactly} represent all the seen samples for \emph{incrementally} calculating prediction loss in each online update round, which can avoid loading all the training data into memory for updating model, and also effectively prevent drastic fluctuation of the model in the presence of noise. Furthermore, we introduce a forgetting factor to set different weights on samples so as to track the data streams' evolving characteristics quickly from the latest samples. Experiments on one synthetic dataset and three real-world datasets validate the effectiveness of the proposed method. In addition, the update speed of MORES is at least 2000 samples processed per second on the three real-world datasets, more than 15 times faster than the state-of-the-art online learning algorithm.

preprint2015arXiv

Emergence of Quantum Nonmagnetic Insulating Phase in Spin-Orbit Coupled Square Lattices

We investigate the metal-insulator transition (MIT) and phase diagram of the half-filled Fermi Hubbard model with Rashba-type spin-orbit coupling (SOC) on a square optical lattice. The interplay between the atomic interactions and SOC results in distinctive features of the MIT. Significantly, in addition to the diverse spin ordered phases, a nonmagnetic insulating phase emerges in a considerably large regime of parameters near the Mott transition. This phase has a finite single-particle gap but vanishing magnetization and spin correlation exhibits a power-law scaling, suggesting a potential algebraic spin-liquid ground state. These results are confirmed by the non-perturbative cluster dynamical mean-field theory.

preprint2015arXiv

Fingertip in the Eye: A cascaded CNN pipeline for the real-time fingertip detection in egocentric videos

We introduce a new pipeline for hand localization and fingertip detection. For RGB images captured from an egocentric vision mobile camera, hand and fingertip detection remains a challenging problem due to factors like background complexity and hand shape variety. To address these issues accurately and robustly, we build a large scale dataset named Ego-Fingertip and propose a bi-level cascaded pipeline of convolutional neural networks, namely, Attention-based Hand Detector as well as Multi-point Fingertip Detector. The proposed method significantly tackles challenges and achieves satisfactorily accurate prediction and real-time performance compared to previous hand and fingertip detection methods.

preprint2015arXiv

Gap Distributions in Circle Packings

We determine the distribution of nearest neighbour spacings between the tangencies to a fixed circle in a class of circle packings generated by reflections. We use a combination of geometric tools and the theory of automorphic forms.

preprint2015arXiv

Iso-oriented monolayer α-MoO3(010) films epitaxially grown on SrTiO3(001)

The ability to synthesis well-ordered two-dimensional materials under ultra-high vacuum and directly characterize them by other techniques in-situ can greatly advance our current understanding on their physical and chemical properties. In this paper, we demonstrate that iso-oriented α-MoO3 films with as low as single monolayer thickness can be reproducibly grown on SrTiO3(001) (STO) substrates by molecular beam epitaxy ( (010)MoO3 || (001)STO, [100]MoO3 || [100]STO or [010]STO) through a self-limiting process. While one in-plane lattice parameter of the MoO3 is very close to that of the SrTiO3 (aMoO3 = 3.96 Å, aSTO = 3.905 Å), the lattice mismatch along other direction is large (~5%, cMoO3 = 3.70 Å), which leads to relaxation as clearly observed from the splitting of streaks in reflection high-energy electron diffraction (RHEED) patterns. A narrow range in the growth temperature is found to be optimal for the growth of monolayer α-MoO3 films. Increasing deposition time will not lead to further increase in thickness, which is explained by a balance between deposition and thermal desorption due to the weak van der Waals force between α-MoO3 layers. Lowering growth temperature after the initial iso-oriented α-MoO3 monolayer leads to thicker α-MoO3(010) films with excellent crystallinity.

preprint2015arXiv

Neutrinos in the holographic dark energy model: constraints from latest measurements of expansion history and growth of structure

The model of holographic dark energy (HDE) with massive neutrinos and/or dark radiation is investigated in detail. The background and perturbation evolutions in the HDE model are calculated. We employ the PPF approach to overcome the gravity instability difficulty (perturbation divergence of dark energy) led by the equation-of-state parameter $w$ evolving across the phantom divide $w=-1$ in the HDE model with $c<1$. We thus derive the evolutions of density perturbations of various components and metric fluctuations in the HDE model. The impacts of massive neutrino and dark radiation on the CMB anisotropy power spectrum and the matter power spectrum in the HDE scenario are discussed. Furthermore, we constrain the models of HDE with massive neutrinos and/or dark radiation by using the latest measurements of expansion history and growth of structure, including the Planck CMB temperature data, the baryon acoustic oscillation data, the JLA supernova data, the Hubble constant direct measurement, the cosmic shear data of weak lensing, the Planck CMB lensing data, and the redshift space distortions data. We find that $\sum m_ν<0.186$ eV (95\% CL) and $N_{\rm eff}=3.75^{+0.28}_{-0.32}$ in the HDE model from the constraints of these data.

preprint2015arXiv

Nonlinear terahertz devices utilizing semiconducting plasmonic metamaterials

The development of responsive metamaterials has enabled the realization of compact tunable photonic devices capable of manipulating the amplitude, polarization, wave vector, and frequency of light. Integration of semiconductors into the active regions of metallic resonators is a proven approach for creating nonlinear metamaterials through optoelectronic control of the semiconductor carrier density. Metal-free subwavelength resonant semiconductor structures offer an alternative approach to create dynamic metamaterials. We present InAs plasmonic disk arrays as a viable resonant metamaterial at terahertz frequencies. Importantly, InAs plasmonic disks exhibit a strong nonlinear response arising from electric field induced intervalley scattering resulting in a reduced carrier mobility thereby damping the plasmonic response. We demonstrate nonlinear perfect absorbers configured as either optical limiters or saturable absorbers, including flexible nonlinear absorbers achieved by transferring the disks to polyimide films. Nonlinear plasmonic metamaterials show potential for use in ultrafast THz optics and for passive protection of sensitive electromagnetic devices.

preprint2015arXiv

On the Local-Global Principle for Integral Apollonian-3 Circle Packings

In this paper we study the integral properties of Apollonian-3 circle packings, which are variants of the standard Apollonian circle packings. Specifically, we study the reduction theory, formulate a local-global conjecture, and prove a density one version of this conjecture. Along the way, we prove a uniform spectral gap for congruence towers of the symmetry group.

preprint2015arXiv

Parsimonious Tensor Response Regression

Aiming at abundant scientific and engineering data with not only high dimensionality but also complex structure, we study the regression problem with a multidimensional array (tensor) response and a vector predictor. Applications include, among others, comparing tensor images across groups after adjusting for additional covariates, which is of central interest in neuroimaging analysis. We propose parsimonious tensor response regression adopting a generalized sparsity principle. It models all voxels of the tensor response jointly, while accounting for the inherent structural information among the voxels. It effectively reduces the number of free parameters, leading to feasible computation and improved interpretation. We achieve model estimation through a nascent technique called the envelope method, which identifies the immaterial information and focuses the estimation based upon the material information in the tensor response. We demonstrate that the resulting estimator is asymptotically efficient, and it enjoys a competitive finite sample performance. We also illustrate the new method on two real neuroimaging studies.

preprint2015arXiv

Phonon and Raman scattering of two-dimensional transition metal dichalcogenides from monolayer, multilayer to bulk material

Two-dimensional (2D) transition metal dichalcogenide (TMD) nanosheets exhibit remarkable electronic and optical properties. The 2D features, sizable bandgaps, and recent advances in the synthesis, characterization, and device fabrication of the representative MoS$_2$, WS$_2$, WSe$_2$, and MoSe$_2$ TMDs make TMDs very attractive in nanoelectronics and optoelectronics. Similar to graphite and graphene, the atoms within each layer in 2D TMDs are joined together by covalent bonds, while van der Waals interactions keep the layers together. This makes the physical and chemical properties of 2D TMDs layer dependent. In this review, we discuss the basic lattice vibrations of monolayer, multilayer, and bulk TMDs, including high-frequency optical phonons, interlayer shear and layer breathing phonons, the Raman selection rule, layer-number evolution of phonons, multiple phonon replica, and phonons at the edge of the Brillouin zone. The extensive capabilities of Raman spectroscopy in investigating the properties of TMDs are discussed, such as interlayer coupling, spin--orbit splitting, and external perturbations. The interlayer vibrational modes are used in rapid and substrate-free characterization of the layer number of multilayer TMDs and in probing interface coupling in TMD heterostructures. The success of Raman spectroscopy in investigating TMD nanosheets paves the way for experiments on other 2D crystals and related van der Waals heterostructures.

preprint2015arXiv

Polytypism and Unexpected Strong Interlayer Coupling of two-Dimensional Layered ReS2

The anisotropic two-dimensional (2D) van der Waals (vdW) layered materials, with both scientific interest and potential application, have one more dimension to tune the properties than the isotropic 2D materials. The interlayer vdW coupling determines the properties of 2D multi-layer materials by varying stacking orders. As an important representative anisotropic 2D materials, multilayer rhenium disulfide (ReS2) was expected to be random stacking and lack of interlayer coupling. Here, we demonstrate two stable stacking orders (aa and a-b) of N layer (NL, N>1) ReS2 from ultralow-frequency and high-frequency Raman spectroscopy, photoluminescence spectroscopy and first-principles density functional theory calculation. Two interlayer shear modes are observed in aa-stacked NL-ReS2 while only one interlayer shear mode appears in a-b-stacked NL-ReS2, suggesting anisotropic-like and isotropic-like stacking orders in aa- and a-b-stacked NL-ReS2, respectively. The frequency of the interlayer shear and breathing modes reveals unexpected strong interlayer coupling in aa- and a-b-NL-ReS2, the force constants of which are 55-90% to those of multilayer MoS2. The observation of strong interlayer coupling and polytypism in multi-layer ReS2 stimulate future studies on the structure, electronic and optical properties of other 2D anisotropic materials.

preprint2015arXiv

Probing $f(R)$ cosmology with sterile neutrinos via measurements of scale-dependent growth rate of structure

In this paper, we constrain the dimensionless Compton wavelength parameter $B_0$ of $f(R)$ gravity as well as the mass of sterile neutrino by using the cosmic microwave background observations, the baryon acoustic oscillation surveys, and the linear growth rate measurements. Since both the $f(R)$ model and the sterile neutrino generally predict scale-dependent growth rates, we utilize the growth rate data measured in different wavenumber bins with the theoretical growth rate approximatively scale-independent in each bin. The employed growth rate data come from the peculiar velocity measurements at $z=0$ in five wavenumber bins, and the redshift space distortions measurements at $z=0.25$ and $z=0.37$ in one wavenumber bin. By constraining the $f(R)$ model alone, we get a tight 95\% upper limit of $\log_{10}B_0<-4.1$. This result is slightly weakened to $\log_{10}B_0<-3.8$ (at 2$σ$ level) once we simultaneously constrain the $f(R)$ model and the sterile neutrino mass, due to the degeneracy between the parameters of the two. For the massive sterile neutrino parameters, we get the effective sterile neutrino mass $m_{ν,{\rm{sterile}}}^{\rm{eff}}<0.62$ eV (2$σ$) and the effective number of relativistic species $N_{\rm eff}<3.90$ (2$σ$) in the $f(R)$ model. As a comparison, we also obtain $m_{ν,{\rm{sterile}}}^{\rm{eff}}<0.56$ eV (2$σ$) and $N_{\rm eff}<3.92$ (2$σ$) in the standard $Λ$CDM model.

preprint2015arXiv

Redshift drift constraints on f(T) gravity

We explore the impact of the Sandage-Loeb (SL) test on the precision of cosmological constraints for $f(T)$ gravity theories. The SL test is an important supplement to current cosmological observations because it measures the redshift drift in the Lyman-$α$ forest in the spectra of distant quasars, covering the "redshift desert" of $2 \lesssim z \lesssim5$. To avoid data inconsistency, we use the best-fit models based on current combined observational data as fiducial models to simulate 30 mock SL test data. We quantify the impact of these SL test data on parameter estimation for $f(T)$ gravity theories. Two typical $f(T)$ models are considered, the power-law model $f(T)_{PL}$ and the exponential-form model $f(T)_{EXP}$. The results show that the SL test can effectively break the existing strong degeneracy between the present-day matter density $Ω_m$ and the Hubble constant $H_0$ in other cosmological observations. For the considered $f(T)$ models, a 30-year observation of the SL test can improve the constraint precision of $Ω_m$ and $H_0$ enormously but cannot effectively improve the constraint precision of the model parameters.

preprint2015arXiv

Redshift drift exploration for interacting dark energy

By detecting redshift drift in the spectra of Lyman-$α$ forest of distant quasars, Sandage-Loeb (SL) test directly measures the expansion of the universe, covering the "redshift desert" of $2 \lesssim z \lesssim5$. Thus this method is definitely an important supplement to the other geometric measurements and will play a crucial role in cosmological constraints. In this paper, we quantify the ability of SL test signal by a CODEX-like spectrograph for constraining interacting dark energy. Four typical interacting dark energy models are considered: (i) $Q=γHρ_c$, (ii) $Q=γHρ_{de}$, (iii) $Q=γH_0ρ_c$, and (iv) $Q=γH_0ρ_{de}$. The results show that for all the considered interacting dark energy models, relative to the current joint SN+BAO+CMB+$H_0$ observations, the constraints on $Ω_m$ and $H_0$ would be improved by about 60\% and 30--40\%, while the constraints on $w$ and $γ$ would be slightly improved, with a 30-yr observation of SL test. We also explore the impact of SL test on future joint geometric observations. In this analysis, we take the model with $Q=γHρ_c$ as an example, and simulate future SN and BAO data based on the space-based project WFIRST. We find that in the future geometric constraints, the redshift drift observations would help break the geometric degeneracies in a meaningful way, thus the measurement precisions of $Ω_m$, $H_0$, $w$, and $γ$ could be substantially improved using future probes.

preprint2015arXiv

Retrieve the Bethe states of quantum integrable models solved via off-diagonal Bethe Ansatz

Based on the inhomogeneous T-Q relation constructed via the off-diagonal Bethe Ansatz, a systematic method for retrieving the Bethe-type eigenstates of integrable models without obvious reference state is developed by employing certain orthogonal basis of the Hilbert space. With the XXZ spin torus model and the open XXX spin-1/2 chain as examples, we show that for a given inhomogeneous T-Q relation and the associated Bethe Ansatz equations, the constructed Bethe-type eigenstate has a well-defined homogeneous limit.

preprint2015arXiv

Software for Wearable Devices: Challenges and Opportunities

Wearable devices are a new form of mobile computer system that provides exclusive and user-personalized services. Wearable devices bring new issues and challenges to computer science and technology. This paper summarizes the development process and the categories of wearable devices. In addition, we present new key issues arising in aspects of wearable devices, including operating systems, database management system, network communication protocol, application development platform, privacy and security, energy consumption, human-computer interaction, software engineering, and big data.

preprint2015arXiv

Statefinder hierarchy exploration of the extended Ricci dark energy

We apply the statefinder hierarchy plus the fractional growth parameter to explore the extended Ricci dark energy (ERDE) model, in which there are two independent coefficients $α$ and $β$. By adjusting them, we plot evolution trajectories of some typical parameters, including Hubble expansion rate $E$, deceleration parameter $q$, the third and fourth order hierarchy $S_3^{(1)}$ and $S_4^{(1)}$ and fractional growth parameter $ε$, respectively, as well as several combinations of them. For the case of variable $α$ and constant $β$, in the low-redshift region the evolution trajectories of $E$ are in high degeneracy and that of $q$ separate somewhat. However, the $Λ$CDM model is confounded with ERDE in both of these two cases. $S_3^{(1)}$ and $S_4^{(1)}$, especially the former, perform much better. They can differentiate well only varieties of cases within ERDE except $Λ$CDM in the low-redshift region. For high-redshift region, combinations $\{S_n^{(1)},ε\}$ can break the degeneracy. Both of $\{S_3^{(1)},ε\}$ and $\{S_4^{(1)},ε\}$ have the ability to discriminate ERDE with $α=1$ from $Λ$CDM, of which the degeneracy cannot be broken by all the before-mentioned parameters. For the case of variable $β$ and constant $α$, $S_3^{(1)}(z)$ and $S_4^{(1)}(z)$ can only discriminate ERDE from $Λ$CDM. Nothing but pairs $\{S_3^{(1)},ε\}$ and $\{S_4^{(1)},ε\}$ can discriminate not only within ERDE but also ERDE from $Λ$CDM. Finally we find that $S_3^{(1)}$ is surprisingly a better choice to discriminate within ERDE itself, and ERDE from $Λ$CDM as well, rather than $S_4^{(1)}$.

preprint2015arXiv

Strong gravitational lensing constraints on holographic dark energy

Strong gravitational lensing (SGL) has provided an important tool for probing galaxies and cosmology. In this paper, we use the SGL data to constrain the holographic dark energy model, as well as models that have the same parameter number, such as the $w$CDM and Ricci dark energy models. We find that only using SGL is difficult to effectively constrain the model parameters. However, when the SGL data are combined with CBS (CMB+BAO+SN) data, the reasonable estimations can be given and the constraint precision is improved to a certain extent, relative to the case of CBS only. Therefore, SGL is an useful way to tighten constraints on model parameters.

preprint2015arXiv

Substrate-free layer-number identification of two-dimensional materials: A case of Mo$_{0.5}$W$_{0.5}$S$_2$ alloy

Any of two or more two-dimensional (2D) materials with similar properties can be alloyed into a new layered material, namely, 2D alloy. Individual monolayer in 2D alloys are kept together by Van der Waals interactions. The property of multilayer alloys is a function of their layer number. Here, we studied the shear (C) and layer-breathing (LB) modes of Mo$_{0.5}$W$_{0.5}$S$_2$ alloy flakes and their link to the layer number of alloy flakes. The study reveals that the disorder effect is absent in the C and LB modes of 2D alloys, and the monatomic chain model can be used to estimate the frequencies of the C and LB modes. We demonstrated how to use the C and LB mode frequency to identify the layer number of alloy flakes deposited on different substrates. This technique is independent of the substrate, stoichiometry, monolayer thickness and complex refractive index of 2D materials, offering a robust and substrate-free approach for layer-number identification of 2D materials.

preprint2015arXiv

Terahertz radiation-induced sub-cycle field electron emission across a split-gap dipole antenna

We use intense terahertz pulses to excite the resonant mode (0.6 THz) of a micro-fabricated dipole antenna with a vacuum gap. The dipole antenna structure enhances the peak amplitude of the in-gap THz electric field by a factor of ~170. Above an in-gap E-field threshold amplitude of ~10 MVcm-1, THz-induced field electron emission is observed (TIFEE) as indicated by the field-induced electric current across the dipole antenna gap. Field emission occurs within a fraction of the driving THz period. Our analysis of the current (I) and incident electric field (E) is in agreement with a Millikan-Lauritsen analysis where log (I) exhibits a linear dependence on 1/E. Numerical estimates indicate that the electrons are accelerated to a value of approximately one tenth of the speed of light.

preprint2014arXiv

A Stochastic Maximum Principle for Processes Driven by G-Brownian Motion and Applications to Finance

In this paper, we consider the stochastic optimal control problems under model risk caused by uncertain volatilities. To have a mathematical consistent framework we use the notion of G-expectation and its corresponding G-Brwonian motion introduced by Peng(2007). Based on the theory of stochastic differential equations on a sublinear expectation space $(Ω,\mathcal{H},\hat{\mathbb{E}})$, we prove a stochastic maximum principle for controlled processes driven by G-Brownian motion. Then we obtain the maximum condition in terms of the $\mathcal{H}$-function plus some convexity conditions constitute sufficient conditions for optimality. Finally, we solve a portfolio optimization problem with ambiguous volatility as an explicitly illustrated example of the main result.

preprint2014arXiv

Algorithms for envelope estimation

Envelopes were recently proposed as methods for reducing estimative variation in multivariate linear regression. Estimation of an envelope usually involves optimization over Grassmann manifolds. We propose a fast and widely applicable one-dimensional (1D) algorithm for estimating an envelope in general. We reveal an important structural property of envelopes that facilitates our algorithm, and we prove both Fisher consistency and root-n-consistency of the algorithm.

preprint2014arXiv

An embryo of protocell membrane: The capsule of graphene oxide

Many signs indicate that the graphene could widely occur on the early Earth. Here, we report a new theory that graphene might be an embryo of protocell membrane, and found several evidences. Firstly, the graphene oxide and phospholipid-graphene oxide composite would curl into capsules in strongly acidic saturated solution of Pb(NO3)2 at low temperature, providing a protective space for biochemical reactions. Secondly, L-animi acids exhibit higher reactivity than D-animi acids for graphene oxides in favor of the formation of left-handed proteins. Thirdly, monolayer graphene with nanopores prepared by unfocused 84Kr25+ has high selectivity for permeation of the monovalent metal ions (Rb+ > K+ > Cs+ > Na+ > Li+), but does not allow Cl- through, which could be attributed to the ion exchange of oxygen-containing groups on the rim of nanopores. It is similar to K+ channels, which would cause efflux of some ions from capsule of graphene oxides with the decrease of pH in the primitive ocean, creating a suitable inner condition for the origin of life. Consequently, the strongly acidic, high salinity and strong radiation as well as temperature changes in the early Earth, regarded as negative factors, would be indispensable for the origin of protocell. In short, graphene bred life, but digested gradually by the evolution.

preprint2014arXiv

ARPES insights on the metallic states of YbB6(001): E(k) dispersion, temporal changes and spatial variation

We report high resolution Angle Resolved PhotoElectron Spectroscopy (ARPES) results on the (001) cleavage surface of YbB$_{6}$, a rare-earth compound which has been recently predicted to host surface electronic states with topological character. We observe two types of well-resolved metallic states, whose Fermi contours encircle the time-reversal invariant momenta of the YbB$_{6}$(001) surface Brillouin zone, and whose full (E,$k$)-dispersion relation can be measured wholly unmasked by states from the rest of the electronic structure. Although the two-dimensional character of these metallic states is confirmed by their lack of out-of-plane dispersion, two new aspects are revealed in these experiments. Firstly, these states do not resemble two branches of opposite, linear velocity that cross at a Dirac point, but rather straightforward parabolas which terminate to high binding energy with a clear band bottom. Secondly, these states are sensitive to time-dependent changes of the YbB$_{6}$ surface under ultrahigh vacuum conditions. Adding the fact that these data from cleaved YbB$_{6}$ surfaces also display spatial variations in the electronic structure, it appears there is little in common between the theoretical expectations for an idealized YbB$_{6}$(001) crystal truncation on the one hand, and these ARPES data from real cleavage surfaces on the other.

preprint2014arXiv

Cosmological constraints on neutrinos after BICEP2

Since the B-mode polarization of the cosmic microwave background (CMB) was detected by the BICEP2 experiment and an unexpectedly large tensor-to-scalar ratio, $r=0.20^{+0.07}_{-0.05}$, was found, the base standard cosmology should at least be extended to the 7-parameter $Λ$CDM+$r$ model. In this paper, we consider the extensions to this base $Λ$CDM+$r$ model by including additional base parameters relevant to neutrinos and/or other neutrino-like relativistic components. Four neutrino cosmological models are considered, i.e., the $Λ$CDM+$r$+$\sum m_ν$, $Λ$CDM+$r$+$N_{\rm eff}$, $Λ$CDM+$r$+$\sum m_ν$+$N_{\rm eff}$, and $Λ$CDM+$r$+$N_{\rm eff}$+$m_{ν,{\rm sterile}}^{\rm eff}$ models. We combine the current data, including the Planck temperature data, the WMAP 9-year polarization data, the baryon acoustic oscillation data, the Hubble constant direct measurement data, the Planck Sunyaev-Zeldovich cluster counts data, the Planck CMB lensing data, the cosmic shear data, and the BICEP2 polarization data, to constrain these neutrino cosmological models. We focus on the constraints on the parameters $\sum m_ν$, $N_{\rm eff}$, and $m_{ν,{\rm sterile}}^{\rm eff}$. We also discuss whether the tension on $r$ between Planck and BICEP2 can be relieved in these neutrino cosmological models.

preprint2014arXiv

Dark radiation from a unified dark fluid model

We present a unified dark fluid model to describe the possible evolutionary behavior of $ΔN_\mathrm{eff}$ in dark radiation. This model can be viewed as an interacting model for the dark sectors, in which dark matter interacts with dark radiation. We show that the evolution of $ΔN_\mathrm{eff}$ can be nicely explained without some drawbacks, such as the blowup of $ΔN_\mathrm{eff}$ and the non-vanishing interaction at the late time.

preprint2014arXiv

Diagnosing holographic dark energy models with statefinder hierarchy

We apply a series of null diagnostics based on the statefinder hierarchy to diagnose different holographic dark energy models including the original holographic dark energy, the new holographic dark energy, the new agegraphic dark energy, and the Ricci dark energy models. We plot the curves of statefinders $S^{(1)}_3$ and $S^{(1)}_4$ versus redshift $z$ and the evolutionary trajectories of $\{S^{(1)}_3, ε\}$ and $\{S^{(1)}_4, ε\}$ for these models, where $ε$ is the fractional growth parameter. Combining the evolution curves with the current values of $S^{(1)}_3$, $S^{(1)}_4$, and $ε$, we find that the statefinder $S^{(1)}_4$ performs better than $S^{(1)}_3$ for diagnosing the holographic dark energy models. In addition, the conjunction of the statefinder hierarchy and the fractional growth parameter is proven to be a useful method to diagnose the holographic dark energy models, especially for breaking the degeneracy of the new agegraphic dark energy model with different parameter values.

preprint2014arXiv

Effects of a time-varying color-luminosity parameter $β$ on the cosmological constraints of modified gravity models

It has been found that, for the Supernova Legacy Survey three-year (SNLS3) data, there is strong evidence for the redshift-evolution of color-luminosity parameter $β$. In previous studies, only dark energy (DE) models are used to explore the effects of a time-varying $β$ on parameter estimation. In this paper, we extend the discussions to the case of modified gravity (MG), by considering Dvali-Gabadadze-Porrati (DGP) model, power-law type $f(T)$ model and exponential type $f(T)$ model. In addition to the SNLS3 data, we also use the latest Planck distance priors data, the galaxy clustering (GC) data extracted from Sloan Digital Sky Survey (SDSS) data release 7 (DR7) and Baryon Oscillation Spectroscopic Survey (BOSS), as well as the direct measurement of Hubble constant $H_0$ from the Hubble Space Telescope (HST) observation. We find that, for both cases of using the supernova (SN) data alone and using the combination of all data, adding a parameter of $β$ can reduce $χ^2$ by $\sim$ 36 for all the MG models, showing that a constant $β$ is ruled out at 6$σ$ confidence level (CL). Moreover, we find that a time-varying $β$ always yields a larger fractional matter density $Ω_{m0}$ and a smaller reduced Hubble constant $h$; in addition, it significantly changes the shapes of 1$σ$ and 2$σ$ confidence regions of various MG models, and thus corrects systematic bias for the parameter estimation. These conclusions are consistent with the results of DE models, showing that $β$'s evolution is completely independent of the cosmological models in the background. Therefore, our work highlights the importance of considering the evolution of $β$ in the cosmology-fits.

preprint2014arXiv

Effects of time-varying $β$ in SNLS3 on constraining interacting dark energy models

It has been found that, for the Supernova Legacy Survey three-year (SNLS3) data, there is strong evidence for the redshift-evolution of color-luminosity parameter $β$. In this paper, adopting the $w$-cold-dark-matter ($w$CDM) model and considering its interacting extensions (with three kinds of interaction between dark sectors), we explore the evolution of $β$ and its effects on parameter estimation. In addition to the SNLS3 data, we also take into account the Planck distance priors data of the cosmic microwave background (CMB), the galaxy clustering (GC) data extracted from SDSS DR7 and BOSS, as well as the direct measurement of Hubble constant from the Hubble Space Telescope (HST) observation. We find that, for all the interacting dark energy (IDE) models, adding a parameter of $β$ can reduce $χ^2$ by $\sim$ 34, indicating that $β_1 = 0$ is ruled out at 5.8$σ$ confidence level (CL). Furthermore, it is found that varying $β$ can significantly change the fitting results of various cosmological parameters: for all the dark energy models considered in this paper, varying $β$ yields a larger $Ω_{c0}$ and a larger $w$; on the other side, varying $β$ yields a smaller $h$ for the $w$CDM model, but has no impact on $h$ for the three IDE models. This implies that there is a degeneracy between $h$ and $γ$. Our work shows that the evolution of $β$ is insensitive to the interaction between dark sectors, and then highlights the importance of considering $β$'s evolution in the cosmology fits.

preprint2014arXiv

Equitable list point arboricity of graphs

A graph $G$ is list point $k$-arborable if, whenever we are given a $k$-list assignment $L(v)$ of colors for each vertex $v\in V(G)$, we can choose a color $c(v)\in L(v)$ for each vertex $v$ so that each color class induces an acyclic subgraph of $G$, and is equitable list point $k$-arborable if $G$ is list point $k$-arborable and each color appears on at most $\lceil |V(G)|/k\rceil$ vertices of $G$. In this paper, we conjecture that every graph $G$ is equitable list point $k$-arborable for every $k\geq \lceil(Δ(G)+1)/2\rceil$ and settle this for complete graphs, 2-degenerate graphs, 3-degenerate claw-free graphs with maximum degree at least 4, and planar graphs with maximum degree at least 8.

preprint2014arXiv

Equitable vertex arboricity of planar graphs

Let $G_1$ be a planar graph such that all cycles of length at most 4 are independent and let $G_2$ be a planar graph without 3-cycles and adjacent 4-cycles. It is proved that the set of vertices of $G_1$ and $G_2$ can be equitably partitioned into $t$ subsets for every $t\geq 3$ so that each subset induces a forest. These results partially confirm a conjecture of Wu, Zhang and Li.

preprint2014arXiv

Exact solution of the one-dimensional super-symmetric t-J model with unparallel boundary fields

The exact solution of the one-dimensional super-symmetric t-J model under generic integrable boundary conditions is obtained via the Bethe ansatz methods. With the coordinate Bethe ansatz, the corresponding R-matrix and K-matrices are derived for the second eigenvalue problem associated with spin degrees of freedom. It is found that the second eigenvalue problem can be transformed to that of the transfer matrix of the inhomogeneous XXX spin chain, which allows us to obtain the spectrum of the Hamiltonian and the associated Bethe ansatz equations by the off-diagonal Bethe ansatz method.

preprint2014arXiv

Exploring the evolution of color-luminosity parameter $β$ and its effects on parameter estimation

It has been found in previous studies that, for the Supernova Legacy Survey three-year (SNLS3) data, there is strong evidence for the redshift-evolution of color-luminosity parameter $β$. In this paper, using three simplest dark energy models ($Λ$CDM, $w$CDM, and CPL), we further explore the evolution of $β$ and its effects on parameter estimation. In addition to the SNLS3 data, we also take into account the Planck distance priors data, as well as the latest galaxy clustering (GC) data extracted from SDSS DR7 and BOSS. We find that, for all the models, adding a parameter of $β$ can reduce $χ^2_{min}$ by $\sim$ 36, indicating that $β_1 = 0$ is ruled out at 6$σ$ confidence levels. In other words, $β$ deviates from a constant at 6$σ$ confidence levels. This conclusion is insensitive to the dark energy models considered, showing the importance of considering the evolution of $β$ in the cosmology-fits. Furthermore, it is found that varying $β$ can significantly change the fitting results of various cosmological parameters: using the SNLS3 data alone, varying $β$ yields a larger $Ω_m$ for the $Λ$CDM model; using the SNLS3+CMB+GC data, varying $β$ yields a larger $Ω_m$ and a smaller $h$ for all the models. Moreover, we find that these results are much closer to those given by the CMB+GC data, compared to the cases of treating $β$ as a constant. This indicates that considering the evolution of $β$ is very helpful for reducing the tension between supernova and other cosmological observations.

preprint2014arXiv

Exploring the full parameter space for an interacting dark energy model with recent observations including redshift-space distortions: Application of the parametrized post-Friedmann approach

Dark energy can modify the dynamics of dark matter if there exists a direct interaction between them. Thus a measurement of the structure growth, e.g., redshift-space distortions (RSD), can provide a powerful tool to constrain the interacting dark energy (IDE) models. For the widely studied $Q=3βHρ_{de}$ model, previous works showed that only a very small coupling ($β\sim\mathcal{O}(10^{-3})$) can survive in current RSD data. However, all these analyses had to assume $w>-1$ and $β>0$ due to the existence of the large-scale instability in the IDE scenario. In our recent work [Phys. Rev. D 90, 063005 (2014)], we successfully solved this large-scale instability problem by establishing a parametrized post-Friedmann (PPF) framework for the IDE scenario. So we, for the first time, have the ability to explore the full parameter space of the IDE models. In this work, we reexamine the observational constraints on the $Q=3βHρ_{de}$ model within the PPF framework. By using the Planck data, the baryon acoustic oscillation data, the JLA sample of supernovae, and the Hubble constant measurement, we get $β=-0.010^{+0.037}_{-0.033}$ ($1σ$). The fit result becomes $β=-0.0148^{+0.0100}_{-0.0089}$ ($1σ$) once we further incorporate the RSD data in the analysis. The error of $β$ is substantially reduced with the help of the RSD data. Compared with the previous results, our results show that a negative $β$ is favored by current observations, and a relatively larger interaction rate is permitted by current RSD data.

preprint2014arXiv

Large-scale stable interacting dark energy model: Cosmological perturbations and observational constraints

Dark energy might interact with cold dark matter in a direct, nongravitational way. However, the usual interacting dark energy models (with constant $w$) suffer from some catastrophic difficulties. For example, the $Q\proptoρ_{\rm c}$ model leads to an early-time large-scale instability, and the $Q\proptoρ_{\rm de}$ model gives rise to the future unphysical result for cold dark matter density (in the case of a positive coupling). In order to overcome these fatal flaws, we propose in this paper an interacting dark energy model (with constant $w$) in which the interaction term is carefully designed to realize that $Q\proptoρ_{\rm de}$ at the early times and $Q\proptoρ_{\rm c}$ in the future, simultaneously solving the early-time superhorizon instability and future unphysical $ρ_{\rm c}$ problems. The concrete form of the interaction term in this model is $Q=3βH \frac{ρ_{\rm{de}}ρ_{\rm{c}}}{ρ_{\rm{de}}+ρ_{\rm{c}}}$, where $β$ is the dimensionless coupling constant. We show that this model is actually equivalent to the decomposed new generalized Chaplygin gas (NGCG) model, with the relation $β=-αw$. We calculate the cosmological perturbations in this model in a gauge-invariant way and show that the cosmological perturbations are stable during the whole expansion history provided that $β>0$. Furthermore, we use the Planck data in conjunction with other astrophysical data to place stringent constraints on this model (with eight parameters), and we find that indeed $β>0$ is supported by the joint constraint at more than 1$σ$ level. The excellent theoretical features and the support from observations all indicate that the decomposed NGCG model deserves more attention and further investigation.

preprint2014arXiv

Measuring growth index in a universe with sterile neutrinos

Consistency tests for the general relativity (GR) can be performed by constraining the growth index $γ$ using the measurements of redshift-space distortions (RSD) in conjunction with other observations. In previous studies, deviations from the GR expected value of $γ\approx 0.55$ at the 2--3$σ$ level were found. In this work, we reconsider the measurement of $γ$ in a universe with sterile neutrinos. We constrain the sterile neutrino cosmological model using the RSD measurements combined with the cosmic microwave background data (Planck temperature data plus WMAP 9-yr polarization data), the baryon acoustic oscillation data, the Hubble constant direct measurement, the Planck Sunyaev-Zeldovich cluster counts data, and the galaxy shear data. We obtain the constraint result of the growth index, $γ=0.584^{+0.047}_{-0.048}$, well consistent with the GR expected value (the consistency is at the 0.6$σ$ level). For the parameters of sterile neutrino, we obtain $N_{\rm{eff}}=3.62^{+0.26}_{-0.42}$ and $m_{ν,{\rm{sterile}}}^{\rm{eff}}=0.48^{+0.11}_{-0.14}$ eV. We also consider the BICEP2 data and perform an analysis on the model with tensor modes. Similar fit results are obtained, showing that once light sterile neutrino is considered in the universe, GR will become well consistent with the current observations.

preprint2014arXiv

Neutrinos and dark energy after Planck and BICEP2: data consistency tests and cosmological parameter constraints

The detection of the B-mode polarization of the cosmic microwave background (CMB) by the BICEP2 experiment implies that the tensor-to-scalar ratio $r$ should be involved in the base standard cosmology. In this paper, we extend the $Λ$CDM+$r$+neutrino/dark radiation models by replacing the cosmological constant with the dynamical dark energy with constant $w$. Four neutrino plus dark energy models are considered, i.e., the $w$CDM+$r+\sum m_ν$, $w$CDM+r + $N_{\rm eff}$, $w$CDM+r + $\sum m_ν$ + $N_{\rm eff}$, and $w$CDM+r + $N_{\rm eff}$ + $m_{ν,{\rm sterile}}^{\rm eff}$ models. The current observational data considered in this paper include the Planck temperature data, the WMAP 9-year polarization data, the baryon acoustic oscillation data, the Hubble constant direct measurement data, the Planck Sunyaev-Zeldovich cluster counts data, the Planck CMB lensing data, the cosmic shear data, and the BICEP2 polarization data. We test the data consistency in the four cosmological models, and then combine the consistent data sets to perform joint constraints on the models. We focus on the constraints on the parameters $w$, $\sum m_ν$, $N_{\rm eff}$, and $m_{ν,{\rm sterile}}^{\rm eff}$.

preprint2014arXiv

Optimal Hybrid Dividend Strategy Under The Markovian Regime-Switching Economy

In this paper, we consider the optimal dividend problem for a company. We describe the surplus process of the company by a diffusion model with regime switching. The aim of the company is to choose a dividend policy to maximize the expected total discounted payments until ruin. In this article, we consider a hybrid dividend strategy, that is, the company is allowed to conduct continuous dividend strategy as well as impulsive dividend strategy. In addition, we consider the change of economy, which is characterized by a markovian regime-switching, and under the setting of two regimes, we solve the problem and obtain the analytical solution for the value function.

preprint2014arXiv

Optimal investment-reinsurance policy under a long-term perspective

In this paper, we assume an insure is allowed to purchase proportional reinsurance and can invest his or her wealth into the financial market where a savings account, stocks and bonds are available. Different from classical optimal investment and reinsurance problem, this paper studies the insurer's long-term investment decision. Under this setting, our model consider the interest risk and the inflation risk. Specifically, we suppose the interest rate follows a stochastic process, while price index is described by a classical model. By solving Hamilton-Jacobi-Bellman equation, the closed-form expression of the optimal policy is obtained. Further, we prove the corresponding verification theorem without the usual Lipschitz condition. In the end, numerical examples are made to illustrate the difference of the optimal polices under Ho-lee model and Vasicek model.

preprint2014arXiv

Parameter estimation with Sandage-Loeb test

The Sandage-Loeb (SL) test directly measures the expansion rate of the universe in the redshift range of $2\lesssim z\lesssim 5$ by detecting redshift drift in the spectra of Lyman-$α$ forest of distant quasars. We discuss the impact of the future SL test data on parameter estimation for the $Λ$CDM, the $w$CDM, and the $w_0w_a$CDM models. To avoid the potential inconsistency with other observational data, we take the best-fitting dark energy model constrained by the current observations as the fiducial model to produce 30 mock SL test data. The SL test data provide an important supplement to the other dark energy probes, since they are extremely helpful in breaking the existing parameter degeneracies. We show that the strong degeneracy between $Ω_m$ and $H_0$ in all the three dark energy models is well broken by the SL test. Compared to the current combined data of type Ia supernovae, baryon acoustic oscillation, cosmic microwave background, and Hubble constant, the 30-yr observation of SL test could improve the constraints on $Ω_m$ and $H_0$ by more than 60\% for all the three models. But the SL test can only moderately improve the constraint on the equation of state of dark energy. We show that a 30-yr observation of SL test could help improve the constraint on constant $w$ by about 25\%, and improve the constraints on $w_0$ and $w_a$ by about 20\% and 15\%, respectively. We also quantify the constraining power of the SL test in the future high-precision joint geometric constraints on dark energy. The mock future supernova and baryon acoustic oscillation data are simulated based on the space-based project JDEM. We find that the 30-yr observation of SL test would help improve the measurement precision of $Ω_m$, $H_0$, and $w_a$ by more than 70\%, 20\%, and 60\%, respectively, for the $w_0w_a$CDM model.

preprint2014arXiv

Parametrized Post-Friedmann Framework for Interacting Dark Energy

Dark energy might directly interact with cold dark matter. However, in such a scenario, an early-time large-scale instability occurs occasionally, which may be due to the incorrect treatment for the pressure perturbation of dark energy as a nonadiabatic fluid. To avoid this nonphysical instability, we establish a new framework to correctly calculate the cosmological perturbations in the interacting dark energy models. Inspired by the well-known parametrized post-Friedmann approach, the condition of the dark energy pressure perturbation is replaced with the relationship between the momentum density of dark energy and that of other components on large scales. By reconciling the perturbation evolutions on the large and small scales, one can complete the perturbation equations system. The large-scale instability can be successfully avoided and the well-behaved density and metric perturbations are obtained within this framework. Our test results show that this new framework works very well and is applicable to all the interacting dark energy models.

preprint2014arXiv

Quantifying the impact of future Sandage-Loeb test data on dark energy constraints

The Sandage-Loeb (SL) test is a unique method to probe dark energy in the "redshift desert" of $2\lesssim z\lesssim 5$, and thus it provides an important supplement to the other dark energy probes. Therefore, it is of great importance to quantify how the future SL test data impact on the dark energy constraints. To avoid the potential inconsistency in data, we use the best-fitting model based on the other geometric measurements as the fiducial model to produce 30 mock SL test data. The 10-yr, 20-yr, and 30-yr observations of SL test are analyzed and compared in detail. We show that compared to the current combined data of type Ia supernovae, baryon acoustic oscillation, cosmic microwave background, and Hubble constant, the 30-yr observation of SL test could improve the constraint on $Ω_m$ by about $80%$ and the constraint on $w$ by about $25%$. Furthermore, the SL test can also improve the measurement of the possible direct interaction between dark energy and dark matter. We show that the SL test 30-yr data could improve the constraint on $γ$ by about $30%$ and $10%$ for the $Q=γHρ_c$ and $Q=γHρ_{de}$ models, respectively.

preprint2014arXiv

Regular and exceptional spectra of the two-qubit quantum Rabi model

Two-qubit system is the foundation of constructing the universal quantum gate. We have studied the two-qubit Rabi model for the general case and its generalizations with dipole, XXX and XYZ Heisenberg qubit-qubit interactions, which are commonly used in quantum computation. Their solutions are presented analytically with eigenstates can be obtained in terms of extended coherent states or Fock states and applied to the construction of the ultrafast two-qubit quantum gate in circuit QED and quantum state storage and transfer. Some novel kinds of quasi-exact solutions are found for specific sets of parameters, causing level crossings within the same parity subspace, which do not appear in the regular spectrum, indicating its non-integrability. These eigenstates are very interesting for quantum computing and single photon experiments because they are formed by just a few Fock states, and in some cases, with at most one photon. They are also easy to prepare, since they exist for all qubit-photon coupling values with constant eigenenergy and the qubit energy splittings can be fine tuned in the experiment in contrast to the coupling.

preprint2014arXiv

Revisit of constraints on holographic dark energy: SNLS3 dataset with the effects of time-varying $β$ and different light-curve fitters

Previous studies have shown that for the Supernova Legacy Survey three-year (SNLS3) data there is strong evidence for the redshift-evolution of color-luminosity parameter $β$ of type Ia supernovae (SN Ia). In this paper, we explore the effects of varying $β$ on the cosmological constraints of holographic dark energy (HDE) model. In addition to the SNLS3 data, we also use Planck distance prior data of cosmic microwave background (CMB), as well as galaxy clustering (GC) data extracted from Sloan Digital Sky Survey (SDSS) data release 7 and Baryon Oscillation Spectroscopic Survey (BOSS). We find that, for the both cases of using SN data alone and using SN+CMB+GC data, involving an additional parameter of $β$ can reduce $χ^2$ by $\sim$ 36; this shows that $β$ deviates from a constant at 6$σ$ confidence levels. Adopting SN+CMB+GC data, we find that compared to the constant $β$ case, varying $β$ yields a larger fractional matter density $Ω_{m0}$ and a smaller reduced Hubble constant $h$; moreover, varying $β$ significantly increases the value of HDE model parameter $c$, leading to $c\approx 0.8$, consistent with the constraint results obtained before Planck. These results indicate that the evolution of $β$ should be taken into account seriously in the cosmological fits. In addition, we find that relative to the differences between the constant $β$ and varying $β(z)$ cases, the effects of different light-curve fitters on parameter estimation are very small.

preprint2014arXiv

Revisiting the holographic dark energy in a non-flat universe: alternative model and cosmological parameter constraints

We propose an alternative model for the holographic dark energy in a non-flat universe. This new model differs from the previous one in that the IR length cutoff $L$ is taken to be exactly the event horizon size in a non-flat universe, which is more natural and theoretically/conceptually concordant with the model of holographic dark energy in a flat universe. We constrain the model using the recent observational data including the type Ia supernova data from SNLS3, the baryon acoustic oscillation data from 6dF, SDSS-DR7, BOSS-DR11, and WiggleZ, the cosmic microwave background data from Planck, and the Hubble constant measurement from HST. In particular, since some previous studies have shown that the color-luminosity parameter $β$ of supernovae is likely to vary during the cosmic evolution, we also consider such a case that $β$ in SNLS3 is time-varying in our data fitting. Compared to the constant $β$ case, the time-varying $β$ case reduces the value of $χ^2$ by about 35 and results in that $β$ deviates from a constant at about 5$σ$ level, well consistent with the previous studies. For the parameter $c$ of the holographic dark energy, the constant $β$ fit gives $c=0.65\pm 0.05$ and the time-varying $β$ fit yields $c=0.72\pm 0.06$. In addition, an open universe is favored (at about 2$σ$) for the model by the current data.

preprint2014arXiv

Revisiting the interacting model of new agegraphic dark energy

In this paper, a new version of the interacting model of new agegraphic dark energy (INADE) is proposed and analyzed in detail. The interaction between dark energy and dark matter is reconsidered. The interaction term $Q=bH_0ρ_{\rm de}^αρ_{\rm dm}^{1-α}$ is adopted, which abandons the Hubble expansion rate $H$ and involves both $ρ_{\rm de}$ and $ρ_{\rm dm}$. Moreover, the new initial condition for the agegraphic dark energy is used, which solves the problem of accommodating baryon matter and radiation in the model. The solution of the model can be given using an iterative algorithm. A concrete example for the calculation of the model is given. Furthermore, the model is constrained by using the combined Planck data (Planck+BAO+SNIa+$H_0$) and the combined WMAP-9 data (WMAP+BAO+SNIa+$H_0$). Three typical cases are considered: (A) $Q=bH_0ρ_{\rm de}$, (B) $Q=bH_0\sqrt{ρ_{\rm de}ρ_{\rm dm}}$, and (C) $Q=bH_0ρ_{\rm dm}$, which correspond to $α=1$, 1/2, and 0, respectively. The departures of the models from the $Λ$CDM model are measured by the $Δ$BIC and $Δ$AIC values. It is shown that the INADE model is better than the NADE model in the fit, and the INADE(A) model is the best in fitting data among the three cases.

preprint2014arXiv

Sterile neutrinos help reconcile the observational results of primordial gravitational waves from Planck and BICEP2

We show that involving a sterile neutrino species in the $Λ$CDM+$r$ model can help relieve the tension about the tensor-to-scalar ratio $r$ between the Planck temperature data and the BICEP2 B-mode polarization data. Such a model is called the $Λ$CDM+$r$+$ν_s$ model in this paper. Compared to the $Λ$CDM+$r$ model, there are two extra parameters, $N_{\rm eff}$ and $m_{ν,{\rm sterile}}^{\rm eff}$, in the $Λ$CDM+$r$+$ν_s$ model. We show that in this model the tension between Planck and BICEP2 can be greatly relieved at the cost of the increase of $n_s$. However, comparing with the $Λ$CDM+$r$+$dn_s/d\ln k$ model that can significantly reduce the tension between Planck and BICEP2 but also makes trouble to inflation due to the large running of the spectral index of order $10^{-2}$ produced, the $Λ$CDM+$r$+$ν_s$ model is much better for inflation. By including a sterile neutrino species in the standard cosmology, besides the tension with BICEP2, the other tensions of Planck with other astrophysical data, such as the $H_0$ direct measurement, the Sunyaev-Zeldovich cluster counts, and the galaxy shear data, can all be significantly relieved. So, this model seems to be an economical choice. Combining the Planck temperature data, the WMAP-9 polarization data, and the baryon acoustic oscillation data with all these astrophysical data (including BICEP2), we find that in the $Λ$CDM+$r$+$ν_s$ model $n_s=0.999\pm 0.011$, $r=0.21^{+0.04}_{-0.05}$, $N_{\rm eff}=3.95\pm 0.33$ and $m_{ν,{\rm sterile}}^{\rm eff}=0.51^{+0.12}_{-0.13}$ eV. Thus, our results prefer $ΔN_{\rm eff}>0$ at the 2.7$σ$ level and a nonzero mass of sterile neutrino at the 3.9$σ$ level.

preprint2014arXiv

Structural Control of Metamaterial Oscillator Strength and Electric Field Enhancement at Terahertz Frequencies

The design of artificial nonlinear materials requires control over the internal resonant charge densities and local electric field distributions. We present a MM design with a structurally controllable oscillator strength and local electric field enhancement at terahertz frequencies. The MM consists of a split ring resonator (SRR) array stacked above an array of nonresonant closed conducting rings. An in-plane, lateral shift of a half unit cell between the SRR and closed ring arrays results in a decrease of the MM oscillator strength by a factor of 4 and a 40% change in the amplitude of the resonant electric field enhancement in the SRR capacitive gap. We use terahertz time-domain spectroscopy and numerical simulations to confirm our results and we propose a qualitative inductive coupling model to explain the observed electromagnetic reponse.

preprint2014arXiv

The edge chromatic number of outer-1-planar graphs

A graph is outer-1-planar if it can be drawn in the plane so that all vertices are on the outer face and each edge is crossed at most once. In this paper, we completely determine the edge chromatic number of outer 1-planar graphs.

preprint2014arXiv

The Interlayer Shear Modes in Twisted Multi-layer Graphenes: Interlayer coupling, Davydov Splitting and Intensity Resonance

Graphene and other two-dimensional crystals can be combined to form various hybrids and heterostructures, creating materials on demand, in which the interlayer coupling at the interface leads to modified physical properties as compared to their constituents. Here, by measuring Raman spectra of shear modes, we probe the coupling at the interface between two artificially-stacked few-layer graphenes rotated with respect to each other. The strength of interlayer coupling between the two interface layers is found to be only 20% of that between Bernal-stacked layers. Nevertheless, this weak coupling manifests itself in a Davydov splitting of the shear mode frequencies in systems consisting of two equivalent graphene multilayers, and in the intensity enhancement of shear modes due to the optical resonance with several optically allowed electronic transitions between conduction and valence bands in the band structures. This study paves way for fundamental understanding into the interface coupling of two-dimensional hybrids and heterostructures.

preprint2014arXiv

The ultra-low-frequency shear modes of 2-4 layer graphenes observed in their scroll structures at edges

The in-plane shear modes between neighbor-layers of 2-4 layer graphenes (LGs) and the corresponding graphene scrolls rolled up by 2-4LGs were investigated by Raman scattering. In contrast to that just one shear mode was observed in 3-4LGs, all the shear modes of 3-4LGs were observed in 3-4 layer scrolls (LSs), whose frequencies agree well with the theoretical predication by both a force-constant model and a linear chain model. In comparison to the broad width (about 12cm$^{-1}$) for the G band in graphite, all the shear modes exhibit an intrinsic line width of about 1.0 cm$^{-1}$. The local electronic structures dependent on the local staking configurations enhance the intensity of the shear modes in corresponding 2-4LSs zones, which makes it possible to observe all the shear modes. It provides a direct evidence that how the band structures of FLGs can be sensitive to local staking configurations. This result can be extended to n layer graphene (n > 4) for the understanding of the basic phonon properties of multi-layer graphenes. This observation of all-scale shear modes can be foreseen in other 2D materials with similar scroll structures.

preprint2014arXiv

Tilt of primordial gravitational wave spectrum in a universe with sterile neutrinos

In this work, we constrain the spectral index $n_t$ of the primordial gravitational wave power spectrum in a universe with sterile neutrinos by using the Planck temperature data, the WMAP 9-year polarization data, the baryon acoustic oscillation data, and the BICEP2 data. We call this model the $Λ$CDM+$r$+$ν_s$+$n_t$ model. The additional massive sterile neutrino species can significantly relieve the tension between the Planck and BICEP2 data, and thus can reduce the possible effects of this tension on the fit results of $n_t$. To constrain the parameters of sterile neutrino, we also utilize the Hubble constant direct measurement data, the Planck Sunyaev-Zeldovich cluster counts data, the Planck CMB lensing data, and the cosmic shear data. We find that due to the fact that the BICEP2 data are most sensitive to the multipole $\ell\sim150$ corresponding to $k\sim0.01$ Mpc$^{-1}$, there exists a strong anticorrelation between $n_t$ and $r_{0.002}$ in the BICEP2 data, and this further results in a strongly blue-tilt spectrum. However, a slightly red-tilt tensor power spectrum is also allowed by the BICEP2 data in the region with larger value of $r_{0.002}$. By using the full data sets, we obtain $m_{ν,{\rm{sterile}}}^{\rm{eff}}=0.48^{+0.11}_{-0.13}$ eV, $N_{\rm{eff}}=3.73^{+0.34}_{-0.37}$, and $n_t=0.96^{+0.48}_{-0.63}$ for the $Λ$CDM+$r$+$ν_s$+$n_t$ model.

preprint2013arXiv

A global fit study on the new agegraphic dark energy model

We perform a global fit study on the new agegraphic dark energy (NADE) model in a non-flat universe by using the MCMC method with the full CMB power spectra data from the WMAP 7-yr observations, the SNIa data from Union2.1 sample, BAO data from SDSS DR7 and WiggleZ Dark Energy Survey, and the latest measurements of $H_0$ from HST. We find that the value of $Ω_{k0}$ is greater than 0 at least at the 3$σ$ confidence levels (CLs), which implies that the NADE model distinctly favors an open universe. Besides, our results show that the value of the key parameter of NADE model, $n=2.673^{+0.053+0.127+0.199}_{-0.077-0.151-0.222}$, at the 1--3$σ$ CLs, where its best-fit value is significantly smaller than those obtained in previous works. We find that the reason leading to such a change comes from the different SNIa samples used. Our further test indicates that there is a distinct tension between the Union2 sample of SNIa and other observations, and the tension will be relieved once the Union2 sample is replaced by the Union2.1 sample. So, the new constraint result of the NADE model obtained in this work is more reasonable than before.

preprint2013arXiv

Confronting brane inflation with Planck and pre-Planck data

In this paper, we compare brane inflation models with the Planck data and the pre-Planck data (which combines WMAP, ACT, SPT, BAO and H0 data). The Planck data prefer a spectral index less than unity at more than 5σconfidence level, and a running of the spectral index at around 2σconfidence level. We find that the KKLMMT model can survive at the level of 2σonly if the parameter $β$ (the conformal coupling between the Hubble parameter and the inflaton) is less than $\mathcal{O}(10^{-3})$, which indicates a certain level of fine-tuning. The IR DBI model can provide a slightly larger negative running of spectral index and red tilt, but in order to be consistent with the non-Gaussianity constraints from Planck, its parameter also needs fine-tuning at some level.

preprint2013arXiv

Decoupling Crossover in Asymmetric Broadside Coupled Split Ring Resonators at Terahertz Frequencies

We investigate the electromagnetic response of asymmetric broadside coupled split ring resonators (ABC-SRRs) as a function of the relative in-plane displacement between the two component SRRs. The asymmetry is defined as the difference in the capacitive gap widths (Δg) between the two resonators comprising a coupled unit. We characterize the response of ABC-SRRs both numerically and experimentally via terahertz time-domain spectroscopy. As with symmetric BC-SRRs (Δg=0 μm), a large redshift in the LC resonance is observed with increasing displacement, resulting from changes in the capacitive and inductive coupling. However, for ABC-SRRs, in-plane shifting between the two resonators by more than 0.375Lo (Lo=SRR sidelength) results in a transition to a response with two resonant modes, associated with decoupling in the ABC-SRRs. For increasing Δg, the decoupling transition begins at the same relative shift (0.375Lo), though with an increase in the oscillator strength of the new mode. This strongly contrasts with symmetric BC-SRRs which present only one resonance for shifts up to 0.75Lo. Since all BC-SRRs are effectively asymmetric when placed on a substrate, an understanding of ABC-SRR behavior is essential for a complete understanding of BC-SRR based metamaterials.

preprint2013arXiv

Drawing complete multipartite graphs on the plane with restrictions on crossings

We introduce the concept of NIC-planar graphs and present the full characterization of NIC-planar complete k-partite graphs.

preprint2013arXiv

Electronic transport in ferromagnetic barriers on the surface of a topological insulator with $δ$ doping

We investigate electron transporting through a two-dimensional ferromagnetic/normal/ferromagnetic tunnel junction on the surface of a three-dimensional topological insulator with taking into $δ$ doping account. It is found that the conductance oscillates with the Fermi energy, the position and the aptitude of the $δ$ doping. Also the conductance depends sensitively on the direction of the magnetization of the two ferromagnets, which originate from the control of the spin flow due to spin-momentum locked. It is found that the conductance is the maximum at the parallel configuration while it is minimum at the antiparallel configuration and vice versa, which may stem from the half wave loss due to the electron wave entering through the antiparallel configuration. These characters are very helpful for making new types of magnetoresistance devices due to the practical applications.

preprint2013arXiv

Gradient type optimization methods for electronic structure calculations

The density functional theory (DFT) in electronic structure calculations can be formulated as either a nonlinear eigenvalue or direct minimization problem. The most widely used approach for solving the former is the so-called self-consistent field (SCF) iteration. A common observation is that the convergence of SCF is not clear theoretically while approaches with convergence guarantee for solving the latter are often not competitive to SCF numerically. In this paper, we study gradient type methods for solving the direct minimization problem by constructing new iterations along the gradient on the Stiefel manifold. Global convergence (i.e., convergence to a stationary point from any initial solution) as well as local convergence rate follows from the standard theory for optimization on manifold directly. A major computational advantage is that the computation of linear eigenvalue problems is no longer needed. The main costs of our approaches arise from the assembling of the total energy functional and its gradient and the projection onto the manifold. These tasks are cheaper than eigenvalue computation and they are often more suitable for parallelization as long as the evaluation of the total energy functional and its gradient is efficient. Numerical results show that they can outperform SCF consistently on many practically large systems.

preprint2013arXiv

Holographic dark energy in a universe with spatial curvature and massive neutrinos: a full Markov Chain Monte Carlo exploration

In this paper, we report the results of constraining the holographic dark energy model with spatial curvature and massive neutrinos, based on a Markov Chain Monte Carlo global fit technique. The cosmic observational data include the full WMAP 7-yr temperature and polarization data, the type Ia supernova data from Union2.1 sample, the baryon acoustic oscillation data from SDSS DR7 and WiggleZ Dark Energy Survey, and the latest measurements of $H_0$ from HST. To deal with the perturbations of dark energy, we adopt the parameterized post-Friedmann method. We find that, for the simplest holographic dark energy model without spatial curvature and massive neutrinos, the phenomenological parameter $c<1$ at more than $4σ$ confidence level. The inclusion of spatial curvature enlarges the error bars and leads to $c<1$ only in about $2.5σ$ range; in contrast, the inclusion of massive neutrinos does not have significant influence on $c$. We also find that, for the holographic dark energy model with spatial curvature but without massive neutrinos, the $3σ$ error bars of the current fractional curvature density $Ω_{k0}$ are still in order of $10^{-2}$; for the model with massive neutrinos but without spatial curvature, the $2σ$ upper bound of the total mass of neutrinos is $\sum m_ν < 0.48$ eV. Moreover, there exists clear degeneracy between spatial curvature and massive neutrinos in the holographic dark energy model, which enlarges the upper bound of $\sum m_ν$ by more than 2 times. In addition, we demonstrate that, making use of the full WMAP data can give better constraints on the holographic dark energy model, compared with the case using the WMAP ``distance priors''.

preprint2013arXiv

Lattice dynamics in mono- and few-layer sheets of WS2 and WSe2

Thickness is one of the fundamental parameters that define the electronic, optical, and thermal properties of two-dimensional (2D) crystals. Phonons in molybdenum disulfide (MoS2) were recently found to exhibit unique thickness dependence due to interplay between short and long range interactions. Here we report Raman spectra of atomically thin sheets of WS2 and WSe2 in the mono- to few-layer thickness regime. We show that, similar to the case of MoS2, the characteristic and modes exhibit stiffening and softening with increasing number of layers, respectively, with a small shift of less than 3 cm-1 due to large mass of the atoms. Thickness dependence is also observed in a series of multiphonon bands arising from overtone, combination, and zone edge phonons, whose intensity exhibit significant enhancement in excitonic resonance conditions. Some of these multiphonon peaks are found to be absent only in monolayers. These features provide a unique fingerprint and rapid identification for monolayer flakes.

preprint2013arXiv

List total coloring of pseudo-outerplanar graphs

A graph is pseudo-outerplanar if each of its blocks has an embedding in the plane so that the vertices lie on a fixed circle and the edges lie inside the disk of this circle with each of them crossing at most one another. It is proved that every pseudo-outerplanar graph with maximum degree Δ\geq 5 is totally (Δ+1)-choosable.

preprint2013arXiv

New initial condition of the new agegraphic dark energy model

The initial condition $Ω_{\rm de}(z_{\rm ini})=n^2(1+z_{\rm ini})^{-2}/4$ at $z_{\rm ini}=2000$ widely used to solve the differential equation of the density of the new agegraphic dark energy (NADE) $Ω_{\rm de}$, makes the NADE model be a single-parameter dark-energy cosmological model. However, we find that this initial condition is only applicable in a flat universe with only dark energy and pressureless matter. In fact, in order to obtain more information from current observational data, such as cosmic microwave background (CMB) and baryon acoustic oscillations (BAO), we need to consider the contribution of radiation. For this situation, the initial condition mentioned above becomes invalid. To overcome this shortage, we investigate the evolution of dark energy in the matter-dominated and radiation-dominated epochs, and obtain a new initial condition $Ω_{\rm de}(z_{\rm ini})=\frac{n^2(1+z_{\rm ini})^{-2}}{4}(1+\sqrt{F(z_{\rm ini})})^2$ at $z_{\rm ini}=2000$, where $F(z)\equiv\frac{Ω_{r0}(1+z)}{Ω_{m0}+Ω_{r0}(1+z)}$ with $Ω_{r0}$ and $Ω_{m0}$ being the current density parameters of radiation and pressureless matter, respectively. This revised initial condition is applicable for the differential equation of $Ω_{\rm de}$ obtained in the standard Friedmann-Robertson-Walker (FRW) universe with dark energy, pressureless matter, radiation, and even spatial curvature, and can still keep the NADE model being a single-parameter model. With the revised initial condition and the observational data of type Ia supernova (SNIa), CMB and BAO, we finally constrain the NADE model. The results show that the single free parameter $n$ of the NADE model can be constrained tightly.

preprint2013arXiv

On r-equitable chromatic threshold of Kronecker products of complete graphs

A graph $G$ is $r$-equitably $k$-colorable if its vertex set can be partitioned into $k$ independent sets, any two of which differ in size by at most $r$. The $r$-equitable chromatic threshold of a graph $G$, denoted by $χ_{r=}^*(G)$, is the minimum $k$ such that $G$ is $r$-equitably $k'$-colorable for all $k'\ge k$. Let $G\times H$ denote the Kronecker product of graphs $G$ and $H$. In this paper, we completely determine the exact value of $χ_{r=}^*(K_m\times K_n)$ for general $m,n$ and $r$. As a consequence, we show that for $r\ge 2$, if $n\ge \frac{1}{r-1}(m+r)(m+2r-1)$ then $K_m\times K_n$ and its spanning supergraph $K_{m(n)}$ have the same $r$-equitable colorability, and in particular $χ_{r=}^*(K_m\times K_n)=χ_{r=}^*(K_{m(n)})$, where $K_{m(n)}$ is the complete $m$-partite graph with $n$ vertices in each part.

preprint2013arXiv

On total colorings of 1-planar graphs

A graph is 1-planar if it can be drawn on the plane so that each edge is crossed by at most one other edge. In this paper, we confirm the total-coloring conjecture for 1-planar graphs with maximum degree at least 13.

preprint2013arXiv

Planck Constraints on Holographic Dark Energy

We perform a detailed investigation on the cosmological constraints on the holographic dark energy (HDE) model by using the Planck data. HDE can provide a good fit to Planck high-l (l>40) temperature power spectrum, while the discrepancy at l=20-40 found in LCDM remains unsolved in HDE. The Planck data alone can lead to strong and reliable constraint on the HDE parameter c. At 68% CL, we get c=0.508+-0.207 with Planck+WP+lensing, favoring the present phantom HDE at > 2sigma CL. Comparably, by using WMAP9 alone we cannot get interesting constraint on c. By combining Planck+WP with the BAO measurements from 6dFGS+SDSS DR7(R)+BOSS DR9, the H0 measurement from HST, the SNLS3 and Union2.1 SNIa data sets, we get 68% CL constraints c=0.484+-0.070, 0.474+-0.049, 0.594+-0.051 and 0.642+-0.066. Constraints can be improved by 2%-15% if we further add the Planck lensing data. Compared with the WMAP9 results, the Planck results reduce the error by 30%-60%, and prefer a phantom-like HDE at higher CL. We find no evident tension between Planck and BAO/HST. Especially, the strong correlation between Omegam h^3 and dark energy parameters is helpful in relieving the tension between Planck and HST. The residual chi^2_{Planck+WP+HST}-chi^2_{Planck+WP} is 7.8 in LCDM, and is reduced to 1.0 or 0.3 if we switch dark energy to the w model or the holographic model. We find SNLS3 is in tension with all other data sets; for Planck+WP, WMAP9 and BAO+HST, the corresponding Delta chi^2 is 6.4, 3.5 and 4.1, respectively. Comparably, Union2.1 is consistent with these data sets, but the combination Union2.1+BAO+HST is in tension with Planck+WP+lensing, corresponding to a Delta chi^2 8.6 (1.4% probability). Thus, it is not reasonable to perform an all-combined (CMB+SNIa+BAO+HST) analysis for HDE when using the Planck data. Our tightest self-consistent constraint is c=0.495+-0.039 obtained from Planck+WP+BAO+HST+lensing.

preprint2013arXiv

Reexamination of inflation in noncommutative space-time after Planck results

An inflationary model in the framework of noncommutative space-time may generate a nontrivial running of the scalar spectral index, but usually induces a large tensor-to-scalar ratio simultaneously. With the latest observational data from the Planck mission, we reexamine the inflationary scenarios in a noncommutative space-time. We find that either the running of the spectral index is tiny compared with the recent observational result, or the tensor-to-scalar ratio is too large to allow a sufficient number of $e$-folds. As examples, we show that the chaotic and power-law inflation models with the noncommutative effects are not favored by the current Planck data.

preprint2013arXiv

Three Dimensional Broadband Tunable Terahertz Metamaterials

We present optically tunable magnetic 3D metamaterials at terahertz (THz) frequencies which exhibit a tuning range of ~30% of the resonance frequency. This is accomplished by fabricating 3D array structures consisting of double-split-ring resonators (DSRRs) on silicon-on-sapphire, fabricated using multilayer electroplating. Photoexcitation of free carriers in the silicon within the capacitive region of the DSRR results in a red-shift of the resonant frequency from 1.74 THz to 1.16 THz. The observed frequency shift leads to a transition from a magnetic-to-bianisotropic response as verified through electromagnetic simulations and parameter retrieval. Our approach extends dynamic metamaterial tuning to magnetic control, and may find applications in switching and modulation, polarization control, or tunable perfect absorbers.

preprint2012arXiv

A conjecture on equitable vertex arboricity of graphs

Wu, Zhang and Li [4] conjectured that the set of vertices of any simple graph $G$ can be equitably partitioned into $\lceil(Δ(G)+1)/2\rceil$ subsets so that each of them induces a forest of $G$. In this note, we prove this conjecture for graphs $G$ with $Δ(G)\geq |G|/2$.

preprint2012arXiv

Constraining dynamical dark energy with a divergence-free parametrization in the presence of spatial curvature and massive neutrinos

In this paper, we report the results of constraining the dynamical dark energy with a divergence-free parameterization, $w(z) = w_{0} + w_{a}(\frac{\ln(2+z)}{1+z}-\ln2)$, in the presence of spatial curvature and massive neutrinos, with the 7-yr WMAP temperature and polarization data, the power spectrum of LRGs derived from SDSS DR7, the Type Ia supernova data from Union2 sample, and the new measurements of $H_0$ from HST, by using a MCMC global fit method. Our focus is on the determinations of the spatial curvature, $Ω_k$, and the total mass of neutrinos, $\sum m_ν$, in such a dynamical dark energy scenario, and the influence of these factors to the constraints on the dark energy parameters, $w_0$ and $w_a$. We show that $Ω_k$ and $\sum m_ν$ can be well constrained in this model; the 95% CL limits are: $-0.0153<Ω_k<0.0167$ and $\sum m_ν<0.56$ eV. Comparing to the case in a flat universe, we find that the error in $w_0$ is amplified by 25.51%, and the error in $w_a$ is amplified by 0.14%; comparing to the case with a zero neutrino mass, we find that the error in $w_0$ is amplified by 12.24%, and the error in $w_a$ is amplified by 1.63%.

preprint2012arXiv

Dark Energy and Fate of the Universe

We explore the ultimate fate of the Universe by using a divergence-free parametrization for dark energy $w(z)=w_0+w_a({\ln (2+z)\over 1+z}-\ln2)$. Unlike the CPL parametrization, this parametrization has well behaved, bounded behavior for both high redshifts and negative redshifts, and thus can genuinely cover many theoretical dark energy models. After constraining the parameter space of this parametrization by using the current cosmological observations, we find that, at the 95.4% confidence level, our Universe can still exist at least 16.7 Gyr before it ends in a big rip. Moreover, for the phantom energy dominated Universe, we find that a gravitationally bound system will be destroyed at a time $t \simeq P\sqrt{2|1+3w(-1)|}/[6π|1+w(-1)|]$, where $P$ is the period of a circular orbit around this system, before the big rip.

preprint2012arXiv

Equitable vertex arboricity of graphs

An equitable $(t,k,d)$-tree-coloring of a graph $G$ is a coloring to vertices of $G$ such that the sizes of any two color classes differ by at most one and the subgraph induced by each color class is a forest of maximum degree at most $k$ and diameter at most $d$. The minimum $t$ such that $G$ has an equitable $(t',k,d)$-tree-coloring for every $t'\geq t$ is called the strong equitable $(k,d)$-vertex-arboricity and denoted by $va^{\equiv}_{k,d}(G)$. In this paper, we give sharp upper bounds for $va^{\equiv}_{1,1}(K_{n,n})$ and $va^{\equiv}_{k,\infty}(K_{n,n})$ by showing that $va^{\equiv}_{1,1}(K_{n,n})=O(n)$ and $va^{\equiv}_{k,\infty}(K_{n,n})=O(n^{\1/2})$ for every $k\geq 2$. It is also proved that $va^{\equiv}_{\infty,\infty}(G)\leq 3$ for every planar graph $G$ with girth at least 5 and $va^{\equiv}_{\infty,\infty}(G)\leq 2$ for every planar graph $G$ with girth at least 6 and for every outerplanar graph. We conjecture that $va^{\equiv}_{\infty,\infty}(G)=O(1)$ for every planar graph and $va^{\equiv}_{\infty,\infty}(G)\leq \lceil\frac{Δ(G)+1}{2}\rceil$ for every graph $G$.

preprint2012arXiv

Holographic Ricci dark energy: Interacting model and cosmological constraints

We extend the holographic Ricci dark energy model to include some direct, non-gravitational interaction between dark energy and dark matter. We consider three phenomenological forms for the interaction term $Q$ in the model, namely, $Q$ is taken proportional to the Hubble expansion rate and the energy densities of dark sectors (taken to be $ρ_{\rm de}$, $ρ_{\rm m}$, and $ρ_{\rm de}+ρ_{\rm m}$, respectively). We obtain a uniform analytical solution to the three interacting models. Furthermore, we constrain the models by using the latest observational data, including the 557 Union2 type Ia supernovae data, the cosmic microwave background anisotropy data from the 7-yr WMAP, and the baryon acoustic oscillation data from the SDSS. We show that in the interacting models of the holographic Ricci dark energy, a more reasonable value of $Ω_{\rm m0}$ will be obtained, and the observations favor a rather strong coupling between dark energy and dark matter.

preprint2012arXiv

Holographic Λ(t)CDM model in a non-flat universe

The holographic $Λ(t)$CDM model in a non-flat universe is studied in this paper. In this model, to keep the form of the stress-energy of the vacuum required by general covariance, the holographic vacuum is enforced to exchange energy with dark matter. It is demonstrated that for the holographic model the best choice for the IR cutoff of the effective quantum field theory is the event horizon size of the universe. We derive the evolution equations of the holographic $Λ(t)$CDM model in a non-flat universe. We constrain the model by using the current observational data, including the 557 Union2 type Ia supernovae data, the cosmic microwave background anisotropy data from the 7-yr WMAP, and the baryon acoustic oscillation data from the SDSS. Our fit results show that the holographic $Λ(t)$CDM model tends to favor a spatially closed universe (the best-fit value of $Ω_{k0}$ is -0.042), and the 95% confidence level range for the spatial curvature is $-0.101<Ω_{k0}<0.040$. We show that the interaction between the holographic vacuum and dark matter induces an energy flow of which the direction is first from vacuum to dark matter and then from dark matter to vacuum. Thus, the holographic $Λ(t)$CDM model is just a time-varying vacuum energy scenario in which the interaction between vacuum and dark matter changes sign during the expansion of the universe.

preprint2012arXiv

Nonlinear terahertz metamaterials via field-enhanced carrier dynamics in GaAs

We demonstrate nonlinear metamaterial split ring resonators (SRRs) on GaAs at terahertz frequencies. For SRRs on doped GaAs films, incident terahertz radiation with peak fields of ~20 - 160 kV/cm drives intervalley scattering. This reduces the carrier mobility and enhances the SRR LC response due to a conductivity decrease in the doped thin film. Above ~160 kV/cm, electric field enhancement within the SRR gaps leads to efficient impact ionization, increasing the carrier density and the conductivity which, in turn, suppresses the SRR resonance. We demonstrate an increase of up to 10 orders of magnitude in the carrier density in the SRR gaps on semi-insulating GaAs substrate. Furthermore, we show that the effective permittivity can be swept from negative to positive values with increasing terahertz field strength in the impact ionization regime, enabling new possibilities for nonlinear metamaterials.

preprint2011arXiv

(1,λ)-embedded graphs and the acyclic edge choosability

A (1,λ)-embedded graph is a graph that can be embedded on a surface with Euler characteristic λ so that each edge is crossed by at most one other edge. A graph G is called α-linear if there exists an integral constant β such that e(G') \leq α v(G')+β for each G'\subseteq G. In this paper, it is shown that every (1,λ)-embedded graph G is 4-linear for all possible λ, and is acyclicly edge-(3Δ(G)+70)-choosable for λ=1,2.

preprint2011arXiv

(2,1)-Total labeling of planar graphs with large maximum degree

The ($d$,1)-total labelling of graphs was introduced by Havet and Yu. In this paper, we prove that, for planar graph $G$ with maximum degree $Δ\geq12$ and $d=2$, the (2,1)-total labelling number $λ_2^T(G)$ is at most $Δ+2$.

preprint2011arXiv

Class two 1-planar graphs with maximum degree six or seven

A graph is 1-planar if it can be drawn on the plane so that each edge is crossed by at most one other edge. In this note we give examples of class two 1-planar graphs with maximum degree six or seven.

preprint2011arXiv

Cosmic age problem revisited in the holographic dark energy model

Because of an old quasar APM 08279+5255 at $z=3.91$, some dark energy models face the challenge of the cosmic age problem. It has been shown by Wei and Zhang [Phys. Rev. D {\bf 76}, 063003 (2007)] that the holographic dark energy model is also troubled with such a cosmic age problem. In order to accommodate this old quasar and solve the age problem, we propose in this Letter to consider the interacting holographic dark energy in a non-flat universe. We show that the cosmic age problem can be eliminated when the interaction and spatial curvature are both involved in the holographic dark energy model.

preprint2011arXiv

Degree conditions for the partition of a graph into triangles and quadrilaterals

For two positive integers $r$ and $s$ with $r\geq 2s-2$, if $G$ is a graph of order $3r+4s$ such that $d(x)+d(y)\geq 4r+4s$ for every $xy\not\in E(G)$, then $G$ independently contains $r$ triangles and $s$ quadrilaterals, which partially prove the El-Zahar's Conjecture.

preprint2011arXiv

Edge covering pseudo-outerplanar graphs with forests

A graph is called pseudo-outerplanar if each block has an embedding on the plane in such a way that the vertices lie on a fixed circle and the edges lie inside the disk of this circle with each of them crossing at most one another. In this paper, we prove that each pseudo-outerplanar graph admits edge decompositions into a linear forest and an outerplanar graph, or a star forest and an outerplanar graph, or two forests and a matching, or $\max\{Δ(G),4\}$ matchings, or $\max\{\lceilΔ(G)/2\rceil,3\}$ linear forests. These results generalize some ones on outerplanar graphs and $K_{2,3}$-minor-free graphs, since the class of pseudo-outerplanar graphs is a larger class than the one of $K_{2,3}$-minor-free graphs.

preprint2011arXiv

Fitting to data of superluminal neutrinos with phenomenological scenarios

We test several phenomenological scenarios of faster-than-light neutrinos, by fitting to the experimental data from OPERA, MINOS and Fermilab79. Our purpose is to see, from the perspective of the current data, whether or not the speed of the superluminal neutrino depends on its energy. We show that the Coleman-Glashow scenario in which the velocity of the neutrino is free of the energy fits the data best. However, the result of SN1987A cannot be explained by this model. We find that a power-law model with the power close to zero can simultaneously explain the results of SN1987A and OPERA+MINOS+Fermilab79.

preprint2011arXiv

Group edge choosability of planar graphs without adjacent short cycles

In this paper, we aim to introduce the group version of edge coloring and list edge coloring, and prove that all 2-degenerate graphs along with some planar graphs without adjacent short cycles is group $(Δ(G)+1)$-edge-choosable while some planar graphs with large girth and maximum degree is group $Δ(G)$-edge-choosable.

preprint2011arXiv

Interacting model of new agegraphic dark energy: observational constraints and age problem

Many dark energy models fail to pass the cosmic age test because of the old quasar APM 08279+5255 at redshift $z=3.91$, the $Λ$CDM model and holographic dark energy models being no exception. In this paper, we focus on the topic of age problem in the new agegraphic dark energy (NADE) model. We determine the age of the universe in the NADE model by fitting the observational data, including type Ia supernovae (SNIa), baryon acoustic oscillations (BAO) and the cosmic microwave background (CMB). We find that the NADE model also faces the challenge of the age problem caused by the old quasar APM 08279+5255. In order to overcome such a difficulty, we consider the possible interaction between dark energy and dark matter. We show that this quasar can be successfully accommodated in the interacting new agegraphic dark energy (INADE) model at the $2σ$ level under the current observational constraints.

preprint2011arXiv

k-forested choosability of graphs with bounded maximum average degree

A proper vertex coloring of a simple graph is $k$-forested if the graph induced by the vertices of any two color classes is a forest with maximum degree less than $k$. A graph is $k$-forested $q$-choosable if for a given list of $q$ colors associated with each vertex $v$, there exists a $k$-forested coloring of $G$ such that each vertex receives a color from its own list. In this paper, we prove that the $k$-forested choosability of a graph with maximum degree $Δ\geq k\geq 4$ is at most $\lceil\fracΔ{k-1}\rceil+1$, $\lceil\fracΔ{k-1}\rceil+2$ or $\lceil\fracΔ{k-1}\rceil+3$ if its maximum average degree is less than 12/5, $8/3 or 3, respectively.

preprint2011arXiv

List (d,1)-total labelling of graphs embedded in surfaces

The (d,1)-total labelling of graphs was introduced by Havet and Yu. In this paper, we consider the list version of (d,1)-total labelling of graphs. Let G be a graph embedded in a surface with Euler characteristic $ε$ whose maximum degree $Δ(G)$ is sufficiently large. We prove that the (d,1)-total choosability $C_{d,1}^T(G)$ of $G$ is at most $Δ(G)+2d$.

preprint2011arXiv

Probing the dynamics of dark energy with divergence-free parametrizations: A global fit study

The CPL parametrization is very important for investigating the property of dark energy with observational data. However, the CPL parametrization only respects the past evolution of dark energy but does not care about the future evolution of dark energy, since $w(z)$ diverges in the distant future. In a recent paper [J.Z. Ma and X. Zhang, Phys.\ Lett.\ B {\bf 699}, 233 (2011)], a robust, novel parametrization for dark energy, $w(z)=w_0+w_1({\ln (2+z)\over 1+z}-\ln2)$, has been proposed, successfully avoiding the future divergence problem in the CPL parametrization. On the other hand, an oscillating parametrization (motivated by an oscillating quintom model) can also avoid the future divergence problem. In this Letter, we use the two divergence-free parametrizations to probe the dynamics of dark energy in the whole evolutionary history. In light of the data from 7-year WMAP temperature and polarization power spectra, matter power spectrum of SDSS DR7, and SN Ia Union2 sample, we perform a full Markov Chain Monte Carlo exploration for the two dynamical dark energy models. We find that the best-fit dark energy model is a quintom model with the EOS across -1 during the evolution. However, though the quintom model is more favored, we find that the cosmological constant still cannot be excluded.

preprint2011arXiv

Probing the dynamics of dark energy with novel parametrizations

We point out that the CPL parametrization has a problem that the equation of state $w(z)$ diverges in the far future, so that this model can only properly describe the past evolution but cannot depict the future evolution. To overcome such a difficulty, in this Letter we propose two novel parametrizations for dark energy, the logarithm form $w(z)=w_0+w_1({\ln (2+z)\over 1+z}-\ln2)$ and the oscillating form $w(z)=w_0+w_1({\sin(1+z)\over 1+z}-\sin(1))$, successfully avoiding the future divergency problem in the CPL parametrization, and use them to probe the dynamics of dark energy in the whole evolutionary history. Our divergency-free parametrizations are proven to be very successful in exploring the dynamical evolution of dark energy and have powerful prediction capability for the ultimate fate of the universe. Constraining the CPL model and the new models with the current observational data, we show that the new models are more favored. The features and the predictions for the future evolution in the new models are discussed in detail.

preprint2011arXiv

Running coupling: Does the coupling between dark energy and dark matter change sign during the cosmological evolution?

In this paper we put forward a running coupling scenario for describing the interaction between dark energy and dark matter. The dark sector interaction in our scenario is free of the assumption that the interaction term $Q$ is proportional to the Hubble expansion rate and the energy densities of dark sectors. We only use a time-variable coupling $b(a)$ (with $a$ the scale factor of the universe) to characterize the interaction $Q$. We propose a parametrization form for the running coupling $b(a)=b_0a+b_e(1-a)$ in which the early-time coupling is given by a constant $b_e$, while today the coupling is given by another constant, $b_0$. For investigating the feature of the running coupling, we employ three dark energy models, namely, the cosmological constant model ($w=-1$), the constant $w$ model ($w=w_0$), and the time-dependent $w$ model ($w(a)=w_0+w_1(1-a)$). We constrain the models with the current observational data, including the type Ia supernova, the baryon acoustic oscillation, the cosmic microwave background, the Hubble expansion rate, and the X-ray gas mass fraction data. The fitting results indicate that a time-varying vacuum scenario is favored, in which the coupling $b(z)$ crosses the noninteracting line ($b=0$) during the cosmological evolution and the sign changes from negative to positive. The crossing of the noninteracting line happens at around $z=0.2-0.3$, and the crossing behavior is favored at about 1$σ$ confidence level. Our work implies that we should pay more attention to the time-varying vacuum model and seriously consider the phenomenological construction of a sign-changeable or oscillatory interaction between dark sectors.

preprint2011arXiv

Total coloring of pseudo-outerplanar graphs

A graph is pseudo-outerplanar if each of its blocks has an embedding in the plane so that the vertices lie on a fixed circle and the edges lie inside the disk of this circle with each of them crossing at most one another. In this paper, the total coloring conjecture is completely confirmed for pseudo-outerplanar graphs. In particular, it is proved that the total chromatic number of every pseudo-outerplanar graph with maximum degree $Δ\geq 5$ is $Δ+1$.

preprint2010arXiv

Automated sampling assessment for molecular simulations using the effective sample size

To quantify the progress in development of algorithms and forcefields used in molecular simulations, a method for the assessment of the sampling quality is needed. We propose a general method to assess the sampling quality through the estimation of the number of independent samples obtained from molecular simulations. This method is applicable to both dynamic and nondynamic methods and utilizes the variance in the populations of physical states to determine the ESS. We test the correctness and robustness of our procedure in a variety of systems--two-state toy model, all-atom butane, coarse-grained calmodulin, all-atom dileucine and Met-enkaphalin. We also introduce an automated procedure to obtain approximate physical states from dynamic trajectories: this procedure allows for sample--size estimation for systems for which physical states are not known in advance.

preprint2010arXiv

Comparison of dark energy models: A perspective from the latest observational data

In this paper, we compare some popular dark energy models under the assumption of a flat universe by using the latest observational data including the type Ia supernovae Constitution compilation, the baryon acoustic oscillation measurement from the Sloan Digital Sky Survey, the cosmic microwave background measurement given by the seven-year Wilkinson Microwave Anisotropy Probe observations and the determination of $H_0$ from the Hubble Space Telescope. Model comparison statistics such as the Bayesian and Akaike information criteria are applied to assess the worth of the models. These statistics favor models that give a good fit with fewer parameters. Based on this analysis, we find that the simplest cosmological constant model that has only one free parameter is still preferred by the current data. For other dynamical dark energy models, we find that some of them, such as the $α$ dark energy, constant $w$, generalized Chaplygin gas, Chevalliear-Polarski-Linder parametrization, and holographic dark energy models, can provide good fits to the current data, and three of them, namely, the Ricci dark energy, agegraphic dark energy, and Dvali-Gabadadze-Porrati models, are clearly disfavored by the data.

preprint2010arXiv

Edge Coloring of Triangle-Free 1-Planar Graphs

it is shown that each triangle-free 1-planar graph with maximum degree $Δ\geq7$ can be $Δ$-colorable by Discharging Method.

preprint2010arXiv

Frequency-tunable metamaterials using broadside-coupled split ring resonators

We present frequency tunable metamaterial designs at terahertz (THz) frequencies using broadside-coupled split ring resonator (BC-SRR) arrays. Frequency tuning, arising from changes in near field coupling, is obtained by in-plane horizontal or vertical displacements of the two SRR layers. For electrical excitation, the resonance frequency continuously redshifts as a function of displacement. The maximum frequency shift occurs for displacement of half a unit cell, with vertical displacement resulting in a shift of 663 GHz (51% of f0) and horizontal displacement yielding a shift of 270 GHz (20% of f0). We also discuss the significant differences in tuning that arise for electrical excitation in comparison to magnetic excitation of BC-SRRs.

preprint2010arXiv

Interacting model of new agegraphic dark energy: Cosmological evolution and statefinder diagnostic

The statefinder diagnosic is a useful method for distinguishing different dark energy models. In this paper, we investigate the new agegraphic dark energy model with interaction between dark energy and matter component by using statefinder parameter pair $\{r, s\}$ and study its cosmological evolution. We plot the trajectories of the new agegraphic dark energy model for different interaction cases in the statefinder plane. As a result, the influence of the interaction on the evolution of the universe is shown in the statefinder diagrams.

preprint2010arXiv

Sandage-Loeb test for the new agegraphic and Ricci dark energy models

The Sandage-Loeb (SL) test is a unique method to explore dark energy at the ``redshift desert'' ($2\lesssim z\lesssim 5$), an era not covered by any other dark energy probes, by directly measuring the temporal variation of the redshift of quasar (QSO) Lyman-$α$ absorption lines. In this paper, we study the prospects for constraining the new agegraphic dark energy (NADE) model and the Ricci dark energy (RDE) model with the SL test. We show that, assuming only a ten-year survey, the SL test can constrain these two models with high significance.

preprint2010arXiv

Structural properties of 1-planar graphs and an application to acyclic edge coloring

A graph is called 1-planar if it can be drawn on the plane so that each edge is crossed by at most one other edge. In this paper, we establish a local property of 1-planar graphs which describes the structure in the neighborhood of small vertices (i.e. vertices of degree no more than seven). Meanwhile, some new classes of light graphs in 1-planar graphs with the bounded degree are found. Therefore, two open problems presented by Fabrici and Madaras [The structure of 1-planar graphs, Discrete Mathematics, 307, (2007), 854-865] are solved. Furthermore, we prove that each 1-planar graph $G$ with maximum degree $Δ(G)$ is acyclically edge $L$-choosable where $L=\max\{2Δ(G)-2,Δ(G)+83\}$.

preprint2010arXiv

Theoretical Limits on Agegraphic Quintessence from Weak Gravity Conjecture

In this paper, we investigate the possible theoretical constraint on the parameter $n$ of the agegraphic quintessence model by considering the requirement of the weak gravity conjecture that the variation of the quintessence scalar field $ϕ$ should be less than the Planck mass $M_{\rm{p}}$. We obtain the theoretical upper bound $n\lesssim 2.5$ that is inconsistent with the current observational constraint result $2.637<n<2.983$ (95.4% CL). The possible implications of the tension between observational and theoretical constraint results are discussed.

preprint2009arXiv

Brane inflation revisited after WMAP five-year results

In this paper, we revisit brane inflation models with the WMAP five-year results. The WMAP five-year data favor a red-tilted power spectrum of primordial fluctuations at the level of two standard deviations, which is the same as the WMAP three-year result qualitatively, but quantitatively the spectral index is slightly greater than the three-year value. This result can bring impacts on brane inflation models. According to the WMAP five-year data, we find that the KKLMMT model can survive at the level of one standard deviation, and the fine-tuning of the parameter $β$ can be alleviated to a certain extent at the level of two standard deviations.

preprint2009arXiv

Heal the world: Avoiding the cosmic doomsday in the holographic dark energy model

The current observational data imply that the universe would end with a cosmic doomsday in the holographic dark energy model. However, unfortunately, the big-rip singularity will ruin the theoretical foundation of the holographic dark energy scenario. To rescue the holographic scenario of dark energy, we employ the braneworld cosmology and incorporate the extra-dimension effects into the holographic theory of dark energy. We find that such a mend could erase the big-rip singularity and leads to a de Sitter finale for the holographic cosmos. Therefore, in the holographic dark energy model, the extra-dimension recipe could heal the world.

preprint2009arXiv

New agegraphic dark energy as a rolling tachyon

Combining the general relativity and the uncertainty relation in quantum mechanics, the energy density of quantum fluctuations of space-time can be viewed as dark energy. The so-called agegraphic dark energy model is just based on this viewpoint, in which the age of the universe is introduced as the length measure. Recently, the new agegraphic dark energy model was proposed, where the dynamical dark energy is measured by the conformal age of the universe. On the other hand, scalar-field dark energy models like tachyon are often regarded as an effective description of some underlying theory of dark energy. In this paper, we show that the new agegraphic dark energy can be described completely by a tachyon scalar-field. We thus reconstruct the potential and the dynamics of the tachyon scalar-field, according to the evolution of the new agegraphic dark energy.

preprint2009arXiv

New Agegraphic Dark Energy in Brans-Dicke Theory

In this paper, we investigate the new agegraphic dark energy model in the framework of Brans-Dicke theory which is a natural extension of the Einstein's general relativity. In this framework the form of the new agegraphic dark energy density takes as $ρ_{q} =3n^2 Φ(t)η^{-2}$, where $η$ is the conformal age of the universe and $Φ(t)$ is the Brans-Dicke scalar field representing the inverse of the time-variable Newton's constant. We derive the equation of state of the new agegraphic dark energy and the deceleration parameter of the universe in the Brans-Dicke theory. It is very interesting to find that in the Brans-Dicke theory the agegraphic dark energy realizes quintom-like behavior, i.e., its equation of state crosses the phantom divide $w=-1$ during the evolution. We also compare the situation of the agegraphic dark energy model in the Brans-Dicke theory with that in the Einstein's theory. In addition, we discuss the new agegraphic dark energy model with interaction in the framework of the Brans-Dicke theory.

preprint2009arXiv

Probing interaction and spatial curvature in the holographic dark energy model

In this paper we place observational constraints on the interaction and spatial curvature in the holographic dark energy model. We consider three kinds of phenomenological interactions between holographic dark energy and matter, i.e., the interaction term $Q$ is proportional to the energy densities of dark energy ($ρ_Λ$), matter ($ρ_{m}$), and matter plus dark energy ($ρ_m+ρ_Λ$). For probing the interaction and spatial curvature in the holographic dark energy model, we use the latest observational data including the type Ia supernovae (SNIa) Constitution data, the shift parameter of the cosmic microwave background (CMB) given by the five-year Wilkinson Microwave Anisotropy Probe (WMAP5) observations, and the baryon acoustic oscillation (BAO) measurement from the Sloan Digital Sky Survey (SDSS). Our results show that the interaction and spatial curvature in the holographic dark energy model are both rather small. Besides, it is interesting to find that there exists significant degeneracy between the phenomenological interaction and the spatial curvature in the holographic dark energy model.

preprint2008arXiv

High Performance Cooperative Transmission Protocols Based on Multiuser Detection and Network Coding

Cooperative transmission is an emerging communication technique that takes advantage of the broadcast nature of wireless channels. However, due to low spectral efficiency and the requirement of orthogonal channels, its potential for use in future wireless networks is limited. In this paper, by making use of multiuser detection (MUD) and network coding, cooperative transmission protocols with high spectral efficiency, diversity order, and coding gain are developed. Compared with the traditional cooperative transmission protocols with single-user detection, in which the diversity gain is only for one source user, the proposed MUD cooperative transmission protocols have the merit that the improvement of one user's link can also benefit the other users. In addition, using MUD at the relay provides an environment in which network coding can be employed. The coding gain and high diversity order can be obtained by fully utilizing the link between the relay and the destination. From the analysis and simulation results, it is seen that the proposed protocols achieve higher diversity gain, better asymptotic efficiency, and lower bit error rate, compared to traditional MUD schemes and to existing cooperative transmission protocols. From the simulation results, the performance of the proposed scheme is near optimal as the performance gap is 0.12dB for average bit error rate (BER) 10^{-6} and 1.04dB for average BER 10^(-3), compared to two performance upper bounds.

preprint2007arXiv

Cooperative Transmission Protocols with High Spectral Efficiency and High Diversity Order Using Multiuser Detection and Network Coding

Cooperative transmission is an emerging communication technique that takes advantages of the broadcast nature of wireless channels. However, due to low spectral efficiency and the requirement of orthogonal channels, its potential for use in future wireless networks is limited. In this paper, by making use of multiuser detection (MUD) and network coding, cooperative transmission protocols with high spectral efficiency, diversity order, and coding gain are developed. Compared with the traditional cooperative transmission protocols with single-user detection, in which the diversity gain is only for one source user, the proposed MUD cooperative transmission protocols have the merits that the improvement of one user's link can also benefit the other users. In addition, using MUD at the relay provides an environment in which network coding can be employed. The coding gain and high diversity order can be obtained by fully utilizing the link between the relay and the destination. From the analysis and simulation results, it is seen that the proposed protocols achieve higher diversity gain, better asymptotic efficiency, and lower bit error rate, compared to traditional MUD and to existing cooperative transmission protocols.

preprint2007arXiv

Statefinder diagnosis in a non-flat universe and the holographic model of dark energy

In this paper, we study the holographic dark energy model in non-flat universe from the statefinder viewpoint. We plot the evolutionary trajectories of the holographic dark energy model for different values of the parameter $c$ as well as for different contributions of spatial curvature, in the statefinder parameter-planes. The statefinder diagrams characterize the properties of the holographic dark energy and show the discrimination between this scenario and other dark energy models. As we show, the contributions of the spatial curvature in the model can be diagnosed out explicitly by the statefinder diagrams. Furthermore, we also investigate the holographic dark energy model in the $w-w'$ plane, which can provide us with a useful dynamical diagnosis complement to the statefinder geometrical diagnosis.

preprint2005arXiv

Statefinder diagnostic for holographic dark energy model

In this paper we study the holographic dark energy model proposed by Li from the statefinder viewpoint. We plot the evolutionary trajectories of the model with $c=1$ in the statefinder parameter-planes. The statefinder diagrams characterize the properties of the holographic dark energy and show the discrimination between this scenario and other dark energy models. We also perform a statefinder diagnostic to the holographic dark energy model in cases of different $c$ which given by three fits to observational data. The result indicates that from the statefinder viewpoint $c$ plays a significant role in this model and should thus be determined seriously by future high precision experiments.

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39 works

Jing-Fei Zhang

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Jing-Fei Zhang contributes to research discovery and scholarly infrastructure.

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19 works

Yun-He Li

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Yun-He Li contributes to research discovery and scholarly infrastructure.

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9 works

Guizhen Liu

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Guizhen Liu contributes to research discovery and scholarly infrastructure.

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8 works

Jian-Liang Wu

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Jian-Liang Wu contributes to research discovery and scholarly infrastructure.

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Xin Zhang

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271 published item(s)

A Tale of Two Problems: Multi-Task Bilevel Learning Meets Equality Constrained Multi-Objective Optimization

Agent-ValueBench: A Comprehensive Benchmark for Evaluating Agent Values

EnergyLens: Predictive Energy-Aware Exploration for Multi-GPU LLM Inference Optimization

Gravitational wave standard sirens: A brief review of cosmological parameter estimation

Implicit Graph, Explicit Retrieval: Towards Efficient and Interpretable Long-horizon Memory for Large Language Models

Loupe: A Generalizable and Adaptive Framework for Image Forgery Detection

MiMo-V2-Flash Technical Report

Multi-messenger standard-siren cosmology for third-generation gravitational-wave detectors: forecasts considering observations of gamma-ray bursts and kilonovae

On Privacy-Preserving Image Transmission in Low-Altitude Networks: A Swin Transformer-Based Framework with Federated Learning

On regional treatment effect assessment using robust MAP priors

RCDN: Real-Centered Detection Network for Robust Face Forgery Identification

SGR: A Stepwise Reasoning Framework for LLMs with External Subgraph Generation

Strong Evidence for Dark Sector Interactions

Visual Latents Know More Than They Say: Unsilencing Latent Reasoning in MLLMs

WorldArena 2.0: Extending Embodied World Model Benchmarking on Modality, Functionality and Platform

WorldVLN: Autoregressive World Action Model for Aerial Vision-Language Navigation

Introduction to the Chinese Space Station Survey Telescope (CSST)

MiMo-Audio: Audio Language Models are Few-Shot Learners

RelServe: Fast LLM Inference Serving on Relational Data

TeleWorld: Towards Dynamic Multimodal Synthesis with a 4D World Model

Efficient Sparse Least Absolute Deviation Regression with Differential Privacy

HEAP: Unsupervised Object Discovery and Localization with Contrastive Grouping

SpeechAgents: Human-Communication Simulation with Multi-Modal Multi-Agent Systems

The Taiji-TianQin-LISA network: Precisely measuring the Hubble constant using both bright and dark sirens

Understanding LLMs: A Comprehensive Overview from Training to Inference

Asymptotic Mutual Information Analysis for Double-scattering MIMO Channels: A New Approach by Gaussian Tools

How Close is ChatGPT to Human Experts? Comparison Corpus, Evaluation, and Detection

A forecast of using fast radio burst observations to constrain holographic dark energy

A Hybrid Parallelization Approach for Distributed and Scalable Deep Learning

A new way to explore cosmological tensions using gravitational waves and strong gravitational lensing

A PDE approach for regret bounds under partial monitoring

Alternating current conductivity and superconducting properties of the holographic effective theory

An introduction to variational inference in Geophysical inverse problems

Anarchic Federated Learning

Approaching a Minimal Topological Electronic Structure in Antiferromagnetic Topological Insulator MnBi2Te4 via Surface Modification

Ballistic Transport Enhanced Heat Convection at Nanoscale Hotspots

Can depth-adaptive BERT perform better on binary classification tasks

Complementary Vanadium Dioxide Metamaterials with Enhanced Modulation Amplitude at Terahertz Frequencies

Constraints on interacting dark energy models from time-delay cosmography with seven lensed quasars

Contrastive-mixup learning for improved speaker verification

Cosmological Parameter Estimation Using Current and Future Observations of Strong Gravitational Lensing

Cosmological search for sterile neutrinos after Planck 2018

Deep Learning for Unsupervised Anomaly Localization in Industrial Images: A Survey

Defective incidence coloring of graphs

Distributional Robust Portfolio Construction based on Investor Aversion

Domain Prompt Learning for Efficiently Adapting CLIP to Unseen Domains

Dynamic Cournot-Nash Equilibrium: The Non-Potential Case

Effects of Small-Scale Absorption Systems on Neutral Islands during the Late Epoch of Reionization

Eliminating Primary Beam Effect in Foreground Subtraction of Neutral Hydrogen Intensity Mapping Survey with Deep Learning

Envelopes and principal component regression

Fast and Robust Sparsity Learning over Networks: A Decentralized Surrogate Median Regression Approach

Galaxy-Scale Test of General Relativity with Strong Gravitational Lensing

Gate-Tunable Spin-Orbit Coupling in a Germanium Hole Double Quantum Dot

Graph Contrastive Learning with Personalized Augmentation

Gravitational lensing by a black hole in effective loop quantum gravity

Gravitational wave constraints on Lorentz and parity violations in gravity: high-order spatial derivative cases

Identifying Chinese Opinion Expressions with Extremely-Noisy Crowdsourcing Annotations

Improving Tese Case Generation for Python Native Libraries Through Constraints on Input Data Structures

Invariant subspaces and explicit Bethe vectors in the integrable open spin $1/2$ $\XYZ$ chain

K-core in percolated dense graph sequences

Linear quadratic leader-follower stochastic differential games for mean-field switching diffusions

Local collective dynamics at equilibrium BCC crystal-melt interfaces

NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data

Next-to-tribimaximal mixing against CP violation and baryon asymmetry signs

On Length Sets of Subarithmetic Hyperbolic Manifolds

Out-of-distribution Detection with Boundary Aware Learning

Outage and Finite-SNR DMT Analysis for IRS-aided MIMO Systems: How Large IRSs Need to Be?

Persistent exchange splitting in a chiral helimagnet Cr1/3NbS2

PLMCL: Partial-Label Momentum Curriculum Learning for Multi-Label Image Classification

Propagation of Chaos of Forward-Backward Stochastic Differential Equations with Graphon Interactions

Secrecy Analysis for IRS-aided Wiretap MIMO Communications: Fundamental Limits and System Design

Shadow and weak deflection angle of a black hole in nonlocal gravity

Stochastic Linear Quadratic Optimal Control Problems with Random Coefficients and Markovian Regime Switching System

Surface wave dispersion inversion using an energy likelihood function