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Lin Li

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preprint2026arXiv

OTora: A Unified Red Teaming Framework for Reasoning-Level Denial-of-Service in LLM Agents

Large Language Models (LLMs) are increasingly deployed as autonomous agents that execute tool-augmented, multi-step tasks, where latency is a critical factor for real-world applications. Yet an overlooked threat is Reasoning-Level Denial-of-Service (R-DoS), in which an attacker preserves task correctness but degrades availability by inflating an agent's reasoning depth or tool-use budget. We introduce OTora, the first unified, two-stage red-teaming framework for instantiating R-DoS attacks. Stage I optimizes an adversarial trigger that induces targeted tool invocations using insertion-aware scoring and dynamic target co-evolution, supporting both black-box and white-box settings. Stage II generates agent-aware reasoning payloads via an ICL-guided genetic search that amplifies overthinking while maintaining correct task outcomes. Across WebShop, Email, and OS agents built on multiple backbone models such as LLaMA-70B and GPT-OSS-120B, OTora achieves up to 10 times increases in reasoning tokens and order-of-magnitude latency slowdowns, all while preserving near-baseline task accuracy. Finally, we discuss mitigation strategies for detecting and constraining abnormal reasoning and latency spikes. The code is available at https://github.com/llm2409/OTora.

preprint2025arXiv

Constrained Language Model Policy Optimization via Risk-aware Stepwise Alignment

When fine-tuning pre-trained Language Models (LMs) to exhibit desired behaviors, maintaining control over risk is critical for ensuring both safety and trustworthiness. Most existing safety alignment methods, such as Safe RLHF and SACPO, typically operate under a risk-neutral paradigm that is insufficient to address the risks arising from deviations from the reference policy and offers limited robustness against rare but potentially catastrophic harmful behaviors. To address this limitation, we propose Risk-aware Stepwise Alignment (RSA), a novel alignment method that explicitly incorporates risk awareness into the policy optimization process by leveraging a class of nested risk measures. Specifically, RSA formulates safety alignment as a token-level risk-aware constrained policy optimization problem and solves it through a stepwise alignment procedure that yields token-level policy updates derived from the nested risk measures. This design offers two key benefits: (1) it mitigates risks induced by excessive model shift away from a reference policy, and (2) it explicitly suppresses low-probability yet high-impact harmful behaviors. Moreover, we provide theoretical analysis on policy optimality under mild assumptions. Experimental results demonstrate that our method achieves high levels of helpfulness while ensuring strong safety and significantly suppresses tail risks, namely low-probability yet high-impact unsafe responses.

preprint2025arXiv

GuidedMorph: Two-Stage Deformable Registration for Breast MRI

Accurately registering breast MR images from different time points enables the alignment of anatomical structures and tracking of tumor progression, supporting more effective breast cancer detection, diagnosis, and treatment planning. However, the complexity of dense tissue and its highly non-rigid nature pose challenges for conventional registration methods, which primarily focus on aligning general structures while overlooking intricate internal details. To address this, we propose \textbf{GuidedMorph}, a novel two-stage registration framework designed to better align dense tissue. In addition to a single-scale network for global structure alignment, we introduce a framework that utilizes dense tissue information to track breast movement. The learned transformation fields are fused by introducing the Dual Spatial Transformer Network (DSTN), improving overall alignment accuracy. A novel warping method based on the Euclidean distance transform (EDT) is also proposed to accurately warp the registered dense tissue and breast masks, preserving fine structural details during deformation. The framework supports paradigms that require external segmentation models and with image data only. It also operates effectively with the VoxelMorph and TransMorph backbones, offering a versatile solution for breast registration. We validate our method on ISPY2 and internal dataset, demonstrating superior performance in dense tissue, overall breast alignment, and breast structural similarity index measure (SSIM), with notable improvements by over 13.01% in dense tissue Dice, 3.13% in breast Dice, and 1.21% in breast SSIM compared to the best learning-based baseline.

preprint2024arXiv

Capacity Results for Multiple-Input Multiple-Output Optical Wireless Communication With Per-Antenna Intensity Constraints

In this paper, we investigate the capacity of a multiple-input multiple-output (MIMO) optical intensity channel (OIC) under per-antenna peak- and average-intensity constraints. We first consider the case where the average intensities of input are required to be equal to preassigned constants due to the requirement of illumination quality and color temperature. When the channel graph of the MIMO OIC is strongly connected, we prove that the strongest eigen-subchannel must have positive channel gains, which simplifies the capacity analysis. Then we derive various capacity bounds by utilizing linear precoding, generalized entropy power inequality, and QR decomposition, etc. These bounds are numerically verified to approach the capacity in the low or high signal-to-noise ratio regime. Specifically, when the channel rank is one less than the number of transmit antennas, we derive an equivalent capacity expression from the perspective of convex geometry, and new lower bounds are derived based on this equivalent expression. Finally, the developed results are extended to the more general case where the average intensities of input are required to be no larger than preassigned constants.

preprint2023arXiv

Towards A Unified Conformer Structure: from ASR to ASV Task

Transformer has achieved extraordinary performance in Natural Language Processing and Computer Vision tasks thanks to its powerful self-attention mechanism, and its variant Conformer has become a state-of-the-art architecture in the field of Automatic Speech Recognition (ASR). However, the main-stream architecture for Automatic Speaker Verification (ASV) is convolutional Neural Networks, and there is still much room for research on the Conformer based ASV. In this paper, firstly, we modify the Conformer architecture from ASR to ASV with very minor changes. Length-Scaled Attention (LSA) method and Sharpness-Aware Minimizationis (SAM) are adopted to improve model generalization. Experiments conducted on VoxCeleb and CN-Celeb show that our Conformer based ASV achieves competitive performance compared with the popular ECAPA-TDNN. Secondly, inspired by the transfer learning strategy, ASV Conformer is natural to be initialized from the pretrained ASR model. Via parameter transferring, self-attention mechanism could better focus on the relationship between sequence features, brings about 11% relative improvement in EER on test set of VoxCeleb and CN-Celeb, which reveals the potential of Conformer to unify ASV and ASR task. Finally, we provide a runtime in ASV-Subtools to evaluate its inference speed in production scenario. Our code is released at https://github.com/Snowdar/asv-subtools/tree/master/doc/papers/conformer.md.

preprint2022arXiv

Analysis of a Direct Separation Method Based on Adaptive Chirplet Transform for Signals with Crossover Instantaneous Frequencies

In many applications, it is necessary to retrieve the sub-signal building blocks of a multi-component signal, which is usually non-stationary in real-world and real-life applications. Empirical mode decomposition (EMD), synchrosqueezing transform (SST), signal separation operation (SSO), and iterative filtering decomposition (IFD) have been proposed and developed for this purpose. However, these computational methods are restricted by the specification of well-separation of the sub-signal frequency curves for multi-component signals. On the other hand, the chirplet transform-based signal separation scheme (CT3S) that extends SSO from the two-dimensional "time-frequency" plane to the three-dimensional "time-frequency-chirp rate" space was recently proposed in our recent work to remove the frequency-separation specification, and thereby allowing "frequency crossing". The main objective of this present paper is to carry out an in-depth error analysis study of instantaneous frequency estimation and component recovery for the CT3S method.

preprint2022arXiv

Approximation Properties of Deep ReLU CNNs

This paper focuses on establishing $L^2$ approximation properties for deep ReLU convolutional neural networks (CNNs) in two-dimensional space. The analysis is based on a decomposition theorem for convolutional kernels with a large spatial size and multi-channels. Given the decomposition result, the property of the ReLU activation function, and a specific structure for channels, a universal approximation theorem of deep ReLU CNNs with classic structure is obtained by showing its connection with one-hidden-layer ReLU neural networks (NNs). Furthermore, approximation properties are obtained for one version of neural networks with ResNet, pre-act ResNet, and MgNet architecture based on connections between these networks.

preprint2022arXiv

Deep Representation Decomposition for Rate-Invariant Speaker Verification

While promising performance for speaker verification has been achieved by deep speaker embeddings, the advantage would reduce in the case of speaking-style variability. Speaking rate mismatch is often observed in practical speaker verification systems, which may actually degrade the system performance. To reduce intra-class discrepancy caused by speaking rate, we propose a deep representation decomposition approach with adversarial learning to learn speaking rate-invariant speaker embeddings. Specifically, adopting an attention block, we decompose the original embedding into an identity-related component and a rate-related component through multi-task training. Additionally, to reduce the latent relationship between the two decomposed components, we further propose a cosine mapping block to train the parameters adversarially to minimize the cosine similarity between the two decomposed components. As a result, identity-related features become robust to speaking rate and then are used for verification. Experiments are conducted on VoxCeleb1 data and HI-MIA data to demonstrate the effectiveness of our proposed approach.

preprint2022arXiv

Direct Signal Separation Via Extraction of Local Frequencies with Adaptive Time-Varying Parameters

Real-world phenomena that can be formulated as signals are often affected by a number of factors and appear as multi-component modes. To understand and process such phenomena, "divide-and-conquer" is probably the most common strategy to address the problem. In other words, the captured signal is decomposed into signal components for each individual component to be processed. Unfortunately, for signals that are superimposition of non-stationary amplitude-frequency modulated (AM-FM) components, the "divide-and-conquer" strategy is bound to fail, since there is no way to be sure that the decomposed components take on the AM-FM formulations which are necessary for the extraction of their instantaneous frequencies (IFs) and amplitudes (IAs). In this paper, we propose an adaptive signal separation operation (ASSO) for effective and accurate separation of a single-channel blind-source multi-component signal, via introducing a time-varying parameter that adapts locally to IFs and using linear chirp (linear frequency modulation) signals to approximate components at each time instant. We derive more accurate component recovery formulae based on the linear chirp signal local approximation. In addition, a recovery scheme, together with a ridge detection method, is also proposed to extract the signal components one by one, and the time-varying parameter is updated for each component. The proposed method is suitable for engineering implementation, being capable of separating complicated signals into their components or sub-signals and reconstructing the signal trend directly. Numerical experiments on synthetic and real-world signals are presented to demonstrate our improvement over the previous attempts.

preprint2022arXiv

Dual Space Graph Contrastive Learning

Unsupervised graph representation learning has emerged as a powerful tool to address real-world problems and achieves huge success in the graph learning domain. Graph contrastive learning is one of the unsupervised graph representation learning methods, which recently attracts attention from researchers and has achieved state-of-the-art performances on various tasks. The key to the success of graph contrastive learning is to construct proper contrasting pairs to acquire the underlying structural semantics of the graph. However, this key part is not fully explored currently, most of the ways generating contrasting pairs focus on augmenting or perturbating graph structures to obtain different views of the input graph. But such strategies could degrade the performances via adding noise into the graph, which may narrow down the field of the applications of graph contrastive learning. In this paper, we propose a novel graph contrastive learning method, namely \textbf{D}ual \textbf{S}pace \textbf{G}raph \textbf{C}ontrastive (DSGC) Learning, to conduct graph contrastive learning among views generated in different spaces including the hyperbolic space and the Euclidean space. Since both spaces have their own advantages to represent graph data in the embedding spaces, we hope to utilize graph contrastive learning to bridge the spaces and leverage advantages from both sides. The comparison experiment results show that DSGC achieves competitive or better performances among all the datasets. In addition, we conduct extensive experiments to analyze the impact of different graph encoders on DSGC, giving insights about how to better leverage the advantages of contrastive learning between different spaces.

preprint2022arXiv

Graph Convolutional Network Based Semi-Supervised Learning on Multi-Speaker Meeting Data

Unsupervised clustering on speakers is becoming increasingly important for its potential uses in semi-supervised learning. In reality, we are often presented with enormous amounts of unlabeled data from multi-party meetings and discussions. An effective unsupervised clustering approach would allow us to significantly increase the amount of training data without additional costs for annotations. Recently, methods based on graph convolutional networks (GCN) have received growing attention for unsupervised clustering, as these methods exploit the connectivity patterns between nodes to improve learning performance. In this work, we present a GCN-based approach for semi-supervised learning. Given a pre-trained embedding extractor, a graph convolutional network is trained on the labeled data and clusters unlabeled data with "pseudo-labels". We present a self-correcting training mechanism that iteratively runs the cluster-train-correct process on pseudo-labels. We show that this proposed approach effectively uses unlabeled data and improves speaker recognition accuracy.

preprint2022arXiv

Josephson-Coulomb drag effect between graphene and LaAlO3/SrTiO3 interfacial superconductor

Coulomb drag refers to the phenomenon that a charge current in one electronic circuit induces a responsive current in a neighboring circuit solely through Coulomb interactions. For conventional interactions between fermionic particles such as electrons, the as-induced drag current in the passive layer is orders of magnitude weaker than the active current due to strong dielectric screening effect between the two. Here we propose a 'super' Coulomb drag effect between an active normal conductor and a passive superconductor of Josephson junction arrays, whereby the passive current can greatly exceed the active. The drag force originates from the interactions between the substantially enhanced dynamical quantum fluctuations of the superconducting phases in the passive layer and the normal electrons in the active layer. We demonstrate this effect in the devices composed of monolayer graphene and LaAlO3/SrTiO3 heterointerface, an inherently non-uniform superconductor described by Josephson junction arrays. Remarkable drag signal is observed in the superconducting transition regime of the LaAlO3/SrTiO3 interface, with its sign independent of the carrier type in the graphene layer. The estimated passive-to-active ratio can reach about 0.3 at the optimal gate voltage and the temperature dependence follows that of the typical Josephson energy between superconducting puddles. Strikingly, the ratio ought to be as large as 10^5 at zero temperature by theoretical extrapolation. From engineering perspective, our device may work as current or voltage transformers, and the drag mechanism lays the foundation for synchronizing Josephson-junction-array-based terahertz radiators.

preprint2022arXiv

Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation

The Scene Graph Generation (SGG) task aims to detect all the objects and their pairwise visual relationships in a given image. Although SGG has achieved remarkable progress over the last few years, almost all existing SGG models follow the same training paradigm: they treat both object and predicate classification in SGG as a single-label classification problem, and the ground-truths are one-hot target labels. However, this prevalent training paradigm has overlooked two characteristics of current SGG datasets: 1) For positive samples, some specific subject-object instances may have multiple reasonable predicates. 2) For negative samples, there are numerous missing annotations. Regardless of the two characteristics, SGG models are easy to be confused and make wrong predictions. To this end, we propose a novel model-agnostic Label Semantic Knowledge Distillation (LS-KD) for unbiased SGG. Specifically, LS-KD dynamically generates a soft label for each subject-object instance by fusing a predicted Label Semantic Distribution (LSD) with its original one-hot target label. LSD reflects the correlations between this instance and multiple predicate categories. Meanwhile, we propose two different strategies to predict LSD: iterative self-KD and synchronous self-KD. Extensive ablations and results on three SGG tasks have attested to the superiority and generality of our proposed LS-KD, which can consistently achieve decent trade-off performance between different predicate categories.

preprint2022arXiv

Learning to Prove Trigonometric Identities

Automatic theorem proving with deep learning methods has attracted attentions recently. In this paper, we construct an automatic proof system for trigonometric identities. We define the normalized form of trigonometric identities, design a set of rules for the proof and put forward a method which can generate theoretically infinite trigonometric identities. Our goal is not only to complete the proof, but to complete the proof in as few steps as possible. For this reason, we design a model to learn proof data generated by random BFS (rBFS), and it is proved theoretically and experimentally that the model can outperform rBFS after a simple imitation learning. After further improvement through reinforcement learning, we get AutoTrig, which can give proof steps for identities in almost as short steps as BFS (theoretically shortest method), with a time cost of only one-thousandth. In addition, AutoTrig also beats Sympy, Matlab and human in the synthetic dataset, and performs well in many generalization tasks.

preprint2022arXiv

MealRec: A Meal Recommendation Dataset

Bundle recommendation systems aim to recommend a bundle of items for a user to consider as a whole. They have become a norm in modern life and have been applied to many real-world settings, such as product bundle recommendation, music playlist recommendation and travel package recommendation. However, compared to studies of bundle recommendation approaches in areas such as online shopping and digital music services, research on meal recommendations for restaurants in the hospitality industry has made limited progress, due largely to the lack of high-quality benchmark datasets. A publicly available dataset specialising in meal recommendation research for the research community is in urgent demand. In this paper, we introduce a meal recommendation dataset (MealRec) that aims to facilitate future research. MealRec is constructed from the user review records of Allrecipe.com, covering 1,500+ users, 7,200+ recipes and 3,800+ meals. Each recipe is described with rich information, such as ingredients, instructions, pictures, category and tags, etc; and each meal is three-course, consisting of an appetizer, a main dish and a dessert. Furthermore, we propose a category-constrained meal recommendation model that is evaluated through comparative experiments with several state-of-the-art bundle recommendation methods on MealRec. Experimental results confirm the superiority of our model and demonstrate that MealRec is a promising testbed for meal recommendation related research. The MealRec dataset and the source code of our proposed model are available at https://github.com/WUT-IDEA/MealRec for access and reproducibility.

preprint2022arXiv

Normalized solutions for Schrödinger-Bopp-Podolsky system

In this paper, we study the following energy functional originates from the Schrödinger-Bopp-Podolsky system $$I(u)=\frac{1}{2}\int_{\mathbb{R}^{3}}|\nabla u|^{2}dx+\frac{1}{4}\int_{\mathbb{R}^{3}} ϕ_{u}u^{2}dx-\frac{1}{p}\int_{\mathbb{R}^{3}}|u|^{p}dx$$ constrained on $B_ρ=\left\{u\in H^{1}(\mathbb{R}^{3},C):\ \left\|u\right\|_{2}=ρ\right\},$ where $ρ>0.$ As such constrained problem $I(u)$ is bounded from below on $B_ρ$ when $p\in(2,\frac{10}{3}).$ We use minimizing method to get a normalized solution.

preprint2022arXiv

Probabilistic Guaranteed Path Planning for Safe Urban Air Mobility Using Chance Constrained RRT

Safety is a critical concern for the success of urban air mobility, especially in dynamic and uncertain environments. This paper proposes a path planning algorithm based on RRT in conjunction with chance constraints in the presence of uncertain obstacles. The chance-constrained formulation for Gaussian distributed obstacles is developed by converting the probabilistic constraints to deterministic constraints in terms of distribution parameters. The probabilistic feasible region at every time step can be established through the simulation of the system state and the evaluation of convex constraints. Through establishing chance-constrained RRT, the algorithm not only enjoys the benefits of sampling-based algorithms but also incorporates uncertainty into the formulation. Simulation results demonstrate that the planning for a trajectory connecting the starting and goal point in accordance with the requirement of probabilistic obstacle avoidance can be achieved by the utilization of this algorithm.

preprint2022arXiv

ReLU Deep Neural Networks from the Hierarchical Basis Perspective

We study ReLU deep neural networks (DNNs) by investigating their connections with the hierarchical basis method in finite element methods. First, we show that the approximation schemes of ReLU DNNs for $x^2$ and $xy$ are composition versions of the hierarchical basis approximation for these two functions. Based on this fact, we obtain a geometric interpretation and systematic proof for the approximation result of ReLU DNNs for polynomials, which plays an important role in a series of recent exponential approximation results of ReLU DNNs. Through our investigation of connections between ReLU DNNs and the hierarchical basis approximation for $x^2$ and $xy$, we show that ReLU DNNs with this special structure can be applied only to approximate quadratic functions. Furthermore, we obtain a concise representation to explicitly reproduce any linear finite element function on a two-dimensional uniform mesh by using ReLU DNNs with only two hidden layers.

preprint2022arXiv

Scattered Image Reconstruction at Near-infrared Based on Spatial Modulation Instability

We present a method of near-infrared image reconstruction based on spatial modulation instability in a photorefractive strontium barium niobate crystal. The conditions that lead to the formation of modulation instability at near-infrared are discussed depending on the theory of modulation instability gain. Experimental results of scattered image reconstruction at the 1064 nm wavelength show the maximum cross-correlation coefficient and cross-correlation gain are 0.57 and 2.09 respectively. This method is expected to be an aid for near-infrared imaging technologies.

preprint2022arXiv

The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation

Unbiased SGG has achieved significant progress over recent years. However, almost all existing SGG models have overlooked the ground-truth annotation qualities of prevailing SGG datasets, i.e., they always assume: 1) all the manually annotated positive samples are equally correct; 2) all the un-annotated negative samples are absolutely background. In this paper, we argue that both assumptions are inapplicable to SGG: there are numerous "noisy" groundtruth predicate labels that break these two assumptions, and these noisy samples actually harm the training of unbiased SGG models. To this end, we propose a novel model-agnostic NoIsy label CorrEction strategy for SGG: NICE. NICE can not only detect noisy samples but also reassign more high-quality predicate labels to them. After the NICE training, we can obtain a cleaner version of SGG dataset for model training. Specifically, NICE consists of three components: negative Noisy Sample Detection (Neg-NSD), positive NSD (Pos-NSD), and Noisy Sample Correction (NSC). Firstly, in Neg-NSD, we formulate this task as an out-of-distribution detection problem, and assign pseudo labels to all detected noisy negative samples. Then, in Pos-NSD, we use a clustering-based algorithm to divide all positive samples into multiple sets, and treat the samples in the noisiest set as noisy positive samples. Lastly, in NSC, we use a simple but effective weighted KNN to reassign new predicate labels to noisy positive samples. Extensive results on different backbones and tasks have attested to the effectiveness and generalization abilities of each component of NICE.

preprint2022arXiv

The Effects of Dynamic Learning and the Forgetting Process on an Optimizing Modelling for Full-Service Repair Pricing Contracts for Medical Devices

In order to improve the profitability and customer service management of original equipment manufacturers (OEMs) in a market where full-service (FS) and on-call service (OS) co-exist, this article extends the optimizing modelling for pricing FS repair contracts with the effects of dynamic learning and forgetting. Along with considering autonomous learning in maintenance practice, this study also analyses how induced learning and forgetting process in a workplace put impact on the pricing optimizing model of FS contracts in the portfolio of FS and OS. A numerical analysis based on real data from a medical industry proves that the enhanced FS pricing model discussed here has two main advantages: (1) It could prominently improve repair efficiency, and (2) It help OEMs gain better profits compared to the original FS model and the sole OS maintenance. Sensitivity analysis shows that if internal failure rate increases, the optimized FS price rises gradually until reaching the maximum value, and profitability to the OEM increases overall; if frequency of induced learning goes up, the optimal FS price rises after a short-term downward trend, with a stable profitability to the OEM.

preprint2022arXiv

Towards Real-Time Visual Tracking with Graded Color-names Features

MeanShift algorithm has been widely used in tracking tasks because of its simplicity and efficiency. However, the traditional MeanShift algorithm needs to label the initial region of the target, which reduces the applicability of the algorithm. Furthermore, it is only applicable to the scene with a large overlap rate between the target area and the candidate area. Therefore, when the target speed is fast, the target scale change, shape deformation or the target occlusion occurs, the tracking performance will be deteriorated. In this paper, we address the challenges above-mentioned by developing a tracking method that combines the background models and the graded features of color-names under the MeanShift framework. This method significantly improve performance in the above scenarios. In addition, it facilitates the balance between detection accuracy and detection speed. Experimental results demonstrate the validation of the proposed method.

preprint2022arXiv

Video is All You Need: Attacking PPG-based Biometric Authentication

Unobservable physiological signals enhance biometric authentication systems. Photoplethysmography (PPG) signals are convenient owning to its ease of measurement and are usually well protected against remote adversaries in authentication. Any leaked PPG signals help adversaries compromise the biometric authentication systems, and the advent of remote PPG (rPPG) enables adversaries to acquire PPG signals through restoration. While potentially dangerous, rPPG-based attacks are overlooked because existing methods require the victim's PPG signals. This paper proposes a novel spoofing attack approach that uses the waveforms of rPPG signals extracted from video clips to fool the PPG-based biometric authentication. We develop a new PPG restoration model that does not require leaked PPG signals for adversarial attacks. Test results on state-of-art PPG-based biometric authentication show that the signals recovered through rPPG pose a severe threat to PPG-based biometric authentication.

preprint2022arXiv

What is Next when Sequential Prediction Meets Implicitly Hard Interaction?

Hard interaction learning between source sequences and their next targets is challenging, which exists in a myriad of sequential prediction tasks. During the training process, most existing methods focus on explicitly hard interactions caused by wrong responses. However, a model might conduct correct responses by capturing a subset of learnable patterns, which results in implicitly hard interactions with some unlearned patterns. As such, its generalization performance is weakened. The problem gets more serious in sequential prediction due to the interference of substantial similar candidate targets. To this end, we propose a Hardness Aware Interaction Learning framework (HAIL) that mainly consists of two base sequential learning networks and mutual exclusivity distillation (MED). The base networks are initialized differently to learn distinctive view patterns, thus gaining different training experiences. The experiences in the form of the unlikelihood of correct responses are drawn from each other by MED, which provides mutual exclusivity knowledge to figure out implicitly hard interactions. Moreover, we deduce that the unlikelihood essentially introduces additional gradients to push the pattern learning of correct responses. Our framework can be easily extended to more peer base networks. Evaluation is conducted on four datasets covering cyber and physical spaces. The experimental results demonstrate that our framework outperforms several state-of-the-art methods in terms of top-k based metrics.

preprint2021arXiv

CFNS Ad-Hoc meeting on Radiative Corrections Whitepaper

Current precision scattering experiments and even more so many experiments planed for the Electron Ion Collider will be limited by systematics. From the theory side, a fundamental source of systematic uncertainty is the correct treatment of radiative effects. To gauge the current state of technique and knowledge, help the cross-pollination between different direction of nuclear physics, and to give input to the yellow report process, the community met in an ad-hoc workshop hosted by the Center for Frontiers in Nuclear Science, Stony Brook University. This whitepaper is a collection of contributions to this workshop.

preprint2021arXiv

Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation

User purchasing prediction with multi-behavior information remains a challenging problem for current recommendation systems. Various methods have been proposed to address it via leveraging the advantages of graph neural networks (GNNs) or multi-task learning. However, most existing works do not take the complex dependencies among different behaviors of users into consideration. They utilize simple and fixed schemes, like neighborhood information aggregation or mathematical calculation of vectors, to fuse the embeddings of different user behaviors to obtain a unified embedding to represent a user's behavioral patterns which will be used in downstream recommendation tasks. To tackle the challenge, in this paper, we first propose the concept of hyper meta-path to construct hyper meta-paths or hyper meta-graphs to explicitly illustrate the dependencies among different behaviors of a user. How to obtain a unified embedding for a user from hyper meta-paths and avoid the previously mentioned limitations simultaneously is critical. Thanks to the recent success of graph contrastive learning, we leverage it to learn embeddings of user behavior patterns adaptively instead of assigning a fixed scheme to understand the dependencies among different behaviors. A new graph contrastive learning based framework is proposed by coupling with hyper meta-paths, namely HMG-CR, which consistently and significantly outperforms all baselines in extensive comparison experiments.

preprint2021arXiv

Observation of Aharonov-Bohm effect in PbTe nanowire networks

We report phase coherent electron transport in PbTe nanowire networks with a loop geometry. Magneto-conductance shows Aharonov-Bohm (AB) oscillations with periods of $h/e$ and $h/2e$ in flux. The amplitude of $h/2e$ oscillations is enhanced near zero magnetic field, possibly due to interference between time-reversal paths. Temperature dependence of the AB amplitudes suggests a phase coherence length $\sim$ 8 - 12 $μ$m at 50 mK. This length scale is larger than the typical geometry of PbTe-based hybrid semiconductor-superconductor nanowire devices.

preprint2021arXiv

Selective area epitaxy of PbTe-Pb hybrid nanowires on a lattice-matched substrate

Topological quantum computing is based on braiding of Majorana zero modes encoding topological qubits. A promising candidate platform for Majorana zero modes is semiconductor-superconductor hybrid nanowires. The realization of topological qubits and braiding operations requires scalable and disorder-free nanowire networks. Selective area growth of in-plane InAs and InSb nanowires, together with shadow-wall growth of superconductor structures, have demonstrated this scalability by achieving various network structures. However, the noticeable lattice mismatch at the nanowire-substrate interface, acting as a disorder source, imposes a serious obstacle along with this roadmap. Here, combining selective area and shadow-wall growth, we demonstrate the fabrication of PbTe-Pb hybrid nanowires - another potentially promising Majorana system - on a nearly perfectly lattice-matched substrate CdTe, all done in one molecular beam epitaxy chamber. Transmission electron microscopy shows the single-crystal nature of the PbTe nanowire and its atomically sharp and clean interfaces to the CdTe substrate and the Pb overlayer, without noticeable inter-diffusion or strain. The nearly ideal interface condition, together with the strong screening of charge impurities due to the large dielectric constant of PbTe, hold promise towards a clean nanowire system to study Majorana zero modes and topological quantum computing.

preprint2020arXiv

A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts

The emergence and rapid progress of the Internet have brought ever-increasing impact on financial domain. How to rapidly and accurately mine the key information from the massive negative financial texts has become one of the key issues for investors and decision makers. Aiming at the issue, we propose a sentiment analysis and key entity detection approach based on BERT, which is applied in online financial text mining and public opinion analysis in social media. By using pre-train model, we first study sentiment analysis, and then we consider key entity detection as a sentence matching or Machine Reading Comprehension (MRC) task in different granularity. Among them, we mainly focus on negative sentimental information. We detect the specific entity by using our approach, which is different from traditional Named Entity Recognition (NER). In addition, we also use ensemble learning to improve the performance of proposed approach. Experimental results show that the performance of our approach is generally higher than SVM, LR, NBM, and BERT for two financial sentiment analysis and key entity detection datasets.

preprint2020arXiv

Analysis of Adaptive Synchrosqueezing Transform with a Time-varying Parameter

The synchrosqueezing transform (SST) was developed recently to separate the components of non-stationary multicomponent signals. The continuous wavelet transform-based SST (WSST) reassigns the scale variable of the continuous wavelet transform of a signal to the frequency variable and sharpens the time-frequency representation. The WSST with a time-varying parameter, called the adaptive WSST, was introduced very recently in the paper "Adaptive synchrosqueezing transform with a time-varying parameter for non-stationary signal separation". The well-separated conditions of non-stationary multicomponent signals with the adaptive WSST and a method to select the time-varying parameter were proposed in that paper. In addition, simulation experiments in that paper show that the adaptive WSST is very promising in estimating the instantaneous frequency of a multicomponent signal, and in accurate component recovery. However the theoretical analysis of the adaptive WSST has not been studied. In this paper, we carry out such analysis and obtain error bounds for component recovery with the adaptive WSST and the 2nd-order adaptive WSST. These results provide a mathematical guarantee to non-stationary multicomponent signal separation with the adaptive WSST.

preprint2020arXiv

Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach

Few-shot learning is a challenging problem that has attracted more and more attention recently since abundant training samples are difficult to obtain in practical applications. Meta-learning has been proposed to address this issue, which focuses on quickly adapting a predictor as a base-learner to new tasks, given limited labeled samples. However, a critical challenge for meta-learning is the representation deficiency since it is hard to discover common information from a small number of training samples or even one, as is the representation of key features from such little information. As a result, a meta-learner cannot be trained well in a high-dimensional parameter space to generalize to new tasks. Existing methods mostly resort to extracting less expressive features so as to avoid the representation deficiency. Aiming at learning better representations, we propose a meta-learning approach with complemented representations network (MCRNet) for few-shot image classification. In particular, we embed a latent space, where latent codes are reconstructed with extra representation information to complement the representation deficiency. Furthermore, the latent space is established with variational inference, collaborating well with different base-learners, and can be extended to other models. Finally, our end-to-end framework achieves the state-of-the-art performance in image classification on three standard few-shot learning datasets.

preprint2020arXiv

Effects of biaxial strain and local constant potential on electronic structure of monolayer SnSe

We use the modified Becke-Johnson exchange potential (mBJ) with the spin-orbit coupling effect (SOC) to study effects of biaxial strain and local constant potential on electronic structure of monolayer SnSe. Our results show the fundamental band gap size can be tuned via biaxial strain. Compressive strain (tensile strain) can narrow (enlarge) band gap, and compressive strain causes the transition from quasi-direct to indirect band gap. Moreover, considering that any tuning of electronic structure is realized by changing the periodic potential distribution in the crystalline, we directly add constant potential (CP) to muffin-tin spheres. The results demonstrate that positive and negative CPs can narrow and enlarge band gap, respectively. At CP of 0.9 Ry, semiconductor-metal transition appears, and interestingly a new type of nearly linear dispersions occur at band edge. Our work is good for inspiring more experimental and further theoretical research works.

preprint2020arXiv

Many-body chiral edge currents and sliding phases of atomic spinwaves in momentum-space lattice

Collective excitations (spinwaves) of long-lived atomic hyperfine states can be synthesized into a Bose-Hubbard model in momentum space. We explore many-body ground states and dynamics of a two-leg momentum-space lattice formed by two coupled hyperfine states. Essential ingredients of this setting are a staggered artificial magnetic field engineered by lasers that couple the spinwave states, and a state-dependent long-range interaction, which is induced by laser-dressing a hyperfine state to a Rydberg state. The Rydberg dressed two-body interaction gives rise to a state-dependent blockade in momentum space, and can amplify staggered flux induced anti-chiral edge currents in the many-body ground state in the presence of magnetic flux. When the Rydberg dressing is applied to both hyperfine states, exotic sliding insulating and superfluid/supersolid phases emerge. Due to the Rydberg dressed long-range interaction, spinwaves slide along a leg of the momentum-space lattice without costing energy. Our study paves a route to the quantum simulation of topological phases and exotic dynamics with interacting spinwaves of atomic hyperfine states in momentum-space lattice.

preprint2020arXiv

Null Gravitational Redshift by a Reissner-Nordström Black Hole in the Strong Field Limit

The gravitational shift of electromagnetic frequency in the strong field limit is usually investigated under the common scenario, where the light receiver is far away from the central body while the emitter is in the strong-field region of the lens. In this paper, the gravitational frequency shift of light caused by a Reissner-Nordström (RN) black hole is studied numerically in the traditional strong-field scenario, as well as in the scenario where both the light emission and reception events happen in the strong-field region of the black hole. In order to obtain the numerical results of the gravitational redshift, we first derive the exact null equations of motion in the RN geometry in harmonic coordinates. For a given light observer, a new numerical technique is proposed in the integration of the geodesic equations to determine the spatial position of the emitter, considering the fact that their spatial positions are not always known simultaneously. Our work might be helpful to the related observations for probing strong gravity.

preprint2020arXiv

Performance of the T-matrix based master equation for Coulomb drag in double quantum dots

Recently, novel Coulomb drag mechanisms in capacitively coupled double quantum dots were uncovered by the T-matrix based master equation (TME). The TME is so far the primary approach to studying Coulomb drag in the weak-coupling regime; however, its accuracy and reliability remain unexplored. Here, we evaluate the performance of the TME for Coulomb drag via a comparison with numerically exact results obtained by the hierarchical equation-of-motion approach. We find that the TME can capture qualitative current evolutions versus dot levels, temperature, and effective coupling strengths, but only partially succeeds at the quantitative level. Specifically, the TME gives highly inaccurate drag currents when large charge fluctuations on dots exist and the fourth-order tunneling processes make a leading-order contribution. This failure of the TME is attributed to the combined effect of the unique drag mechanisms and its overlook of the fourth-order single-electron tunnelings. We identify the reliable regions to facilitate further quantitative studies on Coulomb drag by the TME.

preprint2020arXiv

Possible Formation Scenarios of ZTF J153932.16+502738.8-A Gravitational Source Close to the Peak of LISA's Sensitivity

ZTF J153932.16+502738.8 (ZTFJ1539) is an eclipsing double-white-dwarf system with an orbital period of 6.91 minutes, and is a significant source of LISA detecting gravitational wave. However, the massive white dwarf (WD) with mass of about 0.61 M$_\odot$ has a high effective temperature (48900 K), and the lower mass WD with mass of about 0.21 M$_{\odot}$ has a low effective temperature($<$10000 K). It is challenging the popular theory of binary evolution. We investigate the formation of ZTFJ1539 via nova and Algol scenarios. Assuming that the massive WD in ZTFJ1539 just experiences a thermalnuclear runaway, nova scenario can explain the effective temperatures of two WDs in ZTFJ1539. However, in order to enlarging a semi-detached orbit of about 4---5 minutes to a detached orbit of about 7 minutes, nova scenario needs a much high kick velocity of about 200 km s$^{-1}$ during nova eruption. The high kick velocity can result in high eccentricity of about 0.2---0.6. Algol scenario can also produce ZTFJ1539 if we take a high efficient parameter for ejecting common envelope and enhance the mass-loss rate via stellar wind trigger by tidal effect.

preprint2020arXiv

Taking the pulse of COVID-19: A spatiotemporal perspective

The sudden outbreak of the Coronavirus disease (COVID-19) swept across the world in early 2020, triggering the lockdowns of several billion people across many countries, including China, Spain, India, the U.K., Italy, France, Germany, and most states of the U.S. The transmission of the virus accelerated rapidly with the most confirmed cases in the U.S., and New York City became an epicenter of the pandemic by the end of March. In response to this national and global emergency, the NSF Spatiotemporal Innovation Center brought together a taskforce of international researchers and assembled implemented strategies to rapidly respond to this crisis, for supporting research, saving lives, and protecting the health of global citizens. This perspective paper presents our collective view on the global health emergency and our effort in collecting, analyzing, and sharing relevant data on global policy and government responses, geospatial indicators of the outbreak and evolving forecasts; in developing research capabilities and mitigation measures with global scientists, promoting collaborative research on outbreak dynamics, and reflecting on the dynamic responses from human societies.

preprint2019arXiv

The Formation of Bimodal Dust Species in Nova Ejecta

The formation of bimodal dust species (namely the silicate and amorphous carbon dust grains coexistent) in a nova eruption is an open problem. According to the nova model simulated by Modules for Experiments in Stellar Astrophysics code, we calculate the formation and growth of carbon (C) and forsterite (Mg2SiO4) dust grains purely in nova ejecta for the fee-expansion model and the radiative shock model by assuming spherical geometry of the nova ejecta. In these models, the chemical properties of pre-existing circumstellar medium are not taken into account. In the free-expansion model, the nova ejecta is not an idea environment for dust nucleation. However, it can efficiently produce dust in the radiative shock model. We estimate that every nova can produce C grains with an average mass of about $10^{-9}$ and $10^{-8}$ ${\rm M_\odot}$, and Mg$_2$SiO$_4$ grains with an average mass of about $10^{-8}$ and $10^{-7}$ ${\rm M_\odot}$. Based on the mass of ejected gas, the ratio of dust to gas is about 1\%. The C grains form first after several or tens of days of nova eruption. After that, the Mg$_2$SiO$_4$ grains begin to grow in tens of days, which is consistent with observations.

preprint2018arXiv

ReLU Deep Neural Networks and Linear Finite Elements

In this paper, we investigate the relationship between deep neural networks (DNN) with rectified linear unit (ReLU) function as the activation function and continuous piecewise linear (CPWL) functions, especially CPWL functions from the simplicial linear finite element method (FEM). We first consider the special case of FEM. By exploring the DNN representation of its nodal basis functions, we present a ReLU DNN representation of CPWL in FEM. We theoretically establish that at least $2$ hidden layers are needed in a ReLU DNN to represent any linear finite element functions in $Ω\subseteq \mathbb{R}^d$ when $d\ge2$. Consequently, for $d=2,3$ which are often encountered in scientific and engineering computing, the minimal number of two hidden layers are necessary and sufficient for any CPWL function to be represented by a ReLU DNN. Then we include a detailed account on how a general CPWL in $\mathbb R^d$ can be represented by a ReLU DNN with at most $\lceil\log_2(d+1)\rceil$ hidden layers and we also give an estimation of the number of neurons in DNN that are needed in such a representation. Furthermore, using the relationship between DNN and FEM, we theoretically argue that a special class of DNN models with low bit-width are still expected to have an adequate representation power in applications. Finally, as a proof of concept, we present some numerical results for using ReLU DNNs to solve a two point boundary problem to demonstrate the potential of applying DNN for numerical solution of partial differential equations.

preprint2016arXiv

A New ZrCuSiAs-Type Superconductor: ThFeAsN

We report the first nitrogen-containing iron-pnictide superconductor ThFeAsN, which is synthesized by a solid-state reaction in an evacuated container. The compound crystallizes in a ZrCuSiAs-type structure with the space group P4/nmm and lattice parameters a=4.0367(1) Å and c=8.5262(2) Å at 300 K. The electrical resistivity and dc magnetic susceptibility measurements indicate superconductivity at 30 K for the nominally undoped ThFeAsN.

preprint2016arXiv

Cross-Domain Entity Resolution in Social Media

The challenge of associating entities across multiple domains is a key problem in social media understanding. Successful cross-domain entity resolution provides integration of information from multiple sites to create a complete picture of user and community activities, characteristics, and trends. In this work, we examine the problem of entity resolution across Twitter and Instagram using general techniques. Our methods fall into three categories: profile, content, and graph based. For the profile-based methods, we consider techniques based on approximate string matching. For content-based methods, we perform author identification. Finally, for graph-based methods, we apply novel cross-domain community detection methods and generate neighborhood-based features. The three categories of methods are applied to a large graph of users in Twitter and Instagram to understand challenges, determine performance, and understand fusion of multiple methods. Final results demonstrate an equal error rate less than 1%.

preprint2016arXiv

Field-induced resistivity plateau and unsaturated negative magnetoresistance in topological semimetal TaSb2

Several prominent transport properties have been identified as key signatures of topologicalmaterials. One is the resistivity plateau at low temperatures as observed in several topological insulators (TIs), another is the negative magnetoresistance (MR) when the applied magnetic field is parallel to the current direction as observed in several topological semimetals (TSMs) including Dirac semimetals (DSMs) and Weyl semimetals (WSMs). Usually, these two exotic phenomena emerge in distinct materials with or without time reversal symmetry (TRS), respectively. Here we report the discovery of a new member in TSMs, TaSb2, which clearly exhibits both of these phenomena in a single material. This compound crystallizes in a base-centered monoclinic, centrosymmetric structure, and is metallic with a low carrier density in the zero field. While applying magnetic field it exhibits insulating behavior before appearance of a resistivity plateau below Tc =13 K. In the plateau regime, the ultrahigh carrier mobility and extreme magnetoresistance (XMR) for the field perpendicular to the current are observed as in DSMs and WSMs, in addition to a quantum oscillation behavior with non-trivial Berry phases. In contrast to the most known DSMs and WSMs, the negative MR in TaSb2 does not saturate up to 9 T, which, together with the almost linear Hall resistivity, manifests itself an electron-hole non-compensated TMS. These findings indicate that the resistivity plateau could be a generic feature of topology-protected metallic states even in the absence of TRS and compatible with the negative MR depending on the field direction. Our experiment extends a materials basis represented by TaSb2 as a new platform for future theoretical investigations and device applications of topological materials.

preprint2016arXiv

Higher-order Landau-Devonshire theory for BaTiO3

The higher-order Landau-Devonshire theory for BaTiO3 is proposed. The structural stabilities of some Landau potentials proposed for the phenomenology of BaTiO3 until now are discussed in the framework of the singularity theory. We confirm that the structurally stable Landau potential has to contain at least all the invariants up to eighth power when the three parameters vary in the experiment. We propose the tenth order Landau potential for adequate description of the various experimental data for BaTiO3 crystal. We show that the results of the phenomenology based on the tenth potential are in good agreement with experimental data on the spontaneous polarization, dielectric constants, dielectric susceptibility and piezoelectric coefficients versus the temperature and the electric field.

preprint2016arXiv

Kondo screening of Andreev bound states in an N-QD-S system

Motivated by experimental observation of the Kondo-enhanced Andreev transport [R. S. Deacon \textit{et al.}, PRB \textbf{81}, 121308(R) (2010)] in a hybrid normal metal-quantum dot-superconductor (N-QD-S) device, we theoretically study the Kondo effect in such a device and clarify the different roles played by the normal and superconducting leads. Due to the Andreev reflection in a QD-S system, a pair of Andreev energy levels form in the superconducting gap, which is able to carry the magnetic moment if the ground state of the QD is a magnetic doublet. In this sense, the Andreev energy levels play a role of effective impurity levels. When the normal lead is coupled to the QD-S system, on the one hand, the Andreev energy levels broaden to form the so-called Andreev bound states (ABSs), on the other hand, it can screen the magnetic moment of the ABSs. By tuning the couplings between the QD and the normal (superconducting) leads, the ABSs can simulate the Kondo, mixed-valence, and even empty orbit regimes of the usual single-impurity Anderson model. The above picture is confirmed by the Green's function calculation of the hybrid N-QD-S Anderson model and is also able to explain qualitatively experimental phenomena observed by Deacon \textit{et al.}. These results can further stimulate related experimental study in the N-QD-S systems.

preprint2016arXiv

Matching Community Structure Across Online Social Networks

The discovery of community structure in networks is a problem of considerable interest in recent years. In online social networks, often times, users are simultaneously involved in multiple social media sites, some of which share common social relationships. It is of great interest to uncover a shared community structure across these networks. However, in reality, users typically identify themselves with different usernames across social media sites. This creates a great difficulty in detecting the community structure. In this paper, we explore several approaches for community detection across online social networks with limited knowledge of username alignment across the networks. We refer to the known alignment of usernames as seeds. We investigate strategies for seed selection and its impact on networks with a different fraction of overlapping vertices. The goal is to study the interplay between network topologies and seed selection strategies, and to understand how it affects the detected community structure. We also propose several measures to assess the performance of community detection and use them to measure the quality of the detected communities in both Twitter-Twitter networks and Twitter-Instagram networks.

preprint2016arXiv

Modeling Group Dynamics Using Probabilistic Tensor Decompositions

We propose a probabilistic modeling framework for learning the dynamic patterns in the collective behaviors of social agents and developing profiles for different behavioral groups, using data collected from multiple information sources. The proposed model is based on a hierarchical Bayesian process, in which each observation is a finite mixture of an set of latent groups and the mixture proportions (i.e., group probabilities) are drawn randomly. Each group is associated with some distributions over a finite set of outcomes. Moreover, as time evolves, the structure of these groups also changes; we model the change in the group structure by a hidden Markov model (HMM) with a fixed transition probability. We present an efficient inference method based on tensor decompositions and the expectation-maximization (EM) algorithm for parameter estimation.

preprint2016arXiv

Precise tuning of the Curie temperature of (Ga,Mn)As-based magnetic semiconductors by hole compensation: Support for valence-band ferromagnetism

For the prototype diluted ferromagnetic semiconductor (Ga,Mn)As, there is a fundamental concern about the electronic states near the Fermi level, i.e., whether the Fermi level resides in a well-separated impurity band derived from Mn doping (impurity-band model) or in the valence band that is already merged with the Mn-derived impurity band (valence-band model). We investigate this question by carefully shifting the Fermi level by means of carrier compensation. We use helium-ion implantation, a standard industry technology, to precisely compensate the hole doping of GaAs-based diluted ferromagnetic semiconductors while keeping the Mn concentration constant. We monitor the change of Curie temperature ($T_C$) and conductivity. For a broad range of samples including (Ga,Mn)As and (Ga,Mn)(As,P) with various Mn and P concentrations, we observe a smooth decrease of $T_C$ with carrier compensation over a wide temperature range while the conduction is changed from metallic to insulating. The existence of $T_C$ below 10\,K is also confirmed in heavily compensated samples. Our experimental results are naturally explained within the valence-band picture.

preprint2016arXiv

Saliency Detection with Spaces of Background-based Distribution

In this letter, an effective image saliency detection method is proposed by constructing some novel spaces to model the background and redefine the distance of the salient patches away from the background. Concretely, given the backgroundness prior, eigendecomposition is utilized to create four spaces of background-based distribution (SBD) to model the background, in which a more appropriate metric (Mahalanobis distance) is quoted to delicately measure the saliency of every image patch away from the background. After that, a coarse saliency map is obtained by integrating the four adjusted Mahalanobis distance maps, each of which is formed by the distances between all the patches and background in the corresponding SBD. To be more discriminative, the coarse saliency map is further enhanced into the posterior probability map within Bayesian perspective. Finally, the final saliency map is generated by properly refining the posterior probability map with geodesic distance. Experimental results on two usual datasets show that the proposed method is effective compared with the state-of-the-art algorithms.

preprint2016arXiv

Superconductivity and abnormal pressure effect in Sr0.5La0.5FBiSe2 superconductor

Through the solid state reaction method, we synthesized a new BiSe2-based superconductor Sr0:5La0:5FBiSe2 with superconducting transition temperature Tc?3.8 K. A strong diamagnetic signal below Tc in susceptibility ?(T) is observed indicating the bulk nature of superconductivity. Different to most BiS2-based compounds where superconductivity develops from a semiconducting-like normal state, the present compound exhibits a metallic behavior down to Tc. Under weak magnetic field or pressure, however, a remarkable crossover from metallic to insulating behaviors takes place around Tmin where the resistivity picks up a local minimum. With increasing pressure, Tc decreases monotonously and Tmin shifts to high temperatures, while the absolute value of the normal state resistivity at low temperatures first decreases and then increases with pressure up to 2.5 GPa. These results imply that the electronic structure of Sr0:5La0:5FBiSe2 may be different to those in the other BiS2-based systems.

preprint2016arXiv

The Entropy of Nonrotating Isolated Horizons in Lovelock Theory from Loop Quantum Gravity

In this paper, the BF theory method is applied to the nonrotating isolated horizons in Lovelock theory. The final entropy matches the Wald entropy formula for this theory. We also confirm the conclusion got by Bodendorfer et. al. that the entropy is related to the flux operator rather than the area operator in general diffeomorphic-invariant theory.

preprint2016arXiv

Wave vector star channel and star channel group in the reciprocal lattice space of crystal

In the paper, a new method determining the wave vector star channel in the reciprocal lattice space of crystal in the light of the translational symmetry breaking is proposed, and, in order to consider the phase transitions according to the wave vector star channel, the conception of wave vector star channel group is adopted. By this method, it is revealed that the phase transitions in crystal are induced not by any arbitrary combinations of arms of the given star, but by the selected combinations of arms which are satisfied by symmetry of the parent phase. The wave vector star channel group is defined as the set of elements of space group leaving the wave vector star channel invariant. We show that the conception of wave vector star channel group can be efficiently used in studying the translational symmetry breaking related to all the Lifshitz wave vector stars of 230 space groups.

preprint2016arXiv

Wave vector substar group in reciprocal lattice space and its representation

In the paper we establish the new conception of the wave vector substar group and its representation that, in the study on translational symmetry breaking of crystal, can only consider the particular arms of wave vector star taking part in phase transition, but not all arms of wave vector star, unlike the traditional Landau theory. We show that using the new conception, we can effectively investigate the interesting physical properties of crystals associated with translational symmetry breaking. We can see that the studies on the complicated phase transitions related to reducible representations, such as phase transitions in perovskite KMnF3 multiferroics crystal and high temperature superconductor La2/3Mg1/2W1/2O3 (La4Mg3W3O18) compound, are much simplified by the new conception, and the theory of the wave vector substar group and its representation becomes a powerful mathematical tool being able to promote actively the study of various symmetry breaking phenomena of solid state crystals .

preprint2015arXiv

A Closed-Loop UL Power Control Scheme for Interference Mitigation in Dynamic TD-LTE Systems

The TD-LTE system is envisaged to adopt dynamic time division duplexing (TDD) transmissions for small cells to adapt their communication service to the fast variation of downlink (DL) and uplink (UL) traffic demands. However, different DL/UL directions for the same subframe in adjacent cells will result in new destructive interference components, i.e., eNB-to-eNB and UE-to-UE, with levels that can significantly differ from one subframe to another. In this paper, a feasible UL power control mechanism is proposed to manage eNB-to-eNB interference, where different UL power control parameters are set based on different interference level. We consider the geometric location information and the subframe set selection process about adjacent eNBs when the interference level is estimated. The performance of the proposed scheme is evaluated through system level simulations and it is shown that the scheme can achieve preferable improvement in terms of UL average and 5%-ile packet throughputs compared with the original scheme without power control. Also, the UE-to-UE interference is not worse when the UE transmit power become higher.

preprint2015arXiv

Cluster spin-glass ground state in quasi-one-dimensional KCr$_{3}$As$_{3}$

We report structural and physical properties of a new quasi-one-dimensional Cr-based compound, KCr$_{3}$As$_{3}$, which is prepared by potassium deintercalation from the superconductive K$_{2}$Cr$_{3}$As$_{3}$. KCr$_{3}$As$_{3}$ adopts the TlFe$_{3}$Te$_{3}$-type structure with space group $P6_{3}$/$m$ (No. 176). The high-temperature magnetic susceptibility obeys the Curie-Weiss law with an effective magnetic moment of 0.68 $μ_{\mathrm{B}}$/Cr. Below 56 K the susceptibility deviates from the high-temperature Curie-Weiss behavior, coinciding with the rapid increase in resistivity, which suggests formation of spin clusters. The short-range spin correlations are also supported by the specific-heat data. The title material does not exhibit bulk superconductivity; instead, it shows a cluster spin-glass state below $\sim$ 5 K.

preprint2015arXiv

Coexistence of superconductivity and ferromagnetism in Sr0.5Ce0.5FBiS2

Through the combination of X-ray diffraction, electrical transport, magnetic susceptibility, and the heat capacity measurements, we studied the effect of Ce doping in the newly discovered SrFBiS${2}$ system. It is found that Sr${0.5}$Ce${0.5}$FBiS${2}$ undergoes a second-order transition below $7.5 K, followed by a superconducting transition with the critical temperature Tc=2.8 K. Our transport, specific heat and DC-magnetization results suggest the presence of bulk ferromagneticcorrelation of Ce ion below 7.5 K that coexist with superconductivity when the temperature is further lowered below 2.8 K.

preprint2015arXiv

Fano resonance in a normal metal/ferromagnet-quantum dot-superconductor device

We investigate theoretically the Andreev transport through a quantum dot strongly coupled with a normal metal/ferromagnet and a superconductor (N/F-QD-S), in which the interplay between the Kondo resonance and the Andreev bound states (ABSs) has not been clearly clarified yet. Here we show that the interference between the Kondo resonance and the ABSs modifies seriously the lineshape of the Kondo resonance, which manifests as a Fano resonance. The ferromagnetic lead with spin-polarization induces an effective field, which leads to splitting both of the Kondo resonance and the ABSs. The spin-polarization together with the magnetic field applied provides an alternative way to tune the lineshape of the Kondo resonances, which is dependent of the relative positions of the Kondo resonance and of the ABSs. These results indicate that the interplay between the Kondo resonance and the ABSs can significantly affect the Andreev transport, which could be tested by experiments.

preprint2015arXiv

G-Bean: an ontology-graph based web tool for biomedical literature retrieval

Currently, most people use PubMed to search the MEDLINE database, an important bibliographical information source for life science and biomedical information. However, PubMed has some drawbacks that make it difficult to find relevant publications pertaining to users' individual intentions, especially for non-expert users. To ameliorate the disadvantages of PubMed, we developed G-Bean, a graph based biomedical search engine, to search biomedical articles in MEDLINE database more efficiently.G-Bean addresses PubMed's limitations with three innovations: parallel document index creation,ontology-graph based query expansion, and retrieval and re-ranking of documents based on user's search intention.Performance evaluation with 106 OHSUMED benchmark queries shows that G-Bean returns more relevant results than PubMed does when using these queries to search the MEDLINE database. PubMed could not even return any search result for some OHSUMED queries because it failed to form the appropriate Boolean query statement automatically from the natural language query strings. G-Bean is available at http://bioinformatics.clemson.edu/G-Bean/index.php.G-Bean addresses PubMed's limitations with ontology-graph based query expansion, automatic document indexing, and user search intention discovery. It shows significant advantages in finding relevant articles from the MEDLINE database to meet the information need of the user.

preprint2015arXiv

High Resolution Image Reconstruction Method for a Double-plane PET System with Changeable Spacing

Positron Emission Mammography (PEM) imaging systems with the ability in detection of millimeter-sized tumors were developed in recent years. And some of them have been well used in clinical applications. In consideration of biopsy application, a double-plane detector configuration is practical for the convenience of breast immobilization. However, the serious blurring effect in the double-plane system with changeable spacing for different breast size should be studied. Methods: We study a high resolution reconstruction method applicable for a double-plane PET system with a changeable detector spacing. Geometric and blurring components should be calculated at real time for different detector distance. Accurate geometric sensitivity is obtained with a tube area model. Resolution recovery is achieved by estimating blurring effects derived from simulated single gamma response information. Results: The results show that the new geometric modeling gives a more finite and smooth sensitivity weight in double-plane system. The blurring component yields contrast recovery levels that could not be reached without blurring modeling, as well as better visual recovery of the smallest spheres and better delineation of the structures in the reconstructed images. Statistical noise has lower variance at the voxel level with blurring modeling than without at matched resolution. Conclusion: In the distance-changeable double-plane PET, the finite resolution modeling during reconstruction achieves resolution recovery, without noise amplification.

preprint2015arXiv

Optimizing thermoelectric performances of low-temperature SnSe compounds by electronic structure design

Recently SnSe compound was reported to have a peak thermoelectric figure-5 of-merit (ZT) of 2.62 at 923 K, but the ZT values at temperatures below 750 K are relatively low. In this work, the electronic structures of SnSe are calculated using the density functional theory, and the electro- and thermo-transport properties upon varying chemical potential (or carrier density) are evaluated by the semi-classic Boltzmann transport theory, showing that the calculated ZT values along the a10 and c-axes below 675 K are in agreement with reported values, but that along the b-axis can be as high as 2.57 by optimizing the carrier concentration to ~3.6*1019 cm-3. It is revealed that a mixed ionic-covalent bonding and heavy-light band overlapping near the valence band are the reasons for the higher thermoelectric performance

preprint2015arXiv

Superconductivity in a new layered nickel-selenide CsNi2Se2

The physical properties of CsNi$_{2}$Se$_{2}$ were characterized by electrical resistivity, magnetization and specific heat measurements. We found that the stoichiometric CsNi$_{2}$Se$_{2}$ compound is a superconductor with a transition temperature \textit{T$_{c}$}=2.7K. A large Sommerfeld coefficient $γ$$_{n}$ ($\sim$77.90 mJ/mol$\cdot$K$^{-2}$), was obtained from the normal state electronic specific heat. However, the Kadowaki-Woods ratio of CsNi$_{2}$Se$_{2}$ was estimated to be about 0.041$\times$10$^{-5}$ $μΩ$$\cdot$cm(mol$\cdot$K/mJ)$^{2}$, indicating the absence of strong electron-electron correlations in this compound. In the superconducting state, we found that the zero-field electronic specific heat data, $C_{es}(T)$ (0.5K $\leq$ T $<$ 2.6K), can be well fitted with a two-gap BCS model. The comparison with the results of the density functional theory (DFT) calculations suggested that the large $γ$$_{n}$ in the nickel-selenide superconductors may be related to the large Density of States (DOS) at the fermi surface.

preprint2015arXiv

Thermoelectric-induced unitary Cooper pair splitting efficiency

Thermoelectric effect is exploited to optimize the Cooper pair splitting efficiency in a Y-shaped junction, which consists of two normal leads coupled to an $s$-wave superconductor via double noninteracting quantum dots. Here, utilizing temperature difference rather than bias voltage between the two normal leads, and tuning the two dot levels such that the transmittance of elastic cotunneling process is particle-hole symmetric, we find currents flowing through the normal leads are totally contributed from the splitting of Cooper pairs emitted from the superconductor. Such a unitary splitting efficiency is significantly better than the efficiencies obtained in experiments so far.

preprint2014arXiv

A Smartphone Controlled Handheld Microfluidic Liquid Handling System

Microfluidics and lab-on-a-chip technologies have made it possible to manipulate small volume liquids with unprecedented resolution, automation and integration. However, most current microfluidic systems still rely on bulky off-chip infrastructures such as compressed pressure sources, syringe pumps and computers to achieve complex liquid manipulation functions. Here, we present a handheld automated microfluidic liquid handling system controlled by a smartphone, which is enabled by combining elastomeric on-chip valves and a compact pneumatic system. As a demonstration, we show that the system can automatically perform all the liquid handling steps of a bead-based sandwich immunoassay on a multi-layer PDMS chip without any human intervention. The footprint of the system is 6 by 10.5 by 16.5cm, and the total weight is 829g including battery. Powered by a 12.8V 1500mAh Li battery, the system consumed 2.2W on average during the immunoassay and lasted for 8.7 hrs. This handheld microfluidic liquid handling platform is generally applicable to many biochemical and cell-based assays requiring complex liquid manipulation and sample preparation steps such as FISH, PCR, flow cytometry and nucleic acid sequencing. In particular, the integration of this technology with read-out biosensors may help enable the realization of the long-sought Tricorder-like handheld in-vitro diagnostic (IVD) systems.

preprint2014arXiv

Attenuation correction with Region Growing Method used in the Positron Emission Mammography System

Positron Emission Mammography imaging system (PEMi) is a nuclear medicine diagnosis method dedicated for breast imaging. It provides a better resolution in detection of millimeter-sized breast tumors than whole-body PET. To address the requirement of semi-quantitative analysis with the radiotracer concentration map of the breast, a new attenuation correction method based on three-dimensional seeded region growing image segmentation (3DSRG-AC) solution was developed. The method gives a 3D connected region as the segmentation result instead of image slices. The continuously segmentation property makes this new method free of activity variation of breast tissues. Threshold value chosen is the key point for the segmentation process. The first valley of the grey level histogram of the reconstruction image is set as the lower threshold, which works fine in clinical application. Results show that attenuation correction for PEMi improves the image quality and the quantitative accuracy of radioactivity distribution determination. Attenuation correction also improves the probabilities to detect small and early breast tumors.

preprint2014arXiv

Azimuthal distribution of mean transverse rapidity in relativistic heavy ion collisions

By the sample generated by the AMPT with string melting model, we compare the azimuthal distributions of multiplicity, total transverse rapidity, and mean transverse rapidity. It is demonstrated that the azimuthal distribution of mean transverse rapidity is a good probe of the radial kinetic expansion. The anisotropic part of the distribution characterizes the anisotropic nature of the radial expansion, and isotropic part is combinations of thermal motion and isotropic radial expansion.

preprint2014arXiv

Disentangling defect-induced ferromagnetism in SiC

We present a detailed investigation of the magnetic properties in SiC single crystals bombarded with neon ions. Through careful measuring of the magnetization of virgin and irradiated SiC, we decompose the magnetization of SiC into paramagnetic, superparamagnetic, and ferromagnetic contributions. The ferromagnetic contribution persists well above room temperature and exhibits a pronounced magnetic anisotropy. We qualitatively explain the magnetic properties as a result of the intrinsic clustering tendency of defects.

preprint2014arXiv

Electronic phase diagram in the new BiS2-based Sr1-xLaxFBiS2 system

In this paper, we systematically study the electron doping effect in a new BiS2-based system Sr1-xLaxFBiS2(0<x<0.7) through multiple techniques of X-ray diffraction, electrical transport, magnetic susceptibility, and Hall effect measurements. The parent compound SrFBiS2 is found to possess a semiconducting-like ground state, with thermally activation energy Eg=38 meV. By the partial substitution of La for Sr, superconductivity emerges when x > 0.3, reaching its maximal superconducting transition temperature Tc=3.5 K at x=0.55. In the normal state of superconducting samples, it is clearly seen that there exists a crossover from metallic to semiconducting state below a temperature Tmin, which shifts to lower temperatures with increasing La content. Based on these measurements, the associated electronic phase diagram of Sr1-xLaxFBiS2 system has thus been established.

preprint2014arXiv

Modeling and Analysis of SLED

SLED is a crucial component for C-band microwave acceleration unit of SXFEL. To study the behavior of SLED (SLAC Energy Doubler), mathematic model is commonly built and analyzed. In this paper, a new method is proposed to build the model of SLED at SINAP. With this method, the parameters of the two cavities can be analyzed separately. Also it is suitable to study parameter optimization of SLED and analyze the effect from the parameters variations. Simulation results of our method are also presented.

preprint2014arXiv

Possible Supersymmetric Kinematics

The contraction method in different limits to obtain 22 different realizations of kinematical algebras is applied to study the supersymmetric extension of \AdS\ algebra and its contractions. It is shown that $\frak{p}_2$ $\frak{h}_-$, $\frak{p}'$, $\frak{c}_2$ and $\frak{g}'$ algebras, in addition to $\frak{d}_-$, $\frak{p}$, $\frak{n}_-$, $\frak{g}$ and $\frak{c}$ algebras, have supersymmetric extension, while $\frak{n}_{-2}$, $\frak{g}_2$ and $\frak{g}'_2$ algebras have no supersymmetric extension. The connections among the superalgebras are established.

preprint2014arXiv

Sensing Subjective Well-being from Social Media

Subjective Well-being(SWB), which refers to how people experience the quality of their lives, is of great use to public policy-makers as well as economic, sociological research, etc. Traditionally, the measurement of SWB relies on time-consuming and costly self-report questionnaires. Nowadays, people are motivated to share their experiences and feelings on social media, so we propose to sense SWB from the vast user generated data on social media. By utilizing 1785 users' social media data with SWB labels, we train machine learning models that are able to "sense" individual SWB from users' social media. Our model, which attains the state-by-art prediction accuracy, can then be used to identify SWB of large population of social media users in time with very low cost.

preprint2014arXiv

Structural and magnetic properties of irradiated SiC

We present a comprehensive structural characterization of ferromagnetic SiC single crystals induced by Ne ion irradiation. The ferromagnetism has been confirmed by electron spin resonance and possible transition metal impurities can be excluded to be the origin of the observed ferromagnetism. Using X-ray diffraction and Rutherford backscattering/channeling spectroscopy, we estimate the damage to the crystallinity of SiC which mutually influences the ferromagnetism in SiC.

preprint2013arXiv

Graphics Processing Unit acceleration of the Random Phase Approximation in the projector augmented wave method

The Random Phase Approximation (RPA) for correlation energy in the grid-based projector augmented wave (gpaw) code is accelerated by porting to the Graphics Processing Unit (GPU) architecture. The acceleration is achieved by grouping independent vectors/matrices and transforming the implementation from being memory bound to being computation/latency bound. With this approach, both the CPU and GPU implementations have been enhanced. We tested the GPU implementation on a few representative systems: molecules (O2), bulk solids (Li2O and MoO3) and molecules adsorbed on metal surfaces (N2/Ru(0001) and CO/Ni(111)). Improvements from 10+ to 40+ have been achieved (8-GPUs versus 8-CPUs). A realistic RPA calculation for CO/Ni(111) surface can be finished in 5.5 h using 8 GPUs. It is thus promising to employ non-self-consistent RPA for routine surface chemistry simulations.

preprint2013arXiv

Influence of initial size on higher cumulant ratios of net-proton number fluctuations

With the help of AMPT default model, we study the influence of initial size (centrality of collisions) on higher cumulant ratios of net-proton distributions. If the centrality is presented by impact parameter, there is a strong centrality dependent, in particular, in those peripheral collisions. This dependence is slightly reduced if the centrality is presented by number of participant, or charged multiplicity. However, the dynamical ratios are almost centrality independent. So the centrality dependence of dynamical ratios at RHIC beam energy scan are presented.

preprint2013arXiv

Kernel methods on spike train space for neuroscience: a tutorial

Over the last decade several positive definite kernels have been proposed to treat spike trains as objects in Hilbert space. However, for the most part, such attempts still remain a mere curiosity for both computational neuroscientists and signal processing experts. This tutorial illustrates why kernel methods can, and have already started to, change the way spike trains are analyzed and processed. The presentation incorporates simple mathematical analogies and convincing practical examples in an attempt to show the yet unexplored potential of positive definite functions to quantify point processes. It also provides a detailed overview of the current state of the art and future challenges with the hope of engaging the readers in active participation.

preprint2013arXiv

Poisson-Nernst-Planck Model of Bipolar Nano uidic Diode Based on Bulletlike Nanopore

Bipolar nanofluidic diode is based on nanopore with positive and negative surface charges separated by a junction. This paper investigates the effects of the pore structure, taking the bullet-like pore as an example, on the ion current rectification. The Poisson-Nernst-Planck Modelings show that the ion current rectification behavior can be greatly influenced by the shape of the pore. The bipolar nanofluidic diode with more tapered tip has significantly higher ion current rectification degree. The modelling results indicate that special design of the nanopore is necessary for the performance of the bipolar nanofluidic diode.

preprint2012arXiv

Azimuthal distributions of radial momentum and velocity in relativistic heavy ion collisions

Azimuthal distributions of radial (transverse) momentum, mean radial momentum, and mean radial velocity of final state particles are suggested for relativistic heavy ion collisions. Using transport model AMPT with string melting, these distributions for Au + Au collisions at 200 GeV are presented and studied. It is demonstrated that the distribution of total radial momentum is more sensitive to the anisotropic expansion, as the anisotropies of final state particles and their associated transverse momentums are both counted in the measure. The mean radial velocity distribution is compared with the radial °ow velocity. The thermal motion contributes an isotropic constant to mean radial velocity.

preprint2012arXiv

Compensation effect in carbon nanotube quantum dots coupled to polarized electrodes in the presence of spin-orbit coupling

We study theoretically the Kondo effect in carbon nanotube quantum dot attached to polarized electrodes. Since both spin and orbit degrees of freedom are involved in such a system, the electrode polarization contains the spin- and orbit-polarizations as well as the Kramers polarization in the presence of the spin-orbit coupling. In this paper we focus on the compensation effect of the effective fields induced by different polarizations by applying magnetic field. The main results are i) while the effective fields induced by the spin- and orbit-polarizations remove the degeneracy in the Kondo effect, the effective field induced by the Kramers polarization enhances the degeneracy through suppressing the spin-orbit coupling; ii) while the effective field induced by the spin-polarization can not be compensated by applying magnetic field, the effective field induced by the orbit-polarization can be compensated; and iii) the presence of the spin-orbit coupling does not change the compensation behavior observed in the case without the spin-orbit coupling. These results are observable in an ultraclean carbon-nanotube quantum dot attached to ferromagnetic contacts under a parallel applied magnetic field along the tube axis and it would deepen our understanding on the Kondo physics of the carbon nanotube quantum dot.

preprint2012arXiv

Kondo effect of an adatom in graphene and its scanning tunneling spectroscopy

We study the Kondo effect of a single magnetic adatom on the surface of graphene. It was shown that the unique linear dispersion relation near the Dirac points in graphene makes it more easy to form the local magnetic moment, which simply means that the Kondo resonance can be observed in a more wider parameter region than in the metallic host. The result indicates that the Kondo resonance indeed can form ranged from the Kondo regime, to the mixed valence, even to the empty orbital regime. While the Kondo resonance displays as a sharp peak in the first regime, it has a peak-dip structure and/or an anti-resonance in the remaining two regimes, which result from the Fano resonance due to the significant background leaded by dramatically broadening of the impurity level in graphene. We also study the scanning tunneling microscopy (STM) spectra of the adatom and they show obvious particle-hole asymmetry when the chemical potential is tuned by the gate voltages applied to the graphene. Finally, we explore the influence of the direct tunneling channel between the STM tip and the graphene on the Kondo resonance and find that the lineshape of the Kondo resonance is unaffected, which can be attributed to unusual large asymmetry factor in graphene. Our study indicates that the graphene is an ideal platform to study systematically the Kondo physics and these results are useful to further stimulate the relevant experimental studies on the system.

preprint2012arXiv

Measurement of anisotropic radial flow in relativistic heavy ion collisions

We suggest the azimuthal distribution of mean transverse (radial) rapidity of the final state particles as a more direct measure of the transverse motion of the source than the standard azimuthal multiplicity distribution. Using a sample generated by the AMPT model with string melting, we demonstrate that the azimuthal amplitude of the suggested distribution characterizes the anisotropic radial flow, and coincides with the parameter of anisotropic radial rapidity extracted from a generalized blast-wave parametrization.

preprint2012arXiv

The First Infrared Telescope in Tibet Plateau, China

We plan to install an infrared telescope at the new site of Tibet, China. The primary mirror diameter is 50cm, and the focal ratio F8. The Xenics 640\times512 near infrared camera is employed, equipped with a dedicated high speed InGaAs detector array, working up to 1.7μm. The new site is located on 5100m mountain, near Gar town, Ali, where is an excellent site for both infrared and submillimeter observations. The telescope will be remotely controlled through internet. The goal of IRT is to make site testing, detect variable stars, and search for extrasolar planets.

preprint2012arXiv

Type IIn Supernova SN 2010jl: Optical Observations for Over 500 Days After Explosion

We present extensive optical observations of a Type IIn supernova (SN) 2010jl for the first 1.5 years after the discovery. The UBVRI light curves demonstrated an interesting two-stage evolution during the nebular phase, which almost flatten out after about 90 days from the optical maximum. SN 2010jl has one of the highest intrinsic H_alpha luminosity ever recorded for a SN IIn, especially at late phase, suggesting a strong interaction of SN ejecta with the dense circumstellar material (CSM) ejected by the progenitor. This is also indicated by the remarkably strong Balmer lines persisting in the optical spectra. One interesting spectral evolution about SN 2010jl is the appearance of asymmetry of the Balmer lines. These lines can be well decomposed into a narrow component and an intermediate-width component. The intermediate-width component showed a steady increase in both strength and blueshift with time until t ~ 400 days after maximum, but it became less blueshifted at t ~ 500 days when the line profile appeared relatively symmetric again. Owing to that a pure reddening effect will lead to a sudden decline of the light curves and a progressive blueshift of the spectral lines, we therefore propose that the asymmetric profiles of H lines seen in SN 2010jl is unlikely due to the extinction by newly formed dust inside the ejecta, contrary to the explanation by some early studies. Based on a simple CSM-interaction model, we speculate that the progenitor of SN 2010jl may suffer a gigantic mass loss (~ 30-50 M_sun) in a few decades before explosion. Considering a slow moving stellar wind (e.g., ~ 28 km/s) inferred for the preexisting, dense CSM shell and the extremely high mass-loss rate (1-2 M_sun per yr), we suggest that the progenitor of SN 2010jl might have experienced a red supergiant stage and explode finally as a post-red supergiant star with an initial mass above 30-40 M_sun.

preprint2012arXiv

Visible Spectrum Circular Dichroism in Extrinsic Chirality Metamaterials

We present the new planar extrinsic chirality metamaterial (ECM) design that manifests giant circular dichroism (CD) in the visible spectrum range rather than usual near-infrared and terahertz range. Effects of incident beam angles and meta-molecules unit sizes on the CD spectrums were theoretically analyzed; Physical mechanism was illustrated in new figures of asymmetrical current excitation in neighboring unit cells.

preprint2011arXiv

Rise and fall of defect induced ferromagnetism in SiC single crystals

6H-SiC (silicon carbide) single crystals containing VSi-VC divacancies are investigated with respect to magnetic and structural properties. We found that an initial increase of structural disorder leads to pronounced ferromagnetic properties at room temperature. Further introduction of disorder lowers the saturation magnetization and is accompanied with the onset of lattice amorphization. Close to the threshold of full amorphization, also divacancy clusters are formed and the saturation magnetization nearly drops to zero.

preprint2010arXiv

An effective method of calculating the non-Markovianity $\mathcal{N}$ for single channel open systems

We propose an effective method which can simplify the optimization of the increase of the trace distance over all pairs of initial states in calculating the non-Markovianity $\mathcal{N}$ for single channel open systems. For the amplitude damping channel, we can unify the results of Breuer $et$ $al$. [Phys. Rev. Lett. \bf 103\rm, 210401 (2009)] in the large-detuning case and the results of Xu $et$ $al$. [Phys. Rev. A \bf 81\rm, 044105 (2010)] in the resonant case; furthermore, for the general off-resonant cases we can obtain a very tight lower bound of $\mathcal{N}$. As another application of our method, we also discuss $\mathcal{N}$ for the non-Markovian depolarizing channel.

preprint2010arXiv

Tailoring the magnetism of GaMnAs films by ion irradiation

Ion irradiation of semiconductors is a well understood method to tune the carrier concentration in a controlled manner. We show that the ferromagnetism in GaMnAs films, known to be hole-mediated, can be modified by He ion irradiation. The coercivity can be increased by more than three times. The magnetization, Curie temperature and the saturation field along the out-of-plane hard axis all decrease as the fluence increases. The electrical and structural characterization of the irradiated GaMnAs layers indicates that the controlled tailoring of magnetism results from a compensation of holes by generated electrical defects.

preprint2010arXiv

The optical microscopy with virtual image breaks a record: 50-nm resolution imaging is demonstrated

We demonstrate a new 'microsphere nanoscope' that uses ordinary SiO2 microspheres as superlenses to create a virtual image of the object in near field. The magnified virtual image greatly overcomes the diffraction limit. We are able to resolve clearly 50-nm objects under a standard white light source in both transmission and reflection modes. The resolution achieved for white light opens a new opportunity to image viruses, DNA and molecules in real time.

preprint2009arXiv

On-chip Single Nanoparticle Detection and Sizing by Mode Splitting in an Ultra-high-Q Microresonator

The ability to detect and size individual nanoparticles with high resolution is crucial to understanding behaviours of single particles and effectively using their strong size-dependent properties to develop innovative products. We report real-time, in-situ detection and sizing of single nanoparticles, down to 30 nm in radius, using mode-splitting in a monolithic ultra-high-Q whispering-gallery-mode (WGM) microtoroid resonator. Particle binding splits a WGM into two spectrally shifted resonance modes, forming a self-referenced detection scheme. This technique provides superior noise suppression and enables extracting accurate size information in a single-shot measurement. Our method requires neither labelling of the particles nor apriori information on their presence in the medium, providing an effective platform to study nanoparticles at single particle resolution.

preprint2005arXiv

$D_s$ Asymmetry in Photoproduction

By adopting two models of strange and antistrange quark distributions inside nucleon, the light-cone meson-baryon fluctuation model and the effective chiral quark model, we calculate the $D_s^+ - D_s^-$ asymmetry in photoproduction in the framework of heavy-quark recombination mechanism. We find that the effect of asymmetry of strange sea to the $D_s$ asymmetry is considerable and depending on the different models. Therefore, we expect that with the further study in electroproduction, e.g. at HERA and CEBAF, the experimental measurements on the $D_s^+ - D_s^-$ asymmetry may impose a strong restriction on the strange-antistrange distribution asymmetry models.

preprint2004arXiv

$B\to Kπ$ Decays with $1/m_b$ Corrections in $QCD$ Factorization

It is commonly believed that a careful investigation of the subleading terms is crucial for a better understanding of the $QCD$ factorization in charmless B decays. In this work the penguin-dominated $B\to Kπ$ decays are discussed systematically, including the subleading corrections in $1/m_b$ due to soft and hard gluons, besides the annihilation contributions. Soft-gluon effects for all the relevant 4-quark effective operators are calculated within the framework of the light-cone QCD sum rules (LCSR). Our observation is that such soft and hard corrections are less important than the annihilation effects, enhancing only the branching ratios by a few percent; the resultant increase in the branching ratios due to the overall ${\cal O}(1/m_b)$ effects is between about $(22-27)%$ of the QCD factorization results with the ${\cal O}(α_s)$ corrections, as the weak phase $γ(=\textmd{Im}V_{ub}^*)$ ranges from $40^0$ to $80^0$. Impacts of the involved uncertainties are discussed in some details.

Lin Li

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88 published item(s)

OTora: A Unified Red Teaming Framework for Reasoning-Level Denial-of-Service in LLM Agents

Constrained Language Model Policy Optimization via Risk-aware Stepwise Alignment

GuidedMorph: Two-Stage Deformable Registration for Breast MRI

Capacity Results for Multiple-Input Multiple-Output Optical Wireless Communication With Per-Antenna Intensity Constraints

Towards A Unified Conformer Structure: from ASR to ASV Task

Analysis of a Direct Separation Method Based on Adaptive Chirplet Transform for Signals with Crossover Instantaneous Frequencies

Approximation Properties of Deep ReLU CNNs

Deep Representation Decomposition for Rate-Invariant Speaker Verification

Direct Signal Separation Via Extraction of Local Frequencies with Adaptive Time-Varying Parameters

Dual Space Graph Contrastive Learning

Graph Convolutional Network Based Semi-Supervised Learning on Multi-Speaker Meeting Data

Josephson-Coulomb drag effect between graphene and LaAlO3/SrTiO3 interfacial superconductor

Label Semantic Knowledge Distillation for Unbiased Scene Graph Generation

Learning to Prove Trigonometric Identities

MealRec: A Meal Recommendation Dataset

Normalized solutions for Schrödinger-Bopp-Podolsky system

Probabilistic Guaranteed Path Planning for Safe Urban Air Mobility Using Chance Constrained RRT

ReLU Deep Neural Networks from the Hierarchical Basis Perspective

Scattered Image Reconstruction at Near-infrared Based on Spatial Modulation Instability

The Devil is in the Labels: Noisy Label Correction for Robust Scene Graph Generation

The Effects of Dynamic Learning and the Forgetting Process on an Optimizing Modelling for Full-Service Repair Pricing Contracts for Medical Devices

Towards Real-Time Visual Tracking with Graded Color-names Features

Video is All You Need: Attacking PPG-based Biometric Authentication

What is Next when Sequential Prediction Meets Implicitly Hard Interaction?

CFNS Ad-Hoc meeting on Radiative Corrections Whitepaper

Hyper Meta-Path Contrastive Learning for Multi-Behavior Recommendation

Observation of Aharonov-Bohm effect in PbTe nanowire networks

Selective area epitaxy of PbTe-Pb hybrid nanowires on a lattice-matched substrate

A BERT based Sentiment Analysis and Key Entity Detection Approach for Online Financial Texts

Analysis of Adaptive Synchrosqueezing Transform with a Time-varying Parameter

Complementing Representation Deficiency in Few-shot Image Classification: A Meta-Learning Approach

Effects of biaxial strain and local constant potential on electronic structure of monolayer SnSe

Many-body chiral edge currents and sliding phases of atomic spinwaves in momentum-space lattice

Null Gravitational Redshift by a Reissner-Nordström Black Hole in the Strong Field Limit

Performance of the T-matrix based master equation for Coulomb drag in double quantum dots

Possible Formation Scenarios of ZTF J153932.16+502738.8-A Gravitational Source Close to the Peak of LISA's Sensitivity

Taking the pulse of COVID-19: A spatiotemporal perspective

The Formation of Bimodal Dust Species in Nova Ejecta

ReLU Deep Neural Networks and Linear Finite Elements

A New ZrCuSiAs-Type Superconductor: ThFeAsN

Cross-Domain Entity Resolution in Social Media

Field-induced resistivity plateau and unsaturated negative magnetoresistance in topological semimetal TaSb2

Higher-order Landau-Devonshire theory for BaTiO3

Kondo screening of Andreev bound states in an N-QD-S system

Matching Community Structure Across Online Social Networks

Modeling Group Dynamics Using Probabilistic Tensor Decompositions

Precise tuning of the Curie temperature of (Ga,Mn)As-based magnetic semiconductors by hole compensation: Support for valence-band ferromagnetism

Saliency Detection with Spaces of Background-based Distribution

Superconductivity and abnormal pressure effect in Sr0.5La0.5FBiSe2 superconductor

The Entropy of Nonrotating Isolated Horizons in Lovelock Theory from Loop Quantum Gravity

Wave vector star channel and star channel group in the reciprocal lattice space of crystal

Wave vector substar group in reciprocal lattice space and its representation

A Closed-Loop UL Power Control Scheme for Interference Mitigation in Dynamic TD-LTE Systems

Cluster spin-glass ground state in quasi-one-dimensional KCr$_{3}$As$_{3}$

Coexistence of superconductivity and ferromagnetism in Sr0.5Ce0.5FBiS2

Fano resonance in a normal metal/ferromagnet-quantum dot-superconductor device

G-Bean: an ontology-graph based web tool for biomedical literature retrieval

High Resolution Image Reconstruction Method for a Double-plane PET System with Changeable Spacing

Optimizing thermoelectric performances of low-temperature SnSe compounds by electronic structure design

Superconductivity in a new layered nickel-selenide CsNi2Se2

Thermoelectric-induced unitary Cooper pair splitting efficiency

A Smartphone Controlled Handheld Microfluidic Liquid Handling System

Attenuation correction with Region Growing Method used in the Positron Emission Mammography System

Azimuthal distribution of mean transverse rapidity in relativistic heavy ion collisions

Disentangling defect-induced ferromagnetism in SiC

Electronic phase diagram in the new BiS2-based Sr1-xLaxFBiS2 system

Modeling and Analysis of SLED

Possible Supersymmetric Kinematics

Sensing Subjective Well-being from Social Media

Structural and magnetic properties of irradiated SiC

Graphics Processing Unit acceleration of the Random Phase Approximation in the projector augmented wave method

Influence of initial size on higher cumulant ratios of net-proton number fluctuations

Kernel methods on spike train space for neuroscience: a tutorial

Poisson-Nernst-Planck Model of Bipolar Nano uidic Diode Based on Bulletlike Nanopore