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Hao Xu

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preprint2026arXiv

Effective dark matter component presents a robust signature of negative pressure by the DESI observations

Comprehensive cosmological analysis of an effective non-standard dark matter(NSDM) model, characterized by an equation of state $w_{\mathrm{dm}} = w_2 a^2$, which allows for mild deviations from the previously assumed pressureless cold dark matter, is elaborated in the present work. This effective description framework is the scenarios that matter contents coupled to three distinct single-parameter dynamical dark energy models: i.e, the thawing scalar field, the Modified Emergent Dark Energy(MEDE) scenario, and the constant-$w$ model. We constrain these frameworks by using the latest cosmological probes, including the Planck 2018 Cosmic Microwave Background(CMB) distance priors, the Baryon Acoustic Oscillation(BAO) measurements from the Data Release 2 of the Dark Energy Spectroscopic Instrument(DESI), and three compilations of Type Ia Supernovae(SN Ia) namely the Dark Energy Survey Year 5 (DESY5) compilation, the Union3 compilation, and the PantheonPlus (PP) sample. Across all three dark energy scenarios and all dataset combinations, we find a consistent preference for negative values of the parameter $w_2$. Furthermore, this result is robust against the choice of dark energy parametrization, suggesting a model-independent deviation from "standard" cold dark matter. This result indicates that the dark matter fluid possesses a small but non-vanishing negative pressure, meaning a non-cold nature. While the inferred Hubble constant $H_0$ remains consistent with the Planck $Λ$CDM value and does not fully alleviate the $H_0$ tension with local measurements, the persistent detection of $w_2 < 0$ across a wide range of independent cosmological probes provides compelling evidence for new physics in the dark matter sector -- suggesting that dark matter may be better described as an effective fluid endowed with a mild negative pressure, rather than as a perfectly cold, pressureless substance.

preprint2026arXiv

How well can off-the-shelf LLMs elucidate molecular structures from mass spectra using chain-of-thought reasoning?

Mass spectrometry (MS) is a powerful analytical technique for identifying small molecules, yet determining complete molecular structures directly from tandem mass spectra (MS/MS) remains a long-standing challenge due to complex fragmentation patterns and the vast diversity of chemical space. Recent progress in large language models (LLMs) has shown promise for reasoning-intensive scientific tasks, but their capability for chemical interpretation is still unclear. In this work, we introduce a Chain-of-Thought (CoT) prompting framework and benchmark that evaluate how LLMs reason about mass spectral data to predict molecular structures. We formalize expert chemists' reasoning steps-such as double bond equivalent (DBE) analysis, neutral loss identification, and fragment assembly-into structured prompts and assess multiple state-of-the-art LLMs (Claude-3.5-Sonnet, GPT-4o-mini, and Llama-3 series) in a zero-shot setting using the MassSpecGym dataset. Our evaluation across metrics of SMILES validity, formula consistency, and structural similarity reveals that while LLMs can produce syntactically valid and partially plausible structures, they fail to achieve chemical accuracy or link reasoning to correct molecular predictions. These findings highlight both the interpretive potential and the current limitations of LLM-based reasoning for molecular elucidation, providing a foundation for future work that combines domain knowledge and reinforcement learning to achieve chemically grounded AI reasoning.

preprint2026arXiv

Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index

Language models are trained mainly on massive text data from the Internet, and it becomes increasingly important to understand this data source. Exact-match search engines enable searching in large text corpora - counting string appearances and retrieving the enclosing documents - yet the high storage overhead hinders their application on Internet-scale data. We present infini-gram mini, an efficient and scalable system that can make petabyte-level text corpora searchable. Based on the FM-index data structure (Ferragina and Manzini, 2000), which simultaneously indexes and compresses text, our system creates indexes with size only 44% of the corpus. Infini-gram mini greatly improves upon the best existing implementation of FM-index in terms of indexing speed (18$\times$) and memory use during both indexing (3.2$\times$ reduction) and querying (down to a negligible amount). We index 83TB of Internet text in 99 days with a single CPU node with 128 vCPUs (or 19 hours if using 137 such nodes). We show one important use case of infini-gram mini in a large-scale analysis of benchmark contamination. We find several core LM evaluation benchmarks to be heavily contaminated in Internet crawls (up to 74.2% in GSM8K), which could lead to overestimating the capabilities of language models if trained on such data. We host a benchmark contamination bulletin to share the contamination rate of many core and community-contributed benchmarks. We also release a web interface and an API endpoint to serve general search queries on infini-gram mini indexes.

preprint2026arXiv

Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models

Spatio-Temporal (ST) Foundation Models (STFMs) promise cross-dataset generalization, yet joint ST pretraining is computationally expensive and grapples with the heterogeneity of domain-specific spatial patterns. Substantially extending our preliminary conference version, we present FactoST-v2, an enhanced factorized framework redesigned for full weight transfer and arbitrary-length generalization. FactoST-v2 decouples universal temporal learning from domain-specific spatial adaptation. The first stage pretrains a minimalist encoder-only backbone using randomized sequence masking to capture invariant temporal dynamics, enabling probabilistic quantile prediction across variable horizons. The second stage employs a streamlined adapter to rapidly inject spatial awareness via meta adaptive learning and prompting. Comprehensive evaluations across diverse domains demonstrate that FactoST-v2 achieves state-of-the-art accuracy with linear efficiency - significantly outperforming existing foundation models in zero-shot and few-shot scenarios while rivaling domain-specific expert baselines. This factorized paradigm offers a practical, scalable path toward truly universal STFMs. Code is available at https://github.com/CityMind-Lab/FactoST.

preprint2026arXiv

STT-Arena: A More Realistic Environment for Tool-Using with Spatio-Temporal Dynamics

Large language models (LLMs) deployed in real-world agentic applications must be capable of replanning and adapting when mid-task disruptions invalidate their prior decisions. Existing dynamic benchmarks primarily measure whether LLMs can detect temporal changes in a timely manner, leaving the complementary challenge of adaptive replanning under spatio-temporal dynamics largely unexplored. We introduce STT-Arena (Spatio-Temporal Tool-Use Arena), a benchmark of 227 high-quality interactive tasks spanning nine spatio-temporal conflict types and four solvability levels. Each task is grounded in a realistic, executable environment equipped with injected spatio-temporal triggers that can abruptly invalidate an ongoing plan, forcing the model to detect the state shift and construct a revised execution strategy. Extensive evaluation of frontier LLMs reveals that even the SOTA proprietary models, including Claude-4.6-Opus, achieves less than 40\% overall accuracies, highlighting the fundamental difficulty of spatio-temporal dynamic reasoning. Systematic analysis of failure trajectories uncovers three recurring error modes of existing models: Stale-State Execution, Misdiagnosis of Dynamic Triggers, and Missing Post-Adaptation Verification. Guided by these findings, we propose an iterative trajectory refinement technique that eliminates these failure patterns from training data, and combine it with online RL to produce STT-Agent-4B which outperforms frontier LLMs on STT-Arena.

preprint2026arXiv

Towards Open Diversity-Aware Social Interactions

Social Media and the Internet have catalyzed an unprecedented potential for exposure to human diversity in terms of demographics, talents, opinions, knowledge, and the like. However, this potential has not come with new, much-needed, instruments and skills to harness it. This paper presents our work on promoting richer and deeper social relations through the design and development of the "Internet of Us", an online platform that uses diversity-aware Artificial Intelligence to mediate and empower human social interactions. We discuss the multiple facets of diversity in social settings, the multidisciplinary work that is required to reap the benefits of diversity, and the vision for a diversity-aware hybrid human-AI society.

preprint2026arXiv

Zero-Shot Chinese Character Recognition via Global-Local Dual-Branch Alignment and Hierarchical Inference

Chinese character categories are extremely large, and unseen characters frequently arise in open-world scenarios, making zero-shot Chinese character recognition an important yet challenging problem. Existing IDS-based retrieval methods usually encode a character image and its ideographic description sequence into a single global vector for matching. Although efficient, such holistic alignment often under-models local component differences. Moreover, directly introducing patch-token level fine-grained interaction suffers from both the noise of structural operators in IDS and the high cost of full-candidate retrieval.To address these issues, we propose a Global-Local Hierarchical Perception Network (GL-HPN), which jointly learns global and local representations of character images and IDS sequences within a unified cross-modal alignment framework. The global branch supports efficient coarse recall, while the local branch improves component-level discrimination through patch-token interaction. We further introduce a structure filtering mask to suppress structurally meaningful but visually non-entity IDS operators in local similarity aggregation. On top of this, we design a coarse-to-fine hierarchical inference strategy that performs global retrieval over the full candidate set and local reranking only on Top-$K$ candidates, followed by parameter-free multiplicative fusion of normalized posterior scores. Experimental results show that GL-HPN achieves competitive performance across multiple zero-shot splits, performs especially well under low-resource settings, and substantially reduces the inference cost of large-scale candidate retrieval.

preprint2025arXiv

Specific Absorption Rate-Aware Multiuser MIMO Assisted by Fluid Antenna System

With the development of the upcoming sixth-generation (6G) wireless networks, there is a pressing need for innovative technologies capable of satisfying heightened performance indicators. Fluid antenna system (FAS) is proposed recently as a promising technique to achieve higher data rates and more diversity gains by dynamically changing the positions of the antennas to form a more desirable channel. However, worries regarding the possibly harmful effects of electromagnetic (EM) radiation emitted by devices have arisen as a result of the rapid evolution of advanced techniques in wireless communication systems. Specific absorption rate (SAR) is a widely adopted metric to quantify EM radiation worldwide. In this paper, we investigate the SAR-aware multiuser multiple-input multiple-output (MIMO) communications assisted by FAS. In particular, a two-layer iterative algorithm is proposed to minimize the SAR value under signal-to-interference-plus-noise ratio (SINR) and FAS constraints. Moreover, the minimum weighted SINR maximization problem under SAR and FAS constraints is studied by finding its relationship with the SAR minimization problem. Simulation results verify that the proposed SAR-aware FAS design outperforms the adaptive backoff and fixed-position antenna designs.

preprint2024arXiv

Channel Estimation for FAS-assisted Multiuser mmWave Systems

This letter investigates the challenge of channel estimation in a multiuser millimeter-wave (mmWave) time-division duplexing (TDD) system. In this system, the base station (BS) employs a multi-antenna uniform linear array (ULA), while each mobile user is equipped with a fluid antenna system (FAS). Accurate channel state information (CSI) plays a crucial role in the precise placement of antennas in FAS. Traditional channel estimation methods designed for fixed-antenna systems are inadequate due to the high dimensionality of FAS. To address this issue, we propose a low-sample-size sparse channel reconstruction (L3SCR) method, capitalizing on the sparse propagation paths characteristic of mmWave channels. In this approach, each fluid antenna only needs to switch and measure the channel at a few specific locations. By observing this reduced-dimensional data, we can effectively extract angular and gain information related to the sparse channel, enabling us to reconstruct the full CSI. Simulation results demonstrate that our proposed method allows us to obtain precise CSI with minimal hardware switching and pilot overhead. As a result, the system sum-rate approaches the upper bound achievable with perfect CSI.

preprint2024arXiv

FuRPE: Learning Full-body Reconstruction from Part Experts

In the field of full-body reconstruction, the scarcity of annotated data often impedes the efficacy of prevailing methods. To address this issue, we introduce FuRPE, a novel framework that employs part-experts and an ingenious pseudo ground-truth selection scheme to derive high-quality pseudo labels. These labels, central to our approach, equip our network with the capability to efficiently learn from the available data. Integral to FuRPE is a unique exponential moving average training strategy and expert-derived feature distillation strategy. These novel elements of FuRPE not only serve to further refine the model but also to reduce potential biases that may arise from inaccuracies in pseudo labels, thereby optimizing the network's training process and enhancing the robustness of the model. We apply FuRPE to train both two-stage and fully convolutional single-stage full-body reconstruction networks. Our exhaustive experiments on numerous benchmark datasets illustrate a substantial performance boost over existing methods, underscoring FuRPE's potential to reshape the state-of-the-art in full-body reconstruction.

preprint2023arXiv

A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch

Mitosis detection is one of the challenging problems in computational pathology, and mitotic count is an important index of cancer grading for pathologists. However, current counts of mitotic nuclei rely on pathologists looking microscopically at the number of mitotic nuclei in hot spots, which is subjective and time-consuming. In this paper, we propose a two-stage cascaded network, named FoCasNet, for mitosis detection. In the first stage, a detection network named M_det is proposed to detect as many mitoses as possible. In the second stage, a classification network M_class is proposed to refine the results of the first stage. In addition, the attention mechanism, normalization method, and hybrid anchor branch classification subnet are introduced to improve the overall detection performance. Our method achieves the current highest F1-score of 0.888 on the public dataset ICPR 2012. We also evaluated our method on the GZMH dataset released by our research team for the first time and reached the highest F1-score of 0.563, which is also better than multiple classic detection networks widely used at present. It confirmed the effectiveness and generalization of our method. The code will be available at: https://github.com/antifen/mitosis-nuclei-detection.

preprint2023arXiv

Fluid Antenna-Assisted MIMO Transmission Exploiting Statistical CSI

In conventional multiple-input multiple-output (MIMO) communication systems, the positions of antennas are fixed. To take full advantage of spatial degrees of freedom, a new technology called fluid antenna (FA) is proposed to obtain higher achievable rate and diversity gain. Most existing works on FA exploit instantaneous channel state information (CSI). However, in FA-assisted systems, it is difficult to obtain instantaneous CSI since changes in the antenna position will lead to channel variation. In this letter, we investigate a FA-assisted MIMO system using relatively slow-varying statistical CSI. Specifically, in the criterion of rate maximization, we propose an algorithmic framework for transmit precoding and transmit/receive FAs position designs with statistical CSI. Simulation results show that our proposed algorithm in FA-assisted systems significantly outperforms baselines in terms of rate performance.

preprint2023arXiv

Semi-Supervised Learning with Pseudo-Negative Labels for Image Classification

Semi-supervised learning frameworks usually adopt mutual learning approaches with multiple submodels to learn from different perspectives. To avoid transferring erroneous pseudo labels between these submodels, a high threshold is usually used to filter out a large number of low-confidence predictions for unlabeled data. However, such filtering can not fully exploit unlabeled data with low prediction confidence. To overcome this problem, in this work, we propose a mutual learning framework based on pseudo-negative labels. Negative labels are those that a corresponding data item does not belong. In each iteration, one submodel generates pseudo-negative labels for each data item, and the other submodel learns from these labels. The role of the two submodels exchanges after each iteration until convergence. By reducing the prediction probability on pseudo-negative labels, the dual model can improve its prediction ability. We also propose a mechanism to select a few pseudo-negative labels to feed into submodels. In the experiments, our framework achieves state-of-the-art results on several main benchmarks. Specifically, with our framework, the error rates of the 13-layer CNN model are 9.35% and 7.94% for CIFAR-10 with 1000 and 4000 labels, respectively. In addition, for the non-augmented MNIST with only 20 labels, the error rate is 0.81% by our framework, which is much smaller than that of other approaches. Our approach also demonstrates a significant performance improvement in domain adaptation.

preprint2023arXiv

TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification

Cross-domain text classification aims to transfer models from label-rich source domains to label-poor target domains, giving it a wide range of practical applications. Many approaches promote cross-domain generalization by capturing domain-invariant features. However, these methods rely on unlabeled samples provided by the target domains, which renders the model ineffective when the target domain is agnostic. Furthermore, the models are easily disturbed by shortcut learning in the source domain, which also hinders the improvement of domain generalization ability. To solve the aforementioned issues, this paper proposes TACIT, a target domain agnostic feature disentanglement framework which adaptively decouples robust and unrobust features by Variational Auto-Encoders. Additionally, to encourage the separation of unrobust features from robust features, we design a feature distillation task that compels unrobust features to approximate the output of the teacher. The teacher model is trained with a few easy samples that are easy to carry potential unknown shortcuts. Experimental results verify that our framework achieves comparable results to state-of-the-art baselines while utilizing only source domain data.

preprint2022arXiv

2D Human Pose Estimation: A Survey

Human pose estimation aims at localizing human anatomical keypoints or body parts in the input data (e.g., images, videos, or signals). It forms a crucial component in enabling machines to have an insightful understanding of the behaviors of humans, and has become a salient problem in computer vision and related fields. Deep learning techniques allow learning feature representations directly from the data, significantly pushing the performance boundary of human pose estimation. In this paper, we reap the recent achievements of 2D human pose estimation methods and present a comprehensive survey. Briefly, existing approaches put their efforts in three directions, namely network architecture design, network training refinement, and post processing. Network architecture design looks at the architecture of human pose estimation models, extracting more robust features for keypoint recognition and localization. Network training refinement tap into the training of neural networks and aims to improve the representational ability of models. Post processing further incorporates model-agnostic polishing strategies to improve the performance of keypoint detection. More than 200 research contributions are involved in this survey, covering methodological frameworks, common benchmark datasets, evaluation metrics, and performance comparisons. We seek to provide researchers with a more comprehensive and systematic review on human pose estimation, allowing them to acquire a grand panorama and better identify future directions.

preprint2022arXiv

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers

In recent years, deep learning has made brilliant achievements in Environmental Microorganism (EM) image classification. However, image classification of small EM datasets has still not obtained good research results. Therefore, researchers need to spend a lot of time searching for models with good classification performance and suitable for the current equipment working environment. To provide reliable references for researchers, we conduct a series of comparison experiments on 21 deep learning models. The experiment includes direct classification, imbalanced training, and hyperparameter tuning experiments. During the experiments, we find complementarities among the 21 models, which is the basis for feature fusion related experiments. We also find that the data augmentation method of geometric deformation is difficult to improve the performance of VTs (ViT, DeiT, BotNet and T2T-ViT) series models. In terms of model performance, Xception has the best classification performance, the ViT model consumes the least time for training, and the ShuffleNet-V2 model has the least number of parameters.

preprint2022arXiv

A Two-Phase Paradigm for Joint Entity-Relation Extraction

An exhaustive study has been conducted to investigate span-based models for the joint entity and relation extraction task. However, these models sample a large number of negative entities and negative relations during the model training, which are essential but result in grossly imbalanced data distributions and in turn cause suboptimal model performance. In order to address the above issues, we propose a two-phase paradigm for the span-based joint entity and relation extraction, which involves classifying the entities and relations in the first phase, and predicting the types of these entities and relations in the second phase. The two-phase paradigm enables our model to significantly reduce the data distribution gap, including the gap between negative entities and other entities, as well as the gap between negative relations and other relations. In addition, we make the first attempt at combining entity type and entity distance as global features, which has proven effective, especially for the relation extraction. Experimental results on several datasets demonstrate that the spanbased joint extraction model augmented with the two-phase paradigm and the global features consistently outperforms previous state-of-the-art span-based models for the joint extraction task, establishing a new standard benchmark. Qualitative and quantitative analyses further validate the effectiveness the proposed paradigm and the global features.

preprint2022arXiv

Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology

Background: Breast cancer has the highest prevalence in women globally. The classification and diagnosis of breast cancer and its histopathological images have always been a hot spot of clinical concern. In Computer-Aided Diagnosis (CAD), traditional classification models mostly use a single network to extract features, which has significant limitations. On the other hand, many networks are trained and optimized on patient-level datasets, ignoring the application of lower-level data labels. Method: This paper proposes a deep ensemble model based on image-level labels for the binary classification of benign and malignant lesions of breast histopathological images. First, the BreaKHis dataset is randomly divided into a training, validation and test set. Then, data augmentation techniques are used to balance the number of benign and malignant samples. Thirdly, considering the performance of transfer learning and the complementarity between each network, VGG16, Xception, ResNet50, DenseNet201 are selected as the base classifiers. Result: In the ensemble network model with accuracy as the weight, the image-level binary classification achieves an accuracy of $98.90\%$. In order to verify the capabilities of our method, the latest Transformer and Multilayer Perception (MLP) models have been experimentally compared on the same dataset. Our model wins with a $5\%-20\%$ advantage, emphasizing the ensemble model's far-reaching significance in classification tasks. Conclusion: This research focuses on improving the model's classification performance with an ensemble algorithm. Transfer learning plays an essential role in small datasets, improving training speed and accuracy. Our model has outperformed many existing approaches in accuracy, providing a method for the field of auxiliary medical diagnosis.

preprint2022arXiv

BDPGO: Balanced Distributed Pose Graph Optimization Framework for Swarm Robotics

Distributed pose graph optimization (DPGO) is one of the fundamental techniques of swarm robotics. Currently, the sub-problems of DPGO are built on the native poses. Our validation proves that this approach may introduce an imbalance in the sizes of the sub-problems in real-world scenarios, which affects the speed of DPGO optimization, and potentially increases communication requirements. In addition, the coherence of the estimated poses is not guaranteed when the robots in the swarm fail, or partial robots are disconnected. In this paper, we propose BDPGO, a balanced distributed pose graph optimization framework using the idea of decoupling the robot poses and DPGO. BDPGO re-distributes the poses in the pose graph to the robot swarm in a balanced way by introducing a two-stage graph partitioning method to build balanced subproblems. Our validation demonstrates that BDPGO significantly improves the optimization speed without changing the specific algorithm of DPGO in realistic datasets. What's more, we also validate that BDPGO is robust to robot failure, changes in the wireless network. BDPGO has capable of keeps the coherence of the estimated poses in these situations. The framework also has the potential to be applied to other collaborative simultaneous localization and mapping (CSLAM) problems involved in distributedly solving the factor graph.

preprint2022arXiv

Capacitively coupled distinct mechanical resonators for room temperature phonon-cavity electromechanics

Coupled electromechanical resonators that can be independently driven/detected and easily integrated with external circuits are essential for exploring mechanical modes based signal processing. Here, we present a room temperature phonon-cavity electromechanical system, consisting of two distinct resonators: a silicon nitride electromechanical drum capacitively coupled to an aluminum one. We demonstrate electromechanically induced transparency and amplification in a two-tone driving scheme and observe the phonon-cavity force affecting the mechanical damping rates of both movable objects. We also develop an analytical model based on linearly coupled motion equations, which captures the optomechanical features in the classical limit and enables to fit quantitatively our measurements. Our results open up new possibilities in the study of phonon-cavity based signal processing in the classical and potentially in the future in the quantum regimes.

preprint2022arXiv

Capacity Scaling Law in Massive MIMO with Antenna Selection

Antenna selection is capable of handling the cost and complexity issues in massive multiple-input multiple-output (MIMO) channels. The sum-rate capacity of a multiuser massive MIMO uplink channel is characterized under the Nakagami fading. A mathematically tractable sum-rate capacity upper bound is derived for the considered system. Moreover, for a sufficiently large base station (BS) antenna number, a deterministic equivalent (DE) of the sum-rate bound is derived. Based on this DE, the sum-rate capacity is shown to grow double logarithmically with the number of BS antennas. The validity of the analytical result is confirmed by numerical experiments.

preprint2022arXiv

CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising

Degraded images commonly exist in the general sources of character images, leading to unsatisfactory character recognition results. Existing methods have dedicated efforts to restoring degraded character images. However, the denoising results obtained by these methods do not appear to improve character recognition performance. This is mainly because current methods only focus on pixel-level information and ignore critical features of a character, such as its glyph, resulting in character-glyph damage during the denoising process. In this paper, we introduce a novel generic framework based on glyph fusion and attention mechanisms, i.e., CharFormer, for precisely recovering character images without changing their inherent glyphs. Unlike existing frameworks, CharFormer introduces a parallel target task for capturing additional information and injecting it into the image denoising backbone, which will maintain the consistency of character glyphs during character image denoising. Moreover, we utilize attention-based networks for global-local feature interaction, which will help to deal with blind denoising and enhance denoising performance. We compare CharFormer with state-of-the-art methods on multiple datasets. The experimental results show the superiority of CharFormer quantitatively and qualitatively.

preprint2022arXiv

Coupled electronic and magnetic excitations in the cuprates and their role in the superconducting transition

The formation of Cooper pairs, a bound state of two electrons of opposite spin and momenta by exchange of a phonon [1], is a defining feature of conventional superconductivity. In the cuprate high temperature superconductors, even though it has been established that the superconducting state also consists of Cooper pairs, the pairing mechanism remains intensely debated. Here we investigate superconducting pairing in the Bi2Sr2CaCu2O8+δ(Bi2212) cuprate by employing spectral functions obtained directly from angle-resolved photoemission (ARPES) experiments as input to the Bethe-Salpeter gap equation. Assuming that Cooper pairing is driven solely by spin fluctuations, we construct the single-loop spin-fluctuation-mediated pairing interaction, and use it to compute the eigenfunctions and eigenvalues of the Bethe-Salpeter equation in the particle-particle channel for multiple Bi2212 samples. The key point of our results is that, as the temperature is reduced, the leading eigenvalue increases upon approaching Tc, reaching a value of approximately 1 at the Tc corresponding to each doping value, indicating a superconducting transition with d-wave eigenfunctions. This suggests that spin fluctuations can approximately account for Tc and, consequently, mediate pairing in the cuprate high temperature superconductors.

preprint2022arXiv

Distributed Information Bottleneck for a Primitive Gaussian Diamond Channel with Rayleigh Fading

This paper considers the distributed information bottleneck (D-IB) problem for a primitive Gaussian diamond channel with two relays and Rayleigh fading. Due to the bottleneck constraint, it is impossible for the relays to inform the destination node of the perfect channel state information (CSI) in each realization. To evaluate the bottleneck rate, we provide an upper bound by assuming that the destination node knows the CSI and the relays can cooperate with each other, and also three achievable schemes with simple symbol-by-symbol relay processing and compression. Numerical results show that the lower bounds obtained by the proposed achievable schemes can come close to the upper bound on a wide range of relevant system parameters.

preprint2022arXiv

EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation

Environmental microorganisms (EMs) are ubiquitous around us and have an important impact on the survival and development of human society. However, the high standards and strict requirements for the preparation of environmental microorganism (EM) data have led to the insufficient of existing related databases, not to mention the databases with GT images. This problem seriously affects the progress of related experiments. Therefore, This study develops the Environmental Microorganism Dataset Sixth Version (EMDS-6), which contains 21 types of EMs. Each type of EM contains 40 original and 40 GT images, in total 1680 EM images. In this study, in order to test the effectiveness of EMDS-6. We choose the classic algorithms of image processing methods such as image denoising, image segmentation and target detection. The experimental result shows that EMDS-6 can be used to evaluate the performance of image denoising, image segmentation, image feature extraction, image classification, and object detection methods.

preprint2022arXiv

Ensemble Multi-Relational Graph Neural Networks

It is well established that graph neural networks (GNNs) can be interpreted and designed from the perspective of optimization objective. With this clear optimization objective, the deduced GNNs architecture has sound theoretical foundation, which is able to flexibly remedy the weakness of GNNs. However, this optimization objective is only proved for GNNs with single-relational graph. Can we infer a new type of GNNs for multi-relational graphs by extending this optimization objective, so as to simultaneously solve the issues in previous multi-relational GNNs, e.g., over-parameterization? In this paper, we propose a novel ensemble multi-relational GNNs by designing an ensemble multi-relational (EMR) optimization objective. This EMR optimization objective is able to derive an iterative updating rule, which can be formalized as an ensemble message passing (EnMP) layer with multi-relations. We further analyze the nice properties of EnMP layer, e.g., the relationship with multi-relational personalized PageRank. Finally, a new multi-relational GNNs which well alleviate the over-smoothing and over-parameterization issues are proposed. Extensive experiments conducted on four benchmark datasets well demonstrate the effectiveness of the proposed model.

preprint2022arXiv

Entropy production and correlation spreading in the interaction between particle detector and thermal baths

Entropy production is the key to the second law of thermodynamics, and it is well defined by considering a joint unitary evolution of a system $S$ and a thermal environment $E$. However, due to the diversity of the initial state and Hamiltonian of the system and environment, it is hard to evaluate the characterisation of entropy production. In the present work, we propose that the evolution of $S$ and $E$ can be solved non-perturbatively in the framework of Gaussian quantum mechanics (GQM). We study the entropy production and correlation spreading in the interaction between Unruh-DeWitt-like particle detector and thermal baths, where the particle detector is set to be a harmonic oscillator and the thermal baths are made of interacting and noninteracting Gaussian states. We can observe that the entropy production implies quantum recurrence and shows periodicity. In the case of interacting bath, the correlation propagates in a periodic system and leads to a revival of the initial state. Our analysis can be extended to any other models in the framework of GQM, and it may also shed some light on the AdS/CFT correspondence.

preprint2022arXiv

FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration

Data association is important in the point cloud registration. In this work, we propose to solve the partial-to-partial registration from a new perspective, by introducing multi-level feature interactions between the source and the reference clouds at the feature extraction stage, such that the registration can be realized without the attentions or explicit mask estimation for the overlapping detection as adopted previously. Specifically, we present FINet, a feature interaction-based structure with the capability to enable and strengthen the information associating between the inputs at multiple stages. To achieve this, we first split the features into two components, one for rotation and one for translation, based on the fact that they belong to different solution spaces, yielding a dual branches structure. Second, we insert several interaction modules at the feature extractor for the data association. Third, we propose a transformation sensitivity loss to obtain rotation-attentive and translation-attentive features. Experiments demonstrate that our method performs higher precision and robustness compared to the state-of-the-art traditional and learning-based methods. Code is available at https://github.com/megvii-research/FINet.

preprint2022arXiv

How Anti-de Sitter Black Holes Reach Thermal Equilibrium

It is commonly known in the literature that large black holes in anti-de Sitter spacetimes (with reflective boundary condition) are in thermal equilibrium with their Hawking radiation. Focusing on black holes with event horizon of toroidal topology, we study a simple model to understand explicitly how this thermal equilibrium is reached under Hawking evaporation. It is shown that it is possible for a large toroidal black hole to evolve into a small (but stable) one.

preprint2022arXiv

Landauer's principle in Qubit-Cavity quantum field theory Interaction in Vacuum and Thermal States

Landauer's principle has seen a boom of interest in the last few years due to the growing interest in quantum information sciences. However, its relevance and validity in the contexts of quantum field theory (QFT) remain surprisingly unexplored. In the present paper, we consider Landauer's principle in qubit-cavity QFT interaction perturbatively, in which the initial state of the cavity QFT is chosen to be a vacuum or thermal state. In the vacuum case, the QFT always absorbs heat and jumps to excited states. For the qubit at rest, its entropy decreases, whereas if the qubit accelerates, it may also gain energy and it increases its entropy due to the Unruh effect. For the thermal state, the QFT can both absorb and release heat, depending on its temperature and the initial state of the qubit, and the higher-order perturbations can excite or deexcite the initial state to a higher or lower state. Landauer's principle is valid in all the cases we consider. We hope that this paper will pave the way for future explorations of Landauer's principle in QFT and gravity theories.

preprint2022arXiv

Lens Antenna Arrays-Assisted mmWave MU-MIMO Uplink Transmission: Joint Beam Selection and Phase-Only Beamforming Design

This paper considers a lens antenna array-assisted millimeter wave (mmWave) multiuser multiple-input multiple-output (MU-MIMO) system. The base station's beam selection matrix and user terminals' phase-only beamformers are jointly designed with the aim of maximizing the uplink sum rate. In order to deal with the formulated mixed-integer optimization problem, a penalty dual decomposition (PDD)-based iterative algorithm is developed via capitalizing on the weighted minimum mean square error (WMMSE), block coordinate descent (BCD), and minorization-maximization (MM) techniques. Moreover, a low-complexity sequential optimization (SO)-based algorithm is proposed at the cost of a slight sum rate performance loss. Numerical results demonstrate that the proposed methods can achieve higher sum rates than state-of-the-art methods.

preprint2022arXiv

Linear Array Network for Low-light Image Enhancement

Convolution neural networks (CNNs) based methods have dominated the low-light image enhancement tasks due to their outstanding performance. However, the convolution operation is based on a local sliding window mechanism, which is difficult to construct the long-range dependencies of the feature maps. Meanwhile, the self-attention based global relationship aggregation methods have been widely used in computer vision, but these methods are difficult to handle high-resolution images because of the high computational complexity. To solve this problem, this paper proposes a Linear Array Self-attention (LASA) mechanism, which uses only two 2-D feature encodings to construct 3-D global weights and then refines feature maps generated by convolution layers. Based on LASA, Linear Array Network (LAN) is proposed, which is superior to the existing state-of-the-art (SOTA) methods in both RGB and RAW based low-light enhancement tasks with a smaller amount of parameters. The code is released in https://github.com/cuiziteng/LASA_enhancement.

preprint2022arXiv

Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation

Modeling the evolution of user preference is essential in recommender systems. Recently, dynamic graph-based methods have been studied and achieved SOTA for recommendation, majority of which focus on user's stable long-term preference. However, in real-world scenario, user's short-term preference evolves over time dynamically. Although there exists sequential methods that attempt to capture it, how to model the evolution of short-term preference with dynamic graph-based methods has not been well-addressed yet. In particular: 1) existing methods do not explicitly encode and capture the evolution of short-term preference as sequential methods do; 2) simply using last few interactions is not enough for modeling the changing trend. In this paper, we propose Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation (LSTSR) to capture the evolution of short-term preference under dynamic graph. Specifically, we explicitly encode short-term preference and optimize it via memory mechanism, which has three key operations: Message, Aggregate and Update. Our memory mechanism can not only store one-hop information, but also trigger with new interactions online. Extensive experiments conducted on five public datasets show that LSTSR consistently outperforms many state-of-the-art recommendation methods across various lines.

preprint2022arXiv

Metaverse Native Communication: A Blockchain and Spectrum Prospective

Metaverse depicts a vista of constructing a virtual environment parallel to the real world so people can communicate with others and objects through digital entities. In the real world, communication relies on identities and addresses that are recognized by authorities, no matter the link is established via post, email, mobile phone, or landline. Metaverse, however, is different from the real world, which requires a single identity belongs to the individual. This identity can be an encrypted virtual address in the metaverse but no one can trace or verify it. In order to achieve such addresses to hide individuals in the metaverse, re-mapping the virtual address to the individual's identity and a specific spectrum to support the address-based communication for the metaverse are needed. Therefore, metaverse native or meta-native communications based on blockchain could be a promising solution to directly connect entities with their native encrypted addresses that gets rid of the existing network services based on IP, cellular, HTTP, etc. This paper proposes a vision of blockchain, encrypted address and address-based access model for all users, devices, services, etc. to contribute to the metaverse. Furthermore, the allocation architecture of a designated spectrum for the metaverse is proposed to remove the barrier to access to the metaverse/blockchain in response to the initiatives of metaverse and decentralized Internet.

preprint2022arXiv

Neither Fast Nor Slow: How to Fly Through Narrow Tunnels

Nowadays, multirotors are playing important roles in abundant types of missions. During these missions, entering confined and narrow tunnels that are barely accessible to humans is desirable yet extremely challenging for multirotors. The restricted space and significant ego airflow disturbances induce control issues at both fast and slow flight speeds, meanwhile bringing about problems in state estimation and perception. Thus, a smooth trajectory at a proper speed is necessary for safe tunnel flights. To address these challenges, in this letter, a complete autonomous aerial system that can fly smoothly through tunnels with dimensions narrow to 0.6 m is presented. The system contains a motion planner that generates smooth mini-jerk trajectories along the tunnel center lines, which are extracted according to the map and Euclidean Distance Field (EDF), and its practical speed range is obtained through computational fluid dynamics (CFD) and flight data analyses. Extensive flight experiments on the quadrotor are conducted inside multiple narrow tunnels to validate the planning framework as well as the robustness of the whole system.

preprint2022arXiv

Neutron spectroscopy evidence for a possible magnetic-field-induced gapless quantum-spin-liquid phase in a Kitaev material $α$-RuCl$_3$

As one of the most promising Kitaev quantum-spin-liquid (QSL) candidates, $α$-RuCl$_3$ has received a great amount of attention. However, its ground state exhibits a long-range zigzag magnetic order, which defies the QSL phase. Nevertheless, the magnetic order is fragile and can be completely suppressed by applying an external magnetic field. Here, we explore the evolution of magnetic excitations of $α$-RuCl$_3$ under an in-plane magnetic field, by carrying out inelastic neutron scattering measurements on high-quality single crystals. Under zero field, there exist spin-wave excitations near the $M$ point and a continuum near the $\mitΓ$ point, which are believed to be associated with the zigzag magnetic order and fractional excitations of the Kitaev QSL state, respectively. By increasing the magnetic field, the spin-wave excitations gradually give way to the continuous excitations. On the verge of the critical field $μ_0H_{\rm c}=7.5$ T, the former vanish and only the latter is left, indicating the emergence of a pure QSL state. By further increasing the field strength, the excitations near the $\mitΓ$ point become more intense. By following the gap evolution of the excitations near the $\mitΓ$ point, we are able to establish a phase diagram composed of three interesting phases, including a gapped zigzag order phase at low fields, possibly-gapless QSL phase near $μ_0H_{\rm c}$, and gapped partially polarized phase at high fields. These results demonstrate that an in-plane magnetic field can drive $α$-RuCl$_3$ into a long-sought QSL state near the critical field.

preprint2022arXiv

OJXPerf: Featherlight Object Replica Detection for Java Programs

Memory bloat is an important source of inefficiency in complex production software, especially in software written in managed languages such as Java. Prior approaches to this problem have focused on identifying objects that outlive their life span. Few studies have, however, looked into whether and to what extent myriad objects of the same type are identical. A quantitative assessment of identical objects with code-level attribution can assist developers in refactoring code to eliminate object bloat, and favor reuse of existing object(s). The result is reduced memory pressure, reduced allocation and garbage collection, enhanced data locality, and reduced re-computation, all of which result in superior performance. We develop OJXPerf, a lightweight sampling-based profiler, which probabilistically identifies identical objects. OJXPerf employs hardware performance monitoring units (PMU) in conjunction with hardware debug registers to sample and compare field values of different objects of the same type allocated at the same calling context but potentially accessed at different program points. The result is a lightweight measurement, a combination of object allocation contexts and usage contexts ordered by duplication frequency. This class of duplicated objects is relatively easier to optimize. OJXPerf incurs 9% runtime and 6% memory overheads on average. We empirically show the benefit of OJXPerf by using its profiles to instruct us to optimize a number of Java programs, including well-known benchmarks and real-world applications. The results show a noticeable reduction in memory usage (up to 11%) and a significant speedup (up to 25%).

preprint2022arXiv

Omni-swarm: A Decentralized Omnidirectional Visual-Inertial-UWB State Estimation System for Aerial Swarms

Decentralized state estimation is one of the most fundamental components of autonomous aerial swarm systems in GPS-denied areas yet it still remains a highly challenging research topic. Omni-swarm, a decentralized omnidirectional visual-inertial-UWB state estimation system for aerial swarms, is proposed in this paper to address this research niche. To solve the issues of observability, complicated initialization, insufficient accuracy, and lack of global consistency, we introduce an omnidirectional perception front-end in Omni-swarm. It consists of stereo wide-FoV cameras and ultra-wideband sensors, visual-inertial odometry, multi-drone map-based localization, and visual drone tracking algorithms. The measurements from the front-end are fused with graph-based optimization in the back-end. The proposed method achieves centimeter-level relative state estimation accuracy while guaranteeing global consistency in the aerial swarm, as evidenced by the experimental results. Moreover, supported by Omni-swarm, inter-drone collision avoidance can be accomplished without any external devices, demonstrating the potential of Omni-swarm as the foundation of autonomous aerial swarms.

preprint2022arXiv

On the Ergodic Capacity of Reconfigurable Intelligent Surface (RIS)-Aided MIMO Channels

Reconfigurable intelligent surfaces (RISs) have emerged as a promising technique to enhance the system spectral efficiency. This paper investigates the ergodic channel capacity (ECC) of an RIS-aided multiple-input multiple-output channel under the assumption that the transmitter-RIS, RIS-receiver, and transmitter-receiver channels contain deterministic line-of-sight paths. Novel expressions are derived to characterize the upper and lower bounds of the ECC. To unveil more system insights, asymptotic analyses are performed to the system ECC in the limit of large signal-to-noise ratio (SNR) and number of reflecting elements (REs). Theoretical analyses suggest that the RIS's deployment can shape the ECC curve by influencing its high-SNR power offset and the ECC can get improved by increasing the number of REs.

preprint2022arXiv

RCRN: Real-world Character Image Restoration Network via Skeleton Extraction

Constructing high-quality character image datasets is challenging because real-world images are often affected by image degradation. There are limitations when applying current image restoration methods to such real-world character images, since (i) the categories of noise in character images are different from those in general images; (ii) real-world character images usually contain more complex image degradation, e.g., mixed noise at different noise levels. To address these problems, we propose a real-world character restoration network (RCRN) to effectively restore degraded character images, where character skeleton information and scale-ensemble feature extraction are utilized to obtain better restoration performance. The proposed method consists of a skeleton extractor (SENet) and a character image restorer (CiRNet). SENet aims to preserve the structural consistency of the character and normalize complex noise. Then, CiRNet reconstructs clean images from degraded character images and their skeletons. Due to the lack of benchmarks for real-world character image restoration, we constructed a dataset containing 1,606 character images with real-world degradation to evaluate the validity of the proposed method. The experimental results demonstrate that RCRN outperforms state-of-the-art methods quantitatively and qualitatively.

preprint2022arXiv

Research on Stable Obstacle Avoidance Control Strategy for Tracked Intelligent Transportation Vehicles in Non-structural Environment Based on Deep Learning

Existing intelligent driving technology often has a problem in balancing smooth driving and fast obstacle avoidance, especially when the vehicle is in a non-structural environment, and is prone to instability in emergency situations. Therefore, this study proposed an autonomous obstacle avoidance control strategy that can effectively guarantee vehicle stability based on Attention-long short-term memory (Attention-LSTM) deep learning model with the idea of humanoid driving. First, we designed the autonomous obstacle avoidance control rules to guarantee the safety of unmanned vehicles. Second, we improved the autonomous obstacle avoidance control strategy combined with the stability analysis of special vehicles. Third, we constructed a deep learning obstacle avoidance control through experiments, and the average relative error of this system was 15%. Finally, the stability and accuracy of this control strategy were verified numerically and experimentally. The method proposed in this study can ensure that the unmanned vehicle can successfully avoid the obstacles while driving smoothly.

preprint2022arXiv

Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs

On-device ML accelerators are becoming a standard in modern mobile system-on-chips (SoC). Neural architecture search (NAS) comes to the rescue for efficiently utilizing the high compute throughput offered by these accelerators. However, existing NAS frameworks have several practical limitations in scaling to multiple tasks and different target platforms. In this work, we provide a two-pronged approach to this challenge: (i) a NAS-enabling infrastructure that decouples model cost evaluation, search space design, and the NAS algorithm to rapidly target various on-device ML tasks, and (ii) search spaces crafted from group convolution based inverted bottleneck (IBN) variants that provide flexible quality/performance trade-offs on ML accelerators, complementing the existing full and depthwise convolution based IBNs. Using this approach we target a state-of-the-art mobile platform, Google Tensor SoC, and demonstrate neural architectures that improve the quality-performance pareto frontier for various computer vision (classification, detection, segmentation) as well as natural language processing tasks.

preprint2022arXiv

Some Discussions on PHY Security in DF Relay

Physical layer (PHY) security in decode-and-forward (DF) relay systems is discussed. Based on the types of wiretap links, the secrecy performance of three typical secure DF relay models is analyzed. Different from conventional works in this field, rigorous derivations of the secrecy channel capacity are provided from an information-theoretic perspective. Meanwhile, closed-form expressions are derived to characterize the secrecy outage probability (SOP). For the sake of unveiling more system insights, asymptotic analyses are performed on the SOP for a sufficiently large signal-to-noise ratio (SNR). The analytical results are validated by computer simulations and are in excellent agreement.

preprint2022arXiv

Stripes and spin-density waves in the doped two-dimensional Hubbard model: ground state phase diagram

We determine the spin and charge orders in the ground state of the doped two-dimensional (2D) Hubbard model in its simplest form, namely with only nearest-neighbor hopping and on-site repulsion. At half-filling, the ground state is known to be an anti-ferromagnetic Mott insulator. Doping Mott insulators is believed to be relevant to the superconductivity observed in cuprates. A variety of candidates have been proposed for the ground state of the doped 2D Hubbard model. A recent work employing a combination of several state-of-the-art numerical many-body methods, established the stripe order as the ground state near $1/8$ doping at strong interactions. In this work, we apply one of these methods, the cutting-edge constrained-path auxiliary field quantum Monte Carlo method with self-consistently optimized gauge constraints, to systematically study the model as a function of doping and interaction strength. With careful finite size scaling based on large-scale computations, we map out the ground state phase diagram in terms of its spin and charge order. We find that modulated antiferromagnetic order persists from near half-filling to about $1/5$ doping. At lower interaction strengths or larger doping, these ordered states are best described as spin-density waves, with essentially delocalized holes and modest oscillations in charge correlations. When the charge correlations are stronger (large interaction or small doping), they are best described as stripe states, with the holes more localized near the node in the antiferromagnetic spin order. In both cases, we find that the wavelength in the charge correlations is consistent with so-called filled stripes in the pure Hubbard model.

preprint2022arXiv

Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces

Recent state-of-the-art performances of Vision Transformers (ViT) in computer vision tasks demonstrate that a general-purpose architecture, which implements long-range self-attention, could replace the local feature learning operations of convolutional neural networks. In this paper, we extend ViTs to surfaces by reformulating the task of surface learning as a sequence-to-sequence learning problem, by proposing patching mechanisms for general surface meshes. Sequences of patches are then processed by a transformer encoder and used for classification or regression. We validate our method on a range of different biomedical surface domains and tasks: brain age prediction in the developing Human Connectome Project (dHCP), fluid intelligence prediction in the Human Connectome Project (HCP), and coronary artery calcium score classification using surfaces from the Scottish Computed Tomography of the Heart (SCOT-HEART) dataset, and investigate the impact of pretraining and data augmentation on model performance. Results suggest that Surface Vision Transformers (SiT) demonstrate consistent improvement over geometric deep learning methods for brain age and fluid intelligence prediction and achieve comparable performance on calcium score classification to standard metrics used in clinical practice. Furthermore, analysis of transformer attention maps offers clear and individualised predictions of the features driving each task. Code is available on Github: https://github.com/metrics-lab/surface-vision-transformers

preprint2022arXiv

Underwater Acoustic Communication Channel Modeling using Reservoir Computing

Underwater acoustic (UWA) communications have been widely used but greatly impaired due to the complicated nature of the underwater environment. In order to improve UWA communications, modeling and understanding the UWA channel is indispensable. However, there exist many challenges due to the high uncertainties of the underwater environment and the lack of real-world measurement data. In this work, the capability of reservoir computing and deep learning has been explored for modeling the UWA communication channel accurately using real underwater data collected from a water tank with disturbance and from Lake Tahoe. We leverage the capability of reservoir computing for modeling dynamical systems and provided a data-driven approach to modeling the UWA channel using Echo State Network (ESN). In addition, the potential application of transfer learning to reservoir computing has been examined. Experimental results show that ESN is able to model chaotic UWA channels with better performance compared to popular deep learning models in terms of mean absolute percentage error (MAPE), specifically, ESN has outperformed deep neural network by 2% and as much as 40% in benign and chaotic UWA respectively.

preprint2021arXiv

Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)

Partial differential equations (PDEs) are concise and understandable representations of domain knowledge, which are essential for deepening our understanding of physical processes and predicting future responses. However, the PDEs of many real-world problems are uncertain, which calls for PDE discovery. We propose the symbolic genetic algorithm (SGA-PDE) to discover open-form PDEs directly from data without prior knowledge about the equation structure. SGA-PDE focuses on the representation and optimization of PDE. Firstly, SGA-PDE uses symbolic mathematics to realize the flexible representation of any given PDE, transforms a PDE into a forest, and converts each function term into a binary tree. Secondly, SGA-PDE adopts a specially designed genetic algorithm to efficiently optimize the binary trees by iteratively updating the tree topology and node attributes. The SGA-PDE is gradient-free, which is a desirable characteristic in PDE discovery since it is difficult to obtain the gradient between the PDE loss and the PDE structure. In the experiment, SGA-PDE not only successfully discovered nonlinear Burgers' equation, Korteweg-de Vries (KdV) equation, and Chafee-Infante equation, but also handled PDEs with fractional structure and compound functions that cannot be solved by conventional PDE discovery methods.

preprint2021arXiv

Dynamics in direct two-photon transition by frequency combs

Two-photon resonance transition technology has been proven to have a wide range of applications,it's limited by the available wavelength of commercial lasers.The application of optical comb technology with direct two-photon transition (DTPT) will not be restricted by cw lasers.This article will further theoretically analyze the dynamics effects of the DTPT process driven by optical frequency combs. In a three-level atomic system, the population of particles and the amount of momentum transfer on atoms are increased compared to that of the DTPT-free process. The 17% of population increasement in 6-level system of cesium atoms has verified that DTPT process has a robust enhancement on the effect of momentum transfer. It can be used to excite the DTPTs of rubidium and cesium simultaneously with the same mode-locked laser. And this technology has potential applications in cooling different atoms to obtain polar cold molecules, as well as high-precision spectroscopy measurement.

preprint2021arXiv

Enabling Longitudinal Exploratory Analysis of Clinical COVID Data

As the COVID-19 pandemic continues to impact the world, data is being gathered and analyzed to better understand the disease. Recognizing the potential for visual analytics technologies to support exploratory analysis and hypothesis generation from longitudinal clinical data, a team of collaborators worked to apply existing event sequence visual analytics technologies to a longitudinal clinical data from a cohort of 998 patients with high rates of COVID-19 infection. This paper describes the initial steps toward this goal, including: (1) the data transformation and processing work required to prepare the data for visual analysis, (2) initial findings and observations, and (3) qualitative feedback and lessons learned which highlight key features as well as limitations to address in future work.

preprint2021arXiv

Experimental Extraction and Simulation of Charge Trapping during Endurance of FeFET with TiN/HfZrO/SiO2/Si (MFIS) Gate Structure

We investigate the charge trapping during endurance fatigue of FeFET with TiN/Hf0.5Zr0.5O2/SiO2/Si (MFIS) gate structure. We propose a method of experimentally extracting the number of trapped charges during the memory operation, by measuring the charges in the metal gate and Si substrate. We verify that the amount of trapped charges increases during the endurance fatigue process. This is the first time that the trapped charges are directly experimentally extracted and verified to increase during endurance fatigue. Moreover, we model the interplay between the trapped charges and ferroelectric polarization switching during endurance fatigue. Through the consistency of experimental results and simulated data, we demonstrate that as the memory window decreases: 1) The ferroelectric characteristic of Hf0.5Zr0.5O2 is not degraded. 2) The trap density in the upper bandgap of the gate stacks increases. 3) The reason for memory window decrease is increased trapped electrons after program operation but not related to hole trapping/de-trapping. Our work is helpful to study the charge trapping behavior of FeFET and the related endurance fatigue process.

preprint2021arXiv

How Much Communication Resource is Needed to Run a Wireless Blockchain Network?

Blockchain is built on a peer-to-peer network that relies on frequent communications among the distributively located nodes. In particular, the consensus mechanisms (CMs), which play a pivotal role in blockchain, are communication resource-demanding and largely determines blockchain security bound and other key performance metrics such as transaction throughput, latency and scalability. Most blockchain systems are designed in a stable wired communication network running in advanced devices under the assumption of sufficient communication resource provision. However, it is envisioned that the majority of the blockchain node peers will be connected through the wireless network in the future. Constrained by the highly dynamic wireless channel and scarce frequency spectrum, communication can significantly affect blockchain's key performance metrics. Hence, in this paper, we present wireless blockchain networks (WBN) under various commonly used CMs and we answer the question of how much communication resource is needed to run such a network. We first present the role of communication in the four stages of the blockchain procedure. We then discuss the relationship between the communication resource provision and the WBNs performance, for three of the most used blockchain CMs namely, Proof-of-Work (PoW), practical Byzantine Fault Tolerant (PBFT) and Raft. Finally, we provide analytical and simulated results to show the impact of the communication resource provision on blockchain performance.

preprint2021arXiv

Impact of Interlayer and Ferroelectric Materials on Charge Trapping during Endurance Fatigue of FeFET with TiN/HfxZr1-xO2/interlayer/Si (MFIS) Gate Structure

We study the impact of different interlayers and ferroelectric materials on charge trapping during the endurance fatigue of Si FeFET with TiN/HfxZr1-xO2/interlayer/Si (MFIS) gate stack. We have fabricated FeFET devices with different interlayers (SiO2 or SiON) and HfxZr1-xO2 materials (x=0.75, 0.6, 0.5), and directly extracted the charge trapping during endurance fatigue. We find that: 1) The introduction of the N element in the interlayer suppresses charge trapping and defect generation, and improves the endurance characteristics. 2) As the spontaneous polarization (Ps) of the HfxZr1-xO2 decreases from 25.9 μC/cm2 (Hf0.5Zr0.5O2) to 20.3 μC/cm2 (Hf0.6Zr0.4O2), the charge trapping behavior decreases, resulting in the slow degradation rate of memory window (MW) during program/erase cycling; in addition, when the Ps further decreases to 8.1 μC/cm2 (Hf0.75Zr0.25O2), the initial MW nearly disappears (only ~0.02 V). Thus, the reduction of Ps could improve endurance characteristics. On the contract, it can also reduce the MW. Our work helps design the MFIS gate stack to improve endurance characteristics.

preprint2021arXiv

Multistability of Small Reaction Networks

For three typical sets of small reaction networks (networks with two reactions, one irreversible and one reversible reaction, or two reversible-reaction pairs), we completely answer the challenging question: what is the smallest subset of all multistable networks such that any multistable network outside of the subset contains either more species or more reactants than any network in this subset?

preprint2021arXiv

Study of the hidden charm $D\bar{D}^*$ interactions in chiral effective field theory

We study the chiral interactions of the hidden charm $D\bar{D}^*$ system within chiral effective field theory. Chiral Lagrangians are constructed by incorporating the chiral symmetry, heavy quark symmetry as well as proper charge conjugation properties of the heavy mesons. The interacting potentials of the $S$-wave $D\bar{D}^*$ are calculated up to second chiral order at 1-loop level, where complete two-pion exchange interactions are included. We further investigate the behaviors of the potentials in coordinate space, as well as their bound state properties. Our studies indicate that there exists a interacting strength ordering among considered four channels: $\text{str.}[0^+(1^{++})]>\text{str.}[0^-(1^{+-})] > \text{str.}[1^+(1^{+-})] > \text{str.}[1^-(1^{++})]$ where str. stands for the strength of the $D\bar{D}^*$ interaction. Moreover, we find that $X(3872)$ can be treated as a good candidate of $0^+(1^{++})$ molecular state. There also tends to form $0^-(1^{+-})$ and $1^+(1^{+-})$ molecular states and we expect the experiments to search for the predicted multi-structures around the $D\bar{D}^*$ mass region.

preprint2020arXiv

An Achievable Region for the Multiple Access Wiretap Channels with Confidential and Open Messages

This paper investigates the capacity region of a discrete memoryless (DM) multiple access wiretap (MAC-WT) channel where, besides confidential messages, the users have also open messages to transmit. All these messages are intended for the legitimate receiver but only the confidential messages need to be protected from the eavesdropper. By using random coding, we find an achievable secrecy rate region, within which perfect secrecy can be realized, i.e., all users can communicate with the legitimate receiver with arbitrarily small probability of error, while the confidential information leaked to the eavesdropper tends to zero.

preprint2020arXiv

Black Hole Evaporation in Hořava-Lifshitz Gravity

Hořava-Lifshitz (HL) gravity was formulated in hope of solving the non-renormalization problem in Einstein gravity and the ghost problem in higher derivative gravity theories by violating Lorentz invariance. In this work we consider the spherically symmetric neutral AdS black hole evaporation process in HL gravity in various spacetime dimensions $d$, and with detailed balance violation parameter $0\leqslant ε^2\leqslant 1$. We find that the lifetime of the black holes under Hawking evaporation is dimensional dependent, with $d=4,5$ behave differently from $d\geqslant 6$. For the case of $ε=0$, in $d=4,5$, the black hole admits zero temperature state, and the lifetime of the black hole is always infinite. This phenomenon obeys the third law of black hole thermodynamics, and implies that the black holes become an effective remnant towards the end of the evaporation. As $d\geqslant 6$, however, the lifetime of black hole does not diverge with any initial black hole mass, and it is bounded by a time of the order of $\ell^{d-1}$, similar to the case of Schwarzschild-AdS in Einstein gravity (which corresponds to $ε^2=1$), though for the latter this holds for all $d\geqslant 4$. The case of $0<ε^2<1$ is also qualitatively similar with $ε=0$.

preprint2020arXiv

Blockchain-enabled Resource Management and Sharing for 6G Communications

The sixth generation (6G) network must provide performance superior to previous generations in order to meet the requirements of emerging services and applications, such as multi-gigabit transmission rate, even higher reliability, sub 1 millisecond latency and ubiquitous connection for Internet of Everything. However, with the scarcity of spectrum resources, efficient resource management and sharing is crucial to achieve all these ambitious requirements. One possible technology to enable all of this is blockchain, which has recently gained significance and will be of paramount importance to 6G networks and beyond due to its inherent properties. In particular, the integration of blockchain in 6G will enable the network to monitor and manage resource utilization and sharing efficiently. Hence, in this article, we discuss the potentials of blockchain for resource management and sharing in 6G using multiple application scenarios namely, Internet of things, device-to-device communications, network slicing, and inter-domain blockchain ecosystems.

preprint2020arXiv

Computational LEGO Technic Design

We introduce a method to automatically compute LEGO Technic models from user input sketches, optionally with motion annotations. The generated models resemble the input sketches with coherently-connected bricks and simple layouts, while respecting the intended symmetry and mechanical properties expressed in the inputs. This complex computational assembly problem involves an immense search space, and a much richer brick set and connection mechanisms than regular LEGO. To address it, we first comprehensively model the brick properties and connection mechanisms, then formulate the construction requirements into an objective function, accounting for faithfulness to input sketch, model simplicity, and structural integrity. Next, we model the problem as a sketch cover, where we iteratively refine a random initial layout to cover the input sketch, while guided by the objective. At last, we provide a working system to analyze the balance, stress, and assemblability of the generated model. To evaluate our method, we compared it with four baselines and professional designs by a LEGO expert, demonstrating the superiority of our automatic designs. Also, we recruited several users to try our system, employed it to create models of varying forms and complexities, and physically built most of them.

preprint2020arXiv

Cosmic Censorship and the Evolution of d-Dimensional Charged Evaporating Black Holes

The cosmic censorship conjecture essentially states that naked singularities should not form from generic initial conditions. Since black hole parameters can change their values under Hawking evaporation, one has to ask whether it is possible to reach extremality by simply waiting for the black hole to evaporate. If so a slight perturbation would likely render the singularity naked. Fortunately, at least for the case of asymptotically flat 4-dimensional Reissner-Nordström black hole, Hiscock and Weems showed that it can never reach extremality despite the fact that for a sufficiently massive black hole, its charge-to-mass ratio can increase during Hawking evaporation. Hence cosmic censorship is never violated by Hawking emission. However, we know that under some processes, it is easier to violate cosmic censorship in higher dimensions, therefore it is crucial to generalize Hiscock and Weems model to dimensions above four to check cosmic censorship. We found that Hawking evaporation cannot lead to violation of cosmic censorship even in higher dimensional Reissner-Nordström spacetimes. Morerover, it seems to be more difficult to reach extremality as number of dimension increases.

preprint2020arXiv

Discovery potential for the LHCb fully-charm tetraquark $X(6900)$ state via $\bar{p}p$ annihilation reaction

Inspired by the observation of the fully-charm tetraquark $X(6900)$ state at LHCb, the production of $X(6900)$ in $\bar{p}p\rightarrow J/ψJ/ψ$ reaction is studied within an effective Lagrangian approach and Breit-Wigner formula. The numerical results show that the cross section of $X(6900)$ at the c.m. energy of 6.9 GeV is much larger than that from the background contribution. Moreover, we estimate dozens of signal events can be detected by D0 experiment, which indicates that searching for the $X(6900)$ via antiproton-proton scattering may be a very important and promising way. Therefore, related experiments are suggested to be carried out.

preprint2020arXiv

DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data

In recent years, data-driven methods have been developed to learn dynamical systems and partial differential equations (PDE). The goal of such work is discovering unknown physics and the corresponding equations. However, prior to achieving this goal, major challenges remain to be resolved, including learning PDE under noisy data and limited discrete data. To overcome these challenges, in this work, a deep-learning based data-driven method, called DL-PDE, is developed to discover the governing PDEs of underlying physical processes. The DL-PDE method combines deep learning via neural networks and data-driven discovery of PDE via sparse regressions. In the DL-PDE, a neural network is first trained, and then a large amount of meta-data is generated, and the required derivatives are calculated by automatic differentiation. Finally, the form of PDE is discovered by sparse regression. The proposed method is tested with physical processes, governed by groundwater flow equation, convection-diffusion equation, Burgers equation and Korteweg-de Vries (KdV) equation, for proof-of-concept and applications in real-world engineering settings. The proposed method achieves satisfactory results when data are noisy and limited.

preprint2020arXiv

DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm

Data-driven methods have recently been developed to discover underlying partial differential equations (PDEs) of physical problems. However, for these methods, a complete candidate library of potential terms in a PDE are usually required. To overcome this limitation, we propose a novel framework combining deep learning and genetic algorithm, called DLGA-PDE, for discovering PDEs. In the proposed framework, a deep neural network that is trained with available data of a physical problem is utilized to generate meta-data and calculate derivatives, and the genetic algorithm is then employed to discover the underlying PDE. Owing to the merits of the genetic algorithm, such as mutation and crossover, DLGA-PDE can work with an incomplete candidate library. The proposed DLGA-PDE is tested for discovery of the Korteweg-de Vries (KdV) equation, the Burgers equation, the wave equation, and the Chaffee-Infante equation, respectively, for proof-of-concept. Satisfactory results are obtained without the need for a complete candidate library, even in the presence of noisy and limited data.

preprint2020arXiv

Mean Field Game and Decentralized Intelligent Adaptive Pursuit Evasion Strategy for Massive Multi-Agent System under Uncertain Environment

In this paper, a novel decentralized intelligent adaptive optimal strategy has been developed to solve the pursuit-evasion game for massive Multi-Agent Systems (MAS) under uncertain environment. Existing strategies for pursuit-evasion games are neither efficient nor practical for large population multi-agent system due to the notorious "Curse of dimensionality" and communication limit while the agent population is large. To overcome these challenges, the emerging mean field game theory is adopted and further integrated with reinforcement learning to develop a novel decentralized intelligent adaptive strategy with a new type of adaptive dynamic programing architecture named the Actor-Critic-Mass (ACM). Through online approximating the solution of the coupled mean field equations, the developed strategy can obtain the optimal pursuit-evasion policy even for massive MAS under uncertain environment. In the proposed ACM learning based strategy, each agent maintains five neural networks, which are 1) the critic neural network to approximate the solution of the HJI equation for each individual agent; 2) the mass neural network to estimate the population density function (i.e., mass) of the group; 3) the actor neural network to approximate the decentralized optimal strategy, and 4) two more neural networks are designed to estimate the opponents' group mass as well as the optimal cost function. Eventually, a comprehensive numerical simulation has been provided to demonstrate the effectiveness of the designed strategy.

preprint2020arXiv

Recently observed $P_c$ as molecular states and possible mixture of $P_c(4457)$

Recently observed spectrum of $P_c$ states exhibits a strong link to $Σ_c \bar{D}^{(*)}$ thresholds. In spite of successful molecular interpretations, we still push forward to wonder whether there exist finer structures. Utilizing the effecitve lagrangians respecting heavy quark symmetry and chiral symmetry, as well as instantaneous Bethe-Salpeter equations, we investigate the $Σ_c \bar{D}^{(*)}$ interactions and three $P_c$ states. We confirm that $P_c(4312)$ and $P_c(4440)$ are good candidates of $Σ_c \bar{D}$ and $Σ_c \bar{D}^{*}$ molecules with spin-$\frac12$, respectively. Unlike other molecular calculations, our results indicate $P_c(4457)$ signal might be a mixture of spin-$\frac32$ and spin-$\frac12$ $Σ_c \bar{D}^{*}$ molecules, where the latter one appears to be an excitation of $P_c(4440)$. Therefore we conclude that, confronting three LHCb $P_c$ signals, there may exist not three, but four molecular states.

preprint2020arXiv

The Clock and Control System for the ATLAS Liquid Argon Calorimeter Phase-I Upgrade

A Liquid-argon Trigger Digitizer Board (LTDB) is being developed to upgrade the ATLAS Liquid Argon Calorimeter Phase-I trigger electronics. The LTDB located at the front end needs to obtain the clock signals and be configured and monitored remotely from the back end. A clock and control system is being developed for the LTDB and the major functions of the system have been evaluated. The design and evaluation of the clock and control system are presented in this paper.

preprint2020arXiv

TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network

We introduce the first neural optimization framework to solve a classical instance of the tiling problem. Namely, we seek a non-periodic tiling of an arbitrary 2D shape using one or more types of tiles: the tiles maximally fill the shape's interior without overlaps or holes. To start, we reformulate tiling as a graph problem by modeling candidate tile locations in the target shape as graph nodes and connectivity between tile locations as edges. Further, we build a graph convolutional neural network, coined TilinGNN, to progressively propagate and aggregate features over graph edges and predict tile placements. TilinGNN is trained by maximizing the tiling coverage on target shapes, while avoiding overlaps and holes between the tiles. Importantly, our network is self-supervised, as we articulate these criteria as loss terms defined on the network outputs, without the need of ground-truth tiling solutions. After training, the runtime of TilinGNN is roughly linear to the number of candidate tile locations, significantly outperforming traditional combinatorial search. We conducted various experiments on a variety of shapes to showcase the speed and versatility of TilinGNN. We also present comparisons to alternative methods and manual solutions, robustness analysis, and ablation studies to demonstrate the quality of our approach.

preprint2020arXiv

Tri-graph Information Propagation for Polypharmacy Side Effect Prediction

The use of drug combinations often leads to polypharmacy side effects (POSE). A recent method formulates POSE prediction as a link prediction problem on a graph of drugs and proteins, and solves it with Graph Convolutional Networks (GCNs). However, due to the complex relationships in POSE, this method has high computational cost and memory demand. This paper proposes a flexible Tri-graph Information Propagation (TIP) model that operates on three subgraphs to learn representations progressively by propagation from protein-protein graph to drug-drug graph via protein-drug graph. Experiments show that TIP improves accuracy by 7%+, time efficiency by 83$\times$, and space efficiency by 3$\times$.

preprint2020arXiv

Unruh Quantum Otto heat engine with level degeneracy

We investigate the Unruh quantum Otto heat engine with level degeneracy. An effectively two level system, where the ground state is non-degenerate and the excited state is $n$-fold degenerate, is acting as the working substance, and the vacuum of massless free scalar field serves as a thermal bath via the Unruh effect. We calculate the heat and work at each step of the Unruh quantum Otto cycle and study the features of the heat engine. The efficiency of the heat engine depends only on the excited energy values of the two level system, not on its level degeneracy. However, the degeneracy acts as a kind of thermodynamic resource and helps us to extract more work than in the non-degenerate case. The extractable work has a finite upper bound, corresponding to $n\rightarrow \infty$.

preprint2016arXiv

Galaxy Properties and UV Escape Fractions During Epoch of Reionization: Results from the Renaissance Simulations

Cosmic reionization is thought to be primarily fueled by the first generations of galaxies. We examine their stellar and gaseous properties, focusing on the star formation rates and the escape of ionizing photons, as a function of halo mass, redshift, and environment using the full suite of the {\it Renaissance Simulations} with an eye to provide better inputs to global reionization simulations. This suite, carried out with the adaptive mesh refinement code Enzo, is unprecedented in terms of their size and physical ingredients. The simulations probe overdense, average, and underdense regions of the universe of several hundred comoving Mpc$^3$, each yielding a sample of over 3,000 halos in the mass range $10^7 - 10^{9.5}~\Ms$ at their final redshifts of 15, 12.5, and 8, respectively. In the process, we simulate the effects of radiative and supernova feedback from 5,000 to 10,000 metal-free (Population III) stars in each simulation. We find that halos as small as $10^7~\Ms$ are able to form stars due to metal-line cooling from earlier enrichment by massive Population III stars. However, we find such halos do not form stars continuously. Using our large sample, we find that the galaxy-halo occupation fraction drops from unity at virial masses above $10^{8.5}~\Ms$ to $\sim$50\% at $10^8 ~\Ms$ and $\sim$10\% at $10^7~\Ms$, quite independent of redshift and region. Their average ionizing escape fraction is $\sim$5\% in the mass range $10^8 - 10^9~\Ms$ and increases with decreasing halo mass below this range, reaching 40--60\% at $10^7~\Ms$. Interestingly, we find that the escape fraction varies between 10--20\% in halos with virial masses $\sim 3 \times 10^9~\Ms$. Taken together, our results confirm the importance of the smallest galaxies as sources of ionizing radiation contributing to the reionization of the universe.

preprint2016arXiv

Heat kernel coefficients on Kahler manifolds

Polterovich proved a remarkable closed formula for heat kernel coefficients of the Laplace operator on compact Riemannian manifolds involving powers of Laplacians acting on the distance function. In the case of Kähler manifolds, we prove a combinatorial formula for powers of the complex Laplacian and use it to derive an explicit graph theoretic formula for the numerics in heat coefficients as a linear combination of metric jets based on Polterovich's formula.

preprint2016arXiv

Implication the observed $ψ(3770)\to p\bar{p}π^0$ for studying the $p\bar{p}\to ψ(3770)π^0$ process

We study the charmonium $p \bar{p} \to ψ(3770) π^0$ reaction using effective lagrangian approach where the contributions from well established $N^*$ states are considered, and all parameters are fixed in the process of $e^+e^- \to p \bar{p}π^0$ at center of mass energy $\sqrt{s} = 3.773$ GeV. The experimental data on the line shape of the mass distribution of the $e^+e^- \to p\bar{p}π^0$ can be well reproduced. Based on the studying of $e^+e^- \to p \bar{p}π^0$, the total and differential cross sections of the $p \bar{p} \to ψ(3770) π^0$ reaction are predicted. At the same time we evaluated also the cross sections of the $p \bar{p} \to ψ(3686) π^0$ reaction. It is shown that the contribution of nucleon pole to this reaction is largest close to the reaction threshold. However, the interference between nucleon pole and the other nucleon resonance can still change the angle distributions significantly. Those theoretical results may be test by the future experiments at $\overline{\mbox{P}}$ANDA.

preprint2016arXiv

Late Pop III Star Formation During the Epoch of Reionization: Results from the Renaissance Simulations

We present results on the formation of Pop III stars at redshift 7.6 from the Renaissance Simulations, a suite of extremely high-resolution and physics-rich radiation transport hydrodynamics cosmological adaptive-mesh refinement simulations of high redshift galaxy formation performed on the Blue Waters supercomputer. In a survey volume of about 220 comoving Mpc$^3$, we found 14 Pop III galaxies with recent star formation. The surprisingly late formation of Pop III stars is possible due to two factors: (i) the metal enrichment process is local and slow, leaving plenty of pristine gas to exist in the vast volume; and (ii) strong Lyman-Werner radiation from vigorous metal-enriched star formation in early galaxies suppresses Pop III formation in ("not so") small primordial halos with mass less than $\sim$ 3 $\times$ 10$^7$ M$_\odot$. We quantify the properties of these Pop III galaxies and their Pop III star formation environments. We look for analogues to the recently discovered luminous Ly $α$ emitter CR7 (Sobral et al. 2015), which has been interpreted as a Pop III star cluster within or near a metal-enriched star forming galaxy. We find and discuss a system similar to this in some respects, however the Pop III star cluster is far less massive and luminous than CR7 is inferred to be.

preprint2016arXiv

Length Scales and Turbulent Properties of Magnetic Fields in Simulated Galaxy Clusters

Additional physics beyond standard hydrodynamics is needed to fully model the intracluster medium (ICM); however, as we move to more sophisticated models, it is important to consider the role of magnetic fields and the way the fluid approximation breaks down. This paper represents a first step towards developing a self-consistent model of the ICM by characterizing the statistical properties of magnetic fields in cosmological simulations of galaxy clusters. We find that plasma conditions are largely homogeneous across a range of cluster masses and relaxation states. We also find that the magnetic field length scales are resolution dependent and not based on any particular physical process. Energy transfer mechanisms and scales are also identified, and imply the existence of small scale dynamo action. The scales of the small scale dynamo are resolution limited and driven by numerical resistivity and viscosity.

preprint2016arXiv

Maxwell's equal area law for Lovelock Thermodynamics

We present the construction of Maxwell's equal area law for the Guass-Bonnet AdS black holes in $d=5,6$ and third order Lovelock AdS black holes in $d=7,8$. The equal area law can be used to find the number and location of the points of intersection in the plots of Gibbs free energy, so that we can get the thermodynamically preferred solution which corresponds to the first order phase transition. We obtain the radius of the small and large black holes in the phase transition which share the same Gibbs free energy. The case with two critical points is explored in much more details. The latent heat is also studied.

preprint2016arXiv

Object Boundary Guided Semantic Segmentation

Semantic segmentation is critical to image content understanding and object localization. Recent development in fully-convolutional neural network (FCN) has enabled accurate pixel-level labeling. One issue in previous works is that the FCN based method does not exploit the object boundary information to delineate segmentation details since the object boundary label is ignored in the network training. To tackle this problem, we introduce a double branch fully convolutional neural network, which separates the learning of the desirable semantic class labeling with mask-level object proposals guided by relabeled boundaries. This network, called object boundary guided FCN (OBG-FCN), is able to integrate the distinct properties of object shape and class features elegantly in a fully convolutional way with a designed masking architecture. We conduct experiments on the PASCAL VOC segmentation benchmark, and show that the end-to-end trainable OBG-FCN system offers great improvement in optimizing the target semantic segmentation quality.

preprint2016arXiv

Thermodynamics and holographic entanglement entropy for spherical black holes in 5D Gauss-Bonnet gravity

The holographic entanglement entropy is studied numerically in (4+1)-dimensional spherically symmetric Gauss-Bonnet AdS black hole spacetime with compact boundary. On the bulk side the black hole spacetime undergoes a van der Waals-like phase transition in the extended phase space, which is reviewed with emphasis on the behavior on the temperature-entropy plane. On the boundary, we calculated the regularized HEE of a disk region of different sizes. We find strong numerical evidence for the failure of equal area law for isobaric curves on the temperature-HEE plane and for the correctness of first law of entanglement entropy, and briefly give an explanation for why the latter may serve as a reason for the former, i.e. the failure of equal area law on the temperature-HEE plane.

preprint2016arXiv

Understanding $B^-\rightarrow X(3823)K^-$ via rescattering mechanism and predicting $B^-\to η_{c2} (^1D_2)/ψ_3(^3D_3)K^-$

We study the observed $B^-\rightarrow X(3823)K^-$ decay via rescattering mechanism and show that this branching ratio is well reproduced by this mechanism. We further extend this theoretical framework to investigate the decays of $B^-\toη_{c2} (^1D_2)/ψ_3(^3D_3)K^-$, where the $η_{c2} (^1D_2)$ and the $ψ_3(^3D_3)$ are $D$-wave charmonium partners of the $X(3823)$. Our results show that the branching ratios, $B^-\to η_{c2}(^1D_2) K^-$ and $B^-\to ψ_3(^3D_3) K^-$, are of the order of $10^{-5}$, which can be accessible at LHCb, Belle and forthcoming BelleII.

preprint2016arXiv

Using $X(3823)\to J/ψπ^+π^-$ to identify coupled-channel effects

Very recently, the Belle and BESIII experiments observed a new charmonium-like state $X(3823)$, which is a good candidate for the $D$-wave charmonium $ψ(1^3D_2)$. Because the $X(3823)$ is just near the $D\bar{D}^*$ threshold, the decay $X(3823)\to J/ψπ^+π^-$ can be a golden channel to test the significance of coupled-channel effects. In this work, this decay is considered including both the hidden-charm dipion and the usual quantum chromodynamics multipole expansion (QCDME) contributions. The partial decay width, the dipion invariant mass spectrum distribution $\mathrm{d}Γ[X(3823)\to J/ψπ^+π^-]/\mathrm{d}m_{π^+π^-}$, and the corresponding $\mathrm{d}Γ[X(3823)\to J/ψπ^+π^-]/\mathrm{d}\cosθ$ distribution are computed. Many parameters are determined from existing experimental data, so the results depend mainly only on one unknown phase between the QCDME and hidden-charm dipion amplitudes.

preprint2016arXiv

X-ray Background at High Redshifts from Pop III Remnants: Results from Pop III star formation rates in the Renaissance Simulations

Due to their long mean free paths, X-rays are expected to have many significant impacts globally on the properties of the intergalactic medium (IGM) by their heating and ionizing processes on large scales. At high redshifts, X-rays from Population (Pop) III binaries might have important effects on cosmic reionization and the Lyman alpha forest. As a continuation of our previous work on Pop III binary X-rays (Xu et al. 2014), we use the Pop III distribution and evolution from the Renaissance Simulations, a suite of self-consistent cosmological radiation hydrodynamics simulations of the formation of the first galaxies, to calculate the X-ray luminosity density and background over the redshift range 20 > z > 7.6. As we find that Pop III star formation continues at a low, nearly constant rate to the end of reionization, X-rays are being continuously produced at significant rates compared to other possible X-ray sources, such as AGNs and normal X-ray binaries during the same period of time. We estimate that Pop III binaries produce approximately 6 eV of energy in the X-rays per hydrogen atom. We calculate the X-ray background for different monochromatic photon energies. KeV X-rays redshift and accumulate to produce a strong X-ray background spectrum extending to roughly 500 eV. The X-ray background is strong enough to heat the IGM to ~ 1000 K and to ionize a few percent of the neutral hydrogen. These effects are important for an understanding of the neutral hydrogen hyperfine transition 21-cm line signatures, the Ly alpha forest, and optical depth of the CMB to Thomson scattering.

preprint2015arXiv

Evaluation of commercial ADC radiation tolerance for accelerator experiments

Electronic components used in high energy physics experiments are subjected to a radiation background composed of high energy hadrons, mesons and photons. These particles can induce permanent and transient effects that affect the normal device operation. Ionizing dose and displacement damage can cause chronic damage which disable the device permanently. Transient effects or single event effects are in general recoverable with time intervals that depend on the nature of the failure. The magnitude of these effects is technology dependent with feature size being one of the key parameters. Analog to digital converters are components that are frequently used in detector front end electronics, generally placed as close as possible to the sensing elements to maximize signal fidelity. We report on radiation effects tests conducted on 17 commercially available analog to digital converters and extensive single event effect measurements on specific twelve and fourteen bit ADCs that presented high tolerance to ionizing dose. Mitigation strategies for single event effects (SEE) are discussed for their use in the large hadron collider environment.

preprint2015arXiv

Probing The Ultraviolet Luminosity Function of the Earliest Galaxies with the Renaissance Simulations

In this paper, we present the first results from the Renaissance Simulations, a suite of extremely high-resolution and physics-rich AMR calculations of high redshift galaxy formation performed on the Blue Waters supercomputer. These simulations contain hundreds of well-resolved galaxies at $z \sim 25-8$, and make several novel, testable predictions. Most critically, we show that the ultraviolet luminosity function of our simulated galaxies is consistent with observations of high-z galaxy populations at the bright end of the luminosity function (M$_{1600} \leq -17$), but at lower luminosities is essentially flat rather than rising steeply, as has been inferred by Schechter function fits to high-z observations, and has a clearly-defined lower limit in UV luminosity. This behavior of the luminosity function is due to two factors: (i) the strong dependence of the star formation rate on halo virial mass in our simulated galaxy population, with lower-mass halos having systematically lower star formation rates and thus lower UV luminosities; and (ii) the fact that halos with virial masses below $\simeq 2 \times 10^8$ M$_\odot$ do not universally contain stars, with the fraction of halos containing stars dropping to zero at $\simeq 7 \times 10^6$ M$_\odot$. Finally, we show that the brightest of our simulated galaxies may be visible to current and future ultra-deep space-based surveys, particularly if lensed regions are chosen for observation.

preprint2015arXiv

Spatially Extended 21 cm Signal from Strongly Clustered UV and X-Ray Sources in the Early Universe

We present our prediction for the local 21 cm differential brightness temperature ($δT_{b}$) from a set of strongly clustered sources of Population III (Pop III) and II (Pop II) objects in the early Universe, by a numerical simulation of their formation and radiative feedback. These objects are located inside a highly biased environment, which is a rare, high-density peak ("Rarepeak") extending to $\sim7$ comoving Mpc. We study the impact of ultraviolet (UV) and X-ray photons on the intergalactic medium (IGM) and the resulting $δT_{b}$, when Pop III stars are assumed to emit X-ray photons by forming X-ray binaries very efficiently. We parameterize the rest-frame spectral energy distribution (SED) of X-ray photons, which regulates X-ray photon-trapping, IGM-heating, secondary Lyman-alpha pumping and the resulting morphology of $δT_{b}$. A combination of emission ($δT_{b}>0$) and absorption ($δT_{b}<0$) regions appears in varying amplitudes and angular scales. The boost of the signal by the high-density environment ($δ\sim0.64$) and on a relatively large scale combine to make Rarepeak a discernible, spatially-extended ($θ\sim10'$) object for 21 cm observation at $13\lesssim z\lesssim17$, which is found to be detectable as a single object by SKA with integration time of $\sim1000$ hours. Power spectrum analysis by some of the SKA precursors (LOFAR, MWA, PAPER) of such rare peaks is found difficult due to the rarity of these peaks, and the contribution only by these rare peaks to the total power spectrum remains subdominant compared to that by all astrophysical sources.

preprint2014arXiv

Extended phase space thermodynamics for third order Lovelock black holes in diverse dimensions

Treating the cosmological constant as thermodynamic pressure and its conjugate as thermodynamic volume, we investigate the critical behavior of the third order Lovelock black holes in diverse dimensions. For black hole horizons with different normalized sectional curvature $k=0,\pm1$, the corresponding critical behaviors differ drastically. For $k=0$, there is no critical point in the extended thermodynamic phase space. For $k=-1$, there is a single critical point in any dimension $d\geq 7$, and for $k=+1$, there is a single critical point in $7$ dimension and two critical points in $8,9,10,11$ dimensions. We studied the corresponding phase structures in all possible cases.

preprint2014arXiv

Gauss-Bonnet coupling constant as a free thermodynamical variable and the associated criticality

The thermodynamic phase space of Gauss-Bonnet (GB) AdS black holes is extended, taking the inverse of the GB coupling constant as a new thermodynamic pressure $P_{\mathrm{GB}}$. We studied the critical behavior associated with $P_{\mathrm{GB}}$ in the extended thermodynamic phase space at fixed cosmological constant and electric charge. The result shows that when the black holes are neutral, the associated critical points can only exist in five dimensional GB-AdS black holes with spherical topology, and the corresponding critical exponents are identical to those for Van der Waals system. For charged GB-AdS black holes, it is shown that there can be only one critical point in five dimensions (for black holes with either spherical or hyperbolic topologies), which also requires the electric charge to be bounded within some appropriate range; while in $d>5$ dimensions, there can be up to two different critical points at the same electric charge, and the phase transition can occur only at temperatures which are not in between the two critical values.

preprint2014arXiv

Heating the IGM by X-rays from Population III Binaries in High Redshift Galaxies

Due to their long mean free path, X-rays are expected to have an important impact on cosmic reionization by heating and ionizing the IGM on large scales, especially after simulations have suggested that Population III stars may form in pairs at redshifts as high as 20 - 30. We use the Pop III distribution and evolution from a self-consistent cosmological radiation hydrodynamics simulation of the formation of the first galaxies and a simple Pop III X-ray binaries model to estimate their X-ray output in a high density region larger than 100 comoving (Mpc)$^3$. We then combine three different methods --- ray tracing, a one-zone model, and X-ray background modeling --- to investigate the X-ray propagation, intensity distribution, and long term effects on the IGM thermal and ionization state. The efficiency and morphology of photo-heating and photo-ionization are dependent on the photon energies. The sub-keV X-rays only impact the IGM near the sources while the keV photons contribute significantly to the X-ray background and heat and ionize the IGM smoothly. The X-rays just below 1 keV are most efficient in heating and ionizing the IGM. We find that the IGM might be heated to over 100 K by $z=10$ and the high density source region might reach 10$^4$ K, limited by atomic hydrogen cooling. This may be important for predicting the 21-cm neutral hydrogen signals. But, on the other hand, the free electrons from X-ray ionizations are not enough to contribute significantly to the optical depth of CMB to the Thomson scattering.

preprint2014arXiv

Newly observed $B(5970)$ and the predictions of its spin and strange partners

Using the effective Lagrangian approach, we study the decay behavior of the newly observed $B(5970)$ meson, which is assigned as the $2^3S_1$ state in the $B$ meson family. What is more important is that we predict the information of the partial and total decay decay widths of $B(2^1S_0)$, $B_s(2^1S_0)$ and $B_s(2^3S_1)$ as the spin and strange partners of $B(5970)$, which is valuable to future experimental search for these missing states.

preprint2014arXiv

Numerical analysis of the production of $D^{(*)}(3000)$, $D_{sJ}(3040)$ and their partners through the semileptonic decays of $B_{(s)}$ mesons in terms of the light-front quark model

Inspired by the newly observed $D^{(*)}(3000)$, and $D_{sJ}(3040)$ states, in this work we study the production of $D^{(*)}(3000)$, $D_{sJ}(3040)$ and their partners through the semileptonic decays of $B_{(s)}$ mesons, where the light-front Quark model is applied to the whole calculation. Our numerical results indicate that the $B_{(s)}$ semileptonic decays into the $2P$ states of the charmed or charmed-strange meson family have considerable branching ratios, which shows that these semileptonic decays can be accessible at future experiments, especially LHCb and the forthcoming Belle II.

preprint2014arXiv

Scaling Relations for Galaxies Prior to Reionization

The first galaxies in the Universe are the building blocks of all observed galaxies. We present scaling relations for galaxies forming at redshifts $z \ge 15$ when reionization is just beginning. We utilize the ``Rarepeak' cosmological radiation hydrodynamics simulation that captures the complete star formation history in over 3,300 galaxies, starting with massive Population III stars that form in dark matter halos as small as ~$10^6 M_\odot$. We make various correlations between the bulk halo quantities, such as virial, gas, and stellar masses and metallicities and their respective accretion rates, quantifying a variety of properties of the first galaxies up to halo masses of $10^9 M_\odot$. Galaxy formation is not solely relegated to atomic cooling halos with virial temperatures greater than $10^4$ K, where we find a dichotomy in galaxy properties between halos above and below this critical mass scale. Halos below the atomic cooling limit have a stellar mass -- halo mass relationship $\log M_\star \simeq 3.5 + 1.3\log(M_{\rm vir} / 10^7 M_\odot)$. We find a non-monotonic relationship between metallicity and halo mass for the smallest galaxies. Their initial star formation events enrich the interstellar medium and subsequent star formation to a median of $10^{-2} Z_\odot$ and $10^{-1.5} Z_\odot$, respectively, in halos of total mass $10^7 M_\odot$ that is then diluted by metal-poor inflows, well beyond Population III pre-enrichment levels of $10^{-3.5} Z_\odot$. The scaling relations presented here can be employed in models of reionization, galaxy formation and chemical evolution in order to consider these galaxies forming prior to reionization.

preprint2013arXiv

An explicit formula of hitting times for random walks on graphs

We prove an explicit formula of hitting times in terms of enumerations of spanning trees for random walks on general connected graphs. We apply the formula to improve Lawler's bound of hitting times for general graphs, prove a sharp bound of hitting times for adjacent vertices and derive closed formulas of hitting times for some special graphs.

preprint2013arXiv

Enzo: An Adaptive Mesh Refinement Code for Astrophysics

This paper describes the open-source code Enzo, which uses block-structured adaptive mesh refinement to provide high spatial and temporal resolution for modeling astrophysical fluid flows. The code is Cartesian, can be run in 1, 2, and 3 dimensions, and supports a wide variety of physics including hydrodynamics, ideal and non-ideal magnetohydrodynamics, N-body dynamics (and, more broadly, self-gravity of fluids and particles), primordial gas chemistry, optically-thin radiative cooling of primordial and metal-enriched plasmas (as well as some optically-thick cooling models), radiation transport, cosmological expansion, and models for star formation and feedback in a cosmological context. In addition to explaining the algorithms implemented, we present solutions for a wide range of test problems, demonstrate the code's parallel performance, and discuss the Enzo collaboration's code development methodology.

preprint2013arXiv

Finite Horizon Adaptive Optimal Distributed Power Allocation for Enhanced Cognitive Radio Network in the Presence of Channel Uncertainties

In this paper, novel enhanced Cognitive Radio Network is considered by using power control where secondary users are allowed to use wireless resources of the primary users when primary users are deactivated, but also allow secondary users to coexist with primary users while primary users are activated by managing interference caused from secondary users to primary users. Therefore, a novel finite horizon adaptive optimal distributed power allocation scheme is proposed by incorporating the effect of channel uncertainties for enhanced cognitive radio network in the presence of wireless channel uncertainties under two cases. In Case 1, proposed scheme can force the Signal-to-interference (SIR) of the secondary users to converge to a higher target value for increasing network throughput when primary users' are not communicating within finite horizon. Once primary users are activated as in the Case 2, proposed scheme can not only force the SIR of primary users to converge to a higher target SIR, but also force the SIR of secondary users to converge to a lower value for regulating their interference to Pus during finite time period. In order to mitigate the attenuation of SIR due to channel uncertainties the proposed novel finite horizon adaptive optimal distributed power allocation allows the SIR of both primary users' and secondary users' to converge to a desired target SIR while minimizing the energy consumption within finite horizon. Simulation results illustrate that this novel finite horizon adaptive optimal distributed power allocation scheme can converge much faster and cost less energy than others by adapting to the channel variations optimally.

preprint2013arXiv

Hybrid Affinity Propagation

In this paper, we address a problem of managing tagged images with hybrid summarization. We formulate this problem as finding a few image exemplars to represent the image set semantically and visually, and solve it in a hybrid way by exploiting both visual and textual information associated with images. We propose a novel approach, called homogeneous and heterogeneous message propagation ($\text{H}^\text{2}\text{MP}$). Similar to the affinity propagation (AP) approach, $\text{H}^\text{2}\text{MP}$ reduce the conventional \emph{vector} message propagation to \emph{scalar} message propagation to make the algorithm more efficient. Beyond AP that can only handle homogeneous data, $\text{H}^\text{2}\text{MP}$ generalizes it to exploit extra heterogeneous relations and the generalization is non-trivial as the reduction to scalar messages from vector messages is more challenging. The main advantages of our approach lie in 1) that $\text{H}^\text{2}\text{MP}$ exploits visual similarity and in addition the useful information from the associated tags, including the associations relation between images and tags and the relations within tags, and 2) that the summary is both visually and semantically satisfactory. In addition, our approach can also present a textual summary to a tagged image collection, which can be used to automatically generate a textual description. The experimental results demonstrate the effectiveness and efficiency of the roposed approach.

preprint2013arXiv

Image Tag Refinement by Regularized Latent Dirichlet Allocation

Tagging is nowadays the most prevalent and practical way to make images searchable. However, in reality many manually-assigned tags are irrelevant to image content and hence are not reliable for applications. A lot of recent efforts have been conducted to refine image tags. In this paper, we propose to do tag refinement from the angle of topic modeling and present a novel graphical model, regularized Latent Dirichlet Allocation (rLDA). In the proposed approach, tag similarity and tag relevance are jointly estimated in an iterative manner, so that they can benefit from each other, and the multi-wise relationships among tags are explored. Moreover, both the statistics of tags and visual affinities of images in the corpus are explored to help topic modeling. We also analyze the superiority of our approach from the deep structure perspective. The experiments on tag ranking and image retrieval demonstrate the advantages of the proposed method.

preprint2013arXiv

MHD Turbulence and Cosmic Ray Reacceleration in Galaxy Clusters

Cosmological MHD simulations of galaxy cluster formation show a significant amplification of seed magnetic fields. We developed a novel method to decompose cluster magnetized turbulence into modes and showed that the fraction of the fast mode is fairly large, around 1/4 in terms of energy. This is larger than that was estimated before, which implies that cluster turbulence interacts with cosmic rays rather efficiently. We propose a framework to deal with electron and proton reacceleration in galaxy clusters that includes feedback on turbulence. In particular, we establish a new upper limit on proton and electron fluxes based on turbulence intensity. These findings, along with detailed modeling of reacceleration, will help to reconcile the observed giant radio haloes and the unobserved diffuse gamma-ray emission from these clusters.

preprint2013arXiv

Population III Stars and Remnants in High Redshift Galaxies

Recent simulations of Population III star formation have suggested that some fraction form in binary systems, in addition to having a characteristic mass of tens of solar masses. The deaths of metal-free stars result in the initial chemical enrichment of the universe and the production of the first stellar-mass black holes. Here we present a cosmological adaptive mesh refinement simulation of an overdense region that forms a few 10^9 Msun dark matter halos and over 13,000 Population III stars by redshift 15. We find that most halos do not form Population III stars until they reach Mvir ~ 10^7 Msun because this biased region is quickly enriched from both Population III and galaxies, which also produce high levels of ultraviolet radiation that suppress H2 formation. Nevertheless, Population III stars continue to form, albeit in more massive halos, at a rate of ~ 10^{-4} Msun yr^{-1} Mpc^{-3} at redshift 15. The most massive starless halo has a mass of 7 x 10^7 Msun, which could host massive black hole formation through the direct gaseous collapse scenario. We show that the multiplicity of the Population III remnants grows with halo mass above 10^8 Msun, culminating in 50 remnants located in 10^9 Msun halos on average. This has implications that high mass X-ray binaries and intermediate mass black holes that originate from metal-free stars may be abundant in high-redshift galaxies.

preprint2013arXiv

Supermassive Population III Supernovae and the Birth of the First Quasars

The existence of supermassive black holes as early as z ~ 7 is one of the great unsolved problems in cosmological structure formation. One leading theory argues that they are born during catastrophic baryon collapse in z ~ 15 protogalaxies in strong Lyman-Werner UV backgrounds. Atomic line cooling in such galaxies fragments baryons into massive clumps that are thought to directly collapse to 10^4 - 10^5 solar-mass black holes. We have now discovered that some of these fragments can instead become supermassive stars that eventually explode as pair-instability supernovae with energies of ~ 10^55 erg, the most energetic explosions in the universe. We have calculated light curves and spectra for supermassive Pop III PI SNe with the Los Alamos RAGE and SPECTRUM codes. We find that they will be visible in NIR all-sky surveys by Euclid out to z ~ 10 - 15 and by WFIRST and WISH out to z ~ 15 - 20, perhaps revealing the birthplaces of the first quasars.

preprint2012arXiv

A closed formula for the asymptotic expansion of the Bergman kernel

We prove a graph theoretic closed formula for coefficients in the Tian-Yau-Zelditch asymptotic expansion of the Bergman kernel. The formula is expressed in terms of the characteristic polynomial of the directed graphs representing Weyl invariants. The proof relies on a combinatorial interpretation of a recursive formula due to M. Englis and A. Loi.

preprint2012arXiv

An explicit formula for the Berezin star product

We prove an explicit formula of the Berezin star product on Kaehler manifolds. The formula is expressed as a summation over certain strongly connected digraphs. The proof relies on a combinatorial interpretation of Englis' work on the asymptotic expansion of the Laplace integral.

preprint2012arXiv

Comparisons of Cosmological MHD Galaxy Cluster Simulations to Radio Observations

Radio observations of galaxy clusters show that there are $μ$G magnetic fields permeating the intra-cluster medium (ICM), but it is hard to accurately constrain the strength and structure of the magnetic fields without the help of advanced computer simulations. We present qualitative comparisons of synthetic VLA observations of simulated galaxy clusters to radio observations of Faraday Rotation Measure (RM) and radio halos. The cluster formation is modeled using adaptive mesh refinement (AMR) magneto-hydrodynamic (MHD) simulations with the assumption that the initial magnetic fields are injected into the ICM by active galactic nuclei (AGNs) at high redshift. In addition to simulated clusters in Xu et al. (2010, 2011), we present a new simulation with magnetic field injections from multiple AGNs. We find that the cluster with multiple injection sources is magnetized to a similar level as in previous simulations with a single AGN. The RM profiles from simulated clusters, both $|RM|$ and the dispersion of RM ($σ_{RM}$), are consistent at a first-order with the radial distribution from observations. The correlations between the $σ_{RM}$ and X-ray surface brightness from simulations are in a broad agreement with the observations, although there is an indication that the simulated clusters could be slightly over-dense and less magnetized with respect to those in the observed sample. In addition, the simulated radio halos agree with the observed correlations between the radio power versus the cluster X-ray luminosity and between the radio power versus the radio halo size. These studies show that the cluster wide magnetic fields that originate from AGNs and are then amplified by the ICM turbulence (Xu et al. 2010) match observations of magnetic fields in galaxy clusters.

preprint2012arXiv

Cosmological MHD Simulations of Galaxy Cluster Radio Relics: Insights and Warnings for Observations

Non-thermal radio emission from cosmic ray electrons in the vicinity of merging galaxy clusters is an important tracer of cluster merger activity, and is the result of complex physical processes that involve magnetic fields, particle acceleration, gas dynamics, and radiation. In particular, objects known as radio relics are thought to be the result of shock-accelerated electrons that, when embedded in a magnetic field, emit synchrotron radiation in the radio wavelengths. In order to properly model this emission, we utilize the adaptive mesh refinement simulation of the magnetohydrodynamic evolution of a galaxy cluster from cosmological initial conditions. We locate shock fronts and apply models of cosmic ray electron acceleration that are then input into radio emission models. We have determined the thermodynamic properties of this radio-emitting plasma and constructed synthetic radio observations to compare to observed galaxy clusters. We find a significant dependence of the observed morphology and radio relic properties on the viewing angle of the cluster, raising concerns regarding the interpretation of observed radio features in clusters. We also find that a given shock should not be characterized by a single Mach number. We find that the bulk of the radio emission comes from gas with T>5x10^7, ρ~10^(-28)-10^(-27) g/cm^3, with magnetic field strengths of 0.1-1.0 μG and shock Mach numbers of M~3-6. We present an analysis of the radio spectral index which suggests that the spatial variation of the spectral index can mimic synchrotron aging. Finally, we examine the polarization fraction and position angle of the simulated radio features, and compare to observations.

preprint2012arXiv

On a formula of Gammelgaard for Berezin-Toeplitz quantization

We give a proof of a slightly refined version of Gammelgaard's graph theoretic formula for Berezin-Toeplitz quantization on (pseudo-)Kaehler manifolds. Our proof has the merit of giving an alternative approach to Karabegov-Schlichenmaier's identification theorem. We also identify the dual Karabegov-Bordemann-Waldmann star product.

preprint2012arXiv

The Two States of Star Forming Clouds

We examine the effects of self-gravity and magnetic fields on supersonic turbulence in isothermal molecular clouds with high resolution simulations and adaptive mesh refinement. These simulations use large root grids (512^3) to capture turbulence and four levels of refinement to capture high density, for an effective resolution of 8,196^3. Three Mach 9 simulations are performed, two super-Alfvénic and one trans-Alfvénic. We find that gravity splits the clouds into two populations, one low density turbulent state and one high density collapsing state. The low density state exhibits properties similar to non-self-gravitating in this regime, and we examine the effects of varied magnetic field strength on statistical properties: the density probability distribution function is approximately lognormal; velocity power spectral slopes decrease with field strength; alignment between velocity and magnetic field increases with field; the magnetic field probability distribution can be fit to a stretched exponential. The high density state is characterized by self-similar spheres; the density PDF is a power-law; collapse rate decreases with increasing mean field; density power spectra have positive slopes, P(ρ,k) \propto k; thermal-to-magnetic pressure ratios are unity for all simulations; dynamic-to-magnetic pressure ratios are larger than unity for all simulations; magnetic field distribution is a power-law. The high Alfvén Mach numbers in collapsing regions explain recent observations of magnetic influence decreasing with density. We also find that the high density state is found in filaments formed by converging flows, consistent with recent Herschel observations. Possible modifications to existing star formation theories are explored.

preprint2012arXiv

Weyl invariant polynomial and deformation quantization on Kahler manifolds

Given a polynomial P of partial derivatives of the Kahler metric, expressed as a linear combination of directed multigraphs, we prove a simple criterion in terms of the coefficients for $P$ to be an invariant polynomial, i.e. invariant under the transformation of coordinates. As applications, we prove an explicit composition formula for covariant differential operators under a canonical basis, also known as invariant differential operators in the case of bounded symmetric domains. We also prove a general explicit formula of star products on Kahler manifolds.

preprint2011arXiv

A remark on Mirzakhani's asymptotic formulae

In this note, we answer a question of Mirzakhani on asymptotic behavior of the one-point volume polynomial of moduli spaces of curves. We also present some applications of Mirzakhani's asymptotic formulae of Weil-Petersson volumes.

preprint2011arXiv

Comparing Numerical Methods for Isothermal Magnetized Supersonic Turbulence

We employ simulations of supersonic super-Alfvenic turbulence decay as a benchmark test problem to assess and compare the performance of nine astrophysical MHD methods actively used to model star formation. The set of nine codes includes: ENZO, FLASH, KT-MHD, LL-MHD, PLUTO, PPML, RAMSES, STAGGER, and ZEUS. We present a comprehensive set of statistical measures designed to quantify the effects of numerical dissipation in these MHD solvers. We compare power spectra for basic fields to determine the effective spectral bandwidth of the methods and rank them based on their relative effective Reynolds numbers. We also compare numerical dissipation for solenoidal and dilatational velocity components to check for possible impacts of the numerics on small-scale density statistics. Finally, we discuss convergence of various characteristics for the turbulence decay test and impacts of various components of numerical schemes on the accuracy of solutions. We show that the best performing codes employ a consistently high order of accuracy for spatial reconstruction of the evolved fields, transverse gradient interpolation, conservation law update step, and Lorentz force computation. The best results are achieved with divergence-free evolution of the magnetic field using the constrained transport method, and using little to no explicit artificial viscosity. Codes which fall short in one or more of these areas are still useful, but they must compensate higher numerical dissipation with higher numerical resolution. This paper is the largest, most comprehensive MHD code comparison on an application-like test problem to date. We hope this work will help developers improve their numerical algorithms while helping users to make informed choices in picking optimal applications for their specific astrophysical problems.

preprint2011arXiv

Evolution and Distribution of Magnetic Fields from AGNs in Galaxy Clusters II. The Effects of Cluster Size and Dynamical State

Theory and simulations suggest that magnetic fields from radio jets and lobes powered by their central super massive black holes can be an important source of magnetic fields in the galaxy clusters. This is paper II in a series of studies where we present self-consistent high-resolution adaptive mesh refinement cosmological magnetohydrodynamic (MHD) simulations that simultaneously follow the formation of a galaxy cluster and evolution of magnetic fields ejected by an active galactic nucleus (AGN). We studied 12 different galaxy clusters with virial masses ranging from 1 $\times$ 10$^{14}$ to 2 $\times$ 10$^{15}$ M$_{\odot}$. In this work we examine the effects of the mass and merger history on the final magnetic properties. We find that the evolution of magnetic fields is qualitatively similar to those of previous studies. In most clusters, the injected magnetic fields can be transported throughout the cluster and be further amplified by the intra-cluster medium (ICM) turbulence during the cluster formation process with hierarchical mergers, while the amplification history and the magnetic field distribution depend on the cluster formation and magnetism history. This can be very different for different clusters. The total magnetic energies in these clusters are between 4 $\times$ 10$^{57}$ and $10^{61}$ erg, which is mainly decided by the cluster mass, scaling approximately with the square of the total mass. Dynamically older relaxed clusters usually have more magnetic fields in their ICM. The dynamically very young clusters may be magnetized weakly since there is not enough time for magnetic fields to be amplified.

preprint2011arXiv

Formal pseudodifferential operators and Witten's r-spin numbers

We derive an effective recursion for Witten's r-spin intersection numbers, using Witten's conjecture relating r-spin numbers to the Gel'fand-Dikii hierarchy (Theorem 4.1). Consequences include closed-form descriptions of the intersection numbers (for example, in terms of gamma functions: Propositions 5.2 and 5.4, Corollary 5.5). We use these closed-form descriptions to prove Harer-Zagier's formula for the Euler characteristic of M_{g,1}. Finally in Section 6, we extend Witten's series expansion formula for the Landau-Ginzburg potential to study r-spin numbers in the small phase space in genus zero. Our key tool is the calculus of formal pseudodifferential operators, and is partially motivated by work of Brezin and Hikami.

preprint2010arXiv

Computing top intersections in the tautological ring of $M_g$

We derive effective recursion formulae of top intersections in the tautological ring $R^*(M_g)$ of the moduli space of curves of genus $g\geq 2$. As an application, we prove a convolution-type tautological relation in $R^{g-2}(M_g)$.

preprint2010arXiv

Descendent integrals and tautological rings of moduli spaces of curves

The main objective of this paper is to give a summary of our recent work on recursion formulae for intersection numbers on moduli spaces of curves and their applications. We also present a conjectural relation between tautological rings and the mock theta function.

preprint2010arXiv

Evolution and Distribution of Magnetic Fields from AGNs in Galaxy Clusters. I. The Effect of Injection Energy and Redshift

We present a series of cosmological magnetohydrodynamic (MHD) simulations that simultaneously follow the formation of a galaxy cluster and evolution of magnetic fields ejected by an Active Galactic Nucleus (AGN). Specifically, we investigate the influence of both the epoch of AGN (z $\sim$ 3-0.5) and the AGN energy ($\sim$ 3 $\times$ 10$^{57}$ - 2 $\times$ 10$^{60}$ ergs)on the final magnetic field distribution in a relatively massive cluster (M$_{vir}$ $\sim$10$^{15}$ M$_\odot$). We find that as long as the AGN magnetic fields are ejected before the major mergers in the cluster formation history, magnetic fields can be transported throughout the cluster and can be further amplified by the intra-cluster medium (ICM) turbulence cause by hierarchical mergers during the cluster formation process. The total magnetic energy in the cluster can reach $\sim$ $10^{61}$ ergs, with micro Gauss fields distributed over $\sim$ Mpc scale. The amplification of the total magnetic energy by the ICM turbulence can be significant, up to $\sim$1000 times in some cases. Therefore even weak magnetic fields from AGNs can be used to magnetize the cluster to the observed level. The final magnetic energy in the ICM is determined by the ICM turbulent energy, with a weak dependence on the AGN injection energy. We discuss the properties of magnetic fields throughout the cluster and the synthetic Faraday rotation measure maps they produce. We also show that high spatial resolution over most of the magnetic regions of the cluster is very important to capture the small scale dynamo process and maintain the magnetic field structure in our simulations.

preprint2010arXiv

Mass and Magnetic distributions in Self Gravitating Super Alfvenic Turbulence with AMR

In this work, we present the mass and magnetic distributions found in a recent Adaptive Mesh Refinement (AMR) MHD simulation of supersonic, \sa, self gravitating turbulence. Powerlaw tails are found in both volume density and magnetic field probability density functions, with $P(ρ) \propto ρ^{-1.67}$ and $P(B)\propto B^{-2.74}$. A power law is also found between magnetic field strength and density, with $B\propto ρ^{0.48}$, throughout the collapsing gas. The mass distribution of gravitationally bound cores is shown to be in excellent agreement with recent observation of prestellar cores. The mass to flux distribution of cores is also found to be in excellent agreement with recent Zeeman splitting measurements.

preprint2009arXiv

A proof of the Faber intersection number conjecture

We prove the famous Faber intersection number conjecture and other more general results by using a recursion formula of $n$-point functions for intersection numbers on moduli spaces of curves. We also present some vanishing properties of Gromov-Witten invariants.

preprint2009arXiv

Cosmological AMR MHD with Enzo

In this work, we present MHDEnzo, the extension of the cosmological code Enzo to include the effects magnetic fields through the ideal MHD approximation. We use a higher order Godunov Riemann solver for the computation of interface fluxes. We use two constrained transport methods to compute the electric field from those interface fluxes, which simultaneously advances the induction equation and maintains the divergence of the magnetic field. A third order divergence free reconstruction technique is used to interpolate the magnetic fields in the block structured AMR framework already extant in Enzo. This reconstruction also preserves the divergence of the magnetic field to machine precision. We use operator splitting to include gravity and cosmological expansion. We then present a series of cosmological and non cosmological tests problems to demonstrate the quality of solution resulting from this combination of solvers.

preprint2009arXiv

Intersection numbers and automorphisms of stable curves

Due to the orbifold singularities, the intersection numbers on the moduli space of curves $\bar{\sM}_{g,n}$ are in general rational numbers rather than integers. We study the properties of the denominators of these intersection numbers and their relationship with the orders of automorphism groups of stable curves. In particular, we prove a conjecture of Itzykson and Zuber. We also present a conjectural multinomial type numerical property for Hodge integrals.

preprint2009arXiv

Mirzakharni's recursion formula is equivalent to the Witten-Kontsevich theorem

In this paper, we give a proof of Mirzakhani's recursion formula of Weil-Petersson volumes of moduli spaces of curves using the Witten-Kontsevich theorem. We also describe properties of intersections numbers involving higher degree $κ$ classes.

preprint2009arXiv

Plans for PANDA Online Computing

The PANDA experiment will not use any hardware trigger, i.e. all raw data are streaming in the data acquisition with a bandwidth of ~280 GB/s. The PANDA Online System is designed to perform data reduction by a factor of ~800 by reconstruction algorithms programmed in VHDL (Very High Speed Integrated Circuit Hardware Description Language) on FPGAs (Field Programmable Gate Arrays).

preprint2009arXiv

Recursion formulae of higher Weil-Petersson volumes

In this paper we study effective recursion formulae for computing intersection numbers of mixed $ψ$ and $κ$ classes on moduli spaces of curves. By using the celebrated Witten-Kontsevich theorem, we generalize Mulase-Safnuk form of Mirzakhani's recursion and prove a recursion formula of higher Weil-Petersson volumes. We also present recursion formulae to compute intersection pairings in the tautological rings of moduli spaces of curves.

preprint2009arXiv

The n-point functions for intersection numbers on moduli spaces of curves

Using the celebrated Witten-Kontsevich theorem, we prove a recursive formula of the $n$-point functions for intersection numbers on moduli spaces of curves. It has been used to prove the Faber intersection number conjecture and motivated us to find some conjectural vanishing identities for Gromov-Witten invariants. The latter has been proved recently by X. Liu and R. Pandharipande. We also give a combinatorial interpretation of $n$-point functions in terms of summation over binary trees.

preprint2009arXiv

Turbulence and Dynamo in Galaxy Cluster Medium: Implications on the Origin of Cluster Magnetic Fields

We present self-consistent cosmological magnetohydrodynamic (MHD) simulations that simultaneously follow the formation of a galaxy cluster and the magnetic field ejection by an active galactic nucleus (AGN). We find that the magnetic fields ejected by the AGNs, though initially distributed in relatively small volumes, can be transported throughout the cluster and be further amplified by the intra-cluster medium (ICM) turbulence during the cluster formation process. The ICM turbulence is shown to be generated and sustained by the frequent mergers of smaller halos. Furthermore, a cluster-wide dynamo process is shown to exist in the ICM and amplify the magnetic field energy and flux. The total magnetic energy in the cluster can reach $\sim$ $10^{61}$ ergs while micro Gauss ($μ$G) fields can distribute over $\sim$ Mpc scales throughout the whole cluster. This finding shows that magnetic fields from AGNs, being further amplified by the ICM turbulence through small-scale dynamo processes, can be the origin of cluster-wide magnetic fields.

preprint2007arXiv

New properties of the intersection numbers on moduli spaces of curves

We present certain new properties about the intersection numbers on moduli spaces of curves $\bar{\sM}_{g,n}$, including a simple explicit formula of $n$-point functions and several new identities of intersection numbers. In particular we prove a new identity, which together with a conjectural identity implies the famous Faber's conjecture about relations in $\mathcal R^{g-2}(\sM_g)$. These new identities clarify the mysterious constant in Faber's conjecture and uncover novel combinatorial structures of intersection numbers. We also discuss some numerical properties of Hodge integrals which have provided numerous inspirations for this work.

preprint2007arXiv

New results of intersection numbers on moduli spaces of curves

We present a series of new results we obtained recently about the intersection numbers of tautological classes on moduli spaces of curves, including a simple formula of the n-point functions for Witten's $τ$ classes, an effective recursion formula to compute higher Weil-Petersson volumes, several new recursion formulae of intersection numbers and our proof of a conjecture of Itzykson and Zuber concerning denominators of intersection numbers. We also present Virasoro and KdV properties of generating functions of general mixed $κ$ and $ψ$ intersections.

Hao Xu

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121 published item(s)

Effective dark matter component presents a robust signature of negative pressure by the DESI observations

How well can off-the-shelf LLMs elucidate molecular structures from mass spectra using chain-of-thought reasoning?

Infini-gram mini: Exact n-gram Search at the Internet Scale with FM-Index

Learning to Factorize and Adapt: A Versatile Approach Toward Universal Spatio-Temporal Foundation Models

STT-Arena: A More Realistic Environment for Tool-Using with Spatio-Temporal Dynamics

Towards Open Diversity-Aware Social Interactions

Zero-Shot Chinese Character Recognition via Global-Local Dual-Branch Alignment and Hierarchical Inference

Specific Absorption Rate-Aware Multiuser MIMO Assisted by Fluid Antenna System

Channel Estimation for FAS-assisted Multiuser mmWave Systems

FuRPE: Learning Full-body Reconstruction from Part Experts

A novel dataset and a two-stage mitosis nuclei detection method based on hybrid anchor branch

Fluid Antenna-Assisted MIMO Transmission Exploiting Statistical CSI

Semi-Supervised Learning with Pseudo-Negative Labels for Image Classification

TACIT: A Target-Agnostic Feature Disentanglement Framework for Cross-Domain Text Classification

2D Human Pose Estimation: A Survey

A Comparative Study of Deep Learning Classification Methods on a Small Environmental Microorganism Image Dataset (EMDS-6): from Convolutional Neural Networks to Visual Transformers

A Two-Phase Paradigm for Joint Entity-Relation Extraction

Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology

BDPGO: Balanced Distributed Pose Graph Optimization Framework for Swarm Robotics

Capacitively coupled distinct mechanical resonators for room temperature phonon-cavity electromechanics

Capacity Scaling Law in Massive MIMO with Antenna Selection

CharFormer: A Glyph Fusion based Attentive Framework for High-precision Character Image Denoising

Coupled electronic and magnetic excitations in the cuprates and their role in the superconducting transition

Distributed Information Bottleneck for a Primitive Gaussian Diamond Channel with Rayleigh Fading

EMDS-6: Environmental Microorganism Image Dataset Sixth Version for Image Denoising, Segmentation, Feature Extraction, Classification and Detection Methods Evaluation

Ensemble Multi-Relational Graph Neural Networks

Entropy production and correlation spreading in the interaction between particle detector and thermal baths

FINet: Dual Branches Feature Interaction for Partial-to-Partial Point Cloud Registration

How Anti-de Sitter Black Holes Reach Thermal Equilibrium

Landauer's principle in Qubit-Cavity quantum field theory Interaction in Vacuum and Thermal States

Lens Antenna Arrays-Assisted mmWave MU-MIMO Uplink Transmission: Joint Beam Selection and Phase-Only Beamforming Design

Linear Array Network for Low-light Image Enhancement

Long Short-Term Preference Modeling for Continuous-Time Sequential Recommendation

Metaverse Native Communication: A Blockchain and Spectrum Prospective

Neither Fast Nor Slow: How to Fly Through Narrow Tunnels

Neutron spectroscopy evidence for a possible magnetic-field-induced gapless quantum-spin-liquid phase in a Kitaev material $α$-RuCl$_3$

OJXPerf: Featherlight Object Replica Detection for Java Programs

Omni-swarm: A Decentralized Omnidirectional Visual-Inertial-UWB State Estimation System for Aerial Swarms

On the Ergodic Capacity of Reconfigurable Intelligent Surface (RIS)-Aided MIMO Channels

RCRN: Real-world Character Image Restoration Network via Skeleton Extraction

Research on Stable Obstacle Avoidance Control Strategy for Tracked Intelligent Transportation Vehicles in Non-structural Environment Based on Deep Learning

Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs

Some Discussions on PHY Security in DF Relay

Stripes and spin-density waves in the doped two-dimensional Hubbard model: ground state phase diagram

Surface Vision Transformers: Flexible Attention-Based Modelling of Biomedical Surfaces

Underwater Acoustic Communication Channel Modeling using Reservoir Computing

Any equation is a forest: Symbolic genetic algorithm for discovering open-form partial differential equations (SGA-PDE)

Dynamics in direct two-photon transition by frequency combs

Enabling Longitudinal Exploratory Analysis of Clinical COVID Data

Experimental Extraction and Simulation of Charge Trapping during Endurance of FeFET with TiN/HfZrO/SiO2/Si (MFIS) Gate Structure

How Much Communication Resource is Needed to Run a Wireless Blockchain Network?

Impact of Interlayer and Ferroelectric Materials on Charge Trapping during Endurance Fatigue of FeFET with TiN/HfxZr1-xO2/interlayer/Si (MFIS) Gate Structure

Multistability of Small Reaction Networks

Study of the hidden charm $D\bar{D}^*$ interactions in chiral effective field theory

An Achievable Region for the Multiple Access Wiretap Channels with Confidential and Open Messages

Black Hole Evaporation in Hořava-Lifshitz Gravity

Blockchain-enabled Resource Management and Sharing for 6G Communications

Computational LEGO Technic Design

Cosmic Censorship and the Evolution of d-Dimensional Charged Evaporating Black Holes

Discovery potential for the LHCb fully-charm tetraquark $X(6900)$ state via $\bar{p}p$ annihilation reaction

DL-PDE: Deep-learning based data-driven discovery of partial differential equations from discrete and noisy data

DLGA-PDE: Discovery of PDEs with incomplete candidate library via combination of deep learning and genetic algorithm

Mean Field Game and Decentralized Intelligent Adaptive Pursuit Evasion Strategy for Massive Multi-Agent System under Uncertain Environment

Recently observed $P_c$ as molecular states and possible mixture of $P_c(4457)$

The Clock and Control System for the ATLAS Liquid Argon Calorimeter Phase-I Upgrade

TilinGNN: Learning to Tile with Self-Supervised Graph Neural Network

Tri-graph Information Propagation for Polypharmacy Side Effect Prediction

Unruh Quantum Otto heat engine with level degeneracy

Galaxy Properties and UV Escape Fractions During Epoch of Reionization: Results from the Renaissance Simulations

Heat kernel coefficients on Kahler manifolds

Implication the observed $ψ(3770)\to p\bar{p}π^0$ for studying the $p\bar{p}\to ψ(3770)π^0$ process

Late Pop III Star Formation During the Epoch of Reionization: Results from the Renaissance Simulations

Length Scales and Turbulent Properties of Magnetic Fields in Simulated Galaxy Clusters

Maxwell's equal area law for Lovelock Thermodynamics