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Jun Xu

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preprint2026arXiv

The Attention Market: Interpreting Online Fair Re-ranking as Manifold Optimization under Walrasian Equilibrium

Fair re-ranking aims to promote long-tail items and enhance diversity within groups in information retrieval. While previous research on online fairness-aware re-ranking has shown promising outcomes, our comprehensive evaluation of online fair re-ranking methods over 20 settings reveals significant performance disparities among existing methods. To uncover the root causes of these inconsistencies, we reformulate fair re-ranking within an attentional market framework governed by a Walrasian Equilibrium, where the fairness is treated as a taxation cost. This market-based formulation is then coupled with manifold optimization, demonstrating that seeking this equilibrium is equivalent to performing gradient descent on a specific ranking manifold constructed by the market. Different re-ranking settings induce distinct manifold geometries, and these intrinsic geometric differences dictate the gradient landscapes and optimization trajectories. We propose ManifoldRank, an efficient online fair re-ranking algorithm. ManifoldRank adjusts gradients to align with the ranking manifold, considering various contextual settings. On the supply side, it incorporates a gradient adjustment based on different fairness requirements, accounting for associated costs. On the demand side, it empirically predicts an additional gradient adjustment term derived from the ranking scores. By integrating these two gradient adjustments, ManifoldRank effectively balances fairness and accuracy. Experimental results across multiple datasets confirm ManifoldRank's effectiveness.

preprint2025arXiv

A precise proof of the n-variable Bekic principle

We provide a proof of the $n$-ary Bekič principle, which states that a vectorial fixpoint of size $n$ can be written in terms of nested fixpoints in each coordinate according to lexicographic order. The proof is inductive.

preprint2025arXiv

Large Language Model Sourcing: A Survey

Due to the black-box nature of large language models (LLMs) and the realism of their generated content, issues such as hallucinations, bias, unfairness, and copyright infringement have become significant. In this context, sourcing information from multiple perspectives is essential. This survey presents a systematic investigation organized around four interrelated dimensions: Model Sourcing, Model Structure Sourcing, Training Data Sourcing, and External Data Sourcing. Moreover, a unified dual-paradigm taxonomy is proposed that classifies existing sourcing methods into prior-based (proactive traceability embedding) and posterior-based (retrospective inference) approaches. Traceability across these dimensions enhances the transparency, accountability, and trustworthiness of LLMs deployment in real-world applications.

preprint2023arXiv

Revisiting angular momentum conservation in transport simulations of intermediate-energy heavy-ion collisions

Based on the well-calibrated IBUU transport model, we have studied the dynamical effect of incorporating rigorous angular momentum conservation in each collision of particles with homework setups. The constraint of the rigorous angular momentum conservation requires in-plane collisions and side jumps of particles after their collision. Since the option is not unique, we have compared two typical prescriptions with the original one. While the results depend quantitatively on the choice of the prescription, we found that the angular momentum conservation generally reduces local density fluctuations and thus the collision rate, and may have some influence on the density evolution, the collective flow, and even the pion production in transport simulations of intermediate-energy heavy-ion collisions.

preprint2022arXiv

A Model-Agnostic Causal Learning Framework for Recommendation using Search Data

Machine-learning based recommender systems(RSs) has become an effective means to help people automatically discover their interests. Existing models often represent the rich information for recommendation, such as items, users, and contexts, as embedding vectors and leverage them to predict users' feedback. In the view of causal analysis, the associations between these embedding vectors and users' feedback are a mixture of the causal part that describes why an item is preferred by a user, and the non-causal part that merely reflects the statistical dependencies between users and items, for example, the exposure mechanism, public opinions, display position, etc. However, existing RSs mostly ignored the striking differences between the causal parts and non-causal parts when using these embedding vectors. In this paper, we propose a model-agnostic framework named IV4Rec that can effectively decompose the embedding vectors into these two parts, hence enhancing recommendation results. Specifically, we jointly consider users' behaviors in search scenarios and recommendation scenarios. Adopting the concepts in causal analysis, we embed users' search behaviors as instrumental variables (IVs), to help decompose original embedding vectors in recommendation, i.e., treatments. IV4Rec then combines the two parts through deep neural networks and uses the combined results for recommendation. IV4Rec is model-agnostic and can be applied to a number of existing RSs such as DIN and NRHUB. Experimental results on both public and proprietary industrial datasets demonstrate that IV4Rec consistently enhances RSs and outperforms a framework that jointly considers search and recommendation.

preprint2022arXiv

A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems

Accurate recommendation and reliable explanation are two key issues for modern recommender systems. However, most recommendation benchmarks only concern the prediction of user-item ratings while omitting the underlying causes behind the ratings. For example, the widely-used Yahoo!R3 dataset contains little information on the causes of the user-movie ratings. A solution could be to conduct surveys and require the users to provide such information. In practice, the user surveys can hardly avoid compliance issues and sparse user responses, which greatly hinders the exploration of causality-based recommendation. To better support the studies of causal inference and further explanations in recommender systems, we propose a novel semi-synthetic data generation framework for recommender systems where causal graphical models with missingness are employed to describe the causal mechanism of practical recommendation scenarios. To illustrate the use of our framework, we construct a semi-synthetic dataset with Causal Tags And Ratings (CTAR), based on the movies as well as their descriptive tags and rating information collected from a famous movie rating website. Using the collected data and the causal graph, the user-item-ratings and their corresponding user-item-tags are automatically generated, which provides the reasons (selected tags) why the user rates the items. Descriptive statistics and baseline results regarding the CTAR dataset are also reported. The proposed data generation framework is not limited to recommendation, and the released APIs can be used to generate customized datasets for other research tasks.

preprint2022arXiv

Bayesian inference of finite-nuclei observables based on the KIDS model

Bayesian analyses on both isoscalar and isovector nuclear interaction parameters are carried out based on the Korea-IBS-Daegu-SKKU (KIDS) model under the constraints of nuclear structure data of $^{208}$Pb and $^{120}$Sn. Under the constraint of the neutron-skin thickness, it is found that incorporating the curvature parameter $K_{sym}$ of nuclear symmetry energy as an independent variable significantly broadens the posterior probability distribution function (PDF) of the slope parameter $L$, and affects the related correlations. Typically, the anticorrelation between $L$ and the symmetry energy at saturation density disappears, while a positive correlation between $L$ and $K_{sym}$ is observed. Under the constraint of the isoscalar giant monopole resonance (ISGMR), incorporating the skewness parameter as an independent variable also significantly broadens the posterior PDF of the nuclear matter incompressibility $K_0$. Even with the broad uncertainties of higher-order parameters of the equation of state (EOS), robust constraints of $L<90$ MeV and $K_0<270$ MeV are obtained. Our study quantifies the consistency between the constraints on $L$ from the neutron-skin data of PREXII and isovector giant dipole resonance (IVGDR) data, and the constraints on $K_0$ from the data of ISGMR in $^{208}$Pb and $^{120}$Sn.

preprint2022arXiv

Building Embedded Systems Like It's 1996

Embedded devices are ubiquitous. However, preliminary evidence shows that attack mitigations protecting our desktops/servers/phones are missing in embedded devices, posing a significant threat to embedded security. To this end, this paper presents an in-depth study on the adoption of common attack mitigations on embedded devices. Precisely, it measures the presence of standard mitigations against memory corruptions in over 10k Linux-based firmware of deployed embedded devices. The study reveals that embedded devices largely omit both user-space and kernel-level attack mitigations. The adoption rates on embedded devices are multiple times lower than their desktop counterparts. An equally important observation is that the situation is not improving over time. Without changing the current practices, the attack mitigations will remain missing, which may become a bigger threat in the upcoming IoT era. Throughout follow-up analyses, we further inferred a set of factors possibly contributing to the absence of attack mitigations. The exemplary ones include massive reuse of non-protected software, lateness in upgrading outdated kernels, and restrictions imposed by automated building tools. We envision these will turn into insights towards improving the adoption of attack mitigations on embedded devices in the future.

preprint2022arXiv

Dynamic Message Propagation Network for RGB-D Salient Object Detection

This paper presents a novel deep neural network framework for RGB-D salient object detection by controlling the message passing between the RGB images and depth maps on the feature level and exploring the long-range semantic contexts and geometric information on both RGB and depth features to infer salient objects. To achieve this, we formulate a dynamic message propagation (DMP) module with the graph neural networks and deformable convolutions to dynamically learn the context information and to automatically predict filter weights and affinity matrices for message propagation control. We further embed this module into a Siamese-based network to process the RGB image and depth map respectively and design a multi-level feature fusion (MFF) module to explore the cross-level information between the refined RGB and depth features. Compared with 17 state-of-the-art methods on six benchmark datasets for RGB-D salient object detection, experimental results show that our method outperforms all the others, both quantitatively and visually.

preprint2022arXiv

Error-free approximation of explicit linear MPC through lattice piecewise affine expression

In this paper, the disjunctive and conjunctive lattice piecewise affine (PWA) approximations of explicit linear model predictive control (MPC) are proposed. The training data are generated uniformly in the domain of interest, consisting of the state samples and corresponding affine control laws, based on which the lattice PWA approximations are constructed. Re-sampling of data is also proposed to guarantee that the lattice PWA approximations are identical to explicit MPC control law in the unique order (UO) regions containing the sample points as interior points. Additionally, under mild assumptions, the equivalence of the two lattice PWA approximations guarantees that the approximations are error-free in the domain of interest. The algorithms for deriving statistically error-free approximation to the explicit linear MPC are proposed and the complexity of the entire procedure is analyzed, which is polynomial with respect to the number of samples. The performance of the proposed approximation strategy is tested through two simulation examples, and the result shows that with a moderate number of sample points, we can construct lattice PWA approximations that are equivalent to optimal control law of the explicit linear MPC.

preprint2022arXiv

Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction

As an essential operation of legal retrieval, legal case matching plays a central role in intelligent legal systems. This task has a high demand on the explainability of matching results because of its critical impacts on downstream applications -- the matched legal cases may provide supportive evidence for the judgments of target cases and thus influence the fairness and justice of legal decisions. Focusing on this challenging task, we propose a novel and explainable method, namely \textit{IOT-Match}, with the help of computational optimal transport, which formulates the legal case matching problem as an inverse optimal transport (IOT) problem. Different from most existing methods, which merely focus on the sentence-level semantic similarity between legal cases, our IOT-Match learns to extract rationales from paired legal cases based on both semantics and legal characteristics of their sentences. The extracted rationales are further applied to generate faithful explanations and conduct matching. Moreover, the proposed IOT-Match is robust to the alignment label insufficiency issue commonly in practical legal case matching tasks, which is suitable for both supervised and semi-supervised learning paradigms. To demonstrate the superiority of our IOT-Match method and construct a benchmark of explainable legal case matching task, we not only extend the well-known Challenge of AI in Law (CAIL) dataset but also build a new Explainable Legal cAse Matching (ELAM) dataset, which contains lots of legal cases with detailed and explainable annotations. Experiments on these two datasets show that our IOT-Match outperforms state-of-the-art methods consistently on matching prediction, rationale extraction, and explanation generation.

preprint2022arXiv

Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis

Due to the constraints of the imaging device and high cost in operation time, computer tomography (CT) scans are usually acquired with low intra-slice resolution. Improving the intra-slice resolution is beneficial to the disease diagnosis for both human experts and computer-aided systems. To this end, this paper builds a novel medical slice synthesis to increase the between-slice resolution. Considering that the ground-truth intermediate medical slices are always absent in clinical practice, we introduce the incremental cross-view mutual distillation strategy to accomplish this task in the self-supervised learning manner. Specifically, we model this problem from three different views: slice-wise interpolation from axial view and pixel-wise interpolation from coronal and sagittal views. Under this circumstance, the models learned from different views can distill valuable knowledge to guide the learning processes of each other. We can repeat this process to make the models synthesize intermediate slice data with increasing inter-slice resolution. To demonstrate the effectiveness of the proposed approach, we conduct comprehensive experiments on a large-scale CT dataset. Quantitative and qualitative comparison results show that our method outperforms state-of-the-art algorithms by clear margins.

preprint2022arXiv

Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework

Tumor infiltration of the recurrent laryngeal nerve (RLN) is a contraindication for robotic thyroidectomy and can be difficult to detect via standard laryngoscopy. Ultrasound (US) is a viable alternative for RLN detection due to its safety and ability to provide real-time feedback. However, the tininess of the RLN, with a diameter typically less than 3mm, poses significant challenges to the accurate localization of the RLN. In this work, we propose a knowledge-driven framework for RLN localization, mimicking the standard approach surgeons take to identify the RLN according to its surrounding organs. We construct a prior anatomical model based on the inherent relative spatial relationships between organs. Through Bayesian shape alignment (BSA), we obtain the candidate coordinates of the center of a region of interest (ROI) that encloses the RLN. The ROI allows a decreased field of view for determining the refined centroid of the RLN using a dual-path identification network, based on multi-scale semantic information. Experimental results indicate that the proposed method achieves superior hit rates and substantially smaller distance errors compared with state-of-the-art methods.

preprint2022arXiv

MobileSal: Extremely Efficient RGB-D Salient Object Detection

The high computational cost of neural networks has prevented recent successes in RGB-D salient object detection (SOD) from benefiting real-world applications. Hence, this paper introduces a novel network, MobileSal, which focuses on efficient RGB-D SOD using mobile networks for deep feature extraction. However, mobile networks are less powerful in feature representation than cumbersome networks. To this end, we observe that the depth information of color images can strengthen the feature representation related to SOD if leveraged properly. Therefore, we propose an implicit depth restoration (IDR) technique to strengthen the mobile networks' feature representation capability for RGB-D SOD. IDR is only adopted in the training phase and is omitted during testing, so it is computationally free. Besides, we propose compact pyramid refinement (CPR) for efficient multi-level feature aggregation to derive salient objects with clear boundaries. With IDR and CPR incorporated, MobileSal performs favorably against state-of-the-art methods on six challenging RGB-D SOD datasets with much faster speed (450fps for the input size of 320 $\times$ 320) and fewer parameters (6.5M). The code is released at https://mmcheng.net/mobilesal.

preprint2022arXiv

Non-negative Sparse and Collaborative Representation for Pattern Classification

Sparse representation (SR) and collaborative representation (CR) have been successfully applied in many pattern classification tasks such as face recognition. In this paper, we propose a novel Non-negative Sparse and Collaborative Representation (NSCR) for pattern classification. The NSCR representation of each test sample is obtained by seeking a non-negative sparse and collaborative representation vector that represents the test sample as a linear combination of training samples. We observe that the non-negativity can make the SR and CR more discriminative and effective for pattern classification. Based on the proposed NSCR, we propose a NSCR based classifier for pattern classification. Extensive experiments on benchmark datasets demonstrate that the proposed NSCR based classifier outperforms the previous SR or CR based approach, as well as state-of-the-art deep approaches, on diverse challenging pattern classification tasks.

preprint2022arXiv

Person-job fit estimation from candidate profile and related recruitment history with co-attention neural networks

Existing online recruitment platforms depend on automatic ways of conducting the person-job fit, whose goal is matching appropriate job seekers with job positions. Intuitively, the previous successful recruitment records contain important information, which should be helpful for the current person-job fit. Existing studies on person-job fit, however, mainly focus on calculating the similarity between the candidate resumes and the job postings on the basis of their contents, without taking the recruiters' experience (i.e., historical successful recruitment records) into consideration. In this paper, we propose a novel neural network approach for person-job fit, which estimates person-job fit from candidate profile and related recruitment history with co-attention neural networks (named PJFCANN). Specifically, given a target resume-job post pair, PJFCANN generates local semantic representations through co-attention neural networks and global experience representations via graph neural networks. The final matching degree is calculated by combining these two representations. In this way, the historical successful recruitment records are introduced to enrich the features of resumes and job postings and strengthen the current matching process. Extensive experiments conducted on a large-scale recruitment dataset verify the effectiveness of PJFCANN compared with several state-of-the-art baselines. The codes are released at: https://github.com/CCIIPLab/PJFCANN.

preprint2022arXiv

Spinodal Enhancement of Light Nuclei Yield Ratio in Relativistic Heavy Ion Collisions

Using a relativistic transport model to describe the evolution of the quantum chromodynamic matter produced in Au+Au collisions at $\sqrt{s_{NN}}=3-200$ GeV, we study the effect of a first-order phase transition in the equation of state of this matter on the yield ratio $N_tN_p/ N_d^2$ ($tp/d^2$) of produced proton ($p$), deuteron ($d$), and triton ($t$). We find that the large density inhomogeneities generated by the spinodal instability during the first-order phase transition can survive the fast expansion of the subsequent hadronic matter and lead to an enhanced $tp/d^2$ in central collisions at $\sqrt{s_{NN}}=3-5$ GeV as seen in the experiments by the STAR Collaboration and the E864 Collaboration. However, this enhancement subsides with increasing collision centrality, and the resulting almost flat centrality dependence of $tp/d^2$ at $\sqrt{s_{NN}}=3$ GeV can also be used as a signal for the first-order phase transition.

preprint2022arXiv

swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor

The flourish of deep learning frameworks and hardware platforms has been demanding an efficient compiler that can shield the diversity in both software and hardware in order to provide application portability. Among the existing deep learning compilers, TVM is well known for its efficiency in code generation and optimization across diverse hardware devices. In the meanwhile, the Sunway many-core processor renders itself as a competitive candidate for its attractive computational power in both scientific computing and deep learning workloads. This paper combines the trends in these two directions. Specifically, we propose swTVM that extends the original TVM to support ahead-of-time compilation for architecture requiring cross-compilation such as Sunway. In addition, we leverage the architecture features during the compilation such as core group for massive parallelism, DMA for high bandwidth memory transfer and local device memory for data locality, in order to generate efficient codes for deep learning workloads on Sunway. The experiment results show that the codes generated by swTVM achieves 1.79x on average compared to the state-of-the-art deep learning framework on Sunway, across six representative benchmarks. This work is the first attempt from the compiler perspective to bridge the gap of deep learning and Sunway processor particularly with productivity and efficiency in mind. We believe this work will encourage more people to embrace the power of deep learning and Sunway many-core processor.

preprint2022arXiv

The Security War in File Systems: An Empirical Study from A Vulnerability-Centric Perspective

This paper presents a systematic study on the security of modern file systems, following a vulnerability-centric perspective. Specifically, we collected 377 file system vulnerabilities committed to the CVE database in the past 20 years. We characterize them from four dimensions that include why the vulnerabilities appear, how the vulnerabilities can be exploited, what consequences can arise, and how the vulnerabilities are fixed. This way, we build a deep understanding of the attack surfaces faced by file systems, the threats imposed by the attack surfaces, and the good and bad practices in mitigating the attacks in file systems. We envision that our study will bring insights towards the future development of file systems, the enhancement of file system security, and the relevant vulnerability mitigating solutions.

preprint2022arXiv

Transport Model Comparison Studies of Intermediate-Energy Heavy-Ion Collisions

Transport models are the main method to obtain physics information from low to relativistic-energy heavy-ion collisions. The Transport Model Evaluation Project (TMEP) has been pursued to test the robustness of transport model predictions in reaching consistent conclusions from the same type of physical model. Calculations under controlled conditions of physical input and set-up were performed with various participating codes. These included both calculations of nuclear matter in a box with periodic boundary conditions, and more realistic calculations of heavy-ion collisions. In this intermediate review, we summarize and discuss the present status of the project. We also provide condensed descriptions of the 26 participating codes, which contributed to some part of the project. These include the major codes in use today. We review the main results of the studies completed so far. They show, that in box calculations the differences between the codes can be well understood and a convergence of the results can be reached. These studies also highlight the systematic differences between the two families of transport codes, known as BUU and QMD type codes. However, when the codes were compared in full heavy-ion collisions using different physical models, as recently for pion production, they still yielded substantially different results. This calls for further comparisons of heavy-ion collisions with controlled models and of box comparisons of important ingredients, like momentum-dependent fields, which are currently underway. We often indicate improved strategies in performing transport simulations and thus provide guidance to code developers. Results of transport simulations of heavy-ion collisions from a given code will have more significance if the code can be validated against benchmark calculations such as the ones summarized in this review.

preprint2022arXiv

Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals

Most dialog systems posit that users have figured out clear and specific goals before starting an interaction. For example, users have determined the departure, the destination, and the travel time for booking a flight. However, in many scenarios, limited by experience and knowledge, users may know what they need, but still struggle to figure out clear and specific goals by determining all the necessary slots. In this paper, we identify this challenge and make a step forward by collecting a new human-to-human mixed-type dialog corpus. It contains 5k dialog sessions and 168k utterances for 4 dialog types and 5 domains. Within each session, an agent first provides user-goal-related knowledge to help figure out clear and specific goals, and then help achieve them. Furthermore, we propose a mixed-type dialog model with a novel Prompt-based continual learning mechanism. Specifically, the mechanism enables the model to continually strengthen its ability on any specific type by utilizing existing dialog corpora effectively.

preprint2022arXiv

WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

Lung cancer is the leading cause of cancer death worldwide, and adenocarcinoma (LUAD) is the most common subtype. Exploiting the potential value of the histopathology images can promote precision medicine in oncology. Tissue segmentation is the basic upstream task of histopathology image analysis. Existing deep learning models have achieved superior segmentation performance but require sufficient pixel-level annotations, which is time-consuming and expensive. To enrich the label resources of LUAD and to alleviate the annotation efforts, we organize this challenge WSSS4LUAD to call for the outstanding weakly-supervised semantic segmentation (WSSS) techniques for histopathology images of LUAD. Participants have to design the algorithm to segment tumor epithelial, tumor-associated stroma and normal tissue with only patch-level labels. This challenge includes 10,091 patch-level annotations (the training set) and over 130 million labeled pixels (the validation and test sets), from 87 WSIs (67 from GDPH, 20 from TCGA). All the labels were generated by a pathologist-in-the-loop pipeline with the help of AI models and checked by the label review board. Among 532 registrations, 28 teams submitted the results in the test phase with over 1,000 submissions. Finally, the first place team achieved mIoU of 0.8413 (tumor: 0.8389, stroma: 0.7931, normal: 0.8919). According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS. Cutmix data augmentation has been widely adopted to generate more reliable samples. With the success of this challenge, we believe that WSSS approaches with patch-level annotations can be a complement to the traditional pixel annotations while reducing the annotation efforts. The entire dataset has been released to encourage more researches on computational pathology in LUAD and more novel WSSS techniques.

preprint2021arXiv

On the ideal shortest vector problem over random rational primes

Any ideal in a number field can be factored into a product of prime ideals. In this paper we study the prime ideal shortest vector problem (SVP) in the ring $ \Z[x]/(x^{2^n} + 1) $, a popular choice in the design of ideal lattice based cryptosystems. We show that a majority of rational primes lie under prime ideals admitting a polynomial time algorithm for SVP. Although the shortest vector problem of ideal lattices underpins the security of Ring-LWE cryptosystem, this work does not break Ring-LWE, since the security reduction is from the worst case ideal SVP to the average case Ring-LWE, and it is one-way.

preprint2021arXiv

Privacy-preserving Channel Estimation in Cell-free Hybrid Massive MIMO Systems

We consider a cell-free hybrid massive multiple-input multiple-output (MIMO) system with $K$ users and $M$ access points (APs), each with $N_a$ antennas and $N_r< N_a$ radio frequency (RF) chains. When $K\ll M{N_a}$, efficient uplink channel estimation and data detection with reduced number of pilots can be performed based on low-rank matrix completion. However, such a scheme requires the central processing unit (CPU) to collect received signals from all APs, which may enable the CPU to infer the private information of user locations. We therefore develop and analyze privacy-preserving channel estimation schemes under the framework of differential privacy (DP). As the key ingredient of the channel estimator, two joint differentially private noisy matrix completion algorithms based respectively on Frank-Wolfe iteration and singular value decomposition are presented. We provide an analysis on the tradeoff between the privacy and the channel estimation error. In particular, we show that the estimation error can be mitigated while maintaining the same privacy level by increasing the payload size with fixed pilot size; and the scaling laws of both the privacy-induced and privacy-independent error components in terms of payload size are characterized. Simulation results are provided to further demonstrate the tradeoff between privacy and channel estimation performance.

preprint2020arXiv

AGNs are not that cool: revisiting the intrinsic AGN far-infrared spectral energy distribution

We investigate the intrinsic spectral energy distribution (SED) of active galactic nuclei (AGNs) at infrared (IR) bands with 42 $z < 0.5$ optically luminous Palomar Green survey quasars through SED decomposition. We decompose the SEDs of the 42 quasars by combining an AGN IR template library Siebenmorgen2015 that covers a wide range of the AGN parameter space with three commonly used galaxy template libraries. We determine the median AGN SED from the best-fitting results. The far-IR (FIR) contribution of our median AGN SED is significantly smaller than that of Symeonidis et al. 2016, but roughly consistent with that of Lyu et al. 2017. The AGN IR SED becomes cooler with increasing bolometric luminosity, which might be due to that more luminous AGNs might have stronger radiative feedback to change torus structures and/or their tori might have higher metallicities. Our conclusions do not depend on the choice of galaxy template libraries. However, since the predicted polycyclic aromatic hydrocarbon (PAH) emission line flux is galaxy template-dependent, cautions should be taken on deriving galaxy FIR contribution from PAH fluxes.

preprint2020arXiv

An Empirical Study on Benchmarks of Artificial Software Vulnerabilities

Recently, various techniques (e.g., fuzzing) have been developed for vulnerability detection. To evaluate those techniques, the community has been developing benchmarks of artificial vulnerabilities because of a shortage of ground-truth. However, people have concerns that such vulnerabilities cannot represent reality and may lead to unreliable and misleading results. Unfortunately, there lacks research on handling such concerns. In this work, to understand how close these benchmarks mirror reality, we perform an empirical study on three artificial vulnerability benchmarks - LAVA-M, Rode0day and CGC (2669 bugs) and various real-world memory-corruption vulnerabilities (80 CVEs). Furthermore, we propose a model to depict the properties of memory-corruption vulnerabilities. Following this model, we conduct intensive experiments and data analyses. Our analytic results reveal that while artificial benchmarks attempt to approach the real world, they still significantly differ from reality. Based on the findings, we propose a set of strategies to improve the quality of artificial benchmarks.

preprint2020arXiv

Auto-captions on GIF: A Large-scale Video-sentence Dataset for Vision-language Pre-training

In this work, we present Auto-captions on GIF, which is a new large-scale pre-training dataset for generic video understanding. All video-sentence pairs are created by automatically extracting and filtering video caption annotations from billions of web pages. Auto-captions on GIF dataset can be utilized to pre-train the generic feature representation or encoder-decoder structure for video captioning, and other downstream tasks (e.g., sentence localization in videos, video question answering, etc.) as well. We present a detailed analysis of Auto-captions on GIF dataset in comparison to existing video-sentence datasets. We also provide an evaluation of a Transformer-based encoder-decoder structure for vision-language pre-training, which is further adapted to video captioning downstream task and yields the compelling generalizability on MSR-VTT. The dataset is available at \url{http://www.auto-video-captions.top/2020/dataset}.

preprint2020arXiv

Bilateral Attention Network for RGB-D Salient Object Detection

Most existing RGB-D salient object detection (SOD) methods focus on the foreground region when utilizing the depth images. However, the background also provides important information in traditional SOD methods for promising performance. To better explore salient information in both foreground and background regions, this paper proposes a Bilateral Attention Network (BiANet) for the RGB-D SOD task. Specifically, we introduce a Bilateral Attention Module (BAM) with a complementary attention mechanism: foreground-first (FF) attention and background-first (BF) attention. The FF attention focuses on the foreground region with a gradual refinement style, while the BF one recovers potentially useful salient information in the background region. Benefitted from the proposed BAM module, our BiANet can capture more meaningful foreground and background cues, and shift more attention to refining the uncertain details between foreground and background regions. Additionally, we extend our BAM by leveraging the multi-scale techniques for better SOD performance. Extensive experiments on six benchmark datasets demonstrate that our BiANet outperforms other state-of-the-art RGB-D SOD methods in terms of objective metrics and subjective visual comparison. Our BiANet can run up to 80fps on $224\times224$ RGB-D images, with an NVIDIA GeForce RTX 2080Ti GPU. Comprehensive ablation studies also validate our contributions.

preprint2020arXiv

Centrality fluctuations and decorrelations in heavy-ion collisions

The centrality or the number of initial-state sources $V$ of the system produced in heavy ion collision is a concept that is not uniquely defined and subject to significant theoretical and experimental uncertainties. We argue that a more robust connection between the initial-state sources with final-state multiplicity could be established from the event-by-event multiplicity correlation between two subevents separated in pseudorapidity, $N_a$ vs $N_b$. This correlation is sensitive to two main types of centrality fluctuations (CF): 1) particle production for each source $p(n)$ which smears the relation between $V$ and $N_a$ used for experimental centrality, and 2) decorrelations between the sources in the two subevents $V_b$ and $V_a$. The CF is analyzed in terms of cumulants of $V_b$ and $N_b$ as a function of $N_a$, i.e. experimental centrality is defined with $N_a$. We found that the mean values $\langle V_b\rangle_{N_a}$ and $\langle N_b\rangle_{N_a}$ increase linearly with $N_a$ in mid-central collisions, but flatten out in ultra-central collisions. Such non-linear behavior is sensitive to the centrality resolution of $N_a$. In the presence of centrality decorrelations, the scaled variances $\langle(δV_b)^2\rangle/\langle V_b\rangle$ and $\langle(δN_b)^2\rangle/\langle N_b\rangle$ are found to decrease linearly with $N_a$ in mid-central collisions, while the $p(n)$ leads to another sharp decrease in the ultra-central region. The higher-order cumulants of $V_b$ and $N_b$ show interesting but rather complex behaviors which deserve further studies. Our results suggest that one can use the cumulants of the two-dimensional multiplicity correlation, especially the mean and variance, to constrain the particle production mechanism as well as the longitudinal fluctuations of the initial-state sources.

preprint2020arXiv

Conditional Variational Image Deraining

Image deraining is an important yet challenging image processing task. Though deterministic image deraining methods are developed with encouraging performance, they are infeasible to learn flexible representations for probabilistic inference and diverse predictions. Besides, rain intensity varies both in spatial locations and across color channels, making this task more difficult. In this paper, we propose a Conditional Variational Image Deraining (CVID) network for better deraining performance, leveraging the exclusive generative ability of Conditional Variational Auto-Encoder (CVAE) on providing diverse predictions for the rainy image. To perform spatially adaptive deraining, we propose a spatial density estimation (SDE) module to estimate a rain density map for each image. Since rain density varies across different color channels, we also propose a channel-wise (CW) deraining scheme. Experiments on synthesized and real-world datasets show that the proposed CVID network achieves much better performance than previous deterministic methods on image deraining. Extensive ablation studies validate the effectiveness of the proposed SDE module and CW scheme in our CVID network. The code is available at \url{https://github.com/Yingjun-Du/VID}.

preprint2020arXiv

Discovering Dialog Structure Graph for Open-Domain Dialog Generation

Learning interpretable dialog structure from human-human dialogs yields basic insights into the structure of conversation, and also provides background knowledge to facilitate dialog generation. In this paper, we conduct unsupervised discovery of dialog structure from chitchat corpora, and then leverage it to facilitate dialog generation in downstream systems. To this end, we present a Discrete Variational Auto-Encoder with Graph Neural Network (DVAE-GNN), to discover a unified human-readable dialog structure. The structure is a two-layer directed graph that contains session-level semantics in the upper-layer vertices, utterance-level semantics in the lower-layer vertices, and edges among these semantic vertices. In particular, we integrate GNN into DVAE to fine-tune utterance-level semantics for more effective recognition of session-level semantic vertex. Furthermore, to alleviate the difficulty of discovering a large number of utterance-level semantics, we design a coupling mechanism that binds each utterance-level semantic vertex with a distinct phrase to provide prior semantics. Experimental results on two benchmark corpora confirm that DVAE-GNN can discover meaningful dialog structure, and the use of dialog structure graph as background knowledge can facilitate a graph grounded conversational system to conduct coherent multi-turn dialog generation.

preprint2020arXiv

Isospin effect on baryon and charge fluctuations from the pNJL model

We have studied the possible isospin corrections on the skewness and kurtosis of net-baryon and net-charge fluctuations in the isospin asymmetric matter formed in relativistic heavy-ion collisions at RHIC-BES energies, based on a 3-flavor polyakov-looped Nambu-Jona-Lasinio model. With typical scalar-isovector and vector-isovector couplings leading to the splitting of $u$ and $d$ quark chiral phase transition boundaries and critical points, we have observed dramatic isospin effects on the susceptibilities, especially those of net-charge fluctuations. Reliable experimental measurements at even lower collision energies are encouraged to confirm the observed isospin effects.

preprint2020arXiv

Joint Inference on Truth/Rumor and Their Sources in Social Networks

In the contemporary era of information explosion, we are often faced with the mixture of massive \emph{truth} (true information) and \emph{rumor} (false information) flooded over social networks. Under such circumstances, it is very essential to infer whether each claim (e.g., news, messages) is a truth or a rumor, and identify their \emph{sources}, i.e., the users who initially spread those claims. While most prior arts have been dedicated to the two tasks respectively, this paper aims to offer the joint inference on truth/rumor and their sources. Our insight is that a joint inference can enhance the mutual performance on both sides. To this end, we propose a framework named SourceCR, which alternates between two modules, i.e., \emph{credibility-reliability training} for truth/rumor inference and \emph{division-querying} for source detection, in an iterative manner. To elaborate, the former module performs a simultaneous estimation of claim credibility and user reliability by virtue of an Expectation Maximization algorithm, which takes the source reliability outputted from the latter module as the initial input. Meanwhile, the latter module divides the network into two different subnetworks labeled via the claim credibility, and in each subnetwork launches source detection by applying querying of theoretical budget guarantee to the users selected via the estimated reliability from the former module. The proposed SourceCR is provably convergent, and algorithmic implementable with reasonable computational complexity. We empirically validate the effectiveness of the proposed framework in both synthetic and real datasets, where the joint inference leads to an up to 35\% accuracy of credibility gain and 29\% source detection rate gain compared with the separate counterparts.

preprint2020arXiv

Learning to Learn Kernels with Variational Random Features

In this work, we introduce kernels with random Fourier features in the meta-learning framework to leverage their strong few-shot learning ability. We propose meta variational random features (MetaVRF) to learn adaptive kernels for the base-learner, which is developed in a latent variable model by treating the random feature basis as the latent variable. We formulate the optimization of MetaVRF as a variational inference problem by deriving an evidence lower bound under the meta-learning framework. To incorporate shared knowledge from related tasks, we propose a context inference of the posterior, which is established by an LSTM architecture. The LSTM-based inference network can effectively integrate the context information of previous tasks with task-specific information, generating informative and adaptive features. The learned MetaVRF can produce kernels of high representational power with a relatively low spectral sampling rate and also enables fast adaptation to new tasks. Experimental results on a variety of few-shot regression and classification tasks demonstrate that MetaVRF delivers much better, or at least competitive, performance compared to existing meta-learning alternatives.

preprint2020arXiv

Learning to Learn with Variational Information Bottleneck for Domain Generalization

Domain generalization models learn to generalize to previously unseen domains, but suffer from prediction uncertainty and domain shift. In this paper, we address both problems. We introduce a probabilistic meta-learning model for domain generalization, in which classifier parameters shared across domains are modeled as distributions. This enables better handling of prediction uncertainty on unseen domains. To deal with domain shift, we learn domain-invariant representations by the proposed principle of meta variational information bottleneck, we call MetaVIB. MetaVIB is derived from novel variational bounds of mutual information, by leveraging the meta-learning setting of domain generalization. Through episodic training, MetaVIB learns to gradually narrow domain gaps to establish domain-invariant representations, while simultaneously maximizing prediction accuracy. We conduct experiments on three benchmarks for cross-domain visual recognition. Comprehensive ablation studies validate the benefits of MetaVIB for domain generalization. The comparison results demonstrate our method outperforms previous approaches consistently.

preprint2020arXiv

NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising

Non-local self similarity (NSS) is a powerful prior of natural images for image denoising. Most of existing denoising methods employ similar patches, which is a patch-level NSS prior. In this paper, we take one step forward by introducing a pixel-level NSS prior, i.e., searching similar pixels across a non-local region. This is motivated by the fact that finding closely similar pixels is more feasible than similar patches in natural images, which can be used to enhance image denoising performance. With the introduced pixel-level NSS prior, we propose an accurate noise level estimation method, and then develop a blind image denoising method based on the lifting Haar transform and Wiener filtering techniques. Experiments on benchmark datasets demonstrate that, the proposed method achieves much better performance than previous non-deep methods, and is still competitive with existing state-of-the-art deep learning based methods on real-world image denoising. The code is publicly available at https://github.com/njusthyk1972/NLH.

preprint2020arXiv

Nucleus giant resonances from an improved isospin-dependent Boltzmann-Uehling-Uhlenbeck transport approach

We have studied the isoscalar giant quadruple resonance (ISGQR) and the isovector giant dipole resonance (IVGDR) in $^{208}$Pb based on an improved isospin-dependent Boltzmann-Uehling-Uhlenbeck transport approach using an improved isospin- and momentum-dependent interaction. With the isoscalar nucleon effective mass and the nucleon-nucleon cross section which reproduces respectively the excitation energy and the width of the ISGQR strength function, the slope parameter of the symmetry energy and the neutron-proton effective mass splitting are constrained respectively within $36<L<62$ MeV and $0.08δ<(m_{n0}^*-m_{p0}^*)/m<0.42δ$, by comparing the resulting centroid energy of the IVGDR and the electric dipole polarizability with the experimental data. It is found that nucleon-nucleon collisions have considerable effects on the resulting electric dipole polarizability, which needs to be measured more accurately in order to pin down isovector nuclear interactions.

preprint2020arXiv

Pre-training for Video Captioning Challenge 2020 Summary

The Pre-training for Video Captioning Challenge 2020 Summary: results and challenge participants' technical reports.

preprint2020arXiv

Privacy for All: Demystify Vulnerability Disparity of Differential Privacy against Membership Inference Attack

Machine learning algorithms, when applied to sensitive data, pose a potential threat to privacy. A growing body of prior work has demonstrated that membership inference attack (MIA) can disclose specific private information in the training data to an attacker. Meanwhile, the algorithmic fairness of machine learning has increasingly caught attention from both academia and industry. Algorithmic fairness ensures that the machine learning models do not discriminate a particular demographic group of individuals (e.g., black and female people). Given that MIA is indeed a learning model, it raises a serious concern if MIA ``fairly'' treats all groups of individuals equally. In other words, whether a particular group is more vulnerable against MIA than the other groups. This paper examines the algorithmic fairness issue in the context of MIA and its defenses. First, for fairness evaluation, it formalizes the notation of vulnerability disparity (VD) to quantify the difference of MIA treatment on different demographic groups. Second, it evaluates VD on four real-world datasets, and shows that VD indeed exists in these datasets. Third, it examines the impacts of differential privacy, as a defense mechanism of MIA, on VD. The results show that although DP brings significant change on VD, it cannot eliminate VD completely. Therefore, fourth, it designs a new mitigation algorithm named FAIRPICK to reduce VD. An extensive set of experimental results demonstrate that FAIRPICK can effectively reduce VD for both with and without the DP deployment.

preprint2020arXiv

Properties of strange quark stars with isovector interactions

We study the properties of strange quark stars by employing a 3-flavor Nambu-Jona-Lasinio model with both scalar-isovector and vector-isovector interactions. Using the constraint on the vector-isoscalar interaction strength obtained from the elliptic flow splitting between particles and their antiparticles in relativistic heavy-ion collisions, we investigate the dependence of the properties of strange quark stars on the vector-isovector and the scalar-isovector interactions, and compare the results with the state-of-art astrophysical constraints on the compact star radius and mass as well as its tidal deformability from the GW170817 event. Results from our study reinforce the prospect of using both heavy-ion collisions and astrophysical observations to provide constraints on the isovector coupling strength in quark matter and thus the quark matter equation of state as well as the QCD phase structure at finite isospin chemical potentials.

preprint2020arXiv

QPS-r: A Cost-Effective Crossbar Scheduling Algorithm and Its Stability and Delay Analysis

In an input-queued switch, a crossbar schedule, or a matching between the input ports and the output ports needs to be computed in each switching cycle, or time slot. Designing switching algorithms with very low computational complexity, that lead to high throughput and small delay is a challenging problem. There appears to be a fundamental tradeoff between the computational complexity of the switching algorithm and the resultants throughput and delay. Parallel maximal matching algorithms (adapted for switching) appear to have stricken a sweet spot in this tradeoff, and prior work has shown the following performance guarantees. Using maximal matchings in every time slot results in at least 50% switch throughput and order-optimal (i.e., independent of the switch size N) average delay bounds for various traffic arrival processes. On the other hand, their computational complexity can be as low as $O(log^2N)$ per port/processor, which is much lower than those of the algorithms such as maximum weighted matching which ensures better throughput performance. In this work, we propose QPS-r, a parallel iterative switching algorithm that has the lowest possible computational complexity: O(1) per port. Using Lyapunov stability analysis, we show that the throughput and delay performance is identical to that of maximal matching algorithm. Although QPS-r builds upon an existing technique called Queue-Proportional Sampling (QPS), in this paper, we provide analytical guarantees on its throughput and delay under i.i.d. traffic as well as a Markovian traffic model which can model many realistic traffic patterns. We also demonstrate that QPS-3 (running 3 iterations) has comparable empirical throughput and delay performances as iSLIP (running $log_2 N$ iterations), a refined and optimized representative maximal matching algorithm adapted for switching.

preprint2020arXiv

SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval

In learning-to-rank for information retrieval, a ranking model is automatically learned from the data and then utilized to rank the sets of retrieved documents. Therefore, an ideal ranking model would be a mapping from a document set to a permutation on the set, and should satisfy two critical requirements: (1)~it should have the ability to model cross-document interactions so as to capture local context information in a query; (2)~it should be permutation-invariant, which means that any permutation of the inputted documents would not change the output ranking. Previous studies on learning-to-rank either design uni-variate scoring functions that score each document separately, and thus failed to model the cross-document interactions; or construct multivariate scoring functions that score documents sequentially, which inevitably sacrifice the permutation invariance requirement. In this paper, we propose a neural learning-to-rank model called SetRank which directly learns a permutation-invariant ranking model defined on document sets of any size. SetRank employs a stack of (induced) multi-head self attention blocks as its key component for learning the embeddings for all of the retrieved documents jointly. The self-attention mechanism not only helps SetRank to capture the local context information from cross-document interactions, but also to learn permutation-equivariant representations for the inputted documents, which therefore achieving a permutation-invariant ranking model. Experimental results on three large scale benchmarks showed that the SetRank significantly outperformed the baselines include the traditional learning-to-rank models and state-of-the-art Neural IR models.

preprint2020arXiv

SoK: All You Ever Wanted to Know About x86/x64 Binary Disassembly But Were Afraid to Ask

Disassembly of binary code is hard, but necessary for improving the security of binary software. Over the past few decades, research in binary disassembly has produced many tools and frameworks, which have been made available to researchers and security professionals. These tools employ a variety of strategies that grant them different characteristics. The lack of systematization, however, impedes new research in the area and makes selecting the right tool hard, as we do not understand the strengths and weaknesses of existing tools. In this paper, we systematize binary disassembly through the study of nine popular, open-source tools. We couple the manual examination of their code bases with the most comprehensive experimental evaluation (thus far) using 3,788 binaries. Our study yields a comprehensive description and organization of strategies for disassembly, classifying them as either algorithm or else heuristic. Meanwhile, we measure and report the impact of individual algorithms on the results of each tool. We find that while principled algorithms are used by all tools, they still heavily rely on heuristics to increase code coverage. Depending on the heuristics used, different coverage-vs-correctness trade-offs come in play, leading to tools with different strengths and weaknesses. We envision that these findings will help users pick the right tool and assist researchers in improving binary disassembly.

preprint2020arXiv

Space- and Computationally-Efficient Set Reconciliation via Parity Bitmap Sketch (PBS)

Set reconciliation is a fundamental algorithmic problem that arises in many networking, system, and database applications. In this problem, two large sets A and B of objects (bitcoins, files, records, etc.) are stored respectively at two different network-connected hosts, which we name Alice and Bob respectively. Alice and Bob communicate with each other to learn $AΔB$, the difference between A and B, and as a result the reconciled set $A\bigcup B$. Current set reconciliation schemes are based on either Invertible Bloom Filters (IBF) or Error-Correction Codes (ECC). The former has a low computational complexity of O(d), where d is the cardinality of $AΔB$, but has a high communication overhead that is several times larger than the theoretical minimum. The latter has a low communication overhead close to the theoretical minimum, but has a much higher computational complexity of $O(d^2)$. In this work, we propose Parity Bitmap Sketch (PBS), an ECC- based set reconciliation scheme that gets the better of both worlds: PBS has both a low computational complexity of O(d) just like IBF-based solutions and a low communication overhead of roughly twice the theoretical minimum. A separate contribution of this work is a novel rigorous analytical framework that can be used for the precise calculation of various performance metrics and for the near-optimal parameter tuning of PBS.

preprint2020arXiv

STAR: A Structure and Texture Aware Retinex Model

Retinex theory is developed mainly to decompose an image into the illumination and reflectance components by analyzing local image derivatives. In this theory, larger derivatives are attributed to the changes in reflectance, while smaller derivatives are emerged in the smooth illumination. In this paper, we utilize exponentiated local derivatives (with an exponent γ) of an observed image to generate its structure map and texture map. The structure map is produced by been amplified with γ > 1, while the texture map is generated by been shrank with γ < 1. To this end, we design exponential filters for the local derivatives, and present their capability on extracting accurate structure and texture maps, influenced by the choices of exponents γ. The extracted structure and texture maps are employed to regularize the illumination and reflectance components in Retinex decomposition. A novel Structure and Texture Aware Retinex (STAR) model is further proposed for illumination and reflectance decomposition of a single image. We solve the STAR model by an alternating optimization algorithm. Each sub-problem is transformed into a vectorized least squares regression, with closed-form solutions. Comprehensive experiments on commonly tested datasets demonstrate that, the proposed STAR model produce better quantitative and qualitative performance than previous competing methods, on illumination and reflectance decomposition, low-light image enhancement, and color correction. The code is publicly available at https://github.com/csjunxu/STAR.

preprint2020arXiv

Univariate ReLU neural network and its application in nonlinear system identification

ReLU (rectified linear units) neural network has received significant attention since its emergence. In this paper, a univariate ReLU (UReLU) neural network is proposed to both modelling the nonlinear dynamic system and revealing insights about the system. Specifically, the neural network consists of neurons with linear and UReLU activation functions, and the UReLU functions are defined as the ReLU functions respect to each dimension. The UReLU neural network is a single hidden layer neural network, and the structure is relatively simple. The initialization of the neural network employs the decoupling method, which provides a good initialization and some insight into the nonlinear system. Compared with normal ReLU neural network, the number of parameters of UReLU network is less, but it still provide a good approximation of the nonlinear dynamic system. The performance of the UReLU neural network is shown through a Hysteretic benchmark system: the Bouc-Wen system. Simulation results verify the effectiveness of the proposed method.

preprint2020arXiv

Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient

Deep Q-learning algorithms often suffer from poor gradient estimations with an excessive variance, resulting in unstable training and poor sampling efficiency. Stochastic variance-reduced gradient methods such as SVRG have been applied to reduce the estimation variance (Zhao et al. 2019). However, due to the online instance generation nature of reinforcement learning, directly applying SVRG to deep Q-learning is facing the problem of the inaccurate estimation of the anchor points, which dramatically limits the potentials of SVRG. To address this issue and inspired by the recursive gradient variance reduction algorithm SARAH (Nguyen et al. 2017), this paper proposes to introduce the recursive framework for updating the stochastic gradient estimates in deep Q-learning, achieving a novel algorithm called SRG-DQN. Unlike the SVRG-based algorithms, SRG-DQN designs a recursive update of the stochastic gradient estimate. The parameter update is along an accumulated direction using the past stochastic gradient information, and therefore can get rid of the estimation of the full gradients as the anchors. Additionally, SRG-DQN involves the Adam process for further accelerating the training process. Theoretical analysis and the experimental results on well-known reinforcement learning tasks demonstrate the efficiency and effectiveness of the proposed SRG-DQN algorithm.

preprint2019arXiv

Compact fiber optical interferometer technique to measure picometer displacements in biological piezoelectric materials

A simple and robust fiber optical interferometer was developed to non-invasively study the weak piezoelectric effect from thin samples. A biological sample from inter-molt dactyl clubs obtained from the mantis shrimp was used as the test sample. The non-contact technique can measure displacements better than 0.5 picometer for samples subjected to large electric fields. The approach utilizes the phase dependent detection of an oscillating cavity at different frequencies from 0.5 kHz to 2.0 kHz. The piezoelectric constant of the biological samples was calculated from the optical interference fringes and determined to be in the range of 0.3-0.5 pm/V. The noise of 20 fm/Hz^0.5 in the setup is primarily due to thermally associated strains from current flow to the sample electrodes during the measurement.

preprint2019arXiv

Investigation of HIV-1 Gag binding with RNAs and Lipids using Atomic Force Microscopy

Atomic Force Microscopy was utilized to study the morphology of Gag, ΨRNA, and their binding complexes with lipids in a solution environment with 0.1Å vertical and 1nm lateral resolution. TARpolyA RNA was used as a RNA control. The lipid used was phospha-tidylinositol-(4,5)-bisphosphate (PI(4,5)P2). The morphology of specific complexes Gag-ΨRNA, Gag-TARpolyA RNA, Gag-PI(4,5)P2 and PI(4,5)P2-ΨRNA-Gag were studied. They were imaged on either positively or negatively charged mica substrates depending on the net charges carried. Gag and its complexes consist of monomers, dimers and tetramers, which was confirmed by gel electrophoresis. The addition of specific ΨRNA to Gag is found to increase Gag multimerization. Non-specific TARpolyA RNA was found not to lead to an increase in Gag multimerization. The addition PI(4,5)P2 to Gag increases Gag multimerization, but to a lesser extent than ΨRNA. When both ΨRNA and PI(4,5)P2 are present Gag undergoes comformational changes and an even higher degree of multimerization.

preprint2019arXiv

RANet: Ranking Attention Network for Fast Video Object Segmentation

Despite online learning (OL) techniques have boosted the performance of semi-supervised video object segmentation (VOS) methods, the huge time costs of OL greatly restrict their practicality. Matching based and propagation based methods run at a faster speed by avoiding OL techniques. However, they are limited by sub-optimal accuracy, due to mismatching and drifting problems. In this paper, we develop a real-time yet very accurate Ranking Attention Network (RANet) for VOS. Specifically, to integrate the insights of matching based and propagation based methods, we employ an encoder-decoder framework to learn pixel-level similarity and segmentation in an end-to-end manner. To better utilize the similarity maps, we propose a novel ranking attention module, which automatically ranks and selects these maps for fine-grained VOS performance. Experiments on DAVIS-16 and DAVIS-17 datasets show that our RANet achieves the best speed-accuracy trade-off, e.g., with 33 milliseconds per frame and J&F=85.5% on DAVIS-16. With OL, our RANet reaches J&F=87.1% on DAVIS-16, exceeding state-of-the-art VOS methods. The code can be found at https://github.com/Storife/RANet.

preprint2019arXiv

Spectro-polarimetric Observations at the NVST: I. Instrumental Polarization Calibration and Primary Measurements

This paper is devoted to the primary spectro-polarimetric observation performed at the New Vacuum Solar Telescope of China since 2017, and our aim is to precisely evaluate the real polarimetric accuracy and sensitivity of this polarimetry by using full Stokes spectro-polarimetric observations of the photospheric line Fe I 532.4 nm. In the work, we briefly describe the salient characteristic of the NVST as a polarimeter in technology and then characterize its instrumental polarization based on the operation in 2017 and 2019. It is verified that the calibration method making use of the instrumental polarization calibration unit (ICU) is stable and credible. The calibration accuracy can reach up to 3$\times 10^{-3}$ . Based on the scientific observation of the NOAA 12645 on April 5th, 2017, we estimate that the residual cross-talk from Stokes $I$ to Stokes $Q$, $U$ and $V$, after the instrumental polarization calibration, is about 4$\times10^{-3}$ on average, which is consistent with the calibration accuracy and close to the photon noise. The polarimetric sensitivity (i.e., the detection limit) for polarized light is of the order of $10^{-3}$ with an integration time over 20 seconds. Slow modulation rate is indeed an issue for the present system. The present NVST polarimeter is expected to be integrated with an high-order adaptive optics system and a field scanner to realize 2D magnetic field vector measurements in the following instrumentation update.

preprint2016arXiv

A Study of MatchPyramid Models on Ad-hoc Retrieval

Deep neural networks have been successfully applied to many text matching tasks, such as paraphrase identification, question answering, and machine translation. Although ad-hoc retrieval can also be formalized as a text matching task, few deep models have been tested on it. In this paper, we study a state-of-the-art deep matching model, namely MatchPyramid, on the ad-hoc retrieval task. The MatchPyramid model employs a convolutional neural network over the interactions between query and document to produce the matching score. We conducted extensive experiments to study the impact of different pooling sizes, interaction functions and kernel sizes on the retrieval performance. Finally, we show that the MatchPyramid models can significantly outperform several recently introduced deep matching models on the retrieval task, but still cannot compete with the traditional retrieval models, such as BM25 and language models.

preprint2016arXiv

Equations of motion of test particles for solving the spin-dependent Boltzmann-Vlasov equation

A consistent derivation of the equations of motion (EOMs) of test particles for solving the spin-dependent Boltzmann-Vlasov equation is presented. The resulting EOMs in phase space are similar to the canonical equations in Hamiltonian dynamics, and the EOM of spin is the same as that in the Heisenburg picture of quantum mechanics. Considering further the quantum nature of spin and choosing the direction of total angular momentum in heavy-ion reactions as a reference of measuring nucleon spin, the EOMs of spin-up and spin-down nucleons are given separately. The key elements affecting the spin dynamics in heavy-ion collisions are identified. The resulting EOMs provide a solid foundation for using the test-particle approach in studying spin dynamics in heavy-ion collisions at intermediate energies. Future comparisons of model simulations with experimental data will help constrain the poorly known in-medium nucleon spin-orbit coupling relevant for understanding properties of rare isotopes and their astrophysical impacts.

preprint2016arXiv

Incorporating Semantic Knowledge into Latent Matching Model in Search

The relevance between a query and a document in search can be represented as matching degree between the two objects. Latent space models have been proven to be effective for the task, which are often trained with click-through data. One technical challenge with the approach is that it is hard to train a model for tail queries and tail documents for which there are not enough clicks. In this paper, we propose to address the challenge by learning a latent matching model, using not only click-through data but also semantic knowledge. The semantic knowledge can be categories of queries and documents as well as synonyms of words, manually or automatically created. Specifically, we incorporate semantic knowledge into the objective function by including regularization terms. We develop two methods to solve the learning task on the basis of coordinate descent and gradient descent respectively, which can be employed in different settings. Experimental results on two datasets from an app search engine demonstrate that our model can make effective use of semantic knowledge, and thus can significantly enhance the accuracies of latent matching models, particularly for tail queries.

preprint2016arXiv

Internet of Things Applications: Animal Monitoring with Unmanned Aerial Vehicle

In animal monitoring applications, both animal detection and their movement prediction are major tasks. While a variety of animal monitoring strategies exist, most of them rely on mounting devices. However, in real world, it is difficult to find these animals and install mounting devices. In this paper, we propose an animal monitoring application by utilizing wireless sensor networks (WSNs) and unmanned aerial vehicle (UAV). The objective of the application is to detect locations of endangered species in large-scale wildlife areas and monitor movement of animals without any attached devices. In this application, sensors deployed throughout the observation area are responsible for gathering animal information. The UAV flies above the observation area and collects the information from sensors. To achieve the information efficiently, we propose a path planning approach for the UAV based on a Markov decision process (MDP) model. The UAV receives a certain amount of reward from an area if some animals are detected at that location. We solve the MDP using Q-learning such that the UAV prefers going to those areas that animals are detected before. Meanwhile, the UAV explores other areas as well to cover the entire network and detects changes in the animal positions. We first define the mathematical model underlying the animal monitoring problem in terms of the value of information (VoI) and rewards. We propose a network model including clusters of sensor nodes and a single UAV that acts as a mobile sink and visits the clusters. Then, one MDP-based path planning approach is designed to maximize the VoI while reducing message delays. The effectiveness of the proposed approach is evaluated using two real-world movement datasets of zebras and leopard. Simulation results show that our approach outperforms greedy, random heuristics and the path planning based on the traveling salesman problem.

preprint2016arXiv

Isospin properties of quark matter from a 3-flavor NJL model

We have studied the properties of hot and dense quark matter based on the 3-flavor Nambu-Jona-Lasinio (NJL) model as well as its Polyakov-loop extension (pNJL) with scalar-isovector and vector-isovector couplings. Provided a considerable large isospin asymmetry or isospin chemical potential, isospin splittings of constituent mass, chiral phase transition boundary, and critical point for $u$ and $d$ quarks can be observed for positive isovector coupling constants but are suppressed for negative ones. The quark matter symmetry energy decreases with the increasing isovector coupling constant, and is mostly enhanced in the pNJL model than in the NJL model. A positive scalar-isovector coupling constant is more likely to lead to an unstable isospin asymmetric quark matter. The isovector coupling has been further found to affect particle fractions as well as the equation of state in hybrid stars. Possible effects on the isospin properties of quark matter have also been discussed if the strangeness sector is further broken among the flavor symmetry.

preprint2016arXiv

Kibble-Zurek dynamics in an array of coupled binary Bose condensates

Universal dynamics of spontaneous symmetry breaking is central to understanding the universal behavior of spontaneous defect formation in various system from the early universe, condensed-matter systems to ultracold atomic systems. We explore the universal real-time dynamics in an array of coupled binary atomic Bose-Einstein condensates in optical lattices, which undergo a spontaneous symmetry breaking from the symmetric Rabi oscillation to the broken-symmetry self-trapping. In addition to Goldstone modes, there exist gapped Higgs mode whose excitation gap vanishes at the critical point. In the slow passage through the critical point, we analytically find that the symmetry-breaking dynamics obeys the Kibble-Zurek mechanism. From the scalings of bifurcation delay and domain formation, we numerically extract two Kibble-Zurek exponents $b_{1}=ν/(1+νz)$ and $b_{2}=1/(1+νz)$, which give the static correlation-length critical exponent $ν$ and the dynamic critical exponent $z$. Our approach provides an efficient way to simultaneous determination of the critical exponents $ν$ and $z$ for a continuous phase transition.

preprint2016arXiv

Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN

Semantic matching, which aims to determine the matching degree between two texts, is a fundamental problem for many NLP applications. Recently, deep learning approach has been applied to this problem and significant improvements have been achieved. In this paper, we propose to view the generation of the global interaction between two texts as a recursive process: i.e. the interaction of two texts at each position is a composition of the interactions between their prefixes as well as the word level interaction at the current position. Based on this idea, we propose a novel deep architecture, namely Match-SRNN, to model the recursive matching structure. Firstly, a tensor is constructed to capture the word level interactions. Then a spatial RNN is applied to integrate the local interactions recursively, with importance determined by four types of gates. Finally, the matching score is calculated based on the global interaction. We show that, after degenerated to the exact matching scenario, Match-SRNN can approximate the dynamic programming process of longest common subsequence. Thus, there exists a clear interpretation for Match-SRNN. Our experiments on two semantic matching tasks showed the effectiveness of Match-SRNN, and its ability of visualizing the learned matching structure.

preprint2016arXiv

Semantic Regularities in Document Representations

Recent work exhibited that distributed word representations are good at capturing linguistic regularities in language. This allows vector-oriented reasoning based on simple linear algebra between words. Since many different methods have been proposed for learning document representations, it is natural to ask whether there is also linear structure in these learned representations to allow similar reasoning at document level. To answer this question, we design a new document analogy task for testing the semantic regularities in document representations, and conduct empirical evaluations over several state-of-the-art document representation models. The results reveal that neural embedding based document representations work better on this analogy task than conventional methods, and we provide some preliminary explanations over these observations.

preprint2016arXiv

Spin transport in intermediate-energy heavy-ion collisions as a probe of in-medium spin-orbit interactions

The spin up-down splitting of collective flows in intermediate-energy heavy-ion collisions as a result of the nuclear spin-orbit interaction is investigated within a spin- and isospin-dependent Boltzmann-Uehing-Uhlenbeck transport model SIBUU12. Using a Skyrme-type spin-orbit coupling quadratic in momentum, we found that the spin splittings of the directed flow and elliptic flow are largest in peripheral Au+Au collisions at beam energies of about 100-200 MeV/nucleon, and the effect is considerable even in smaller systems especially for nucleons with high transverse momenta. The collective flows of light clusters of different spin states are also investigated using an improved dynamical coalescence model with spin. Our study can be important in understanding the properties of in-medium nuclear spin-orbit interactions once the spin-dependent observables proposed in this work can be measured.

preprint2016arXiv

Text Matching as Image Recognition

Matching two texts is a fundamental problem in many natural language processing tasks. An effective way is to extract meaningful matching patterns from words, phrases, and sentences to produce the matching score. Inspired by the success of convolutional neural network in image recognition, where neurons can capture many complicated patterns based on the extracted elementary visual patterns such as oriented edges and corners, we propose to model text matching as the problem of image recognition. Firstly, a matching matrix whose entries represent the similarities between words is constructed and viewed as an image. Then a convolutional neural network is utilized to capture rich matching patterns in a layer-by-layer way. We show that by resembling the compositional hierarchies of patterns in image recognition, our model can successfully identify salient signals such as n-gram and n-term matchings. Experimental results demonstrate its superiority against the baselines.

preprint2016arXiv

Understanding transport simulations of heavy-ion collisions at 100 and 400 AMeV: Comparison of heavy ion transport codes under controlled conditions

Transport simulations are very valuable for extracting physics information from heavy-ion collision experiments. With the emergence of many different transport codes in recent years, it becomes important to estimate their robustness in extracting physics information from experiments. We report on the results of a transport code comparison project. 18 commonly used transport codes were included in this comparison: 9 Boltzmann-Uehling-Uhlenbeck-type codes and 9 Quantum-Molecular-Dynamics-type codes. These codes have been required to simulate Au+Au collisions using the same physics input for mean fields and for in-medium nucleon-nucleon cross sections, as well as the same initialization set-up, the impact parameter, and other calculational parameters at 100 and 400 AMeV incident energy. Among the codes we compare one-body observables such as rapidity and transverse flow distributions. We also monitor non-observables such as the initialization of the internal states of colliding nuclei and their stability, the collision rates and the Pauli blocking. We find that not completely identical initializations constitute partly for different evolutions. Different strategies to determine the collision probabilities, and to enforce the Pauli blocking, also produce considerably different results. There is a substantial spread in the predictions for the observables, which is much smaller at the higher incident energy. We quantify the uncertainties in the collective flow resulting from the simulation alone as about $30\%$ at 100 AMeV and $13\%$ at 400 AMeV, respectively. We propose further steps within the code comparison project to test the different aspects of transport simulations in a box calculation of infinite nuclear matter. This should, in particular, improve the robustness of transport model predictions at lower incident energies where abundant amounts of data are available.

preprint2016arXiv

Universal spatiotemporal dynamics of spontaneous superfluidity breakdown in the presence of synthetic gauge fields

According to the famous Kibble-Zurek mechanism (KZM), the universality of spontaneous defect generation in continuous phase transitions (CPTs) can be understood by the critical slowing down. In most CPTs of atomic Bose-Einstein condensates (BECs), the universality of spontaneous defect generations has been explained by the divergent relaxation time associated with the nontrivial gapless Bogoliubov excitations. However, for atomic BECs in synthetic gauge fields, their spontaneous superfluidity breakdown is resulted from the divergent correlation length associated with the zero Landau critical velocity. Here, by considering an atomic BEC ladder subjected to a synthetic magnetic field, we reveal that the spontaneous superfluidity breakdown obeys the KZM. The Kibble-Zurek scalings are derived from the Landau critical velocity which determines the correlation length. In further, the critical exponents are numerically extracted from the critical spatial-temporal dynamics of the bifurcation delay and the spontaneous vortex generation. Our study provides a general way to explore and understand the spontaneous superfluidity breakdown in CPTs from a single-well dispersion to a double-well one, such as, BECs in synthetic gauge fields, spin-orbit coupled BECs, and BECs in shaken optical lattices.

preprint2015arXiv

A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

Matching natural language sentences is central for many applications such as information retrieval and question answering. Existing deep models rely on a single sentence representation or multiple granularity representations for matching. However, such methods cannot well capture the contextualized local information in the matching process. To tackle this problem, we present a new deep architecture to match two sentences with multiple positional sentence representations. Specifically, each positional sentence representation is a sentence representation at this position, generated by a bidirectional long short term memory (Bi-LSTM). The matching score is finally produced by aggregating interactions between these different positional sentence representations, through $k$-Max pooling and a multi-layer perceptron. Our model has several advantages: (1) By using Bi-LSTM, rich context of the whole sentence is leveraged to capture the contextualized local information in each positional sentence representation; (2) By matching with multiple positional sentence representations, it is flexible to aggregate different important contextualized local information in a sentence to support the matching; (3) Experiments on different tasks such as question answering and sentence completion demonstrate the superiority of our model.

preprint2015arXiv

Cluster Gutzwiller study of Bose-Hubbard ladder: ground-state phase diagram and many-body Landau-Zener dynamics

We present a cluster Gutzwiller mean-field study for ground states and time-evolution dynamics in the Bose-Hubbard ladder (BHL), which can be realized by loading Bose atoms in double-well optical lattices. In our cluster mean-field approach, we treat each double-well unit of two lattice sites as a coherent whole for composing the cluster Gutzwiller ansatz, which may remain some residual correlations in each two-site unit. For a unbiased BHL, in addition to conventional superfluid phase and integer Mott insulator phases, we find that there are exotic fractional insulator phases if the inter-chain tunneling is much stronger than the intra-chain one. The fractional insulator phases can not be found by using a conventional mean-field treatment based upon the single-site Gutzwiller ansatz. For a biased BHL, we find there appear single-atom tunneling and interaction blockade if the system is dominated by the interplay between the on-site interaction and the inter-chain bias. In the many-body Landau-Zener process, in which the inter-chain bias is linearly swept from negative to positive or vice versa, our numerical results are qualitatively consistent with the experimental observation [Nat. Phys. \textbf{7}, 61 (2011)]. Our cluster bosonic Gutzwiller treatment is of promising perspectives in exploring exotic quantum phases and time-evolution dynamics of bosonic particles in superlattices.

preprint2015arXiv

Data Allocation in a Heterogeneous Disk Array - HDA with Multiple RAID Levels for Database Applications

We consider the allocation of Virtual Arrays (VAs) in a Heterogeneous Disk Array (HDA). Each VA holds groups of related objects and datasets such as files, relational tables, which has similar performance and availability characteristics. We evaluate single-pass data allocation methods for HDA using a synthetic stream of allocation requests, where each VA is characterized by its RAID level, disk loads and space requirements. The goal is to maximize the number of allocated VAs and maintain high disk bandwidth and capacity utilization, while balancing disk loads. Although only RAID1 (basic mirroring) and RAID5 (rotated parity arrays) are considered in the experimental study, we develop the analysis required to estimate disk loads for other RAID levels. Since VA loads vary significantly over time, the VA allocation is carried out at the peak load period, while ensuring that disk bandwidth is not exceeded at other high load periods. Experimental results with a synthetic stream of allocation requests show that allocation methods minimizing the maximum disk bandwidth and capacity utilization or their variance across all disks yield the maximum number of allocated VAs. HDA saves disk bandwidth, since a single RAID level accommodating the most stringent availability requirements for a small subset of objects would incur an unnecessarily high overhead for updating check blocks or data replicas for all objects. The number of allocated VAs can be increased by adopting the clustered RAID5 paradigm, which exploits the tradeoff between redundancy and bandwidth utilization. Since rebuild can be carried out at the level of individual VAs, prioritizing rebuild of VAs with higher access rates can improve overall performance.

preprint2015arXiv

Dynamical effects of spin-dependent interactions in low- and intermediate-energy heavy-ion reactions

It is well known that non-central nuclear forces, such as the spin-orbital coupling and the tensor force, play important roles in understanding many interesting features of nuclear structures. However, their dynamical effects in nuclear reactions are poorly known since only the spin-averaged observables are normally studied both experimentally and theoretically. Realizing that spin-sensitive observables in nuclear reactions may carry useful information about the in-medium properties of non-central nuclear interactions, besides earlier studies using the time-dependent Hartree-Fock approach to understand effects of spin-orbital coupling on the threshold energy and spin polarization in fusion reactions, some efforts have been made recently to explore dynamical effects of non-central nuclear forces in intermediate-energy heavy-ion collisions using transport models. The focuses of these studies have been on investigating signatures of the density and isospin dependence of the form factor in the spin-dependent single-nucleon potential. Interestingly, some useful probes were identified in the model studies while so far there is still no data to compare with. In this brief review, we summarize the main physics motivations as well as the recent progress in understanding the spin dynamics and identifying spin-sensitive observables in heavy-ion reactions at intermediate energies. We hope the interesting, important, and new physics potentials identified in the spin dynamics of heavy-ion collisions will stimulate more experimental work in this direction.

preprint2015arXiv

Dynamical generation of dark solitons in spin-orbit-coupled Bose-Einstein condensates

We numerically investigate the ground state, the Raman-driving dynamics and the nonlinear excitations of a realized spin-orbit-coupled Bose-Einstein condensate in a one-dimensional harmonic trap. Depending on the Raman coupling and the interatomic interactions, three ground-state phases are identified: stripe, plane wave and zero-momentum phases. A narrow parameter regime with coexistence of stripe and zero-momentum or plane wave phases in real space is found. Several sweep progresses across different phases by driving the Raman coupling linearly in time is simulated and the non-equilibrium dynamics of the system in these sweeps are studied. We find kinds of nonlinear excitations, with the particular dark solitons excited in the sweep from the stripe phase to the plane wave or zero-momentum phase within the trap. Moreover, the number and the stability of the dark solitons can be controlled in the driving, which provide a direct and easy way to generate dark solitons and study their dynamics and interaction properties.

preprint2015arXiv

Extraordinary focusing of sound above a soda can array without time reversal

Recently, Lemoult et al. [Phys. Rev. Lett. 107, 064301 (2011)] used time reversal to focus sound above an array of soda cans into a spot much smaller than the acoustic wavelength in air. In this study, we show that equally sharp focusing can be achieved without time reversal, by arranging transducers around a nearly circular array of soda cans. The size of the focal spot at the center of the array is made progressively smaller as the frequency approaches the Helmholtz resonance frequency of a can from below, and, near the resonance, becomes smaller than the size of a single can. We show that the locally resonant metamaterial formed by soda cans supports a guided wave at frequencies below the Helmholtz resonance frequency. The small focal spot results from a small wavelength of this guided wave near the resonance in combination with a near field effect making the acoustic field concentrate at the opening of a can. The focusing is achieved with propagating rather than evanescent waves. No sub-diffraction-limited focusing is observed if the diffraction limit is defined with respect to the wavelength of the guided mode in the metamaterial medium rather than the wavelength of the bulk wave in air.

preprint2015arXiv

Influence of neutron-skin thickness on $π^{-}/π^{+}$ ratio in Pb+Pb collisions

Within an isospin- and momentum-dependent transport model IBUU11 using as an input nucleon density profiles from Hartree-Fock calculations based on a modified Skyrme-like (MSL) model, we study the influence of the uncertainty of the neutron skin thickness on the $π^{-}/π^{+}$ ratio in both central and peripheral Pb+Pb collisions at beam energies of 400 MeV/nucleon and 1000 MeV/nucleon. Within the current experimental uncertainty range of neutron skin in $^{208}$Pb, while the neutron skin effect on the \rpi ratio is negligible in central reactions at both energies, it increases gradually with increasing impact parameter and becomes comparable with or even larger than the symmetry energy effect in peripheral collisions especially at 400 MeV/nucleon. Moreover, we found that while the \rpi ratio is larger with a softer \esym in central collisions, above certain impact parameters depending on the size of the neutron skin, a stiffer \esym can lead to a larger \rpi ratio as most of the pions are produced at densities below the saturation density in these peripheral reactions. Thus, a clear impact parameter selection is important to extract reliable information about the \esym at suprasaturation densities (size of neutron skin) from the $π^-/π^+$ ratio in central (peripheral) heavy-ion collisions.

preprint2015arXiv

Instantly Obsoleting the Address-code Associations: A New Principle for Defending Advanced Code Reuse Attack

Fine-grained Address Space Randomization has been considered as an effective protection against code reuse attacks such as ROP/JOP. However, it only employs a one-time randomization, and such a limitation has been exploited by recent just-in-time ROP and side channel ROP, which collect gadgets on-the-fly and dynamically compile them for malicious purposes. To defeat these advanced code reuse attacks, we propose a new defense principle: instantly obsoleting the address-code associations. We have initialized this principle with a novel technique called virtual space page table remapping and implemented the technique in a system CHAMELEON. CHAMELEON periodically re-randomizes the locations of code pages on-the-fly. A set of techniques are proposed to achieve our goal, including iterative instrumentation that instruments a to-be-protected binary program to generate a re-randomization compatible binary, runtime virtual page shuffling, and function reordering and instruction rearranging optimizations. We have tested CHAMELEON with over a hundred binary programs. Our experiments show that CHAMELEON can defeat all of our tested exploits by both preventing the exploit from gathering sufficient gadgets, and blocking the gadgets execution. Regarding the interval of our re-randomization, it is a parameter and can be set as short as 100ms, 10ms or 1ms. The experiment results show that CHAMELEON introduces on average 11.1%, 12.1% and 12.9% performance overhead for these parameters, respectively.

preprint2015arXiv

Investigating the scaling of higher-order flows in relativistic heavy-ion collisions

The modified number of constituent quark (NCQ) scaling $v_{n}/n_{q}^{n/2} \sim KE_{T}/n_{q}$ for mesons and baryons and the scaling relation $v_{n} \sim v_{2}^{n/2}$ for higher-order anisotropic flows, which were observed experimentally, have been investigated at top RHIC energy. It has been found that the modified NCQ scaling can not be obtained from the naive coalescence even by taking into account event-by-event fluctuations but may be due to hadronic afterburner or thermal freeze-out. In addition, we observed that the behavior of the $v_{n}/v_{2}^{n/2}$ ratio is sensitive to the partonic interaction, and it is different for mesons and baryons from the naive coalescence but is expected to be almost the same experimentally.

preprint2015arXiv

Isospin splitting of nucleon effective mass and shear viscosity of nuclear matter

Based on an improved isospin- and momentum-dependent interaction, we have studied the qualitative effect of isospin splitting of nucleon effective mass on the specific shear viscosity of neutron-rich nuclear matter from a relaxation time approach. It is seen that for $m_n^\star>m_p^\star$, the relaxation time of neutrons is smaller and the neutron flux between flow layers is weaker, leading to a smaller specific shear viscosity of neutron-rich matter compared to the case for $m_n^\star<m_p^\star$. The effect is larger in nuclear matter at higher densities, lower temperatures, and larger isospin asymmetries, but it doesn't affect much the behavior of the specific shear viscosity near nuclear liquid-gas phase transition.

preprint2015arXiv

Mean-field potential effects on particle and antiparticle elliptic flows in the beam-energy scan program at RHIC

The elliptic flow splitting between particles and their antiparticles has recently been observed by the STAR Collaboration in the beam-energy scan program at the Relativistic Heavy Ion Collider. In studies based on transport models, we have found that this splitting can be explained by the different mean-field potentials acting on particles and their antiparticles in the produced baryon-rich matter. In particular, we have shown that the experimentally measured relative elliptic flow difference can help constrain the vector coupling constant in the Nambu-Jona-Lasinio model used in describing the partonic stage of heavy-ion collisions. This information is useful for locating the critical point in the QCD phase diagram and thus understanding the phase structure of QCD.

preprint2015arXiv

Revisit of the neutron/proton ratio puzzle in intermediate-energy heavy-ion collisions

Incorporating a newly improved isospin- and momentum-dependent interaction in the isospin-dependent Boltzmann-Uehling-Uhlenbeck transport model IBUU11, we have investigated relative effects of the density dependence of nuclear symmetry energy $E_{sym}(ρ)$ and the neutron-proton effective mass splitting $m^*_n-m^*_p$ on the neutron/proton ratio of free nucleons and those in light clusters. It is found that the $m^*_n-m^*_p$ has a relatively stronger effect than the $E_{sym}(ρ)$ and the assumption of $m^*_n\leq m^*_p$ leads to a higher neutron/proton ratio. Moreover, this finding is independent of the in-medium nucleon-nucleon cross sections used. However, results of our calculations using the $E_{sym}(ρ)$ and $m^*_n-m^*_p$ both within their current uncertainty ranges are all too low compared to the recent NSCL/MSU double neutron/proton ratio data from central $^{124}$Sn+$^{124}$Sn and $^{112}$Sn+$^{112}$Sn collisions at 50 and 120 MeV/u, thus calling for new mechanisms to explain the puzzlingly high neutron/proton ratio observed in the experiments.

preprint2015arXiv

Thermal properties of asymmetric nuclear matter with an improved isospin- and momentum-dependent interaction

Thermal properties of asymmetric nuclear matter, including the temperature dependence of the symmetry energy, single-particle properties, and differential isospin fractionation, are investigated with different neutron-proton effective mass splittings using an improved isospin- and momentum-dependent interaction. In this improved interaction, the momentum-dependence of the isoscalar single-particle potential at saturation density is well fitted to that extracted from optical model analyses of proton-nucleus scattering data up to nucleon kinetic energy of 1 GeV, and the isovector properties, i.e., the slope of the nuclear symmetry energy, the momentum-dependence of the symmetry potential, and the symmetry energy at saturation density can be flexibly adjusted via three parameters $x$, $y$, and $z$, respectively. Our results indicate that the nucleon phase-space distribution in equilibrium, the temperature dependence of the symmetry energy, and the differential isospin fractionation can be significantly affected by the isospin splitting of nucleon effective mass.

preprint2014arXiv

A novel minimal in vitro system for analyzing HIV-1 Gag mediated budding

A biomimetic minimalist model membrane was used to study the mechanism and kinetics of cell-free in vitro HIV-1 Gag budding from a giant unilamellar vesicle (GUV). Real time interaction of Gag, RNA and lipid leading to the formation of mini-vesicles was measured using confocal microscopy. Gag forms resolution limited punctae on the GUV lipid membrane. Introduction of the Gag and urea to a GUV solution containing RNA led to the budding of mini-vesicles on the inside surface of the GUV. The GUV diameter showed a linear decrease in time due to bud formation. Both bud formation and decrease in GUV size were proportional to Gag concentration. In the absence of RNA, addition of urea to GUVs incubated with Gag also resulted in subvesicle formation but exterior to the surface. These observations suggest the possibility that clustering of GAG proteins leads to membrane invagination even in the absence of host cell proteins. The method presented here is promising, and allows for systematic study of the dynamics of assembly of immature HIV and help classify the hierarchy of factors that impact the Gag protein initiated assembly of retroviruses such as HIV.

preprint2014arXiv

Breaking the EOS-Gravity Degeneracy with Masses and Pulsating Frequencies of Neutron Stars

A thorough understanding of many astrophysical phenomena associated with compact objects requires reliable knowledge about both the equation of state (EOS)of super-dense nuclear matter and the theory of strong-field gravity simultaneously because of the EOS-gravity degeneracy. Currently, variations of the neutron star (NS) mass-radius correlation from using alternative gravity theories are much larger than those from changing the NS matter EOS within known constraints. At least two independent observables are required to break the EOS-gravity degeneracy. Using model EOSs for hybrid stars and a Yukawa-type non-Newtonian gravity, we investigate both the mass-radius correlation and pulsating frequencies of NSs. While the maximum mass of NSs increases, the frequencies of the $f$, $p_1$, $p_2$, and $w_I$ pulsating modes are found to decrease with the increasing strength of the Yukawa-type non-Newtonian gravity, providing a useful reference for future determination simultaneously of both the strong-field gravity and the supranuclear EOS by combining data of x-ray and gravitational wave emissions of neutron stars.

preprint2014arXiv

Near-field Holographic Retrieval of an Isolated Subwavelength Hole in a Thin Metallic Film

Using a holographic approach, we experimentally study the near-field intensity distribution of light squeezed through an isolated subwavelength plasmonic hole in a thin metallic film. Our experiments revealed an in-plane electric dipole moment excited near the isolated hole. By analyzing the fringe patterns formed between the in-plane dipole and plane wave illumination, both the transmission coefficient and phase shift of the dipole can be retrieved. We also observed opposite phases of the excited dipoles from the subwavelength dent and protrusion in the metallic film, in good agreement with the prediction from our model. Our approach can be used to study the microscopic process of the light-structure interaction for the plasmonic and nanophotonic systems with potential applications in high density optical data storages.

preprint2014arXiv

New Vacuum Solar Telescope and Observations with High Resolution

The New Vacuum Solar Telescope (NVST) is a 1 meter vacuum solar telescope that aims to observe the fine structures on the Sun. The main tasks of NVST are high resolution imaging and spectral observations, including the measurements of solar magnetic field. NVST is the primary ground-based facility of Chinese solar community in this solar cycle. It is located by the Fuxian Lake of southwest China, where the seeing is good enough to perform high resolution observations. In this paper, we first introduce the general conditions of Fuxian Solar Observatory and the primary science cases of NVST. Then, the basic structures of this telescope and instruments are described in detail. Finally, some typical high resolution data of solar photosphere and chromosphere are also shown.

preprint2014arXiv

Photon-induced sideband transitions in a many-body Landau-Zener process

We investigate the many-body Landau-Zener (LZ) process in a two-site Bose-Hubbard model driven by a time-periodic field. We find that the driving field may induce sideband transitions in addition to the main LZ transitions. These photon-induced sideband transitions are a signature of the photon-assisted tunneling in our many-body LZ process. In the strong interaction regime, we develop an analytical theory for understanding the sideband transitions, which is confirmed by our numerical simulation. Furthermore, we discuss the quantization of the driving field. In the effective model of the quantized driving field, the sideband transitions can be understood as the LZ transitions between states of different "photon" numbers.

preprint2014arXiv

Probing isospin- and momentum-dependent nuclear effective interactions in neutron-rich matter

The single-particle potentials for nucleons and hyperons in neutron-rich matter generally depends on the density and isospin asymmetry of the medium as well as the momentum and isospin of the particle. It further depends on the temperature of the matter if the latter is in thermal equilibrium. We review here the extension of a Gogny-type isospin- and momentum-dependent interaction in several aspects made in recent years and their applications in studying intermediate-energy heavy ion collisions, thermal properties of asymmetric nuclear matter and properties of neutron stars. The importance of the isospin- and momentum-dependence of the single-particle potential, especially the momentum dependence of the isovector potential, is clearly revealed throughout these studies.

preprint2014arXiv

Spin-orbit coupling and the up-down differential transverse flow in intermediate-energy heavy-ion collisions

To explore the strength, isospin dependence, and density dependence of the in-medium spin-orbit coupling that is poorly known but relevant for understanding the structure of rare isotopes, the nucleon spin up-down differential transverse flow is studied systematically by varying the beam energy and impact parameter of Au+Au collisions within a spin-isospin dependent transport model recently developed for investigating spin-related phenomena in intermediate-energy heavy-ion collisions. The optimal reaction conditions for delineating and disentangling effects of different terms in the spin-orbit coupling are discussed.

preprint2014arXiv

Temperature dependent characteristics of GaSb p-channel MOSFETs with Si-implanted source and drain

GaSb p-channel MOSFETs with an atomic layer deposited Al2O3 gate dielectric and a self-aligned Si implanted source/drain are demonstrated. Thermal anneal conditions are optimized for the source/drain impurity activation. Temperature dependent electrical characteristics are investigated. Different electrical behaviors are observed in two different temperature regions and the mechanisms underneath are proposed. Off-state drain current is generation current dominated in the low temperature regions and is diffusion current dominated in the high temperature regions.

preprint2013arXiv

Effects of ozone post deposition treatment on interfacial and electrical characteristics of atomic-layer-deposited Al2O3 and HfO2 films on GaSb substrates

Atomic-layer-deposited Al2O3 and HfO2 films on GaSb substrates were treated by in-situ ozone post deposition treatment (PDT). The effects of ozone PDT on the interfacial and electrical properties of Al2O3 and HfO2 gate dielectric films on GaSb substrates were investigated carefully. It is found that the dielectric quality and the interfacial properties of the Al2O3 and HfO2 films are improved by ozone PDT. After in-situ ozone PDT for 5 min, the Al2O3 and HfO2 films on GaSb substrates exhibit improved electrical and interfacial properties, such as reduced frequency dispersion, gate leakage current, border traps and interface traps. Interface trap density is reduced by ~24% for the Al2O3/GaSb stacks and ~27% for the HfO2/GaSb stacks. In-situ ozone PDT is proved to be a promising technique in improving the quality of high-k gate stacks on GaSb substrates.

preprint2013arXiv

Elliptic flow splitting as a probe of the QCD phase structure at finite baryon chemical potential

Using a partonic transport model based on the 3-flavor Nambu-Jona-Lasinio model and a relativistic hadronic transport model to describe, respectively, the evolution of the initial partonic and the final hadronic phase of heavy-ion collisions at energies carried out in the Beam-Energy Scan program of the Relativistic Heavy Ion Collider, we have studied the effects of both the partonic and hadronic mean-field potentials on the elliptic flow of particles relative to that of their antiparticles. We find that to reproduce the measured relative elliptic flow differences between nucleons and antinucleons as well as between kaons and antikaons requires a vector coupling constant as large as 0.5 to 1.1 times the scalar coupling constant in the Nambu-Jona-Lasinio model. Implications of our results in understanding the QCD phase structure at finite baryon chemical potential are discussed.

preprint2013arXiv

Energy dependence of pion in-medium effects on π^-/π^+ ratio in heavy-ion collisions

Within the framework of a thermal model with its parameters fitted to the results from an isospin-dependent Boltzmann-Uehling-Uhlenbeck (IBUU) transport model, we study the pion in-medium effect on the charged-pion ratio in heavy-ion collisions at various energies. We find that due to the cancellation between the effects from pion-nucleon s-wave and p-wave interactions in nuclear medium, the π^-/π^+ ratio generally decreases after including the pion in-medium effect. The effect is larger at lower collision energies as a result of narrower pion spectral functions at lower temperatures.

preprint2013arXiv

Ferroelectric-Gated Terahertz Plasmonics on Graphene

Inspired by recent advancement of low-power ferroelectic-gated memories and transistors, we propose a design of ferroelectic-gated nanoplasmonic devices based on graphene sheets clamped in ferroelectric crystals. We show that the two-dimensional plasmons in graphene strongly couple with the phonon-polaritons in ferroelectrics at terahertz frequencies, leading to characteristic modal wavelength of the order of 100--200 nm at only 3--4 THz. By patterning the ferroelectrics into different domains, one can produce compact on-chip plasmonic waveguides, which exhibit negligible crosstalk even at 50 nm separation distance. Harnessing the memory effect of ferroelectrics, low-power electro-optical switching can be achieved on these plasmonic waveguides.

preprint2013arXiv

Improved Interfacial and Electrical Properties of GaSb Metal Oxide Semiconductor Devices Passivated with Acidic (NH4)2S Solution

Surface passivation with acidic (NH4)2S solution is shown to be effective in improving the interfacial and electrical properties of HfO2/GaSb metal oxide semiconductor devices. Compared with control samples, those treated with acidic (NH4)2S solution showed great improvements in frequency dispersion, gate leakage current and interface trap density. These improvements were attributed to the acidic (NH4)2S solution enhancing passivation of the substrates, which was analyzed from the perspective of chemical mechanism and confirmed by X-ray photoelectron spectroscopy and high-resolution cross-sectional transmission electron microscopy.

preprint2013arXiv

Measurement of line widths and permanent electric dipole moment change of the Ce 4f-5d transition in Y_2SiO_5 for a qubit readout scheme in rare-earth ion based quantum computing

In this work the inhomogeneous (zero-phonon line) and homogeneous line widths, and one projection of the permanent electric dipole moment change for the Ce 4f-5d transition in Y_2SiO_5 were measured in order to investigate the possibility for using Ce as a sensor to detect the hyperfine state of a spatially close-lying Pr or Eu ion. The experiments were carried out on Ce doped or Ce-Pr co-doped single Y_2SiO_5 crystals. The homogeneous line width was measured to be about 3 MHz, which is essentially limited by the excited state lifetime. Based on the line width measurements, the oscillator strength, absorption cross section and saturation intensity were calculated to be about 9*10^-7, 5*10^-19 m^2 and 1*10^7 W/m^2, respectively. One projection of the difference in permanent dipole moment, Delt_miu_Ce, between the ground and excited states of the Ce ion was measured as 6.3 * 10^-30 C*m, which is about 26 times as large as that of Pr ions. The measurements done on Ce ions indicate that the Ce ion is a promising candidate to be used as a probe to read out a single qubit ion state for the quantum computing using rare-earth ions.

preprint2013arXiv

Nonlinear dissipative dynamics of a two-component atomic condensate coupling with a continuum

We investigate the nonlinear dissipative coherence bifurcation and population dynamics of a two-component atomic Bose-Einstein condensate coupling with a continuum. The coupling between the two-component condensates and the continuum brings effective dissipations to the two-component condensates. The steady states and the coherence bifurcation depend on both dissipation and the nonlinear interaction between condensed atoms. The coherence among condensed atoms may be even enhanced by the effective dissipations. The combination of dissipation and nonlinearity allows one to control the switching between different self-trapped states or the switching between a self-trapped state and a non-self-trapped state.

preprint2013arXiv

Nuclear constraints on non-Newtonian gravity at femtometer scale

Effects of the non-Newtonian gravity on properties of finite nuclei are studied by consistently incorporating both the direct and exchange contribution of the Yukawa potential in the Hartree-Fock approach using a well-tested Skyrme force for the strong interaction. It is shown for the first time that the strength of the Yukawa term in the non-Newtonian gravity is limited to $\log(|α|)<1.75/[λ(\rm fm)]^{0.54} + 33.6$ within the length scale of $λ=1-10$ fm in order for the calculated properties of finite nuclei not to be in conflict with accurate experimental data available.

preprint2013arXiv

Probing in-medium spin-orbit potential with intermediate-energy heavy-ion collisions

Incorporating for the first time both the spin and isospin degrees of freedom explicitly in transport model simulations of intermediate-energy heavy-ion collisions, we observe that a local spin polarization appears during collision process. Most interestingly, it is found that the nucleon spin up-down differential transverse flow is a sensitive probe of the spin-orbit interaction, providing a novel approach to probe both the density and isospin dependence of the in-medium spin-orbit coupling that is important for understanding the structure of rare isotopes and synthesis of superheavy elements.

preprint2013arXiv

Shear viscosity of neutron-rich nucleonic matter near its liquid-gas phase transition

Within a relaxation time approach using free nucleon-nucleon cross sections modified by the in-medium nucleon masses that are determined from an isospin- and momentum-dependent effective nucleon-nucleon interaction, we investigate the specific shear viscosity ($η/s$) of neutron-rich nucleonic matter near its liquid-gas phase transition. It is found that as the nucleonic matter is heated at fixed pressure or compressed at fixed temperature, its specific shear viscosity shows a valley shape in the temperature or density dependence, with the minimum located at the boundary of the phase transition. Moreover, the value of $η/s$ drops suddenly at the first-order liquid-gas phase transition temperature, reaching as low as $4\sim5$ times the KSS bound of $\hbar/4π$. However, it varies smoothly for the second-order liquid-gas phase transition. Effects of the isospin degree of freedom and the nuclear symmetry energy on the value of $η/s$ are also discussed.

preprint2013arXiv

Shear viscosity of nuclear matter

In this talk I report my recent study on the shear viscosity of neutron-rich nuclear matter from a relaxation time approach. An isospin- and momentum-dependent interaction is used in the study. Effects of density, temperature, and isospin asymmetry of nuclear matter on its shear viscosity have been discussed. Similar to the symmetry energy, the symmetry shear viscosity is defined and its density and temperature dependence are studied.

preprint2013arXiv

Tailoring exciton dynamics by elastic strain-gradient in semiconductors

As device miniaturization approaches the atomic limit, it becomes highly desirable to exploit novel paradigms for tailoring electronic structures and carrier dynamics in materials. Elastic strain can in principle be applied to achieve reversible and fast control of such properties, but it remains a great challenge to create and utilize precisely controlled inhomogeneous deformation in semiconductors. Here, we take a combined experimental and theoretical approach to demonstrate that elastic strain-gradient can be created controllably and reversibly in ZnO micro/nanowires. In particular, we show that the inhomogeneous strain distribution creates an effective field that fundamentally alters the dynamics of the neutral excitons. As the basic principles behind these results are quite generic and applicable to most semiconductors, this work points to a novel route to a wide range of applications in electronics, optoelectronics, and photochemistry.

preprint2012arXiv

Disentangling effects of collision geometry and symmetry energy in U+U collisions

Effects of the collision geometry on experimental observables that are known to be sensitive to the high-density behavior of nuclear symmetry energy are examined in U+U collisions at 0.52 GeV/nucleon using an isospin- and momentum-dependent interaction within the framework of IBUU transport model. It is found that the neutron-proton differential flow in tip-tip collisions and the difference of neutron and proton elliptic flow in body-body collisions are more sensitive to the symmetry energy at supra-saturation densities compared with collisions of spherical nuclei of the same masses. In addition, the n/p ratio of pre-equilibrium nucleons is found to be slightly more sensitive to the symmetry energy in tip-tip collisions, and the collision geometry affects the π^-/π^+ ratio significantly.

preprint2012arXiv

Effects of hadronic potentials on elliptic flows in relativistic heavy ion collisions

Within the framework of a multiphase transport (AMPT) model that includes both initial partonic and final hadronic interactions, we show that including mean-field potentials in the hadronic phase leads to a splitting of the elliptic flows of particles and their antiparticles, providing thus a plausible explanation of the different elliptic flows between $p$ and ${\bar p}$, $K^+$ and $K^-$, and $π^+$ and $π^-$ observed in recent Beam Energy Scan (BES) program at the Relativistic Heavy-Ion Collider (RHIC).

preprint2012arXiv

Generic Constraints on the Relativistic Mean-Field and Skyrme-Hartree-Fock Models from the Pure Neutron Matter Equation of State

We study the nuclear symmetry energy S(rho) and related quantities of nuclear physics and nuclear astrophysics predicted generically by relativistic mean-field (RMF) and Skyrme-Hartree-Fock (SHF) models. We establish a simple prescription for preparing equivalent RMF and SHF parametrizations starting from a minimal set of empirical constraints on symmetric nuclear matter, nuclear binding energy and charge radii, enforcing equivalence of their Lorenz effective masses, and then using the pure neutron matter (PNM) equation of state (EoS) obtained from ab-initio calculations to optimize the pure isovector parameters in the RMF and SHF models. We find the resulting RMF and SHF parametrizations give broadly consistent predictions of the symmetry energy J and its slope parameter L at saturation density within a tight range of <~2 MeV and <~6 MeV respectively, but that clear model dependence shows up in the predictions of higher-order symmetry energy parameters, leading to important differences in (a) the slope of the correlation between J and L from the confidence ellipse, (b) the isospin-dependent part of the incompressibility of nuclear matter K_tau, (c) the symmetry energy at supra-saturation densities, and (d) the predicted neutron star radii. The model dependence can lead to about 1-2 km difference in predictions of the neutron star radius given identical predicted values of J, L and symmetric nuclear matter (SNM) saturation properties. Allowing the full freedom in the effective masses in both models leads to constraints of 30<~J<~31.5 MeV, 35<~L<~60 MeV, -330<~K_tau<~-216 MeV for the RMF model as a whole and 30<~J<~33 MeV, 28<~L<~65 MeV, -420<~K_tau<~-325 MeV for the SHF model as a whole. Notably, given PNM constraints, these results place RMF and SHF models as a whole at odds with some constraints on K_tau inferred from giant monopole resonance and neutron skin experimental results.

preprint2012arXiv

Non-Newtonian gravity in finite nuclei

In this talk, we report our recent study of constraining the non-Newtonian gravity at femtometer scale. We incorporate the Yukawa-type non-Newtonian gravitational potential consistently to the Skyrme functional form using the exact treatment for the direct contribution and density-matrix expansion method for the exchange contribution. The effects from the non-Newtonian potential on finite nuclei properties are then studied together with a well-tested Skyrme force. Assuming that the framework without non-Newtonian gravity can explain the binding energies and charge radii of medium to heavy nuclei within 2% error, we set an upper limit for the strength of the non-Newtonian gravitational potential at femtometer scale.

preprint2012arXiv

Optical Curtain Effect: Extraordinary Optical Transmission Enhanced by Antireflection

In this paper, we employ an antireflective coating which comprises of inverted pi shaped metallic grooves to manipulate the behaviour of a TM-polarized plane wave transmitted through a periodic nanoslit array. At normal incidence, such scheme can not only retain the optical curtain effect in the output region, but also generate the extraordinary transmission of light through the nanoslits with the total transmission efficiency as high as 90%. Besides, we show that the spatially invariant field distribution in the output region as well as the field distribution of resonant modes around the inverted pi shaped grooves can be reproduced immaculately when the system is excited by an array of point sources beneath the inverted pi shaped grooves. In further, we investigate the influence of center-groove and side-corners of the inverted pi shaped grooves on suppressing the reflection of light, respectively. Based on our work, it shows promising potential in applications of enhancing the extraction efficiency as well as controlling the beaming pattern of light emitting diodes.

preprint2012arXiv

Pure Neutron Matter Constraints and Nuclear Symmetry Energy

In this review, we will discuss the results of our recent work to study the general optimization of the pure isovector parameters of the popular relativistic mean-field (RMF) and Skyrme-Hartree-Fock (SHF) nuclear energy-density functionals (EDFs), using constraints on the pure neutron matter (PNM) equation of state (EoS) from recent {\sl ab initio} calculations. By using RMF and SHF parameterizations that give equivalent predictions for ground-state properties of doubly magic nuclei and properties of symmetric nuclear matter (SNM) and PNM, we found that such optimization leads to broadly consistent symmetry energy $J$ and its slope parameter $L$ at saturation density within a tight range of $σ(J) < 2$ MeV and $σ(L) < 6$ MeV. We demonstrate that a clear model dependence shows up (a) in the curvature parameter of the symmetry energy $K_{\rm sym}$, (b) the symmetry energy at supra-saturation densities, and (c) the radius of neutron stars.

preprint2011arXiv

A Thin Film Broadband Absorber Based on Multi-sized Nanoantennas

We experimentally demonstrate an infrared broadband absorber for TM polarized light based on an array of nanostrip antennas of several different sizes. The broadband property is due to the collective effect of magnetic responses excited by these nano-antennas at distinct wavelengths. By manipulating the differences of the nanostrip widths, the measured spectra clearly validate our design for the purpose of broadening the absorption band. The present broadband absorber works very well in a wide angular range.

preprint2011arXiv

Higher-order anisotropic flows and dihadron correlations in Pb-Pb collisions at $\sqrt{s_{NN}}=2.76$ TeV in a multiphase transport model

Using a multiphase transport model that includes both initial partonic and final hadronic scatterings, we have studied higher-order anisotropic flows as well as dihadron correlations as functions of pseudorapidity and azimuthal angular differences between trigger and associated particles in Pb-Pb collisions at $\sqrt{s_{NN}}=2.76$ TeV. With parameters in the model determined previously from fitting the measured multiplicity density of mid-pseudorapidity charged particles in central collisions and their elliptic flow in mid-central collisions, the calculated higher-order anisotropic flows from the two-particle cumulant method reproduce approximately those measured by the ALICE Collaboration, except at small centralities where they are slightly overestimated. Similar to experimental results, the two-dimensional dihadron correlations at most central collisions show a ridge structure at the near side and a broad structure at the away side. The short- and long-range dihadron azimuthal correlations, corresponding to small and large pseudorapidity differences, respectively, are studied for triggering particles with different transverse momenta and are found to be qualitatively consistent with experimental results from the CMS Collaboration. The relation between the short-range and long-range dihadron correlations with that induced by back-to-back jet pairs produced from initial hard collisions is also discussed.

preprint2011arXiv

Imprints of Nuclear Symmetry Energy on Properties of Neutron Stars

Significant progress has been made in recent years in constraining the density dependence of nuclear symmetry energy using terrestrial nuclear laboratory data. Around and below the nuclear matter saturation density, the experimental constraints start to merge in a relatively narrow region. At supra-saturation densities, there are, however, still large uncertainties. After summarizing the latest experimental constraints on the density dependence of nuclear symmetry energy, we highlight a few recent studies examining imprints of nuclear symmetry energy on the binding energy, energy release during hadron-quark phase transitions as well as the $w$-mode frequency and damping time of gravitational wave emission of neutron stars.

preprint2011arXiv

Nonlinear quantum interferometry with Bose condensed atoms

In quantum interferometry, it is vital to control and utilize nonlinear interactions for achieving high-precision measurements. Attribute to their long coherent time and high controllability, ultracold atoms including Bose condensed atoms have been widely used for implementing quantum interferometry. Here, we review the recent progresses in theoretical studies of quantum interferometry with Bose condensed atoms. In particular, we focus on the nonlinear phenomena induced by the atom-atom interaction and how to control and utilize these nonlinear phenomena. Under the mean-field description, due to the atom-atom interaction, matter-wave solitons appear in the interference patterns, and macroscopic quantum self-trapping exists in the Bose-Josephson junctions. Under the many-body description, the atom-atom interaction can generate non-classical entanglement, which may be utilized to achieve high-precision measurements beyond the standard quantum limit.

preprint2011arXiv

Pb-Pb collisions at $\sqrt{s_{NN}}=2.76$ TeV in a multiphase transport model

The multiplicity and elliptic flow of charged particles produced in Pb-Pb collisions at center of mass energy $\sqrt{s_{NN}}=2.76$ TeV from the Large Hadron Collider are studied in a multiphase transport (AMPT) model. With the standard parameters in the HIJING model, which is used as initial conditions for subsequent partonic and hadronic scatterings in the AMPT model, the resulting multiplicity of final charged particles at mid-pseudorapidity is consistent with the experimental data measured by the ALICE Collaboration. This value is, however, increased by about 25% if the final-state partonic and hadronic scatterings are turned off. Because of final-state scatterings, particular those among partons, the final elliptic flow of charged hadrons is also consistent with the ALICE data if a smaller but more isotropic parton scattering cross section than previously used in the AMPT model for describing the charged hadron elliptic flow in heavy ion collisions at the Relativistic Heavy Ion Collider is used. The resulting transverse momentum spectra of charged particles as well as the centrality dependence of their multiplicity density and the elliptic flow are also in reasonable agreement with the ALICE data. Furthermore, the multiplicities, transverse momentum spectra and elliptic flows of identified hadrons such as protons, kaons and pions are predicted.

preprint2011arXiv

Specific viscosity of neutron-rich nuclear matter from a relaxation time approach

The specific viscosity of neutron-rich nuclear matter is studied from the relaxation time approach using an isospin- and momentum-dependent interaction and the nucleon-nucleon cross sections taken as those from the experimental data modified by the in-medium effective masses as used in the IBUU transport model calculations. The relaxation time of neutrons is larger while that of protons is smaller in neutron-rich nuclear matter compared with that in symmetric nuclear matter, and this leads to a larger specific viscosity in neutron-rich nuclear matter. In addition, the specific viscosity decreases with increasing temperature because of more frequent collisions and weaker Pauli blocking effect at higher temperatures. At lower temperatures the specific viscosity increases with increasing density due to the Pauli blocking effect, while at higher temperatures it slightly decreases with increasing density as a result of smaller in-medium effective masses at higher densities.

preprint2011arXiv

The effect of triangular flow on di-hadron azimuthal correlations in relativistic heavy ion collisions

Using the AMPT model for relativistic heavy ion collisions, we have studied the di-hadron azimuthal angular correlations triggered by emitted jets in Au+Au collisions at center of mass energy $\sqrt{s_{NN}}=200$ GeV and impact parameter $b=8$ fm. A double-peak structure for the associated particles at the away side of trigger particles is obtained after subtracting background correlations due to the elliptic flow. Both the near-side peak and the away-side double peaks in the azimuthal angular correlations are, however, significantly suppressed (enhanced) in events of small (large) triangular flow, which are present as a result of fluctuations in the initial collision geometry. After subtraction of background correlations due to the triangular flow, the away-side double peaks change into a single peak with broad shoulders on both sides. The away side of the di-hadron correlations becomes essentially a single peak after further subtraction of higher-order flows.

preprint2011arXiv

Triangular flow in heavy ion collisions in a multiphase transport model

We have obtained a new set of parameters in a multiphase transport (AMPT) model that are able to describe both the charged particle multiplicity density and elliptic flow measured in Au+Au collisions at center of mass energy $\sqrt{s_{NN}}=200$ GeV at the Relativistic Heavy Ion Collider (RHIC), although they still give somewhat softer transverse momentum spectra. We then use the model to predict the triangular flow due to fluctuations in the initial collision geometry and study its effect relative to those from other harmonic components of anisotropic flows on the di-hadron azimuthal correlations in both central and mid-central collisions.

preprint2011arXiv

Ultra-broadband Light Absorption by a Sawtooth Anisotropic Metamaterial Slab

We present an ultra broadband thin-film infrared absorber made of saw-toothed anisotropic metamaterial. Absorbtivity of higher than 95% at normal incidence is supported in a wide range of frequencies, where the full absorption width at half maximum is about 86%. Such property is retained well at a very wide range of incident angles too. Light of shorter wavelengths are harvested at upper parts of the sawteeth of smaller widths, while light of longer wavelengths are trapped at lower parts of larger tooth widths. This phenomenon is explained by the slowlight modes in anisotropic metamaterial waveguide. Our study can be applied in the field of designing photovoltaic devices and thermal emitters.

preprint2010arXiv

$J/ψ$ production and elliptic flow in relativistic heavy-ion collisions

Using a two-component model for charmonium production, which includes contributions from both the initial hard nucleon-nucleon scattering and from the regeneration in the quark-gluon plasma, we study the nuclear modification factor $R_{AA}$ and elliptic flow $v_2$ of $J/ψ$ in relativistic heavy ion collisions. For the expansion dynamics of produced hot dense matter, we introduce a schematic fireball model with its transverse acceleration determined from the pressure gradient inside the fireball and azimuthally anisotropic expansion parameterized to reproduce measured $v_2$ of light hadrons. We assume that light hadrons freeze out at the temperature of 120 MeV while charmonia at 160 MeV, similar to the kinetic and chemical freeze-out temperatures in the statistical model, respectively. For the properties of charmonia in the quark-gluon plasma, we use the screening mass between their charm and anticharm quarks and their dissociation cross sections given by the perturbative QCD (pQCD) in the leading order and up to the next-to-leading order, respectively. For the relaxation time of charm and anticharm quarks in the quark-gluon plasma, we also use the one calculated in the leading order of pQCD. Modeling the effect of higher-order corrections in pQCD by introducing multiplicative factors to the dissociation cross section of charmonia and the elastic scattering cross sections of charm and anticharm quarks, we find that this effect is small for the $R_{AA}$ of $J/ψ$ as they suppress the number of initially produced $J/ψ$ but enhance the number of regenerated ones. The higher-order corrections increase, however, the $v_2$ of $J/ψ$. Our results suggest that the $v_2$ of $J/ψ$ can play an important role in discriminating between $J/ψ$ production from the initial hard collisions and from the regeneration in the quark-gluon plasma.

preprint2010arXiv

Density matrix expansion for the MDI interaction

By assuming that the isospin- and momentum-dependent MDI interaction has a form similar to the Gogny-like effective two-body interaction with a Yukawa finite-range term and the momentum dependence only originates from the finite-range exchange interaction, we determine its parameters by comparing the predicted potential energy density functional in uniform nuclear matter with what has been usually given and used extensively in transport models for studying isospin effects in intermediate-energy heavy-ion collisions as well as in investigating the properties of hot asymmetric nuclear matter and neutron star matter. We then use the density matrix expansion to derive from the resulting finite-range exchange interaction an effective Skyrme-like zero-range interaction with density-dependent parameters. As an application, we study the transition density and pressure at the inner edge of neutron star crusts using the stability conditions derived from the linearized Vlasov equation for the neutron star matter.

preprint2010arXiv

Density slope of the nuclear symmetry energy from the neutron skin thickness of heavy nuclei

Expressing explicitly the parameters of the standard Skyrme interaction in terms of the macroscopic properties of asymmetric nuclear matter, we show in the Skyrme-Hartree-Fock approach that unambiguous correlations exist between observables of finite nuclei and nuclear matter properties. We find that existing data on neutron skin thickness $Δr_{np}$ of Sn isotopes give an important constraint on the symmetry energy $E_{sym}({ρ_{0}})$ and its density slope $L$ at saturation density ${ρ_{0}}$. Combining these constraints with those from recent analyses of isospin diffusion and double neutron/proton ratio in heavy-ion collisions at intermediate energies leads to a more stringent limit on $L$ approximately independent of $E_{sym}({ρ_{0}})$. The implication of these new constraints on the $Δr_{np}$ of $^{208}$Pb as well as the core-crust transition density and pressure in neutron stars is discussed.

preprint2010arXiv

Energy release from hadron-quark phase transition in neutron stars and the axial $w$-mode of gravitational waves

Describing the hyperonic and quark phases of neutron stars with an isospin- and momentum-dependent effective interaction for the baryon octet and the MIT bag model, respectively, and using the Gibbs conditions to construct the mixed phase, we study the energy release due to the hadron-quark phase transition. Moreover, the frequency and damping time of the first axial $w$-mode of gravitational waves are studied for both hyperonic and hybrid stars. We find that the energy release is much more sensitive to the bag constant than the density dependence of the nuclear symmetry energy. Also, the frequency of the $w$-mode is found to be significantly different with or without the hadron-quark phase transition and depends strongly on the value of the bag constant. Effects of the density dependence of the nuclear symmetry energy become, however, important for large values of the bag constant that lead to higher hadron-quark transition densities.

preprint2010arXiv

Isospin- and momentum-dependent effective interactions for the baryon octet and the properties of hybrid stars

The isospin- and momentum-dependent MDI interaction, which has been extensively used in intermediate-energy heavy-ion reactions to study the properties of asymmetric nuclear matter, is extended to include the nucleon-hyperon and hyperon-hyperon interactions by assuming same density, momentum and isospin dependence as for the nucleon-nucleon interaction. The parameters in these interactions are determined from the empirical hyperon single-particle potentials in symmetric nuclear matter at saturation density. The extended MDI interaction is then used to study in the mean-field approximation the equation of state of hypernuclear matter and also the properties of hybrid stars by including the phase transition from the hypernuclear matter to the quark matter at high densities. In particular, the effects of attractive and repulsive $Σ$N interactions and different values of symmetry energies on the hybrid star properties are investigated.

preprint2010arXiv

Isospin-dependent pion in-medium effects on charged pion ratio in heavy ion collisions

Using results from the chiral perturbation theory for the s-wave interaction and the delta-resonance model for the p-wave interaction of pions with nucleons, we have evaluated the spectral functions of pions in asymmetric nuclear matter with unequal proton and neutron densities. We find that in hot dense neutron-rich matter the strength of the spectral function of positively charged pion at low energies is somewhat larger than that of negatively charged pion. In a thermal model, this isospin-dependent effect slightly reduces the ratio of negatively charged to positively charged pions that are produced in heavy ion collisions induced by radioactive beams. Relevance of our results to the determination of the nuclear symmetry energy from measured ratio of negatively to positively charged pions produced in heavy ion collisions is discussed.

preprint2010arXiv

Medium effects on charged pion ratio in heavy ion collisions

We have recently studied in the delta-resonance--nucleon-hole model the dependence of the pion spectral function in hot dense asymmetric nuclear matter on the charge of the pion due to the pion p-wave interaction in nuclear medium. In a thermal model, this isospin-dependent effect enhances the ratio of negatively charged to positively charged pions in neutron-rich nuclear matter, and the effect is comparable to that due to the uncertainties in the theoretically predicted stiffness of nuclear symmetry energy at high densities. This effect is, however, reversed if we also take into account the s-wave interaction of the pion in nuclear medium as given by chiral perturbation theory, resulting instead in a slightly reduced ratio of negatively charged to positively charged pions. Relevance of our results to the determination of the nuclear symmetry energy from the ratio of negatively to positively charged pions produced in heavy ion collisions is discussed.

preprint2010arXiv

Thin Film Absorbers Based on Plasmonic Phase Resonances

We demonstrate an efficient double-layer light absorber by exciting plasmonic phase resonances. We show that the addition of grooves can cause mode splitting of the plasmonic waveguide cavity modes and all the new resonant modes exhibit large absorptivity greater than 90%. Some of the generated absorption peaks have wide-angle characteristics. Furthermore, we find that the proposed structure is fairly insensitive to the alignment error between different layers. The proposed plasmonic nano-structure designs may have exciting potential applications in thin film solar cells, thermal emitters, novel infrared detectors, and highly sensitive bio-sensors.

preprint2010arXiv

Transition density and pressure in hot neutron stars

Using the momentum-dependent MDI effective interaction for nucleons, we have studied the transition density and pressure at the boundary between the inner crust and liquid core of hot neutron stars. We find that their values are larger in neutrino-trapped neutron stars than in neutrino-free neutron stars. Furthermore, both are found to decrease with increasing temperature of a neutron star as well as increasing slope parameter of the nuclear symmetry energy, except that the transition pressure in neutrino-trapped neutron stars for the case of small symmetry energy slope parameter first increases and then decreases with increasing temperature. We have also studied the effect of the nuclear symmetry energy on the critical temperature above which the inner crust in a hot neutron star disappears and found that with increasing value of the symmetry energy slope parameter, the critical temperature decreases slightly in neutrino-trapped neutron stars but first decreases and then increases in neutrino-free neutron stars.

preprint2009arXiv

Incompressibility of asymmetric nuclear matter

The incompressibility $K_sat(δ)$ of isospin asymmetric nuclear matter at its saturation density. Our results show that in the expansion of $K_sat(δ)$ in powers of isospin asymmetry $δ$, i.e., $K_sat(δ)$=K_{0}+K_{sat,2}δ^{2}+K_{sat,4}δ^{4}+O(δ^{6})$, the magnitude of the 4th-order K_{sat,4} parameter is generally small. The 2nd-order K_{sat,2} parameter thus essentially characterizes the isospin dependence of the incompressibility of asymmetric nuclear matter at saturation density. Furthermore, the K_{sat,2} can be expressed as K_{sat,2}=K_{sym}-6L-J_{0}/{K_{0}L in terms of the slope parameter $L$ and the curvature parameter $K_{\mathrm{sym}}$ of the symmetry energy and the third-order derivative parameter $J_0$ of the energy of symmetric nuclear matter at saturation density, and we find the higher order $J_0$ contribution to K_{sat,2} generally cannot be neglected. Also, we have found a linear correlation between K_{sym} and $L$ as well as between $J_{0}/K_{0}$ and $K_{0}$. Using these correlations together with the empirical constraints on $K_{0}$ and $L$, the nuclear symmetry energy $E_sym(ρ_{0})$ at normal nuclear density, and the nucleon effective mass, we have obtained an estimated value of K_{sat,2}=-370 +- 120 MeV for the 2nd-order parameter in the isospin asymmetry expansion of the incompressibility of asymmetric nuclear matter at its saturation density.

preprint2009arXiv

Nuclear constraints on properties of neutron star crusts

The transition density $ρ_{t}$ and pressure $P_{t}$ at the inner edge separating the liquid core from the solid crust of neutron stars are systematically studied using a modified Gogny (MDI) and 47 popular Skyrme interactions within well established dynamical and thermodynamical methods. It is shown that the widely used parabolic approximation to the full Equation of State (EOS) of isospin asymmetric nuclear matter may lead to huge errors in estimating the ρ_{t} and P_{t}, especially for stiffer symmetry energy functionals $E_{sym}(ρ)$. The ρ_{t} and P_{t} decrease roughly linearly with the increasing slope parameter $L$ of the $E_{sym}(ρ)$ using the full EOS within both methods. It is also shown that the thickness, fractional mass and moment of inertia of neutron star crust are all very sensitive to the parameter $L$ through the $ρ_{t}$. Moreover, it is shown that the $E_{sym}(ρ)$ constrained in the same sub-saturation density range as the neutron star crust by the isospin diffusion data in heavy-ion collisions at intermediate energies limits the transition density and pressure to 0.040$ fm^-3}< ρ_{t} < 0.065$ fm^-3 and 0.01 MeV/fm^3 < P_{t} < 0.26$ MeV/fm^3, respectively. These constrained values for the transition density and pressure are significantly lower than their fiducial values currently used in the literature. Furthermore, the mass-radius relation and several other properties closely related to the neutron star crust are studied by using the MDI interaction. It is found that the newly constrained $ρ_t$ and $P_t$ together with the earlier estimate of $ΔI/I>0.014$ for the crustal fraction of the moment of inertia of the Vela pulsar impose a stringent constraint of R>= 4.7+4.0M/M_sun km for the radius $R$ and mass $M$ of neutron stars.

preprint2009arXiv

Nuclear constraints on the inner edge of neutron star crusts

We show that the widely used parabolic approximation to the Equation of State (EOS) of asymmetric nuclear matter leads systematically to significantly higher core-crust transition densities and pressures. Using an EOS for neutron-rich nuclear matter constrained by the isospin diffusion data from heavy-ion reactions in the same sub-saturation density range as the neutron star crust, the density and pressure at the inner edge separating the liquid core from the solid crust of neutron stars are determined to be 0.040 fm$^{-3}$ $\leq ρ_{t}\leq 0.065$ fm$^{-3}$ and 0.01 MeV/fm$^{3}$ $\leq P_{t}\leq 0.26$ MeV/fm$^{3}$, respectively. Implications of these constraints on the Vela pulsar are discussed.

preprint2009arXiv

Transition Density and Pressure at the Inner Edge of Neutron Star Crusts

Using the nuclear symmetry energy that has been recently constrained by the isospin diffusion data in intermediate-energy heavy ion collisions, we have studied the transition density and pressure at the inner edge of neutron star crusts, and they are found to be 0.040 fm$^{-3}$ $\leq ρ_{t}\leq 0.065$ fm$^{-3}$ and 0.01 MeV/fm$^{3}$ $\leq P_{t}\leq 0.26$ MeV/fm$^{3}$, respectively, in both the dynamical and thermodynamical approaches. We have also found that the widely used parabolic approximation to the equation of state of asymmetric nuclear matter gives significantly higher values of core-crust transition density and pressure, especially for stiff symmetry energies. With these newly determined transition density and pressure, we have obtained an improved relation between the mass and radius of neutron stars.

preprint2006arXiv

Density of states of a two dimensional XY model from Wang-Landau algorithm

Using Wang-landau algorithm combined with analytic method, the density of states of two dimensional XY model on square lattices of sizes $16\times16$, $24\times24$ and $32\times32$ is accurately calculated. Thermodynamic quantities, such as internal energy, free energy, entropy and specific heat are obtained from the resulted density of states by numerical integration. From the entropy curve symptoms of phase transition is observed. A general method of calculation of the density of states of continuous models by simulation combined with analytical method is proposed.

preprint2004arXiv

MgB2 thick film grown on Stainless steel substrate with ductility

MgB2 thick film was synthesized on the stainless steel substrate by the technique of hybrid physical-chemical vapor deposition (HPCVD), using Mg ingot and B2H6 as the raw materials. The film thickness is about 10 microns. The scanning electron microscope (SEM) images reveal that it is consisting of highly dense MgB2 crystals with the size ranging from 0.2 to 3 microns. The superconducting transition occurs at 38 K (Tc, onset) and ends at 27 K (Tc, zero), giving the transition width of 11 K. The fabricated film exhibits high ductility and remains attached to the substrate after it was bent to a curvature of about 200 microns.

Jun Xu

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126 published item(s)

The Attention Market: Interpreting Online Fair Re-ranking as Manifold Optimization under Walrasian Equilibrium

A precise proof of the n-variable Bekic principle

Large Language Model Sourcing: A Survey

Revisiting angular momentum conservation in transport simulations of intermediate-energy heavy-ion collisions

A Model-Agnostic Causal Learning Framework for Recommendation using Search Data

A Semi-Synthetic Dataset Generation Framework for Causal Inference in Recommender Systems

Bayesian inference of finite-nuclei observables based on the KIDS model

Building Embedded Systems Like It's 1996

Dynamic Message Propagation Network for RGB-D Salient Object Detection

Error-free approximation of explicit linear MPC through lattice piecewise affine expression

Explainable Legal Case Matching via Inverse Optimal Transport-based Rationale Extraction

Incremental Cross-view Mutual Distillation for Self-supervised Medical CT Synthesis

Localizing the Recurrent Laryngeal Nerve via Ultrasound with a Bayesian Shape Framework

MobileSal: Extremely Efficient RGB-D Salient Object Detection

Non-negative Sparse and Collaborative Representation for Pattern Classification

Person-job fit estimation from candidate profile and related recruitment history with co-attention neural networks

Spinodal Enhancement of Light Nuclei Yield Ratio in Relativistic Heavy Ion Collisions

swTVM: Towards Optimized Tensor Code Generation for Deep Learning on Sunway Many-Core Processor

The Security War in File Systems: An Empirical Study from A Vulnerability-Centric Perspective

Transport Model Comparison Studies of Intermediate-Energy Heavy-Ion Collisions

Where to Go for the Holidays: Towards Mixed-Type Dialogs for Clarification of User Goals

WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

On the ideal shortest vector problem over random rational primes

Privacy-preserving Channel Estimation in Cell-free Hybrid Massive MIMO Systems

AGNs are not that cool: revisiting the intrinsic AGN far-infrared spectral energy distribution

An Empirical Study on Benchmarks of Artificial Software Vulnerabilities

Auto-captions on GIF: A Large-scale Video-sentence Dataset for Vision-language Pre-training

Bilateral Attention Network for RGB-D Salient Object Detection

Centrality fluctuations and decorrelations in heavy-ion collisions

Conditional Variational Image Deraining

Discovering Dialog Structure Graph for Open-Domain Dialog Generation

Isospin effect on baryon and charge fluctuations from the pNJL model

Joint Inference on Truth/Rumor and Their Sources in Social Networks

Learning to Learn Kernels with Variational Random Features

Learning to Learn with Variational Information Bottleneck for Domain Generalization

NLH: A Blind Pixel-level Non-local Method for Real-world Image Denoising

Nucleus giant resonances from an improved isospin-dependent Boltzmann-Uehling-Uhlenbeck transport approach

Pre-training for Video Captioning Challenge 2020 Summary

Privacy for All: Demystify Vulnerability Disparity of Differential Privacy against Membership Inference Attack

Properties of strange quark stars with isovector interactions

QPS-r: A Cost-Effective Crossbar Scheduling Algorithm and Its Stability and Delay Analysis

SetRank: Learning a Permutation-Invariant Ranking Model for Information Retrieval

SoK: All You Ever Wanted to Know About x86/x64 Binary Disassembly But Were Afraid to Ask

Space- and Computationally-Efficient Set Reconciliation via Parity Bitmap Sketch (PBS)

STAR: A Structure and Texture Aware Retinex Model

Univariate ReLU neural network and its application in nonlinear system identification

Variance Reduction for Deep Q-Learning using Stochastic Recursive Gradient

Compact fiber optical interferometer technique to measure picometer displacements in biological piezoelectric materials

Investigation of HIV-1 Gag binding with RNAs and Lipids using Atomic Force Microscopy

RANet: Ranking Attention Network for Fast Video Object Segmentation

Spectro-polarimetric Observations at the NVST: I. Instrumental Polarization Calibration and Primary Measurements

A Study of MatchPyramid Models on Ad-hoc Retrieval

Equations of motion of test particles for solving the spin-dependent Boltzmann-Vlasov equation

Incorporating Semantic Knowledge into Latent Matching Model in Search

Internet of Things Applications: Animal Monitoring with Unmanned Aerial Vehicle

Isospin properties of quark matter from a 3-flavor NJL model

Kibble-Zurek dynamics in an array of coupled binary Bose condensates

Match-SRNN: Modeling the Recursive Matching Structure with Spatial RNN

Semantic Regularities in Document Representations

Spin transport in intermediate-energy heavy-ion collisions as a probe of in-medium spin-orbit interactions

Text Matching as Image Recognition

Understanding transport simulations of heavy-ion collisions at 100 and 400 AMeV: Comparison of heavy ion transport codes under controlled conditions

Universal spatiotemporal dynamics of spontaneous superfluidity breakdown in the presence of synthetic gauge fields

A Deep Architecture for Semantic Matching with Multiple Positional Sentence Representations

Cluster Gutzwiller study of Bose-Hubbard ladder: ground-state phase diagram and many-body Landau-Zener dynamics

Data Allocation in a Heterogeneous Disk Array - HDA with Multiple RAID Levels for Database Applications

Dynamical effects of spin-dependent interactions in low- and intermediate-energy heavy-ion reactions

Dynamical generation of dark solitons in spin-orbit-coupled Bose-Einstein condensates

Extraordinary focusing of sound above a soda can array without time reversal

Influence of neutron-skin thickness on $π^{-}/π^{+}$ ratio in Pb+Pb collisions

Instantly Obsoleting the Address-code Associations: A New Principle for Defending Advanced Code Reuse Attack

Investigating the scaling of higher-order flows in relativistic heavy-ion collisions

Isospin splitting of nucleon effective mass and shear viscosity of nuclear matter

Mean-field potential effects on particle and antiparticle elliptic flows in the beam-energy scan program at RHIC