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Fan Yang

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Computer Vision cond-mat.supr-con cond-mat.mes-hall cond-mat.str-el Machine Learning physics.optics quant-ph cond-mat.mtrl-sci astro-ph.GA Artificial Intelligence math.PR astro-ph.SR math.CO math.DS physics.app-ph astro-ph.IM cond-mat.quant-gas Distributed, Parallel, and Cluster Computing math.LO math.ST Statistics Theory Cryptography and Security eess.IV physics.atom-ph astro-ph.EP Computation and Language eess.SP math-ph math.MP Methodology Computational Geometry Information Theory Logic in Computer Science math.IT math.OC Numerical Analysis physics.flu-dyn cond-mat.stat-mech Data Structures and Algorithms eess.SY Information Retrieval math.CA math.FA math.NA math.SP physics.comp-ph Systems and Control Applications Biological Physics Computational Complexity cond-mat.other cs.CY Databases Emerging Technologies Hardware Architecture hep-th Human-Computer Interaction math.QA math.RT Networking and Internet Architecture Neural and Evolutionary Computing physics.chem-ph physics.class-ph physics.ins-det physics.plasm-ph physics.space-ph Programming Languages q-fin.PR Robotics Social and Information Networks

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preprint2026arXiv

GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents

We present GLM-5V-Turbo, a step toward native foundation models for multimodal agents. As foundation models are increasingly deployed in real environments, agentic capability depends not only on language reasoning, but also on the ability to perceive, interpret, and act over heterogeneous contexts such as images, videos, webpages, documents, GUIs. GLM-5V-Turbo is built around this objective: multimodal perception is integrated as a core component of reasoning, planning, tool use, and execution, rather than as an auxiliary interface to a language model. This report summarizes the main improvements behind GLM-5V-Turbo across model design, multimodal training, reinforcement learning, toolchain expansion, and integration with agent frameworks. These developments lead to strong performance in multimodal coding, visual tool use, and framework-based agentic tasks, while preserving competitive text-only coding capability. More importantly, our development process offers practical insights for building multimodal agents, highlighting the central role of multimodal perception, hierarchical optimization, and reliable end-to-end verification.

preprint2026arXiv

Trapping Attacker in Dilemma: Examining Internal Correlations and External Influences of Trigger for Defending GNN Backdoors

GNNs have become a standard tool for learning on relational data, yet they remain highly vulnerable to backdoor attacks. Prior defenses often depend on inspecting specific subgraph patterns or node features, and thus can be circumvented by adaptive attackers. We propose PRAETORIAN, a new defense that targets intrinsic requirements of effective GNN backdoors rather than surface-level cues. Our key observation is that flipping a victim node's prediction requires substantial influence on the victim: attackers tend to either inject many trigger nodes or rely on a small set of highly influential ones. Building on this observation, PRAETORIAN (i) analyzes internal correlations within potential trigger subgraphs to detect abnormally large injected structures, and (ii) quantifies external node influence to identify triggers with disproportionate impact. Across our evaluations, PRAETORIAN reduces the average attack success rate (ASR) to 0.55% with only a 0.62% drop in clean accuracy (CA), whereas state-of-the-art defenses still yield an average ASR of >20% and a CA drop of >3% under the same conditions. Moreover, PRAETORIAN remains effective against a range of adaptive attacks, forcing adversaries to either inject many trigger nodes to achieve high ASR (>80%), which incurs a >10% CA drop, or preserve CA at the cost of limiting ASR to 18.1%. Overall, PRAETORIAN constrains attackers to an unfavorable trade-off between efficacy and detectability.

preprint2026arXiv

Unifying Sparse Attention with Hierarchical Memory for Scalable Long-Context LLM Serving

Long-context LLM serving is bottlenecked by the cost of attending over ever-growing KV caches. Dynamic sparse attention promises relief by accessing only a small, query-dependent subset of the KV state per decoding step and extending the KV storage to CPU memory. In practice, however, these algorithmic savings rarely translate into end-to-end system-level gains because sparse methods typically operate at different granularities and thus rely on ad hoc, per-algorithm implementations. At the same time, hierarchical KV storage introduces a new systems bottleneck: retrieving fine-grained, irregular KV subsets across the GPU-CPU boundary can easily erase the benefits of sparsity. We present SPIN, a sparse-attention-aware inference framework that co-designs the execution pipeline with hierarchical KV storage through three techniques: (1) a unified partition abstraction that maps different sparsity granularities onto a shared page-based KV substrate; (2) a locality-aware KV cache manager that dynamically sizes per-request HBM budgets and uses a GPU-friendly bucketed LRU policy to cut PCIe round-trips; and (3) a two-level hierarchical metadata layout sized to the active working set rather than the worst-case address space. Built on vLLM with three representative sparse attention algorithms, SPIN delivers 1.66-5.66x higher end-to-end throughput and 7-9x lower TTFT than vLLM, and reduces TPOT by up to 58% over the original sparse-attention implementations.

preprint2025arXiv

Evidence of anisotropic three-dimensional weak-localization in TiSe$_{2}$ nanoflakes

TiSe$_2$ is a typical transition-metal dichalcogenide known for its charge-density wave order. In this study, we report the observation of an unusual anisotropic negative magnetoresistance in exfoliated TiSe$_2$ nanoflakes at low temperatures. Unlike the negative magnetoresistance reported in most other transition-metal dichalcogenides, our results cannot be explained by either the conventional two-dimensional weak localization effect or the Kondo effect. A comprehensive analysis of the data suggests that the observed anisotropic negative magnetoresistance in TiSe$_2$ flakes is most likely caused by the three-dimensional weak localization effect. Our findings contribute to a deeper understanding of the phase-coherent transport processes in TiSe$_2$.

preprint2024arXiv

Disentangle Estimation of Causal Effects from Cross-Silo Data

Estimating causal effects among different events is of great importance to critical fields such as drug development. Nevertheless, the data features associated with events may be distributed across various silos and remain private within respective parties, impeding direct information exchange between them. This, in turn, can result in biased estimations of local causal effects, which rely on the characteristics of only a subset of the covariates. To tackle this challenge, we introduce an innovative disentangle architecture designed to facilitate the seamless cross-silo transmission of model parameters, enriched with causal mechanisms, through a combination of shared and private branches. Besides, we introduce global constraints into the equation to effectively mitigate bias within the various missing domains, thereby elevating the accuracy of our causal effect estimation. Extensive experiments conducted on new semi-synthetic datasets show that our method outperforms state-of-the-art baselines.

preprint2023arXiv

Active RIS vs. Passive RIS: Which Will Prevail in 6G?

As a revolutionary paradigm for controlling wireless channels, reconfigurable intelligent surfaces (RISs) have emerged as a candidate technology for future 6G networks. However, due to the "multiplicative fading" effect, the existing passive RISs only achieve limited capacity gains in many scenarios with strong direct links. In this paper, the concept of active RISs is proposed to overcome this fundamental limitation. Unlike passive RISs that reflect signals without amplification, active RISs can amplify the reflected signals via amplifiers integrated into their elements. To characterize the signal amplification and incorporate the noise introduced by the active components, we develop and verify the signal model of active RISs through the experimental measurements based on a fabricated active RIS element. Based on the verified signal model, we further analyze the asymptotic performance of active RISs to reveal the substantial capacity gain they provide for wireless communications. Finally, we formulate the sum-rate maximization problem for an active RIS aided multi-user multiple-input single-output (MU-MISO) system and a joint transmit beamforming and reflect precoding scheme is proposed to solve this problem. Simulation results show that, in a typical wireless system, passive RISs can realize only a limited sum-rate gain of 22%, while active RISs can achieve a significant sum-rate gain of 130%, thus overcoming the "multiplicative fading" effect.

preprint2023arXiv

An empirical process framework for covariate balance in causal inference

We propose a new perspective for the evaluation of matching procedures by considering the complexity of the function class they belong to. Under this perspective we provide theoretical guarantees on post-matching covariate balance through a finite sample concentration inequality. We apply this framework to coarsened exact matching as well as matching using the propensity score and suggest how to apply it to other algorithms. Simulation studies are used to evaluate the procedures.

preprint2023arXiv

Interacting Bose gas across a narrow Feshbach resonance

We use a two-channel model to investigate an interacting Bose gas across a narrow Feshbach resonance within a field path integral approach. The ground state properties show strong deviation from that of a broad Feshbach resonance or a single channel interaction. The deviation can be interpreted by the strong energy dependence of two-body scattering length near a narrow Feshbach resonance. As the density increases, the chemical potential and energy per particle are found to saturate while the inverse compressibility and phonon velocity undergo a significant reduction. We also take Gaussian fluctuations into account and calculate the ground state energy correction as well as the quantum depletion.

preprint2023arXiv

Reconfigurable Metasurface: A Systematic Categorization and Recent Advances

Considering the rapid progress of theory, design, fabrication and applications, metasurface (MTS) has become a new research frontier in microwave, terahertz and optical bands. Reconfigurable metasurface (R-MTS) can dynamically modulate electromagnetic (EM) wave with unparalleled flexibility, which leads to great research tide in recent years. Numerous R-MTSs with powerful capabilities and various functions are presented explosively. In light of the five dimensions of EM wave, this review proposes a unified model to describe the interactions among R-MTS, EM wave and EM information, and suggests information bit allocation strategy to categorize different types of R-MTSs systematically. As recent advances of R-MTS, 1-bit and 2-bit elements manipulating different wave dimensions are reviewed respectively in detail. Finally, this review discusses the future research trends of R-MTS. Hopefully, R-MTSs with diverse dimensions and functions can propel the next generation of communication, detection, sensing, imaging and computing applications.

preprint2023arXiv

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup

Subpopulation shift widely exists in many real-world machine learning applications, referring to the training and test distributions containing the same subpopulation groups but varying in subpopulation frequencies. Importance reweighting is a normal way to handle the subpopulation shift issue by imposing constant or adaptive sampling weights on each sample in the training dataset. However, some recent studies have recognized that most of these approaches fail to improve the performance over empirical risk minimization especially when applied to over-parameterized neural networks. In this work, we propose a simple yet practical framework, called uncertainty-aware mixup (UMIX), to mitigate the overfitting issue in over-parameterized models by reweighting the ''mixed'' samples according to the sample uncertainty. The training-trajectories-based uncertainty estimation is equipped in the proposed UMIX for each sample to flexibly characterize the subpopulation distribution. We also provide insightful theoretical analysis to verify that UMIX achieves better generalization bounds over prior works. Further, we conduct extensive empirical studies across a wide range of tasks to validate the effectiveness of our method both qualitatively and quantitatively. Code is available at https://github.com/TencentAILabHealthcare/UMIX.

preprint2023arXiv

Variable Fofana's Spaces and their Pre-dual

In this paper, we introduce the variable Fofana's spaces $(L^{p(\cdot)},L^q)^α(\mathbb{R}^n)$ where $1< p(\cdot)<\infty$ and $1\leq q,α\leq\infty$, then show some properties and establish the pre-dual of those spaces which are contributed to prove the necessary conditions of fractional integral commutators' boundedness. As applications, the characterization of fractional integral operators and commutators on variable Fofana's spaces are discussed, which are new result even for the classical Fofana's spaces.

preprint2022arXiv

A Calibration Approach to Transportability and Data-Fusion with Observational Data

Two important considerations in clinical research studies are proper evaluations of internal and external validity. While randomized clinical trials can overcome several threats to internal validity, they may be prone to poor external validity. Conversely, large prospective observational studies sampled from a broadly generalizable population may be externally valid, yet susceptible to threats to internal validity, particularly confounding. Thus, methods that address confounding and enhance transportability of study results across populations are essential for internally and externally valid causal inference, respectively. These issues persist for another problem closely related to transportability known as data-fusion. We develop a calibration method to generate balancing weights that address confounding and sampling bias, thereby enabling valid estimation of the target population average treatment effect. We compare the calibration approach to two additional doubly-robust methods that estimate the effect of an intervention on an outcome within a second, possibly unrelated target population. The proposed methodologies can be extended to resolve data-fusion problems that seek to evaluate the effects of an intervention using data from two related studies sampled from different populations. A simulation study is conducted to demonstrate the advantages and similarities of the different techniques. We also test the performance of the calibration approach in a motivating real data example comparing whether the effect of biguanides versus sulfonylureas - the two most common oral diabetes medication classes for initial treatment - on all-cause mortality described in a historical cohort applies to a contemporary cohort of US Veterans with diabetes.

preprint2022arXiv

A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services as Example

Though widely used in industry, traditional task-oriented dialogue systems suffer from three bottlenecks: (i) difficult ontology construction (e.g., intents and slots); (ii) poor controllability and interpretability; (iii) annotation-hungry. In this paper, we propose to represent utterance with a simpler concept named Dialogue Action, upon which we construct a tree-structured TaskFlow and further build task-oriented chatbot with TaskFlow as core component. A framework is presented to automatically construct TaskFlow from large-scale dialogues and deploy online. Our experiments on real-world after-sale customer services show TaskFlow can satisfy the major needs, as well as reduce the developer burden effectively.

preprint2022arXiv

A Multi-Person Video Dataset Annotation Method of Spatio-Temporally Actions

Spatio-temporal action detection is an important and challenging problem in video understanding. However, the application of the existing large-scale spatio-temporal action datasets in specific fields is limited, and there is currently no public tool for making spatio-temporal action datasets, it takes a lot of time and effort for researchers to customize the spatio-temporal action datasets, so we propose a multi-Person video dataset Annotation Method of spatio-temporally actions.First, we use ffmpeg to crop the videos and frame the videos; then use yolov5 to detect human in the video frame, and then use deep sort to detect the ID of the human in the video frame. By processing the detection results of yolov5 and deep sort, we can get the annotation file of the spatio-temporal action dataset to complete the work of customizing the spatio-temporal action dataset. https://github.com/Whiffe/Custom-ava-dataset_Custom-Spatio-Temporally-Action-Video-Dataset

preprint2022arXiv

A note on the edge choosability of $K_{5}$-minor free graphs

For a planar graph $G$, Borodin stated that $G$ is $(Δ+1)$-edge-choosable if $Δ\geq9$ and later Bonamy showed that $G$ is $9$-edge-choosable if $Δ=8$. At the same time, Borodin et al. proved that $G$ is $Δ$-edge-choosable if $Δ\geq12$. In the paper, we extend these results to $K_5$-minor free graphs.

preprint2022arXiv

Active RISs: Signal Modeling, Asymptotic Analysis, and Beamforming Design

Reconfigurable intelligent surfaces (RISs) have emerged as a candidate technology for future 6G networks. However, due to the "multiplicative fading" effect, the existing passive RISs only achieve a negligible capacity gain in environments with strong direct links. In this paper, the concept of active RISs is studied to overcome this fundamental limitation. Unlike the existing passive RISs that reflect signals without amplification, active RISs can amplify the reflected signals via amplifiers integrated into their elements. To characterize the signal amplification and incorporate the noise introduced by the active components, we verify the signal model of active RISs through the experimental measurements on a fabricated active RIS element. Based on the verified signal model, we formulate the sum-rate maximization problem for an active RIS aided multi-user multiple-input single-output (MU-MISO) system and a joint transmit precoding and reflect beamforming algorithm is proposed to solve this problem. Simulation results show that, in a typical wireless system, the existing passive RISs can realize only a negligible sum-rate gain of 3%, while the active RISs can achieve a significant sum-rate gain of 62%, thus overcoming the "multiplicative fading" effect. Finally, we develop a 64-element active RIS aided wireless communication prototype, and the significant gain of active RISs is validated by field test.

preprint2022arXiv

Actor-identified Spatiotemporal Action Detection -- Detecting Who Is Doing What in Videos

The success of deep learning on video Action Recognition (AR) has motivated researchers to progressively promote related tasks from the coarse level to the fine-grained level. Compared with conventional AR which only predicts an action label for the entire video, Temporal Action Detection (TAD) has been investigated for estimating the start and end time for each action in videos. Taking TAD a step further, Spatiotemporal Action Detection (SAD) has been studied for localizing the action both spatially and temporally in videos. However, who performs the action, is generally ignored in SAD, while identifying the actor could also be important. To this end, we propose a novel task, Actor-identified Spatiotemporal Action Detection (ASAD), to bridge the gap between SAD and actor identification. In ASAD, we not only detect the spatiotemporal boundary for instance-level action but also assign the unique ID to each actor. To approach ASAD, Multiple Object Tracking (MOT) and Action Classification (AC) are two fundamental elements. By using MOT, the spatiotemporal boundary of each actor is obtained and assigned to a unique actor identity. By using AC, the action class is estimated within the corresponding spatiotemporal boundary. Since ASAD is a new task, it poses many new challenges that cannot be addressed by existing methods: i) no dataset is specifically created for ASAD, ii) no evaluation metrics are designed for ASAD, iii) current MOT performance is the bottleneck to obtain satisfactory ASAD results. To address those problems, we contribute to i) annotate a new ASAD dataset, ii) propose ASAD evaluation metrics by considering multi-label actions and actor identification, iii) improve the data association strategies in MOT to boost the MOT performance, which leads to better ASAD results. The code is available at https://github.com/fandulu/ASAD.

preprint2022arXiv

Actuarial-consistency and two-step actuarial valuations: a new paradigm to insurance valuation

This paper introduces new valuation schemes called actuarial-consistent valuations for insurance liabilities which depend on both financial and actuarial risks, which imposes that all actuarial risks are priced via standard actuarial principles. We propose to extend standard actuarial principles by a new actuarial-consistent procedure, which we call "two-step actuarial valuations". \bluebis{In the case valuations are coherent}, we show that actuarial-consistent valuations are equivalent to two-step actuarial valuations. We also discuss the connection with "two-step market-consistent valuations" from Pelsser and Stadje (2014). In particular, we discuss how the dependence structure between actuarial and financial risks impacts both actuarial-consistent and market-consistent valuations.

preprint2022arXiv

ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization

Quantization is a technique to reduce the computation and memory cost of DNN models, which are getting increasingly large. Existing quantization solutions use fixed-point integer or floating-point types, which have limited benefits, as both require more bits to maintain the accuracy of original models. On the other hand, variable-length quantization uses low-bit quantization for normal values and high-precision for a fraction of outlier values. Even though this line of work brings algorithmic benefits, it also introduces significant hardware overheads due to variable-length encoding and decoding. In this work, we propose a fixed-length adaptive numerical data type called ANT to achieve low-bit quantization with tiny hardware overheads. Our data type ANT leverages two key innovations to exploit the intra-tensor and inter-tensor adaptive opportunities in DNN models. First, we propose a particular data type, flint, that combines the advantages of float and int for adapting to the importance of different values within a tensor. Second, we propose an adaptive framework that selects the best type for each tensor according to its distribution characteristics. We design a unified processing element architecture for ANT and show its ease of integration with existing DNN accelerators. Our design results in 2.8$\times$ speedup and 2.5$\times$ energy efficiency improvement over the state-of-the-art quantization accelerators.

preprint2022arXiv

Anti-resonances and sharp analysis of Maryland localization for all parameters

We develop the technique to prove localization through the analysis of eigenfunctions in presence of both exponential frequency resonances and exponential phase barriers (anti-resonances) and use it to prove localization for the Maryland model for all parameters.

preprint2022arXiv

BBA-net: A bi-branch attention network for crowd counting

In the field of crowd counting, the current mainstream CNN-based regression methods simply extract the density information of pedestrians without finding the position of each person. This makes the output of the network often found to contain incorrect responses, which may erroneously estimate the total number and not conducive to the interpretation of the algorithm. To this end, we propose a Bi-Branch Attention Network (BBA-NET) for crowd counting, which has three innovation points. i) A two-branch architecture is used to estimate the density information and location information separately. ii) Attention mechanism is used to facilitate feature extraction, which can reduce false responses. iii) A new density map generation method combining geometric adaptation and Voronoi split is introduced. Our method can integrate the pedestrian's head and body information to enhance the feature expression ability of the density map. Extensive experiments performed on two public datasets show that our method achieves a lower crowd counting error compared to other state-of-the-art methods.

preprint2022arXiv

Branching Brownian motion in a periodic environment and existence of pulsating travelling waves

We study the limits of the additive and derivative martingales of one-dimensional branching Brownian motion in a periodic environment. Then we prove the existence of pulsating travelling wave solutions of the corresponding F-KPP equation in the supercritical and critical cases by representing the solutions probabilistically in terms of the limits of the additive and derivative martingales. We also prove that there is no pulsating travelling wave solution in the subcritical case. Our main tools are the spine decomposition and martingale change of measures.

preprint2022arXiv

Branching Brownian motion in a periodic environment and uniqueness of pulsating travelling waves

Using one-dimensional branching Brownian motion in a periodic environment, we give probabilistic proofs of the asymptotics and uniqueness of pulsating travelling waves of the F-KPP equation in a periodic environment. This paper is a sequel to [Ren et al. Branching Brownian motion in a periodic environment and existence of pulsating travelling waves], in which we proved the existence of the pulsating travelling waves in the supercritical and critical cases using the limits of the additive and derivative martingales of branching Brownian motion in a periodic environment.

preprint2022arXiv

Characterizing counterfactuals and dependencies over causal and generalized causal teams

We analyze the causal-observational languages that were introduced in Barbero and Sandu (2018), which allow discussing interventionist counterfactuals and functional dependencies in a unified framework. In particular, we systematically investigate the expressive power of these languages in causal team semantics, and we provide complete natural deduction calculi for each language. Furthermore, we introduce a generalized semantics which allows representing uncertainty about the causal laws, and analyze the expressive power and proof theory of the causal-observational languages over this enriched semantics.

preprint2022arXiv

Chiral SO(4) spin-valley density wave and degenerate topological superconductivity in magic-angle-twisted bilayer-graphene

Starting from a realistic extended Hubbard model for a $p_{x,y}$-orbital tight-binding model on the Honeycomb lattice, we perform a thorough investigation on the possible electron instabilities in the MA-TBG near the van Hove (VH) dopings. Here we focus on the interplay between the approximate SU(2)$\times$SU(2) symmetry and the $D_3$ symmetry, which leads to intriguing quantum states relevant to recent experiments, as revealed by our systematic RPA based calculations followed by a succeeding mean-field energy minimization for the ground state energy. At the SU(2)$\times$SU(2) symmetric point, the degenerate inter-valley SDW and VDW are mixed into a new state of matter dubbed as the chiral SO(4) spin-valley DW. This state simultaneously hosts three 4-component vectorial spin-valley DW orders with each adopting one wave vector, and the polarization directions of the three DW orders are mutually perpendicular to one another. %in the $\mathbb{R}^4$ space. In the presence of a tiny inter-valley exchange interaction with coefficient $J_H\to 0^{-}$ which breaks the SU(2)$\times$SU(2) symmetry, a pure chiral SDW state is obtained. In the case of $J_H\to 0^{+}$, a nematic VDW+SDW state emerges which possesses a stripy distribution of the charge density, consistent with the recent STM observations. On the aspect of SC, while the triplet $p+ip$ and singlet $d+id$ topological SCs are degenerate at $J_H=0$ near the VH dopings, the former (latter) is favored for $J_H\to 0^{-}$ ($J_H\to 0^{+}$). In addition, the two asymmetric doping-dependent behaviors of the obtained pairing phase diagram are well consistent with experiments.

preprint2022arXiv

Class-Aware Contrastive Semi-Supervised Learning

Pseudo-label-based semi-supervised learning (SSL) has achieved great success on raw data utilization. However, its training procedure suffers from confirmation bias due to the noise contained in self-generated artificial labels. Moreover, the model's judgment becomes noisier in real-world applications with extensive out-of-distribution data. To address this issue, we propose a general method named Class-aware Contrastive Semi-Supervised Learning (CCSSL), which is a drop-in helper to improve the pseudo-label quality and enhance the model's robustness in the real-world setting. Rather than treating real-world data as a union set, our method separately handles reliable in-distribution data with class-wise clustering for blending into downstream tasks and noisy out-of-distribution data with image-wise contrastive for better generalization. Furthermore, by applying target re-weighting, we successfully emphasize clean label learning and simultaneously reduce noisy label learning. Despite its simplicity, our proposed CCSSL has significant performance improvements over the state-of-the-art SSL methods on the standard datasets CIFAR100 and STL10. On the real-world dataset Semi-iNat 2021, we improve FixMatch by 9.80% and CoMatch by 3.18%. Code is available https://github.com/TencentYoutuResearch/Classification-SemiCLS.

preprint2022arXiv

Client Selection and Bandwidth Allocation for Federated Learning: An Online Optimization Perspective

Federated learning (FL) can train a global model from clients' local data set, which can make full use of the computing resources of clients and performs more extensive and efficient machine learning on clients with protecting user information requirements. Many existing works have focused on optimizing FL accuracy within the resource constrained in each individual round, however there are few works comprehensively consider the optimization for latency, accuracy and energy consumption over all rounds in wireless federated learning. Inspired by this, in this paper, we investigate FL in wireless network where client selection and bandwidth allocation are two crucial factors which significantly affect the latency, accuracy and energy consumption of clients. We formulate the optimization problem as a mixed-integer problem, which is to minimize the cost of time and accuracy within the long-term energy constrained over all rounds. To address this optimization, we propose the Perround Energy Drift Plus Cost (PEDPC) algorithm in an online perspective, and the performance of the PEDPC algorithm is verified in simulation results in terms of latency, accuracy and energy consumption in IID and NON-IID dat distributions.

preprint2022arXiv

Complete logics for elementary team properties

In this paper, we introduce a logic based on team semantics, called FOT, whose expressive power is elementary, i.e., coincides with first-order logic both on the level of sentences and (possibly open) formulas, and we also show that a sublogic of FOT, called FOT${}^\downarrow$, captures exactly downward closed elementary (or first-order) team properties. We axiomatize completely the logic FOT, and also extend the known partial axiomatization of dependence logic to dependence logic enriched with the logical constants in FOT${}^\downarrow$.

preprint2022arXiv

Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss

Data-driven methods have achieved notable performance on intent detection, which is a task to comprehend user queries. Nonetheless, they are controversial for over-confident predictions. In some scenarios, users do not only care about the accuracy but also the confidence of model. Unfortunately, mainstream neural networks are poorly calibrated, with a large gap between accuracy and confidence. To handle this problem defined as confidence calibration, we propose a model using the hyperspherical space and rebalanced accuracy-uncertainty loss. Specifically, we project the label vector onto hyperspherical space uniformly to generate a dense label representation matrix, which mitigates over-confident predictions due to overfitting sparce one-hot label matrix. Besides, we rebalance samples of different accuracy and uncertainty to better guide model training. Experiments on the open datasets verify that our model outperforms the existing calibration methods and achieves a significant improvement on the calibration metric.

preprint2022arXiv

Cooper instability and superconductivity on the Penrose lattice

Bulk superconductivity (SC) has recently been observed in the Al-Zn-Mg quasicrystal (QC). To settle several fundamental issues of the SC on the QC, we use an attractive Hubbard model to perform a systematic study on the Penrose lattice. The first issue is the Cooper instability of the QC, i.e., no Fermi surface under an infinitesimal attractive interaction. Starting from the two-electron problem outside the filled Fermi-sea, we analytically prove that an infinitesimal Hubbard attraction can lead to the Cooper instability as long as the density of state is nonzero at the Fermi level, which provides the basis of the SC on the QC. Our numerical results yield that the Cooper pairing always takes place between the two time-reversal states, satisfying the Anderson's theorem. On this theorem, we perform a mean-field (MF) study at both zero and finite temperatures. The MF study also shows that an arbitrarily weak attraction can lead to the pairing order, with the resulted pairing state being well described by the BCS theory and the thermal dynamic behaviors being well consistent with experimental results. The second issue is about the superfluid density on the QC without translational symmetry. It's clarified that although the normal state of the system locates at the critical point of the metal-insulator transition, the pairing state exhibits real SC, carrying finite superfluid density that can be verified by the Meissner effect, consistent with experiment also. These revealed properties of the SC on the Penrose lattice are universal for all QCs.

preprint2022arXiv

Decay of multi-point correlation functions in $\mathbb{Z}^d$

We prove multi-point correlation bounds in $\mathbb{Z}^d$ for arbitrary $d\geq 1$ with symmetrized distances, answering open questions proposed by Sims-Warzel \cite{SW} and Aza-Bru-Siqueira Pedra \cite{ABP}. As applications, we prove multi-point correlation bounds for the Ising model on $\mathbb{Z}^d$, and multi-point dynamical localization in expectation for uniformly localized disordered systems, which provides the first examples of this conjectured phenomenon by Bravyi-König \cite{BK}.

preprint2022arXiv

Demo: low-power communications based on RIS and AI for 6G

Ultra-massive multiple-input-multiple-output (UM-MIMO) is promising to meet the high rate requirements for future 6G. However, due to the large number of antennas and high path loss, the hardware power consumption and computing power consumption of UM-MIMO will be unaffordable. To address this problem, we implement a low-power communication system based on reconfigurable intelligent surface (RIS) and artificial intelligence (AI) for 6G. For hardware design, we employ a 256-element RIS at the base station to replace the traditional phased array. Moreover, a 2304-element RIS is developed as a relay to assist communication with much reduced transmit power. For software implementation, we develop an AI-based transmission design to reduce computing power consumption. By jointly designing the hardware and software, this prototype can realize real-time 4K video transmission with much reduced power consumption.

preprint2022arXiv

Detecting and Monitoring Tidal Dissipation of Hot Jupiters in the Era of SiTian

Transit Timing Variation (TTV) of hot Jupiters provides direct observational evidence of planet tidal dissipation. Detecting tidal dissipation through TTV needs high precision transit timings and long timing baselines. In this work, we predict and discuss the potential scientific contribution of SiTian Survey in detecting and analyzing exoplanet TTV. We develop a tidal dissipation detection pipeline for SiTian Survey that aims at time-domain astronomy with 72 1-meter optical telescopes. The pipeline includes the modules of light curve deblending, transit timing obtaining, and TTV modeling. SiTian is capable to detect more than 25,000 exoplanets among which we expect $\sim$50 sources showing evidence of tidal dissipation. We present detection and analysis of tidal dissipating targets, based on simulated SiTian light curves of XO-3b and WASP-161b. The transit light curve modeling gives consistent results within 1$σ$ to input values of simulated light curves. Also, the parameter uncertainties predicted by Monte-Carlo Markov Chain are consistent with the distribution obtained from simulating and modeling the light curve 1000 times. The timing precision of SiTian observations is $\sim$ 0.5 minutes with one transit visit. We show that differences between TTV origins, e.g., tidal dissipation, apsidal precession, multiple planets, would be significant, considering the timing precision and baseline. The detection rate of tidal dissipating hot Jupiters would answer a crucial question of whether the planet migrates at an early formation stage or random stages due to perturbations, e.g., planet scattering, secular interaction. SiTian identified targets would be constructive given that the sample would extend tenfold.

preprint2022arXiv

Deterministic Photon Sorting in Waveguide QED Systems

Sorting quantum fields into different modes according to their Fock-space quantum numbers is a highly desirable quantum operation. In this Letter, we show that a pair of two-level emitters, chirally coupled to a waveguide, may scatter single- and two-photon components of an input pulse into orthogonal temporal modes with a fidelity $\gtrsim 0.9997$. We develop a general theory to characterize and optimize this process and observe an interesting dynamics in the two-photon scattering regime: while the first emitter gives rise to a complex multimode field, the second emitter recombines the field amplitudes and the net two-photon scattering induces a self-time reversal of the pulse mode. The presented scheme can be employed to construct logic elements for propagating photons, such as a deterministic nonlinear-sign gate with a fidelity $\gtrsim 0.9995$

preprint2022arXiv

Differentially Private Counterfactuals via Functional Mechanism

Counterfactual, serving as one emerging type of model explanation, has attracted tons of attentions recently from both industry and academia. Different from the conventional feature-based explanations (e.g., attributions), counterfactuals are a series of hypothetical samples which can flip model decisions with minimal perturbations on queries. Given valid counterfactuals, humans are capable of reasoning under ``what-if'' circumstances, so as to better understand the model decision boundaries. However, releasing counterfactuals could be detrimental, since it may unintentionally leak sensitive information to adversaries, which brings about higher risks on both model security and data privacy. To bridge the gap, in this paper, we propose a novel framework to generate differentially private counterfactual (DPC) without touching the deployed model or explanation set, where noises are injected for protection while maintaining the explanation roles of counterfactual. In particular, we train an autoencoder with the functional mechanism to construct noisy class prototypes, and then derive the DPC from the latent prototypes based on the post-processing immunity of differential privacy. Further evaluations demonstrate the effectiveness of the proposed framework, showing that DPC can successfully relieve the risks on both extraction and inference attacks.

preprint2022arXiv

Discrete-time Simulation of Stochastic Volterra Equations

We study discrete-time simulation schemes for stochastic Volterra equations, namely the Euler and Milstein schemes, and the corresponding Multi-Level Monte-Carlo method. By using and adapting some results from Zhang [22], together with the Garsia-Rodemich-Rumsey lemma, we obtain the convergence rates of the Euler scheme and Milstein scheme under the supremum norm. We then apply these schemes to approximate the expectation of functionals of such Volterra equations by the (Multi-Level) Monte-Carlo method, and compute their complexity.

preprint2022arXiv

Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings

Vector quantization (VQ) based ANN indexes, such as Inverted File System (IVF) and Product Quantization (PQ), have been widely applied to embedding based document retrieval thanks to the competitive time and memory efficiency. Originally, VQ is learned to minimize the reconstruction loss, i.e., the distortions between the original dense embeddings and the reconstructed embeddings after quantization. Unfortunately, such an objective is inconsistent with the goal of selecting ground-truth documents for the input query, which may cause severe loss of retrieval quality. Recent works identify such a defect, and propose to minimize the retrieval loss through contrastive learning. However, these methods intensively rely on queries with ground-truth documents, whose performance is limited by the insufficiency of labeled data. In this paper, we propose Distill-VQ, which unifies the learning of IVF and PQ within a knowledge distillation framework. In Distill-VQ, the dense embeddings are leveraged as "teachers", which predict the query's relevance to the sampled documents. The VQ modules are treated as the "students", which are learned to reproduce the predicted relevance, such that the reconstructed embeddings may fully preserve the retrieval result of the dense embeddings. By doing so, Distill-VQ is able to derive substantial training signals from the massive unlabeled data, which significantly contributes to the retrieval quality. We perform comprehensive explorations for the optimal conduct of knowledge distillation, which may provide useful insights for the learning of VQ based ANN index. We also experimentally show that the labeled data is no longer a necessity for high-quality vector quantization, which indicates Distill-VQ's strong applicability in practice.

preprint2022arXiv

Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices

Emotion recognition in smart eyewear devices is highly valuable but challenging. One key limitation of previous works is that the expression-related information like facial or eye images is considered as the only emotional evidence. However, emotional status is not isolated; it is tightly associated with people's visual perceptions, especially those sentimental ones. However, little work has examined such associations to better illustrate the cause of different emotions. In this paper, we study the emotionship analysis problem in eyewear systems, an ambitious task that requires not only classifying the user's emotions but also semantically understanding the potential cause of such emotions. To this end, we devise EMOShip, a deep-learning-based eyewear system that can automatically detect the wearer's emotional status and simultaneously analyze its associations with semantic-level visual perceptions. Experimental studies with 20 participants demonstrate that, thanks to the emotionship awareness, EMOShip not only achieves superior emotion recognition accuracy over existing methods (80.2% vs. 69.4%), but also provides a valuable understanding of the cause of emotions. Pilot studies with 20 participants further motivate the potential use of EMOShip to empower emotion-aware applications, such as emotionship self-reflection and emotionship life-logging.

preprint2022arXiv

Edge statistics of large dimensional deformed rectangular matrices

We consider the edge statistics of large dimensional deformed rectangular matrices of the form $Y_t=Y+\sqrt{t}X,$ where $Y$ is a $p \times n$ deterministic signal matrix whose rank is comparable to $n$, $X$ is a $p\times n$ random noise matrix with centered i.i.d. entries with variance $n^{-1}$, and $t>0$ gives the noise level. This model is referred to as the interference-plus-noise matrix in the study of massive multiple-input multiple-output (MIMO) system, which belongs to the category of the so-called signal-plus-noise model. For the case $t=1$, the spectral statistics of this model have been studied to a certain extent in the literature. In this paper, we study the singular value and singular vector statistics of $Y_t$ around the right-most edge of the singular value spectrum in the harder regime $n^{-2/3}\ll t \ll 1$. This regime is harder than the $t=1$ case, because on one hand, the edge behavior of the empirical spectral distribution (ESD) of $YY^\top$ has a strong effect on the edge statistics of $Y_tY_t^\top$ since $t\ll 1$ is "small", while on the other hand, the edge statistics of $Y_t$ is also not merely a perturbation of those of $Y$ since $t\gg n^{-2/3}$ is "large". Under certain regularity assumptions on $Y,$ we prove the edge universality, eigenvalues rigidity and eigenvector delocalization for the matrices $Y_tY_t^\top$ and $Y_t^\top Y_t$. These results can be used to estimate and infer the massive MIMO system. To prove the main results, we analyze the edge behavior of the asymptotic ESD for $Y_tY_t^\top$, and establish some sharp local laws on the resolvent of $Y_tY_t^\top$. These results can be of independent interest, and used as useful inputs for many other problems regarding the spectral statistics of $Y_t$.

preprint2022arXiv

Existence and uniqueness of equilibrium states for systems with specification at a fixed scale

We consider the uniqueness of equilibrium states for dynamical systems that satisfy certain weak, non-uniform versions of specification, expansivity, and the Bowen property at a fixed scale. Following Climenhaga-Thompson's approach which was originally due to Bowen and Franco, we prove that equilibrium states are unique even when the weak specification assumption only holds on a small collection of orbit segments.

preprint2022arXiv

FAR Planner: Fast, Attemptable Route Planner using Dynamic Visibility Update

The problem of path planning in unknown environments remains a challenging problem - as the environment is gradually observed during the navigation, the underlying planner has to update the environment representation and replan, promptly and constantly, to account for the new observations. In this paper, we present a visibility graph-based planning framework capable of dealing with navigation tasks in both known and unknown environments. The planner employs a polygonal representation of the environment and constructs the representation by extracting edge points around obstacles to form enclosed polygons. With that, the method dynamically updates a global visibility graph using a two-layered data structure, expanding the visibility edges along with the navigation and removing edges that become occluded by newly observed obstacles. When navigating in unknown environments, the method is attemptable in discovering a way to the goal by picking up the environment layout on the fly, updating the visibility graph, and fast re-planning corresponding to the newly observed environment. We evaluate the method in simulated and real-world settings. The method shows the capability to attempt and navigate through unknown environments, reducing the travel time by up to 12-47% from search-based methods: A*, D* Lite, and more than 24-35% than sampling-based methods: RRT*, BIT*, and SPARS.

preprint2022arXiv

Geo-Localization via Ground-to-Satellite Cross-View Image Retrieval

The large variation of viewpoint and irrelevant content around the target always hinder accurate image retrieval and its subsequent tasks. In this paper, we investigate an extremely challenging task: given a ground-view image of a landmark, we aim to achieve cross-view geo-localization by searching out its corresponding satellite-view images. Specifically, the challenge comes from the gap between ground-view and satellite-view, which includes not only large viewpoint changes (some parts of the landmark may be invisible from front view to top view) but also highly irrelevant background (the target landmark tend to be hidden in other surrounding buildings), making it difficult to learn a common representation or a suitable mapping. To address this issue, we take advantage of drone-view information as a bridge between ground-view and satellite-view domains. We propose a Peer Learning and Cross Diffusion (PLCD) framework. PLCD consists of three parts: 1) a peer learning across ground-view and drone-view to find visible parts to benefit ground-drone cross-view representation learning; 2) a patch-based network for satellite-drone cross-view representation learning; 3) a cross diffusion between ground-drone space and satellite-drone space. Extensive experiments conducted on the University-Earth and University-Google datasets show that our method outperforms state-of-the-arts significantly.

preprint2022arXiv

High-order harmonic generation in X-ray range from laser induced noble gas multivalent ions

Sub-femtosecond x-ray burst is powerful tool for probing and imaging electronic and concomitant atomic motion in attosecond physics. For years, x-ray source (above 2 keV) had mainly been obtained from X-ray free electron laser (XFEL) or synchrotron radiation, which are high energy consumption, high cost and huge volume. Here we propose a low-cost and small-size method to generate X-ray source. We experimentally obtained high photon energy spectrum (~ 5.2 keV) through both atom and multiple valence state ions using a near-infrared 1.45 μm driving laser interacting with krypton gas, according to our knowledge, which is the highest photon energy generated through high-order harmonic generation up to now. In our scheme, multi-keV photon energy can be achieved with a relaxed requirement on experimental conditions, and make time-resolved studies more accessible to many laboratories that are capable of producing high energy photon extending to hard x-ray region. Furthermore, our scheme minimizes the influence of X-ray fluorescence process on detection, and can also be utilized to study the quantum-optical nature of high-order harmonic generation.

preprint2022arXiv

Improving Generalization of Metric Learning via Listwise Self-distillation

Most deep metric learning (DML) methods employ a strategy that forces all positive samples to be close in the embedding space while keeping them away from negative ones. However, such a strategy ignores the internal relationships of positive (negative) samples and often leads to overfitting, especially in the presence of hard samples and mislabeled samples. In this work, we propose a simple yet effective regularization, namely Listwise Self-Distillation (LSD), which progressively distills a model's own knowledge to adaptively assign a more appropriate distance target to each sample pair in a batch. LSD encourages smoother embeddings and information mining within positive (negative) samples as a way to mitigate overfitting and thus improve generalization. Our LSD can be directly integrated into general DML frameworks. Extensive experiments show that LSD consistently boosts the performance of various metric learning methods on multiple datasets.

preprint2022arXiv

Kinetic-scale flux ropes: Observations and applications of kinetic equilibrium models

Magnetic flux ropes with helical field lines and strong core field are ubiquitous structures in space plasmas. Recently, kinetic-scale flux ropes have been identified by high-resolution observations from Magnetospheric Multiscale (MMS) spacecraft in the magnetosheath, which have drawn a lot of attention because of their non-ideal behavior and internal structures. Detailed investigation of flux rope structure and dynamics requires development of realistic kinetic models. In this paper, we generalize an equilibrium model to reconstruct a kinetic-scale flux rope previously reported via MMS observations. The key features in the magnetic field and electron pitch-angle distribution measurements of all four satellites are simultaneously reproduced in this reconstruction. Besides validating the model, our results also indicate that the anisotropic features previously attributed to asymmetric magnetic topologies in the magnetosheath can be alternatively explained by the spacecraft motion in the flux rope rest frame.

preprint2022arXiv

Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification

Adversarial attacks are often considered as threats to the robustness of Deep Neural Networks (DNNs). Various defending techniques have been developed to mitigate the potential negative impact of adversarial attacks against task predictions. This work analyzes adversarial attacks from a different perspective. Namely, adversarial examples contain implicit information that is useful to the predictions i.e., image classification, and treat the adversarial attacks against DNNs for data self-expression as extracted abstract representations that are capable of facilitating specific learning tasks. We propose an algorithmic framework that leverages the advantages of the DNNs for data self-expression and task-specific predictions, to improve image classification. The framework jointly learns a DNN for attacking Variational Autoencoder (VAE) networks and a DNN for classification, coined as Attacking VAE for Improve Classification (AVIC). The experiment results show that AVIC can achieve higher accuracy on standard datasets compared to the training with clean examples and the traditional adversarial training.

preprint2022arXiv

Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach

Rule sets are highly interpretable logical models in which the predicates for decision are expressed in disjunctive normal form (DNF, OR-of-ANDs), or, equivalently, the overall model comprises an unordered collection of if-then decision rules. In this paper, we consider a submodular optimization based approach for learning rule sets. The learning problem is framed as a subset selection task in which a subset of all possible rules needs to be selected to form an accurate and interpretable rule set. We employ an objective function that exhibits submodularity and thus is amenable to submodular optimization techniques. To overcome the difficulty arose from dealing with the exponential-sized ground set of rules, the subproblem of searching a rule is casted as another subset selection task that asks for a subset of features. We show it is possible to write the induced objective function for the subproblem as a difference of two submodular (DS) functions to make it approximately solvable by DS optimization algorithms. Overall, the proposed approach is simple, scalable, and likely to be benefited from further research on submodular optimization. Experiments on real datasets demonstrate the effectiveness of our method.

preprint2022arXiv

Learning Optical Flow with Adaptive Graph Reasoning

Estimating per-pixel motion between video frames, known as optical flow, is a long-standing problem in video understanding and analysis. Most contemporary optical flow techniques largely focus on addressing the cross-image matching with feature similarity, with few methods considering how to explicitly reason over the given scene for achieving a holistic motion understanding. In this work, taking a fresh perspective, we introduce a novel graph-based approach, called adaptive graph reasoning for optical flow (AGFlow), to emphasize the value of scene/context information in optical flow. Our key idea is to decouple the context reasoning from the matching procedure, and exploit scene information to effectively assist motion estimation by learning to reason over the adaptive graph. The proposed AGFlow can effectively exploit the context information and incorporate it within the matching procedure, producing more robust and accurate results. On both Sintel clean and final passes, our AGFlow achieves the best accuracy with EPE of 1.43 and 2.47 pixels, outperforming state-of-the-art approaches by 11.2% and 13.6%, respectively.

preprint2022arXiv

Lifshitz transition enhanced triplet $p_z$-wave superconductivity in hydrogen doped KCr$_3$As$_3$

The recently synthesized air-insensitive hydrogen doped KCr$_3$As$_3$ superconductor has aroused great research interests. This material has, for the first time in the research area of the quasi-one-dimensional Cr-based superconductivity (SC), realized a tunability through charge doping, which will potentially significantly push the development of this area. Here based on the band structure from first-principle calculations, we construct a six-band tight-binding (TB) model equipped with multi-orbital Hubbard interactions, and adopt the random-phase-approximation approach to study the hydrogen-doping dependence of the pairing symmetry and superconducting $T_c$. Under the rigid-band approximation, our pairing phase diagram is occupied by the triplet $p_z$-wave pairing through out the hydrogen-doping regime $x\in (0.4,1)$ in which SC has been experimentally detected. Remarkably, the $x$-dependence of $T_c$ shows a peak at the 3D-quasi-1D Lifshitz transition point, although the total density of state exhibit a dip there. A thorough investigation of the band structure reveals type-II van-Hove singularities (VHSs) in the $γ$ band, which favor the formation of the triplet SC. It turns out that the $γ$- Fermi surface (FS) comprises two flat quasi-1D FS sheets almost parallel to the $k_z=0$ plane and six almost perpendicular tube-like FS sheets, and the type-II VHS just lies in the boundary between these two FS parts. Furthermore, the $\left|k_z\right|$ of the VH planes reaches the maximum near the Lifshitz-transition point, which pushes the $T_c$ of the $p_z$-wave SC to the maximum. Our results appeal more experimental access into this intriguing superconductor.

preprint2022arXiv

Limited-memory BFGS Optimisation of Phase-Only Computer-Generated Hologram for Fraunhofer Diffraction

We implement a novel limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS) optimisation algorithm with cross entropy (CE) loss function, to produce phase-only computer-generated hologram (CGH) for holographic displays, with validation on a binary-phase modulation holographic projector.

preprint2022arXiv

Limiting distribution of the sample canonical correlation coefficients of high-dimensional random vectors

Consider two high-dimensional random vectors $\widetilde{\mathbf x}\in\mathbb R^p$ and $\widetilde{\mathbf y}\in\mathbb R^q$ with finite rank correlations. More precisely, suppose that $\widetilde{\mathbf x}=\mathbf x+A\mathbf z$ and $\widetilde{\mathbf y}=\mathbf y+B\mathbf z$, for independent random vectors $\mathbf x\in\mathbb R^p$, $\mathbf y\in\mathbb R^q$ and $\mathbf z\in\mathbb R^r$ with iid entries of mean 0 and variance 1, and two deterministic matrices $A\in\mathbb R^{p\times r}$ and $B\in\mathbb R^{q\times r}$ . With $n$ iid observations of $(\widetilde{\mathbf x},\widetilde{\mathbf y})$, we study the sample canonical correlations between them. In this paper, we focus on the high-dimensional setting with a rank-$r$ correlation. Let $t_1\ge\cdots\ge t_r$ be the squares of the population canonical correlation coefficients (CCC) between $\widetilde{\mathbf x}$ and $\widetilde{\mathbf y}$, and $\widetildeλ_1\ge\cdots\ge\widetildeλ_r$ be the squares of the largest $r$ sample CCC. Under certain moment assumptions on the entries of $\mathbf x$, $\mathbf y$ and $\mathbf z$, we show that there exists a threshold $t_c\in(0, 1)$ such that if $t_i>t_c$, then $\sqrt{n}(\widetildeλ_i-θ_i)$ converges in law to a centered normal distribution, where $θ_i>λ_+$ is a fixed outlier location determined by $t_i$. Our results extend the ones in [4] for Gaussian vectors. Moreover, we find that the variance of the limiting distribution of $\sqrt{n}(\widetildeλ_i-θ_i)$ also depends on the fourth cumulants of the entries of $\mathbf x$, $\mathbf y$ and $\mathbf z$, a phenomenon that cannot be observed in the Gaussian case.

preprint2022arXiv

Liouvillian Skin Effect in an Exactly Solvable Model

The interplay between dissipation, topology and sensitivity to boundary conditions has recently attracted tremendous amounts of attention at the level of effective non-Hermitian descriptions. Here we exactly solve a quantum mechanical Lindblad master equation describing a dissipative topological Su-Schrieffer-Heeger (SSH) chain of fermions for both open boundary condition (OBC) and periodic boundary condition (PBC). We find that the extreme sensitivity on the boundary conditions associated with the non-Hermitian skin effect is directly reflected in the rapidities governing the time evolution of the density matrix giving rise to a Liouvillian skin effect. This leads to several intriguing phenomena including boundary sensitive damping behavior, steady state currents in finite periodic systems, and diverging relaxation times in the limit of large systems. We illuminate how the role of topology in these systems differs in the effective non-Hermitian Hamiltonian limit and the full master equation framework.

preprint2022arXiv

Localization for magnetic quantum walks

We prove Anderson localization for all Diophantine frequencies and all non-resonant phases for a model that arises from 2D quantum walk model subject to an external magnetic field. This is the first localization result for all Diophantine frequencies in the magnetic quantum walk and the quasi-periodic CMV setting. We also obtain sharp asymptotics of the localized eigenfunctions.

preprint2022arXiv

On sufficient conditions for planar graphs to be 5-flexible

In this paper, we study the flexibility of two planar graph classes $\mathcal{H}_1$, $\mathcal{H}_2$, where $\mathcal{H}_1$, $\mathcal{H}_2$ denote the set of all hopper-free planar graphs and house-free planar graphs, respectively. Let $G$ be a planar graph with a list assignment $L$. Suppose a preferred color is given for some of the vertices. We prove that if $G\in \mathcal{H}_1$ or $G\in \mathcal{H}_2$ such that all lists have size at least $5$, then there exists an $L$-coloring respecting at least a constant fraction of the preferences.

preprint2022arXiv

On the discrete-time simulation of the rough Heston model

We study Euler-type discrete-time schemes for the rough Heston model, which can be described by a stochastic Volterra equation (with non-Lipschtiz coefficient functions), or by an equivalent integrated variance formulation. Using weak convergence techniques, we prove that the limits of the discrete-time schemes are solution to some modified Volterra equations. Such modified equations are then proved to share the same unique solution as the initial equations, which implies the convergence of the discrete-time schemes. Numerical examples are also provided in order to evaluate different derivative options prices under the rough Heston model.

preprint2022arXiv

Optimally Weighted PCA for High-Dimensional Heteroscedastic Data

Modern data are increasingly both high-dimensional and heteroscedastic. This paper considers the challenge of estimating underlying principal components from high-dimensional data with noise that is heteroscedastic across samples, i.e., some samples are noisier than others. Such heteroscedasticity naturally arises, e.g., when combining data from diverse sources or sensors. A natural way to account for this heteroscedasticity is to give noisier blocks of samples less weight in PCA by using the leading eigenvectors of a weighted sample covariance matrix. We consider the problem of choosing weights to optimally recover the underlying components. In general, one cannot know these optimal weights since they depend on the underlying components we seek to estimate. However, we show that under some natural statistical assumptions the optimal weights converge to a simple function of the signal and noise variances for high-dimensional data. Surprisingly, the optimal weights are not the inverse noise variance weights commonly used in practice. We demonstrate the theoretical results through numerical simulations and comparisons with existing weighting schemes. Finally, we briefly discuss how estimated signal and noise variances can be used when the true variances are unknown, and we illustrate the optimal weights on real data from astronomy.

preprint2022arXiv

Propositional union closed team logics

In this paper, we study several propositional team logics that are closed under unions, including propositional inclusion logic. We prove that all these logics are expressively complete, and we introduce sound and complete systems of natural deduction for these logics. We also discuss the locality property and its connection with interpolation in these logics.

preprint2022arXiv

ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classification

Whole slide image (WSI) classification often relies on deep weakly supervised multiple instance learning (MIL) methods to handle gigapixel resolution images and slide-level labels. Yet the decent performance of deep learning comes from harnessing massive datasets and diverse samples, urging the need for efficient training pipelines for scaling to large datasets and data augmentation techniques for diversifying samples. However, current MIL-based WSI classification pipelines are memory-expensive and computation-inefficient since they usually assemble tens of thousands of patches as bags for computation. On the other hand, despite their popularity in other tasks, data augmentations are unexplored for WSI MIL frameworks. To address them, we propose ReMix, a general and efficient framework for MIL based WSI classification. It comprises two steps: reduce and mix. First, it reduces the number of instances in WSI bags by substituting instances with instance prototypes, i.e., patch cluster centroids. Then, we propose a ``Mix-the-bag'' augmentation that contains four online, stochastic and flexible latent space augmentations. It brings diverse and reliable class-identity-preserving semantic changes in the latent space while enforcing semantic-perturbation invariance. We evaluate ReMix on two public datasets with two state-of-the-art MIL methods. In our experiments, consistent improvements in precision, accuracy, and recall have been achieved but with orders of magnitude reduced training time and memory consumption, demonstrating ReMix's effectiveness and efficiency. Code is available.

preprint2022arXiv

Sample canonical correlation coefficients of high-dimensional random vectors with finite rank correlations

Consider two random vectors $\widetilde{\mathbf x} \in \mathbb R^p$ and $\widetilde{\mathbf y} \in \mathbb R^q$ of the forms $\widetilde{\mathbf x}=A\mathbf z+\mathbf C_1^{1/2}\mathbf x$ and $\widetilde{\mathbf y}=B\mathbf z+\mathbf C_2^{1/2}\mathbf y$, where $\mathbf x\in \mathbb R^p$, $\mathbf y\in \mathbb R^q$ and $\mathbf z\in \mathbb R^r$ are independent vectors with i.i.d. entries of mean 0 and variance 1, $\mathbf C_1$ and $\mathbf C_2$ are $p \times p$ and $q\times q$ deterministic covariance matrices, and $A$ and $B$ are $p\times r$ and $q\times r$ deterministic matrices. With $n$ independent observations of $(\widetilde{\mathbf x},\widetilde{\mathbf y})$, we study the sample canonical correlations between $\widetilde{\mathbf x} $ and $\widetilde{\mathbf y}$. We consider the high-dimensional setting with finite rank correlations. Let $t_1\ge t_2 \ge \cdots\ge t_r$ be the squares of the nontrivial population canonical correlation coefficients, and let $\widetildeλ_1 \ge\widetildeλ_2\ge\cdots\ge\widetildeλ_{p\wedge q}$ be the squares of the sample canonical correlation coefficients. If the entries of $\mathbf x$, $\mathbf y$ and $\mathbf z$ are i.i.d. Gaussian, then the following dichotomy has been shown in [7] for a fixed threshold $t_c \in(0, 1)$: for $1\le i \le r$, if $t_i < t_c$, then $\widetildeλ_i$ converges to the right-edge $λ_+$ of the limiting eigenvalue spectrum of the sample canonical correlation matrix; if $t_i>t_c$, then $\widetildeλ_i$ converges to a deterministic limit $θ_i \in (λ_+,1)$ determined by $t_i$. In this paper, we prove that these results hold universally under the sharp fourth moment conditions on the entries of $\mathbf x$ and $\mathbf y$. Moreover, we prove the results in full generality, in the sense that they also hold for near-degenerate $t_i$'s and for $t_i$'s that are close to the threshold $t_c$.

preprint2022arXiv

SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration

In this paper, we present a second order spatial compatibility (SC^2) measure based method for efficient and robust point cloud registration (PCR), called SC^2-PCR. Firstly, we propose a second order spatial compatibility (SC^2) measure to compute the similarity between correspondences. It considers the global compatibility instead of local consistency, allowing for more distinctive clustering between inliers and outliers at early stage. Based on this measure, our registration pipeline employs a global spectral technique to find some reliable seeds from the initial correspondences. Then we design a two-stage strategy to expand each seed to a consensus set based on the SC^2 measure matrix. Finally, we feed each consensus set to a weighted SVD algorithm to generate a candidate rigid transformation and select the best model as the final result. Our method can guarantee to find a certain number of outlier-free consensus sets using fewer samplings, making the model estimation more efficient and robust. In addition, the proposed SC^2 measure is general and can be easily plugged into deep learning based frameworks. Extensive experiments are carried out to investigate the performance of our method. Code will be available at \url{https://github.com/ZhiChen902/SC2-PCR}.

preprint2022arXiv

Second-harmonic generation from singular metasurfaces

We present a theoretical study of second-harmonic generation from a singular metasurface. The singular metasurfaces strongly interact with the incident light, where the large field enhancement forms an intense surface polarization that generates the second-harmonic field. By using transformation optics, the calculation of nonlinear optical response is converted from the metasurface frame to that of a simple slab geometry, largely reducing the complexity of the problem. In addition, the singular metasurface exhibits a weak dependence on the incident angle of light, which can be potentially used as an all-angle device for harmonic generations. Finally, we study the symmetry dependence of second-harmonic generation in the far field for the singular metasurface and show how to enhance the conversion efficiency under normal incidence by breaking the surface inversion symmetry.

preprint2022arXiv

Spin Supersolidity in Nearly Ideal Easy-axis Triangular Quantum Antiferromagnet Na$_2$BaCo(PO$_4$)$_2$

Prototypical models and their material incarnations are cornerstones to the understanding of quantum magnetism. Here we show theoretically that the recently synthesized magnetic compound Na$_2$BaCo(PO$_4$)$_2$ (NBCP) is a rare, nearly ideal material realization of the $S=1/2$ triangular-lattice antiferromagnet with significant easy-axis spin exchange anisotropy. By combining the automatic parameter searching and tensor-network simulations, we establish a microscopic model description of this material with realistic model parameters, which can not only fit well the experimental thermodynamic data but also reproduce the measured magnetization curves without further adjustment of parameters. According to the established model, the NBCP hosts a spin supersolid state that breaks both the lattice translation symmetry and the spin rotational symmetry. Such a state is a spin analogue of the long-sought supersolid state, thought to exist in solid Helium and optical lattice systems, and share similar traits. The NBCP therefore represents an ideal material-based platform to explore the physics of supersolidity as well as its quantum and thermal melting.

preprint2022arXiv

SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation

Quantization of deep neural networks (DNN) has been proven effective for compressing and accelerating DNN models. Data-free quantization (DFQ) is a promising approach without the original datasets under privacy-sensitive and confidential scenarios. However, current DFQ solutions degrade accuracy, need synthetic data to calibrate networks, and are time-consuming and costly. This paper proposes an on-the-fly DFQ framework with sub-second quantization time, called SQuant, which can quantize networks on inference-only devices with low computation and memory requirements. With the theoretical analysis of the second-order information of DNN task loss, we decompose and approximate the Hessian-based optimization objective into three diagonal sub-items, which have different areas corresponding to three dimensions of weight tensor: element-wise, kernel-wise, and output channel-wise. Then, we progressively compose sub-items and propose a novel data-free optimization objective in the discrete domain, minimizing Constrained Absolute Sum of Error (or CASE in short), which surprisingly does not need any dataset and is even not aware of network architecture. We also design an efficient algorithm without back-propagation to further reduce the computation complexity of the objective solver. Finally, without fine-tuning and synthetic datasets, SQuant accelerates the data-free quantization process to a sub-second level with >30% accuracy improvement over the existing data-free post-training quantization works, with the evaluated models under 4-bit quantization. We have open-sourced the SQuant framework at https://github.com/clevercool/SQuant.

preprint2022arXiv

Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation

The architectural advancements in deep neural networks have led to remarkable leap-forwards across a broad array of computer vision tasks. Instead of relying on human expertise, neural architecture search (NAS) has emerged as a promising avenue toward automating the design of architectures. While recent achievements in image classification have suggested opportunities, the promises of NAS have yet to be thoroughly assessed on more challenging tasks of semantic segmentation. The main challenges of applying NAS to semantic segmentation arise from two aspects: (i) high-resolution images to be processed; (ii) additional requirement of real-time inference speed (i.e., real-time semantic segmentation) for applications such as autonomous driving. To meet such challenges, we propose a surrogate-assisted multi-objective method in this paper. Through a series of customized prediction models, our method effectively transforms the original NAS task into an ordinary multi-objective optimization problem. Followed by a hierarchical pre-screening criterion for in-fill selection, our method progressively achieves a set of efficient architectures trading-off between segmentation accuracy and inference speed. Empirical evaluations on three benchmark datasets together with an application using Huawei Atlas 200 DK suggest that our method can identify architectures significantly outperforming existing state-of-the-art architectures designed both manually by human experts and automatically by other NAS methods.

preprint2022arXiv

Time Series Data Augmentation for Deep Learning: A Survey

Deep learning performs remarkably well on many time series analysis tasks recently. The superior performance of deep neural networks relies heavily on a large number of training data to avoid overfitting. However, the labeled data of many real-world time series applications may be limited such as classification in medical time series and anomaly detection in AIOps. As an effective way to enhance the size and quality of the training data, data augmentation is crucial to the successful application of deep learning models on time series data. In this paper, we systematically review different data augmentation methods for time series. We propose a taxonomy for the reviewed methods, and then provide a structured review for these methods by highlighting their strengths and limitations. We also empirically compare different data augmentation methods for different tasks including time series classification, anomaly detection, and forecasting. Finally, we discuss and highlight five future directions to provide useful research guidance.

preprint2022arXiv

Tracy-Widom distribution for heterogeneous Gram matrices with applications in signal detection

Detection of the number of signals corrupted by high-dimensional noise is a fundamental problem in signal processing and statistics. This paper focuses on a general setting where the high-dimensional noise has an unknown complicated heterogeneous variance structure. We propose a sequential test which utilizes the edge singular values (i.e., the largest few singular values) of the data matrix. It also naturally leads to a consistent sequential testing estimate of the number of signals. We describe the asymptotic distribution of the test statistic in terms of the Tracy-Widom distribution. The test is shown to be accurate and have full power against the alternative, both theoretically and numerically. The theoretical analysis relies on establishing the Tracy-Widom law for a large class of Gram type random matrices with non-zero means and completely arbitrary variance profiles, which can be of independent interest.

preprint2022arXiv

Transit Timing Variation of XO-3b: Evidence for Tidal Evolution of Hot Jupiter with High Eccentricity

Observed transit timing variation (TTV) potentially reveals the period decay caused by star-planet tidal interaction which can explain the orbital migration of hot Jupiters. We report the TTV of XO-3b, using TESS observed timings and archival timings. We generate a photometric pipeline to produce light curves from raw TESS images and find the difference between our pipeline and TESS PDC is negligible for timing analysis. TESS timing presents a shift of 17.6 minutes (80 $σ$), earlier than the prediction from the previous ephemeris. The best linear fit for all timings available gives a Bayesian Information Criterion (BIC) value of 439. A quadratic function is a better model with a BIC of 56. The period derivative obtained from a quadratic function is -6.2$\times$10$^{-9}$$\pm$2.9$\times$10$^{-10}$ per orbit, indicating an orbital decay timescale 1.4 Myr. We find that the orbital period decay can be well explained by tidal interaction. The `modified tidal quality factor' $Q_{p}'$ would be 1.8$\times$10$^{4}$$\pm$8$\times$10$^{2}$ if we assume the decay is due to the tide in the planet; whereas $Q_{*}'$ would be 1.5$\times$10$^{5}$$\pm$6$\times$10$^{3}$ if tidal dissipation is predominantly in the star. The precession model is another possible origin to explain the observed TTVs. We note that the follow-up observations of occultation timing and radial velocity monitoring are needed for fully discriminating the different models.

preprint2022arXiv

Transmissive RIS for 6G Communications: Design, Prototyping, and Experimental Demonstrations

Reconfigurable intelligent surface (RIS) has been widely considered as a key technique to improve spectral efficiency for 6G communications. Compared with most existing research that only focuses on the reflective RIS, the design and prototyping of a novel transmissive RIS are presented in this paper, and its enhancement to the RIS-aided communication system is experimentally demonstrated. The 2-bit transmissive RIS element utilizes the penetration structure, which combines a 1-bit current reversible dipole and a 90° digital phase shifter based on a quadrature hybrid coupler. A transmissive RIS prototype with 16$\times$16 elements is designed, fabricated, and measured to verify the proposed design. The measured phase shift and insertion loss of the RIS element validate the 2-bit phase modulation capability. Being illuminated by a horn feed, the prototype achieves a maximum broadside gain of 22.0 dBi at 27 GHz, and the two-dimensional beamforming capability with scan angles up to $\pm$60° is validated. The experimental results of the RIS-aided communication system verify that by introducing the extra gain and beam steering capability, the transmissive RIS is able to achieve a higher data rate, reduce the transmit power, improve the transmission capability through obstacles, and dynamically adapt to the signal propagation direction.

preprint2022arXiv

Uniqueness of equilibrium states for Lorenz attractors in any dimension

In this note, we consider the thermodynamic formalism for Lorenz attractors of flows in any dimension. Under a mild condition on the Hölder continuous potential function $ϕ$, we prove that for an open and dense subset of $C^1$ vector fields, every Lorenz attractor supports a unique equilibrium state. In particular, we obtain the uniqueness for the measure of maximal entropy.

preprint2022arXiv

UniVIP: A Unified Framework for Self-Supervised Visual Pre-training

Self-supervised learning (SSL) holds promise in leveraging large amounts of unlabeled data. However, the success of popular SSL methods has limited on single-centric-object images like those in ImageNet and ignores the correlation among the scene and instances, as well as the semantic difference of instances in the scene. To address the above problems, we propose a Unified Self-supervised Visual Pre-training (UniVIP), a novel self-supervised framework to learn versatile visual representations on either single-centric-object or non-iconic dataset. The framework takes into account the representation learning at three levels: 1) the similarity of scene-scene, 2) the correlation of scene-instance, 3) the discrimination of instance-instance. During the learning, we adopt the optimal transport algorithm to automatically measure the discrimination of instances. Massive experiments show that UniVIP pre-trained on non-iconic COCO achieves state-of-the-art transfer performance on a variety of downstream tasks, such as image classification, semi-supervised learning, object detection and segmentation. Furthermore, our method can also exploit single-centric-object dataset such as ImageNet and outperforms BYOL by 2.5% with the same pre-training epochs in linear probing, and surpass current self-supervised object detection methods on COCO dataset, demonstrating its universality and potential.

preprint2021arXiv

A concise review of Rydberg atom based quantum computation and quantum simulation

Quantum information processing based on Rydberg atoms emerged as a promising direction two decades ago. Recent experimental and theoretical progresses have shined exciting light on this avenue. In this concise review, we will briefly introduce the basics of Rydberg atoms and their recent applications in associated areas of neutral atom quantum computation and simulation. We shall also include related discussions on quantum optics with Rydberg atomic ensembles, which are increasingly used to explore quantum computation and quantum simulation with photons.

preprint2021arXiv

ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation

Interpreting human actions requires understanding the spatial and temporal context of the scenes. State-of-the-art action detectors based on Convolutional Neural Network (CNN) have demonstrated remarkable results by adopting two-stream or 3D CNN architectures. However, these methods typically operate in a non-real-time, ofline fashion due to system complexity to reason spatio-temporal information. Consequently, their high computational cost is not compliant with emerging real-world scenarios such as service robots or public surveillance where detection needs to take place at resource-limited edge devices. In this paper, we propose ACDnet, a compact action detection network targeting real-time edge computing which addresses both efficiency and accuracy. It intelligently exploits the temporal coherence between successive video frames to approximate their CNN features rather than naively extracting them. It also integrates memory feature aggregation from past video frames to enhance current detection stability, implicitly modeling long temporal cues over time. Experiments conducted on the public benchmark datasets UCF-24 and JHMDB-21 demonstrate that ACDnet, when integrated with the SSD detector, can robustly achieve detection well above real-time (75 FPS). At the same time, it retains reasonable accuracy (70.92 and 49.53 frame mAP) compared to other top-performing methods using far heavier configurations. Codes will be available at https://github.com/dginhac/ACDnet.

preprint2021arXiv

An entropy dichotomy for singular star flows

We show that non-trivial chain recurrent classes for generic $C^1$ star flows satisfy a dichotomy: either they have zero topological entropy, or they must be isolated. Moreover, chain recurrent classes for generic star flows with zero entropy must be sectional hyperbolic, and cannot be detected by any non-trivial ergodic invariant probability. As a result, we show that $C^1$ generic star flows have only finitely many Lyapunov stable chain recurrent classes.

preprint2021arXiv

Aqueous humor dynamics in human eyes: a lattice Boltzmann study

This paper presents a lattice Boltzmann model to simulate the aqueous humor (AH) dynamics in the human eyes by involving incompressible Navier-Stokes flow, heat convection and diffusion, and Darcy seepage flow. Verifying simulations indicate that the model is stable, convergent and robust. Further investigations were carried out, including the effects of heat convection and buoyancy, AH production rate, permeability of trabecular meshwork, viscosity of AH and anterior chamber angle on intraocular pressure (IOP). The heat convection and diffusion can significantly affect the flow patterns in the healthy eye, and the IOP can be controlled by increasing the anterior chamber angle or decreasing the secretion rate, the drainage resistance and viscosity of AH. However, the IOP is insensitive to the viscosity of the AH, which may be one of the causes that the viscosity would not have been considered as a factor for controlling the IOP. It's interesting that all these factors have more significant influences on the IOP in pathologic eyes than healthy ones. The temperature difference and the eye-orientation have obvious influence on the cornea and iris wall shear stresses. The present model and simulation results are expected to provide an alternative tool and theoretical reference for the study of AH dynamics.

preprint2021arXiv

Cascade Network with Guided Loss and Hybrid Attention for Finding Good Correspondences

Finding good correspondences is a critical prerequisite in many feature based tasks. Given a putative correspondence set of an image pair, we propose a neural network which finds correct correspondences by a binary-class classifier and estimates relative pose through classified correspondences. First, we analyze that due to the imbalance in the number of correct and wrong correspondences, the loss function has a great impact on the classification results. Thus, we propose a new Guided Loss that can directly use evaluation criterion (Fn-measure) as guidance to dynamically adjust the objective function during training. We theoretically prove that the perfect negative correlation between the Guided Loss and Fn-measure, so that the network is always trained towards the direction of increasing Fn-measure to maximize it. We then propose a hybrid attention block to extract feature, which integrates the Bayesian attentive context normalization (BACN) and channel-wise attention (CA). BACN can mine the prior information to better exploit global context and CA can capture complex channel context to enhance the channel awareness of the network. Finally, based on our Guided Loss and hybrid attention block, a cascade network is designed to gradually optimize the result for more superior performance. Experiments have shown that our network achieves the state-of-the-art performance on benchmark datasets. Our code will be available in https://github.com/wenbingtao/GLHA.

preprint2021arXiv

Defending SVMs against Poisoning Attacks: the Hardness and DBSCAN Approach

Adversarial machine learning has attracted a great amount of attention in recent years. In a poisoning attack, the adversary can inject a small number of specially crafted samples into the training data which make the decision boundary severely deviate and cause unexpected misclassification. Due to the great importance and popular use of support vector machines (SVM), we consider defending SVM against poisoning attacks in this paper. We study two commonly used strategies for defending: designing robust SVM algorithms and data sanitization. Though several robust SVM algorithms have been proposed before, most of them either are in lack of adversarial-resilience, or rely on strong assumptions about the data distribution or the attacker's behavior. Moreover, the research on their complexities is still quite limited. We are the first, to the best of our knowledge, to prove that even the simplest hard-margin one-class SVM with outliers problem is NP-complete, and has no fully PTAS unless P$=$NP (that means it is hard to achieve an even approximate algorithm). For the data sanitization defense, we link it to the intrinsic dimensionality of data; in particular, we provide a sampling theorem in doubling metrics for explaining the effectiveness of DBSCAN (as a density-based outlier removal method) for defending against poisoning attacks. In our empirical experiments, we compare several defenses including the DBSCAN and robust SVM methods, and investigate the influences from the intrinsic dimensionality and data density to their performances.

preprint2021arXiv

Efficient Visual Recognition with Deep Neural Networks: A Survey on Recent Advances and New Directions

Visual recognition is currently one of the most important and active research areas in computer vision, pattern recognition, and even the general field of artificial intelligence. It has great fundamental importance and strong industrial needs. Deep neural networks (DNNs) have largely boosted their performances on many concrete tasks, with the help of large amounts of training data and new powerful computation resources. Though recognition accuracy is usually the first concern for new progresses, efficiency is actually rather important and sometimes critical for both academic research and industrial applications. Moreover, insightful views on the opportunities and challenges of efficiency are also highly required for the entire community. While general surveys on the efficiency issue of DNNs have been done from various perspectives, as far as we are aware, scarcely any of them focused on visual recognition systematically, and thus it is unclear which progresses are applicable to it and what else should be concerned. In this paper, we present the review of the recent advances with our suggestions on the new possible directions towards improving the efficiency of DNN-related visual recognition approaches. We investigate not only from the model but also the data point of view (which is not the case in existing surveys), and focus on three most studied data types (images, videos and points). This paper attempts to provide a systematic summary via a comprehensive survey which can serve as a valuable reference and inspire both researchers and practitioners who work on visual recognition problems.

preprint2021arXiv

Further studies on numerical instabilities of Godunov-type schemes for strong shocks

In this paper, continuous research is undertaken to explore the underlying mechanism of numerical shock instabilities of Godunov-type schemes for strong shocks. By conducting dissipation analysis of Godunov-type schemes and a sequence of numerical experiments, we are able to clarify that the instability may be attributed to insufficient entropy production inside the numerical shock structure. As a result, a general entropy-control technique for improving the robustness of various Godunov-type schemes at strong shocks is developed. It plays a part in guaranteeing that enough entropy is produced inside the numerical shock structure. Furthermore, such a modified approach does not introduce any additional numerical dissipation on linear degenerate waves to suppress the shock instability. Numerical results that are obtained for various test cases indicate that the proposed methods have a good performance in terms of accuracy and robustness.

preprint2021arXiv

Generative Counterfactuals for Neural Networks via Attribute-Informed Perturbation

With the wide use of deep neural networks (DNN), model interpretability has become a critical concern, since explainable decisions are preferred in high-stake scenarios. Current interpretation techniques mainly focus on the feature attribution perspective, which are limited in indicating why and how particular explanations are related to the prediction. To this end, an intriguing class of explanations, named counterfactuals, has been developed to further explore the "what-if" circumstances for interpretation, and enables the reasoning capability on black-box models. However, generating counterfactuals for raw data instances (i.e., text and image) is still in the early stage due to its challenges on high data dimensionality and unsemantic raw features. In this paper, we design a framework to generate counterfactuals specifically for raw data instances with the proposed Attribute-Informed Perturbation (AIP). By utilizing generative models conditioned with different attributes, counterfactuals with desired labels can be obtained effectively and efficiently. Instead of directly modifying instances in the data space, we iteratively optimize the constructed attribute-informed latent space, where features are more robust and semantic. Experimental results on real-world texts and images demonstrate the effectiveness, sample quality as well as efficiency of our designed framework, and show the superiority over other alternatives. Besides, we also introduce some practical applications based on our framework, indicating its potential beyond the model interpretability aspect.

preprint2021arXiv

LAMOST Time-Domain Survey: First Results of four $K$2 plates

From Oct. 2019 to Apr. 2020, LAMOST performs a time-domain spectroscopic survey of four $K$2 plates with both low- and med-resolution observations. The low-resolution spectroscopic survey gains 282 exposures ($\approx$46.6 hours) over 25 nights, yielding a total of about 767,000 spectra, and the med-resolution survey takes 177 exposures ($\approx$49.1 hours) over 27 nights, collecting about 478,000 spectra. More than 70%/50% of low-resolution/med-resolution spectra have signal-to-noise ratio higher than 10. We determine stellar parameters (e.g., $T_{\rm eff}$, log$g$, [Fe/H]) and radial velocity (RV) with different methods, including LASP, DD-Payne, and SLAM. In general, these parameter estimations from different methods show good agreement, and the stellar parameter values are consistent with those of APOGEE. We use the $Gaia$ DR2 RV data to calculate a median RV zero point (RVZP) for each spectrograph exposure by exposure, and the RVZP-corrected RVs agree well with the APOGEE data. The stellar evolutionary and spectroscopic masses are estimated based on the stellar parameters, multi-band magnitudes, distances and extinction values. Finally, we construct a binary catalog including about 2700 candidates by analyzing their light curves, fitting the RV data, calculating the binarity parameters from med-resolution spectra, and cross-matching the spatially resolved binary catalog from $Gaia$ EDR3. The LAMOST TD survey is expected to get breakthrough in various scientific topics, such as binary system, stellar activity, and stellar pulsation, etc.

preprint2021arXiv

Large evanescently-induced Brillouin scattering at the surrounding of a nanofibre

Brillouin scattering has been widely exploited for advanced photonics functionalities such as microwave photonics, signal processing, sensing, lasing, and more recently in micro- and nano-photonic waveguides. So far, all the works have focused on the opto-acoustic interaction driven from the core region of micro- and nano-waveguides. Here we observe, for the first time, an efficient Brillouin scattering generated by an evanescent field nearby a sub-wavelength waveguide embedded in a pressurised gas cell, with a maximum gain coefficient of $18.90 \pm 0.17$ m$^{-1}$W$^{-1}$. This gain is 11 times larger than the highest Brillouin gain obtained in a hollow-core fibre and 79 times larger than in a standard single-mode fibre. The realisation of strong free-space Brillouin scattering from a waveguide benefits from the flexibility of confined light while providing a direct access to the opto-acoustic interaction, as required in free-space optoacoustics such as Brillouin spectroscopy and microscopy. Therefore, our work creates an important bridge between Brillouin scattering in waveguides, Brillouin spectroscopy and microscopy, and opens new avenues in light-sound interactions, optomechanics, sensing, lasing and imaging.

preprint2021arXiv

LTD064402+245919: A Subgiant with a 1-3 M$_{\odot}$ Undetected Companion Identified from LAMOST-TD Data

Single-line spectroscopic binaries recently contribute to the stellar-mass black hole discovery, independently of the X-ray transient method. We report the identification of a single-line binary system LTD064402+245919, with an orbital period of 14.50 days. The observed component is a subgiant with a mass of 2.77$\pm$0.68M$_{\odot}$, radius 15.5$\pm$2.5R$_{\odot}$, effective temperature $T_{\rm eff}$ 4500$\pm$200K, and surface gravity log\emph{g} 2.5$\pm$0.25dex. The discovery makes use of the LAMOST time-domain (LAMOST-TD) and ZTF survey. Our general-purpose software pipeline applies the Lomb-Scargle periodogram to determine the orbital period and uses machine-learning to classify the variable type from the folded light curves. We apply a combined model to estimate the orbital parameters from both the light and radial velocity curves, taking constraints on the primary star mass, mass function, and detection limit of secondary luminosity into consideration. We obtain a radial velocity semi-amplitude of 44.6$\pm$1.5 km s$^{-1}$, mass ratio of 0.73$\pm$0.07, and an undetected component mass of 2.02$\pm$0.49M$_{\odot}$ when the type of the undetected component is not set. We conclude that the inclination is not well constrained, and that the secondary mass is larger than 1M$_{\odot}$ when the undetected component is modelled as a compact object. According to our investigations using an MCMC simulation, increasing the spectra SNR by a factor of 3 would enable the secondary light to be distinguished (if present). The algorithm and software in this work are able to serve as general-purpose tools for the identification of compact objects quiescent in X-rays.

preprint2021arXiv

Mechanically Modulated Sideband and Squeezing Effects of Membrane Resonators

We investigate the sideband spectra of a driven nonlinear mode with its eigenfrequency being modulated at a low frequency (< 1 kHz). This additional parametric modulation leads to prominent antiresonance lineshapes in the sideband spectra, which can be controlled through the vibration state of the driven mode. We also establish a direct connection between the antiresonance frequency and the squeezing of thermal fluctuation in the system. Our work not only provides a simple and robust method for squeezing characterization but also opens a new possibility toward sideband applications.

preprint2021arXiv

Radiative cooling of colored paint based on Fe3+ doped Y2Ce2O7

Materials with both low absorption of incoming solar radiation and high emittance in mid-infrared band can be applied for daytime radiative cooling. Current state-of-the-art materials for passive radiative cooling often utilize a combination of solar reflector and infrared emitter by different structures, or even by expensive nanofabricated photonic structures, which limits the applications in practice. In this study, possessing these two specified radiative properties, pure Y2Ce2O7 is demonstrated with a performance of passive radiative cooling. With a bandgap at 375.7 nm, the prepared Y2Ce2O7 shows a high solar reflectance of 91%, while with lattice strain and distortion of various bonds (e.g., Y-O, Ce-O), it also shows a high emittance of 0.96 in MIR band. More attracting, the aesthetics performance of Y2Ce2O7 can be modified by doping Fe3+ ions to change its color from ivory white to light yellow or red with high NIR reflection and MIR emission, indicating that the Y2Ce2-xFexO7 shows a better cooling performance than a common paint with a similar color. According to the field demonstration of cooling performance at noon time, the Y2Ce2O7 and Y2Ce1.9Fe0.1O7 paints are 2.2 K and 1.8 K lower than the common white and umber paints, respectively, while at night, all paints are 2.3 K lower than the ambient air. If applied on the envelop of a building, the simulation shows that the Y2Ce2O7 and Y2Ce1.9Fe0.1O7 paints save 54.45% and 21.14% energy consumption compared with a common white and umber paints, respectively, in a hot season. The demonstrated Y2Ce2-xFexO7 holds potentials for energy-saving applications in hot climates.

preprint2021arXiv

ReMOTS: Self-Supervised Refining Multi-Object Tracking and Segmentation

We aim to improve the performance of Multiple Object Tracking and Segmentation (MOTS) by refinement. However, it remains challenging for refining MOTS results, which could be attributed to that appearance features are not adapted to target videos and it is also difficult to find proper thresholds to discriminate them. To tackle this issue, we propose a self-supervised refining MOTS (i.e., ReMOTS) framework. ReMOTS mainly takes four steps to refine MOTS results from the data association perspective. (1) Training the appearance encoder using predicted masks. (2) Associating observations across adjacent frames to form short-term tracklets. (3) Training the appearance encoder using short-term tracklets as reliable pseudo labels. (4) Merging short-term tracklets to long-term tracklets utilizing adopted appearance features and thresholds that are automatically obtained from statistical information. Using ReMOTS, we reached the $1^{st}$ place on CVPR 2020 MOTS Challenge 1, with an sMOTSA score of $69.9$.

preprint2021arXiv

Revisiting KELT-19Ab, WASP-156b and WASP-121b in the TESS Era

We present a re-analysis of transit depths of KELT-19Ab, WASP-156b, and WASP-121b, including data from the Transiting Exoplanet Survey Satellite (TESS). The large $\sim$21$\arcsec$ TESS pixels and point spread function result in significant contamination of the stellar flux by nearby objects. We use Gaia data to fit for and remove this contribution, providing general-purpose software for this correction. We find all three sources have a larger inclination, compared to earlier work. For WASP-121b, we find significantly smaller values (13.5 degrees) of the inclination when using the 30 minutes cadence data compared to the 2 minutes cadence data. Using simulations, we demonstrate that the radius ratio of exoplanet to star ($R_{p}/R_{\ast}$) is biased small relative to data taken with a larger sampling interval although oversampling corrections mitigate the bias. This is particularly important for deriving sub-percent transit differences between bands. We find the radius ratio of exoplanet to star ($R_{p}/R_{\ast}$) in the TESS band is 7.5$σ$ smaller than previous work for KELT-19Ab, but consistent to within $\sim$2$σ$ for WASP-156b and WASP-121b. The difference could be due to specific choices in the analysis, not necessarily due to the presence of atmospheric features. The result for KELT-19Ab possibly favors a haze-dominated atmosphere. We do not find evidence for the $\sim$0.95\,$μ$m water feature contaminating transit depths in the TESS band for these stars but show that with photometric precision of 500ppm and with a sampling of about 200 observations across the entire transit, this feature could be detectable in a more narrow $z-$band.

preprint2021arXiv

Scheduling a single parallel-batching machine with non-identical job sizes and incompatible job families

We study the scheduling of jobs on a single parallel-batching machine with non-identical job sizes and incompatible job families. Jobs from the same family have the same processing time and can be loaded into a batch, as long as the batch size respects the machine capacity. The objective is to minimize the total weighted completion time. The problem combines two classic combinatorial problems, namely bin packing and single machine scheduling. We develop three new mixed-integer linear-programming formulations, namely an assignment-based formulation, a time-indexed formulation (TIF), and a set-partitioning formulation (SPF). We also propose a column generation (CG) algorithm for the SPF, which is the basis for a branch-and-price (B&P) algorithm and a CG-based heuristic. We develop a preprocessing method to reduce the formulation size. A heuristic framework based on proximity search is also developed using the TIF. The SPF and B&P can solve instances with non-unit and unit job durations to optimality with up to 80 and 150 jobs within reasonable runtime limits, respectively. The proposed heuristics perform better than the methods from the literature.

preprint2021arXiv

Superresolving second-order correlation imaging using synthesized colored noise speckles

We present a novel method to synthesize non-trivial speckles that can enable superresolving second-order correlation imaging. The speckles acquire a unique anti-correlation in the spatial intensity fluctuation by introducing the blue noise spectrum to the input light fields through amplitude modulation. Illuminating objects with the blue noise speckle patterns can lead to a sub-diffraction limit imaging system with a resolution more than three times higher than first-order imaging, which is comparable to the resolving power of ninth order correlation imaging with thermal light. Our method opens a new route towards non-trivial speckle generation by tailoring amplitudes of the input light fields and provides a versatile scheme for constructing superresolving imaging and microscopy systems without invoking complicated higher-order correlations.

preprint2021arXiv

The Spectroscopic Binaries from LAMOST Medium-Resolution Survey (MRS). I. Searching for Double-lined Spectroscopic Binaries (SB2s) with Convolutional Neural Network

We developed a convolutional neural network (CNN) model to distinguish the double-lined spectroscopic binaries (SB2s) from others based on single exposure medium-resolution spectra ($R\sim 7,500$). The training set consists of a large set of mock spectra of single stars and binaries synthesized based on the MIST stellar evolutionary model and ATLAS9 atmospheric model. Our model reaches a novel theoretic false positive rate by adding a proper penalty on the negative sample (e.g., 0.12\% and 0.16\% for the blue/red arm when the penalty parameter $Λ=16$). Tests show that the performance is as expected and favors FGK-type Main-sequence binaries with high mass ratio ($q \geq 0.7$) and large radial velocity separation ($Δv \geq 50\,\mathrm{km\,s^{-1}}$). Although the real false positive rate can not be estimated reliably, validating on eclipsing binaries identified from Kepler light curves indicates that our model predicts low binary probabilities at eclipsing phases (0, 0.5, and 1.0) as expected. The color-magnitude diagram also helps illustrate its feasibility and capability of identifying FGK MS binaries from spectra. We conclude that this model is reasonably reliable and can provide an automatic approach to identify SB2s with period $\lesssim 10$ days. This work yields a catalog of binary probabilities for over 5 million spectra of 1 million sources from the LAMOST medium-resolution survey (MRS), and a catalog of 2198 SB2 candidates whose physical properties will be analyzed in our following-up paper. Data products are made publicly available at the journal as well as our Github website.

preprint2021arXiv

Towards Fast, Accurate and Stable 3D Dense Face Alignment

Existing methods of 3D dense face alignment mainly concentrate on accuracy, thus limiting the scope of their practical applications. In this paper, we propose a novel regression framework named 3DDFA-V2 which makes a balance among speed, accuracy and stability. Firstly, on the basis of a lightweight backbone, we propose a meta-joint optimization strategy to dynamically regress a small set of 3DMM parameters, which greatly enhances speed and accuracy simultaneously. To further improve the stability on videos, we present a virtual synthesis method to transform one still image to a short-video which incorporates in-plane and out-of-plane face moving. On the premise of high accuracy and stability, 3DDFA-V2 runs at over 50fps on a single CPU core and outperforms other state-of-the-art heavy models simultaneously. Experiments on several challenging datasets validate the efficiency of our method. Pre-trained models and code are available at https://github.com/cleardusk/3DDFA_V2.

preprint2021arXiv

Two-orbital model for possible superconductivity pairing mechanism in nickelates

The newly synthesized strontium doped RNiO$_2$ (R=Nd, La) superconductors have stimulated extensive interests in understanding their pairing mechanism and pairing nature. Here we study the pairing mechanism in this family from a two-orbital model comprising the Ni- $3d_{x^2-y^2}$- and $3d_{xy}$- orbitals, equipped with extended Hubbard interactions and induced low-energy effective superexchange interactions. We then study the pairing symmetry in this system by using large scale variational Monte Carlo approach. Our results yield the intraorbital $d_{x^2-y^2}$-wave singlet pairing as the leading pairing symmetry in the nickelates, which is analogous to the cuprates. However, there exist two important differences between the physical properties of the two families due to the fact that at the low Sr-doping regime, while the Ni-$3d_{x^2-y^2}$ orbitals remain half-filled and singly-occupied to form a Mott-insulating background, the Ni-$3d_{xy}$ orbitals accommodate nearly all the extra doped holes, which move freely on this background. The first difference lies in the single-particle aspect: while the $3d_{x^2-y^2}$ degree of freedom remains Mott insulating with spectra weight pinned down at zero at low dopings, the $3d_{xy}$ one behaves as Fermi liquid with spectra weight near 1. The second difference lies in the pairing aspect: while the huge intra-$3d_{x^2-y^2}$-orbital pairing gap is actually a pseudo gap which has nothing to do with the SC, the small intra-$3d_{xy}$-orbital pairing gap serves as the true superconducting pairing gap, which is related to the $T_c$ via the BCS relation. Both differences can be verified by the angle-resolved photo-emission spectrum.

preprint2020arXiv

A Catalog of Short Period Spectroscopic and Eclipsing Binaries Identified from the LAMOST & PTF Surveys

Binaries play key roles in determining stellar parameters and exploring stellar evolution models. We build a catalog of 88 eclipsing binaries with spectroscopic information, taking advantage of observations from both the Large Sky Area Multi-Object fiber Spectroscopic Telescope (LAMOST) and the Palomar Transient Factory (PTF) surveys. A software pipeline is constructed to identify binary candidates by examining their light curves. The orbital periods of binaries are derived from the Lomb-Scargle method. The key distinguishing features of eclipsing binaries are recognized by a new filter \textit{Flat Test}. We classify the eclipsing binaries by applying Fourier analysis on the light curves. Among all the binary stars, 13 binaries are identified as eclipsing binaries for the first time. The catalog contains information: position, primary eclipsing magnitude and time, eclipsing depth, the number of photometry and radial velocity observations, largest radial velocity difference, binary type, the effective temperature of observable star $T_{\rm eff}$, and surface gravity of observable star log \emph{g}. The false-positive probability is calculated by using both a Monte Carlo simulation and real data from the SDSS Stripe 82 Standard Catalog. The binaries in the catalog are mostly with a period of less than one day. The period distribution shows a 0.22-day cut-off which is consistent with the low probability of an eclipsing binary rotating with such a period.

preprint2020arXiv

A Data-Dependent Algorithm for Querying Earth Mover's Distance with Low Doubling Dimensions

In this paper, we consider the following query problem: given two weighted point sets $A$ and $B$ in the Euclidean space $\mathbb{R}^d$, we want to quickly determine that whether their earth mover's distance (EMD) is larger or smaller than a pre-specified threshold $T\geq 0$. The problem finds a number of important applications in the fields of machine learning and data mining. In particular, we assume that the dimensionality $d$ is not fixed and the sizes $|A|$ and $|B|$ are large. Therefore, most of existing EMD algorithms are not quite efficient to solve this problem due to their high complexities. Here, we consider the problem under the assumption that $A$ and $B$ have low doubling dimensions, which is common for high-dimensional data in real world. Inspired by the geometric method {\em net tree}, we propose a novel ``data-dependent'' algorithm to avoid directly computing the EMD between $A$ and $B$, so as to solve this query problem more efficiently. We also study the performance of our method on synthetic and real datasets. The experimental results suggest that our method can save a large amount of running time comparing with existing EMD algorithms.

preprint2020arXiv

A Framework for Covariate Balance using Bregman Distances

A common goal in observational research is to estimate marginal causal effects in the presence of confounding variables. One solution to this problem is to use the covariate distribution to weight the outcomes such that the data appear randomized. The propensity score is a natural quantity that arises in this setting. Propensity score weights have desirable asymptotic properties, but they often fail to adequately balance covariate data in finite samples. Empirical covariate balancing methods pose as an appealing alternative by exactly balancing the sample moments of the covariate distribution. With this objective in mind, we propose a framework for estimating balancing weights by solving a constrained convex program where the criterion function to be optimized is a Bregman distance. We then show that the different distances in this class render identical weights to those of other covariate balancing methods. A series of numerical studies are presented to demonstrate these similarities.

preprint2020arXiv

A Mysterious Ring in Dark Space?

We report the discovery of a low-surface-brightness (27.42 mag arcsec^(-2) in g band) nebula, which has a ring-like shape in the Beijing-Arizona Sky Survey (BASS). Positive detections have been found in multiband data from far ultraviolet to far infrared, except the z band from BASS and W1, W2 from the Wide-field Infrared Survey Explorer. The reddening of the nebula E(B - V) ~ 0.02 mag is estimated from Infrared Astronomical Satellite (IRAS) 100 micron intensity and HI column density. With the help of the 3D reddening map from Pan-STARRS 1, the Two Micron All Sky Survey, and Gaia, the distance to the nebula of about 500 pc from Earth is derived. Such a low-surface-brightness nebula whose energy can be interpreted by the diffuse Galactic light could account for the optical counterpart of the infrared cirrus, which was detected by IRAS more than 30 yr ago. The ring-like structure might be the ultimate phase of an evolved planetary nebula, while the central white dwarf star has been ejected from the nebula for an unclear reason. On the other hand, the ring structure being a superposition of two close filaments might be another reasonable explanation. Considering the lack of spectroscopic data and uncertainty in the distance measurement, these interpretations need to be checked by future observations.

preprint2020arXiv

A scanning quantum cryogenic atom microscope at 6 K

The Scanning Quantum Cryogenic Atom Microscope (SQCRAMscope) is a quantum sensor in which a quasi-1D quantum gas images electromagnetic fields emitted from a nearby sample. We report improvements to the microscope. Cryogen usage is reduced by replacing the liquid cryostat with a closed-cycle system and modified cold finger, and cryogenic cooling is enhanced by adding a radiation shield. The minimum accessible sample temperature is reduced from 35 K to 5.8 K while maintaining low sample vibrations. A new sample mount is easier to exchange, and quantum gas preparation is streamlined.

preprint2020arXiv

An Embarrassingly Simple Approach for Trojan Attack in Deep Neural Networks

With the widespread use of deep neural networks (DNNs) in high-stake applications, the security problem of the DNN models has received extensive attention. In this paper, we investigate a specific security problem called trojan attack, which aims to attack deployed DNN systems relying on the hidden trigger patterns inserted by malicious hackers. We propose a training-free attack approach which is different from previous work, in which trojaned behaviors are injected by retraining model on a poisoned dataset. Specifically, we do not change parameters in the original model but insert a tiny trojan module (TrojanNet) into the target model. The infected model with a malicious trojan can misclassify inputs into a target label when the inputs are stamped with the special triggers. The proposed TrojanNet has several nice properties including (1) it activates by tiny trigger patterns and keeps silent for other signals, (2) it is model-agnostic and could be injected into most DNNs, dramatically expanding its attack scenarios, and (3) the training-free mechanism saves massive training efforts comparing to conventional trojan attack methods. The experimental results show that TrojanNet can inject the trojan into all labels simultaneously (all-label trojan attack) and achieves 100% attack success rate without affecting model accuracy on original tasks. Experimental analysis further demonstrates that state-of-the-art trojan detection algorithms fail to detect TrojanNet attack. The code is available at https://github.com/trx14/TrojanNet.

preprint2020arXiv

Averages Along the Primes: Improving and Sparse Bounds

Consider averages along the prime integers $ \mathbb P $ given by \begin{equation*} \mathcal{A}_N f (x) = N ^{-1} \sum_{ p \in \mathbb P \;:\; p\leq N} (\log p) f (x-p). \end{equation*} These averages satisfy a uniform scale-free $ \ell ^{p}$-improving estimate. For all $ 1< p < 2$, there is a constant $ C_p$ so that for all integer $ N$ and functions $ f$ supported on $ [0,N]$, there holds \begin{equation*} N ^{-1/p' }\lVert \mathcal{A}_N f\rVert_{\ell^{p'}} \leq C_p N ^{- 1/p} \lVert f\rVert_{\ell^p}. \end{equation*} The maximal function $ \mathcal{A}^{\ast} f =\sup_{N} \lvert \mathcal{A}_N f \rvert$ satisfies $ (p,p)$ sparse bounds for all $ 1< p < 2$. The latter are the natural variants of the scale-free bounds. As a corollary, $ \mathcal{A}^{\ast} $ is bounded on $ \ell ^{p} (w)$, for all weights $ w$ in the Muckenhoupt $A_p$ class. No prior weighted inequalities for $ \mathcal{A}^{\ast} $ were known.

preprint2020arXiv

Axiomatizing first-order consequences in inclusion logic

Inclusion logic is a variant of dependence logic that was shown to have the same expressive power as positive greatest fixed-point logic. Inclusion logic is not axiomatizable in full, but its first-order consequences can be axiomatized. In this paper, we provide such an explicit partial axiomatization by introducing a system of natural deduction for inclusion logic that is sound and complete for first-order consequences in inclusion logic.

preprint2020arXiv

Birds of a Feather Flock Together: Satirical News Detection via Language Model Differentiation

Satirical news is regularly shared in modern social media because it is entertaining with smartly embedded humor. However, it can be harmful to society because it can sometimes be mistaken as factual news, due to its deceptive character. We found that in satirical news, the lexical and pragmatical attributes of the context are the key factors in amusing the readers. In this work, we propose a method that differentiates the satirical news and true news. It takes advantage of satirical writing evidence by leveraging the difference between the prediction loss of two language models, one trained on true news and the other on satirical news, when given a new news article. We compute several statistical metrics of language model prediction loss as features, which are then used to conduct downstream classification. The proposed method is computationally effective because the language models capture the language usage differences between satirical news documents and traditional news documents, and are sensitive when applied to documents outside their domains.

preprint2020arXiv

Cascade Graph Neural Networks for RGB-D Salient Object Detection

In this paper, we study the problem of salient object detection (SOD) for RGB-D images using both color and depth information.A major technical challenge in performing salient object detection fromRGB-D images is how to fully leverage the two complementary data sources. Current works either simply distill prior knowledge from the corresponding depth map for handling the RGB-image or blindly fuse color and geometric information to generate the coarse depth-aware representations, hindering the performance of RGB-D saliency detectors.In this work, we introduceCascade Graph Neural Networks(Cas-Gnn),a unified framework which is capable of comprehensively distilling and reasoning the mutual benefits between these two data sources through a set of cascade graphs, to learn powerful representations for RGB-D salient object detection. Cas-Gnn processes the two data sources individually and employs a novelCascade Graph Reasoning(CGR) module to learn powerful dense feature embeddings, from which the saliency map can be easily inferred. Contrast to the previous approaches, the explicitly modeling and reasoning of high-level relations between complementary data sources allows us to better overcome challenges such as occlusions and ambiguities. Extensive experiments demonstrate that Cas-Gnn achieves significantly better performance than all existing RGB-DSOD approaches on several widely-used benchmarks.

preprint2020arXiv

Cascade Network with Guided Loss and Hybrid Attention for Two-view Geometry

In this paper, we are committed to designing a high-performance network for two-view geometry. We first propose a Guided Loss and theoretically establish the direct negative correlation between the loss and Fn-measure by dynamically adjusting the weights of positive and negative classes during training, so that the network is always trained towards the direction of increasing Fn-measure. By this way, the network can maintain the advantage of the cross-entropy loss while maximizing the Fn-measure. We then propose a hybrid attention block to extract feature, which integrates the bayesian attentive context normalization (BACN) and channel-wise attention (CA). BACN can mine the prior information to better exploit global context and CA can capture complex channel context to enhance the channel awareness of the network. Finally, based on our Guided Loss and hybrid attention block, a cascade network is designed to gradually optimize the result for more superior performance. Experiments have shown that our network achieves the state-of-the-art performance on benchmark datasets.

preprint2020arXiv

Convergence of eigenvector empirical spectral distribution of sample covariance matrices

The eigenvector empirical spectral distribution (VESD) is a useful tool in studying the limiting behavior of eigenvalues and eigenvectors of covariance matrices. In this paper, we study the convergence rate of the VESD of sample covariance matrices to the deformed Marčenko-Pastur (MP) distribution. Consider sample covariance matrices of the form $Σ^{1/2} X X^* Σ^{1/2}$, where $X=(x_{ij})$ is an $M\times N$ random matrix whose entries are independent random variables with mean zero and variance $N^{-1}$, and $Σ$ is a deterministic positive-definite matrix. We prove that the Kolmogorov distance between the expected VESD and the deformed MP distribution is bounded by $N^{-1+ε}$ for any fixed $ε>0$, provided that the entries $\sqrt{N}x_{ij}$ have uniformly bounded 6th moments and $|N/M-1|\ge τ$ for some constant $τ>0$. This result improves the previous one obtained in \cite{XYZ2013}, which gave the convergence rate $O(N^{-1/2})$ assuming $i.i.d.$ $X$ entries, bounded 10th moment, $Σ=I$ and $M<N$. Moreover, we also prove that under the finite $8$th moment assumption, the convergence rate of the VESD is $O(N^{-1/2+ε})$ almost surely for any fixed $ε>0$, which improves the previous bound $N^{-1/4+ε}$ in \cite{XYZ2013}.

preprint2020arXiv

Counterfactuals and dependencies on causal teams: expressive power and deduction systems

We analyze the causal-observational languages that were introduced in Barbero and Sandu (2018), which allow discussing interventionist counterfactuals and functional dependencies in a unified framework. In particular, we systematically investigate the expressive power of these languages in causal team semantics, and we provide complete natural deduction calculi for each language. As an intermediate step towards the completeness, we axiomatize the languages over a generalized version of causal team semantics, which turns out to be interesting also in its own right.

preprint2020arXiv

Counting of Teams in First-Order Team Logics

We study descriptive complexity of counting complexity classes in the range from #P to #$\cdot$NP. A corollary of Fagin's characterization of NP by existential second-order logic is that #P can be logically described as the class of functions counting satisfying assignments to free relation variables in first-order formulae. In this paper we extend this study to classes beyond #P and extensions of first-order logic with team semantics. These team-based logics are closely related to existential second-order logic and its fragments, hence our results also shed light on the complexity of counting for extensions of FO in Tarski's semantics. Our results show that the class #$\cdot$NP can be logically characterized by independence logic and existential second-order logic, whereas dependence logic and inclusion logic give rise to subclasses of #$\cdot$NP and #P , respectively. Our main technical result shows that the problem of counting satisfying assignments for monotone Boolean $Σ_1$-formulae is #$\cdot$NP-complete as well as complete for the function class generated by dependence logic.

preprint2020arXiv

Entropy theory for sectional hyperbolic flows

We use entropy theory as a new tool to study sectional hyperbolic flows in any dimension. We show that for $C^1$ flows, every sectional hyperbolic set $Λ$ is entropy expansive, and the topological entropy varies continuously with the flow. Furthermore, if $Λ$ is Lyapunov stable, then it has positive entropy; in addition, if $Λ$ is a chain recurrent class, then it contains a periodic orbit. As a corollary, we prove that for $C^1$ generic flows, every Lorenz-like class is an attractor.

preprint2020arXiv

Fairness in Deep Learning: A Computational Perspective

Deep learning is increasingly being used in high-stake decision making applications that affect individual lives. However, deep learning models might exhibit algorithmic discrimination behaviors with respect to protected groups, potentially posing negative impacts on individuals and society. Therefore, fairness in deep learning has attracted tremendous attention recently. We provide a review covering recent progresses to tackle algorithmic fairness problems of deep learning from the computational perspective. Specifically, we show that interpretability can serve as a useful ingredient to diagnose the reasons that lead to algorithmic discrimination. We also discuss fairness mitigation approaches categorized according to three stages of deep learning life-cycle, aiming to push forward the area of fairness in deep learning and build genuinely fair and reliable deep learning systems.

preprint2020arXiv

Flexibility of planar graphs without $C_4$ and $C_5$

Let $G$ be a $\{C_4, C_5\}$-free planar graph with a list assignment $L$. Suppose a preferred color is given for some of the vertices. We prove that if all lists have size at least four, then there exists an $L$-coloring respecting at least a constant fraction of the preferences.

preprint2020arXiv

From Topological Superconductivity to Quantum Hall States in Coupled Wires

We present a theoretical study of the interplay between topological p-wave superconductivity, orbital magnetic fields and quantum Hall phases in coupled wire systems. First, we calculate the phase diagram and physical observables of a fermionic ladder made of two coupled Kitaev chains, and discuss the presence of two and four Majorana zero modes. Second, we analyze hybrid systems consisting of a Kitaev chain coupled to a Luttinger liquid. By tuning the magnetic field and the carrier density, we identify quantum Hall and charge density wave phases, as well as regimes in which superconductivity is induced in the second chain by proximity effect. Finally, we consider two-dimensional systems made of weakly coupled ladders. There, we engineer a $p+ip$ superconductor and describe a generalization of the $ν=1/2$ fractional quantum Hall phase. These phases might be realized in solid-state or cold-atom nanowires.

preprint2020arXiv

Galaxy Optical Variability of Virgo Cluster: New Tracer for Environmental Influences on Galaxies

We investigate the relationship between the optical variability of galaxies and their distances from the centre of the Virgo Cluster using Palomar Transient Factory data. We define the ratio between the standard deviation of the galaxy brightness and the mean value of the standard deviation as a measure of a galaxy's optical variability. A sample of 814 Virgo galaxies with 230263 observations shows a monotonically decreasing trend of optical variability with increasing clustercentric distance. The variability level inside the cluster is 3.2$σ$ higher than the level outside. We fit the variability with a linear function and find that the data reject a distance-independent model. We examine 217 background galaxies for comparison and find no significant trend in galaxy variability. We assess the relation with Monte Carlo simulation by rebuilding the brightness of each galaxy. The simulation shows a monotonically decreasing relation for member galaxy variability and a distance-independent relation for background galaxies. Our result is consistent with the theory that the cold gas flowing inwards the cluster centre fuels AGN activity. This work is a new implementation of the method using optical variability to investigate the relation between galaxies evolution and their environment.

preprint2020arXiv

Hybrid Graph Neural Networks for Crowd Counting

Crowd counting is an important yet challenging task due to the large scale and density variation. Recent investigations have shown that distilling rich relations among multi-scale features and exploiting useful information from the auxiliary task, i.e., localization, are vital for this task. Nevertheless, how to comprehensively leverage these relations within a unified network architecture is still a challenging problem. In this paper, we present a novel network structure called Hybrid Graph Neural Network (HyGnn) which targets to relieve the problem by interweaving the multi-scale features for crowd density as well as its auxiliary task (localization) together and performing joint reasoning over a graph. Specifically, HyGnn integrates a hybrid graph to jointly represent the task-specific feature maps of different scales as nodes, and two types of relations as edges:(i) multi-scale relations for capturing the feature dependencies across scales and (ii) mutual beneficial relations building bridges for the cooperation between counting and localization. Thus, through message passing, HyGnn can distill rich relations between the nodes to obtain more powerful representations, leading to robust and accurate results. Our HyGnn performs significantly well on four challenging datasets: ShanghaiTech Part A, ShanghaiTech Part B, UCF_CC_50 and UCF_QNRF, outperforming the state-of-the-art approaches by a large margin.

preprint2020arXiv

Imaging Nematic Transitions in Iron-Pnictide Superconductors with a Quantum Gas

The SQCRAMscope is a recently realized Scanning Quantum CRyogenic Atom Microscope that utilizes an atomic Bose-Einstein condensate to measure magnetic fields emanating from solid-state samples. The quantum sensor does so with unprecedented DC sensitivity at micron resolution from room-to-cryogenic temperatures. An additional advantage of the SQCRAMscope is the preservation of optical access to the sample: Magnetometry imaging of, e.g., electron transport may be performed in concert with other imaging techniques. This multimodal imaging capability can be brought to bear with great effect in the study of nematicity in iron-pnictide high-temperature superconductors, where the relationship between electronic and structural symmetry-breaking resulting in a nematic phase is under debate. Here, we combine the SQCRAMscope with an in situ microscope that measures optical birefringence near the surface. This enables simultaneous and spatially resolved detection of both bulk and near-surface manifestations of nematicity via transport and structural deformation channels, respectively. By performing the first local measurement of emergent resistivity anisotropy in iron pnictides, we observe sharp, nearly concurrent transport and structural transitions. More broadly, these measurements demonstrate the SQCRAMscope's ability to reveal important insights into the physics of complex quantum materials.

preprint2020arXiv

Improving estimates for discrete polynomial averages

For a polynomial $P$ mapping the integers into the integers, define an averaging operator $A_{N} f(x):=\frac{1}{N}\sum_{k=1}^N f(x+P(k))$ acting on functions on the integers. We prove sufficient conditions for the $\ell^{p}$-improving inequality \begin{equation*} \|A_N f\|_{\ell^q(\mathbb{Z})} \lesssim_{P,p,q} N^{-d(\frac{1}{p}-\frac{1}{q})} \|f\|_{\ell^p(\mathbb{Z})}, \qquad N \in\mathbb{N}, \end{equation*} where $1\leq p \leq q \leq \infty$. For a range of quadratic polynomials, the inequalities established are sharp, up to the boundary of the allowed pairs of $(p,q)$. For degree three and higher, the inequalities are close to being sharp. In the quadratic case, we appeal to discrete fractional integrals as studied by Stein and Wainger. In the higher degree case, we appeal to the Vinogradov Mean Value Theorem, recently established by Bourgain, Demeter, and Guth.

preprint2020arXiv

Intrinsic topological superconductivity with exactly flat surface bands in the quasi-one-dimensional A$_2$Cr$_3$As$_3$ (A=Na, K, Rb, Cs) superconductors

A spin-U(1)-symmetry protected momentum-dependent integer-$Z$-valued topological invariant is proposed to time-reversal-invariant (TRI) superconductivity (SC) whose nonzero value will lead to exactly flat surface band(s). The theory is applied to the weakly spin-orbit coupled quasi-1D A$_2$Cr$_3$As$_3$ (A=Na, K, Rb, Cs) superconductors family with highest $T_c$ up to 8.6 K with $p_z$-wave pairing in the $S_z=0$ channel. It's found that up to the leading atomic spin-orbit-coupling (SOC), the whole (001) surface Brillouin zone is covered with exactly-flat surface bands, with some regime hosting three flat bands and the remaining part hosting two. Such exactly-flat surface bands will lead to very sharp zero-bias conductance peak in the scanning tunneling microscopic spectrum. When a tiny subleading spin-flipping SOC is considered, the surface bands will only be slightly split. The application of this theory can be generalized to other unconventional superconductors with weak SOC, particularly to those with mirror-reflection symmetry.

preprint2020arXiv

Kohn-Luttinger mechanism driven exotic topological superconductivity on the Penrose lattice

The Kohn-Luttinger mechanism for unconventional superconductivity (SC) driven by weak repulsive electron-electron interactions on a periodic lattice is generalized to the quasicrystal (QC) via a real-space perturbative approach. The repulsive Hubbard model on the Penrose lattice is studied as an example, on which a classification of the pairing symmetries is performed and a pairing phase diagram is obtained. Two remarkable properties of these pairing states are revealed, due to the combination of the presence of the point-group symmetry and the lack of translation symmetry on this lattice. Firstly, the spin and spacial angular momenta of a Cooper pair is de-correlated: for each pairing symmetry, both spin-singlet and spin-triplet pairings are possible even in the weak-pairing limit. Secondly, the pairing states belonging to the 2D irreducible representations of the $D_5$ point group can be time-reversal-symmetry-breaking topological SCs carrying spontaneous bulk super current and spontaneous vortices. These two remarkable properties are general for the SCs on all QCs, and are rare on periodic lattices. Our work starts the new area of unconventional SCs driven by repulsive interactions on the QC.

preprint2020arXiv

LAMOST Medium-Resolution Spectroscopic Survey (LAMOST-MRS): Scientific goals and survey plan

Since September 2018, LAMOST starts a new 5-year medium-resolution spectroscopic survey (MRS) using bright/gray nights. We present the scientific goals of LAMOST-MRS and propose a near optimistic strategy of the survey. A complete footprint is also provided. Not only the regular medium-resolution survey, but also a time-domain spectroscopic survey is being conducted since 2018 and will be end in 2023. According to the detailed survey plan, we expect that LAMOST-MRS can observe about 2 million stellar spectra with ~7500 and limiting magnitude of around G=15 mag. Moreover, it will also provide about 200 thousand stars with averagely 60-epoch observations and limiting magnitude of G~14 mag. These high quality spectra will give around 20 elemental abundances, rotational velocities, emission line profiles as well as precise radial velocity with uncertainty less than 1 km/s. With these data, we expect that LAMOST can effectively leverage sciences on stellar physics, e.g. exotic binary stars, detailed observation of many types of variable stars etc., planet host stars, emission nebulae, open clusters, young pre-main-sequence stars etc.

preprint2020arXiv

LaSOT: A High-quality Large-scale Single Object Tracking Benchmark

Despite great recent advances in visual tracking, its further development, including both algorithm design and evaluation, is limited due to lack of dedicated large-scale benchmarks. To address this problem, we present LaSOT, a high-quality Large-scale Single Object Tracking benchmark. LaSOT contains a diverse selection of 85 object classes, and offers 1,550 totaling more than 3.87 million frames. Each video frame is carefully and manually annotated with a bounding box. This makes LaSOT, to our knowledge, the largest densely annotated tracking benchmark. Our goal in releasing LaSOT is to provide a dedicated high quality platform for both training and evaluation of trackers. The average video length of LaSOT is around 2,500 frames, where each video contains various challenge factors that exist in real world video footage,such as the targets disappearing and re-appearing. These longer video lengths allow for the assessment of long-term trackers. To take advantage of the close connection between visual appearance and natural language, we provide language specification for each video in LaSOT. We believe such additions will allow for future research to use linguistic features to improve tracking. Two protocols, full-overlap and one-shot, are designated for flexible assessment of trackers. We extensively evaluate 48 baseline trackers on LaSOT with in-depth analysis, and results reveal that there still exists significant room for improvement. The complete benchmark, tracking results as well as analysis are available at http://vision.cs.stonybrook.edu/~lasot/.

preprint2020arXiv

Learning to Detect Head Movement in Unconstrained Remote Gaze Estimation in the Wild

Unconstrained remote gaze estimation remains challenging mostly due to its vulnerability to the large variability in head-pose. Prior solutions struggle to maintain reliable accuracy in unconstrained remote gaze tracking. Among them, appearance-based solutions demonstrate tremendous potential in improving gaze accuracy. However, existing works still suffer from head movement and are not robust enough to handle real-world scenarios. Especially most of them study gaze estimation under controlled scenarios where the collected datasets often cover limited ranges of both head-pose and gaze which introduces further bias. In this paper, we propose novel end-to-end appearance-based gaze estimation methods that could more robustly incorporate different levels of head-pose representations into gaze estimation. Our method could generalize to real-world scenarios with low image quality, different lightings and scenarios where direct head-pose information is not available. To better demonstrate the advantage of our methods, we further propose a new benchmark dataset with the most rich distribution of head-gaze combination reflecting real-world scenarios. Extensive evaluations on several public datasets and our own dataset demonstrate that our method consistently outperforms the state-of-the-art by a significant margin.

preprint2020arXiv

Machine Learning Explanations to Prevent Overtrust in Fake News Detection

Combating fake news and misinformation propagation is a challenging task in the post-truth era. News feed and search algorithms could potentially lead to unintentional large-scale propagation of false and fabricated information with users being exposed to algorithmically selected false content. Our research investigates the effects of an Explainable AI assistant embedded in news review platforms for combating the propagation of fake news. We design a news reviewing and sharing interface, create a dataset of news stories, and train four interpretable fake news detection algorithms to study the effects of algorithmic transparency on end-users. We present evaluation results and analysis from multiple controlled crowdsourced studies. For a deeper understanding of Explainable AI systems, we discuss interactions between user engagement, mental model, trust, and performance measures in the process of explaining. The study results indicate that explanations helped participants to build appropriate mental models of the intelligent assistants in different conditions and adjust their trust accordingly for model limitations.

preprint2020arXiv

Make Skeleton-based Action Recognition Model Smaller, Faster and Better

Although skeleton-based action recognition has achieved great success in recent years, most of the existing methods may suffer from a large model size and slow execution speed. To alleviate this issue, we analyze skeleton sequence properties to propose a Double-feature Double-motion Network (DD-Net) for skeleton-based action recognition. By using a lightweight network structure (i.e., 0.15 million parameters), DD-Net can reach a super fast speed, as 3,500 FPS on one GPU, or, 2,000 FPS on one CPU. By employing robust features, DD-Net achieves the state-of-the-art performance on our experimental datasets: SHREC (i.e., hand actions) and JHMDB (i.e., body actions). Our code will be released with this paper later.

preprint2020arXiv

MSDU-net: A Multi-Scale Dilated U-net for Blur Detection

Blur detection is the separation of blurred and clear regions of an image, which is an important and challenging task in computer vision. In this work, we regard blur detection as an image segmentation problem. Inspired by the success of the U-net architecture for image segmentation, we design a Multi-Scale Dilated convolutional neural network based on U-net, which we call MSDU-net. The MSDU-net uses a group of multi-scale feature extractors with dilated convolutions to extract texture information at different scales. The U-shape architecture of the MSDU-net fuses the different-scale texture features and generates a semantic feature which allows us to achieve better results on the blur detection task. We show that using the MSDU-net we are able to outperform other state of the art blur detection methods on two publicly available benchmarks.

preprint2020arXiv

NNIL-formulas revisited: universal models and finite model property

NNIL-formulas, introduced by Visser in 1983-1984 in a study of $Σ_1$-subsitutions in Heyting Arithmetic, are intuitionistic propositional formulas that does not allow nesting of implication to the left. The first results about these formulas were obtained in a paper of 1995 by Visser et al. In particular, it was shown that NNIL-formulas are exactly the formulas preserved under taking submodels of Kripke models. Recently Bezhanishvili and de Jongh observed that NNIL-formulas are also reflected by color-preserving monotonic maps of Kripke models. In the present paper, we first show how this observation leads to the conclusion that NNIL-formulas are preserved by arbitrary substructures not necessarily satisfying the topo-subframe condition. Then we apply it to construct universal models for NNIL. It follows from the properties of these universal models that NNIL-formulas are also exactly the formulas that are reflected by color-preserving monotonic maps. By using the method developed in constructing the universal models, we give a new direct proof that the logics axiomatized by NNIL-axioms have the finite model property.

preprint2020arXiv

Novel self-epitaxy for inducing superconductivity in the topological insulator (Bi1-xSbx)2Te3

Using the superconducting proximity effect for engineering a topological superconducting state in a topological insulator (TI) is a promising route to realize Majorana fermions. However, epitaxial growth of a superconductor on the TI surface to achieve a good proximity effect has been a challenge. We discovered that simply depositing Pd on thin films of the TI material (Bi$_{1-x}$Sb$_x$)$_2$Te$_3$ leads to an epitaxial self-formation of PdTe$_2$ superconductor having the superconducting transition temperature of ~1 K. This self-formed superconductor proximitizes the TI, which is confirmed by the appearance of a supercurrent in Josephson-junction devices made on (Bi$_{1-x}$Sb$_x$)$_2$Te$_3$. This self-epitaxy phenomenon can be conveniently used for fabricating TI-based superconducting nanodevices to address the superconducting proximity effect in TIs.

preprint2020arXiv

On Metric DBSCAN with Low Doubling Dimension

The density based clustering method {\em Density-Based Spatial Clustering of Applications with Noise (DBSCAN)} is a popular method for outlier recognition and has received tremendous attention from many different areas. A major issue of the original DBSCAN is that the time complexity could be as large as quadratic. Most of existing DBSCAN algorithms focus on developing efficient index structures to speed up the procedure in low-dimensional Euclidean space. However, the research of DBSCAN in high-dimensional Euclidean space or general metric space is still quite limited, to the best of our knowledge. In this paper, we consider the metric DBSCAN problem under the assumption that the inliers (excluding the outliers) have a low doubling dimension. We apply a novel randomized $k$-center clustering idea to reduce the complexity of range query, which is the most time consuming step in the whole DBSCAN procedure. Our proposed algorithms do not need to build any complicated data structures and are easy to be implemented in practice. The experimental results show that our algorithms can significantly outperform the existing DBSCAN algorithms in terms of running time.

preprint2020arXiv

PIC-Net: Point Cloud and Image Collaboration Network for Large-Scale Place Recognition

Place recognition is one of the hot research fields in automation technology and is still an open issue, Camera and Lidar are two mainstream sensors used in this task, Camera-based methods are easily affected by illumination and season changes, LIDAR cannot get the rich data as the image could , In this paper, we propose the PIC-Net (Point cloud and Image Collaboration Network), which use attention mechanism to fuse the features of image and point cloud, and mine the complementary information between the two. Furthermore, in order to improve the recognition performance at night, we transform the night image into the daytime style. Comparison results show that the collaboration of image and point cloud outperform both image-based and point cloud-based method, the attention strategy and day-night-transform could further improve the performance.

preprint2020arXiv

Possible evidence of hydrogen emission in the first-overtone and multi-mode RR Lyrae variables

The nature of shock waves in non-fundamental mode RR Lyrae stars remains a mystery because of limited spectroscopic observations. We apply a pattern recognition algorithm on spectroscopic data from SDSS and LAMOST and report the first evidence of hydrogen emission in first-overtone and multi-mode RR Lyrae stars showing the "first apparition", which is the most prominent observational characteristic of shock in RR Lyrae variables. We find ten RRc stars in SDSS, ten RRc stars in LAMOST, and three RRd stars in LAMOST that show blueshifted Balmer emissions. The emission features possibly indicate the existence of shock waves. We calculate the radial velocities of the emission lines, which are related to the physical conditions occurring in the radiative zone of shock waves. Using photometric observations from ZTF, we present a detailed light curve analysis for the frequency components in one of our RRd stars with hydrogen emission, RRdl3, for possible modulations. With the enormous volume of upcoming spectral observations of variable stars, our study raises the possibility of connecting the unexplained Blazhko effect to shock waves in non-fundamental mode RR Lyrae stars.

preprint2020arXiv

Random band matrices in the delocalized phase, III: Averaging fluctuations

We consider a general class of symmetric or Hermitian random band matrices $H=(h_{xy})_{x,y \in \llbracket 1,N\rrbracket^d}$ in any dimension $d\ge 1$, where the entries are independent, centered random variables with variances $s_{xy}=\mathbb E|h_{xy}|^2$. We assume that $s_{xy}$ vanishes if $|x-y|$ exceeds the band width $W$, and we are interested in the mesoscopic scale with $1\ll W\ll N$. Define the {\it{generalized resolvent}} of $H$ as $G(H,Z):=(H - Z)^{-1}$, where $Z$ is a deterministic diagonal matrix with entries $Z_{xx}\in \mathbb C_+$ for all $x$. Then we establish a precise high-probability bound on certain averages of polynomials of the resolvent entries. As an application of this fluctuation averaging result, we give a self-contained proof for the delocalization of random band matrices in dimensions $d\ge 2$. More precisely, for any fixed $d\ge 2$, we prove that the bulk eigenvectors of $H$ are delocalized in certain averaged sense if $N\le W^{1+\frac{d}{2}}$. This improves the corresponding results in \cite{HeMa2018} under the assumption $N\ll W^{1+\frac{d}{d+1}}$, and in \cite{ErdKno2013,ErdKno2011} under the assumption $N\ll W^{1+\frac{d}{6}}$. For 1D random band matrices, our fluctuation averaging result was used in \cite{PartII,PartI} to prove the delocalization conjecture and bulk universality for random band matrices with $N\ll W^{4/3}$.

preprint2020arXiv

Region-of-interest micro-focus CT based on an all-optical inverse Compton scattering source

Micro-focus computed tomography (CT), enabling the reconstruction of hyperfine structure within objects, is a powerful nondestructive testing tool in many fields. Current X-ray sources for micro-focus CT are typically limited by their relatively low photon energy and low flux. An all-optical inverse Compton scattering source (AOCS) based on laser wakefield accelerator (LWFA) can generate intense quasi-monoenergetic X/gamma-ray pulses in the keV-MeV range with micron-level source size, and its potential application for micro-focus CT has become very attractive in recent years due to the fast pace progress made in LWFA. Here we report the first experimental demonstration of high-fidelity micro-focus CT using AOCS (~70 keV) by imaging and reconstructing a test object with complex inner structures. A region-of-interest (ROI) CT method is adopted to utilize the relatively small field-of-view (FOV) of AOCS to obtain high-resolution reconstruction. This demonstration of the ROI micro-focus CT based on AOCS is a key step for its application in the field of hyperfine nondestructive testing.

preprint2020arXiv

Relational State-Space Model for Stochastic Multi-Object Systems

Real-world dynamical systems often consist of multiple stochastic subsystems that interact with each other. Modeling and forecasting the behavior of such dynamics are generally not easy, due to the inherent hardness in understanding the complicated interactions and evolutions of their constituents. This paper introduces the relational state-space model (R-SSM), a sequential hierarchical latent variable model that makes use of graph neural networks (GNNs) to simulate the joint state transitions of multiple correlated objects. By letting GNNs cooperate with SSM, R-SSM provides a flexible way to incorporate relational information into the modeling of multi-object dynamics. We further suggest augmenting the model with normalizing flows instantiated for vertex-indexed random variables and propose two auxiliary contrastive objectives to facilitate the learning. The utility of R-SSM is empirically evaluated on synthetic and real time-series datasets.

preprint2020arXiv

Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

Recently, increasing attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network makes specific decisions. In this paper, we develop a novel post-hoc visual explanation method called Score-CAM based on class activation mapping. Unlike previous class activation mapping based approaches, Score-CAM gets rid of the dependence on gradients by obtaining the weight of each activation map through its forward passing score on target class, the final result is obtained by a linear combination of weights and activation maps. We demonstrate that Score-CAM achieves better visual performance and fairness for interpreting the decision making process. Our approach outperforms previous methods on both recognition and localization tasks, it also passes the sanity check. We also indicate its application as debugging tools. Official code has been released.

preprint2020arXiv

Spectroscopic evidence for a spin and valley polarized metallic state in a non-magic-angle twisted bilayer graphene

In the magic-angle twisted bilayer graphene (MA-TBG), strong electron-electron (e-e) correlations caused by the band-flattening lead to many exotic quantum phases such as superconductivity, correlated insulator, ferromagnetism, and quantum anomalous Hall effects, when its low-energy van Hove singularities (VHSs) are partially filled. Here our high-resolution scanning tunneling microscope and spectroscopy measurements demonstrate that the e-e correlation in a non-magic-angle TBG with a twist angle θ = 1.49 still plays an important role in determining its electronic properties. Our most interesting observation on that sample is that when one of its VHS is partially filled, the one associated peak in the spectrum splits into four peaks. Our analysis based on the continuum model suggests that such a one-to-four split of the VHS originates from the formation of an interaction-driven spin-valley-polarized metallic state near the VHS, lifting both the spin and valley degeneracies. Our results for this non-magic-angle TBG reveal a new symmetry-breaking phase, which has not been identified in the MA-TBG or in other systems.

preprint2020arXiv

Speech can produce jet-like transport relevant to asymptomatic spreading of virus

Many scientific reports document that asymptomatic and presymptomatic individuals contribute to the spread of COVID-19, probably during conversations in social interactions. Droplet emission occurs during speech, yet few studies document the flow to provide the transport mechanism. This lack of understanding prevents informed public health guidance for risk reduction and mitigation strategies, e.g. the "six-foot rule". Here we analyze flows during breathing and speaking, including phonetic features, using order-of-magnitudes estimates, numerical simulations, and laboratory experiments. We document the spatio-temporal structure of the expelled air flow. Phonetic characteristics of plosive sounds like 'P' lead to enhanced directed transport, including jet-like flows that entrain the surrounding air. We highlight three distinct temporal scaling laws for the transport distance of exhaled material including (i) transport over a short distance ($<$ 0.5 m) in a fraction of a second, with large angular variations due to the complexity of speech, (ii) a longer distance, approximately 1 m, where directed transport is driven by individual vortical puffs corresponding to plosive sounds, and (iii) a distance out to about 2 m, or even further, where sequential plosives in a sentence, corresponding effectively to a train of puffs, create conical, jet-like flows. The latter dictates the long-time transport in a conversation. We believe that this work will inform thinking about the role of ventilation, aerosol transport in disease transmission for humans and other animals, and yield a better understanding of linguistic aerodynamics, i.e., aerophonetics.

preprint2020arXiv

Spiked separable covariance matrices and principal components

We introduce a class of separable sample covariance matrices of the form $\widetilde{\mathcal{Q}}_1:=\widetilde A^{1/2} X \widetilde B X^* \widetilde A^{1/2}.$ Here $\widetilde{A}$ and $\widetilde{B}$ are positive definite matrices whose spectrums consist of bulk spectrums plus several spikes, i.e. larger eigenvalues that are separated from the bulks. Conceptually, we call $\widetilde{\mathcal{Q}}_1$ a \emph{spiked separable covariance matrix model}. On the one hand, this model includes the spiked covariance matrix as a special case with $\widetilde{B}=I$. On the other hand, it allows for more general correlations of datasets. In particular, for spatio-temporal dataset, $\widetilde{A}$ and $\widetilde{B}$ represent the spatial and temporal correlations, respectively. In this paper, we study the outlier eigenvalues and eigenvectors, i.e. the principal components, of the spiked separable covariance model $\widetilde{\mathcal{Q}}_1$. We prove the convergence of the outlier eigenvalues $\widetilde λ_i$ and the generalized components (i.e. $\langle \mathbf v, \widetilde{\mathbfξ}_i \rangle$ for any deterministic vector $\mathbf v$) of the outlier eigenvectors $\widetilde{\mathbfξ}_i$ with optimal convergence rates. Moreover, we also prove the delocalization of the non-outlier eigenvectors. We state our results in full generality, in the sense that they also hold near the so-called BBP transition and for degenerate outliers. Our results highlight both the similarity and difference between the spiked separable covariance matrix model and the spiked covariance model. In particular, we show that the spikes of both $\widetilde{A}$ and $\widetilde{B}$ will cause outliers of the eigenvalue spectrum, and the eigenvectors can help us to select the outliers that correspond to the spikes of $\widetilde{A}$ (or $\widetilde{B}$).

preprint2020arXiv

Tricritical physics in two-dimensional $p$-wave superfluids

We study effects of quantum fluctuations on two-dimensional $p+ip$ superfluids near resonance. In the standard paradigm, phase transitions between superfluids and zero density vacuum are continuous. When strong quantum fluctuations near resonance are taken into account, the line of continuous phase transitions terminates at two multicritical points near resonance, between which the transitions are expected to be first-order ones. The size of the window where first-order phase transitions occur is shown to be substantial when the coupling is strong. Near first-order transitions, superfluids self-contract due to phase separations between superfluids and vacuum.

preprint2020arXiv

Using Panoramic Videos for Multi-person Localization and Tracking in a 3D Panoramic Coordinate

3D panoramic multi-person localization and tracking are prominent in many applications, however, conventional methods using LiDAR equipment could be economically expensive and also computationally inefficient due to the processing of point cloud data. In this work, we propose an effective and efficient approach at a low cost. First, we obtain panoramic videos with four normal cameras. Then, we transform human locations from a 2D panoramic image coordinate to a 3D panoramic camera coordinate using camera geometry and human bio-metric property (i.e., height). Finally, we generate 3D tracklets by associating human appearance and 3D trajectory. We verify the effectiveness of our method on three datasets including a new one built by us, in terms of 3D single-view multi-person localization, 3D single-view multi-person tracking, and 3D panoramic multi-person localization and tracking. Our code and dataset are available at \url{https://github.com/fandulu/MPLT}.

preprint2020arXiv

XGLUE: A New Benchmark Dataset for Cross-lingual Pre-training, Understanding and Generation

In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks. Comparing to GLUE(Wang et al., 2019), which is labeled in English for natural language understanding tasks only, XGLUE has two main advantages: (1) it provides 11 diversified tasks that cover both natural language understanding and generation scenarios; (2) for each task, it provides labeled data in multiple languages. We extend a recent cross-lingual pre-trained model Unicoder(Huang et al., 2019) to cover both understanding and generation tasks, which is evaluated on XGLUE as a strong baseline. We also evaluate the base versions (12-layer) of Multilingual BERT, XLM and XLM-R for comparison.

preprint2020arXiv

Yangian doubles of classical types and their vertex representations

The Yangian double $\text{DY}_{\hbar}(\mathfrak{g}_N)$ is introduced for the classical types of $\mathfrak{g}_N=\mathfrak{o}_{2n+1}$, $\mathfrak{sp}_{2n}$, $\mathfrak{o}_{2n}$. Via the Gauss decomposition of the generator matrix, the Yangian double is given the Drinfeld presentation. In addition, bosonization of level $1$ realizations for the Yangian double $\text{DY}_{\hbar}(\mathfrak{g}_N)$ of non-simply-laced types are explicitly constructed.

preprint2019arXiv

Directional spreading of a viscous droplet on a conical fibre

If a droplet is placed on a substrate with a conical shape it spontaneously starts to spread in the direction of a growing fibre radius. We describe this capillary spreading dynamics by developing a lubrication approximation on a cone and by the perturbation method of matched asymptotic expansions. Our results show that the droplet appears to adopt a quasi-static shape and the predictions of the droplet shape and spreading velocity from the two mathematical models are in excellent agreement for a wide range of slip lengths, cone angles and equilibrium contact angles. At the contact line regions, a large pressure gradient is generated by the mismatch between the equilibrium contact angle and the apparent contact angle that maintains the viscous flow. It is the conical shape of the substrate that breaks the front/rear droplet symmetry in terms of the apparent contact angle, which is larger at the thicker part of the cone than that at its thinner part. Consequently, the droplet is predicted to move from the cone tip to its base, consistent with experimental observations.

preprint2019arXiv

Irregular changes in Hα emission line of V423 Aur observed by LAMOST Medium-Resolution Spectrographs

We obtained 7 spectra of the Be star V423 Aur on Dec. 5th, 2017 using the LAMOST Medium-Resolution Spectrograph with exposures from 600 to 1200 seconds. These spectra show the irregular Hα emission line profile variations (LPVs). In the seven spectra, from the 4th to 7th, the left part of Hα profile even show excess. However, no variation can be seen from the follow-up observation of photometry by 1.26-m telescope and High-Resolution spectra by 2.16-m telescope. According to the High-Resolution spectra, we conclude that it is a B7V type star with E(B-V)= 0.709{\pm}0.036 and its vsini is {\sim} 221.8km/s. The short-term Hα LPVs could be explained as a result of the transient ejection of matter from rotating disk or shell around V423 Aur.

preprint2019arXiv

Nonlocal effects in plasmonic metasurfaces with almost touching surfaces

Geometrical singularities in plasmonic metasurfaces have recently been proposed for the enhancement of light-matter interactions, owing to their broadband light-harvesting properties and extreme plasmon confinement. However, the large plasmon momenta thus achieved lead to failure of local descriptions of the optical response of metals. Here we study a class of metasurfaces consisting of a periodic metal slab with a smooth modulation of its thickness. When the thinnest part shrinks, the two surfaces almost touch, forming a near-singular point. Using transformation optics, we show analytically how nonlocal effects, such as a blueshift of the resonance peaks and a reduced density of states, become important and cannot be ignored in this singular regime. The method developed in this paper is very general and can be used to model a variety of metasurfaces, providing valuable insight in the current context of ultra-thin plasmonic structures.

preprint2019arXiv

Observation of stimulated Brillouin scattering in silicon nitride integrated waveguides

Silicon nitride (Si3N4) has emerged as a promising material for integrated nonlinear photonics and has been used for broadband soliton microcombs and low-pulse-energy supercontinuum generation. Therefore understanding all nonlinear optical properties of Si3N4 is important. So far, only stimulated Brillouin scattering (SBS) has not been reported. Here we observe, for the first time, backward SBS in fully cladded Si3N4 waveguides. The Brillouin gain spectrum exhibits an unusual multi-peak structure resulting from hybridization with with high-overtone bulk acoustic resonances (HBARs) of the silica cladding. The reported intrinsic Si3N4 Brillouin gain at 25 GHz is estimated as 7x10^-13 m/W. Moreover, the magnitude of the Si3N4 photoelastic constant is estimated as |p12| = 0.047 +/- 0.004. Since SBS imposes an optical power limitation for waveguides, our results explain the capability of Si3N4 to handle high optical power, central for integrated nonlinear photonics.

preprint2019arXiv

Reconfigurable all-dielectric metalens with diffraction limited performance

Active metasurfaces, whose optical properties can be modulated post-fabrication, have emerged as an intensively explored field in recent years. The efforts to date, however, still face major performance limitations in tuning range, optical quality, and efficiency especially for non mechanical actuation mechanisms. In this paper, we introduce an active metasurface platform combining phase tuning covering the full 2$π$ range and diffraction-limited performance using an all-dielectric, low-loss architecture based on optical phase change materials (O-PCMs). We present a generic design principle enabling switching of metasurfaces between two arbitrary phase profiles and propose a new figure-of-merit (FOM) tailored for active meta-optics. We implement the approach to realize a high-performance varifocal metalens operating at 5.2 $μ$m wavelength. The metalens is constructed using Ge2Sb2Se4Te1 (GSST), an O-PCM with a large refractive index contrast ($Δ$ n > 1) and unique broadband low-loss characteristics in both amorphous and crystalline states. The reconfigurable metalens features focusing efficiencies above 20% at both states for linearly polarized light and a record large switching contrast ratio of 29.5 dB. We further validated aberration-free imaging using the metalens at both optical states, which represents the first experimental demonstration of a non-mechanical active metalens with diffraction-limited performance.

preprint2019arXiv

Tackling Challenges in Seebeck Coefficient Measurement of Ultra-High Resistance Samples with an AC Technique

Seebeck coefficient is a widely-studied semiconductor property. Conventional Seebeck coefficient measurements are based on DC voltage measurement. Normally this is performed on samples with low resistances below a few Mohm level. Meanwhile, certain semiconductors are highly intrinsic and resistive, many examples can be found in optical and photovoltaic materials. The hybrid halide perovskites that have gained extensive attention recently are a good example. Few credible studies exist on the Seebeck coefficient of, CH3NH3PbI3, for example. We report here an AC technique based Seebeck coefficient measurement, which makes high quality voltage measurement on samples with resistances up to 100Gohm. This is achieved through a specifically designed setup to enhance sample isolation and reduce meter loading. As a demonstration, we performed Seebeck coefficient measurement of a CH3NH3PbI3 thin film at dark and found S = +550 microV/K. Such property of this material has not been successfully studied before.

preprint2019arXiv

Task Decomposition and Synchronization for Semantic Biomedical Image Segmentation

Semantic segmentation is essentially important to biomedical image analysis. Many recent works mainly focus on integrating the Fully Convolutional Network (FCN) architecture with sophisticated convolution implementation and deep supervision. In this paper, we propose to decompose the single segmentation task into three subsequent sub-tasks, including (1) pixel-wise image segmentation, (2) prediction of the class labels of the objects within the image, and (3) classification of the scene the image belonging to. While these three sub-tasks are trained to optimize their individual loss functions of different perceptual levels, we propose to let them interact by the task-task context ensemble. Moreover, we propose a novel sync-regularization to penalize the deviation between the outputs of the pixel-wise segmentation and the class prediction tasks. These effective regularizations help FCN utilize context information comprehensively and attain accurate semantic segmentation, even though the number of the images for training may be limited in many biomedical applications. We have successfully applied our framework to three diverse 2D/3D medical image datasets, including Robotic Scene Segmentation Challenge 18 (ROBOT18), Brain Tumor Segmentation Challenge 18 (BRATS18), and Retinal Fundus Glaucoma Challenge (REFUGE18). We have achieved top-tier performance in all three challenges.

preprint2019arXiv

Valence bond fluctuations in the Kitaev spin model

We introduce valence bond fluctuations, or bipartite fluctuations associated to bond-bond correlation functions, to characterize quantum spin liquids and their entanglement properties. Using analytical and numerical approaches, we find an identical scaling law between valence bond fluctuations and entanglement entropy in the two-dimensional Kitaev spin model and in one-dimensional chain analogues. We also show how these valence bond fluctuations can locate quantum phase transitions between the three gapped and the gapless Majorana semi-metal phases in the honeycomb model. We then study the effect of a uniform magnetic field along the 111 direction opening a gap in the intermediate phase which becomes topological. We still obtain a robust signal to characterize the transitions towards the three gapped phases. The linear scaling behavior of such bipartite fluctuations in two dimensions is also distinguishable from the one in the Neel magnetic state.

preprint2016arXiv

A Memristor Crossbar-Based Computation Scheme with High Precision

The memristor is promising to be the basic cell of next-generation computation systems. Compared to the traditional MOSFET device, the memristor is efficient over energy and area. But one of the biggest challenges faced with researchers is how to program a memristor's resistance precisely. Recently, an algorithm designed to save 8 valid bits in each memristor is proposed, but this is still not sufficient for precise computation. In this paper, we propose a crossbar-based memristor computation scheme supporting precise computations whose operands have 32 valid bits. As a brief introduction, in a multiplication with two operands, one operand is programmed as input signal, and the other operand is saved into a so-called crossbar structure, which contains a group of memristors, and each memristor saves several valid bits, usually one or two bits only. The computation results,i.e. the multiplication of the two operands, are contained in the outputs of the crossbar structure together with noise. Analog-to-Digital Converters (ADCs) are then used to extract the valid bits, which are the most significant bits of outputs. These valid bits can be combined together with Digital-to-Analog Converters(DACs) to get the final results. What's more, the precision of this computation scheme can be adjusted according to the definition of the user, 32 valid bits at most, so it is qualified for different application contexts.

preprint2016arXiv

All-fiber hydrogen sensor based on stimulated Raman gain spectroscopy with a 1550 nm hollow-core fiber

We report a highly sensitive all-fiber hydrogen sensor based on continuous-wave stimulated Raman gain spectroscopy with a hollow-core photonic crystal fiber operating around 1550 nm. A pump-probe configuration is used, in which the frequency difference between the pump and the probe lasers is tuned to the S0(0) transition of para-hydrogen with a Raman shift of 354 cm-1. Preliminary experiments demonstrate a detection limit down to 17 ppm with a 250 s averaging time, and further improvement is possible. The all-fiber configuration operating in the telecommunication wavelength band would enable cost-effective and compact sensors for high sensitivity and high-resolution trace analysis.

preprint2016arXiv

Data Centers as Dispatchable Loads to Harness Stranded Power

We analyze how both traditional data center integration and dispatchable load integration affect power grid efficiency. We use detailed network models, parallel optimization solvers, and thousands of renewable generation scenarios to perform our analysis. Our analysis reveals that significant spillage and stranded power will be observed in power grids as wind power levels are increased. A counter-intuitive finding is that collocating data centers with inflexible loads next to wind farms has limited impacts on renewable portfolio standard (RPS) goals because it provides limited system-level flexibility and can in fact increase stranded power and fossil-fueled generation. In contrast, optimally placing data centers that are dispatchable (with flexible loads) provides system-wide flexibility, reduces stranded power, and improves efficiency. In short, optimally placed dispatchable computing loads can enable better scaling to high RPS. We show that these dispatchable computing loads are powered to 60~80\% of their requested capacity, indicating that there are significant economic incentives provided by stranded power.

preprint2016arXiv

Extreme Scaling of Supercomputing with Stranded Power: Costs and Capabilities

Power consumption (supply, heat, cost) and associated carbon emissions (environmental impact) are increasingly critical challenges in scaling supercomputing to Exascale and beyond. We proposes to exploit stranded power, renewable energy that has no value to the power grid, for scaling supercomputers, Zero-Carbon Cloud (ZCCloud), and showing that stranded power can be employed effectively to expand computing [1]. We build on those results with a new analysis of stranded power, characterizing temporal, geographic, and interval properties. We simulate production supercomputing workloads and model datacenter total-cost-of-ownership (TCO), assessing the costs and capabilities of stranded-power based supercomputing. Results show that the ZCCloud approach is cost-effective today in regions with high cost power. The ZCCloud approach reduces TCO by 21-45%, and improves cost-effectiveness up to 34%. We study many scenarios. With higher power price, cheaper computing hardware and higher system power density, benefits rise to 55%, 97% and 116% respectively. Finally, we study future extreme-scale systems, showing that beyond terascale, projected power requirements in excess of 100MW make ZCCloud up to 45% lower cost, for a fixed budget, increase peak PFLOPS achievable by 80%.

preprint2016arXiv

Far-field linear optical superresolution via heterodyne detection in a higher-order local oscillator mode

The Rayleigh limit has so far applied to all microscopy techniques that rely on linear optical interaction and detection in the far field. Here we demonstrate that detecting the light emitted by an object in higher-order transverse electromagnetic modes (TEMs) can help achieving sub-Rayleigh precision for a variety of microscopy-related tasks. Using optical heterodyne detection in TEM01, we measure the position of coherently and incoherently emitting objects to within 0.0015 and 0.012 of the Rayleigh limit, respectively, and determine the distance between two incoherently emitting slits positioned within 0.28 of the Rayleigh limit with a precision of 0.019 of the Rayleigh limit. Extending our technique to higher-order TEMs enables full imaging with resolution significantly below the Rayleigh limit in a way that is reminiscent of quantum tomography of optical states.

preprint2016arXiv

FGK 22 $μ$m Excess Stars in LAMOST DR2 Stellar Catalog

Since the release of LAMOST (the Large Sky Area Multi-Object Fiber Spectroscopic Telescope) catalog, we have the opportunity to use the LAMOST DR2 stellar catalog and \emph{WISE All-sky catalog} to search for 22 $μ$m excess candidates. In this paper, we present 10 FGK candidates which show an excess in the infrared (IR) at 22 $μ$m. The ten sources are all the newly identified 22 $μ$m excess candidates. Of these 10 stars, 5 stars are F type and 5 stars are G type. The criterion for selecting candidates is $K_s-[22]_{μm}\geq0.387$. In addition, we present the spectral energy distributions (SEDs) covering wavelength from optical to mid-infrared band. Most of them show an obvious excess from 12 $μ$m band and three candidates even show excess from 3.4 $μ$m. To characterize the amount of dust, we also estimate the fractional luminosity of ten 22 $μ$m excess candidates.

preprint2016arXiv

Hitting times distribution and extreme value law for flows

For flows whose return map on a cross section has sufficient mixing property, we show that the hitting time distribution of the flow to balls is exponential in limit. We also establish a link between the extreme value distribution of the flow and its hitting time distribution, generalizing a previous work by Freitas et al. in the discrete time case. Finally we show that for maps that can be modeled by Young`s tower with polynomial tail, the extreme value law holds.

preprint2016arXiv

Lightweight Fault Tolerance in Large-Scale Distributed Graph Processing

The success of Google's Pregel framework in distributed graph processing has inspired a surging interest in developing Pregel-like platforms featuring a user-friendly "think like a vertex" programming model. Existing Pregel-like systems support a fault tolerance mechanism called checkpointing, which periodically saves computation states as checkpoints to HDFS, so that when a failure happens, computation rolls back to the latest checkpoint. However, a checkpoint in existing systems stores a huge amount of data, including vertex states, edges, and messages sent by vertices, which significantly degrades the failure-free performance. Moreover, the high checkpointing cost prevents frequent checkpointing, and thus recovery has to replay all the computations from a state checkpointed some time ago. In this paper, we propose a novel checkpointing approach which only stores vertex states and incremental edge updates to HDFS as a lightweight checkpoint (LWCP), so that writing an LWCP is typically tens of times faster than writing a conventional checkpoint. To recover from the latest LWCP, messages are generated from the vertex states, and graph topology is recovered by replaying incremental edge updates. We show how to realize lightweight checkpointing with minor modifications of the vertex-centric programming interface. We also apply the same idea to a recently-proposed log-based approach for fast recovery, to make it work efficiently in practice by significantly reducing the cost of garbage collection of logs. Extensive experiments on large real graphs verified the effectiveness of LWCP in improving both failure-free performance and the performance of recovery.

preprint2016arXiv

Nowhere-zero 9-flows in 3-edge-connected signed graphs

A signed graph is a graph with a positive or negative sign on each edge. Regarding each edge as two half edges, an orientation of a signed graph is an assignment of a direction to each of its half edges such that the two half edges of a positive edge receive the same direction and that of a negative edge receive opposite directions. A signed graph with such an orientation is called a bidirected graph. A nowhere-zero $k$-flow of a bidirected graph is an assignment of an integer from $\{-(k-1), \ldots, -1, 1, \ldots, (k-1)\}$ to each of its half edges such that Kirchhoff's law is respected, that is, the total incoming flow is equal to the total outgoing flow at each vertex. A signed graph is said to admit a nowhere-zero $k$-flow if it has an orientation such that the corresponding bidirected graph admits a nowhere-zero $k$-flow. It was conjectured by Bouchet that every signed graph admitting a nowhere-zero $k$-flow for some integer $k \ge 2$ admits a nowhere-zero 6-flow. In this paper we prove that every $3$-edge-connected signed graph admitting a nowhere-zero $k$-flow for some $k$ admits a nowhere-zero $9$-flow.

preprint2016arXiv

Poisson Law for returns of Maps on Compact Manifolds

We consider invariant measures of maps on manifolds whose correlations decay at a sufficient rate and which satisfy a geometric contraction property. We then prove the that the limiting distribution of returns to geometric balls is Poissonian. This does not assume an tower construction. The decay of correlations is used to show that the independence generated results in the Poisson distribution for returns that are sufficiently separated. A geometric contraction property is then used to show that short return times have a vanishing contribution to the return times distribution. We then also show that the set of very short returns which are of a small linear order of the logarithm of the radius of the balls has a vanishing measure. We obtain error terms which decay polynomially in the logarithm of the radius. We also obtain a extreme value law for such systems.

preprint2016arXiv

Quegel: A General-Purpose Query-Centric Framework for Querying Big Graphs

Pioneered by Google's Pregel, many distributed systems have been developed for large-scale graph analytics. These systems expose the user-friendly "think like a vertex" programming interface to users, and exhibit good horizontal scalability. However, these systems are designed for tasks where the majority of graph vertices participate in computation, but are not suitable for processing light-workload graph queries where only a small fraction of vertices need to be accessed. The programming paradigm adopted by these systems can seriously under-utilize the resources in a cluster for graph query processing. In this work, we develop a new open-source system, called Quegel, for querying big graphs, which treats queries as first-class citizens in the design of its computing model. Users only need to specify the Pregel-like algorithm for a generic query, and Quegel processes light-workload graph queries on demand using a novel superstep-sharing execution model to effectively utilize the cluster resources. Quegel further provides a convenient interface for constructing graph indexes, which significantly improve query performance but are not supported by existing graph-parallel systems. Our experiments verified that Quegel is highly efficient in answering various types of graph queries and is up to orders of magnitude faster than existing systems.

preprint2016arXiv

Reverse degradation of nickel graphene junction by hydrogen annealing

Metal contacts are fundamental building components for graphene based electronic devices and their properties are greatly influenced by interface quality during device fabrication, leading to resistance variation. Here we show that nickel graphene junction degrades after air exposure, due to interfacial oxidation, thus creating a tunneling barrier. Most importantly, we demonstrate that hydrogen annealing at moderate temperature (300 0C) is an effective technique to reverse the degradation.

preprint2016arXiv

Selective Inference for Group-Sparse Linear Models

We develop tools for selective inference in the setting of group sparsity, including the construction of confidence intervals and p-values for testing selected groups of variables. Our main technical result gives the precise distribution of the magnitude of the projection of the data onto a given subspace, and enables us to develop inference procedures for a broad class of group-sparse selection methods, including the group lasso, iterative hard thresholding, and forward stepwise regression. We give numerical results to illustrate these tools on simulated data and on health record data.

preprint2016arXiv

Structural Multi-type Sequent Calculus for Inquisitive Logic

In this paper, we define a multi-type calculus for inquisitive logic, which is sound, complete and enjoys Belnap-style cut-elimination and subformula property. Inquisitive logic is the logic of inquisitive semantics, a semantic framework developed by Groenendijk, Roelofsen and Ciardelli which captures both assertions and questions in natural language. Inquisitive logic is sound and complete w.r.t. the so-called state semantics (also known as team semantics). The Hilbert-style presentation of inquisitive logic is not closed under uniform substitution; indeed, some occurrences of formulas are restricted to a certain subclass of formulas, called flat formulas. This and other features make the quest for analytic calculi for this logic not straightforward. We develop a certain algebraic and order-theoretic analysis of the team semantics, which provides the guidelines for the design of a multi-type environment which accounts for two domains of interpretation, for flat and for general formulas, as well as for their interaction. This multi-type environment in its turn provides the semantic environment for the multi-type calculus for inquisitive logic we introduce in this paper.

preprint2016arXiv

Switching of Charge-Current-Induced Spin Polarization in the Topological Insulator BiSbTeSe2

The charge-current-induced spin polarization is a key property of topological insulators for their applications in spintronics. However, topological surface states are expected to give rise to only one type of spin polarization for a given current direction, which has been a limiting factor for spin manipulations. Here we report that in devices based on the bulk-insulating topological insulator BiSbTeSe2, an unexpected switching of spin polarization was observed upon changing the chemical potential. The spin polarization expected from the topological surface states was detected in a heavily electron-doped device, whereas the opposite polarization was reproducibly observed in devices with low carrier densities. We propose that the latter type of spin polarization stems from topologically-trivial two-dimensional states with a large Rashba spin splitting, which are caused by a strong band bending at the surface of BiSbTeSe2 beneath the ferromagnetic electrode used as a spin detector. This finding paves the way for realizing the "spin transistor" operation in future topological spintronic devices.

preprint2016arXiv

Tensor and Its Tucker Core: the Invariance Relationships

In [13], Hillar and Lim famously demonstrated that "multilinear (tensor) analogues of many efficiently computable problems in numerical linear algebra are NP-hard". Despite many recent advancements, the state-of-the-art methods for computing such `tensor analogues' still suffer severely from the curse of dimensionality. In this paper we show that the Tucker core of a tensor however, retains many properties of the original tensor, including the CP rank, the border rank, the tensor Schatten quasi norms, and the Z-eigenvalues. When the core tensor is smaller than the original tensor, this property leads to considerable computational advantages as confirmed by our numerical experiments. In our analysis, we in fact work with a generalized Tucker-like decomposition that can accommodate any full column-rank factor matrices.

preprint2016arXiv

Zero-sum 6-flows in 5-regular graphs

Let $G$ be a graph. A zero-sum flow of $G$ is an assignment of non-zero real numbers to the edges of $G$ such that the sum of the values of all edges incident with each vertex is zero. Let $k$ be a natural number. A zero-sum $k$-flow is a flow with values from the set $\{\pm1, \ldots, \pm(k - 1)\}$. In this paper, we prove that every 5-regular graph admits a zero-sum 6-flow.

preprint2015arXiv

Efficiency Enhancement in Organic Solar Cells by Incorporating Silica-coated Gold Nanorods at the Buffer/Active interface

The performance of organic solar cells (OSCs) can be greatly improved by incorporating silica-coated gold nanorods (Au@SiO2 NRs) at the interface between the hole transporting layer and the active layer due to the plasmonic effect. The silica shell impedes the aggregation effect of the Au NRs in ethanol solution as well as the server charge recombination on the surface of the Au NRs otherwise they would bring forward serious reduction in open circuit voltage when incorporating the Au NRs at the positions in contact with the active materials. As a result, while the high open circuit voltage being maintained, the optimized plasmonic OSCs possess an increased short circuit current, and correspondingly an elevated power conversion efficiency with the enhancement factor of ~11%. The origin of performance improvement in OSCs with the Au@SiO2 NRs was analyzed systematically using morphological, electrical, optical characterizations along with theoretical simulation. It is found that the broadband enhancement in absorption, which yields the broadband enhancement in exciton generation in the active layer, is the major factor contributing to the increase in the short circuit current density. Simulation results suggest that the excitation of the transverse and longitudinal surface plasmon resonances of individual NRs as well as their mutual coupling can generate strong electric field near the vicinity of the NRs, thereby an improved exciton generation profile in the active layer. The incorporation of Au@SiO2 NRs at the interface between the hole transporting layer and the active layer also improves hole extraction in the OSCs.

preprint2015arXiv

Entry times distribution for mixing systems

We consider the return times dynamics to Bowen balls for continuous maps on metric spaces which have invariant probability measures with certain mixing properties. These mixing properties are satisfied for instance by systems that allow Young tower constructions. We show that the higher order return times to Bowen balls are in the limit Poisson distributed. We also provide a general result for the asymptotic behavior of the recurrence time for Bowen balls for ergodic systems and those with specification.

preprint2015arXiv

Evidence for spin-triplet odd-parity superconductivity close to type-II van Hove singularities

Searching for unconventional Cooper pairing states has been at the heart of superconductivity research since the discovery of BCS superconductors. In particular, spin-triplet odd-parity pairing states were recently revisited due to the possibility of tuning towards topological superconductors. In this context, it is interesting to note a recent proposal that such a spin-triplet pairing instability occurs when the band filling is near van Hove singularities (vHS) associated with momenta away from time-reversal invariant momenta named type-II vHS. However, this result was obtained within a weak coupling renormalization group with Fermi surface patch approximation. To explore superconducting instabilities beyond this weak coupling Fermi surface patch approximation, we perform systematic study on Hubbard model in a two-dimensional square lattice using three different methods: random phase approximation, large-scale dynamical mean field theory simulations with continuous time quantum Monte Carlo (CTQMC) impurity solver, and large-scale dynamical cluster simulations with CTQMC cluster solver. We find, in a wide doping range centered around the type-II van Hove filling, a two-fold degenerate, spin-triplet, odd-parity $p$-wave pairing state emerges due to repulsive interaction, when the Fermi surface is not sufficiently nested. Possible relevance of our findings to the recently discovered superconductors LaO$_{1-x}$F$_{x}$BiS$_{2}$, Ir$_{1-x}$Pt$_{x}$Te$_{2}$ and proposed doped BC$_{3}$ are also discussed.

preprint2015arXiv

Experimental consequences of $p_z$-wave spin triplet superconductivity in A$_2$Cr$_3$As$_3$

The experimental observable properties of the triplet $p_z$-wave pairing state, proposed by Wu {\em et al.} [arXiv:1503.06707] in quasi-one dimensional A$_2$Cr$_3$As$_3$ materials, are theoretically investigated. This pairing state is characterized by the line nodes on the $k_z=0$ plane on the Fermi surfaces. Based on the three-band tight binding model, we obtain the specific heat, superfluid density, Knight shift and spin relaxation rate and find that all these properties at low temperature ($T\ll T_c$) show powerlaw behaviors and are consistent available experiments. Particularly, the superfluid density determined by the $p_z$-wave pairing state in this quasi-one dimensional system is anisotropic: the in-plane superfluid density varies as $Δρ_{\parallel}\sim T$ but the out-plane one varies as $Δρ_{\perp}\sim T^3$ at low temperature. The anisotropic upper critical field reported in experiment is consistent with the $S_z=0$ (i.e., $(\uparrow\downarrow+\downarrow\uparrow)$) $p_z$-wave pairing state. We also suggest the phase-sensitive dc-SQUID measurements to pin down the triplet $p_z$-wave pairing state.

preprint2015arXiv

Exploiting Local Features from Deep Networks for Image Retrieval

Deep convolutional neural networks have been successfully applied to image classification tasks. When these same networks have been applied to image retrieval, the assumption has been made that the last layers would give the best performance, as they do in classification. We show that for instance-level image retrieval, lower layers often perform better than the last layers in convolutional neural networks. We present an approach for extracting convolutional features from different layers of the networks, and adopt VLAD encoding to encode features into a single vector for each image. We investigate the effect of different layers and scales of input images on the performance of convolutional features using the recent deep networks OxfordNet and GoogLeNet. Experiments demonstrate that intermediate layers or higher layers with finer scales produce better results for image retrieval, compared to the last layer. When using compressed 128-D VLAD descriptors, our method obtains state-of-the-art results and outperforms other VLAD and CNN based approaches on two out of three test datasets. Our work provides guidance for transferring deep networks trained on image classification to image retrieval tasks.

preprint2015arXiv

Itinerant Ferromagnetism and $p+ip'$ Superconductivity in Doped Bilayer Silicene

We study the electronic instabilities of doped bilayer silicene using the random phase approximation. In contrast to the singlet $d+id'$ superconductivity at the low doping region, we find that the system is an itinerant ferromagnet in the narrow doping regions around the Van Hove singularities, and a triplet $p+ip'$ superconductor in the vicinity of these regions. Adding a weak Kane-Mele spin-orbit coupling to the system further singles out the time-reversal invariant equal-spin helical $p+ip'$ pairing as the leading instability. The triplet pairing identified here is driven by the ferromagnetic fluctuations, which become strong and enhance the superconducting critical temperature remarkably near the phase boundaries between ferromagnetism and superconductivity.

preprint2015arXiv

Member candidates of the star clusters from LAMOST DR2 data

In this work, we provide 2189 photometric- and kinematic-selected member candidates of 24 star clusters from the LAMOST DR2 catalog. We perform two-step membership identification: selection along the stellar track in the color-magnitude diagram, i.e., photometric identification, and the selection from the distribution of radial velocities, i.e. the kinematic identification. We find that the radial velocity from the LAMOST data are very helpful in the membership identification. The mean probability of membership is 40\% for the radial velocity selected sample. With these 24 star clusters, we investigate the performance of the radial velocity and metallicity estimated in the LAMOST pipeline. We find that the systematic offset in radial velocity and metallicity are $0.85\pm1.26$\,\kms\ and $-0.08\pm0.04$\,dex, with dispersions of $5.47_{-0.71}^{+1.16}$\,\kms\ and $0.13_{-0.02}^{+0.04}$\,dex, respectively. Finally, we propose that the photometric member candidates of the clusters covered by the LAMOST footprints should be assigned higher priority so that more member stars can be observed.

preprint2015arXiv

Possible Electric-Field-Induced Superconducting States in Doped Silicene

Silicene has been synthesized recently, with experimental evidence showing possible superconductivity in the doped case. The noncoplanar low-buckled structure of this material inspires us to study the pairing symmetry of the doped system under a perpendicular external electric field. Our study reveals that the electric field induces an interesting quantum phase transition from the singlet chiral $d + id'$-wave superconducting phase to the triplet $f$-wave one. The emergence of the $f$-wave pairing results from the sublattice-symmetry-breaking caused by the electric field and the ferromagnetic-like intra-sublattice spin correlations at low dopings. Due to the enhanced density of states, the superconducting critical temperature of the system is enhanced by the electric field remarkably. Furthermore, we design a particular dc SQUID experiment to detect the quantum phase transition predicted here. Our results, if confirmed, will inject a new vitality to the familiar Si-based industry through adopting doped silicene as a tunable platform to study different types of exotic unconventional superconductivities.

preprint2015arXiv

Semiconductor Nanowire Light Emitting Diodes Grown on Metal: A Direction towards Large Scale Fabrication of Nanowire Devices

Bottom up nanowires are attractive for realizing semiconductor devices with extreme heterostructures because strain relaxation through the nanowire sidewalls allows the combination of highly lattice mismatched materials without creating dislocations. The resulting nanowires are used to fabricate light emitting diodes (LEDs), lasers, solar cells and sensors. However, expensive single crystalline substrates are commonly used as substrates for nanowire heterostructures as well as for epitaxial devices, which limits the manufacturability of nanowire devices. Here, we demonstrate nanowire LEDs directly grown and electrically integrated on metal. Optical and structural measurements reveal high-quality, vertically-aligned GaN nanowires on molybdenum and titanium films. Transmission electron microscopy confirms the composition variation in the polarization-graded AlGaN nanowire LEDs. Blue to green electroluminescence is observed from InGaN quantum well active regions, while GaN active regions exhibit ultraviolet emission. These results demonstrate a pathway for large-scale fabrication of solid state lighting and optoelectronics on metal foils or sheets.

preprint2015arXiv

Symmetric Ternary Quantum Homomorphic Encryption Schemes Based on the Ternary Quantum One-Time Pad

Aiming at a ternary quantum logic circuit, four symmetric ternary quantum homomorphic encryption schemes, based on ternary quantum one-time protocol, were presented. First, for a one-qutrit rotation gate, a homomorphic quantum encryption scheme was constructed. Second, in view of the synthesis of a 3x3 general unitary transformation, another one-qutrit quantum homomorphic encryption scheme was proposed. Third, according to the one-qutrit scheme, the two-qutrit quantum homomorphic encryption scheme about GCX(m') gate was constructed and was further generalized to the n-qutrit unitary matrix case. Finally, the security of these schemes was analyzed from two perspectives. It could be concluded that the attacker can correctly guess the encryption key with a maximum probability ${p_k} = {1 \mathord{\left/ {\vphantom {1 {3^{3n}}}} \right. \kern-\nulldelimiterspace} {3^{3n}}}$, thus it can better protect the privacy of users' data. Moreover these schemes can be well integrated into future quantum remote server architecture, and the computational security of the user's private quantum information can be well solved in a distributed computing environment.

preprint2015arXiv

Time-reversal-invariant topological superconductivity in n-doped BiH

Intrinsic and symmetry protected topological states have attracted lots of interest in condensed matter physics recently. In particular, time reversal symmetry protected fermion topological insulators have been theoretically predicted and experimentally verified. However despite considerable experimental and theoretical works, definitive evidence for time reversal invariant topological superconductivity is still lacking. Here we propose that upon electron doping the hydrogenated single bilayer Bi, namely BiH, will exhibit time reversal invariant topological superconductivity. If confirmed experimentally this material will constitute the first example of TRI topological superconductor.

preprint2015arXiv

Topological Odd-Parity Superconductivity at Type-II 2D Van Hove Singularities

We study unconventional superconductivity induced by weak repulsive interactions in 2D electronic systems at Van Hove singularity (VHS) where density of states is logarithmically divergent. We define two types of VHS. For systems at type-I VHS, weak repulsive interactions generically induce unconventional singlet pairing. However and more interestingly, for type-II VHS renormalization group (RG) analysis shows that weak repulsive interactions favor triplet pairing (e.g. p-wave) when the Fermi surface is not sufficiently nested. For type-II VHS systems respecting tetragonal symmetry, topological superconductivity (either chiral p+ip pairing or time-reversal invariant $Z_2$ p+ip pairing) occurs generally. We shall also discuss relevance of our study to materials including recently discovered superconductors LaO$_{1-x}$F$_x$BiS$_2$ which can be tuned to type-II VHS by doping.

preprint2015arXiv

Topological p+ip superconductivity in doped graphene-like single-sheet materials BC$_3$

We theoretically study exotic superconducting phases in graphenelike single-sheet material BC$_3$ doped to its type-II Van Hove singularity whose saddle point momenta are {\it not} time-reversal-invariant. From combined renormalization group analysis and RPA calculations, we show that the dominant superconducting instability induced by weak repulsive interactions is in the time-reversal-invariant $p+ip$ pairing channel because of the interplay among dominant ferromagnetic fluctuations, subleading spin fluctuations at finite momentum, and spin-orbit coupling. Such time-reversal-invariant $p+ip$ superconductivity has nontrivial $\mathbb{Z}_2$ topological invariant. Our results show that doped BC$_3$ provides a promising route to realize a genuine 2D helical $p+ip$ superconductor.

preprint2015arXiv

Triplet $p_z$-wave pairing in quasi one dimensional A$_2$Cr$_3$As$_3$ superconductors

We construct minimum effective models to investigate the pairing symmetry in the newly discovered quasi-one-dimensional superconductor K$_2$Cr$_3$As$_3$. We show that a minimum three-band model based on the $d_{z^2}$, $d_{xy}$ and $d_{x^2-y^2}$ orbitals of one Cr sublattice can capture the band structures near Fermi surfaces. In both weak and strong coupling limits, the standard random phase approximation (RPA) and mean-field solutions consistently yield the triplet $p_z$-wave pairing as the leading pairing symmetry for physically realistic parameters. The triplet pairing is driven by the ferromagnetic fluctuations within the sublattice. The gap function of the pairing state possesses line gap nodes on the $k_z=0$ plane on the Fermi surfaces.

preprint2014arXiv

Controllable growth of 1-7 layers of graphene by chemical vapour deposition

We report that graphene films with thickness ranging from 1 to 7 layers can be controllably synthesized on the surface of polycrystalline copper by a chemical vapour deposition method. The number of layers of graphene is controlled precisely by regulating the flow ratio of CH4 and H2, the reaction pressure, the temperature and the reaction time. The synthesized graphene films were characterized by scanning electron microscopy, transmission electron microscopy, selected area electron diffraction, X-ray diffraction and Raman spectroscopy. In addition, the graphene films transferred from copper to other substrates are found to have a good optical transmittance that makes them suitable for transparent conductive materials.

preprint2014arXiv

Electrical detection of the spin polarization due to charge flow in the surface state of the topological insulator Bi_1.5 Sb_0.5 Te_1.7 Se_1.3

We detected the spin polarization due to charge flow in the spin non-degenerate surface state of a three dimensional topological insulator by means of an all-electrical method. The charge current in the bulk-insulating topological insulator Bi1.5Sb0.5Te1.7Se1.3 (BSTS) was injected/extracted through a ferromagnetic electrode made of Ni80Fe20, and an unusual current-direction-dependent magnetoresistance gives evidence for the appearance of spin polarization which leads to a spin-dependent resistance at the BSTS/Ni80Fe20 interface. In contrast, our control experiment on Bi2Se3 gave null result. These observations demonstrate the importance of the Fermi-level control for the electrical detection of the spin polarization in topological insulators.

preprint2014arXiv

Entry times distribution for dynamical balls on metric spaces

We show that the entry and return times for dynamic balls (Bowen balls) is exponential for systems that have an $α$-mixing invariant measure with certain regularities. We also show that systems modeled by Young's tower has exponential hitting time distribution for dynamical balls

preprint2014arXiv

Estimation of causal effects using instrumental variables with nonignorable missing covariates: Application to effect of type of delivery NICU on premature infants

Understanding how effective high-level NICUs (neonatal intensive care units that have the capacity for sustained mechanical assisted ventilation and high volume) are compared to low-level NICUs is important and valuable for both individual mothers and for public policy decisions. The goal of this paper is to estimate the effect on mortality of premature babies being delivered in a high-level NICU vs. a low-level NICU through an observational study where there are unmeasured confounders as well as nonignorable missing covariates. We consider the use of excess travel time as an instrumental variable (IV) to control for unmeasured confounders. In order for an IV to be valid, we must condition on confounders of the IV---outcome relationship, for example, month prenatal care started must be conditioned on for excess travel time to be a valid IV. However, sometimes month prenatal care started is missing, and the missingness may be nonignorable because it is related to the not fully measured mother's/infant's risk of complications. We develop a method to estimate the causal effect of a treatment using an IV when there are nonignorable missing covariates as in our data, where we allow the missingness to depend on the fully observed outcome as well as the partially observed compliance class, which is a proxy for the unmeasured risk of complications. A simulation study shows that under our nonignorable missingness assumption, the commonly used estimation methods, complete-case analysis and multiple imputation by chained equations assuming missingness at random, provide biased estimates, while our method provides approximately unbiased estimates. We apply our method to the NICU study and find evidence that high-level NICUs significantly reduce deaths for babies of small gestational age, whereas for almost mature babies like 37 weeks, the level of NICUs makes little difference. A sensitivity analysis is conducted to assess the sensitivity of our conclusions to key assumptions about the missing covariates. The method we develop in this paper may be useful for many observational studies facing similar issues of unmeasured confounders and nonignorable missing data as ours.

preprint2014arXiv

Imaginary geometric phases of quantum trajectories

A quantum object can accumulate a geometric phase when it is driven along a trajectory in a parameterized state space with non-trivial gauge structures. Inherent to quantum evolutions, a system can not only accumulate a quantum phase but may also experience dephasing, or quantum diffusion. Here we show that the diffusion of quantum trajectories can also be of geometric nature as characterized by the imaginary part of the geometric phase. Such an imaginary geometric phase results from the interference of geometric phase dependent fluctuations around the quantum trajectory. As a specific example, we study the quantum trajectories of the optically excited electron-hole pairs, driven by an elliptically polarized terahertz field, in a material with non-zero Berry curvature near the energy band extremes. While the real part of the geometric phase leads to the Faraday rotation of the linearly polarized light that excites the electron-hole pair, the imaginary part manifests itself as the polarization ellipticity of the terahertz sidebands. This discovery of geometric quantum diffusion extends the concept of geometric phases.

preprint2014arXiv

Nonlinear optical response induced by non-Abelian Berry curvature in time-reversal-invariant insulators

We propose a general framework of nonlinear optics induced by non-Abelian Berry curvature in time-reversal-invariant (TRI) insulators. We find that the third-order response of a TRI insulator under optical and terahertz light fields is directly related to the integration of the non-Abelian Berry curvature over the Brillouin zone. We apply the result to insulators with rotational symmetry near the band edge. Under resonant excitations, the optical susceptibility is proportional to the flux of the Berry curvature through the iso-energy surface, which is equal to the Chern number of the surface times $2π$. For the III-V compound semiconductors, microscopic calculations based on the six-band model give a third-order susceptibility with the Chern number of the iso-energy surface equal to three.

preprint2014arXiv

Possible Triplet $p+ip$ Superconductivity in Graphene at Low Filling

We study the Hubbard model on the honeycomb lattice with nearest-neighbor hopping ($t>0$) and next-nearest-neighbor one ($t'<0$). When $t'<-t/6$, the single-particle spectrum is featured by the continuously distributed Van-Hove saddle points at the band bottom, where the density of states diverges in power-law. We investigate possible unconventional superconductivity in such system with Fermi level close to the band bottom by employing both random phase approximation and determinant quantum Monte-Carlo approaches. Our study reveals a possible triplet $p+ip$ superconductivity in this system with appropriate interactions. Our results might provide a possible route to look for triplet superconductivity with relatively-high transition temperature in a low-filled graphene and other similar systems.

preprint2014arXiv

Realizing degree sequences as $Z_3$-connected graphs

An integer-valued sequence $π=(d_1, \ldots, d_n)$ is {\em graphic} if there is a simple graph $G$ with degree sequence of $π$. We say the $π$ has a realization $G$. Let $Z_3$ be a cyclic group of order three. A graph $G$ is {\em $Z_3$-connected} if for every mapping $b:V(G)\to Z_3$ such that $\sum_{v\in V(G)}b(v)=0$, there is an orientation of $G$ and a mapping $f: E(G)\to Z_3-\{0\}$ such that for each vertex $v\in V(G)$, the sum of the values of $f$ on all the edges leaving from $v$ minus the sum of the values of $f$ on the all edges coming to $v$ is equal to $b(v)$. If an integer-valued sequence $π$ has a realization $G$ which is $Z_3$-connected, then $π$ has a {\em $Z_3$-connected realization} $G$. Let $π=(d_1, \ldots, d_n)$ be a graphic sequence with $d_1\ge \ldots \ge d_n\ge 3$. We prove in this paper that if $d_1\ge n-3$, then either $π$ has a $Z_3$-connected realization unless the sequence is $(n-3, 3^{n-1})$ or is $(k, 3^k)$ or $(k^2, 3^{k-1})$ where $k=n-1$ and $n$ is even; if $d_{n-5}\ge 4$, then either $π$ has a $Z_3$-connected realization unless the sequence is $(5^2, 3^4)$ or $(5, 3^5)$.

preprint2014arXiv

Terahertz electron-hole recollisions in GaAs/AlGaAs quantum wells: robustness to scattering by optical phonons and thermal fluctuations

Electron-hole recollisions are induced by resonantly injecting excitons with a near-IR laser at frequency $f_{\text{NIR}}$ into quantum wells driven by a ~10 kV/cm field oscillating at $f_{\text{THz}} = 0.57$ THz. At $T=12$ K, up to 18 sidebands are observed at frequencies $f_{\text{sideband}}=f_{\text{NIR}}+2n f_{\text{THz}}$, with $-8 \le 2n \le 28$. Electrons and holes recollide with total kinetic energies up to 57 meV, well above the $E_{\text{LO}} = 36$ meV threshold for longitudinal optical (LO) phonon emission. Sidebands with order up to $2n=22$ persist up to room temperature. A simple model shows that LO phonon scattering suppresses but does not eliminate sidebands associated with kinetic energies above $E_{\text{LO}}$.

preprint2014arXiv

The K giant stars from the LAMOST survey data I: identification, metallicity, and distance

We present a support vector machine classifier to identify the K giant stars from the LAMOST survey directly using their spectral line features. The completeness of the identification is about 75% for tests based on LAMOST stellar parameters. The contamination in the identified K giant sample is lower than 2.5%. Applying the classification method to about 2 million LAMOST spectra observed during the pilot survey and the first year survey, we select 298,036 K giant candidates. The metallicities of the sample are also estimated with uncertainty of $0.13\sim0.29$\,dex based on the equivalent widths of Mg$_{\rm b}$ and iron lines. A Bayesian method is then developed to estimate the posterior probability of the distance for the K giant stars, based on the estimated metallicity and 2MASS photometry. The synthetic isochrone-based distance estimates have been calibrated using 7 globular clusters with a wide range of metallicities. The uncertainty of the estimated distance modulus at $K=11$\,mag, which is the median brightness of the K giant sample, is about 0.6\,mag, corresponding to $\sim30$% in distance. As a scientific verification case, the trailing arm of the Sagittarius stream is clearly identified with the selected K giant sample. Moreover, at about 80\,kpc from the Sun, we use our K giant stars to confirm a detection of stream members near the apo-center of the trailing tail. These rediscoveries of the features of the Sagittarius stream illustrate the potential of the LAMOST survey for detecting substructures in the halo of the Milky Way.

preprint2014arXiv

Three-Dimensional Nanoporous Graphene Substrate for Surface-Enhanced Raman Scattering

We synthesized three-dimensional nanoporous graphene films by a chemical vapor deposition method with nanoporous copper as a catalytic substrate. The resulting nanoporous graphene has the same average pore size as the underlying copper substrate. Our surface-enhanced Raman scattering (SERS) investigation indicates that the nanoporosity of graphene significantly improves the SERS efficiency of graphene as a substrate as compared to planar graphene substrates.

preprint2014arXiv

Top gating of epitaxial (Bi_{1-x}Sb_x)2Te3 topological insulator thin films

The tunability of the chemical potential for a wide range encompassing the Dirac point is important for many future devices based on topological insulators. Here we report a method to fabricate highly efficient top gates on epitaxially grown (Bi_{1-x}Sb_x)2Te3 topological insulator thin films without degrading the film quality. By combining an in situ deposited Al2O3 capping layer and a SiN_x dielectric layer deposited at low temperature, we were able to protect the films from degradation during the fabrication processes. We demonstrate that by using this top gate, the carriers in the top surface can be efficiently tuned from n- to p-type. We also show that magnetotransport properties give evidence for decoupled transport through top and bottom surfaces for the entire range of gate voltage, which is only possible in truly bulk-insulating samples.

preprint2014arXiv

Topological Surface Transport in Epitaxial SnTe Thin Films Grown on Bi2Te3

The topological crystalline insulator SnTe has been grown epitaxially on a Bi2Te3 buffer layer by molecular beam epitaxy. In a 30-nm-thick SnTe film, p- and n-type carriers are found to coexist, and Shubnikov--de Haas oscillation data suggest that the n-type carriers are Dirac fermions residing on the SnTe (111) surface. This transport observation of the topological surface state in a p-type topological crystalline insulator became possible due to a downward band bending on the free SnTe surface, which appears to be of intrinsic origin.

preprint2014arXiv

Tuning the polarization-induced free hole density in nanowires graded from GaN to AlN

We report a systematic study of p-type polarization induced doping in graded AlGaN nanowire light emitting diodes grown on silicon wafers by plasma-assisted molecular beam epitaxy. The composition gradient in the p-type base is varied in a set of samples from 0.7 %Al/nm to 4.95 %Al/nm corresponding to negative bound polarization charge densities of 2.2x10^18 cm^-3 to 1.6x10^19 cm^-3. Capacitance measurements and energy band modeling reveal that for gradients greater than or equal to 1.30 %Al/nm, the deep donor concentration is negligible and free hole concentrations roughly equal to the bound polarization charge density are achieved up to 1.6x10^19 cm^-3 at a gradient of 4.95 %Al/nm. Accurate grading lengths in the p- and n-side of the pn-junction are extracted from scanning transmission electron microscopy images and are used to support energy band calculation and capacitance modeling. These results demonstrate the robust nature of p-type polarization doping in nanowires and put an upper bound on the magnitude of deep donor compensation.

preprint2014arXiv

Z3-connectivity with independent number 2

Let G be a 3-edge-connected graph on n vertices. It is proved in this paper that if the number of independent set no more than 2, then either G can be Z3-contracted to one of graphs {K1;K4} or G is one of the graphs in Fig. 1.

preprint2013arXiv

A neighborhood condition for fractional ID-[a,b]-factor-critical graphs

Let $G$ be a graph of order $n$, and let $a$ and $b$ be two integers with $1\leq a\leq b$. Let $h: E(G)\rightarrow [0,1]$ be a function. If $a\leq\sum_{e\ni x}h(e)\leq b$ holds for any $x\in V(G)$, then we call $G[F_h]$ a fractional $[a,b]$-factor of $G$ with indicator function $h$ where $F_h=\{e\in E(G): h(e)>0\}$. A graph $G$ is fractional independent-set-deletable $[a,b]$-factor-critical (in short, fractional ID-$[a,b]$-factor-critical) if $G-I$ has a fractional $[a,b]$-factor for every independent set $I$ of $G$. In this paper, it is proved that if $n\geq\frac{(a+2b)(2a+2b-3)+1}{b}$, $δ(G)\geq\frac{bn}{a+2b}+a$ and $|N_G(x)\cup N_G(y)|\geq\frac{(a+b)n}{a+2b}$ for any two nonadjacent vertices $x,y\in V(G)$, then $G$ is fractional ID-$[a,b]$-factor-critical. Furthermore, it is shown that this result is best possible in some sense.

preprint2013arXiv

Anomalous Cooper pair interference on Bi2Te3 surface

It is believed that the edges of a chiral p-wave superconductor host Majorana modes, relating to a mysterious type of fermions predicted seven decades ago. Much attention has been paid to search for p-wave superconductivity in solid-state systems, including recently those with strong spin-orbit coupling (SOC). However, smoking-gun experiments are still awaited. In this work, we have performed phase-sensitive measurements on particularly designed superconducting quantum interference devices constructing on the surface of topological insulators Bi2Te3, in such a way that a substantial portion of the interference loop is built on the proximity-effect-induced superconducting surface. Two types of Cooper interference patterns have been recognized at low temperatures. One is s-wave like and is contributed by a zero-phase loop inhabited in the bulk of Bi2Te3. The other, being identified to relate to the surface states, is anomalous for that there is a phase shift between the positive and negative bias current directions. The results support that the Cooper pairs on the surface of Bi2Te3 have a 2πBerry phase which makes the superconductivity p_x+ip_y-wave-like. Mesoscopic hybrid rings as constructed in this experiment are presumably arbitrary-phase loops good for studying topological quantum phenomena.

preprint2013arXiv

Central Limit Theorems for Classical Likelihood Ratio Tests for High-Dimensional Normal Distributions

For random samples of size n obtained from p-variate normal distributions, we consider the classical likelihood ratio tests (LRT) for their means and covariance matrices in the high-dimensional setting. These test statistics have been extensively studied in multivariate analysis and their limiting distributions under the null hypothesis were proved to be chi-square distributions as n goes to infinity and p remains fixed. In this paper, we consider the high-dimensional case where both p and n go to infinity with p=n/y in (0, 1]. We prove that the likelihood ratio test statistics under this assumption will converge in distribution to normal distributions with explicit means and variances. We perform the simulation study to show that the likelihood ratio tests using our central limit theorems outperform those using the traditional chi-square approximations for analyzing high-dimensional data.

preprint2013arXiv

d+id' Chiral Superconductivity in Bilayer Silicene

We investigate the structure and physical properties of the undoped bilayer silicene through first-principles calculations and find the system is intrinsically metallic with sizable pocket Fermi surfaces. When realistic electron-electron interaction turns on, the system is identified as a chiral d+id' topological superconductor mediated by the strong spin fluctuation on the border of the antiferromagnetic spin density wave order. Moreover, the tunable Fermi pocket area via strain makes it possible to adjust the spin density wave critical interaction strength near the real one and enables a high superconducting critical temperature.

preprint2013arXiv

DA white dwarfs observed in LAMOST pilot survey

A total of $\sim640,000$ objects from LAMOST pilot survey have been publicly released. In this work, we present a catalog of DA white dwarfs from the entire pilot survey. We outline a new algorithm for the selection of white dwarfs by fitting Sérsic profiles to the Balmer H$β$, H$γ$ and H$δ$ lines of the spectra, and calculating the equivalent width of the CaII K line. 2964 candidates are selected by constraining the fitting parameters and the equivalent width of CaII K line. All the spectra of candidates are visually inspected. We identify 230 (59 of them are already in Villanova and SDSS WD catalog) DA white dwarfs, 20 of which are DA white dwarfs with non-degenerate companions. In addition, 128 candidates are classified as DA white dwarf/subdwarfs, which means the classifications are ambiguous. The result is consistent with the expected DA white dwarf number estimated based on the LEGUE target selection algorithm.

preprint2013arXiv

Defect recombination induced by density-activated carrier diffusion in nonpolar InGaN quantum wells

We report on the observation of carrier-diffusion-induced defect emission at high excitation density in a-plane InGaN single quantum wells. When increasing excitation density in a relatively high regime, we observed the emergence of defect-related emission together with a significant reduction in bandedge emission efficiency. The experimental results can be well explained with the density-activated carrier diffusion from localized states to defect states. Such a scenario of density-activated defect recombination, as confirmed by the dependences of photoluminescence on the excitation photon energy and temperature, is a plausible origin of efficiency droop in a-plane InGaN quantum-well light-emitting diodes.

preprint2013arXiv

Fermiology, Orbital Order, Orbital Fluctuation and Cooper Pairing in Iron-based Superconductors

We address two important issues that arise in recent studies of iron-based superconductivity. (1) Why are the T_c of A_xFe_{2-y}Se_2 and the single unit cell FeSe on SrTiO$_3$ so high despite both only have electron pockets? (2) What (if any) are the effects of orbital order and orbital fluctuation on the Cooper pairing. Our conclusions are summarized in the third paragraph of the paper.

preprint2013arXiv

Giant Faraday rotation induced by Berry phase in bilayer graphene under strong terahertz fields

High-order terahertz (THz) sideband generation (HSG) in semiconductors is a phenomenon with physics similar to high-order harmonic generation but in a much lower frequency regime. It was found that the electron-hole pairs excited by a weak optical laser can accumulate Berry phases along a cyclic path under the driving of a strong THz field. The Berry phases appear as the Faraday rotation angles of the emission signal under short-pulse excitation in monolayer MoS$_2$. In this paper, the theory of Berry phase in THz extreme nonlinear optics is applied to biased bilayer graphene with Bernal stacking, which has similar Bloch band features and optical properties to the monolayer MoS$_2$, such as time-reversal related valleys and valley contrasting optical selection rules. The bilayer graphene has much larger Berry curvature than monolayer MoS$_2$, which leads to a giant Faraday rotation of the optical emission ($\sim$ 1 rad for a THz field with frequency 1 THz and strength 8 kV/cm). This provides opportunities to use bilayer graphene and low-power THz lasers for ultrafast electro-optical devices.

preprint2013arXiv

Phonon-mediated Superconductivity in Silicene

We predict that electron-doped silicene is a good two-dimensional electron-phonon superconductor under biaxial tensile strain by first-principles calculations within rigid band approximation. Superconductivity transition temperature of electron-doped silicene can be increased to be above 10 K by 5% tensile strain. Band structures, phonon dispersive relations, and Eliashberg functions are calculated for detailed analysis. The strong interaction between acoustic phonon modes normal to the silicene plane and the increasing electronic states around the Fermi level induced by tensile strain is mainly responsible for the enhanced critical temperature.

preprint2013arXiv

Reducing the efficiency droop by lateral carrier confinement in InGaN/GaN quantum-well nanorods

Efficiency droop is a major obstacle facing high-power application of InGaN/GaN quantum-well (QW) light-emitting diodes. In this letter, we report the suppression of efficiency droop induced by density-activated defect recombination in nanorod structure of a-plane InGaN/GaN QWs. In the high carrier density regime, the retained emission efficiency in a dry-etched nanorod sample is observed to be over two times higher than that in its parent QW sample. We further argue that the improvement is a combined effect of the amendment contributed by lateral carrier confinement and the deterioration made by surface trapping.

preprint2013arXiv

Total transmission and total reflection of acoustic wave by zero index metamaterials loaded with general solid defects

This work investigates acoustic wave transmission property through a zero index metamaterials (ZIM) waveguide embedded with a general solid defect. Total transmission and total reflection can be achieved by adjusting the parameters of the solid defect. We comprehensively study how longitudinal/transverse wave speeds of the solid defect affect the acoustic wave propagation through the waveguide. A two-dimensional (2D) acoustic crystals (ACs) system with zero index is used to realize these intriguing transmission properties. Thus, our work provides more possibilities to manipulate acoustic wave propagation through ZIM.

preprint2012arXiv

An Algorithm for Preferential Selection of Spectroscopic Targets in LEGUE

We describe a general target selection algorithm that is applicable to any survey in which the number of available candidates is much larger than the number of objects to be observed. This routine aims to achieve a balance between a smoothly-varying, well-understood selection function and the desire to preferentially select certain types of targets. Some target-selection examples are shown that illustrate different possibilities of emphasis functions. Although it is generally applicable, the algorithm was developed specifically for the LAMOST Experiment for Galactic Understanding and Exploration (LEGUE) survey that will be carried out using the Chinese Guo Shou Jing Telescope. In particular, this algorithm was designed for the portion of LEGUE targeting the Galactic halo, in which we attempt to balance a variety of science goals that require stars at fainter magnitudes than can be completely sampled by LAMOST. This algorithm has been implemented for the halo portion of the LAMOST pilot survey, which began in October 2011.

preprint2012arXiv

Berry phases of quantum trajectories in semiconductors under strong terahertz fields

Quantum evolution of particles under strong fields can be essentially captured by a small number of quantum trajectories that satisfy the stationary phase condition in the Dirac-Feynmann path integrals. The quantum trajectories are the key concept to understand extreme nonlinear optical phenomena, such as high-order harmonic generation (HHG), above-threshold ionization (ATI), and high-order terahertz sideband generation (HSG). While HHG and ATI have been mostly studied in atoms and molecules, the HSG in semiconductors can have interesting effects due to possible nontrivial "vacuum" states of band materials. We find that in a semiconductor with non-vanishing Berry curvature in its energy bands, the cyclic quantum trajectories of an electron-hole pair under a strong terahertz field can accumulate Berry phases. Taking monolayer MoS$_2$ as a model system, we show that the Berry phases appear as the Faraday rotation angles of the pulse emission from the material under short-pulse excitation. This finding reveals an interesting transport effect in the extreme nonlinear optics regime.

preprint2012arXiv

Frustrated RVB states in 2D: classifications and short-range correlations

Resonating valence bond (RVB) states are of crucial importance in our intuitive understanding of quantum spin liquids in 2D. We systematically classify short-range bosonic RVB states into symmetric or nematic spin liquids by examining their flux patterns. We further map short-range bosonic RVB states into projected BCS wave functions, on which we perform large-scale Monte Carlo simulations without the minus sign problem. Our results clearly show that both spin and dimer correlations decay exponentially in all the short-range frustrated (non-bipartite or $Z_2$) bosonic RVB states we studied, indicating that they are gapped $Z_2$ quantum spin liquids. Generically, we conjecture that all short-range frustrated bosonic RVB states in 2D have only short-range correlations.

preprint2012arXiv

Functional renormalization group and variational Monte Carlo studies of the electronic instabilities in graphene near 1/4 doping

We study the electronic instabilities of near 1/4 electron doped graphene using the functional renormalization group (FRG) and variational Monte-Carlo method. A modified FRG implementation is utilized to improve the treatment of the von Hove singularity. At 1/4 doping the system is a chiral spin density wave state exhibiting the anomalous quantized Hall effect, or equivalently a Chern insulator. When the doping deviates from 1/4, the $d_{x^2-y^2}+i d_{xy}$ Cooper pairing becomes the leading instability. Our results suggest near 1/4 electron or hole doped graphene is a fertile playground for the search of Chern insulators and superconductors.

preprint2012arXiv

Grand-canonical variational approach for the t-J model

Gutzwiller-projected BCS wave function or the resonating-valence-bond (RVB) state in the 2D extended t-J model is investigated by using the variational Monte Carlo technique. We show that the results of ground-state energy and excitation spectra calculated in the grand-canonical scheme allowing particle number to fluctuate are essentially the same as previous results obtained by fixing the number of particle in the canonical scheme if the grand thermodynamic potential is used for minimization. To account for the effect of Gutzwiller projection, a fugacity factor proposed by Laughlin and Anderson few years ago has to be inserted into the coherence factor of the BCS state. Chemical potential, particle number fluctuation, and phase fluctuation of the RVB state, difficult or even impossible to be calculated in the canonical ensemble, have been directly measured in the grand-canonical picture. We find that except for La-214 materials, the doping dependence of chemical potential is consistent with experimental findings on several cuprates. Similar to what has been reported by scanning tunneling spectroscopy experiments, the tunneling asymmetry becomes much stronger as doping decreases. We found a very large enhancement of phase fluctuation in the underdoped regime.

preprint2012arXiv

LAMOST Experiment for Galactic Understanding and Exploration (LEGUE) The survey science plan

We describe the current plans for a spectroscopic survey of millions of stars in the Milky Way galaxy using the Guo Shou Jing Telescope (GSJT, formerly the Large Area Multi-Object Spectroscopic Telescope - LAMOST). The survey will obtain spectra for 2.5 million stars brighter than $r<19$ during dark/grey time, and 5 million stars brighter than $r<17$ or $J<16$ on nights that are moonlit or have low transparency. The survey will begin in fall of 2012, and will run for at least four years. The telescope design constrains the optimal declination range for observations to $10^\circ<δ<50^\circ$, and site conditions lead to an emphasis on stars in the direction of the Galactic anticenter. The survey is divided into three parts with different target selection strategies: disk, anticenter, and spheroid. The resulting dataset will be used to study the merger history of the Milky Way, the substructure and evolution of the disks, the nature of the first generation of stars through identification of the lowest metallicity stars, and star formation through study of open clusters and the OB associations. Detailed design of the LEGUE survey will be completed after a review of the results of the pilot survey in summer 2012.

preprint2012arXiv

Proximity effect at superconducting Sn-Bi2Se3 interface

We have investigated the conductance spectra of Sn-Bi2Se3 interface junctions down to 250 mK and in different magnetic fields. A number of conductance anomalies were observed below the superconducting transition temperature of Sn, including a small gap different from that of Sn, and a zero-bias conductance peak growing up at lower temperatures. We discussed the possible origins of the smaller gap and the zero-bias conductance peak. These phenomena support that a proximity-effect-induced chiral superconducting phase is formed at the interface between the superconducting Sn and the strong spin-orbit coupling material Bi2Se3.

preprint2012arXiv

Proximity-effect-induced superconductivity in topological insulator-related material Bi2Se3

We have studied the electron transport properties of topological insulator-related material Bi2Se3 near the superconducting Pb-Bi2Se3 interface, and found that a superconducting state is induced over an extended volume in Bi2Se3. This state can carry a Josephson supercurrent, and demonstrates a gap-like structure in the conductance spectra as probed by a normal-metal electrode. The establishment of the gap is not by confining the electrons into a narrow space close to the superconductor-normal metal interface, as previously observed in other systems, but presumably via electron-electron attractive interaction in Bi2Se3.

preprint2012arXiv

Secondary use of data in EHR systems

We show how to use aspect-oriented programming to separate security and trust issues from the logical design of mobile, distributed systems. The main challenge is how to enforce various types of security policies, in particular predictive access control policies - policies based on the future behavior of a program. A novel feature of our approach is that advice is able to analyze the future use of data. We consider a number of different security policies, concerning both primary and secondary use of data, some of which can only be enforced by analysis of process continuations.

preprint2012arXiv

The LEGUE High Latitude Bright Survey Design for the LAMOST Pilot Survey

We describe the footprint and input catalog for bright nights in the LAMOST Pilot Survey, which began in October 2011. Targets are selected from two stripes in the north and south Galactic Cap regions, centered at $α$= 29$^\circ$, with 10$^\circ$ width in declination, covering right ascension of 135$^\circ-290^\circ$ and -30$^\circ$ to 30$^\circ$ respectively. We selected spectroscopic targets from a combination of the SDSS and 2MASS point source catalogs. The catalog of stars defining the field centers (as required by the Shack-Hartmann wavefront sensor at the center of the LAMOST field) consists of all V < 8m stars from the Hipparcos catalog. We employ a statistical selection algorithm that assigns priorities to targets based on their positions in multidimensional color/magnitude space. This scheme overemphasizes rare objects and de-emphasizes more populated regions of magnitude and color phase space, while ensuring a smooth, well-understood selection function. A demonstration of plate design is presented based on the Shack-Hartmann star catalog and an input catalog that was generated by our target selection routines.

preprint2012arXiv

The LEGUE Input Catalogue for Dark Night Observing in the LAMOST Pilot Survey

We outline the design of the dark nights portion of the LAMOST Pilot Survey, which began observations in October 2011. In particular, we focus on Milky Way stellar candidates that are targeted for the LEGUE (LAMOST Experiment for Galactic Understanding and Exploration) survey. We discuss the regions of sky in which spectroscopic candidates were selected, and the motivations for selecting each of these sky areas. Some limitations due to the unique design of the telescope are discussed, including the requirement that a bright (V < 8) star be placed at the center of each plate for wavefront sensing and active optics corrections. The target selection categories and scientific goals motivating them are briefly discussed, followed by a detailed overview of how these selection functions were realized. We illustrate the difference between the overall input catalog - Sloan Digital Sky Survey (SDSS) photometry - and the final targets selected for LAMOST observation.

preprint2012arXiv

The selection of LEGUE disk targets for LAMOST's pilot survey

We describe the target selection algorithm for the low latitude disk portion of the LAMOST Pilot Survey, which aims to test systems in preparation for the LAMOST spectroscopic survey. We use the PPMXL (Roeser et al. 2010) astrometric catalog, which provides positions, proper motions, B/R/I magnitudes (mostly) from USNO-B (Monet et al. 2003) and J/H/Ks from The Two Micron All Sky Survey (2MASS, see Skrutskie et al. 2006) as well. We chose 8 plates along the Galactic plane, in the region $0^\circ<α<67^\circ$ and $42^\circ<δ<59^\circ$, that cover 22 known open clusters with a range of ages. Adjacent plates may have small overlapping. Each plate covers an area $2.5^\circ$ in radius,with central star (for Shack-Hartmann guider) brighter than $\sim8^{\rm th}$ magnitude. For each plate, we create an input catalog in the magnitude range $11.3<Imag<16.3$ and $Bmag$ available from PPMXL. The stars are selected to satisfy the requirements of the fiber positioning system and have a uniform distribution in the $I$ vs. $B-I$ color-magnitude diagram. Our final input catalog consists of 12,000 objects on each of 8 plates that are observable during the winter observing season in Xinglong Station of the National Astronomical Observatory of China.

preprint2012arXiv

The site conditions of the Guo Shou Jing Telescope

The weather at Xinglong Observing Station, where the Guo Shou Jing Telescope (GSJT) is located, is strongly affected by the monsoon climate in north-east China. The LAMOST survey strategy is constrained by these weather patterns. In this paper, we present a statistics on observing hours from 2004 to 2007, and the sky brightness, seeing, and sky transparency from 1995 to 2011 at the site. We investigate effects of the site conditions on the survey plan. Operable hours each month shows strong correlation with season: on average there are 8 operable hours per night available in December, but only 1-2 hours in July and August. The seeing and the sky transparency also vary with seasons. Although the seeing is worse in windy winters, and the atmospheric extinction is worse in the spring and summer, the site is adequate for the proposed scientific program of LAMOST survey. With a Monte Carlo simulation using historical data on the site condition, we find that the available observation hours constrain the survey footprint from 22h to 16h in right ascension; the sky brightness allows LAMOST to obtain the limit magnitude of V = 19.5mag with S/N = 10.

preprint2012arXiv

Ultrafast pump-probe spectroscopic signatures of superconducting and pseudogap phases in YBa2Cu3O7-δ films

Femtosecond pump-probe spectroscopy is applied to identify transient optical signatures of phase transitions in optimally doped YBa2Cu3O7-δ films. To elucidate the dynamics of superconducting and pseudogap phases, the slow thermal component is removed from the time-domain traces of photo-induced reflectivity in a high-flux regime with low frequency pulse rate. The rescaled data exhibit distinct signatures of the phase separation with abrupt changes at the onsets of TSC and TPG in excellent agreement with transport data. Compared to the superconducting phase, the response of the pseudogap phase is characterized by the strongly reduced reflectivity change accompanied by a faster recovery time.

preprint2011arXiv

A General Theory of Additive State Space Abstractions

Informally, a set of abstractions of a state space S is additive if the distance between any two states in S is always greater than or equal to the sum of the corresponding distances in the abstract spaces. The first known additive abstractions, called disjoint pattern databases, were experimentally demonstrated to produce state of the art performance on certain state spaces. However, previous applications were restricted to state spaces with special properties, which precludes disjoint pattern databases from being defined for several commonly used testbeds, such as Rubiks Cube, TopSpin and the Pancake puzzle. In this paper we give a general definition of additive abstractions that can be applied to any state space and prove that heuristics based on additive abstractions are consistent as well as admissible. We use this new definition to create additive abstractions for these testbeds and show experimentally that well chosen additive abstractions can reduce search time substantially for the (18,4)-TopSpin puzzle and by three orders of magnitude over state of the art methods for the 17-Pancake puzzle. We also derive a way of testing if the heuristic value returned by additive abstractions is provably too low and show that the use of this test can reduce search time for the 15-puzzle and TopSpin by roughly a factor of two.

preprint2011arXiv

Aharonov-Casher effect in Bi$_{\rm 2}$Se$_{\rm 3}$ square-ring interferometers

Electrical control of spin dynamics in Bi$_{\rm 2}$Se$_{\rm 3}$ was investigated in ring-type interferometers. Aharonov-Bohm and Altshuler-Aronov-Spivak resistance oscillations against magnetic field, and Aharorov-Casher resistance oscillations against gate voltage were observed in the presence of a Berry phase of $π$. A very large tunability of spin precession angle by gate voltage has been obtained, indicating that Bi$_{\rm 2}$Se$_{\rm 3}$-related materials with strong spin-orbit coupling are promising candidates for constructing novel spintronic devices.

preprint2011arXiv

Magnetic and superconducting instabilities in a hybrid model of itinerant/localized electrons for iron pnictides

We study a unified mechanism for spin-density-wave (SDW) and superconductivity in a minimal model, in which itinerant electrons and local moments coexist as previously proposed for the iron pnictides [EPL, 88, 17010 (2009)]. The phase diagram obtained at the mean field level is in qualitative agreement with the experiment, which shows how the magnetic and superconducting (SC) instabilities are driven by the critial coupling between the itinerant/localized electrons. The spin and charge response functions at the random phase approximation (RPA) level further characterize the dynamical evolution of the system. In particular, the dynamic spin susceptibility displays a Goldstone mode in the SDW phase, which evolves into a gapped resonance-like mode in the superconducting phase. The latter persists all the way into the normal state above Tc, where a strong scattering between the itinerant electrons and local moments is restored, as an essential feature of the model.

preprint2011arXiv

Phase diagram and a possible unified description of intercalated iron selenide superconductors

We propose a theoretical description of the phase diagram and physical properties in A2Fe4Se5-type (A=K, Tl) compounds based on a coexistent local moment and itinerant electron picture. Using neutron scattering and ARPES measurements to fix the general structure of the local moment and itinerant Fermi pockets, we find a superconducting (SC) regime with s-wave pairing at the M pockets and an incipient sign-change s-wave near the Gamma point, which is adjacent to an insulating state at low doping and a charge-density-wave (CDW) state at high doping. The uniform susceptibility and resistivity are found to be consistent with the experiment. The main distinction with iron pnictide superconductors is also discussed.

preprint2011arXiv

Strong Superconducting Proximity Effect in Pb-Bi2Te3 Hybrid Structures

Majarona fermions (MFs) were predicted more than seven decades ago but are yet to be identified [1]. Recently, much attention has been paid to search for MFs in condensed matter systems [2-10]. One of the seaching schemes is to create MF at the interface between an s-wave superconductor (SC) and a 3D topological insulator (TI) [11-13]. Experimentally, progresses have been achieved in the observations of a proximity-effect-induced supercurrent [14-16], a perfect Andreev reflection [17] and a conductance peak at the Fermi level [18]. However, further characterizations are still needed to clarify the nature of the SC-TI interface. In this Letter, we report on a strong proximity effect in Pb-Bi2Te3 hybrid structures, based on which Josephson junctions and superconducting quantum interference devices (SQUIDs) can be constructed. Josephson devices of this type would provide a test-bed for exploring novel phenomena such as MFs in the future.

preprint2011arXiv

The electronic instabilities of the Iron-based superconductors: a variational Monte-Carlo study

We report the first variational Monte Carlo (VMC) study of the iron-based superconductors. We use realistic band structures, and the ordering instabilities/variational ansatzs are suggested by previous functional renormalization group calculations. We examine antiferromagnetism, superconducting pairing, normal state fermi surface distortion, and orbital order in the antiferromagnetic state.

preprint2010arXiv

Collective spin mode in a multi-component system of coupled itinerant and localized electrons

We study collective spin excitations of a magnetically ordered state in a multi-component system composed of both itinerant electrons and local moments. Here the induced spin-density-wave (SDW) ordering of itinerant electrons and the collinear antiferromangetic (AF) ordering of local moments are locked together via a Hund's rule coupling. We show that the Goldstone theorem still holds at the RPA level with the gapless spin wave protected inside the small SDW gap of itinerant electrons, which, however, is fragile in the presence of ion-anisotropy. A gapped out-of-phase spin mode extending over a much wider energy scale above the SDW gap is found to be more robust against the ion-anisotropy, which is mainly contributed by the local moment fluctuations. While the scattering between the Goldstone mode and itinerant electrons diminishes within the SDW gap, the out-of-phase mode will strongly interact with itinerant electrons and thus dominate the spin and charge dynamics in such an ordered phase. Possible relevance of such a model to the iron-pnictides will be also discussed.

preprint2010arXiv

Imprinting Light Phase on Matter Wave Gratings in Superradiance Scattering

Superradiance scattering from a Bose-Einstein condensate is studied with a two-frequency pumping beam. We demonstrate the possibility of fully tuning the backward mode population as a function of the locked initial relative phase between the two frequency components of the pumping beam. This result comes from an imprinting of this initial relative phase on two matter wave gratings, formed by the forward mode or backward mode condensate plus the condensate at rest, so that cooperative scattering is affected. A numerical simulation using a semiclassical model agrees with our observations.

preprint2010arXiv

Observation of a red-blue detuning asymmetry in matter-wave superradiance

We report the first experimental observations of strong suppression of matter-wave superradiance using blue-detuned pump light and demonstrate a pump-laser detuning asymmetry in the collective atomic recoil motion. In contrast to all previous theoretical frameworks, which predict that the process should be symmetric with respect to the sign of the pump-laser detuning, we find that for condensates the symmetry is broken. With high condensate densities and red-detuned light, the familiar distinctive multi-order, matter-wave scattering pattern is clearly visible, whereas with blue-detuned light superradiance is strongly suppressed. In the limit of a dilute atomic gas, however, symmetry is restored.

preprint2010arXiv

Quantum interface between frequency-uncorrelated down-converted entanglement and atomic-ensemble quantum memory

Photonic entanglement source and quantum memory are two basic building blocks of linear-optical quantum computation and long-distance quantum communication. In the past decades, intensive researches have been carried out, and remarkable progress, particularly based on the spontaneous parametric down-converted (SPDC) entanglement source and atomic ensembles, has been achieved. Currently, an important task towards scalable quantum information processing (QIP) is to efficiently write and read entanglement generated from a SPDC source into and out of an atomic quantum memory. Here we report the first experimental realization of a quantum interface by building a 5 MHz frequency-uncorrelated SPDC source and reversibly mapping the generated entangled photons into and out of a remote optically thick cold atomic memory using electromagnetically induced transparency. The frequency correlation between the entangled photons is almost fully eliminated with a suitable pump pulse. The storage of a triggered single photon with arbitrary polarization is shown to reach an average fidelity of 92% for 200 ns storage time. Moreover, polarization-entangled photon pairs are prepared, and one of photons is stored in the atomic memory while the other keeps flying. The CHSH Bell's inequality is measured and violation is clearly observed for storage time up to 1 microsecond. This demonstrates the entanglement is stored and survives during the storage. Our work establishes a crucial element to implement scalable all-optical QIP, and thus presents a substantial progress in quantum information science.

preprint2010arXiv

Transverse Mode Revival of a Light-Compensated Quantum Memory

A long-lived quantum memory was developed based on light-compensated cold $^{87}$Rb atoms in a dipole trap. The lifetime of the quantum memory was improved by 40 folds, from 0.67 ms to 28 ms with the help of a compensation laser beam. Oscillations of the memory efficiency due to the transverse mode breathing of the singly-excited spin wave have been clearly observed and clarified with a Monte-Carlo simulation procedure. With detailed analysis of the decoherence processes of the spin wave in cold atomic ensembles, this experiment provides a benchmark for the further development of high-quality quantum memories.

preprint2007arXiv

Manipulation of the quantum state by Majorana transition in spinor Bose-Einstein condensates

Manipulation of the quantum state by the Majorana transition in spinor BEC system has been realized by altering the rotation frequency of the magnetic field's direction. This kind of manipulation method has no limitation on the transition speed in principle and the system is well closed, which provides a new and superior tool to manipulate quantum states. Using this methord on pulsed atom laser, multicomponent spinor atom laser is generated. We demonstrate that the experiment results are agreed with the theoretical predication.

preprint2006arXiv

Why a local moment induces an antiferromagnetic ordering: An RVB picture

Based on a Gutzwiller projected BCS wavefunction, it is shown that a local S=1/2 moment is present around a vacancy site (zinc impurity) in a form of staggered magnetic moments, which is a direct consequence of the short-ranged resonating-valence-bond (RVB) pairing in the spin background.

Fan Yang

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229 published item(s)

GLM-5V-Turbo: Toward a Native Foundation Model for Multimodal Agents

Trapping Attacker in Dilemma: Examining Internal Correlations and External Influences of Trigger for Defending GNN Backdoors

Unifying Sparse Attention with Hierarchical Memory for Scalable Long-Context LLM Serving

Evidence of anisotropic three-dimensional weak-localization in TiSe$_{2}$ nanoflakes

Disentangle Estimation of Causal Effects from Cross-Silo Data

Active RIS vs. Passive RIS: Which Will Prevail in 6G?

An empirical process framework for covariate balance in causal inference

Interacting Bose gas across a narrow Feshbach resonance

Reconfigurable Metasurface: A Systematic Categorization and Recent Advances

UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup

Variable Fofana's Spaces and their Pre-dual

A Calibration Approach to Transportability and Data-Fusion with Observational Data

A Low-Cost, Controllable and Interpretable Task-Oriented Chatbot: With Real-World After-Sale Services as Example

A Multi-Person Video Dataset Annotation Method of Spatio-Temporally Actions

A note on the edge choosability of $K_{5}$-minor free graphs

Active RISs: Signal Modeling, Asymptotic Analysis, and Beamforming Design

Actor-identified Spatiotemporal Action Detection -- Detecting Who Is Doing What in Videos

Actuarial-consistency and two-step actuarial valuations: a new paradigm to insurance valuation

ANT: Exploiting Adaptive Numerical Data Type for Low-bit Deep Neural Network Quantization

Anti-resonances and sharp analysis of Maryland localization for all parameters

BBA-net: A bi-branch attention network for crowd counting

Branching Brownian motion in a periodic environment and existence of pulsating travelling waves

Branching Brownian motion in a periodic environment and uniqueness of pulsating travelling waves

Characterizing counterfactuals and dependencies over causal and generalized causal teams

Chiral SO(4) spin-valley density wave and degenerate topological superconductivity in magic-angle-twisted bilayer-graphene

Class-Aware Contrastive Semi-Supervised Learning

Client Selection and Bandwidth Allocation for Federated Learning: An Online Optimization Perspective

Complete logics for elementary team properties

Confidence Calibration for Intent Detection via Hyperspherical Space and Rebalanced Accuracy-Uncertainty Loss

Cooper instability and superconductivity on the Penrose lattice

Decay of multi-point correlation functions in $\mathbb{Z}^d$

Demo: low-power communications based on RIS and AI for 6G

Detecting and Monitoring Tidal Dissipation of Hot Jupiters in the Era of SiTian

Deterministic Photon Sorting in Waveguide QED Systems

Differentially Private Counterfactuals via Functional Mechanism

Discrete-time Simulation of Stochastic Volterra Equations

Distill-VQ: Learning Retrieval Oriented Vector Quantization By Distilling Knowledge from Dense Embeddings

Do Smart Glasses Dream of Sentimental Visions? Deep Emotionship Analysis for Eyewear Devices

Edge statistics of large dimensional deformed rectangular matrices

Existence and uniqueness of equilibrium states for systems with specification at a fixed scale

FAR Planner: Fast, Attemptable Route Planner using Dynamic Visibility Update

Geo-Localization via Ground-to-Satellite Cross-View Image Retrieval

High-order harmonic generation in X-ray range from laser induced noble gas multivalent ions

Improving Generalization of Metric Learning via Listwise Self-distillation

Kinetic-scale flux ropes: Observations and applications of kinetic equilibrium models

Learning from Attacks: Attacking Variational Autoencoder for Improving Image Classification

Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach

Learning Optical Flow with Adaptive Graph Reasoning

Lifshitz transition enhanced triplet $p_z$-wave superconductivity in hydrogen doped KCr$_3$As$_3$

Limited-memory BFGS Optimisation of Phase-Only Computer-Generated Hologram for Fraunhofer Diffraction

Limiting distribution of the sample canonical correlation coefficients of high-dimensional random vectors

Liouvillian Skin Effect in an Exactly Solvable Model

Localization for magnetic quantum walks

On sufficient conditions for planar graphs to be 5-flexible

On the discrete-time simulation of the rough Heston model

Optimally Weighted PCA for High-Dimensional Heteroscedastic Data

Propositional union closed team logics

ReMix: A General and Efficient Framework for Multiple Instance Learning based Whole Slide Image Classification

Sample canonical correlation coefficients of high-dimensional random vectors with finite rank correlations

SC^2-PCR: A Second Order Spatial Compatibility for Efficient and Robust Point Cloud Registration

Second-harmonic generation from singular metasurfaces

Spin Supersolidity in Nearly Ideal Easy-axis Triangular Quantum Antiferromagnet Na$_2$BaCo(PO$_4$)$_2$

SQuant: On-the-Fly Data-Free Quantization via Diagonal Hessian Approximation

Surrogate-assisted Multi-objective Neural Architecture Search for Real-time Semantic Segmentation

Time Series Data Augmentation for Deep Learning: A Survey

Tracy-Widom distribution for heterogeneous Gram matrices with applications in signal detection

Transit Timing Variation of XO-3b: Evidence for Tidal Evolution of Hot Jupiter with High Eccentricity

Transmissive RIS for 6G Communications: Design, Prototyping, and Experimental Demonstrations

Uniqueness of equilibrium states for Lorenz attractors in any dimension

UniVIP: A Unified Framework for Self-Supervised Visual Pre-training

A concise review of Rydberg atom based quantum computation and quantum simulation

ACDnet: An action detection network for real-time edge computing based on flow-guided feature approximation and memory aggregation

An entropy dichotomy for singular star flows

Aqueous humor dynamics in human eyes: a lattice Boltzmann study