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Yu Wang

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Machine Learning Computer Vision cond-mat.mes-hall Social and Information Networks Artificial Intelligence cond-mat.str-el Systems and Control astro-ph.CO Computation and Language quant-ph cond-mat.mtrl-sci Distributed, Parallel, and Cluster Computing astro-ph.GA astro-ph.HE Hardware Architecture math.AP Robotics Cryptography and Security eess.AS eess.SP eess.SY math.OC physics.ins-det physics.optics Sound Information Retrieval math.DG Programming Languages Software Engineering astro-ph astro-ph.SR cond-mat.quant-gas cs.CY Databases Formal Languages and Automata Theory math.CV physics.plasm-ph cond-mat.stat-mech eess.IV gr-qc Logic in Computer Science math.DS Methodology Networking and Internet Architecture physics.app-ph physics.atom-ph physics.chem-ph Applications Computation cond-mat.other Emerging Technologies hep-ex hep-ph hep-th math.CT math.GR math.PR math.SG math.ST Multimedia Neural and Evolutionary Computing nlin.AO nlin.PS physics.class-ph physics.data-an physics.flu-dyn physics.med-ph physics.soc-ph Populations and Evolution Quantitative Methods Statistics Theory

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preprint2026arXiv

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

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

preprint2024arXiv

FlashDecoding++: Faster Large Language Model Inference on GPUs

As the Large Language Model (LLM) becomes increasingly important in various domains. However, the following challenges still remain unsolved in accelerating LLM inference: (1) Synchronized partial softmax update. The softmax operation requires a synchronized update operation among each partial softmax result, leading to ~20% overheads for the attention computation in LLMs. (2) Under-utilized computation of flat GEMM. The shape of matrices performing GEMM in LLM inference is flat, leading to under-utilized computation and >50% performance loss after padding zeros in previous designs. (3) Performance loss due to static dataflow. Kernel performance in LLM depends on varied input data features, hardware configurations, etc. A single and static dataflow may lead to a 50.25% performance loss for GEMMs of different shapes in LLM inference. We present FlashDecoding++, a fast LLM inference engine supporting mainstream LLMs and hardware back-ends. To tackle the above challenges, FlashDecoding++ creatively proposes: (1) Asynchronized softmax with unified max value. FlashDecoding++ introduces a unified max value technique for different partial softmax computations to avoid synchronization. (2) Flat GEMM optimization with double buffering. FlashDecoding++ points out that flat GEMMs with different shapes face varied bottlenecks. Then, techniques like double buffering are introduced. (3) Heuristic dataflow with hardware resource adaptation. FlashDecoding++ heuristically optimizes dataflow using different hardware resource considering input dynamics. Due to the versatility of optimizations in FlashDecoding++, FlashDecoding++ can achieve up to 4.86x and 2.18x speedup on both NVIDIA and AMD GPUs compared to Hugging Face implementations. FlashDecoding++ also achieves an average speedup of 1.37x compared to state-of-the-art LLM inference engines on mainstream LLMs.

preprint2024arXiv

FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs

Transformer-based Large Language Models (LLMs) have made a significant impact on various domains. However, LLMs' efficiency suffers from both heavy computation and memory overheads. Compression techniques like sparsification and quantization are commonly used to mitigate the gap between LLM's computation/memory overheads and hardware capacity. However, existing GPU and transformer-based accelerators cannot efficiently process compressed LLMs, due to the following unresolved challenges: low computational efficiency, underutilized memory bandwidth, and large compilation overheads. This paper proposes FlightLLM, enabling efficient LLMs inference with a complete mapping flow on FPGAs. In FlightLLM, we highlight an innovative solution that the computation and memory overhead of LLMs can be solved by utilizing FPGA-specific resources (e.g., DSP48 and heterogeneous memory hierarchy). We propose a configurable sparse DSP chain to support different sparsity patterns with high computation efficiency. Second, we propose an always-on-chip decode scheme to boost memory bandwidth with mixed-precision support. Finally, to make FlightLLM available for real-world LLMs, we propose a length adaptive compilation method to reduce the compilation overhead. Implemented on the Xilinx Alveo U280 FPGA, FlightLLM achieves 6.0$\times$ higher energy efficiency and 1.8$\times$ better cost efficiency against commercial GPUs (e.g., NVIDIA V100S) on modern LLMs (e.g., LLaMA2-7B) using vLLM and SmoothQuant under the batch size of one. FlightLLM beats NVIDIA A100 GPU with 1.2$\times$ higher throughput using the latest Versal VHK158 FPGA.

preprint2024arXiv

LLM-Powered Hierarchical Language Agent for Real-time Human-AI Coordination

AI agents powered by Large Language Models (LLMs) have made significant advances, enabling them to assist humans in diverse complex tasks and leading to a revolution in human-AI coordination. LLM-powered agents typically require invoking LLM APIs and employing artificially designed complex prompts, which results in high inference latency. While this paradigm works well in scenarios with minimal interactive demands, such as code generation, it is unsuitable for highly interactive and real-time applications, such as gaming. Traditional gaming AI often employs small models or reactive policies, enabling fast inference but offering limited task completion and interaction abilities. In this work, we consider Overcooked as our testbed where players could communicate with natural language and cooperate to serve orders. We propose a Hierarchical Language Agent (HLA) for human-AI coordination that provides both strong reasoning abilities while keeping real-time execution. In particular, HLA adopts a hierarchical framework and comprises three modules: a proficient LLM, referred to as Slow Mind, for intention reasoning and language interaction, a lightweight LLM, referred to as Fast Mind, for generating macro actions, and a reactive policy, referred to as Executor, for transforming macro actions into atomic actions. Human studies show that HLA outperforms other baseline agents, including slow-mind-only agents and fast-mind-only agents, with stronger cooperation abilities, faster responses, and more consistent language communications.

preprint2024arXiv

Robust Beamforming Design for Intelligent Reflecting Surface Aided Cognitive Radio Systems with Imperfect Cascaded CSI

In this paper, intelligent reflecting surface (IRS) is introduced to enhance the network performance of cognitive radio (CR) systems. Specifically, we investigate robust beamforming design based on both bounded channel state information (CSI) error model and statistical CSI error model for primary user (PU)-related channels in IRS-aided CR systems. We jointly optimize the transmit precoding (TPC) at the secondary user (SU) transmitter (ST) and phase shifts at the IRS to minimize the ST' s total transmit power subject to the quality of service of SUs, the limited interference imposed on the PU and unit-modulus of the reflective beamforming. The successive convex approximation (SCA) method, Schur's complement, General sign-definiteness principle, inverse Chi-square distribution and penalty convex-concave procedure are invoked for dealing with these intricate constraints. The non-convex optimization problems are transformed into several convex subproblems and efficient algorithms are proposed. Simulation results verify the efficiency of the proposed algorithms and reveal the impacts of CSI uncertainties on ST's minimum transmit power and feasibility rate of the optimization problems. Simulation results also show that the number of transmit antennas at the ST and the number of phase shifts at the IRS should be carefully chosen to balance the channel realization feasibility rate and the total transmit power.

preprint2023arXiv

Giant Nernst effect in the crossover between Fermi liquid and strange metal

The strange-metal state is a crucial problem in condensed matter physics highlighted by its ubiquity in almost all major correlated systems[1-7]. Its understanding could provide important insight into high-Tc superconductivity[2] and quantum criticality[8]. However, with the Fermi liquid theory failing in strange metals, understanding the highly unconventional behaviors has been a long-standing challenge. Fundamental aspects of strange metals remain elusive, including the nature of their charge carriers[1]. Here, we report the observation of a giant Nernst response in the strange-metal state in a two-dimensional superconductor 2M-WS2. A giant Nernst coefficient comparable to the vortex Nernst signal in superconducting cuprates, and its high sensitivity to carrier mobility, are found when the system enters the strange-metal state from the Fermi liquid state. The temperature and magnetic field dependence of the giant Nernst peak rule out the relevance of both Landau quasiparticles and superconductivity. Instead, the giant Nernst peak at the crossover indicates a dramatic change in carrier entropy when entering the strange-metal state. The presence of such an anomalous Nernst response is further confirmed in other iconic strange metals, suggesting its universality and places stringent experimental constraints on the mechanism of strange metals.

preprint2023arXiv

Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes

This thesis focuses on data that has complex spatio-temporal structure and on probabilistic graphical models that learn the structure in an interpretable and scalable manner. We target two research areas of interest: Gaussian graphical models for tensor-variate data and summarization of complex time-varying texts using topic models. This work advances the state-of-the-art in several directions. First, it introduces a new class of tensor-variate Gaussian graphical models via the Sylvester tensor equation. Second, it develops an optimization technique based on a fast-converging proximal alternating linearized minimization method, which scales tensor-variate Gaussian graphical model estimations to modern big-data settings. Third, it connects Kronecker-structured (inverse) covariance models with spatio-temporal partial differential equations (PDEs) and introduces a new framework for ensemble Kalman filtering that is capable of tracking chaotic physical systems. Fourth, it proposes a modular and interpretable framework for unsupervised and weakly-supervised probabilistic topic modeling of time-varying data that combines generative statistical models with computational geometric methods. Throughout, practical applications of the methodology are considered using real datasets. This includes brain-connectivity analysis using EEG data, space weather forecasting using solar imaging data, longitudinal analysis of public opinions using Twitter data, and mining of mental health related issues using TalkLife data. We show in each case that the graphical modeling framework introduced here leads to improved interpretability, accuracy, and scalability.

preprint2023arXiv

Sgap: Towards Efficient Sparse Tensor Algebra Compilation for GPU

Sparse compiler is a promising solution for sparse tensor algebra optimization. In compiler implementation, reduction in sparse-dense hybrid algebra plays a key role in performance. Though GPU provides various reduction semantics that can better utilize the parallel computing and memory bandwidth capacity, the central question is: how to elevate the flexible reduction semantics to sparse compilation theory that assumes serial execution. Specifically, we have to tackle two main challenges: (1) there are wasted parallelism by adopting static synchronization granularity (2) static reduction strategy limits optimization space exploration. We propose Sgap: segment group and atomic parallelism to solve these problems. Atomic parallelism captures the flexible reduction semantics to systematically analyze the optimization space of sparse-dense hybrid algebra on GPU. It is a new optimization technique beyond current compiler-based and open-source runtime libraries. Segment group elevates the flexible reduction semantics to suitable levels of abstraction in the sparse compilation theory. It adopts changeable group size and user-defined reduction strategy to solve challenge (1) and (2), respectively. Finally, we use GPU sparse matrix-matrix multiplication (SpMM) on the TACO compiler as a use case to demonstrate the effectiveness of segment group in reduction semantics elevation. We achieve up to 1.2x speedup over the original TACO's SpMM kernels. We also apply new optimization techniques found by atomic parallelism to an open-source state-of-the-art SpMM library dgSPARSE. We achieve 1.6x - 2.3x speedup on the algorithm tuned with atomic parallelism.

preprint2023arXiv

Surprisingly large anomalous Hall effect and giant negative magnetoresistance in half-topological semimetals

Large intrinsic anomalous Hall effect (AHE) due to the Berry curvature in magnetic topological semimetals is attracting enormous interest due to its fundamental importance and technological relevance. Mechanisms resulting in large intrinsic AHE include diverging Berry curvature in Weyl semimetals, anticrossing nodal rings or points of non-trivial bands, and noncollinear spin structures. Here we show that a half-topological semimetal (HTS) state near a topological critical point can provide a new mechanism for driving an exceptionally large AHE. We reveal this through a systematic experimental and theoretical study of the antiferromagnetic (AFM) half-Heusler compound TbPdBi. We not only observed an unusual AHE with a surprisingly large anomalous Hall angle ΘH (tan ΘH ~ 2, the largest among the antiferromagnets) in its field-driven ferromagnetic (FM) phase, but also found a distinct Hall resistivity peak in the canted AFM phase within a low field range, where its isothermal magnetization is nearly linearly dependent on the field. Moreover, we observed a nearly isotropic, giant negative magnetoresistance with a magnitude of ~98%. Our in-depth theoretical modelling demonstrates that these exotic transport properties originate from the HTS state. A minimal Berry curvature cancellation between the trivial spin-up and nontrivial spin-down bands results not only in an extremely large AHE, but it also enhances the spin polarization of the spin-down bands substantially and thus leads to a giant negative magnetoresistance. Our study advances the understanding of the interplay between band topology and magnetism and offers new clues for materials design for spintronics and other applications.

preprint2023arXiv

Universal bifurcation scenarios in delay-differential equations with one delay

We show that delay-differential equations (DDE) exhibit universal bifurcation scenarios, which are observed in large classes of DDEs with a single delay. Each such universality class has the same sequence of stabilizing or destabilizing Hopf bifurcations. These bifurcation sequences and universality classes can be explicitly described by using the asymptotic continuous spectrum for DDEs with large delays. Here, we mainly study linear DDEs, provide a general transversality result for the delay-induced bifurcations, and consider three most common universality classes. For each of them, we explicitly describe the sequence of stabilizing and destabilizing bifurcations. We also illustrate the implications for a nonlinear Stuart-Landau oscillator with time-delayed feedback.

preprint2022arXiv

A Brief Review of Binary Driven Hypernova

Binary driven hypernova (BdHN) models long gamma-ray burst (GRBs) as occurring in the binary systems involving a carbon-oxygen core (CO$_{\rm core}$) and a companion neutron star (NS) or a black hole (BH). This model, first proposed in 2012, succeeds and improves upon the fireshell model and the induced gravitational collapse (IGC) paradigm. After nearly a decade of development, the BdHN model has reached a nearly complete structure, explaining all the observables of long bursts into its theoretical framework, and has given a refined classification of long GRBs according to the original properties of the progenitors. In this article, we present a summary of the BdHN model and the physical processes at work in each of the envisaged Episodes during its occurrence and lifetime, duly contextualized in the framework of GRB observations.

preprint2022arXiv

A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation

Automatic segmentation of glioma and its subregions is of great significance for diagnosis, treatment and monitoring of disease. In this paper, an augmentation method, called TensorMixup, was proposed and applied to the three dimensional U-Net architecture for brain tumor segmentation. The main ideas included that first, two image patches with size of 128 in three dimensions were selected according to glioma information of ground truth labels from the magnetic resonance imaging data of any two patients with the same modality. Next, a tensor in which all elements were independently sampled from Beta distribution was used to mix the image patches. Then the tensor was mapped to a matrix which was used to mix the one-hot encoded labels of the above image patches. Therefore, a new image and its one-hot encoded label were synthesized. Finally, the new data was used to train the model which could be used to segment glioma. The experimental results show that the mean accuracy of Dice scores are 91.32%, 85.67%, and 82.20% respectively on the whole tumor, tumor core, and enhancing tumor segmentation, which proves that the proposed TensorMixup is feasible and effective for brain tumor segmentation.

preprint2022arXiv

An electron-spin qubit platform assembled atom-by-atom on a surface

Creating a quantum-coherent architecture at the atomic scale has long been an ambition in quantum science and nanotechnology. This ultimate length scale requires the use of fundamental quantum properties of atoms, such as the spin of electrons, which naturally occurs in many solid-state environments and allows high-fidelity operations and readout by electromagnetic means. Despite decades of effort, however, it remains a formidable task to realize an atomic-scale quantum architecture where multiple electron spin qubits can be precisely assembled, controllably coupled, and coherently operated. Electron spin qubits created in dopants in semiconductors and color centers in insulators, for example, can be well controlled individually6-8 but are difficult to couple together into a circuit. On the other hand, multiple magnetic atoms and molecules on surfaces can be coupled to each other by building sophisticated atomic structures using a scanning tunneling microscope (STM), but coherent operation has so far been limited to a single qubit in the tunnel junction. Here we demonstrate an atomic-scale qubit platform by showing atom-by-atom construction, coherent operations, and readout of multiple electron-spin qubits on a surface. To enable the coherent control of remote qubits that are outside the tunnel junction, we complement each electron spin with a local magnetic field gradient from a nearby single-atom magnet. To enable readout of remote qubits, we employ a sensor qubit in the tunnel junction and implement pulsed double electron spin resonance. Using these methods, we demonstrate fast single-, two-, and three-qubit operations in an all-electrical fashion. Our work marks the creation of an Angstrom-scale qubit platform, where quantum functionalities using electron spin arrays, built atom-by-atom on a surface, are now within reach.

preprint2022arXiv

BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification

CT-based bronchial tree analysis plays an important role in the computer-aided diagnosis for respiratory diseases, as it could provide structured information for clinicians. The basis of airway analysis is bronchial tree reconstruction, which consists of bronchus segmentation and classification. However, there remains a challenge for accurate bronchial analysis due to the individual variations and the severe class imbalance. In this paper, we propose a region and structure prior embedded framework named BronchusNet to achieve accurate segmentation and classification of bronchial regions in CT images. For bronchus segmentation, we propose an adaptive hard region-aware UNet that incorporates multi-level prior guidance of hard pixel-wise samples in the general Unet segmentation network to achieve better hierarchical feature learning. For the classification of bronchial branches, we propose a hybrid point-voxel graph learning module to fully exploit bronchial structure priors and to support simultaneous feature interactions across different branches. To facilitate the study of bronchial analysis, we contribute~\textbf{BRSC}: an open-access benchmark of \textbf{BR}onchus imaging analysis with high-quality pixel-wise \textbf{S}egmentation masks and the \textbf{C}lass of bronchial segments. Experimental results on BRSC show that our proposed method not only achieves the state-of-the-art performance for binary segmentation of bronchial region but also exceeds the best existing method on bronchial branches classification by 6.9\%.

preprint2022arXiv

BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition

The development of IoT technology enables a variety of sensors can be integrated into mobile devices. Human Activity Recognition (HAR) based on sensor data has become an active research topic in the field of machine learning and ubiquitous computing. However, due to the inconsistent frequency of human activities, the amount of data for each activity in the human activity dataset is imbalanced. Considering the limited sensor resources and the high cost of manually labeled sensor data, human activity recognition is facing the challenge of highly imbalanced activity datasets. In this paper, we propose Balancing Sensor Data Generative Adversarial Networks (BSDGAN) to generate sensor data for minority human activities. The proposed BSDGAN consists of a generator model and a discriminator model. Considering the extreme imbalance of human activity dataset, an autoencoder is employed to initialize the training process of BSDGAN, ensure the data features of each activity can be learned. The generated activity data is combined with the original dataset to balance the amount of activity data across human activity classes. We deployed multiple human activity recognition models on two publicly available imbalanced human activity datasets, WISDM and UNIMIB. Experimental results show that the proposed BSDGAN can effectively capture the data features of real human activity sensor data, and generate realistic synthetic sensor data. Meanwhile, the balanced activity dataset can effectively help the activity recognition model to improve the recognition accuracy.

preprint2022arXiv

CenterFormer: Center-based Transformer for 3D Object Detection

Query-based transformer has shown great potential in constructing long-range attention in many image-domain tasks, but has rarely been considered in LiDAR-based 3D object detection due to the overwhelming size of the point cloud data. In this paper, we propose CenterFormer, a center-based transformer network for 3D object detection. CenterFormer first uses a center heatmap to select center candidates on top of a standard voxel-based point cloud encoder. It then uses the feature of the center candidate as the query embedding in the transformer. To further aggregate features from multiple frames, we design an approach to fuse features through cross-attention. Lastly, regression heads are added to predict the bounding box on the output center feature representation. Our design reduces the convergence difficulty and computational complexity of the transformer structure. The results show significant improvements over the strong baseline of anchor-free object detection networks. CenterFormer achieves state-of-the-art performance for a single model on the Waymo Open Dataset, with 73.7% mAPH on the validation set and 75.6% mAPH on the test set, significantly outperforming all previously published CNN and transformer-based methods. Our code is publicly available at https://github.com/TuSimple/centerformer

preprint2022arXiv

Chemical reactions in imperfect cavities: enhancement, suppression, and resonance

The use of optical cavities to control chemical reactions has been of great interest recently, following demonstrations of enhancement, suppression, and negligible effects on chemical reaction rates depending on the specific reaction and cavity frequency. In this work, we study the reaction rate inside imperfect cavities, where we introduce a broadening parameter in the spectral density to mimic Fabry-Pérot cavities. We investigate cavity modifications to reaction rates using non-Markovian Langevin dynamics with frictional and random forces to account for the presence of imperfect optical cavities. We demonstrate that in the regime of weak solvent and cavity friction, the cavity can enhance chemical reaction rates. On the other hand, in the high friction regime, cavities can suppress chemical reactions. Furthermore, we find that the broadening of the cavity spectral density gives rise to blue shifts of the resonance conditions and, surprisingly, increases the sharpness of the resonance effect.

preprint2022arXiv

ChemicalX: A Deep Learning Library for Drug Pair Scoring

In this paper, we introduce ChemicalX, a PyTorch-based deep learning library designed for providing a range of state of the art models to solve the drug pair scoring task. The primary objective of the library is to make deep drug pair scoring models accessible to machine learning researchers and practitioners in a streamlined framework.The design of ChemicalX reuses existing high level model training utilities, geometric deep learning, and deep chemistry layers from the PyTorch ecosystem. Our system provides neural network layers, custom pair scoring architectures, data loaders, and batch iterators for end users. We showcase these features with example code snippets and case studies to highlight the characteristics of ChemicalX. A range of experiments on real world drug-drug interaction, polypharmacy side effect, and combination synergy prediction tasks demonstrate that the models available in ChemicalX are effective at solving the pair scoring task. Finally, we show that ChemicalX could be used to train and score machine learning models on large drug pair datasets with hundreds of thousands of compounds on commodity hardware.

preprint2022arXiv

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

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

preprint2022arXiv

Class-Specific Semantic Reconstruction for Open Set Recognition

Open set recognition enables deep neural networks (DNNs) to identify samples of unknown classes, while maintaining high classification accuracy on samples of known classes. Existing methods basing on auto-encoder (AE) and prototype learning show great potential in handling this challenging task. In this study, we propose a novel method, called Class-Specific Semantic Reconstruction (CSSR), that integrates the power of AE and prototype learning. Specifically, CSSR replaces prototype points with manifolds represented by class-specific AEs. Unlike conventional prototype-based methods, CSSR models each known class on an individual AE manifold, and measures class belongingness through AE's reconstruction error. Class-specific AEs are plugged into the top of the DNN backbone and reconstruct the semantic representations learned by the DNN instead of the raw image. Through end-to-end learning, the DNN and the AEs boost each other to learn both discriminative and representative information. The results of experiments conducted on multiple datasets show that the proposed method achieves outstanding performance in both close and open set recognition and is sufficiently simple and flexible to incorporate into existing frameworks.

preprint2022arXiv

CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NAS

One-shot Neural Architecture Search (NAS) has been widely used to discover architectures due to its efficiency. However, previous studies reveal that one-shot performance estimations of architectures might not be well correlated with their performances in stand-alone training because of the excessive sharing of operation parameters (i.e., large sharing extent) between architectures. Thus, recent methods construct even more over-parameterized supernets to reduce the sharing extent. But these improved methods introduce a large number of extra parameters and thus cause an undesirable trade-off between the training costs and the ranking quality. To alleviate the above issues, we propose to apply Curriculum Learning On Sharing Extent (CLOSE) to train the supernet both efficiently and effectively. Specifically, we train the supernet with a large sharing extent (an easier curriculum) at the beginning and gradually decrease the sharing extent of the supernet (a harder curriculum). To support this training strategy, we design a novel supernet (CLOSENet) that decouples the parameters from operations to realize a flexible sharing scheme and adjustable sharing extent. Extensive experiments demonstrate that CLOSE can obtain a better ranking quality across different computational budget constraints than other one-shot supernets, and is able to discover superior architectures when combined with various search strategies. Code is available at https://github.com/walkerning/aw_nas.

preprint2022arXiv

CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance

Transformers have gained much attention by outperforming convolutional neural networks in many 2D vision tasks. However, they are known to have generalization problems and rely on massive-scale pre-training and sophisticated training techniques. When applying to 3D tasks, the irregular data structure and limited data scale add to the difficulty of transformer's application. We propose CodedVTR (Codebook-based Voxel TRansformer), which improves data efficiency and generalization ability for 3D sparse voxel transformers. On the one hand, we propose the codebook-based attention that projects an attention space into its subspace represented by the combination of "prototypes" in a learnable codebook. It regularizes attention learning and improves generalization. On the other hand, we propose geometry-aware self-attention that utilizes geometric information (geometric pattern, density) to guide attention learning. CodedVTR could be embedded into existing sparse convolution-based methods, and bring consistent performance improvements for indoor and outdoor 3D semantic segmentation tasks

preprint2022arXiv

Differentially Private Algorithms for Statistical Verification of Cyber-Physical Systems

Statistical model checking is a class of sequential algorithms that can verify specifications of interest on an ensemble of cyber-physical systems (e.g., whether 99% of cars from a batch meet a requirement on their energy efficiency). These algorithms infer the probability that given specifications are satisfied by the systems with provable statistical guarantees by drawing sufficient numbers of independent and identically distributed samples. During the process of statistical model checking, the values of the samples (e.g., a user's car energy efficiency) may be inferred by intruders, causing privacy concerns in consumer-level applications (e.g., automobiles and medical devices). This paper addresses the privacy of statistical model checking algorithms from the point of view of differential privacy. These algorithms are sequential, drawing samples until a condition on their values is met. We show that revealing the number of the samples drawn can violate privacy. We also show that the standard exponential mechanism that randomizes the output of an algorithm to achieve differential privacy fails to do so in the context of sequential algorithms. Instead, we relax the conservative requirement in differential privacy that the sensitivity of the output of the algorithm should be bounded to any perturbation for any data set. We propose a new notion of differential privacy which we call expected differential privacy. Then, we propose a novel expected sensitivity analysis for the sequential algorithm and proposed a corresponding exponential mechanism that randomizes the termination time to achieve the expected differential privacy. We apply the proposed mechanism to statistical model checking algorithms to preserve the privacy of the samples they draw. The utility of the proposed algorithm is demonstrated in a case study.

preprint2022arXiv

Dual Vision Transformer

Prior works have proposed several strategies to reduce the computational cost of self-attention mechanism. Many of these works consider decomposing the self-attention procedure into regional and local feature extraction procedures that each incurs a much smaller computational complexity. However, regional information is typically only achieved at the expense of undesirable information lost owing to down-sampling. In this paper, we propose a novel Transformer architecture that aims to mitigate the cost issue, named Dual Vision Transformer (Dual-ViT). The new architecture incorporates a critical semantic pathway that can more efficiently compress token vectors into global semantics with reduced order of complexity. Such compressed global semantics then serve as useful prior information in learning finer pixel level details, through another constructed pixel pathway. The semantic pathway and pixel pathway are then integrated together and are jointly trained, spreading the enhanced self-attention information in parallel through both of the pathways. Dual-ViT is henceforth able to reduce the computational complexity without compromising much accuracy. We empirically demonstrate that Dual-ViT provides superior accuracy than SOTA Transformer architectures with reduced training complexity. Source code is available at \url{https://github.com/YehLi/ImageNetModel}.

preprint2022arXiv

Effect of shaping plate apparatus on mechanical properties of 3D printed cement-based materials: Experimental and numerical studies

Precisely controlling the shape of the printed-layers, eliminating the curved sides and internal stress concentration, and increasing the mechanical properties are essential to guarantee the quality of 3D printed cement-based structures. This work aims at achieving the above-mentioned targets through a specially designed shaping plate apparatus. The pressure (stress) distribution in the printed structure with a shaping plate apparatus (SP-3DPC), and the cross-sectional shape, microstructure and mechanical properties of SP-3DPC were systematically investigated. Results indicate that using the shaping plate apparatus may slightly reduce the printing speed, but it can effectively constrain the free expansion of extrudate, control its cross-sectional geometry, and improve the surface finish quality and mechanical properties of the printed structure. This study provides a theoretical basis and technical guidance for the design and application of the shaping plate apparatus.

preprint2022arXiv

Efficient Reinforcement Learning from Demonstration Using Local Ensemble and Reparameterization with Split and Merge of Expert Policies

The current work on reinforcement learning (RL) from demonstrations often assumes the demonstrations are samples from an optimal policy, an unrealistic assumption in practice. When demonstrations are generated by sub-optimal policies or have sparse state-action pairs, policy learned from sub-optimal demonstrations may mislead an agent with incorrect or non-local action decisions. We propose a new method called Local Ensemble and Reparameterization with Split and Merge of expert policies (LEARN-SAM) to improve efficiency and make better use of the sub-optimal demonstrations. First, LEARN-SAM employs a new concept, the lambda-function, based on a discrepancy measure between the current state to demonstrated states to "localize" the weights of the expert policies during learning. Second, LEARN-SAM employs a split-and-merge (SAM) mechanism by separating the helpful parts in each expert demonstration and regrouping them into new expert policies to use the demonstrations selectively. Both the lambda-function and SAM mechanism help boost the learning speed. Theoretically, we prove the invariant property of reparameterized policy before and after the SAM mechanism, providing theoretical guarantees for the convergence of the employed policy gradient method. We demonstrate the superiority of the LEARN-SAM method and its robustness with varying demonstration quality and sparsity in six experiments on complex continuous control problems of low to high dimensions, compared to existing methods on RL from demonstration.

preprint2022arXiv

Explore-Bench: Data Sets, Metrics and Evaluations for Frontier-based and Deep-reinforcement-learning-based Autonomous Exploration

Autonomous exploration and mapping of unknown terrains employing single or multiple robots is an essential task in mobile robotics and has therefore been widely investigated. Nevertheless, given the lack of unified data sets, metrics, and platforms to evaluate the exploration approaches, we develop an autonomous robot exploration benchmark entitled Explore-Bench. The benchmark involves various exploration scenarios and presents two types of quantitative metrics to evaluate exploration efficiency and multi-robot cooperation. Explore-Bench is extremely useful as, recently, deep reinforcement learning (DRL) has been widely used for robot exploration tasks and achieved promising results. However, training DRL-based approaches requires large data sets, and additionally, current benchmarks rely on realistic simulators with a slow simulation speed, which is not appropriate for training exploration strategies. Hence, to support efficient DRL training and comprehensive evaluation, the suggested Explore-Bench designs a 3-level platform with a unified data flow and $12 \times$ speed-up that includes a grid-based simulator for fast evaluation and efficient training, a realistic Gazebo simulator, and a remotely accessible robot testbed for high-accuracy tests in physical environments. The practicality of the proposed benchmark is highlighted with the application of one DRL-based and three frontier-based exploration approaches. Furthermore, we analyze the performance differences and provide some insights about the selection and design of exploration methods. Our benchmark is available at https://github.com/efc-robot/Explore-Bench.

preprint2022arXiv

FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning

Client-wise data heterogeneity is one of the major issues that hinder effective training in federated learning (FL). Since the data distribution on each client may vary dramatically, the client selection strategy can significantly influence the convergence rate of the FL process. Active client selection strategies are popularly proposed in recent studies. However, they neglect the loss correlations between the clients and achieve only marginal improvement compared to the uniform selection strategy. In this work, we propose FedCor -- an FL framework built on a correlation-based client selection strategy, to boost the convergence rate of FL. Specifically, we first model the loss correlations between the clients with a Gaussian Process (GP). Based on the GP model, we derive a client selection strategy with a significant reduction of expected global loss in each round. Besides, we develop an efficient GP training method with a low communication overhead in the FL scenario by utilizing the covariance stationarity. Our experimental results show that compared to the state-of-the-art method, FedCorr can improve the convergence rates by $34\%\sim 99\%$ and $26\%\sim 51\%$ on FMNIST and CIFAR-10, respectively.

preprint2022arXiv

Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning

Specific emitter identification (SEI) is a highly potential technology for physical layer authentication that is one of the most critical supplement for the upper-layer authentication. SEI is based on radio frequency (RF) features from circuit difference, rather than cryptography. These features are inherent characteristic of hardware circuits, which difficult to counterfeit. Recently, various deep learning (DL)-based conventional SEI methods have been proposed, and achieved advanced performances. However, these methods are proposed for close-set scenarios with massive RF signal samples for training, and they generally have poor performance under the condition of limited training samples. Thus, we focus on few-shot SEI (FS-SEI) for aircraft identification via automatic dependent surveillance-broadcast (ADS-B) signals, and a novel FS-SEI method is proposed, based on deep metric ensemble learning (DMEL). Specifically, the proposed method consists of feature embedding and classification. The former is based on metric learning with complex-valued convolutional neural network (CVCNN) for extracting discriminative features with compact intra-category distance and separable inter-category distance, while the latter is realized by an ensemble classifier. Simulation results show that if the number of samples per category is more than 5, the average accuracy of our proposed method is higher than 98\%. Moreover, feature visualization demonstrates the advantages of our proposed method in both discriminability and generalization. The codes of this paper can be downloaded from GitHub(https://github.com/BeechburgPieStar/Few-Shot-Specific-Emitter-Identification-via-Deep-Metric-Ensemble-Learning)

preprint2022arXiv

GypSum: Learning Hybrid Representations for Code Summarization

Code summarization with deep learning has been widely studied in recent years. Current deep learning models for code summarization generally follow the principle in neural machine translation and adopt the encoder-decoder framework, where the encoder learns the semantic representations from source code and the decoder transforms the learnt representations into human-readable text that describes the functionality of code snippets. Despite they achieve the new state-of-the-art performance, we notice that current models often either generate less fluent summaries, or fail to capture the core functionality, since they usually focus on a single type of code representations. As such we propose GypSum, a new deep learning model that learns hybrid representations using graph attention neural networks and a pre-trained programming and natural language model. We introduce particular edges related to the control flow of a code snippet into the abstract syntax tree for graph construction, and design two encoders to learn from the graph and the token sequence of source code, respectively. We modify the encoder-decoder sublayer in the Transformer's decoder to fuse the representations and propose a dual-copy mechanism to facilitate summary generation. Experimental results demonstrate the superior performance of GypSum over existing code summarization models.

preprint2022arXiv

Heuristic Adaptability to Input Dynamics for SpMM on GPUs

Sparse Matrix-Matrix Multiplication (SpMM) has served as fundamental components in various domains. Many previous studies exploit GPUs for SpMM acceleration because GPUs provide high bandwidth and parallelism. We point out that a static design does not always improve the performance of SpMM on different input data (e.g., >85\% performance loss with a single algorithm). In this paper, we consider the challenge of input dynamics from a novel auto-tuning perspective, while following issues remain to be solved: (1) Orthogonal design principles considering sparsity. Orthogonal design principles for such a sparse problem should be extracted to form different algorithms, and further used for performance tuning. (2) Nontrivial implementations in the algorithm space. Combining orthogonal design principles to create new algorithms needs to tackle with new challenges like thread race handling. (3) Heuristic adaptability to input dynamics. The heuristic adaptability is required to dynamically optimize code for input dynamics. To tackle these challenges, we first propose a novel three-loop model to extract orthogonal design principles for SpMM on GPUs. The model not only covers previous SpMM designs, but also comes up with new designs absent from previous studies. We propose techniques like conditional reduction to implement algorithms missing in previous studies. We further propose DA-SpMM, a Data-Aware heuristic GPU kernel for SpMM. DA-SpMM adaptively optimizes code considering input dynamics. Extensive experimental results show that, DA-SpMM achieves 1.26x~1.37x speedup compared with the best NVIDIA cuSPARSE algorithm on average, and brings up to 5.59x end-to-end speedup to applications like Graph Neural Networks.

preprint2022arXiv

High-resolution time-to-digital converters (TDCs) with a bidirectional encoder

A high-resolution time-to-digital converter (TDC) based on wave union (four-edge WU A), dual-sampling, and sub-TDL methods is proposed and implemented in a 16-nm Xilinx UltraScale+ field-programmable gate array (FPGA). We combine WU and dual-sampling techniques to achieve a high resolution. Besides, we use the sub-TDL method and the proposed bidirectional encoder to suppress bubbles and encode four-transition pseudo thermometer codes efficiently. Experimental results indicate the proposed TDC achieves a 0.4 ps resolution with a 450MHz sampling clock and a 3.06 ps RMS precision in the best-case scenario. These characteristics make this design suitable for particle physics, biomedical imaging (such as positron emission tomography, PET), and general-purpose scientific instruments.

preprint2022arXiv

Hyperradial distribution function of few-body problems: a new arena for extreme value theory

This work explores classical capture models for few-body systems via a Monte Carlo method in hyperspherical coordinates. In particular, we focus on van der Waals and charged-induced dipole interactions. As a result, we notice that, independently of the number of particles and interparticle interaction, the capture hyperradial distribution function follows a Fréchet distribution, a special type of the generalized extreme value distribution. Besides, we elaborate on the fundamentals of such universal feature using the general extreme value theory, thus, establishing a connection between extreme value theory and few-body physics.

preprint2022arXiv

Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage

Graph Neural Networks (GNNs) have shown great power in learning node representations on graphs. However, they may inherit historical prejudices from training data, leading to discriminatory bias in predictions. Although some work has developed fair GNNs, most of them directly borrow fair representation learning techniques from non-graph domains without considering the potential problem of sensitive attribute leakage caused by feature propagation in GNNs. However, we empirically observe that feature propagation could vary the correlation of previously innocuous non-sensitive features to the sensitive ones. This can be viewed as a leakage of sensitive information which could further exacerbate discrimination in predictions. Thus, we design two feature masking strategies according to feature correlations to highlight the importance of considering feature propagation and correlation variation in alleviating discrimination. Motivated by our analysis, we propose Fair View Graph Neural Network (FairVGNN) to generate fair views of features by automatically identifying and masking sensitive-correlated features considering correlation variation after feature propagation. Given the learned fair views, we adaptively clamp weights of the encoder to avoid using sensitive-related features. Experiments on real-world datasets demonstrate that FairVGNN enjoys a better trade-off between model utility and fairness. Our code is publicly available at https://github.com/YuWVandy/FairVGNN.

preprint2022arXiv

Improving Out-of-Distribution Robustness via Selective Augmentation

Machine learning algorithms typically assume that training and test examples are drawn from the same distribution. However, distribution shift is a common problem in real-world applications and can cause models to perform dramatically worse at test time. In this paper, we specifically consider the problems of subpopulation shifts (e.g., imbalanced data) and domain shifts. While prior works often seek to explicitly regularize internal representations or predictors of the model to be domain invariant, we instead aim to learn invariant predictors without restricting the model's internal representations or predictors. This leads to a simple mixup-based technique which learns invariant predictors via selective augmentation called LISA. LISA selectively interpolates samples either with the same labels but different domains or with the same domain but different labels. Empirically, we study the effectiveness of LISA on nine benchmarks ranging from subpopulation shifts to domain shifts, and we find that LISA consistently outperforms other state-of-the-art methods and leads to more invariant predictors. We further analyze a linear setting and theoretically show how LISA leads to a smaller worst-group error.

preprint2022arXiv

Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network

Because of the large number of online games available nowadays, online game recommender systems are necessary for users and online game platforms. The former can discover more potential online games of their interests, and the latter can attract users to dwell longer in the platform. This paper investigates the characteristics of user behaviors with respect to the online games on the Steam platform. Based on the observations, we argue that a satisfying recommender system for online games is able to characterize: personalization, game contextualization and social connection. However, simultaneously solving all is rather challenging for game recommendation. Firstly, personalization for game recommendation requires the incorporation of the dwelling time of engaged games, which are ignored in existing methods. Secondly, game contextualization should reflect the complex and high-order properties of those relations. Last but not least, it is problematic to use social connections directly for game recommendations due to the massive noise within social connections. To this end, we propose a Social-aware Contextualized Graph Neural Recommender System (SCGRec), which harnesses three perspectives to improve game recommendation. We conduct a comprehensive analysis of users' online game behaviors, which motivates the necessity of handling those three characteristics in the online game recommendation.

preprint2022arXiv

Latent Heterogeneous Graph Network for Incomplete Multi-View Learning

Multi-view learning has progressed rapidly in recent years. Although many previous studies assume that each instance appears in all views, it is common in real-world applications for instances to be missing from some views, resulting in incomplete multi-view data. To tackle this problem, we propose a novel Latent Heterogeneous Graph Network (LHGN) for incomplete multi-view learning, which aims to use multiple incomplete views as fully as possible in a flexible manner. By learning a unified latent representation, a trade-off between consistency and complementarity among different views is implicitly realized. To explore the complex relationship between samples and latent representations, a neighborhood constraint and a view-existence constraint are proposed, for the first time, to construct a heterogeneous graph. Finally, to avoid any inconsistencies between training and test phase, a transductive learning technique is applied based on graph learning for classification tasks. Extensive experimental results on real-world datasets demonstrate the effectiveness of our model over existing state-of-the-art approaches.

preprint2022arXiv

Learning Monotone Dynamics by Neural Networks

Feed-forward neural networks (FNNs) work as standard building blocks in applying artificial intelligence (AI) to the physical world. They allow learning the dynamics of unknown physical systems (e.g., biological and chemical) {to predict their future behavior}. However, they are likely to violate the physical constraints of those systems without proper treatment. This work focuses on imposing two important physical constraints: monotonicity (i.e., a partial order of system states is preserved over time) and stability (i.e., the system states converge over time) when using FNNs to learn physical dynamics. For monotonicity constraints, we propose to use nonnegative neural networks and batch normalization. For both monotonicity and stability constraints, we propose to learn the system dynamics and corresponding Lyapunov function simultaneously. As demonstrated by case studies, our methods can preserve the stability and monotonicity of FNNs and significantly reduce their prediction errors.

preprint2022arXiv

Learning Robust Recommenders through Cross-Model Agreement

Learning from implicit feedback is one of the most common cases in the application of recommender systems. Generally speaking, interacted examples are considered as positive while negative examples are sampled from uninteracted ones. However, noisy examples are prevalent in real-world implicit feedback. A noisy positive example could be interacted but it actually leads to negative user preference. A noisy negative example which is uninteracted because of unawareness of the user could also denote potential positive user preference. Conventional training methods overlook these noisy examples, leading to sub-optimal recommendations. In this work, we propose a novel framework to learn robust recommenders from implicit feedback. Through an empirical study, we find that different models make relatively similar predictions on clean examples which denote the real user preference, while the predictions on noisy examples vary much more across different models. Motivated by this observation, we propose denoising with cross-model agreement(DeCA) which aims to minimize the KL-divergence between the real user preference distributions parameterized by two recommendation models while maximizing the likelihood of data observation. We employ the proposed DeCA on four state-of-the-art recommendation models and conduct experiments on four datasets. Experimental results demonstrate that DeCA significantly improves recommendation performance compared with normal training and other denoising methods. Codes will be open-sourced.

preprint2022arXiv

Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation

Semantic segmentation with limited annotations, such as weakly supervised semantic segmentation (WSSS) and semi-supervised semantic segmentation (SSSS), is a challenging task that has attracted much attention recently. Most leading WSSS methods employ a sophisticated multi-stage training strategy to estimate pseudo-labels as precise as possible, but they suffer from high model complexity. In contrast, there exists another research line that trains a single network with image-level labels in one training cycle. However, such a single-stage strategy often performs poorly because of the compounding effect caused by inaccurate pseudo-label estimation. To address this issue, this paper presents a Self-supervised Low-Rank Network (SLRNet) for single-stage WSSS and SSSS. The SLRNet uses cross-view self-supervision, that is, it simultaneously predicts several complementary attentive LR representations from different views of an image to learn precise pseudo-labels. Specifically, we reformulate the LR representation learning as a collective matrix factorization problem and optimize it jointly with the network learning in an end-to-end manner. The resulting LR representation deprecates noisy information while capturing stable semantics across different views, making it robust to the input variations, thereby reducing overfitting to self-supervision errors. The SLRNet can provide a unified single-stage framework for various label-efficient semantic segmentation settings: 1) WSSS with image-level labeled data, 2) SSSS with a few pixel-level labeled data, and 3) SSSS with a few pixel-level labeled data and many image-level labeled data. Extensive experiments on the Pascal VOC 2012, COCO, and L2ID datasets demonstrate that our SLRNet outperforms both state-of-the-art WSSS and SSSS methods with a variety of different settings, proving its good generalizability and efficacy.

preprint2022arXiv

Learning-Based Vulnerability Analysis of Cyber-Physical Systems

This work focuses on the use of deep learning for vulnerability analysis of cyber-physical systems (CPS). Specifically, we consider a control architecture widely used in CPS (e.g., robotics), where the low-level control is based on e.g., the extended Kalman filter (EKF) and an anomaly detector. To facilitate analyzing the impact potential sensing attacks could have, our objective is to develop learning-enabled attack generators capable of designing stealthy attacks that maximally degrade system operation. We show how such problem can be cast within a learning-based grey-box framework where parts of the runtime information are known to the attacker, and introduce two models based on feed-forward neural networks (FNN); both models are trained offline, using a cost function that combines the attack effects on the estimation error and the residual signal used for anomaly detection, so that the trained models are capable of recursively generating such effective sensor attacks in real-time. The effectiveness of the proposed methods is illustrated on several case studies.

preprint2022arXiv

LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network

This technical report presents the 1st place winning solution for the Waymo Open Dataset 3D semantic segmentation challenge 2022. Our network, termed LidarMultiNet, unifies the major LiDAR perception tasks such as 3D semantic segmentation, object detection, and panoptic segmentation in a single framework. At the core of LidarMultiNet is a strong 3D voxel-based encoder-decoder network with a novel Global Context Pooling (GCP) module extracting global contextual features from a LiDAR frame to complement its local features. An optional second stage is proposed to refine the first-stage segmentation or generate accurate panoptic segmentation results. Our solution achieves a mIoU of 71.13 and is the best for most of the 22 classes on the Waymo 3D semantic segmentation test set, outperforming all the other 3D semantic segmentation methods on the official leaderboard. We demonstrate for the first time that major LiDAR perception tasks can be unified in a single strong network that can be trained end-to-end.

preprint2022arXiv

Low hardware consumption, resolution-configurable Gray code oscillator time-to-digital converters implemented in 16nm, 20nm and 28nm FPGAs

This paper presents a low hardware consumption, resolution-configurable, automatically calibrating Gray code oscillator time-to-digital converter (TDC) in Xilinx 16nm UltraScale+, 20nm UltraScale and 28nm Virtex-7 field-programmable gate arrays (FPGAs). The proposed TDC has several innovations: 1) a sampling matrix to improve the resolution. 2) a virtual bin calibration method (VBCM) to realize resolution configuration and automatic calibration. 3) a hardware implementation of the VBCM in standard FPGA devices. We implemented and evaluated a 16-channel TDC system in all three FPGAs. The UltraScale+ version achieved the best resolution (least significant bit, LSB) of 20.97 ps with 0.09 LSB averaged peak-peak differential linearity (DNLpk-pk). The UltraScale and Virtex-7 versions achieved the best resolutions of 36.01 ps with 0.10 LSB averaged DNLpk-pk and 34.84 ps with 0.08 LSB averaged DNLpk-pk, respectively.

preprint2022arXiv

Membership Inference Attacks on Knowledge Graphs

Membership inference attacks (MIAs) infer whether a specific data record is used for target model training. MIAs have provoked many discussions in the information security community since they give rise to severe data privacy issues, especially for private and sensitive datasets. Knowledge Graphs (KGs), which describe domain-specific subjects and relationships among them, are valuable and sensitive, such as medical KGs constructed from electronic health records. However, the privacy threat to knowledge graphs is critical but rarely explored. In this paper, we conduct the first empirical evaluation of privacy threats to knowledge graphs triggered by knowledge graph embedding methods (KGEs). We propose three types of membership inference attacks: transfer attacks (TAs), prediction loss-based attacks (PLAs), and prediction correctness-based attacks (PCAs), according to attack difficulty levels. In the experiments, we conduct three inference attacks against four standard KGE methods over three benchmark datasets. In addition, we also propose the attacks against medical KG and financial KG. The results demonstrate that the proposed attack methods can easily explore the privacy leakage of knowledge graphs.

preprint2022arXiv

Multi-Granularity Regularized Re-Balancing for Class Incremental Learning

Deep learning models suffer from catastrophic forgetting when learning new tasks incrementally. Incremental learning has been proposed to retain the knowledge of old classes while learning to identify new classes. A typical approach is to use a few exemplars to avoid forgetting old knowledge. In such a scenario, data imbalance between old and new classes is a key issue that leads to performance degradation of the model. Several strategies have been designed to rectify the bias towards the new classes due to data imbalance. However, they heavily rely on the assumptions of the bias relation between old and new classes. Therefore, they are not suitable for complex real-world applications. In this study, we propose an assumption-agnostic method, Multi-Granularity Regularized re-Balancing (MGRB), to address this problem. Re-balancing methods are used to alleviate the influence of data imbalance; however, we empirically discover that they would under-fit new classes. To this end, we further design a novel multi-granularity regularization term that enables the model to consider the correlations of classes in addition to re-balancing the data. A class hierarchy is first constructed by grouping the semantically or visually similar classes. The multi-granularity regularization then transforms the one-hot label vector into a continuous label distribution, which reflects the relations between the target class and other classes based on the constructed class hierarchy. Thus, the model can learn the inter-class relational information, which helps enhance the learning of both old and new classes. Experimental results on both public datasets and a real-world fault diagnosis dataset verify the effectiveness of the proposed method.

preprint2022arXiv

Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion Recognition

The research and applications of multimodal emotion recognition have become increasingly popular recently. However, multimodal emotion recognition faces the challenge of lack of data. To solve this problem, we propose to use transfer learning which leverages state-of-the-art pre-trained models including wav2vec 2.0 and BERT for this task. Multi-level fusion approaches including coattention-based early fusion and late fusion with the models trained on both embeddings are explored. Also, a multi-granularity framework which extracts not only frame-level speech embeddings but also segment-level embeddings including phone, syllable and word-level speech embeddings is proposed to further boost the performance. By combining our coattention-based early fusion model and late fusion model with the multi-granularity feature extraction framework, we obtain result that outperforms best baseline approaches by 1.3% unweighted accuracy (UA) on the IEMOCAP dataset.

preprint2022arXiv

Multi-UAV Coverage Planning with Limited Endurance in Disaster Environment

For scenes such as floods and earthquakes, the disaster area is large, and rescue time is tight. Multi-UAV exploration is more efficient than a single UAV. Existing UAV exploration work is modeled as a Coverage Path Planning (CPP) task to achieve full coverage of the area in the presence of obstacles. However, the endurance capability of UAV is limited, and the rescue time is urgent. Thus, even using multiple UAVs cannot achieve complete disaster area coverage in time. Therefore, in this paper we propose a multi-Agent Endurance-limited CPP (MAEl-CPP) problem based on a priori heatmap of the disaster area, which requires the exploration of more valuable areas under limited energy. Furthermore, we propose a path planning algorithm for the MAEl-CPP problem, by ranking the possible disaster areas according to their importance through satellite or remote aerial images and completing path planning according to the importance level. Experimental results show that our proposed algorithm is at least twice as effective as the existing method in terms of search efficiency.

preprint2022arXiv

Null controllability for stochastic fourth order parabolic equations

We establish the null controllability for linear stochastic fourth order parabolic equations. Utilizing the duality argument, the null controllability is reduced to the observability for backward fourth order stochastic parabolic equations, and the desired observability estimate is obtained by a new global Carleman estimate. Our Carleman estimate is based on a new fundamental identity for a stochastic fourth order parabolic operator.

preprint2022arXiv

On Structural Explanation of Bias in Graph Neural Networks

Graph Neural Networks (GNNs) have shown satisfying performance in various graph analytical problems. Hence, they have become the \emph{de facto} solution in a variety of decision-making scenarios. However, GNNs could yield biased results against certain demographic subgroups. Some recent works have empirically shown that the biased structure of the input network is a significant source of bias for GNNs. Nevertheless, no studies have systematically scrutinized which part of the input network structure leads to biased predictions for any given node. The low transparency on how the structure of the input network influences the bias in GNN outcome largely limits the safe adoption of GNNs in various decision-critical scenarios. In this paper, we study a novel research problem of structural explanation of bias in GNNs. Specifically, we propose a novel post-hoc explanation framework to identify two edge sets that can maximally account for the exhibited bias and maximally contribute to the fairness level of the GNN prediction for any given node, respectively. Such explanations not only provide a comprehensive understanding of bias/fairness of GNN predictions but also have practical significance in building an effective yet fair GNN model. Extensive experiments on real-world datasets validate the effectiveness of the proposed framework towards delivering effective structural explanations for the bias of GNNs. Open-source code can be found at https://github.com/yushundong/REFEREE.

preprint2022arXiv

Opencpop: A High-Quality Open Source Chinese Popular Song Corpus for Singing Voice Synthesis

This paper introduces Opencpop, a publicly available high-quality Mandarin singing corpus designed for singing voice synthesis (SVS). The corpus consists of 100 popular Mandarin songs performed by a female professional singer. Audio files are recorded with studio quality at a sampling rate of 44,100 Hz and the corresponding lyrics and musical scores are provided. All singing recordings have been phonetically annotated with phoneme boundaries and syllable (note) boundaries. To demonstrate the reliability of the released data and to provide a baseline for future research, we built baseline deep neural network-based SVS models and evaluated them with both objective metrics and subjective mean opinion score (MOS) measure. Experimental results show that the best SVS model trained on our database achieves 3.70 MOS, indicating the reliability of the provided corpus. Opencpop is released to the open-source community WeNet, and the corpus, as well as synthesized demos, can be found on the project homepage.

preprint2022arXiv

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

We consider the flare prediction problem that distinguishes flare-imminent active regions that produce an M- or X-class flare in the future 24 hours, from quiet active regions that do not produce any flare within $\pm 24$ hours. Using line-of-sight magnetograms and parameters of active regions in two data products covering Solar Cycle 23 and 24, we train and evaluate two deep learning algorithms -- CNN and LSTM -- and their stacking ensembles. The decisions of CNN are explained using visual attribution methods. We have the following three main findings. (1) LSTM trained on data from two solar cycles achieves significantly higher True Skill Scores (TSS) than that trained on data from a single solar cycle with a confidence level of at least 0.95. (2) On data from Solar Cycle 23, a stacking ensemble that combines predictions from LSTM and CNN using the TSS criterion achieves significantly higher TSS than the "select-best" strategy with a confidence level of at least 0.95. (3) A visual attribution method called Integrated Gradients is able to attribute the CNN's predictions of flares to the emerging magnetic flux in the active region. It also reveals a limitation of CNN as a flare prediction method using line-of-sight magnetograms: it treats the polarity artifact of line-of-sight magnetograms as positive evidence of flares.

preprint2022arXiv

Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees

This paper proposes a primal-dual framework to learn a stable estimator for linear constrained estimation problems leveraging the moving horizon approach. To avoid the online computational burden in most existing methods, we learn a parameterized function offline to approximate the primal estimate. Meanwhile, a dual estimator is trained to check the suboptimality of the primal estimator during execution time. Both the primal and dual estimators are learned from data using supervised learning techniques, and the explicit sample size is provided, which enables us to guarantee the quality of each learned estimator in terms of feasibility and optimality. This in turn allows us to bound the probability of the learned estimator being infeasible or suboptimal. Furthermore, we analyze the stability of the resulting estimator with a bounded error in the minimization of the cost function. Since our algorithm does not require the solution of an optimization problem during runtime, state estimates can be generated online almost instantly. Simulation results are presented to show the accuracy and time efficiency of the proposed framework compared to online optimization of moving horizon estimation and Kalman filter. To the best of our knowledge, this is the first learning-based state estimator with feasibility and near-optimality guarantees for linear constrained systems.

preprint2022arXiv

Production of polarized particle beams via ultraintense laser pulses

High-energy spin-polarized electron, positron, and $γ$-photon beams have many significant applications in the study of material properties, nuclear structure, particle physics, and high-energy astrophysics. Thus,efficient production of such polarized beams attracts a broad spectrum of research interests. This is driven mainly by the rapid advancements in ultrashort and ultraintense laser technology. Currently available laser pulses can achieve peak intensities in the range of $10^{22}-10^{23}$ Wcm$^{-2}$, with pulse durations of tens of femtoseconds. The dynamics of particles in laser fields of the available intensities is dominated by quantum electrodynamics (QED) and the interaction mechanisms have reached regimes spanned by nonlinear multiphoton absorbtion (strong-field QED processes). In strong-field QED processes, the scattering cross sections obviously depend on the spin and polarization of the particles, and the spin-dependent photon emission and the radiation-reaction effects can be utilized to produce the polarized particles. An ultraintense laser-driven polarized particle source possesses the advantages of high-brilliance and compactness, which could open the way for the unexplored aspects in a range of researches. In this work, we briefly review the seminal conclusions from the study of the polarization effects in strong-field QED processes, as well as the progress made by recent proposals for production of the polarized particles by laser-beam or laser-plasma interactions.

preprint2022arXiv

Provable Boolean Interaction Recovery from Tree Ensemble obtained via Random Forests

Random Forests (RF) are at the cutting edge of supervised machine learning in terms of prediction performance, especially in genomics. Iterative Random Forests (iRF) use a tree ensemble from iteratively modified RF to obtain predictive and stable non-linear or Boolean interactions of features. They have shown great promise for Boolean biological interaction discovery that is central to advancing functional genomics and precision medicine. However, theoretical studies into how tree-based methods discover Boolean feature interactions are missing. Inspired by the thresholding behavior in many biological processes, we first introduce a novel discontinuous nonlinear regression model, called the Locally Spiky Sparse (LSS) model. Specifically, the LSS model assumes that the regression function is a linear combination of piecewise constant Boolean interaction terms. Given an RF tree ensemble, we define a quantity called Depth-Weighted Prevalence (DWP) for a set of signed features S. Intuitively speaking, DWP(S) measures how frequently features in S appear together in an RF tree ensemble. We prove that, with high probability, DWP(S) attains a universal upper bound that does not involve any model coefficients, if and only if S corresponds to a union of Boolean interactions under the LSS model. Consequentially, we show that a theoretically tractable version of the iRF procedure, called LSSFind, yields consistent interaction discovery under the LSS model as the sample size goes to infinity. Finally, simulation results show that LSSFind recovers the interactions under the LSS model even when some assumptions are violated.

preprint2022arXiv

Pure State Tomography with Fourier Transformation

Extracting information from quantum devices has long been a crucial problem in the field of quantum mechanics. By performing elaborate measurements, quantum state tomography, an important and fundamental tool in quantum science and technology, can be used to determine unknown quantum states completely. In this study, we explore methods to determine multi-qubit pure quantum states uniquely and directly. Two adaptive protocols are proposed, with their respective quantum circuits. Herein, two or three observables are sufficient, while the number of measurement outcomes is either the same as or fewer than those in existing methods. Additionally, experiments on the IBM 5-qubit quantum computer, as well as numerical investigations, demonstrate the feasibility of the proposed protocols.

preprint2022arXiv

Quantum Information Scrambling in Quantum Many-body Scarred Systems

Quantum many-body scarred systems host special non-thermal eigenstates that support periodic revival dynamics and weakly break the ergodicity. Here, we study the quantum information scrambling dynamics in quantum many-body scarred systems, with a focus on the "PXP" model. We use the out-of-time-ordered correlator (OTOC) and Holevo information as measures of the information scrambling, and apply an efficient numerical method based on matrix product operators to compute them up to 41 spins. We find that both the OTOC and Holevo information exhibit a linear light cone and periodic oscillations inside the light cone for initial states within the scarred subspace, which is in sharp contrast to thermal or many-body localized systems. The periodic revivals of OTOCs and Holevo information signify unusual breakdown of quantum chaos and are not equivalent to the revival dynamics of state fidelity or local observables studied in the previous literature. To explain the formation of the linear light cone structure, we provide a perturbation-type calculation based on a phenomenological model. In addition, we demonstrate that the OTOC and Holevo information dynamics of the "PXP" model can be measured using the Rydberg-atom quantum simulators with current experimental technologies, and numerically identify the measurable signatures using experimental parameters.

preprint2022arXiv

R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction

Fine-grained visual categorization (FGVC) aims to discriminate similar subcategories, whose main challenge is the large intraclass diversities and subtle inter-class differences. Existing FGVC methods usually select discriminant regions found by a trained model, which is prone to neglect other potential discriminant information. On the other hand, the massive interactions between the sequence of image patches in ViT make the resulting class-token contain lots of redundant information, which may also impacts FGVC performance. In this paper, we present a novel approach for FGVC, which can simultaneously make use of partial yet sufficient discriminative information in environmental cues and also compress the redundant information in class-token with respect to the target. Specifically, our model calculates the ratio of high-weight regions in a batch, adaptively adjusts the masking threshold and achieves moderate extraction of background information in the input space. Moreover, we also use the Information Bottleneck~(IB) approach to guide our network to learn a minimum sufficient representations in the feature space. Experimental results on three widely-used benchmark datasets verify that our approach can achieve outperforming performance than other state-of-the-art approaches and baseline models.

preprint2022arXiv

Robotic Computing on FPGAs: Current Progress, Research Challenges, and Opportunities

Robotic computing has reached a tipping point, with a myriad of robots (e.g., drones, self-driving cars, logistic robots) being widely applied in diverse scenarios. The continuous proliferation of robotics, however, critically depends on efficient computing substrates, driven by real-time requirements, robotic size-weight-and-power constraints, cybersecurity considerations, and dynamically changing scenarios. Within all platforms, FPGA is able to deliver both software and hardware solutions with low power, high performance, reconfigurability, reliability, and adaptivity characteristics, serving as the promising computing substrate for robotic applications. This paper highlights the current progress, design techniques, challenges, and open research challenges in the domain of robotic computing on FPGAs.

preprint2022arXiv

Robust End-to-end Speaker Diarization with Generic Neural Clustering

End-to-end speaker diarization approaches have shown exceptional performance over the traditional modular approaches. To further improve the performance of the end-to-end speaker diarization for real speech recordings, recently works have been proposed which integrate unsupervised clustering algorithms with the end-to-end neural diarization models. However, these methods have a number of drawbacks: 1) The unsupervised clustering algorithms cannot leverage the supervision from the available datasets; 2) The K-means-based unsupervised algorithms that are explored often suffer from the constraint violation problem; 3) There is unavoidable mismatch between the supervised training and the unsupervised inference. In this paper, a robust generic neural clustering approach is proposed that can be integrated with any chunk-level predictor to accomplish a fully supervised end-to-end speaker diarization model. Also, by leveraging the sequence modelling ability of a recurrent neural network, the proposed neural clustering approach can dynamically estimate the number of speakers during inference. Experimental show that when integrating an attractor-based chunk-level predictor, the proposed neural clustering approach can yield better Diarization Error Rate (DER) than the constrained K-means-based clustering approaches under the mismatched conditions.

preprint2022arXiv

Self-Supervised Masking for Unsupervised Anomaly Detection and Localization

Recently, anomaly detection and localization in multimedia data have received significant attention among the machine learning community. In real-world applications such as medical diagnosis and industrial defect detection, anomalies only present in a fraction of the images. To extend the reconstruction-based anomaly detection architecture to the localized anomalies, we propose a self-supervised learning approach through random masking and then restoring, named Self-Supervised Masking (SSM) for unsupervised anomaly detection and localization. SSM not only enhances the training of the inpainting network but also leads to great improvement in the efficiency of mask prediction at inference. Through random masking, each image is augmented into a diverse set of training triplets, thus enabling the autoencoder to learn to reconstruct with masks of various sizes and shapes during training. To improve the efficiency and effectiveness of anomaly detection and localization at inference, we propose a novel progressive mask refinement approach that progressively uncovers the normal regions and finally locates the anomalous regions. The proposed SSM method outperforms several state-of-the-arts for both anomaly detection and anomaly localization, achieving 98.3% AUC on Retinal-OCT and 93.9% AUC on MVTec AD, respectively.

preprint2022arXiv

Topological Interface-State Lasing in a Polymer-Cholesteric Liquid Crystal Superlattice

The advance of topological photonics has heralded a revolution for manipulating light as well as for the development of novel photonic devices such as topological insulator lasers. Here, we demonstrate topological lasing of circular polarization in a polymer-cholesteric liquid crystal (P-CLC) superlattice, tunable in the visible wavelength regime. By use of the femtosecond-laser direct-writing and self-assembling techniques, we establish the P-CLC superlattice with a controlled mini-band structure and a topological interface defect, thereby achieving a low threshold for robust topological lasing at about 0.4 uJ. Thanks to the chiral liquid crystal, not only the emission wavelength is thermally tuned, but the circularly polarized lasing is readily achieved. Our results bring about the possibility to realize compact and integrated topological photonic devices at low cost, as well as to engineer an ideal platform for exploring topological physics that involves light-matter interaction in soft-matter environments.

preprint2022arXiv

Unreliability of two-band model analysis of magnetoresistivities in unveiling temperature-driven Lifshitz transition

Recently, anomalies in the temperature dependences of the carrier density and/or mobility derived from analysis of the magnetoresistivities using the conventional two-band model have been used to unveil intriguing temperature-induced Lifshitz transitions in various materials. For instance, two temperature-driven Lifshitz transitions were inferred to exist in the Dirac nodal-line semimetal ZrSiSe, based on two-band model analysis of the Hall magnetoconductivities where the second band exhibits a change in the carrier type from holes to electrons when the temperature decreases below T = 106 K and a dip is observed in the mobility versus temperature curve at T = 80 K. Here, we revisit the experiments and two-band model analysis on ZrSiSe. We show that the anomalies in the second band may be spurious, because the first band dominates the Hall magnetoconductivities at T > 80 K, making the carrier type and mobility obtained for the second band from the two-band model analysis unreliable. That is, care must be taken in interpreting these anomalies as evidences for temperature-driven Lifshitz transitions. Our skepticism on the existence of such phase transitions in ZrSiSe is further supported by the validation of the Kohler's rule for magnetoresistances at temperatures below 180 K. This work showcases potential issues in interpreting anomalies in the temperature dependence of the carrier density and mobility derived from the analysis of magnetoconductivities or magnetoresistivities using the conventional two-band model.

preprint2022arXiv

Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models

Fast inference of numerical model parameters from data is an important prerequisite to generate predictive models for a wide range of applications. Use of sampling-based approaches such as Markov chain Monte Carlo may become intractable when each likelihood evaluation is computationally expensive. New approaches combining variational inference with normalizing flow are characterized by a computational cost that grows only linearly with the dimensionality of the latent variable space, and rely on gradient-based optimization instead of sampling, providing a more efficient approach for Bayesian inference about the model parameters. Moreover, the cost of frequently evaluating an expensive likelihood can be mitigated by replacing the true model with an offline trained surrogate model, such as neural networks. However, this approach might generate significant bias when the surrogate is insufficiently accurate around the posterior modes. To reduce the computational cost without sacrificing inferential accuracy, we propose Normalizing Flow with Adaptive Surrogate (NoFAS), an optimization strategy that alternatively updates the normalizing flow parameters and surrogate model parameters. We also propose an efficient sample weighting scheme for surrogate model training that preserves global accuracy while effectively capturing high posterior density regions. We demonstrate the inferential and computational superiority of NoFAS against various benchmarks, including cases where the underlying model lacks identifiability. The source code and numerical experiments used for this study are available at https://github.com/cedricwangyu/NoFAS.

preprint2022arXiv

WheaCha: A Method for Explaining the Predictions of Models of Code

Attribution methods have emerged as a popular approach to interpreting model predictions based on the relevance of input features. Although the feature importance ranking can provide insights of how models arrive at a prediction from a raw input, they do not give a clear-cut definition of the key features models use for the prediction. In this paper, we present a new method, called WheaCha, for explaining the predictions of code models. Although WheaCha employs the same mechanism of tracing model predictions back to the input features, it differs from all existing attribution methods in crucial ways. Specifically, WheaCha divides an input program into "wheat" (i.e., the defining features that are the reason for which models predict the label that they predict) and the rest "chaff" for any prediction of a learned code model. We realize WheaCha in a tool, HuoYan, and use it to explain four prominent code models: code2vec, seq-GNN, GGNN, and CodeBERT. Results show (1) HuoYan is efficient - taking on average under twenty seconds to compute the wheat for an input program in an end-to-end fashion (i.e., including model prediction time); (2) the wheat that all models use to predict input programs is made of simple syntactic or even lexical properties (i.e., identifier names); (3) Based on wheat, we present a novel approach to explaining the predictions of code models through the lens of training data.

preprint2021arXiv

A High Spatial Resolution Muon Tomography Prototype System based on Micromegas Detector

Cosmic ray muon has strong penetrating power and no ionizing radiation hazards, which makes it an ideal probe for detecting special nuclear materials. In this paper, a high spatial resolution muon tomography system based on Micromegas detectors is proposed to optimize the imaging time and quality. The proposed system includes eight Micromegas detectors based on the thermal bonding technique and a scalable readout system. In addition, a multiplexing method based on position encoding is developed to reduce the number of electronics channels by order of magnitude. The spatial resolution of the proposed system with encoding readout can reach a value of hundred micrometers. Finally, a tomography test is performed, and test results show that this proposed system can image 2-cm objects and distinguish different materials.

preprint2021arXiv

A Survey of FPGA-Based Robotic Computing

Recent researches on robotics have shown significant improvement, spanning from algorithms, mechanics to hardware architectures. Robotics, including manipulators, legged robots, drones, and autonomous vehicles, are now widely applied in diverse scenarios. However, the high computation and data complexity of robotic algorithms pose great challenges to its applications. On the one hand, CPU platform is flexible to handle multiple robotic tasks. GPU platform has higher computational capacities and easy-touse development frameworks, so they have been widely adopted in several applications. On the other hand, FPGA-based robotic accelerators are becoming increasingly competitive alternatives, especially in latency-critical and power-limited scenarios. With specialized designed hardware logic and algorithm kernels, FPGA-based accelerators can surpass CPU and GPU in performance and energy efficiency. In this paper, we give an overview of previous work on FPGA-based robotic accelerators covering different stages of the robotic system pipeline. An analysis of software and hardware optimization techniques and main technical issues is presented, along with some commercial and space applications, to serve as a guide for future work.

preprint2021arXiv

Brilliant circularly polarized $γ$-ray sources via single-shot laser plasma interaction

Circularly polarized (CP) $γ$-ray sources are versatile for broad applications in nuclear physics, high-energy physics and astrophysics. The laser-plasma based particle accelerators provide accessibility for much higher flux $γ$-ray sources than conventional approaches, in which, however, the circular polarization properties of emitted $γ$-photons are used to be neglected. In this letter, we show that brilliant CP $γ$-ray beams can be generated via the combination of laser plasma wakefield acceleration and plasma mirror techniques. In weakly nonlinear Compton scattering scheme with moderate laser intensities, the helicity of the driving laser can be transferred to the emitted $γ$-photons, and their average polarization degree can reach about $\sim 37\%$ ($21\%$) with a peak brilliance of $\gtrsim 10^{21}~$photons/(s $\cdot$ mm$^2 \cdot$ mrad$^2 \cdot$ 0.1% BW) around 1~MeV (100~MeV). Moreover, our proposed method is easily feasible and robust with respect to the laser and plasma parameters.

preprint2021arXiv

Dissecting the Energy Budget of a Gamma-Ray Burst Fireball

The jet composition and radiative efficiency of GRBs are poorly constrained from the data. If the jet composition is matter-dominated (i.e. a fireball), the GRB prompt emission spectra would include a dominant thermal component originating from the fireball photosphere, and a non-thermal component presumably originating from internal shocks whose radii are greater than the photosphere radius. We propose a method to directly dissect the GRB fireball energy budget into three components and measure their values by combining the prompt emission and early afterglow data. The measured parameters include the initial dimensionless specific enthalpy density ($η$), bulk Lorentz factors at the photosphere radius ($Γ_{\rm ph}$) and before fireball deceleration ($Γ_0$), the amount of mass loading ($M$), as well as the GRB radiative efficiency ($η_γ$). All the parameters can be derived from the data for a GRB with a dominant thermal spectral component, a deceleration bump feature in the early afterglow lightcurve, and a measured redshift. The results only weakly depend on the density $n$ of the interstellar medium when the composition ${\cal Y}$ parameter (typically unity) is specified.

preprint2021arXiv

Enabling Lower-Power Charge-Domain Nonvolatile In-Memory Computing with Ferroelectric FETs

Compute-in-memory (CiM) is a promising approach to alleviating the memory wall problem for domain-specific applications. Compared to current-domain CiM solutions, charge-domain CiM shows the opportunity for higher energy efficiency and resistance to device variations. However, the area occupation and standby leakage power of existing SRAMbased charge-domain CiM (CD-CiM) are high. This paper proposes the first concept and analysis of CD-CiM using nonvolatile memory (NVM) devices. The design implementation and performance evaluation are based on a proposed 2-transistor-1-capacitor (2T1C) CiM macro using ferroelectric field-effect-transistors (FeFETs), which is free from leakage power and much denser than the SRAM solution. With the supply voltage between 0.45V and 0.90V, operating frequency between 100MHz to 1.0GHz, binary neural network application simulations show over 47%, 60%, and 64% energy consumption reduction from existing SRAM-based CD-CiM, SRAM-based current-domain CiM, and RRAM-based current-domain CiM, respectively. For classifications in MNIST and CIFAR-10 data sets, the proposed FeFETbased CD-CiM achieves an accuracy over 95% and 80%, respectively.

preprint2021arXiv

Enhancing fiber atom interferometer by in-fiber laser cooling

We demonstrate an inertia sensitive atom interferometer optically guided inside a 22-cm-long negative curvature hollow-core photonic crystal fiber with an interferometer time of 20 ms. The result prolongs the previous fiber guided atom interferometer time by three orders of magnitude. The improvement arises from the realization of in-fiber Λ-enhanced gray molasses and delta-kick cooling to cool atoms from 32 μK to below 1 μK in 4 ms. The in-fiber cooling overcomes the inevitable heating during the atom loading process and allows a shallow guiding optical potential to minimize decoherence. Our results permit bringing atoms close to source fields for sensing and could lead to compact inertial quantum sensors with a sub-millimeter resolution.

preprint2021arXiv

Generating Synthetic Systems of Interdependent Critical Infrastructure Networks

The lack of data on critical infrastructure systems has hindered the research progress in modeling and optimizing the system performance. This work develops a method for generating Synthetic Interdependent Critical Infrastructure Networks (SICIN) using simulation and non-linear optimization techniques. SICIN consists of three components: (i) determining the location of facilities in individual networks via a modified simulated annealing algorithm, (ii) generating interdependent links based on a novel pseudo-tripartite graph algorithm, and (iii) simulating network flow using nonlinear optimization considering the operations of individual networks and their interdependencies. Two existing systems of interdependent infrastructure networks are used to validate the proposed method. The results demonstrate that SICIN outperforms state-of-the-art simulation methods according to multiple topological and flow measures of similarity between the simulated and real networks.

preprint2021arXiv

Hermes: Decentralized Dynamic Spectrum Access System for Massive Devices Deployment in 5G

With the incoming 5G network, the ubiquitous Internet of Things (IoT) devices can benefit our daily life, such as smart cameras, drones, etc. With the introduction of the millimeter-wave band and the thriving number of IoT devices, it is critical to design new dynamic spectrum access (DSA) system to coordinate the spectrum allocation across massive devices in 5G. In this paper, we present Hermes, the first decentralized DSA system for massive devices deployment. Specifically, we propose an efficient multi-agent reinforcement learning algorithm and introduce a novel shuffle mechanism, addressing the drawbacks of collision and fairness in existing decentralized systems. We implement Hermes in 5G network via simulations. Extensive evaluations show that Hermes significantly reduces collisions and improves fairness compared to the state-of-the-art decentralized methods. Furthermore, Hermes is able to adapt the environmental changes within 0.5 seconds, showing its deployment practicability in dynamic environment of 5G.

preprint2021arXiv

Machine Learning for Electronic Design Automation: A Survey

With the down-scaling of CMOS technology, the design complexity of very large-scale integrated (VLSI) is increasing. Although the application of machine learning (ML) techniques in electronic design automation (EDA) can trace its history back to the 90s, the recent breakthrough of ML and the increasing complexity of EDA tasks have aroused more interests in incorporating ML to solve EDA tasks. In this paper, we present a comprehensive review of existing ML for EDA studies, organized following the EDA hierarchy.

preprint2021arXiv

On the Counter-Rotation of Closed Timelike Curves

While it is tempting to think of closed timelike curves (CTCs) around rotating bodies such as a black hole as being "caused" by the rotation of the source, Andréka et al. pointed out that the underlying physics is not as straightforward since such CTCs are "counter-rotating", i.e., the time orientation (the opening of the local light cones) of the CTCs is opposite to the direction in which the singularity or the ergosphere rotates. It was also suggested that this is a generic phenomenon that calls for a deeper intuitive physical understanding. In this short note we point out -- with Kerr-Taub-NUT as an example -- that CTCs are counter-rotating with respect to the local angular velocity of the spacetime, which makes a physical interpretation of CTCs being "caused" by a rotating source even more problematic.

preprint2021arXiv

Probabilistic Conformance for Cyber-Physical Systems

In system analysis, conformance indicates that two systems simultaneously satisfy the same set of specifications of interest; thus, the results from analyzing one system automatically transfer to the other, or one system can safely replace the other in practice. In this work, we study the probabilistic conformance of cyber-physical systems (CPS). We propose a notion of (approximate) probabilistic conformance for sets of complex specifications expressed by the Signal Temporal Logic (STL). Based on a novel statistical test, we develop the first statistical verification methods for the probabilistic conformance of a wide class of CPS. Using this method, we verify the conformance of the startup time of the widely-used full and simplified model of Toyota powertrain systems, the settling time of model-predictive-control-based and neural-network-based automotive lane-keeping controllers, as well as the maximal voltage deviation of full and simplified power grid systems.

preprint2021arXiv

Synthesis and structure of carbon doped H3S compounds at high pressure

Understanding of recently reported putative close-to-room-temperature superconductivity in C-S-H compounds at 267 GPa demands reproducible synthesis protocol as well as knowledge of its structure and composition. We synthesized C-S-H compounds with various carbon composition at high pressures from elemental C and methane CH4, sulfur S, and molecular hydrogen H2. Here we focus on compounds synthesized using methane as these allow a straightforward determination of their structure and composition by combining single-crystal X-ray diffraction (XRD) and Raman spectroscopy. We applied a two-stage synthesis of ((CH4)x(H2S)(1-x))2H2 compounds by first reacting sulfur and mixed methane-hydrogen fluids and forming CH4 doped H2S crystals at 0.5-3 GPa, and then by growing single crystals of the desired hydrogen rich compound. Raman spectroscopy applied to this material shows the presence of the CH4 molecules incorporated into the lattice and allows to determine the CH4 content, while single-crystal X-ray diffraction results suggest that the methane molecules substitute H2S ones. The structural behavior of these compounds is very similar to the previously investigated methane free compounds demonstrating a transition from Al2Cu type I4/mcm structure to a modulated structure at 20-30 GPa and back to the same basic structure in an extended modification with greatly modified Raman spectra. This latter phase demonstrates a distortion into Pnma structure at 132-159 GPa and then transforms into a common Im-3m H3S phase at higher pressures, however, no structural anomaly is detected near 220 GPa, where a sharp upturn in Tc has been reported.

preprint2021arXiv

TDR-OBCA: A Reliable Planner for Autonomous Driving in Free-Space Environment

This paper presents an optimization-based collision avoidance trajectory generation method for autonomous driving in free-space environments, with enhanced robustness, driving comfort and efficiency. Starting from the hybrid optimization-based framework, we introduces two warm start methods, temporal and dual variable warm starts, to improve the efficiency. We also reformulate the problem to improve the robustness and efficiency. We name this new algorithm TDR-OBCA. With these changes, compared with original hybrid optimization we achieve a 96.67% failure rate decrease with respect to initial conditions, 13.53% increase in driving comforts and 3.33% to 44.82% increase in planner efficiency as obstacles number scales. We validate our results in hundreds of simulation scenarios and hundreds of hours of public road tests in both U.S. and China. Our source code is available at https://github.com/ApolloAuto/apollo.

preprint2021arXiv

Two-orbital model for possible superconductivity pairing mechanism in nickelates

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

preprint2020arXiv

1st Place Solution for Waymo Open Dataset Challenge -- 3D Detection and Domain Adaptation

In this technical report, we introduce our winning solution "HorizonLiDAR3D" for the 3D detection track and the domain adaptation track in Waymo Open Dataset Challenge at CVPR 2020. Many existing 3D object detectors include prior-based anchor box design to account for different scales and aspect ratios and classes of objects, which limits its capability of generalization to a different dataset or domain and requires post-processing (e.g. Non-Maximum Suppression (NMS)). We proposed a one-stage, anchor-free and NMS-free 3D point cloud object detector AFDet, using object key-points to encode the 3D attributes, and to learn an end-to-end point cloud object detection without the need of hand-engineering or learning the anchors. AFDet serves as a strong baseline in our winning solution and significant improvements are made over this baseline during the challenges. Specifically, we design stronger networks and enhance the point cloud data using densification and point painting. To leverage camera information, we append/paint additional attributes to each point by projecting them to camera space and gathering image-based perception information. The final detection performance also benefits from model ensemble and Test-Time Augmentation (TTA) in both the 3D detection track and the domain adaptation track. Our solution achieves the 1st place with 77.11% mAPH/L2 and 69.49% mAPH/L2 respectively on the 3D detection track and the domain adaptation track.

preprint2020arXiv

1st Place Solutions for Waymo Open Dataset Challenges -- 2D and 3D Tracking

This technical report presents the online and real-time 2D and 3D multi-object tracking (MOT) algorithms that reached the 1st places on both Waymo Open Dataset 2D tracking and 3D tracking challenges. An efficient and pragmatic online tracking-by-detection framework named HorizonMOT is proposed for camera-based 2D tracking in the image space and LiDAR-based 3D tracking in the 3D world space. Within the tracking-by-detection paradigm, our trackers leverage our high-performing detectors used in the 2D/3D detection challenges and achieved 45.13% 2D MOTA/L2 and 63.45% 3D MOTA/L2 in the 2D/3D tracking challenges.

preprint2020arXiv

2nd Place Solution for Waymo Open Dataset Challenge -- 2D Object Detection

A practical autonomous driving system urges the need to reliably and accurately detect vehicles and persons. In this report, we introduce a state-of-the-art 2D object detection system for autonomous driving scenarios. Specifically, we integrate both popular two-stage detector and one-stage detector with anchor free fashion to yield a robust detection. Furthermore, we train multiple expert models and design a greedy version of the auto ensemble scheme that automatically merges detections from different models. Notably, our overall detection system achieves 70.28 L2 mAP on the Waymo Open Dataset v1.2, ranking the 2nd place in the 2D detection track of the Waymo Open Dataset Challenges.

preprint2020arXiv

A Complex KBQA System using Multiple Reasoning Paths

Multi-hop knowledge based question answering (KBQA) is a complex task for natural language understanding. Many KBQA approaches have been proposed in recent years, and most of them are trained based on labeled reasoning path. This hinders the system's performance as many correct reasoning paths are not labeled as ground truth, and thus they cannot be learned. In this paper, we introduce an end-to-end KBQA system which can leverage multiple reasoning paths' information and only requires labeled answer as supervision. We conduct experiments on several benchmark datasets containing both single-hop simple questions as well as muti-hop complex questions, including WebQuestionSP (WQSP), ComplexWebQuestion-1.1 (CWQ), and PathQuestion-Large (PQL), and demonstrate strong performance.

preprint2020arXiv

A Comprehensive Survey of Grammar Error Correction

Grammar error correction (GEC) is an important application aspect of natural language processing techniques. The past decade has witnessed significant progress achieved in GEC for the sake of increasing popularity of machine learning and deep learning, especially in late 2010s when near human-level GEC systems are available. However, there is no prior work focusing on the whole recapitulation of the progress. We present the first survey in GEC for a comprehensive retrospect of the literature in this area. We first give the introduction of five public datasets, data annotation schema, two important shared tasks and four standard evaluation metrics. More importantly, we discuss four kinds of basic approaches, including statistical machine translation based approach, neural machine translation based approach, classification based approach and language model based approach, six commonly applied performance boosting techniques for GEC systems and two data augmentation methods. Since GEC is typically viewed as a sister task of machine translation, many GEC systems are based on neural machine translation (NMT) approaches, where the neural sequence-to-sequence model is applied. Similarly, some performance boosting techniques are adapted from machine translation and are successfully combined with GEC systems for enhancement on the final performance. Furthermore, we conduct an analysis in level of basic approaches, performance boosting techniques and integrated GEC systems based on their experiment results respectively for more clear patterns and conclusions. Finally, we discuss five prospective directions for future GEC researches.

preprint2020arXiv

A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS

This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, a.k.a. GATES, to improve the predictor-based neural architecture search. Specifically, different from existing graph-based schemes, GATES models the operations as the transformation of the propagating information, which mimics the actual data processing of neural architecture. GATES is a more reasonable modeling of the neural architectures, and can encode architectures from both the "operation on node" and "operation on edge" cell search spaces consistently. Experimental results on various search spaces confirm GATES's effectiveness in improving the performance predictor. Furthermore, equipped with the improved performance predictor, the sample efficiency of the predictor-based neural architecture search (NAS) flow is boosted. Codes are available at https://github.com/walkerning/aw_nas.

preprint2020arXiv

Adversarial Training for Large Neural Language Models

Generalization and robustness are both key desiderata for designing machine learning methods. Adversarial training can enhance robustness, but past work often finds it hurts generalization. In natural language processing (NLP), pre-training large neural language models such as BERT have demonstrated impressive gain in generalization for a variety of tasks, with further improvement from adversarial fine-tuning. However, these models are still vulnerable to adversarial attacks. In this paper, we show that adversarial pre-training can improve both generalization and robustness. We propose a general algorithm ALUM (Adversarial training for large neural LangUage Models), which regularizes the training objective by applying perturbations in the embedding space that maximizes the adversarial loss. We present the first comprehensive study of adversarial training in all stages, including pre-training from scratch, continual pre-training on a well-trained model, and task-specific fine-tuning. ALUM obtains substantial gains over BERT on a wide range of NLP tasks, in both regular and adversarial scenarios. Even for models that have been well trained on extremely large text corpora, such as RoBERTa, ALUM can still produce significant gains from continual pre-training, whereas conventional non-adversarial methods can not. ALUM can be further combined with task-specific fine-tuning to attain additional gains. The ALUM code is publicly available at https://github.com/namisan/mt-dnn.

preprint2020arXiv

AFDet: Anchor Free One Stage 3D Object Detection

High-efficiency point cloud 3D object detection operated on embedded systems is important for many robotics applications including autonomous driving. Most previous works try to solve it using anchor-based detection methods which come with two drawbacks: post-processing is relatively complex and computationally expensive; tuning anchor parameters is tricky. We are the first to address these drawbacks with an anchor free and Non-Maximum Suppression free one stage detector called AFDet. The entire AFDet can be processed efficiently on a CNN accelerator or a GPU with the simplified post-processing. Without bells and whistles, our proposed AFDet performs competitively with other one stage anchor-based methods on KITTI validation set and Waymo Open Dataset validation set.

preprint2020arXiv

Angular dependence and absorption properties of the anapolemode of Si nano-disks

The polarization- and angle- resolved optical response of the anapole mode in silicon nano-disks array have been experimentally and theoretically investigated. The good agreement betweenmeasured data and simulations yields to a consistent description of the anapole mode behaviorthat exhibits different features for TE or TM polarization excitation. Scattering matrix calculationallows us to disentangle scattered and diffused light contributions and to provide a quantitativeestimation of the absorbance enhancement associated to 2D excitation of the anapole mode. Weperformed the multipolar decomposition of the far-field scattered radiation for both TE and TMpolarizations and unambiguously identified the anapole resonant condition in excellent agreementwith the experimental results over a large range of incident angles. Our findings demonstrate thecontrolled excitation of electromagnetic anapole modes in engineered arrays of silicon nano-disksfor the development of optical nanostructures with enhanced light-matter interaction

preprint2020arXiv

Antiferromagnetism of Double Molybdate LiFe(MoO$_4$)$_2$

The magnetic properties of the spin-5/2 double molybdate LiFe(MoO$_4$)$_2$ have been characterized by heat capacity, magnetic susceptibility, and neutron powder diffraction techniques. Unlike the multiferroic system LiFe(MoO$_4$)$_2$ which exhibits two successive magnetic transitions, LiFe(MoO$_4$)$_2$ undergoes only one antiferromagnetic transition at $T_N$ ~ 23.8 K. Its antiferromagnetic magnetic structure with the commensurate propagation vector k = (0, 0.5, 0) has been determined. Density functional theory calculations confirm the antiferromagnetic ground state and provide a numerical estimate of the relevant exchange coupling constants.

preprint2020arXiv

Application of Pre-training Models in Named Entity Recognition

Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task to extract entities from unstructured data. The previous methods for NER were based on machine learning or deep learning. Recently, pre-training models have significantly improved performance on multiple NLP tasks. In this paper, firstly, we introduce the architecture and pre-training tasks of four common pre-training models: BERT, ERNIE, ERNIE2.0-tiny, and RoBERTa. Then, we apply these pre-training models to a NER task by fine-tuning, and compare the effects of the different model architecture and pre-training tasks on the NER task. The experiment results showed that RoBERTa achieved state-of-the-art results on the MSRA-2006 dataset.

preprint2020arXiv

Approximately Optimal Spatial Design: How Good is it?

The increasing recognition of the association between adverse human health conditions and many environmental substances as well as processes has led to the need to monitor them. An important problem that arises in environmental statistics is the design of the locations of the monitoring stations for those environmental processes of interest. One particular design criterion for monitoring networks that tries to reduce the uncertainty about predictions of unseen processes is called the maximum-entropy design. However, this design criterion involves a hard optimization problem that is computationally intractable for large data sets. Previous work of Wang et al. (2017) examined a probabilistic model that can be implemented efficiently to approximate the underlying optimization problem. In this paper, we attempt to establish statistically sound tools for assessing the quality of the approximations.

preprint2020arXiv

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- I. Survey description and a first look at G9.62+0.19

The "ATOMS," standing for {\it ALMA Three-millimeter Observations of Massive Star-forming regions}, survey has observed 146 active star forming regions with ALMA Band 3, aiming to systematically investigate the spatial distribution of various dense gas tracers in a large sample of Galactic massive clumps, to study the roles of stellar feedback in star formation, and to characterize filamentary structures inside massive clumps. In this work, the observations, data analysis, and example science of the "ATOMS" survey are presented, using a case study for the G9.62+0.19 complex. Toward this source, some transitions, commonly assumed to trace dense gas, including CS $J = 2-1$, HCO$^+$ $J = 1-0$ and HCN $J = 1-0$, are found to show extended gas emission in low density regions within the clump; less than 25\% of their emission is from dense cores. SO, CH$_3$OH, H$^{13}$CN and HC$_3$N show similar morphologies in their spatial distributions and reveal well the dense cores. Widespread narrow SiO emission is present (over $\sim$1 pc), which may be caused by slow shocks from large--scale colliding flows or H{\sc ii} regions. Stellar feedback from an expanding H{\sc ii} region has greatly reshaped the natal clump, significantly changed the spatial distribution of gas, and may also account for the sequential high-mass star formation in the G9.62+0.19 complex. The ATOMS survey data can be jointly analyzed with other survey data, e.g., "MALT90", "Orion B", "EMPIRE", "ALMA\_IMF", and "ALMAGAL", to deepen our understandings of "dense gas" star formation scaling relations and massive proto-cluster formation.

preprint2020arXiv

ATOMS: ALMA Three-millimeter Observations of Massive Star-forming regions -- II. Compact objects in ACA observations and star formation scaling relations

We report studies of the relationships between the total bolometric luminosity ($L_{\rm bol}$ or $L_{\rm TIR}$) and the molecular line luminosities of $J=1-0$ transitions of H$^{13}$CN, H$^{13}$CO$^+$, HCN, and HCO$^+$ with data obtained from ACA observations in the "ATOMS" survey of 146 active Galactic star forming regions. The correlations between $L_{\rm bol}$ and molecular line luminosities $L'_{\rm mol}$ of the four transitions all appear to be approximately linear. Line emission of isotopologues shows as large scatters in $L_{\rm bol}$-$L'_{\rm mol}$ relations as their main line emission. The log($L_{\rm bol}$/$L'_{\rm mol}$) for different molecular line tracers have similar distributions. The $L_{\rm bol}$-to-$L'_{\rm mol}$ ratios do not change with galactocentric distances ($R_{\rm GC}$) and clump masses ($M_{\rm clump}$). The molecular line luminosity ratios (HCN-to-HCO$^+$, H$^{13}$CN-to-H$^{13}$CO$^+$, HCN-to-H$^{13}$CN and HCO$^+$-to-H$^{13}$CO$^+$) all appear constant against $L_{\rm bol}$, dust temperature ($T_{\rm d}$), $M_{\rm clump}$ and $R_{\rm GC}$. Our studies suggest that both the main lines and isotopologue lines are good tracers of the total masses of dense gas in Galactic molecular clumps. The large optical depths of main lines do not affect the interpretation of the slopes in star formation relations. We find that the mean star formation efficiency (SFE) of massive Galactic clumps in the "ATOMS" survey is reasonably consistent with other measures of the SFE for dense gas, even those using very different tracers or examining very different spatial scales.

preprint2020arXiv

Communication Lower Bound in Convolution Accelerators

In current convolutional neural network (CNN) accelerators, communication (i.e., memory access) dominates the energy consumption. This work provides comprehensive analysis and methodologies to minimize the communication for CNN accelerators. For the off-chip communication, we derive the theoretical lower bound for any convolutional layer and propose a dataflow to reach the lower bound. This fundamental problem has never been solved by prior studies. The on-chip communication is minimized based on an elaborate workload and storage mapping scheme. We in addition design a communication-optimal CNN accelerator architecture. Evaluations based on the 65nm technology demonstrate that the proposed architecture nearly reaches the theoretical minimum communication in a three-level memory hierarchy and it is computation dominant. The gap between the energy efficiency of our accelerator and the theoretical best value is only 37-87%.

preprint2020arXiv

Context-Aware Temporal Logic for Probabilistic Systems

In this paper, we introduce the context-aware probabilistic temporal logic (CAPTL) that provides an intuitive way to formalize system requirements by a set of PCTL objectives with a context-based priority structure. We formally present the syntax and semantics of CAPTL and propose a synthesis algorithm for CAPTL requirements. We also implement the algorithm based on the PRISM-games model checker. Finally, we demonstrate the usage of CAPTL on two case studies: a robotic task planning problem, and synthesizing error-resilient scheduler for micro-electrode-dot-array digital microfluidic biochips.

preprint2020arXiv

Deep Gradient Compression: Reducing the Communication Bandwidth for Distributed Training

Large-scale distributed training requires significant communication bandwidth for gradient exchange that limits the scalability of multi-node training, and requires expensive high-bandwidth network infrastructure. The situation gets even worse with distributed training on mobile devices (federated learning), which suffers from higher latency, lower throughput, and intermittent poor connections. In this paper, we find 99.9% of the gradient exchange in distributed SGD is redundant, and propose Deep Gradient Compression (DGC) to greatly reduce the communication bandwidth. To preserve accuracy during compression, DGC employs four methods: momentum correction, local gradient clipping, momentum factor masking, and warm-up training. We have applied Deep Gradient Compression to image classification, speech recognition, and language modeling with multiple datasets including Cifar10, ImageNet, Penn Treebank, and Librispeech Corpus. On these scenarios, Deep Gradient Compression achieves a gradient compression ratio from 270x to 600x without losing accuracy, cutting the gradient size of ResNet-50 from 97MB to 0.35MB, and for DeepSpeech from 488MB to 0.74MB. Deep gradient compression enables large-scale distributed training on inexpensive commodity 1Gbps Ethernet and facilitates distributed training on mobile. Code is available at: https://github.com/synxlin/deep-gradient-compression.

preprint2020arXiv

Design of a novel monolithic parabolic-mirror ion-trap to precisely align the RF null point with the optical focus

We propose a novel ion trap design with the high collection efficiency parabolic-mirror integrated with the ion trap electrodes. This design has three radio frequency (RF) electrodes and eight direct current(DC) compensation electrodes. By carefully adjusting three RF voltages, the parabolic mirror focus can be made precisely coincident with the RF null point. Thus, the aberration and the ion micromotion can be minimized at the same time. This monolithic design can significantly improve the ion-ion entanglement generation speed by extending the photon collecting solid angle beyond $90\%\cdot4π$. Further analysis of the trapping setup shows that the RF voltage variation method relexes machining accuracy to a broad range. This design is expected to be a robust scheme for trapping ion to speed entanglement network node.

preprint2020arXiv

DSA: More Efficient Budgeted Pruning via Differentiable Sparsity Allocation

Budgeted pruning is the problem of pruning under resource constraints. In budgeted pruning, how to distribute the resources across layers (i.e., sparsity allocation) is the key problem. Traditional methods solve it by discretely searching for the layer-wise pruning ratios, which lacks efficiency. In this paper, we propose Differentiable Sparsity Allocation (DSA), an efficient end-to-end budgeted pruning flow. Utilizing a novel differentiable pruning process, DSA finds the layer-wise pruning ratios with gradient-based optimization. It allocates sparsity in continuous space, which is more efficient than methods based on discrete evaluation and search. Furthermore, DSA could work in a pruning-from-scratch manner, whereas traditional budgeted pruning methods are applied to pre-trained models. Experimental results on CIFAR-10 and ImageNet show that DSA could achieve superior performance than current iterative budgeted pruning methods, and shorten the time cost of the overall pruning process by at least 1.5x in the meantime.

preprint2020arXiv

DualLip: A System for Joint Lip Reading and Generation

Lip reading aims to recognize text from talking lip, while lip generation aims to synthesize talking lip according to text, which is a key component in talking face generation and is a dual task of lip reading. In this paper, we develop DualLip, a system that jointly improves lip reading and generation by leveraging the task duality and using unlabeled text and lip video data. The key ideas of the DualLip include: 1) Generate lip video from unlabeled text with a lip generation model, and use the pseudo pairs to improve lip reading; 2) Generate text from unlabeled lip video with a lip reading model, and use the pseudo pairs to improve lip generation. We further extend DualLip to talking face generation with two additionally introduced components: lip to face generation and text to speech generation. Experiments on GRID and TCD-TIMIT demonstrate the effectiveness of DualLip on improving lip reading, lip generation, and talking face generation by utilizing unlabeled data. Specifically, the lip generation model in our DualLip system trained with only10% paired data surpasses the performance of that trained with the whole paired data. And on the GRID benchmark of lip reading, we achieve 1.16% character error rate and 2.71% word error rate, outperforming the state-of-the-art models using the same amount of paired data.

preprint2020arXiv

Emergence of Competing Stripe Phase near the Mott Transition in Ti-doped Bilayer Calcium Ruthenates

We report the nanoscale imaging of Ti-doped bilayer calcium ruthenates during the Mott metal-insulator transition by microwave impedance microscopy. Different from a typical first-order phase transition where coexistence of the two terminal phases takes place, a new metallic stripe phase oriented along the in-plane crystalline axes emerges inside both the G-type antiferromagnetic insulating state and paramagnetic metallic state. The effect of this electronic state can be observed in macroscopic measurements, allowing us to construct a phase diagram that takes into account the energetically competing phases. Our work provides a model approach to correlate the macroscopic properties and mesoscopic phase separation in complex oxide materials.

preprint2020arXiv

Enabling Efficient and Flexible FPGA Virtualization for Deep Learning in the Cloud

FPGAs have shown great potential in providing low-latency and energy-efficient solutions for deep neural network (DNN) inference applications. Currently, the majority of FPGA-based DNN accelerators in the cloud run in a time-division multiplexing way for multiple users sharing a single FPGA, and require re-compilation with $\sim$100 s overhead. Such designs lead to poor isolation and heavy performance loss for multiple users, which are far away from providing efficient and flexible FPGA virtualization for neither public nor private cloud scenarios. To solve these problems, we introduce a novel virtualization framework for instruction architecture set (ISA) based on DNN accelerators by sharing a single FPGA. We enable the isolation by introducing a two-level instruction dispatch module and a multi-core based hardware resources pool. Such designs provide isolated and runtime-programmable hardware resources, further leading to performance isolation for multiple users. On the other hand, to overcome the heavy re-compilation overheads, we propose a tiling-based instruction frame package design and two-stage static-dynamic compilation. Only the light-weight runtime information is re-compiled with $\sim$1 ms overhead, thus the performance is guaranteed for the private cloud. Our extensive experimental results show that the proposed virtualization design achieves 1.07-1.69x and 1.88-3.12x throughput improvement over previous static designs using the single-core and the multi-core architectures, respectively.

preprint2020arXiv

Exceptionally large anomalous Hall effect due to anticrossing of spin-split bands in the antiferromagnetic half-Heusler compound TbPtBi

We have investigated magnetotransport properties and the topological electronic structure of the half-Heusler compound TbPtBi. Our experiments reveal an exceptionally large anomalous Hall effect (AHE) in the canted antiferromagnetic state of TbPtBi with the anomalous Hall angle (AHA) reaching ~0.68-0.76, which is a few times larger than the previously reported record in GdPtBi. First-principles electronic structure and the associated anomalous Hall conductivity were computed in order to interpret the experimental results. Our analysis shows that the AHE in TbPtBi does not originate from the Weyl points but that it is driven by the large net Berry curvature produced by the anticrossing of spin-split bands near the Fermi level in TbPtBi.

preprint2020arXiv

Exchange coupling in a linear chain of three quantum-dot spin qubits in silicon

Quantum gates between spin qubits can be implemented leveraging the natural Heisenberg exchange interaction between two electrons in contact with each other. This interaction is controllable by electrically tailoring the overlap between electronic wavefunctions in quantum dot systems, as long as they occupy neighbouring dots. An alternative route is the exploration of superexchange - the coupling between remote spins mediated by a third idle electron that bridges the distance between quantum dots. We experimentally demonstrate direct exchange coupling and provide evidence for second neighbour mediated superexchange in a linear array of three single-electron spin qubits in silicon, inferred from the electron spin resonance frequency spectra. We confirm theoretically through atomistic modeling that the device geometry only allows for sizeable direct exchange coupling for neighbouring dots, while next nearest neighbour coupling cannot stem from the vanishingly small tail of the electronic wavefunction of the remote dots, and is only possible if mediated.

preprint2020arXiv

Expert Training: Task Hardness Aware Meta-Learning for Few-Shot Classification

Deep neural networks are highly effective when a large number of labeled samples are available but fail with few-shot classification tasks. Recently, meta-learning methods have received much attention, which train a meta-learner on massive additional tasks to gain the knowledge to instruct the few-shot classification. Usually, the training tasks are randomly sampled and performed indiscriminately, often making the meta-learner stuck into a bad local optimum. Some works in the optimization of deep neural networks have shown that a better arrangement of training data can make the classifier converge faster and perform better. Inspired by this idea, we propose an easy-to-hard expert meta-training strategy to arrange the training tasks properly, where easy tasks are preferred in the first phase, then, hard tasks are emphasized in the second phase. A task hardness aware module is designed and integrated into the training procedure to estimate the hardness of a task based on the distinguishability of its categories. In addition, we explore multiple hardness measurements including the semantic relation, the pairwise Euclidean distance, the Hausdorff distance, and the Hilbert-Schmidt independence criterion. Experimental results on the miniImageNet and tieredImageNetSketch datasets show that the meta-learners can obtain better results with our expert training strategy.

preprint2020arXiv

Few-Shot Drum Transcription in Polyphonic Music

Data-driven approaches to automatic drum transcription (ADT) are often limited to a predefined, small vocabulary of percussion instrument classes. Such models cannot recognize out-of-vocabulary classes nor are they able to adapt to finer-grained vocabularies. In this work, we address open vocabulary ADT by introducing few-shot learning to the task. We train a Prototypical Network on a synthetic dataset and evaluate the model on multiple real-world ADT datasets with polyphonic accompaniment. We show that, given just a handful of selected examples at inference time, we can match and in some cases outperform a state-of-the-art supervised ADT approach under a fixed vocabulary setting. At the same time, we show that our model can successfully generalize to finer-grained or extended vocabularies unseen during training, a scenario where supervised approaches cannot operate at all. We provide a detailed analysis of our experimental results, including a breakdown of performance by sound class and by polyphony.

preprint2020arXiv

GE-SpMM: General-purpose Sparse Matrix-Matrix Multiplication on GPUs for Graph Neural Networks

Graph Neural Networks (GNNs) have achieved significant improvements in various domains. Sparse Matrix-Matrix multiplication (SpMM) is a fundamental operator in GNNs, which performs a multiplication between a sparse matrix and a dense matrix. Accelerating SpMM on parallel hardware like GPUs can face the following challenges: From the GNN application perspective, the compatibility needs to be considered. General GNN algorithms require SpMM-like operations (e.g., pooling) between matrices, which are not supported in current high-performance GPU libraries (e.g., Nvidia cuSPARSE). Moreover, the sophisticated preprocessing in previous implementations will lead to heavy data format conversion overheads in GNN frameworks. From the GPU hardware perspective, optimizations in SpMV (Sparse Matrix-Vector) designs on GPUs do not apply well to SpMM. SpMM exposes the column-wise parallelism in the dense output matrix, but straightforward generalization from SpMV leads to inefficient, uncoalesced access to sparse matrix in global memory. The sparse row data can be reused among GPU threads, which is neither possible in SpMM designs inherited from SpMV. To tackle these challenges, we propose GE-SpMM. GE-SpMM performs SpMM-like operation on sparse matrices represented in the most common Compressed Sparse Row (CSR) format, so it can be embedded in GNN frameworks with no preprocessing overheads and support general GNN algorithms. We introduce the Coalesced Row Caching method to process columns in parallel and ensure coalesced access to sparse matrix data. We also present the Coarse-grained Warp Merging to reduce redundant data loading among GPU warps. Experiments on a real-world graph dataset show that GE-SpMM achieves up to 1.41X speedup over Nvidia cuSPARSE and up to 1.81X over GraphBLAST. We also embed GE-SpMM in GNN frameworks and get up to 3.67X speedup over popular GNN models like GCN and GraphSAGE.

preprint2020arXiv

High-energy gamma-photon polarization in nonlinear Breit-Wheeler pair production and gamma-polarimetry

The interaction of an unpolarized electron beam with a counterpropagating ultraintense linearly polarized laser pulse is investigated in the quantum radiation-dominated regime. We employ a semiclassical Monte Carlo method to describe spin-resolved electron dynamics, photon emissions and polarization, and pair production. Abundant high-energy linearly polarized gamma photons are generated intermediately during this interaction via nonlinear Compton scattering, with an average polarization degree of more than 50%, which further interacting with the laser fields produce electron-positron pairs due to nonlinear Breit-Wheeler process. The photon polarization is shown to significantly affect the pair yield by a factor beyond 10%. The considered signature of the photon polarization in the pair's yield can be experimentally identified in a prospective two-stage setup. Moreover, the signature can serve also for the polarimetry of high-energy high-flux gamma photons with a resolution well below 1% with currently achievable laser facilities.

preprint2020arXiv

Hyperproperties for Robotics: Planning via HyperLTL

There is a growing interest on formal methods-based robotic planning for temporal logic objectives. In this work, we extend the scope of existing synthesis methods to hyper-temporal logics. We are motivated by the fact that important planning objectives, such as optimality, robustness, and privacy, (maybe implicitly) involve the interrelation between multiple paths. Such objectives are thus hyperproperties, and cannot be expressed with usual temporal logics like the linear temporal logic (LTL). We show that such hyperproperties can be expressed by HyperLTL, an extension of LTL to multiple paths. To handle the complexity of planning with HyperLTL specifications, we introduce a symbolic approach for synthesizing planning strategies on discrete transition systems. Our planning method is evaluated on several case studies.

preprint2020arXiv

In the Eyes of the Beholder: Analyzing Social Media Use of Neutral and Controversial Terms for COVID-19

During the COVID-19 pandemic, "Chinese Virus" emerged as a controversial term for coronavirus. To some, it may seem like a neutral term referring to the physical origin of the virus. To many others, however, the term is in fact attaching ethnicity to the virus. While both arguments appear reasonable, quantitative analysis of the term's real-world usage is lacking to shed light on the issues behind the controversy. In this paper, we attempt to fill this gap. To model the substantive difference of tweets with controversial terms and those with non-controversial terms, we apply topic modeling and LIWC-based sentiment analysis. To test whether "Chinese Virus" and "COVID-19" are interchangeable, we formulate it as a classification task, mask out these terms, and classify them using the state-of-the-art transformer models. Our experiments consistently show that the term "Chinese Virus" is associated with different substantive topics and sentiment compared with "COVID-19" and that the two terms are easily distinguishable by looking at their context.

preprint2020arXiv

Joint Visual and Wireless Signal Feature based Approach for High-Precision Indoor Localization

The existing localization systems for indoor applications basically rely on wireless signal. With the massive deployment of low-cost cameras, the visual image based localization become attractive as well. However, in the existing literature, the hybrid visual and wireless approaches simply combine the above schemes in a straight forward manner, and fail to explore the interactions between them. In this paper, we propose a joint visual and wireless signal feature based approach for high-precision indoor localization system. In this joint scheme, WiFi signals are utilized to compute the coarse area with likelihood probability and visual images are used to fine-tune the localization result. Based on the numerical results, we show that the proposed scheme can achieve 0.62m localization accuracy with near real-time running time.

preprint2020arXiv

Learning a Static Bug Finder from Data

We present an alternative approach to creating static bug finders. Instead of relying on human expertise, we utilize deep neural networks to train static analyzers directly from data. In particular, we frame the problem of bug finding as a classification task and train a classifier to differentiate the buggy from non-buggy programs using Graph Neural Network (GNN). Crucially, we propose a novel interval-based propagation mechanism that leads to a significantly more efficient, accurate and scalable generalization of GNN. We have realized our approach into a framework, NeurSA, and extensively evaluated it. In a cross-project prediction task, three neural bug detectors we instantiate from NeurSA are effective in catching null pointer dereference, array index out of bound and class cast bugs in unseen code. We compare NeurSA against several static analyzers (e.g. Facebook Infer and Pinpoint) on a set of null pointer dereference bugs. Results show that NeurSA is more precise in catching the real bugs and suppressing the spurious warnings. We also apply NeurSA to several popular Java projects on GitHub and discover 50 new bugs, among which 9 have been fixed, and 3 have been confirmed.

preprint2020arXiv

Learning a Unified Sample Weighting Network for Object Detection

Region sampling or weighting is significantly important to the success of modern region-based object detectors. Unlike some previous works, which only focus on "hard" samples when optimizing the objective function, we argue that sample weighting should be data-dependent and task-dependent. The importance of a sample for the objective function optimization is determined by its uncertainties to both object classification and bounding box regression tasks. To this end, we devise a general loss function to cover most region-based object detectors with various sampling strategies, and then based on it we propose a unified sample weighting network to predict a sample's task weights. Our framework is simple yet effective. It leverages the samples' uncertainty distributions on classification loss, regression loss, IoU, and probability score, to predict sample weights. Our approach has several advantages: (i). It jointly learns sample weights for both classification and regression tasks, which differentiates it from most previous work. (ii). It is a data-driven process, so it avoids some manual parameter tuning. (iii). It can be effortlessly plugged into most object detectors and achieves noticeable performance improvements without affecting their inference time. Our approach has been thoroughly evaluated with recent object detection frameworks and it can consistently boost the detection accuracy. Code has been made available at \url{https://github.com/caiqi/sample-weighting-network}.

preprint2020arXiv

Learning Semantic Program Embeddings with Graph Interval Neural Network

Learning distributed representations of source code has been a challenging task for machine learning models. Earlier works treated programs as text so that natural language methods can be readily applied. Unfortunately, such approaches do not capitalize on the rich structural information possessed by source code. Of late, Graph Neural Network (GNN) was proposed to learn embeddings of programs from their graph representations. Due to the homogeneous and expensive message-passing procedure, GNN can suffer from precision issues, especially when dealing with programs rendered into large graphs. In this paper, we present a new graph neural architecture, called Graph Interval Neural Network (GINN), to tackle the weaknesses of the existing GNN. Unlike the standard GNN, GINN generalizes from a curated graph representation obtained through an abstraction method designed to aid models to learn. In particular, GINN focuses exclusively on intervals for mining the feature representation of a program, furthermore, GINN operates on a hierarchy of intervals for scaling the learning to large graphs. We evaluate GINN for two popular downstream applications: variable misuse prediction and method name prediction. Results show in both cases GINN outperforms the state-of-the-art models by a comfortable margin. We have also created a neural bug detector based on GINN to catch null pointer deference bugs in Java code. While learning from the same 9,000 methods extracted from 64 projects, GINN-based bug detector significantly outperforms GNN-based bug detector on 13 unseen test projects. Next, we deploy our trained GINN-based bug detector and Facebook Infer to scan the codebase of 20 highly starred projects on GitHub. Through our manual inspection, we confirm 38 bugs out of 102 warnings raised by GINN-based bug detector compared to 34 bugs out of 129 warnings for Facebook Infer.

preprint2020arXiv

Monitoring Depression Trend on Twitter during the COVID-19 Pandemic

The COVID-19 pandemic has severely affected people's daily lives and caused tremendous economic loss worldwide. However, its influence on people's mental health conditions has not received as much attention. To study this subject, we choose social media as our main data resource and create by far the largest English Twitter depression dataset containing 2,575 distinct identified depression users with their past tweets. To examine the effect of depression on people's Twitter language, we train three transformer-based depression classification models on the dataset, evaluate their performance with progressively increased training sizes, and compare the model's "tweet chunk"-level and user-level performances. Furthermore, inspired by psychological studies, we create a fusion classifier that combines deep learning model scores with psychological text features and users' demographic information and investigate these features' relations to depression signals. Finally, we demonstrate our model's capability of monitoring both group-level and population-level depression trends by presenting two of its applications during the COVID-19 pandemic. We hope this study can raise awareness among researchers and the general public of COVID-19's impact on people's mental health.

preprint2020arXiv

Novel hydrogen clathrate hydrate

We report a new hydrogen clathrate hydrate synthesized at 1.2 GPa and 298 K documented by single-crystal X-ray diffraction, Raman spectroscopy, and first-principles calculations. The oxygen sublattice of the new clathrate hydrate matches that of ice II, while hydrogen molecules are in the ring cavities, which results in the trigonal R3c or R-3c space group (proton ordered or disordered, respectively) and the composition of (H2O)6H2. Raman spectroscopy and theoretical calculations reveal a hydrogen disordered nature of the new phase C1', distinct from the well-known ordered C1 clathrate, to which this new structure transforms upon compression and/or cooling. This new clathrate phase can be viewed as a realization of a disordered ice II, unobserved before, in contrast to all other ordered ice structures.

preprint2020arXiv

On fibrations of Lie groupoids

As groupoids generalize groups, motivated by group extensions we consider a kind of fibrations of Lie groupoids, called locally topological product Lie groupoid fibrations with fiber $\sf A$, i.e., \[ 1\rightarrow {\sf A} \rightarrow {\sf G} \rightarrow {\sf K}\rightarrow 1 \] where $\sf A,\sf G$ and $\sf K$ are Lie groupoids. Similar to the theory of group extensions, we show that the existence of locally topological product Lie groupoid fibrations with fiber $\sf A$ over $\sf K$ is obstructed by a groupoid cohomology of $H^3_{\bar Λ}({\sf K},Z_{\sf A})$, and these locally topological product Lie groupoid fibrations are classified by $H^2_{\bar Λ}({\sf K},Z_{\sf A})$ once exists. Here $Z_{\sf A}$ is the center of $\sf A$. This generalizes the theory of group extensions, of gerbes over manifolds/groupoids and etc.

preprint2020arXiv

Physical Adversarial Attack on Vehicle Detector in the Carla Simulator

In this paper, we tackle the issue of physical adversarial examples for object detectors in the wild. Specifically, we proposed to generate adversarial patterns to be applied on vehicle surface so that it's not recognizable by detectors in the photo-realistic Carla simulator. Our approach contains two main techniques, an Enlarge-and-Repeat process and a Discrete Searching method, to craft mosaic-like adversarial vehicle textures without access to neither the model weight of the detector nor a differential rendering procedure. The experimental results demonstrate the effectiveness of our approach in the simulator.

preprint2020arXiv

Precisely Predicting Acute Kidney Injury with Convolutional Neural Network Based on Electronic Health Record Data

The incidence of Acute Kidney Injury (AKI) commonly happens in the Intensive Care Unit (ICU) patients, especially in the adults, which is an independent risk factor affecting short-term and long-term mortality. Though researchers in recent years highlight the early prediction of AKI, the performance of existing models are not precise enough. The objective of this research is to precisely predict AKI by means of Convolutional Neural Network on Electronic Health Record (EHR) data. The data sets used in this research are two public Electronic Health Record (EHR) databases: MIMIC-III and eICU database. In this study, we take several Convolutional Neural Network models to train and test our AKI predictor, which can precisely predict whether a certain patient will suffer from AKI after admission in ICU according to the last measurements of the 16 blood gas and demographic features. The research is based on Kidney Disease Improving Global Outcomes (KDIGO) criteria for AKI definition. Our work greatly improves the AKI prediction precision, and the best AUROC is up to 0.988 on MIMIC-III data set and 0.936 on eICU data set, both of which outperform the state-of-art predictors. And the dimension of the input vector used in this predictor is much fewer than that used in other existing researches. Compared with the existing AKI predictors, the predictor in this work greatly improves the precision of early prediction of AKI by using the Convolutional Neural Network architecture and a more concise input vector. Early and precise prediction of AKI will bring much benefit to the decision of treatment, so it is believed that our work is a very helpful clinical application.

preprint2020arXiv

Shift Equivariance in Object Detection

Robustness to small image translations is a highly desirable property for object detectors. However, recent works have shown that CNN-based classifiers are not shift invariant. It is unclear to what extent this could impact object detection, mainly because of the architectural differences between the two and the dimensionality of the prediction space of modern detectors. To assess shift equivariance of object detection models end-to-end, in this paper we propose an evaluation metric, built upon a greedy search of the lower and upper bounds of the mean average precision on a shifted image set. Our new metric shows that modern object detection architectures, no matter if one-stage or two-stage, anchor-based or anchor-free, are sensitive to even one pixel shift to the input images. Furthermore, we investigate several possible solutions to this problem, both taken from the literature and newly proposed, quantifying the effectiveness of each one with the suggested metric. Our results indicate that none of these methods can provide full shift equivariance. Measuring and analyzing the extent of shift variance of different models and the contributions of possible factors, is a first step towards being able to devise methods that mitigate or even leverage such variabilities.

preprint2020arXiv

SONYC-UST-V2: An Urban Sound Tagging Dataset with Spatiotemporal Context

We present SONYC-UST-V2, a dataset for urban sound tagging with spatiotemporal information. This dataset is aimed for the development and evaluation of machine listening systems for real-world urban noise monitoring. While datasets of urban recordings are available, this dataset provides the opportunity to investigate how spatiotemporal metadata can aid in the prediction of urban sound tags. SONYC-UST-V2 consists of 18510 audio recordings from the "Sounds of New York City" (SONYC) acoustic sensor network, including the timestamp of audio acquisition and location of the sensor. The dataset contains annotations by volunteers from the Zooniverse citizen science platform, as well as a two-stage verification with our team. In this article, we describe our data collection procedure and propose evaluation metrics for multilabel classification of urban sound tags. We report the results of a simple baseline model that exploits spatiotemporal information.

preprint2020arXiv

SQLFlow: A Bridge between SQL and Machine Learning

Industrial AI systems are mostly end-to-end machine learning (ML) workflows. A typical recommendation or business intelligence system includes many online micro-services and offline jobs. We describe SQLFlow for developing such workflows efficiently in SQL. SQL enables developers to write short programs focusing on the purpose (what) and ignoring the procedure (how). Previous database systems extended their SQL dialect to support ML. SQLFlow (https://sqlflow.org/sqlflow ) takes another strategy to work as a bridge over various database systems, including MySQL, Apache Hive, and Alibaba MaxCompute, and ML engines like TensorFlow, XGBoost, and scikit-learn. We extended SQL syntax carefully to make the extension working with various SQL dialects. We implement the extension by inventing a collaborative parsing algorithm. SQLFlow is efficient and expressive to a wide variety of ML techniques -- supervised and unsupervised learning; deep networks and tree models; visual model explanation in addition to training and prediction; data processing and feature extraction in addition to ML. SQLFlow compiles a SQL program into a Kubernetes-native workflow for fault-tolerable execution and on-cloud deployment. Current industrial users include Ant Financial, DiDi, and Alibaba Group.

preprint2020arXiv

Statistical Model Checking for Hyperproperties

Hyperproperties have shown to be a powerful tool for expressing and reasoning about information-flow security policies. In this paper, we investigate the problem of statistical model checking (SMC) for hyperproperties. Unlike exhaustive model checking, SMC works based on drawing samples from the system at hand and evaluate the specification with statistical confidence. The main benefit of applying SMC over exhaustive techniques is its efficiency and scalability. To reason about probabilistic hyperproperties, we first propose the temporal logic HyperPCLT* that extends PCTL* and HyperPCTL. We show that HyperPCLT* can express important probabilistic information-flow security policies that cannot be expressed with HyperPCTL. Then, we introduce SMC algorithms for verifying HyperPCLT* formulas on discrete-time Markov chains, based on sequential probability ratio tests (SPRT) with a new notion of multi-dimensional indifference region. Our SMC algorithms can handle both non-nested and nested probability operators for any desired significance level. To show the effectiveness of our technique, we evaluate our SMC algorithms on four case studies focused on information security: timing side-channel vulnerability in encryption, probabilistic anonymity in dining cryptographers, probabilistic noninterference of parallel programs, and the performance of a randomized cache replacement policy that acts as a countermeasure against cache flush attacks.

preprint2020arXiv

Statistically Model Checking PCTL Specifications on Markov Decision Processes via Reinforcement Learning

Probabilistic Computation Tree Logic (PCTL) is frequently used to formally specify control objectives such as probabilistic reachability and safety. In this work, we focus on model checking PCTL specifications statistically on Markov Decision Processes (MDPs) by sampling, e.g., checking whether there exists a feasible policy such that the probability of reaching certain goal states is greater than a threshold. We use reinforcement learning to search for such a feasible policy for PCTL specifications, and then develop a statistical model checking (SMC) method with provable guarantees on its error. Specifically, we first use upper-confidence-bound (UCB) based Q-learning to design an SMC algorithm for bounded-time PCTL specifications, and then extend this algorithm to unbounded-time specifications by identifying a proper truncation time by checking the PCTL specification and its negation at the same time. Finally, we evaluate the proposed method on case studies.

preprint2020arXiv

The Ivory Tower Lost: How College Students Respond Differently than the General Public to the COVID-19 Pandemic

Recently, the pandemic of the novel Coronavirus Disease-2019 (COVID-19) has presented governments with ultimate challenges. In the United States, the country with the highest confirmed COVID-19 infection cases, a nationwide social distancing protocol has been implemented by the President. For the first time in a hundred years since the 1918 flu pandemic, the US population is mandated to stay in their households and avoid public contact. As a result, the majority of public venues and services have ceased their operations. Following the closure of the University of Washington on March 7th, more than a thousand colleges and universities in the United States have cancelled in-person classes and campus activities, impacting millions of students. This paper aims to discover the social implications of this unprecedented disruption in our interactive society regarding both the general public and higher education populations by mining people's opinions on social media. We discover several topics embedded in a large number of COVID-19 tweets that represent the most central issues related to the pandemic, which are of great concerns for both college students and the general public. Moreover, we find significant differences between these two groups of Twitter users with respect to the sentiments they expressed towards the COVID-19 issues. To our best knowledge, this is the first social media-based study which focuses on the college student community's demographics and responses to prevalent social issues during a major crisis.

preprint2020arXiv

The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding

We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models. Built upon PyTorch and Transformers, MT-DNN is designed to facilitate rapid customization for a broad spectrum of NLU tasks, using a variety of objectives (classification, regression, structured prediction) and text encoders (e.g., RNNs, BERT, RoBERTa, UniLM). A unique feature of MT-DNN is its built-in support for robust and transferable learning using the adversarial multi-task learning paradigm. To enable efficient production deployment, MT-DNN supports multi-task knowledge distillation, which can substantially compress a deep neural model without significant performance drop. We demonstrate the effectiveness of MT-DNN on a wide range of NLU applications across general and biomedical domains. The software and pre-trained models will be publicly available at https://github.com/namisan/mt-dnn.

preprint2020arXiv

The Sylvester Graphical Lasso (SyGlasso)

This paper introduces the Sylvester graphical lasso (SyGlasso) that captures multiway dependencies present in tensor-valued data. The model is based on the Sylvester equation that defines a generative model. The proposed model complements the tensor graphical lasso (Greenewald et al., 2019) that imposes a Kronecker sum model for the inverse covariance matrix by providing an alternative Kronecker sum model that is generative and interpretable. A nodewise regression approach is adopted for estimating the conditional independence relationships among variables. The statistical convergence of the method is established, and empirical studies are provided to demonstrate the recovery of meaningful conditional dependency graphs. We apply the SyGlasso to an electroencephalography (EEG) study to compare the brain connectivity of alcoholic and nonalcoholic subjects. We demonstrate that our model can simultaneously estimate both the brain connectivity and its temporal dependencies.

preprint2020arXiv

UniLMv2: Pseudo-Masked Language Models for Unified Language Model Pre-Training

We propose to pre-train a unified language model for both autoencoding and partially autoregressive language modeling tasks using a novel training procedure, referred to as a pseudo-masked language model (PMLM). Given an input text with masked tokens, we rely on conventional masks to learn inter-relations between corrupted tokens and context via autoencoding, and pseudo masks to learn intra-relations between masked spans via partially autoregressive modeling. With well-designed position embeddings and self-attention masks, the context encodings are reused to avoid redundant computation. Moreover, conventional masks used for autoencoding provide global masking information, so that all the position embeddings are accessible in partially autoregressive language modeling. In addition, the two tasks pre-train a unified language model as a bidirectional encoder and a sequence-to-sequence decoder, respectively. Our experiments show that the unified language models pre-trained using PMLM achieve new state-of-the-art results on a wide range of natural language understanding and generation tasks across several widely used benchmarks.

preprint2020arXiv

Verifying Stochastic Hybrid Systems with Temporal Logic Specifications via Model Reduction

We present a scalable methodology to verify stochastic hybrid systems. Using the Mori-Zwanzig reduction method, we construct a finite state Markov chain reduction of a given stochastic hybrid system and prove that this reduced Markov chain is approximately equivalent to the original system in a distributional sense. Approximate equivalence of the stochastic hybrid system and its Markov chain reduction means that analyzing the Markov chain with respect to a suitably strengthened property, allows us to conclude whether the original stochastic hybrid system meets its temporal logic specifications. We present the first statistical model checking algorithms to verify stochastic hybrid systems against correctness properties, expressed in the linear inequality linear temporal logic (iLTL) or the metric interval temporal logic (MITL).

preprint2020arXiv

Video-based Person Re-Identification using Gated Convolutional Recurrent Neural Networks

Deep neural networks have been successfully applied to solving the video-based person re-identification problem with impressive results reported. The existing networks for person re-id are designed to extract discriminative features that preserve the identity information. Usually, whole video frames are fed into the neural networks and all the regions in a frame are equally treated. This may be a suboptimal choice because many regions, e.g., background regions in the video, are not related to the person. Furthermore, the person of interest may be occluded by another person or something else. These unrelated regions may hinder person re-identification. In this paper, we introduce a novel gating mechanism to deep neural networks. Our gating mechanism will learn which regions are helpful for person re-identification and let these regions pass the gate. The unrelated background regions or occluding regions are filtered out by the gate. In each frame, the color channels and optical flow channels provide quite different information. To better leverage such information, we generate one gate using the color channels and another gate using the optical flow channels. These two gates are combined to provide a more reliable gate with a novel fusion method. Experimental results on two major datasets demonstrate the performance improvements due to the proposed gating mechanism.

preprint2019arXiv

Ferromagnetic van der Waals compound MnSb$_{1.8}$Bi$_{0.2}$Te$_4$

The intersection of topology and magnetism represents a new playground to discover novel quantum phenomena and device concepts. In this work, we show that a van der Waals compound MnSb$_{1.8}$Bi$_{0.2}$Te$_4$ exhibits a ferromagnetic ground state with a Curie temperature of 26 K, in contrast to the antiferromagnetic order previously found for other members of the Mn(Sb, Bi)$_2$Te$_4$ family. We employ magneto-transport, bulk magnetization and neutron scattering studies to illustrate the magnetic and electrical properties of MnSb$_{1.8}$Bi$_{0.2}$Te$_4$ and report on the observation of an unusual anomalous Hall effect. Our results are an important step in the synthesis and understanding of ferromagnetic topological insulators.

preprint2019arXiv

Giant room temperature anomalous Hall effect and magnetically tuned topology in the ferromagnetic Weyl semimetal Co2MnAl

Weyl semimetals (WSM) have been extensively studied due to their exotic properties such as topological surface states and anomalous transport phenomena. Their band structure topology is usually predetermined by material parameters and can hardly be manipulated once the material is formed. Their unique transport properties appear usually at very low temperature, which sets challenges for practical device applications. In this work, we demonstrate a way to modify the band topology via a weak magnetic field in a ferromagnetic topological semimetal, Co2MnAl, at room temperature. We observe a tunable, giant anomalous Hall effect, which is induced by the transition between Weyl points and nodal rings as rotating the magnetization axis. The anomalous Hall conductivity is as large as that of a 3D quantum anomalous Hall effect (QAHE), with the Hall angle reaching a record value (21%) at the room temperature among magnetic conductors. Furthermore, we propose a material recipe to generate the giant anomalous Hall effect by gaping nodal rings without requiring the existence of Weyl points. Our work reveals an ideal intrinsically magnetic platform to explore the interplay between magnetic dynamics and topological physics for the development of a new generation of spintronic devices.

preprint2019arXiv

Transport evidence of triply degenerate nodal semimetal YRh6Ge4

We have investigated magnetotransport properties of YRh6Ge4, which was recently predicted to be a triply degenerate nodal semimetal. We find it exhibits remarkable signatures of a chiral anomaly, manifested by large negative longitudinal magnetoresistance, quadratic field dependence of magnetoconductance and planar Hall effect. Furthermore, we have also observed Shubnikov-de Haas (SdH) quantum oscillations in the magnetoresistivity measurements on this material. The analyses of the SdH data reveal two point-like Fermi surfaces and these pockets are found to host nearly massless fermions. The small size of these Fermi pockets is in a good agreement with the theoretical prediction that the triply degenerate point in YRh6Ge4 is much closer to the Fermi level than previously demonstrated triply degenerate nodal semimetals such as MoP and WC. These results suggest YRh6Ge4 may serve as a model system to probe exotic properties of three-component fermions and understand their underlying physics.

preprint2018arXiv

iTeleScope: Intelligent Video Telemetry and Classification in Real-Time using Software Defined Networking

Video continues to dominate network traffic, yet operators today have poor visibility into the number, duration, and resolutions of the video streams traversing their domain. Current approaches are inaccurate, expensive, or unscalable, as they rely on statistical sampling, middle-box hardware, or packet inspection software. We present {\em iTelescope}, the first intelligent, inexpensive, and scalable SDN-based solution for identifying and classifying video flows in real-time. Our solution is novel in combining dynamic flow rules with telemetry and machine learning, and is built on commodity OpenFlow switches and open-source software. We develop a fully functional system, train it in the lab using multiple machine learning algorithms, and validate its performance to show over 95\% accuracy in identifying and classifying video streams from many providers including Youtube and Netflix. Lastly, we conduct tests to demonstrate its scalability to tens of thousands of concurrent streams, and deploy it live on a campus network serving several hundred real users. Our system gives unprecedented fine-grained real-time visibility of video streaming performance to operators of enterprise and carrier networks at very low cost.

preprint2017arXiv

Identifying the tilt angle and correcting the orbital angular momentum spectrum dispersion of misaligned light beam

The axis tilt of light beam in optical system would introduce the dispersion of orbital angular momentum (OAM) spectrum. To deal with it, a two-step method is proposed and demonstrated. First, the tilt angle of optical axis is identified with a deduced relation between the tilt angle and the variation of OAM topological charges with different reference axes, which is obtained with the help of a charge coupled device (CCD) camera. In our experiments, the precision of measured tilt angle is about 10-4rad with OAM orders of -3~3. Using the measured angle value, the additional phase delay due to axis tilt can be calculated so that the dispersion of OAM spectrum can be corrected with a simple formula while the optical axis is not aligned. The experimental results indicate that the original OAM spectrum has been successfully extracted for not only the pure OAM state but also the superposed OAM states.

preprint2016arXiv

Catching Fire via "Likes": Inferring Topic Preferences of Trump Followers on Twitter

In this paper, we propose a framework to infer the topic preferences of Donald Trump's followers on Twitter. We first use latent Dirichlet allocation (LDA) to derive the weighted mixture of topics for each Trump tweet. Then we use negative binomial regression to model the "likes," with the weights of each topic serving as explanatory variables. Our study shows that attacking Democrats such as President Obama and former Secretary of State Hillary Clinton earns Trump the most "likes." Our framework of inference is generalizable to the study of other politicians.

preprint2016arXiv

Characterizing Si:P quantum dot qubits with spin resonance techniques

Quantum dots patterned by atomically precise placement of phosphorus donors in single crystal silicon have long spin lifetimes, advantages in addressability, large exchange tunability, and are readily available few-electron systems. To be utilized as quantum bits, it is important to non-invasively characterise these donor quantum dots post fabrication and extract the number of bound electron and nuclear spins as well as their locations. Here, we propose a metrology technique based on electron spin resonance (ESR) measurements with the on-chip circuitry already needed for qubit manipulation to obtain atomic scale information about donor quantum dots and their spin configurations. Using atomistic tight-binding technique and Hartree self-consistent field approximation, we show that the ESR transition frequencies are directly related to the number of donors, electrons, and their locations through the electron-nuclear hyperfine interaction.

preprint2016arXiv

Deciphering the 2016 U.S. Presidential Campaign in the Twitter Sphere: A Comparison of the Trumpists and Clintonists

In this paper, we study follower demographics of Donald Trump and Hillary Clinton, the two leading candidates in the 2016 U.S. presidential race. We build a unique dataset US2016, which includes the number of followers for each candidate from September 17, 2015 to December 22, 2015. US2016 also includes the geographical location of these followers, the number of their own followers and, very importantly, the profile image of each follower. We use individuals' number of followers and profile images to analyze four dimensions of follower demographics: social status, gender, race and age. Our study shows that in terms of social influence, the Trumpists are more polarized than the Clintonists: they tend to have either a lot of influence or little influence. We also find that compared with the Clintonists, the Trumpists are more likely to be either very young or very old. Our study finds no gender affinity effect for Clinton in the Twitter sphere, but we do find that the Clintonists are more racially diverse.

preprint2016arXiv

Do They All Look the Same? Deciphering Chinese, Japanese and Koreans by Fine-Grained Deep Learning

We study to what extend Chinese, Japanese and Korean faces can be classified and which facial attributes offer the most important cues. First, we propose a novel way of obtaining large numbers of facial images with nationality labels. Then we train state-of-the-art neural networks with these labeled images. We are able to achieve an accuracy of 75.03% in the classification task, with chances being 33.33% and human accuracy 38.89% . Further, we train multiple facial attribute classifiers to identify the most distinctive features for each group. We find that Chinese, Japanese and Koreans do exhibit substantial differences in certain attributes, such as bangs, smiling, and bushy eyebrows. Along the way, we uncover several gender-related cross-country patterns as well. Our work, which complements existing APIs such as Microsoft Cognitive Services and Face++, could find potential applications in tourism, e-commerce, social media marketing, criminal justice and even counter-terrorism.

preprint2016arXiv

Gender Politics in the 2016 U.S. Presidential Election: A Computer Vision Approach

Gender is playing an important role in the 2016 U.S. presidential election, especially with Hillary Clinton becoming the first female presidential nominee and Donald Trump being frequently accused of sexism. In this paper, we introduce computer vision to the study of gender politics and present an image-driven method that can measure the effects of gender in an accurate and timely manner. We first collect all the profile images of the candidates' Twitter followers. Then we train a convolutional neural network using images that contain gender labels. Lastly, we classify all the follower and unfollower images. Through two case studies, one on the `woman card' controversy and one on Sanders followers, we demonstrate how gender is informing the 2016 presidential election. Our framework of analysis can be readily generalized to other case studies and elections.

preprint2016arXiv

High-Performance Complementary III-V Tunnel FETs with Strain Engineering

Strain engineering has recently been explored to improve tunnel field-effect transistors (TFETs). Here, we report design and performance of strained ultra-thin-body (UTB) III-V TFETs by quantum transport simulations. It is found that for an InAs UTB confined in [001] orientation, uniaxial compressive strain in [100] or [110] orientation shrinks the band gap meanwhile reduces (increases) transport (transverse) effective masses. Thus it improves the ON state current of both n-type and p-type UTB InAs TFETs without lowering the source density of states. Applying the strain locally in the source region makes further improvements by suppressing the OFF state leakage. For p-type TFETs, the locally strained area can be extended into the channel to form a quantum well, giving rise to even larger ON state current that is comparable to the n-type ones. Therefore strain engineering is a promising option for improving complementary circuits based on UTB III-V TFETs.

preprint2016arXiv

Pricing the Woman Card: Gender Politics between Hillary Clinton and Donald Trump

In this paper, we propose a data-driven method to measure the impact of the 'woman card' exchange between Hillary Clinton and Donald Trump. Building from a unique dataset of the two candidates' Twitter followers, we first examine the transition dynamics of the two candidates' Twitter followers one week before the exchange and one week after. Then we train a convolutional neural network to classify the gender of the followers and unfollowers, and study how women in particular are reacting to the 'woman card' exchange. Our study suggests that the 'woman card' comment has made women more likely to follow Hillary Clinton, less likely to unfollow her and that it has apparently not affected the gender composition of Trump followers.

preprint2016arXiv

Stability and steady state analysis of distributed cooperative droop controlled DC microgrids

Distributed cooperative droop control consisting of the primary decentralized droop control and the {secondary} distributed correction control is studied in this paper, which aims to achieve an exact current sharing between generators, worked in the voltage control mode, of DC microgrids. For the DC microgrids with the distributed cooperative droop control, the dynamic stability has not been well investigated although its steady performance has been widely reported. This paper focuses on the stability problem and shows it is equivalent to the semistability problem of a class of second-order matrix systems. Some further sufficient conditions as well followed. The steady state is analyzed deeply for some special cases. A DC microgrid of three nodes is simulated on the Matlab/Simulink platform to illustrate the efficacy of analytic results.

preprint2016arXiv

Stochastic De Giorgi Iteration and Regularity of Stochastic Partial Differential Equation

Under general conditions we show that the solution of a stochastic parabolic partial differential equation of the form \[ \partial_t u = \mathrm{div} (A \nabla u) + f(t,x, u) + g_i (t,x,u) \dot{w}^i_t \] is almost surely Hölder continuous in both space and time variables.

preprint2016arXiv

Tactics and Tallies: Inferring Voter Preferences in the 2016 U.S. Presidential Primaries Using Sparse Learning

In this paper, we propose a web-centered framework to infer voter preferences for the 2016 U.S. presidential primaries. Using Twitter data collected from Sept. 2015 to March 2016, we first uncover the tweeting tactics of the candidates and then exploit the variations in the number of 'likes' to infer voters' preference. With sparse learning, we are able to reveal neutral topics as well as positive and negative ones. Methodologically, we are able to achieve a higher predictive power with sparse learning. Substantively, we show that for Hillary Clinton the (only) positive issue area is women's rights. We demonstrate that Hillary Clinton's tactic of linking herself to President Obama resonates well with her supporters but the same is not true for Bernie Sanders. In addition, we show that Donald Trump is a major topic for all the other candidates, and that the women's rights issue is equally emphasized in Sanders' campaign as in Clinton's.

preprint2016arXiv

The environmental dependence of the stellar mass fundamental plane of early-type galaxies

Aims. We investigate the environmental dependence of the stellar mass fundamental plane (FP$_*$) using the early-type galaxy sample from the Sloan Digital Sky Survey Data Release 7 (SDSS DR7). Methods. The FP$_*$ is calculated by replacing the luminosity in the fundamental plane (FP) with stellar mass. Based on the SDSS group catalog, we characterize the galaxy environment according to the mass of the host dark matter halo and the position in the halo. In halos with the same mass bin, the color distributions of central and satellite galaxies are different. Therefore, we calculate FP$_*$ coefficients of galaxies in different environments and compare them with those of the FP to study the contribution of the stellar population. Results. We find that coefficient $a$ of the FP$_*$ is systematically larger than that of the FP, but coefficient $b$ of the FP$_*$ is similar to the FP. Moreover, the environmental dependence of the FP$_*$ is similar to that of the FP. For central galaxies, FP$_*$ coefficients are significantly dependent on the halo mass. For satellite galaxies, the correlation between FP$_*$ coefficients and the halo mass is weak. Conclusions. We conclude that the tilt of the FP is not primarily driven by the stellar population.

preprint2016arXiv

To Follow or Not to Follow: Analyzing the Growth Patterns of the Trumpists on Twitter

In this paper, we analyze the growth patterns of Donald Trump's followers (Trumpists, henceforth) on Twitter. We first construct a random walk model with a time trend to study the growth trend and the effects of public debates. We then analyze the relationship between Trump's activity on Twitter and the growth of his followers. Thirdly, we analyze the effects of such controversial events as calling for Muslim ban and his 'schlonged' remark.

preprint2016arXiv

Voting with Feet: Who are Leaving Hillary Clinton and Donald Trump?

From a crowded field with 17 candidates, Hillary Clinton and Donald Trump have emerged as the two front-runners in the 2016 U.S. presidential campaign. The two candidates each boast more than 5 million followers on Twitter, and at the same time both have witnessed hundreds of thousands of people leave their camps. In this paper we attempt to characterize individuals who have left Hillary Clinton and Donald Trump between September 2015 and March 2016. Our study focuses on three dimensions of social demographics: social capital, gender, and age. Within each camp, we compare the characteristics of the current followers with former followers, i.e., individuals who have left since September 2015. We use the number of followers to measure social capital, and profile images to infer gender and age. For classifying gender, we train a convolutional neural network (CNN). For age, we use the Face++ API. Our study shows that for both candidates followers with more social capital are more likely to leave (or switch camps). For both candidates females make up a larger presence among unfollowers than among current followers. Somewhat surprisingly, the effect is particularly pronounced for Clinton. Lastly, middle-aged individuals are more likely to leave Trump, and the young are more likely to leave Hillary Clinton.

preprint2016arXiv

Will Sanders Supporters Jump Ship for Trump? Fine-grained Analysis of Twitter Followers

In this paper, we study the likelihood of Bernie Sanders supporters voting for Donald Trump instead of Hillary Clinton. Building from a unique time-series dataset of the three candidates' Twitter followers, which we make public here, we first study the proportion of Sanders followers who simultaneously follow Trump (but not Clinton) and how this evolves over time. Then we train a convolutional neural network to classify the gender of Sanders followers, and study whether men are more likely to jump ship for Trump than women. Our study shows that between March and May an increasing proportion of Sanders followers are following Trump (but not Clinton). The proportion of Sanders followers who follow Clinton but not Trump has actually decreased. Equally important, our study suggests that the jumping ship behavior will be affected by gender and that men are more likely to switch to Trump than women.

preprint2015arXiv

A Correlated Study of Optical and X-ray Afterglows of GRBs

We study an extensive sample of 87 GRBs for which there are well sampled and simultaneous optical and X-ray light-curves. We extract the cleanest possible signal of the afterglow component, and compare the temporal behaviors of the X-ray light-curve, observed by Swift XRT, and optical data, observed by UVOT and ground-based telescopes for each individual burst. Overall we find 62\% GRBs that are consistent with the standard afterglow model. When more advanced modeling is invoked, up to 91\% of the bursts in our sample may be consistent with the external shock model. A large fraction of these bursts are consistent with occurring in a constant interstellar density medium (ISM) (61\%) while only 39\% of them occur in a wind-like medium. Only 9 cases have afterglow light-curves that exactly match the standard fireball model prediction, having a single power law decay in both energy bands which are observed during their entire duration. In particular, for the bursts with chromatic behavior additional model assumptions must be made over limited segments of the light-curves in order for these bursts to fully agree with the external shock model. Interestingly, for 54\% of the X-ray and 40\% of the optical band observations the end of the shallow decay ($t^{\sim-0.5}$) period coincides with the jet break ($t^{\sim-p}$) time, causing an abrupt change in decay slope. The fraction of the burst that consistent with the external shock model is independent of the observational epochs in the rest frame of GRBs. Moreover, no cases can be explained by the cooling frequency crossing the X-ray or optical band.

preprint2015arXiv

America Tweets China: A Fine-Grained Analysis of the State and Individual Characteristics Regarding Attitudes towards China

The U.S.-China relationship is arguably the most important bilateral relationship in the 21st century. Typically it is measured through opinion polls, for example, by Gallup and Pew Institute. In this paper, we propose a new method to measure U.S.-China relations using data from Twitter, one of the most popular social networks. Compared with traditional opinion polls, our method has two distinctive advantages. First, our sample size is significantly larger. National opinion polls have at most a few thousand samples. Our data set has 724,146 samples. The large size of our data set enables us to perform state level analysis, which so far even large opinion polls have left unexplored. Second, our method can control for fixed state and date effects. We first demonstrate the existence of inter-state and inter-day variances and then control for these variances in our regression analysis. Empirically, our study is able to replicate the stylized results from opinion polls as well as generate new insights. At the state level, we find New York, Michigan, Indiana and Arizona are the top four most China-friendly states. Wyoming, South Dakota, Kansas and Nevada are most homogeneous. At the individual level, we find attitudes towards China improve as an individual's Twitter experience grows longer and more intense. We also find individuals of Chinese ethnicity are statistically more China-friendly.

preprint2015arXiv

Analysis, Design and Testing of a Novel Quasi-Zero-Stiffness based Sensor System for Measurement of Absolute Vibration Motion

This study presents the analysis and design of a novel quasi-zero-stiffness (QZS) based vibration sensor system for measuring absolute displacement of vibrating platforms/objects. The sensor system is constructed by using positive and negative-stiffness springs, which makes it possible to achieve an equivalent QZS and consequently to create a broadband vibration-free point for absolute displacement measurement in vibrating platforms. Theoretic analysis is conducted for the analysis and design of the influence of structure parameters on system measurement performance. A prototype is designed which can avoid the drawback of instability in existing QZS systems with negative stiffness, and the corresponding data-processing software is developed to fulfill time domain and frequency domain measurements simultaneously. Both simulation and experiment results verify the effectiveness of this novel sensor system.

preprint2015arXiv

Bulk and sub-surface donor bound excitons in silicon under electric fields

The electronic structure of the three-particle donor bound exciton (D$^0$X) in silicon is computed using a large-scale atomic orbital tight-binding method within the Hartree approximation. The calculations yield a transition energy close to the experimentally measured value of 1150 meV in bulk, and show how the transition energy and transition probability can change with applied fields and proximity to surfaces, mimicking the conditions of realistic devices. The spin-resolved transition energy from a neutral donor state (D$^0$) to D$^0$X depends on the three-particle Coulomb energy, and the interface and electric field induced hyperfine splitting and heavy-hole-light-hole splitting. Although the Coulomb energy decreases as a result of Stark shift, the spatial separation of the electron and hole wavefunctions by the field also reduces the transition dipole. A bulk-like D$^0$X dissociates abruptly at a modest electric field, while a D$^0$X at a donor close to an interface undergoes a gradual ionization process. Our calculations take into account the full bandstructure of silicon and the full energy spectrum of the donor including spin directly in the atomic orbital basis and treat the three-particle Coulomb interaction self-consistently to provide quantitative guidance to experiments aiming to realize hybrid opto-electric techniques for addressing donor qubits.

preprint2015arXiv

Controller Synthesis for Linear Time-varying Systems with Adversaries

We present a controller synthesis algorithm for a discrete time reach-avoid problem in the presence of adversaries. Our model of the adversary captures typical malicious attacks envisioned on cyber-physical systems such as sensor spoofing, controller corruption, and actuator intrusion. After formulating the problem in a general setting, we present a sound and complete algorithm for the case with linear dynamics and an adversary with a budget on the total L2-norm of its actions. The algorithm relies on a result from linear control theory that enables us to decompose and precisely compute the reachable states of the system in terms of a symbolic simulation of the adversary-free dynamics and the total uncertainty induced by the adversary. With this decomposition, the synthesis problem eliminates the universal quantifier on the adversary's choices and the symbolic controller actions can be effectively solved using an SMT solver. The constraints induced by the adversary are computed by solving second-order cone programmings. The algorithm is later extended to synthesize state-dependent controller and to generate attacks for the adversary. We present preliminary experimental results that show the effectiveness of this approach on several example problems.

preprint2015arXiv

Controller Synthesis with Inductive Proofs for Piecewise Linear Systems: an SMT-based Algorithm

We present a controller synthesis algorithm for reach-avoid problems for piecewise linear discrete-time systems. Our algorithm relies on SMT solvers and in this paper we focus on piecewise constant control strategies. Our algorithm generates feedback control laws together with inductive proofs of unbounded time safety and progress properties with respect to the reach-avoid sets. Under a reasonable robustness assump- tion, the algorithm is shown to be complete. That is, it either generates a controller of the above type along with a proof of correctness, or it establishes the impossibility of the existence of such controllers. To achieve this, the algorithm iteratively attempts to solve a weakened and strengthened versions of the SMT encoding of the reach-avoid problem. We present preliminary experimental results on applying this algorithm based on a prototype implementation.

preprint2015arXiv

Detecting edge degeneracy in interacting topological insulators through entanglement entropy

The existence of degenerate or gapless edge states is a characteristic feature of topological insulators, but is difficult to detect in the presence of interactons. We propose a new method to obtain the degeneracy of the edge states from the perspective of entanglement entropy, which is very useful to identify interacting topological states. Employing the determinant quantum Monte Carlo technique, we investigate the interaction effect on two representative models of fermionic topological insulators in one and two dimensions, respectively. In the two topologically nontrivial phases, the edge degeneracies are reduced by interactions but remain to be nontrivial.

preprint2015arXiv

Improving the Speed of Response of Learning Algorithms Using Multiple Models

This is the first of a series of papers that the authors propose to write on the subject of improving the speed of response of learning systems using multiple models. During the past two decades, the first author has worked on numerous methods for improving the stability, robustness, and performance of adaptive systems using multiple models and the other authors have collaborated with him on some of them. Independently, they have also worked on several learning methods, and have considerable experience with their advantages and limitations. In particular, they are well aware that it is common knowledge that machine learning is in general very slow. Numerous attempts have been made by researchers to improve the speed of convergence of algorithms in different contexts. In view of the success of multiple model based methods in improving the speed of convergence in adaptive systems, the authors believe that the same approach will also prove fruitful in the domain of learning. In this paper, a first attempt is made to use multiple models for improving the speed of response of the simplest learning schemes that have been studied. i.e. Learning Automata.

preprint2015arXiv

Linear Analysis on Multiple-relaxation-time Lattice Boltzmann Method

The development of multiple-relaxation-time (MRT) Lattice Boltzmann method (LBM) is a significant contribution in improving the numerical behavior, revealing the math and physics mechanism and extending the application of LBM. However, some of the MRT schemes proposed previously are not physically-consistent. In this work, we take D2Q9 as a example to show how to derive physically-consistent MRT-LBM schemes by eigenvalue decomposition of the collision operator. In addition, the scheme is validated by the equivalence to Navier-Stokes equations and numerical simulations.

preprint2015arXiv

Linear Convergence of Adaptively Iterative Thresholding Algorithms for Compressed Sensing

This paper studies the convergence of the adaptively iterative thresholding (AIT) algorithm for compressed sensing. We first introduce a generalized restricted isometry property (gRIP). Then we prove that the AIT algorithm converges to the original sparse solution at a linear rate under a certain gRIP condition in the noise free case. While in the noisy case, its convergence rate is also linear until attaining a certain error bound. Moreover, as by-products, we also provide some sufficient conditions for the convergence of the AIT algorithm based on the two well-known properties, i.e., the coherence property and the restricted isometry property (RIP), respectively. It should be pointed out that such two properties are special cases of gRIP. The solid improvements on the theoretical results are demonstrated and compared with the known results. Finally, we provide a series of simulations to verify the correctness of the theoretical assertions as well as the effectiveness of the AIT algorithm.

preprint2015arXiv

Mott insulating states and quantum phase transitions of correlated SU(2N) Dirac fermions

The interplay between charge and spin degrees of freedom in strongly correlated fermionic systems, in particular of Dirac fermions, is a long-standing problem in condensed matter physics. We investigate the competing orders in the half-filled SU(2N) Hubbard model on a honeycomb lattice, which can be accurately realized in optical lattices with large-spin ultra-cold alkaline-earth fermions. Employing large-scale projector determinant quantum Monte Carlo simulations, we have explored quantum phase transitions from the gapless Dirac semi-metals to the gapped Mott-insulating phases in the SU(4) and SU(6) cases. Both of these Mott-insulating states are found to be columnar valence bond solid (cVBS) and to be absent of the antiferromagnetic Neel ordering and the loop current ordering. Inside the cVBS phases, the dimer ordering is enhanced by increasing fermion components and behaves non-monotonically as the interaction strength increases. Although the transitions generally should be of first order due to a cubic invariance possessed by the cVBS order, the coupling to gapless Dirac fermions can soften the transitions to second order through a non-analytic term in the free energy. Our simulations provide important guidance for the experimental exploration of novel states of matter with ultra-cold alkaline earth fermions.

preprint2015arXiv

NXgraph: An Efficient Graph Processing System on a Single Machine

Recent studies show that graph processing systems on a single machine can achieve competitive performance compared with cluster-based graph processing systems. In this paper, we present NXgraph, an efficient graph processing system on a single machine. With the abstraction of vertex intervals and edge sub-shards, we propose the Destination-Sorted Sub-Shard (DSSS) structure to store a graph. By dividing vertices and edges into intervals and sub-shards, NXgraph ensures graph data access locality and enables fine-grained scheduling. By sorting edges within each sub-shard according to their destination vertices, NXgraph reduces write conflicts among different threads and achieves a high degree of parallelism. Then, three updating strategies, i.e., Single-Phase Update (SPU), Double-Phase Update (DPU), and Mixed-Phase Update (MPU), are proposed in this paper. NXgraph can adaptively choose the fastest strategy for different graph problems according to the graph size and the available memory resources to fully utilize the memory space and reduce the amount of data transfer. All these three strategies exploit streamlined disk access pattern. Extensive experiments on three real-world graphs and five synthetic graphs show that NXgraph can outperform GraphChi, TurboGraph, VENUS, and GridGraph in various situations. Moreover, NXgraph, running on a single commodity PC, can finish an iteration of PageRank on the Twitter graph with 1.5 billion edges in 2.05 seconds; while PowerGraph, a distributed graph processing system, needs 3.6s to finish the same task.

preprint2015arXiv

Spin-lattice relaxation times of single donors and donor clusters in silicon

An atomistic method of calculating the spin-lattice relaxation times ($T_1$) is presented for donors in silicon nanostructures comprising of millions of atoms. The method takes into account the full band structure of silicon including the spin-orbit interaction. The electron-phonon Hamiltonian, and hence the deformation potential, is directly evaluated from the strain-dependent tight-binding Hamiltonian. The technique is applied to single donors and donor clusters in silicon, and explains the variation of $T_1$ with the number of donors and electrons, as well as donor locations. Without any adjustable parameters, the relaxation rates in a magnetic field for both systems are found to vary as $B^5$ in excellent quantitative agreement with experimental measurements. The results also show that by engineering electronic wavefunctions in nanostructures, $T_1$ times can be varied by orders of magnitude.

preprint2015arXiv

The Rationale for Second Level Adaptation

Recently, a new approach to the adaptive control of linear time-invariant plants with unknown parameters (referred to as second level adaptation), was introduced by Han and Narendra in [1]. Based on $N (\geq m+1)$ fixed or adaptive models of the plant, where $m$ is the dimension of the unknown parameter vector, an unknown parameter vector $α\in R^{N}$ is estimated in the new approach, and in turn, is used to control the overall system. Simulation studies were presented in [1] to demonstrate that the new method is significantly better than those that are currently in use. In this paper, we undertake a more detailed examination of the theoretical and practical advantages claimed for the new method. In particular, the need for many models, the proof of stability, and the improvement in performance and robustness are explored in depth both theoretically and experimentally.

preprint2015arXiv

To Love or to Loathe: How is the World Reacting to China's Rise?

China has experienced a spectacular economic growth in recent decades. Its economy grew more than 48 times from 1980 to 2013. How are the other countries reacting to China's rise? Do they see it as an economic opportunity or a security threat? In this paper, we answer this question by analyzing online news reports about China published in Australia, France, Germany, Japan, Russia, South Korea, the UK and the US. More specifically, we first analyze the frequency with which China has appeared in news headlines, which is a measure of China's influence in the world. Second, we build a Naive Bayes classifier to study the evolving nature of the news reports, i.e., whether they are economic or political. We then evaluate the friendliness of the news coverage based on sentiment analysis. Empirical results indicate that there has been increasing news coverage of China in all the countries under study. We also find that the emphasis of the reports is generally shifting towards China's economy. Here Japan and South Korea are exceptions: they are reporting more on Chinese politics. In terms of global sentiment, the picture is quite gloomy. With the exception of Australia and, to some extent, France, all the other countries under examination are becoming less positive towards China.

preprint2014arXiv

$C^{2,α}$ estimates for nonlinear elliptic equations in complex and almost complex geometry

We describe how to use the perturbation theory of Caffarelli to prove Evans-Krylov type $C^{2,α}$ estimates for solutions of nonlinear elliptic equations in complex geometry, assuming a bound on the Laplacian of the solution. Our results can be used to replace the various Evans-Krylov type arguments in the complex geometry literature with a sharper and more unified approach. In addition, our methods extend to almost-complex manifolds, and we use this to obtain a new local estimate for an equation of Donaldson.

preprint2014arXiv

A digital CDS technique and the performance testing

Readout noise is a critical parameter for characterizing the performance of charge-coupled devices (CCDs), which can be greatly reduced by the correlated double sampling (CDS) circuit. However, conventional CDS circuit inevitably introduces new noises since it consists of several active analog components such as operational amplifiers. This paper proposes a digital CDS circuit technique, which transforms the pre-amplified CCD signal into a train of digital presentations by a high-speed data acquisition card directly without the noisy CDS circuit first, then implement the digital CDS algorithm through numerical method. The readout noise of 3.3 e$^{-}$ and the energy resolution of 121 eV@5.9keV can be achieved via the digital CDS technique.

preprint2014arXiv

Competing orders in the 2D half-filled SU(2N) Hubbard model through the pinning field quantum Monte-Carlo simulations

We non-perturbatively investigate the ground state magnetic properties of the 2D half-filled SU($2N$) Hubbard model in the square lattice by using the projector determinant quantum Monte Carlo simulations combined with the method of local pinning fields. Long-range Neel orders are found for both the SU(4) and SU(6) cases at small and intermediate values of $U$. In both cases, the long-range Neel moments exhibit non-monotonic behavior with respect to $U$, which first grow and then drop as $U$ increases. This result is fundamentally different from the SU(2) case in which the Neel moments increase monotonically and saturate. In the SU(6) case, a transition to the columnar dimer phase is found in the strong interaction regime.

preprint2014arXiv

Fixation times in differentiation and evolution in the presence of bottlenecks, deserts, and oases

Cellular differentiation and evolution are stochastic processes that can involve multiple types (or states) of particles moving on a complex, high-dimensional state-space or "fitness" landscape. Cells of each specific type can thus be quantified by their population at a corresponding node within a network of states. Their dynamics across the state-space network involve genotypic or phenotypic transitions that can occur upon cell division, such as during symmetric or asymmetric cell differentiation, or upon spontaneous mutation. Waiting times between transitions can be nonexponentially distributed and reflect e.g., the cell cycle. Here, we use a multi-type branching processes to study first passage time statistics for a single cell to appear in a specific state. We present results for a sequential evolutionary process in which $L$ successive transitions propel a population from a "wild-type" state to a given "terminally differentiated," "resistant," or "cancerous" state. Analytic and numeric results are also found for first passage times across an evolutionary chain containing a node with increased death or proliferation rate, representing a desert/bottleneck or an oasis. Processes involving cell proliferation are shown to be "nonlinear" (even though mean-field equations for the expected particle numbers are linear) resulting in first passage time statistics that depend on the position of the bottleneck or oasis. Our results highlight the sensitivity of stochastic measures to cell division fate and quantify the limitations of using certain approximations and assumptions (such as fixed-population and mean-field assumptions) in evaluating fixation times.

preprint2014arXiv

Length-dependent thermal conductivity in suspended single-layer graphene

Graphene exhibits extraordinary electronic and mechanical properties, and extremely high thermal conductivity. Being a very stable atomically thick membrane that can be suspended between two leads, graphene provides a perfect test platform for studying thermal conductivity in two-dimensional systems, which is of primary importance for phonon transport in low-dimensional materials. Here we report experimental measurements and non-equilibrium molecular dynamics simulations of thermal conduction in suspended single layer graphene as a function of both temperature and sample length. Interestingly and in contrast to bulk materials, when temperature at 300K, thermal conductivity keeps increasing and remains logarithmic divergence with sample length even for sample lengths much larger than the average phonon mean free path. This result is a consequence of the two-dimensional nature of phonons in graphene and provides fundamental understanding into thermal transport in two-dimensional materials.

preprint2014arXiv

Network flow-based simultaneous retiming and slack budgeting for low power design

Low power design has become one of the most significant requirements when CMOS technology entered the nanometer era. Therefore, timing budget is often performed to slow down as many components as possible so that timing slacks can be applied to reduce the power consumption while maintaining the performance of the whole design. Retiming is a procedure that involves the relocation of flip-flops (FFs) across logic gates to achieve faster clocking speed. In this paper we show that the retiming and slack budgeting problem can be formulated to a convex cost dual network flow problem. Both the theoretical analysis and experimental results show the efficiency of our approach which can not only reduce power consumption by 8.9%, but also speedup previous work by 500 times.

preprint2014arXiv

Proton irradiation effect on SCDs

The Low Energy X-ray Telescope is a main payload on the Hard X-ray Modulation Telescope satellite. The swept charge device is selected for the Low Energy X-ray Telescope. As swept charge devices are sensitive to proton irradiation, irradiation test was carried out on the HI-13 accelerator at the China Institute of Atomic Energy. The beam energy was measured to be 10 MeV at the SCD. The proton fluence delivered to the SCD was $3\times10^{8}\mathrm{protons}/\mathrm{cm}^{2}$ over two hours. It is concluded that the proton irradiation affects both the dark current and the charge transfer inefficiency of the SCD through comparing the performance both before and after the irradiation. The energy resolution of the proton-irradiated SCD is 212 eV@5.9 keV at $-60\,^{\circ}\mathrm{C}$, while it before irradiated is 134 eV. Moreover, better performance can be reached by lowering the operating temperature of the SCD on orbit.

preprint2014arXiv

Quantum Monte Carlo simulation of thermodynamic properties of SU(2N) ultracold fermions in optical lattices

We have systematically studied the thermodynamic properties of a two-dimensional half-filled SU(2N) Hubbard model on a square lattice by using the determinant quantum Monte Carlo method. The entropy-temperature relation, the isoentropy curve, and the probability distribution of the onsite occupation number are calculated in both SU(4) and SU(6) cases, which exhibit prominent features of the Pomeranchuk effect. We analyze these thermodynamic behaviors based on charge and spin energy scales. In the charge channel, the interaction strength that marks the crossover from the weak to strong interaction regimes increases with the number of fermion components. In the spin channel, increasing the number of fermion components enhances quantum spin fluctuations, which is shown in the simulations of uniform spin susceptibilities and antiferromagnetic structure factors.

preprint2014arXiv

Simulation of Cone Beam CT System Based on Monte Carlo Method

Adaptive Radiation Therapy (ART) was developed based on Image-guided Radiation Therapy (IGRT) and it is the trend of photon radiation therapy. To get a better use of Cone Beam CT (CBCT) images for ART, the CBCT system model was established based on Monte Carlo program and validated against the measurement. The BEAMnrc program was adopted to the KV x-ray tube. Both IOURCE-13 and ISOURCE-24 were chosen to simulate the path of beam particles. The measured Percentage Depth Dose (PDD) and lateral dose profiles under 1cm water were compared with the dose calculated by DOSXYZnrc program. The calculated PDD was better than 1% within the depth of 10cm. More than 85% points of calculated lateral dose profiles was within 2%. The correct CBCT system model helps to improve CBCT image quality for dose verification in ART and assess the CBCT image concomitant dose risk.

preprint2014arXiv

The Fundamental Plane Relation of Early-Type Galaxies: Environmental Dependence

Using a sample of 70,793 early-type galaxies from SDSS DR7, we study the environmental dependence of the fundamental plane relation. With the help of the galaxy group catalogue based on SDSS DR7, we calculate the fundamental planes in different dark matter halo mass bins for central and satellite galaxies respectively. We find the environmental dependence of the fundamental plane coefficients are similar in $g$, $r$, $i$ and $z$ bands. The environmental dependence for central and satellite galaxies is significantly different. While the fundamental plane coefficients of centrals vary systematically with the halo mass, those of satellites are similar in different halo mass bins. The discrepancy between centrals and satellites are significant in small halos, but negligible in the largest halo mass bins. These results remain the same when we only keep red galaxies, or galaxies with $b/a>0.6$, or galaxies in a specific radius range in the sample. After the correction of the sky background, results are still similar. We suggest that the different environmental effects of the halo mass on centrals and satellites may arise from the different quenching processes of them.

preprint2014arXiv

Throughput Optimal Distributed Traffic Signal Control

We propose a distributed algorithm for controlling traffic signals, allowing constraints such as periodic switching sequences of phases and minimum and maximum green time to be incorporated. Our algorithm is adapted from backpressure routing, which has been mainly applied to communication and power networks. We formally prove that our algorithm ensures global optimality as it leads to maximum network throughput even though the controller is constructed and implemented in a completely distributed manner.

preprint2013arXiv

A Liouville Theorem for the Complex Monge-Ampère Equation

In this note, we derive a Liouville theorem for the complex Monge-Ampère equation. Our result states that if the global solution $u$ of the complex Monge-Ampère equation with constant right-hand side differs from a quadratic polynomial solution by $o(\abs{x}^2)$ at infinity, then $u$ is a quadratic polynomial.

preprint2013arXiv

Measuring the X-ray luminosities of SDSS DR7 clusters from RASS

We use ROSAT All Sky Survey (RASS) broadband X-ray images and the optical clusters identified from SDSS DR7 to estimate the X-ray luminosities around $\sim 65,000$ candidate clusters with masses $\ga 10^{13}\msunh$ based on an Optical to X-ray (OTX) code we develop. We obtain a catalogue with X-ray luminosity for each cluster. This catalog contains 817 clusters (473 at redshift $z\le 0.12$) with $S/N> 3$ in X-ray detection. We find about $65\%$ of these X-ray clusters have their most massive member located near the X-ray flux peak; for the rest $35\%$, the most massive galaxy is separated from the X-ray peak, with the separation following a distribution expected from a NFW profile. We investigate a number of correlations between the optical and X-ray properties of these X-ray clusters, and find that: the cluster X-ray luminosity is correlated with the stellar mass (luminosity) of the clusters, as well as with the stellar mass (luminosity) of the central galaxy and the mass of the halo, but the scatter in these correlations is large. Comparing the properties of X-ray clusters of similar halo masses but having different X-ray luminosities, we find that massive halos with masses $\ga 10^{14}\msunh$ contain a larger fraction of red satellite galaxies when they are brighter in X-ray. ... A cluster catalog containing the optical properties of member galaxies and the X-ray luminosity is available at {\it http://gax.shao.ac.cn/data/Group.html}.

preprint2013arXiv

On a novel distance indicator for Gamma-Ray Bursts associated with Supernovae

It has been proposed that the temporal coincidence of a gamma-ray burst (GRB) and a type Ib/c supernova (SN) can be explained with the concept of induced gravitational collapse (IGC), induced by the matter ejected from an SN Ib/c accreting onto a neutron star (NS). We found a standard luminosity light curve behavior in the late-time X-ray emission of this subclass of GRBs. We interpret this as the result of a common physical mechanism in this particular phase of the X-ray emission, possibly related to the creation of the NS from the SN process. Moreover, this scaling law could be a fundamental tool for estimating the redshift of GRBs that belong to this subclass of events.

preprint2013arXiv

Role of Electronic Structure in the Morphotropic Phase Boundary of TbxDy1-xCo2 Studied by First-principles Calculation

Physically parallel to ferroelectric morphotropic phase boundary, a phase boundary separating two ferromagnetic phase of different crystallographic symmetries was found in TbxDy1-xCo2. High-resolution synchrotron XRD has been carried out to offer experimental evidence for TbxDy1-xCo2. It has been proved that TbxDy1-xCo2 (0.6<x<0.7) is a morphotropic phase boundary and that the crystal structures of tetragonal (x<0.6) and rhombohedral (x>0.7) phase is distorted from a Laves Phase. Here, a first principles calculation provides a theoretical explanation on the origin of MBP in TbxDy1-xCo2 and is also provided for the question of why MPB occurs in TbxDy1-xCo2 alloys.

preprint2012arXiv

A Novel Learning Algorithm for Bayesian Network and Its Efficient Implementation on GPU

Computational inference of causal relationships underlying complex networks, such as gene-regulatory pathways, is NP-complete due to its combinatorial nature when permuting all possible interactions. Markov chain Monte Carlo (MCMC) has been introduced to sample only part of the combinations while still guaranteeing convergence and traversability, which therefore becomes widely used. However, MCMC is not able to perform efficiently enough for networks that have more than 15~20 nodes because of the computational complexity. In this paper, we use general purpose processor (GPP) and general purpose graphics processing unit (GPGPU) to implement and accelerate a novel Bayesian network learning algorithm. With a hash-table-based memory-saving strategy and a novel task assigning strategy, we achieve a 10-fold acceleration per iteration than using a serial GPP. Specially, we use a greedy method to search for the best graph from a given order. We incorporate a prior component in the current scoring function, which further facilitates the searching. Overall, we are able to apply this system to networks with more than 60 nodes, allowing inferences and modeling of bigger and more complex networks than current methods.

preprint2012arXiv

Chandra Observation of a Weak Shock in the Galaxy Cluster A2556

Based on a 21.5 ks \chandra\ observation of A2556, we identify an edge on the surface brightness profile (SBP) at about 160$h_{71}^{-1}$ kpc northeast of the cluster center, and it corresponds to a shock front whose Mach number $\mathcal{M}$ is calculated to be $1.25_{-0.03}^{+0.02}$. No prominent substructure, such as sub-cluster, is found in either optical or X-ray band that can be associated with the edge, suggesting that the conventional super-sonic motion mechanism may not work in this case. As an alternative solution, we propose that the nonlinear steepening of acoustic wave, which is induced by the turbulence of the ICM at the core of the cluster, can be used to explain the origin of the shock front. Although nonlinear steepening weak shock is expected to occur frequently in clusters, why it is rarely observed still remains a question that requires further investigation, including both deeper X-ray observation and extensive theoretical studies.

preprint2012arXiv

Cross identification between X-ray and Optical Clusters of Galaxies in the SDSS DR7 Field

We use the ROSAT all sky survey X-ray cluster catalogs and the optical SDSS DR7 galaxy and group catalogs to cross-identify X-ray clusters with their optical counterparts, resulting in a sample of 201 X-ray clusters in the sky coverage of SDSS DR7. We investigate various correlations between the optical and X-ray properties of these X-ray clusters, and find that the following optical properties are correlated with the X-ray luminosity: the central galaxy luminosity, the central galaxy mass, the characteristic group luminosity ($\propto \Lx^{0.43}$), the group stellar mass ($\propto \Lx^{0.46}$), with typical 1-$σ$ scatter of $\sim 0.67$ in $\log \Lx$. Using the observed number distribution of X-ray clusters, we obtain an unbiased scaling relation between the X-ray luminosity, the central galaxy stellar mass and the characteristic satellite stellar mass as ${\log L_X} = -0.26 + 2.90 [\log (M_{\ast, c} + 0.26 M_{\rm sat}) -12.0]$ (and in terms of luminosities, as ${\log L_X} = -0.15 + 2.38 [\log (L_{c} + 0.72 L_{\rm sat}) -12.0]$). We find that the systematic difference between different halo mass estimations, e.g., using the ranking of characteristic group stellar mass or using the X-ray luminosity scaling relation can be used to constrain cosmology. Comparing the properties of groups of similar stellar mass (or optical luminosities) and redshift that are X-ray luminous or under-luminous, we find that X-ray luminous groups have more faint satellite galaxies and higher red fraction in their satellites. The cross-identified X-ray clusters together with their optical properties are provided in Appendix B.

preprint2012arXiv

Distributed Traffic Signal Control for Maximum Network Throughput

We propose a distributed algorithm for controlling traffic signals. Our algorithm is adapted from backpressure routing, which has been mainly applied to communication and power networks. We formally prove that our algorithm ensures global optimality as it leads to maximum network throughput even though the controller is constructed and implemented in a completely distributed manner. Simulation results show that our algorithm significantly outperforms SCATS, an adaptive traffic signal control system that is being used in many cities.

preprint2012arXiv

Exploring the Cosmic Reionization Epoch in Frequency Space: An Improved Approach to Remove the Foreground in 21 cm Tomography

Aiming to correctly restore the redshifted 21 cm signals emitted by the neutral hydrogen during the cosmic reionization processes, we re-examine the separation approaches based on the quadratic polynomial fitting technique in frequency space to investigate whether they works satisfactorily with complex foreground, by quantitatively evaluate the quality of restored 21 cm signals in terms of sample statistics. We construct the foreground model to characterize both spatial and spectral substructures of the real sky, and use it to simulate the observed radio spectra. By comparing between different separation approaches through statistical analysis of restored 21 cm spectra and corresponding power spectra, as well as their constraints on the mean halo bias $b$ and average ionization fraction $x_e$ of the reionization processes, at $z=8$ and the noise level of 60 mK we find that, although the complex foreground can be well approximated with quadratic polynomial expansion, a significant part of Mpc-scale components of the 21 cm signals (75% for $\gtrsim 6h^{-1}$ Mpc scales and 34% for $\gtrsim 1h^{-1}$ Mpc scales) is lost because it tends to be mis-identified as part of the foreground when single-narrow-segment separation approach is applied. The best restoration of the 21 cm signals and the tightest determination of $b$ and $x_e$ can be obtained with the three-narrow-segment fitting technique as proposed in this paper. Similar results can be obtained at other redshifts.

preprint2012arXiv

Frustrated Bose-Einstein condensates with non-collinear orbital ordering

We investigate the unconventional Bose-Einstein condensations (BEC) with the orbital degree of freedom in the 3D cubic optical lattice, which give rise to various exotic features absent in conventional scalar and spinor BECs. Orbital angular momentum moments are formed on lattice sites breaking time-reversal symmetry spontaneously. Furthermore, they exhibit orbital frustrations and develop a chiral ordering selected by the "order-from-disorder" mechanism.

preprint2012arXiv

Small Perturbation Solutions for Parabolic Equations

Let $ϕ$ be a smooth solution of the parabolic equation $F(D^2u, Du, u, x, t)- u_{t} = 0$: Assume $F$ is uniform elliptic only in a neighborhood of $(D^2ϕ, Dϕ, ϕ, x, t)$, we prove that any solution obtained from small L1-perturbation of $ϕ$ remains smooth.

preprint2012arXiv

Two-Phase ICM in the Central Region of the Rich Cluster of Galaxies Abell 1795: A Joint Chandra, XMM-Newton, and Suzaku View

Based on a detailed analysis of the high-quality Chandra, XMM-Newton, and Suzaku data of the X-ray bright cluster of galaxies Abell 1795, we report clear evidence for a two-phase intracluster medium (ICM) structure, which consists of a cool (with a temperature T = 2.0-2.2 keV) and a hot (T = 5.0-5.7 keV) component that coexist and dominate the X-ray emission at least in the central 80 kpc. A third weak emission component (T = 0.8 keV) is also detected within the innermost 144 kpc and is ascribed to a portion of inter-stellar medium (ISM) of the cD galaxy. Deprojected spectral analysis reveals flat radial temperature distributions for both the hot phase and cool phase components. These results are consistent with the ASCA measurements reported in Xu et al. (1998), and resemble the previous findings for the Centaurus cluster (e.g., Takahashi et al. 2009). By analyzing the emission measure ratio and gas metal abundance maps created from the Chandra data, we find that the cool phase component is more metal-enriched than the hot phase one in 50-100 kpc region, which agrees with that found in M87 (Simionescu et al. 2008). The coexistence of the cool phase and hot phase ICM cannot be realized by bubble uplifting from active galactic nuclei (AGN) alone. Instead, the two-phase ICM properties are better reconciled with a cD corona model (Makishima et al. 2001). (Abridged)

preprint2011arXiv

A Remark On C^{2,α}-Regularity of the Complex Monge-Ampère Equation

We prove the $C^{2,α}$-regularity of the solution $u$ of the equation [\det(u_{\bar{k} j}) = f, \quad f^{1/n} \in C^α, \quad f \geq λ] under the assumption in upper bound of $Δu$. Our result settles down the regularity problem related to the paper \cite{Tian} (also see \cite{Zhang}).

preprint2011arXiv

Are GRB 090423 and Similar bursts due to Superconducting Cosmic strings?

Superconducting cosmic strings may be able to power energetic gamma-ray bursts, in particular at high-redshifts ($z>6$). The ultra-relativistic outflows driven by the superconducting cosmic strings, however, seem too narrow to be consistent with the afterglow data of GRB 090423 and GRB 080913.

preprint2011arXiv

Continuous variable quantum key distribution based on optical entangled states without signal modulation

In this paper, we present the first experimental demonstration on continuous variable quantum key distribution using determinant Einstein-Podolsky-Rosen entangled states of optical field. By means of the instantaneous measurements of the quantum fluctuations of optical modes respectively distributed at sender and receiver, the random bits of secret key are obtained without the need for signal modulation. The post-selection boundaries for the presented entanglement-based scheme against both Gaussian collective and individual attacks are theoretically concluded. The final secret key rates of 84 kbits/s and 3 kbits/s are completed under the collective attack for the transmission efficiency of 80% and 40%, respectively.

preprint2011arXiv

Measure Estimates, Harnack Inequalities and Ricci Lower Bound

On a Riemannian metric-measure space, we establish an Alexandrov-Bakelman-Pucci type measure estimate connecting Bakry-Émery Ricci curvature lower bound, modified Laplacian and the measure of certain special sets. We apply this estimate to prove Harnack inequalities for the modified Laplacian operator and fully non-linear operators. These inequalities seem not available in the literature; And our proof, solely based on the ABP estimate, does not involve any Sobolev inequalities nor gradient estimate. We also propose a question regarding the characterization of Ricci lower bound by the Harnack inequality.

preprint2010arXiv

A Viscosity Approach to the Dirichlet Problem for Complex Monge-Ampère Equations

The Dirichlet problem for complex Monge-Ampére equations with continuous data is considered. In particular, a notion of viscosity solutions is introduced; a comparison principle and a solvability theorem are proved; the equivalence between viscosity and pluripotential solutions is established; and an ABP-type of $L^{\infty}$-estimate is achieved.

preprint2010arXiv

Central gas entropy excess as a direct evidence for AGN feedback in galaxy groups and clusters

By analyzing Chandra X-ray data of a sample of 21 galaxy groups and 19 galaxy clusters, we find that in 31 sample systems there exists a significant central ($R^{<}_{\sim} 10h_{71}^{-1}$ kpc) gas entropy excess ($ΔK_{0}$), which corresponds to $\simeq 0.1-0.5$ keV per gas particle, beyond the power-law model that best fits the radial entropy profile of outer regions. We also find a distinct correlation between the central entropy excess $ΔK_{0}$ and $K$-band luminosity $L_{K}$ of the central dominating galaxies (CDGs), which is scaled as $ΔK_{0} \propto L_{K}^{1.6\pm0.4}$, where $L_{K}$ is tightly associated with the mass of the supermassive black hole hosted in the CDG. In fact, if an effective mass-to-energy conversion-efficiency of 0.02 is assumed for the accretion process, the cumulative AGN feedback $E^{\rm AGN}_{\rm feedback} \simeq ηM_{\rm BH}c^{2}$ yields an extra heating of $\simeq 0.5-17.0$ keV per particle, which is sufficient to explain the central entropy excess. In most cases the AGN contribution can compensate the radiative loss of the X-ray gas within the cooling radius ($\simeq 0.002-2.2$ keV per particle), and apparently exceeds the energy required to deviate the scaling relations from the self-similar predictions ($\simeq 0.2-1.0$ keV per particle). In contrast to the AGN feedback, the extra heating provided by supernova explosions accounts for $\simeq 0.01-0.08$ keV per particle in groups and is almost negligible in clusters. Therefore, the observed correlation between $ΔK_{0}$ and $L_{K}$ can be considered as a direct evidence for AGN feedback in galaxy groups and clusters.

preprint2010arXiv

Experimental generation of 6 dB continuous variable entanglement from a nondegenerate optical parametric amplifier

We experimentally demonstrated that the quantum correlations of amplitude and phase quadratures between signal and idler beams produced from a non-degenerate optical parametric amplifier (NOPA) can be significantly improved by using a mode cleaner in the pump field and reducing the phase fluctuations in phase locking systems. Based on the two technical improvements the quantum entanglement measured with a two-mode homodyne detector is enhanced from ~ 4 dB to ~ 6 dB below the quantum noise limit using the same NOPA and nonlinear crystal.

preprint2010arXiv

Monolayer Graphene as Saturable Absorber in Mode-locked Laser

We demonstrate that the intrinsic properties of monolayer graphene allow it to act as a more effective saturable absorber for mode-locking fiber lasers compared to multilayer graphene. The absorption of monolayer graphene can be saturated at lower excitation intensity compared to multilayer graphene, graphene with wrinkle-like defects, and functionalized graphene. Monolayer graphene has a remarkable large modulation depth of 95.3%, whereas the modulation depth of multilayer graphene is greatly reduced due to nonsaturable absorption and scattering loss. Picoseconds ultrafast laser pulse (1.23 ps) can be generated using monolayer graphene as saturable absorber. Due to the ultrafast relaxation time, larger modulation depth and lower scattering loss of monolayer graphene, it performs better than multilayer graphene in terms of pulse shaping ability, pulse stability and output energy.

preprint2010arXiv

Phonon Transport in Suspended Single Layer Graphene

We report the first temperature dependent phonon transport measurements in suspended Cu-CVD single layer graphene (SLG) from 15K to 380K using microfabricated suspended devices. The thermal conductance per unit cross section $σ$/A increases with temperature and exhibits a peak near T~280K ($\pm$10K) due to the Umklapp process. At low temperatures (T<140K), the temperature dependent thermal conductivity scales as ~T^{1.5}, suggesting that the main contribution to thermal conductance arises from flexural acoustic (ZA) phonons in suspended SLG. The $σ$/A reaches a high value of 1.7$\times10^5 T^{1.5}$ W/m^2K, which is approaching the expected ballistic phonon thermal conductance for two-dimensional graphene sheets. Our results not only clarify the ambiguity in the thermal conductance, but also demonstrate the potential of Cu-CVD graphene for heat related applications.

preprint2010arXiv

Toward demonstrating controlled-X operation based on continuous variable four-partite cluster state and quantum teleporters

One-way quantum computation based on measurement and multipartite cluster entanglement offers the ability to perform a variety of unitary operations only through different choices of measurement bases. Here we present an experimental study toward demonstrating the controlled-X operation, a two-mode gate, in which continuous variable (CV) four-partite cluster states of optical modes are utilized. Two quantum teleportation elements are used for achieving the gate operation of the quantum state transformation from input target and control states to output states. By means of the optical cluster state prepared off-line, the homodyne detection and electronic feeding forward, the information carried by the input control state is transformed to the output target state. The presented scheme of the controlled-X operation based on teleportation can be implemented nonlocally and deterministically. The distortion of the quantum information resulting from the imperfect cluster entanglement is estimated with the fidelity.

preprint2009arXiv

A Joint Chandra and XMM-Newton View of Abell 3158: Massive Off-Centre Cool Gas Clump As A Robust Diagnostic of Merger Stage

By analysing the Chandra and XMM-Newton archived data of the nearby galaxy cluster Abell 3158, which was reported to possess a relatively regular, relaxed morphology in the X-ray band in previous works, we identify a bow edge-shaped discontinuity in the X-ray surface brightness distribution at about $120h_{71}^{-1}$ kpc west of the X-ray peak. This feature is found to be associated with a massive, off-centre cool gas clump, and actually forms the west boundary of the cool clump. We find that the cool gas clump is moving at a subsonic velocity of ~700 km/s toward west on the sky plane. We exclude the possibility that this cool clump was formed by local inhomogeneous radiative cooling in the intra-cluster medium, due to the effectiveness of the thermal conduction on the time-scale of $\sim 0.3$ Gyr. Since no evidence for central AGN activity has been found in Abell 3158, and this cool clump bears many similarities to the off-centre cool gas clumps detected in other merging clusters in terms of their mass, size, location, and thermal properties (e.g. lower temperature and higher abundance as compared with the environment), we speculate that the cool clump in Abell 3158 was caused by a merger event, and is the remnant of the original central cool-core of the main cluster or the infalling sub-cluster. This idea is supported not only by the study of line-of-sight velocity distribution of the cluster member galaxies, but also by the study of gas entropy-temperature correlation. This example shows that the appearance of such massive, off-centre cool gas clumps can be used to diagnose the dynamical state of a cluster, especially when prominent shocks and cold fronts are absent.

preprint2009arXiv

Generalization of correlated electron-ion dynamics from nonequilibrium Green's functions

We present a new formulation of the correlated electron-ion dynamics (CEID) by using equations of motion for nonequilibrium Green's functions, which generalizes CEID to a general nonequilibrium statistical ensemble that allows for a variable total number of electrons. We make a rigorous connection between CEID and diagrammatic perturbation theory, which furthermore allows the key approximations in CEID to be quantified in diagrammatic terms, and, in principle, improved. We compare analytically the limiting behavior of CEID and the self-consistent Born approximation (SCBA) for a general dynamical nonequilibrium state. This comparison shows that CEID and SCBA coincide in the weak electron-phonon coupling limit, while they differ in the large ionic mass limit where we can readily quantify their difference. In particular, we illustrate the relation between CEID and SCBA by perturbation theory at the fourth-order in the coupling strength.

preprint2009arXiv

Metal Enrichment via Ram Pressure Stripping in the IGM of the Compact Galaxy Group RGH 80

By creating and analyzing the two dimensional gas temperature and abundance maps of the RGH 80 compact galaxy group with the high-quality Chandra data, we detect a high-abundance ($\simeq 0.7$ $Z_\odot$) arc, where the metal abundance is significantly higher than the surrounding regions by $\simeq 0.3$ Z_\odot$. This structure shows tight spatial correlations with the member galaxy PGC 046529, as well as with the arm-like feature identified on the X-ray image in the previous work of Randall et al. (2009). Since no apparent signature of AGN activity is found associated with PGC 046529 in multi-band observations, and the gas temperature, metallicity, and mass of the high-abundance arc resemble those of the ISM of typical early-type galaxies, we conclude that this high-abundance structure is the remnant of the ISM of PGC 046529, which was stripped out of the galaxy by ram pressure stripping due to the motion of PGC 046529 in RGH 80. This novel case shows that ram pressure stripping can work efficiently in the metal enrichment process in galaxy groups, as it can in galaxy clusters.

preprint2009arXiv

The nature of red dwarf galaxies

Using dark matter halos traced by galaxy groups selected from the Sloan Digital Sky Survey Data Release 4, we find that about 1/4 of the faint galaxies ($\rmag >-17.05$, hereafter dwarfs) that are the central galaxies in their own halo are not blue and star forming, as expected in standard models of galaxy formation, but are red. In contrast, this fraction is about 1/2 for dwarf satellite galaxies. Many red dwarf galaxies are physically associated with more massive halos. In total, about $\sim 45$% of red dwarf galaxies reside in massive halos as satellites, while another $\sim 25$% have a spatial distribution that is much more concentrated towards their nearest massive haloes than other dwarf galaxies. We use mock catalogs to show that the reddest population of non-satellite dwarf galaxies are distributed within about 3 times the virial radii of their nearest massive halos. We suggest that this population of dwarf galaxies are hosted by low-mass halos that have passed through their massive neighbors, and that the same environmental effects that cause satellite galaxies to become red are also responsible for the red colors of this population of galaxies. We do not find any significant radial dependence of the population of dwarf galaxies with the highest concentrations, suggesting that the mechanisms operating on these galaxies affect color more than structure. However, over 30% of dwarf galaxies are red and isolated and their origin remains unknown.

preprint2007arXiv

A High-Abundance Arc in the Compact Group of Galaxies HCG 62: An AGN- or Merger-Induced Metal Outflow?

We present a Chandra study of the metal distribution in the X-ray bright compact group of galaxies HCG 62. We find that the diffuse X-ray emission is peaked at the core of the central galaxy NGC 4778, and is dominated by the contribution of the hot gas. The diffuse emission is roughly symmetric within \simeq0.25^{\prime}, which is straddled by double-sided X-ray cavities aligned in the northeast-southwest direction. By mapping the emission hardness ratio distributions and by performing the 2-dimensional spectral analysis, we identify a remarkable high-abundance arc region at about 2^{\prime} (33.6h_{70}^{-1} kpc) from the X-ray peak that spans over a vast region from south to northwest, a part of which roughly coinciding with the outer edge of the southwest X-ray cavity. The measured average abundance in this arc is higher than that in its neighboring regions by a factor of about 2, and the abundance ratios therein are nicely consistent with the dominance of the SN Ia yields. We estimate that the mass of iron contained in the arc is >3\times 10^{6}h_{70}^{-2.5} solar mass, which accounts for >3% of the iron synthesized in the galaxy. The high-abundance arc could have been formed by the AGN activities. However, it is also possible that the arc was formed in a recent merger as is implied by the recent optical kinematic study (Spavone et al. 2006), which implies that mergers may be as important as AGN activities in metal redistributions in early-type galaxies and their associated groups or clusters.

preprint2007arXiv

The cross-correlation between galaxies of different luminosities and Colors

We study the cross-correlation between galaxies of different luminosities and colors, using a sample selected from the SDSS Dr 4. Galaxies are divided into 6 samples according to luminosity, and each of these samples is divided into red and blue subsamples. Projected auto-correlation and cross-correlation is estimated for these subsample. At projected separations r_p > 1\mpch, all correlation functions are roughly parallel, although the correlation amplitude depends systematically on luminosity and color. On r_p < 1\mpch, the auto- and cross-correlation functions of red galaxies are significantly enhanced relative to the corresponding power laws obtained on larger scales. Such enhancement is absent for blue galaxies and in the cross-correlation between red and blue galaxies. We esimate the relative bias factor on scales r > 1\mpch for each subsample using its auto-correlation function and cross-correlation functions. The relative bias factors obtained from different methods are similar. For blue galaxies the luminosity-dependence of the relative bias is strong over the luminosity range probed (-23.0<M_r < -18.0),but for red galaxies the dependence is weaker and becomes insignificant for luminosities below L^*. To examine whether a significant stochastic/nonlinear component exists in the bias relation, we study the ratio R_ij= W_{ii}W_{jj}/W_{ij}^2, where W_{ij} is the projected correlation between subsample i and j. We find that the values of R_ij are all consistent with 1 for all-all, red-red and blue-blue samples, however significantly larger than 1 for red-blue samples. For faint red - faint blue samples the values of R_{ij} are as high as ~ 2 on small scales r_p < 1 \mpch and decrease with increasing r_p.

Yu Wang

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201 published item(s)

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

FlashDecoding++: Faster Large Language Model Inference on GPUs

FlightLLM: Efficient Large Language Model Inference with a Complete Mapping Flow on FPGAs

LLM-Powered Hierarchical Language Agent for Real-time Human-AI Coordination

Robust Beamforming Design for Intelligent Reflecting Surface Aided Cognitive Radio Systems with Imperfect Cascaded CSI

Giant Nernst effect in the crossover between Fermi liquid and strange metal

Interpretable and Scalable Graphical Models for Complex Spatio-temporal Processes

Sgap: Towards Efficient Sparse Tensor Algebra Compilation for GPU

Surprisingly large anomalous Hall effect and giant negative magnetoresistance in half-topological semimetals

Universal bifurcation scenarios in delay-differential equations with one delay

A Brief Review of Binary Driven Hypernova

A Data Augmentation Method for Fully Automatic Brain Tumor Segmentation

An electron-spin qubit platform assembled atom-by-atom on a surface

BronchusNet: Region and Structure Prior Embedded Representation Learning for Bronchus Segmentation and Classification

BSDGAN: Balancing Sensor Data Generative Adversarial Networks for Human Activity Recognition

CenterFormer: Center-based Transformer for 3D Object Detection

Chemical reactions in imperfect cavities: enhancement, suppression, and resonance

ChemicalX: A Deep Learning Library for Drug Pair Scoring

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

Class-Specific Semantic Reconstruction for Open Set Recognition

CLOSE: Curriculum Learning On the Sharing Extent Towards Better One-shot NAS

CodedVTR: Codebook-based Sparse Voxel Transformer with Geometric Guidance

Differentially Private Algorithms for Statistical Verification of Cyber-Physical Systems

Dual Vision Transformer

Effect of shaping plate apparatus on mechanical properties of 3D printed cement-based materials: Experimental and numerical studies

Efficient Reinforcement Learning from Demonstration Using Local Ensemble and Reparameterization with Split and Merge of Expert Policies

Explore-Bench: Data Sets, Metrics and Evaluations for Frontier-based and Deep-reinforcement-learning-based Autonomous Exploration

FedCor: Correlation-Based Active Client Selection Strategy for Heterogeneous Federated Learning

Few-Shot Specific Emitter Identification via Deep Metric Ensemble Learning

GypSum: Learning Hybrid Representations for Code Summarization

Heuristic Adaptability to Input Dynamics for SpMM on GPUs

High-resolution time-to-digital converters (TDCs) with a bidirectional encoder

Hyperradial distribution function of few-body problems: a new arena for extreme value theory

Improving Fairness in Graph Neural Networks via Mitigating Sensitive Attribute Leakage

Improving Out-of-Distribution Robustness via Selective Augmentation

Large-scale Personalized Video Game Recommendation via Social-aware Contextualized Graph Neural Network

Latent Heterogeneous Graph Network for Incomplete Multi-View Learning

Learning Monotone Dynamics by Neural Networks

Learning Robust Recommenders through Cross-Model Agreement

Learning Self-Supervised Low-Rank Network for Single-Stage Weakly and Semi-Supervised Semantic Segmentation

Learning-Based Vulnerability Analysis of Cyber-Physical Systems

LidarMultiNet: Unifying LiDAR Semantic Segmentation, 3D Object Detection, and Panoptic Segmentation in a Single Multi-task Network

Low hardware consumption, resolution-configurable Gray code oscillator time-to-digital converters implemented in 16nm, 20nm and 28nm FPGAs

Membership Inference Attacks on Knowledge Graphs

Multi-Granularity Regularized Re-Balancing for Class Incremental Learning

Multi-level Fusion of Wav2vec 2.0 and BERT for Multimodal Emotion Recognition

Multi-UAV Coverage Planning with Limited Endurance in Disaster Environment

Null controllability for stochastic fourth order parabolic equations

On Structural Explanation of Bias in Graph Neural Networks

Opencpop: A High-Quality Open Source Chinese Popular Song Corpus for Singing Voice Synthesis

Predicting Solar Flares Using CNN and LSTM on Two Solar Cycles of Active Region Data

Primal-dual Estimator Learning: an Offline Constrained Moving Horizon Estimation Method with Feasibility and Near-optimality Guarantees

Production of polarized particle beams via ultraintense laser pulses

Provable Boolean Interaction Recovery from Tree Ensemble obtained via Random Forests

Pure State Tomography with Fourier Transformation

Quantum Information Scrambling in Quantum Many-body Scarred Systems

R2-Trans:Fine-Grained Visual Categorization with Redundancy Reduction

Robotic Computing on FPGAs: Current Progress, Research Challenges, and Opportunities

Robust End-to-end Speaker Diarization with Generic Neural Clustering

Self-Supervised Masking for Unsupervised Anomaly Detection and Localization

Topological Interface-State Lasing in a Polymer-Cholesteric Liquid Crystal Superlattice

Unreliability of two-band model analysis of magnetoresistivities in unveiling temperature-driven Lifshitz transition

Variational Inference with NoFAS: Normalizing Flow with Adaptive Surrogate for Computationally Expensive Models

WheaCha: A Method for Explaining the Predictions of Models of Code

A High Spatial Resolution Muon Tomography Prototype System based on Micromegas Detector

A Survey of FPGA-Based Robotic Computing

Brilliant circularly polarized $γ$-ray sources via single-shot laser plasma interaction

Dissecting the Energy Budget of a Gamma-Ray Burst Fireball

Enabling Lower-Power Charge-Domain Nonvolatile In-Memory Computing with Ferroelectric FETs

Enhancing fiber atom interferometer by in-fiber laser cooling

Generating Synthetic Systems of Interdependent Critical Infrastructure Networks

Hermes: Decentralized Dynamic Spectrum Access System for Massive Devices Deployment in 5G

Machine Learning for Electronic Design Automation: A Survey

On the Counter-Rotation of Closed Timelike Curves