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Researcher profile

Yang Xu

Yang Xu contributes to research discovery and scholarly infrastructure.

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Computation and LanguageMachine LearningArtificial IntelligenceComputer VisionRoboticsastro-ph.IMcond-mat.mtrl-scicond-mat.softcond-mat.str-elInformation Theorymath.ITmath.OCNetworking and Internet Architectureastro-ph.GABiological PhysicsComputational Engineering, Finance, and Sciencecs.CYeess.SYhep-phInformation RetrievalNeural and Evolutionary Computingnlin.AOphysics.class-phphysics.med-phphysics.opticsphysics.soc-phquant-phSoftware EngineeringSystems and Control

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Published work

44 published item(s)

preprint2026arXiv

Decoding Scientific Experimental Images: The SPUR Benchmark for Perception, Understanding, and Reasoning

We introduce SPUR, a comprehensive benchmark for scientific experimental image perception, understanding, and reasoning, comprising 4,264 question-answering (QA) pairs derived from 1,084 expert-curated images. SPUR features three key innovations: (1) Panel-Level Fine-Grained Perception: evaluating the visual perception of multimodal large language models (MLLMs) across three dimensions (numerical, morphological, and information localization) on six fine-grained panel types; (2) Cross-Panel Relation Understanding: utilizing complex images with an average of 14.3 panels per sample to evaluate MLLMs' ability to decipher intricate cross-panel relations; (3) Expert-Level Reasoning: assessment of qualitative and quantitative reasoning across five experimental paradigms to determine if models can infer conclusions from evidence as human experts do. Comprehensive evaluation of 20 MLLMs and four multimodal Chain-of-Thought (MCoT) methods reveals that current models fall significantly short of the expert-level requirements for scientific image interpretation, underscoring a critical bottleneck in AI for Science (AI4S) research.

0 citations0 reviewsNo code linkedOpen access
preprint2025arXiv

Anomalous Hall effect and rich magnetic phase diagram of Mn$_{100-x}$Rh$_{x}$ epitaxial films

A series of Mn$_{100-x}$Rh$_x$ ($20 \le x \le 50$) thin films were epitaxially grown on the MgO substrate using magnetron sputtering technique, and were systematically investigated by magnetization, longitudinal electrical resistivity, and transverse Hall resistivity. After optimizing the growth conditions, phase-pure Mn$_{100-x}$Rh$_x$ films with a cubic CsCl-type structure were obtained, and their magnetic phase diagram was built. The manipulation of Rh content leads to a rich magnetic phase diagram, where three different regimes can be identified: for $x < 40$, Mn$_{100-x}$Rh$_x$ films undergo a ferromagnetic (FM) transition below $T_\mathrm{C} \approx$ 330-350 K; for $40 \le x \le 45$, in addition to the FM transition at $T_\mathrm{C} \approx$ 200 K, Mn$_{100-x}$Rh$_x$ films undergo a FM-to-antiferromagnetic (AFM) transition at $T_\mathrm{N} \approx$ 120 K; finally for $x > 45$, only one AFM transition at $T_\mathrm{N} \approx$ 150 K can be tracked. All the Mn$_{100-x}$Rh$_x$ films exhibit distinct anomalous Hall effect in their magnetically ordered state, which is most likely due to the intrinsic Berry-curvature mechanism. In addition, all the anomalous Hall transport properties, including the resistivity, conductivity, and angle exhibit a strong correlation with the magnetic properties of Mn$_{100-x}$Rh$_x$ films, which become most evident for $x$ = 35. Our systematic investigations suggest a strong correlation between magnetic properties and electronic band topology in Mn$_{100-x}$Rh$_x$ films, highlighting their great potential for AFM spintronics.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

A Unified Single-loop Alternating Gradient Projection Algorithm for Nonconvex-Concave and Convex-Nonconcave Minimax Problems

Much recent research effort has been directed to the development of efficient algorithms for solving minimax problems with theoretical convergence guarantees due to the relevance of these problems to a few emergent applications. In this paper, we propose a unified single-loop alternating gradient projection (AGP) algorithm for solving smooth nonconvex-(strongly) concave and (strongly) convex-nonconcave minimax problems. AGP employs simple gradient projection steps for updating the primal and dual variables alternatively at each iteration. We show that it can find an $\varepsilon$-stationary point of the objective function in $\mathcal{O}\left( \varepsilon ^{-2} \right)$ (resp. $\mathcal{O}\left( \varepsilon ^{-4} \right)$) iterations under nonconvex-strongly concave (resp. nonconvex-concave) setting. Moreover, its gradient complexity to obtain an $\varepsilon$-stationary point of the objective function is bounded by $\mathcal{O}\left( \varepsilon ^{-2} \right)$ (resp., $\mathcal{O}\left( \varepsilon ^{-4} \right)$) under the strongly convex-nonconcave (resp., convex-nonconcave) setting. To the best of our knowledge, this is the first time that a simple and unified single-loop algorithm is developed for solving both nonconvex-(strongly) concave and (strongly) convex-nonconcave minimax problems. Moreover, the complexity results for solving the latter (strongly) convex-nonconcave minimax problems have never been obtained before in the literature. Numerical results show the efficiency of the proposed AGP algorithm. Furthermore, we extend the AGP algorithm by presenting a block alternating proximal gradient (BAPG) algorithm for solving more general multi-block nonsmooth nonconvex-(strongly) concave and (strongly) convex-nonconcave minimax problems. We can similarly establish the gradient complexity of the proposed algorithm under these four different settings.

0 citations0 reviewsNo code linkedOpen access
preprint2023arXiv

Bayesian Generalized Kernel Inference for Exploration of Autonomous Robots

This paper concerns realizing highly efficient information-theoretic robot exploration with desired performance in complex scenes. We build a continuous lightweight inference model to predict the mutual information (MI) and the associated prediction confidence of the robot&#39;s candidate actions which have not been evaluated explicitly. This allows the decision-making stage in robot exploration to run with a logarithmic complexity approximately, this will also benefit online exploration in large unstructured, and cluttered places that need more spatial samples to assess and decide. We also develop an objective function to balance the local optimal action with the highest MI value and the global choice with high prediction variance. Extensive numerical and dataset simulations show the desired efficiency of our proposed method without losing exploration performance in different environments. We also provide our open-source implementation codes released on GitHub for the robot community.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

An asperity-based statistical model for the adhesive friction of elastic nominally flat rough contact interfaces

Contact mechanics-based models for the friction of nominally flat rough surfaces have not been able to adequately capture certain key experimentally observed phenomenona, such as the transition from a static friction peak to a lower level of sliding friction and the shear-induced contact area reduction that has been observed in the pre-sliding regime especially for soft materials. Here, we propose a statistical model based on physically-rooted contact mechanics laws describing the micromechanics of individual junctions. The model considers the quasi-static tangential loading, up to full sliding, of the contact between a smooth rigid flat surface and a nominally flat linear elastic rough surface comprising random independent spherical asperities, and accounts for the coupling between adhesion and friction at the micro-junction level. The model qualitatively reproduces both the macroscopic shear-induced contact area reduction and, remarkably, the static friction peak without the need to explicitly introduce two different friction levels. It also demonstrates how the static friction peak and contact area evolution depend on the normal load and certain key microscale interface properties such as surface energy, mode mixity and frictional shear strength. &#34;Tougher&#34; interfaces (i.e. with larger surface energy and smaller mode mixity parameter) are shown to result in a larger real contact area and a more pronounced static friction peak. Overall, this work provides important insights about how key microscale properties operating at the asperity level can combine with the surface statistics to reproduce important macroscopic responses observed in rough frictionalsoft contact experiments.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Axial and Vector Structure Functions for Lepton-Nucleon Scattering, NuFact 2021 Update

We report on an update (2021) of a phenomenological model for inelastic neutrino- and electron-nucleon scattering cross sections using effective leading order parton distribution functions with a new scaling variable $ξ_w$. Non-perturbative effects are well described using the $ξ_w$ scaling variable in combination with multiplicative $K$ factors at low $Q^2$. The model describes all inelastic charged-lepton-nucleon scattering data (HERA/NMC/BCDMS/SLAC/JLab) ranging from very high $Q^2$ to very low $Q^2$ and down to the $Q^2=0$ photo-production region. The model has been developed to be used in analyses of neutrino oscillation experiments in the few GeV region. The 2021 update accounts for the difference between axial and vector structure functions which brings it into much better agreement with neutrino-nucleon total cross section measurements. The model has been developed primarily for hadronic final state masses $W$ above 1.8 GeV. However with additional parameters the model also describes the $average$ neutrino cross sections in the resonance region down to $W$=1.4 GeV.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Confidence-rich Localization and Mapping based on Particle Filter for Robotic Exploration

This paper mainly studies the localization and mapping of range sensing robots in the confidence-rich map (CRM) and then extends it to provide a full state estimate for information-theoretic exploration. Most previous works about active simultaneous localization and mapping and exploration always assumed the known robot poses or utilized inaccurate information metrics to approximate pose uncertainty, resulting in imbalanced exploration performance and efficiency in the unknown environment. This inspires us to extend the confidence-rich mutual information (CRMI) with measurable pose uncertainty. Specifically, we propose a Rao-Blackwellized particle filter-based localization and mapping scheme (RBPF-CLAM) for CRM, then we develop a new closed-form weighting method to improve the localization accuracy without scan matching. We further derive the uncertain CRMI (UCRMI) with the weighted particles by a more accurate approximation. Simulations and experimental evaluations show the localization accuracy and exploration performance of the proposed methods.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Crosslinguistic word order variation reflects evolutionary pressures of dependency and information locality

Languages vary considerably in syntactic structure. About 40% of the world&#39;s languages have subject-verb-object order, and about 40% have subject-object-verb order. Extensive work has sought to explain this word order variation across languages. However, the existing approaches are not able to explain coherently the frequency distribution and evolution of word order in individual languages. We propose that variation in word order reflects different ways of balancing competing pressures of dependency locality and information locality, whereby languages favor placing elements together when they are syntactically related or contextually informative about each other. Using data from 80 languages in 17 language families and phylogenetic modeling, we demonstrate that languages evolve to balance these pressures, such that word order change is accompanied by change in the frequency distribution of the syntactic structures which speakers communicate to maintain overall efficiency. Variability in word order thus reflects different ways in which languages resolve these evolutionary pressures. We identify relevant characteristics that result from this joint optimization, particularly the frequency with which subjects and objects are expressed together for the same verb. Our findings suggest that syntactic structure and usage across languages co-adapt to support efficient communication under limited cognitive resources.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

HistBERT: A Pre-trained Language Model for Diachronic Lexical Semantic Analysis

Contextualized word embeddings have demonstrated state-of-the-art performance in various natural language processing tasks including those that concern historical semantic change. However, language models such as BERT was trained primarily on contemporary corpus data. To investigate whether training on historical corpus data improves diachronic semantic analysis, we present a pre-trained BERT-based language model, HistBERT, trained on the balanced Corpus of Historical American English. We examine the effectiveness of our approach by comparing the performance of the original BERT and that of HistBERT, and we report promising results in word similarity and semantic shift analysis. Our work suggests that the effectiveness of contextual embeddings in diachronic semantic analysis is dependent on the temporal profile of the input text and care should be taken in applying this methodology to study historical semantic change.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Image Captioning In the Transformer Age

Image Captioning (IC) has achieved astonishing developments by incorporating various techniques into the CNN-RNN encoder-decoder architecture. However, since CNN and RNN do not share the basic network component, such a heterogeneous pipeline is hard to be trained end-to-end where the visual encoder will not learn anything from the caption supervision. This drawback inspires the researchers to develop a homogeneous architecture that facilitates end-to-end training, for which Transformer is the perfect one that has proven its huge potential in both vision and language domains and thus can be used as the basic component of the visual encoder and language decoder in an IC pipeline. Meantime, self-supervised learning releases the power of the Transformer architecture that a pre-trained large-scale one can be generalized to various tasks including IC. The success of these large-scale models seems to weaken the importance of the single IC task. However, we demonstrate that IC still has its specific significance in this age by analyzing the connections between IC with some popular self-supervised learning paradigms. Due to the page limitation, we only refer to highly important papers in this short survey and more related works can be found at https://github.com/SjokerLily/awesome-image-captioning.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Improving short-term bike sharing demand forecast through an irregular convolutional neural network

As an important task for the management of bike sharing systems, accurate forecast of travel demand could facilitate dispatch and relocation of bicycles to improve user satisfaction. In recent years, many deep learning algorithms have been introduced to improve bicycle usage forecast. A typical practice is to integrate convolutional (CNN) and recurrent neural network (RNN) to capture spatial-temporal dependency in historical travel demand. For typical CNN, the convolution operation is conducted through a kernel that moves across a &#34;matrix-format&#34; city to extract features over spatially adjacent urban areas. This practice assumes that areas close to each other could provide useful information that improves prediction accuracy. However, bicycle usage in neighboring areas might not always be similar, given spatial variations in built environment characteristics and travel behavior that affect cycling activities. Yet, areas that are far apart can be relatively more similar in temporal usage patterns. To utilize the hidden linkage among these distant urban areas, the study proposes an irregular convolutional Long-Short Term Memory model (IrConv+LSTM) to improve short-term bike sharing demand forecast. The model modifies traditional CNN with irregular convolutional architecture to extract dependency among &#34;semantic neighbors&#34;. The proposed model is evaluated with a set of benchmark models in five study sites, which include one dockless bike sharing system in Singapore, and four station-based systems in Chicago, Washington, D.C., New York, and London. We find that IrConv+LSTM outperforms other benchmark models in the five cities. The model also achieves superior performance in areas with varying levels of bicycle usage and during peak periods. The findings suggest that &#34;thinking beyond spatial neighbors&#34; can further improve short-term travel demand prediction of urban bike sharing systems.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Isometries and MacWilliams Extension Property for Weighted Poset Metric

Let $\mathbf{H}$ be the cartesian product of a family of left modules over a ring $S$, indexed by a finite set $Ω$. We are concerned with the $(\mathbf{P},ω)$-weight on $\mathbf{H}$, where $\mathbf{P}=(Ω,\preccurlyeq_{\mathbf{P}})$ is a poset and $ω:Ω\longrightarrow\mathbb{R}^{+}$ is a weight function. We characterize the group of $(\mathbf{P},ω)$-weight isometries of $\mathbf{H}$, and give a canonical decomposition for semi-simple subcodes of $\mathbf{H}$ when $\mathbf{P}$ is hierarchical. We then study the MacWilliams extension property (MEP) for $(\mathbf{P},ω)$-weight. We show that the MEP implies the unique decomposition property (UDP) of $(\mathbf{P},ω)$, which further implies that $\mathbf{P}$ is hierarchical if $ω$ is identically $1$. For the case that either $\mathbf{P}$ is hierarchical or $ω$ is identically $1$, we show that the MEP for $(\mathbf{P},ω)$-weight can be characterized in terms of the MEP for Hamming weight, and give necessary and sufficient conditions for $\mathbf{H}$ to satisfy the MEP for $(\mathbf{P},ω)$-weight when $S$ is an Artinian simple ring (either finite or infinite). When $S$ is a finite field, in the context of $(\mathbf{P},ω)$-weight, we compare the MEP with other coding theoretic properties including the MacWilliams identity, Fourier-reflexivity of partitions and the UDP, and show that the MEP is strictly stronger than all the rest among them.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

K-Detector: Identifying Duplicate Crash Failures in Large-Scale Software Delivery

After a developer submits code, corresponding test cases arise to ensure the quality of software delivery. Test failures would occur during this period, such as crash, error, and timeout. Since it takes time for developers to resolve them, many duplicate failures will happen during this period. In the delivery practice of SAP HANA, crash triage is considered as the most time-consuming task. If duplicate crash failures can be automatically identified, the degree of automation will be significantly enhanced. To find such duplicates, we propose a training-based mathematical model that utilizes component information of SAP HANA to achieve better crash similarity comparison. We implement our approach in a tool named Knowledge-based Detector (K-Detector), which is verified by 11,208 samples and performs 0.986 in AUC. Furthermore, we have deployed K-Detector to the production environment, and it can save 97% human efforts in crash triage as statistics.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

KE-QI: A Knowledge Enhanced Article Quality Identification Dataset

With so many articles of varying qualities being produced every moment, it is a very urgent task to screen outstanding articles and commit them to social media. To our best knowledge, there is a lack of datasets and mature research works in identifying high-quality articles. Consequently, we conduct some surveys and finalize 7 objective indicators to annotate the quality of 10k articles. During annotation, we find that many characteristics of high-quality articles (e.g., background) rely more on extensive external knowledge than inner semantic information of articles. In response, we link extracted article entities to Baidu Encyclopedia, then propose Knowledge Enhanced article Quality Identification (KE-QI) dataset. To make better use of external knowledge, we propose a compound model which fuses the text and external knowledge information via a gate unit to classify the quality of an article. Our experimental results on KE-QI show that with initialization of our pre-trained Node2Vec model, our model achieves about 78\% $F_1$, outperforming other baselines.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

LayoutLMv2: Multi-modal Pre-training for Visually-Rich Document Understanding

Pre-training of text and layout has proved effective in a variety of visually-rich document understanding tasks due to its effective model architecture and the advantage of large-scale unlabeled scanned/digital-born documents. We propose LayoutLMv2 architecture with new pre-training tasks to model the interaction among text, layout, and image in a single multi-modal framework. Specifically, with a two-stream multi-modal Transformer encoder, LayoutLMv2 uses not only the existing masked visual-language modeling task but also the new text-image alignment and text-image matching tasks, which make it better capture the cross-modality interaction in the pre-training stage. Meanwhile, it also integrates a spatial-aware self-attention mechanism into the Transformer architecture so that the model can fully understand the relative positional relationship among different text blocks. Experiment results show that LayoutLMv2 outperforms LayoutLM by a large margin and achieves new state-of-the-art results on a wide variety of downstream visually-rich document understanding tasks, including FUNSD (0.7895 $\to$ 0.8420), CORD (0.9493 $\to$ 0.9601), SROIE (0.9524 $\to$ 0.9781), Kleister-NDA (0.8340 $\to$ 0.8520), RVL-CDIP (0.9443 $\to$ 0.9564), and DocVQA (0.7295 $\to$ 0.8672). We made our model and code publicly available at \url{https://aka.ms/layoutlmv2}.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

ML4CO-KIDA: Knowledge Inheritance in Dataset Aggregation

The Machine Learning for Combinatorial Optimization (ML4CO) NeurIPS 2021 competition aims to improve state-of-the-art combinatorial optimization solvers by replacing key heuristic components with machine learning models. On the dual task, we design models to make branching decisions to promote the dual bound increase faster. We propose a knowledge inheritance method to generalize knowledge of different models from the dataset aggregation process, named KIDA. Our improvement overcomes some defects of the baseline graph-neural-networks-based methods. Further, we won the $1$\textsuperscript{st} Place on the dual task. We hope this report can provide useful experience for developers and researchers. The code is available at https://github.com/megvii-research/NeurIPS2021-ML4CO-KIDA.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Neural reality of argument structure constructions

In lexicalist linguistic theories, argument structure is assumed to be predictable from the meaning of verbs. As a result, the verb is the primary determinant of the meaning of a clause. In contrast, construction grammarians propose that argument structure is encoded in constructions (or form-meaning pairs) that are distinct from verbs. Decades of psycholinguistic research have produced substantial empirical evidence in favor of the construction view. Here we adapt several psycholinguistic studies to probe for the existence of argument structure constructions (ASCs) in Transformer-based language models (LMs). First, using a sentence sorting experiment, we find that sentences sharing the same construction are closer in embedding space than sentences sharing the same verb. Furthermore, LMs increasingly prefer grouping by construction with more input data, mirroring the behaviour of non-native language learners. Second, in a &#34;Jabberwocky&#34; priming-based experiment, we find that LMs associate ASCs with meaning, even in semantically nonsensical sentences. Our work offers the first evidence for ASCs in LMs and highlights the potential to devise novel probing methods grounded in psycholinguistic research.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Noun2Verb: Probabilistic frame semantics for word class conversion

Humans can flexibly extend word usages across different grammatical classes, a phenomenon known as word class conversion. Noun-to-verb conversion, or denominal verb (e.g., to Google a cheap flight), is one of the most prevalent forms of word class conversion. However, existing natural language processing systems are impoverished in interpreting and generating novel denominal verb usages. Previous work has suggested that novel denominal verb usages are comprehensible if the listener can compute the intended meaning based on shared knowledge with the speaker. Here we explore a computational formalism for this proposal couched in frame semantics. We present a formal framework, Noun2Verb, that simulates the production and comprehension of novel denominal verb usages by modeling shared knowledge of speaker and listener in semantic frames. We evaluate an incremental set of probabilistic models that learn to interpret and generate novel denominal verb usages via paraphrasing. We show that a model where the speaker and listener cooperatively learn the joint distribution over semantic frame elements better explains the empirical denominal verb usages than state-of-the-art language models, evaluated against data from 1) contemporary English in both adult and child speech, 2) contemporary Mandarin Chinese, and 3) the historical development of English. Our work grounds word class conversion in probabilistic frame semantics and bridges the gap between natural language processing systems and humans in lexical creativity.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Optimization of rule-based energy management strategies for hybrid vehicles using dynamic programming

Reducing energy consumption is a key focus for hybrid electric vehicle (HEV) development. The popular vehicle dynamic model used in many energy management optimization studies does not capture the vehicle dynamics that the in-vehicle measurement system does. However, feedback from the measurement system is what the vehicle controller actually uses to manage energy consumption. Therefore, the optimization solely using the model does not represent what the vehicle controller sees in the vehicle. This paper reports the utility factor-weighted energy consumption using a rule-based strategy under a real-world representative drive cycle. In addition, the vehicle test data was used to perform the optimization approach. By comparing results from both rule-based and optimization-based strategies, the areas for further improving rule-based strategy are discussed. Furthermore, recent development of OBD raises a concern about the increase of energy consumption. This paper investigates the energy consumption increase with extensive OBD usage.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Quantile Off-Policy Evaluation via Deep Conditional Generative Learning

Off-Policy evaluation (OPE) is concerned with evaluating a new target policy using offline data generated by a potentially different behavior policy. It is critical in a number of sequential decision making problems ranging from healthcare to technology industries. Most of the work in existing literature is focused on evaluating the mean outcome of a given policy, and ignores the variability of the outcome. However, in a variety of applications, criteria other than the mean may be more sensible. For example, when the reward distribution is skewed and asymmetric, quantile-based metrics are often preferred for their robustness. In this paper, we propose a doubly-robust inference procedure for quantile OPE in sequential decision making and study its asymptotic properties. In particular, we propose utilizing state-of-the-art deep conditional generative learning methods to handle parameter-dependent nuisance function estimation. We demonstrate the advantages of this proposed estimator through both simulations and a real-world dataset from a short-video platform. In particular, we find that our proposed estimator outperforms classical OPE estimators for the mean in settings with heavy-tailed reward distributions.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Reflexivity of Partitions Induced by Weighted Poset Metric and Combinatorial Metric

Let $\mathbf{H}$ be the Cartesian product of a family of finite abelian groups. Via a polynomial approach, we give sufficient conditions for a partition of $\mathbf{H}$ induced by weighted poset metric to be reflexive, which also become necessary for some special cases. Moreover, by examining the roots of the Krawtchouk polynomials, we establish non-reflexive partitions of $\mathbf{H}$ induced by combinatorial metric. When $\mathbf{H}$ is a vector space over a finite field $\mathbb{F}$, we consider the property of admitting MacWilliams identity (PAMI) and the MacWilliams extension property (MEP) for partitions of $\mathbf{H}$. With some invariance assumptions, we show that two partitions of $\mathbf{H}$ admit MacWilliams identity if and only if they are mutually dual and reflexive, and any partition of $\mathbf{H}$ satisfying the MEP is in fact an orbit partition induced by some subgroup of $\Aut_{\mathbb{F}}(\mathbf{H})$, which is necessarily reflexive. As an application of the aforementioned results, we establish partitions of $\mathbf{H}$ induced by combinatorial metric that do not satisfy the MEP, which further enable us to provide counter-examples to a conjecture proposed by Pinheiro, Machado and Firer in \cite{39}.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Revisiting the Persson theory of elastoplastic contact: A simpler closed-form solution and a rigorous proof of boundary conditions

Persson&#39;s theory of contact is extensively used in the study of the purely normal interaction between a nominally flat rough surface and a rigid flat. In the literature, Persson&#39;s theory was successfully applied to the elastoplastic contact problem with a scale-independent hardness $H$. However, it yields a closed-form solution, $P(p, ξ)$, in terms of an infinite sum of sines. In this study, $P(p, ξ)$ is found to have a simpler form which is a superposition of three Gaussian functions. A rigorous proof of the boundary condition $P(p=0, ξ)=P(p=H, ξ) = 0$ is given based on the new solution.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Robust Inertial-aided Underwater Localization based on Imaging Sonar Keyframes

This article focuses on feature-based underwater localization and navigation for autonomous underwater vehicles (AUVs) using 2D imaging sonar measurements. The sparsity of underwater acoustic features and the loss of elevation angle in sonar images may introduce wrong feature matches or insufficient features for optimization-based underwater localization (i.e. under-constrained/degeneracy cases). This motivates us to propose a novel inertial-aided sliding window optimization framework to improve the estimation accuracy and the robustness to front-end outliers. Concretely, we first discriminate under-constrained/ well-constrained sonar frames and define sonar keyframes (SKFs) based on the Jacobian matrix derived from odometry and sonar measurements. To utilize the past well-constrained SKFs mostly, we design a size-adjustable windowed back-end optimization scheme based on singular values. We also prove that the landmark triangulation failure (navigation problem) caused by sonar motion can be solved in 2D scenes. Comparative simulation and evaluation on a public dataset show the proposed method outperforms the existing ones in pose estimation and robustness even without loop closure and also ensures the real-time performance for online applications.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

The Machine Learning for Combinatorial Optimization Competition (ML4CO): Results and Insights

Combinatorial optimization is a well-established area in operations research and computer science. Until recently, its methods have focused on solving problem instances in isolation, ignoring that they often stem from related data distributions in practice. However, recent years have seen a surge of interest in using machine learning as a new approach for solving combinatorial problems, either directly as solvers or by enhancing exact solvers. Based on this context, the ML4CO aims at improving state-of-the-art combinatorial optimization solvers by replacing key heuristic components. The competition featured three challenging tasks: finding the best feasible solution, producing the tightest optimality certificate, and giving an appropriate solver configuration. Three realistic datasets were considered: balanced item placement, workload apportionment, and maritime inventory routing. This last dataset was kept anonymous for the contestants.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Ultrafast disinfection of SARS-CoV-2 viruses

The wide use of surgical masks has been proven effective for mitigating the spread of respiration diseases, such as COVID-19, alongside social distance control, vaccines, and other efforts. With the newly reported variants, such as Delta and Omicron, a higher spread rate had been found compared to the initial strains. People might get infected even by inhaling fewer loading of viruses. More frequent sterilization of surgical masks is needed to protect the wearers. However, it is challenging to sterilize the commodity surgical masks with a fast and effective method. Herein, we reported the sterilization of the SARS-CoV-2 viruses within an ultra-short time, while retaining the mask performance. Silver thin film is coated on commercial polyimide film by physical vapor deposition and patterned by laser scribing to form a Joule heating electrode. Another layer of the gold thin film was coated onto the opposite side of the device to promote the uniformity of the Joule heating through nano-heat transfer regulation. As a result, the surgical mask can be heated to inactivation temperature within a short time and with high uniformity. By Joule-heating the surgical mask with the temperature at 90 °C for 3 minutes, the inactivation of the SARS-CoV-2 showed an efficacy of 99.89%. Normal commodity surgical masks can be sterilized faster, more frequently, and efficiently against SARS-CoV-2 viruses and the new invariants.

0 citations0 reviewsNo code linkedOpen access
preprint2022arXiv

Ultrasensitive refractive index sensor with rotatory biased weak measurement

A modified weak measurement scheme, rotatory biased weak measurement, is proposed to significantly improve the sensitivity and resolution of the refractive index sensor on a total reflection structure. This method introduces an additional phase in the post-selected procedure and generates an extinction point in the spectrum distribution. The biased post-selection makes smaller coupling strength available, which leads to an enhancement of phase sensitivity and refractive index sensitivity. In rotatory biased weak measurement, we achieve an enhanced refractive index sensitivity of 13605 nm/RIU compared to 1644 nm/RIU in standard weak measurement. The performance of sensors with different sensitivity is analyzed, and we find the optimal refractive index resolution of sensors increases with sensitivity. In this work, we demonstrate an optimal refractive index resolution of $4\times10^{-7}$ RIU on a total reflection structure. The rabbit anti-mouse IgG and mouse IgG binding reaction experiments demonstrate that our system has a high response to the concentration of IgG in a wide range and the limit of detection is 15 ng/mL. The improvements in this work are helpful to the optimizations of other optical sensors with weak measurement.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

Magnetotransport of dirty-limit van Hove singularity quasiparticles

Tuning of electronic density-of-states singularities is a common route to unconventional metal physics. Conceptually, van Hove singularities are realized only in clean two-dimensional systems. Little attention has therefore been given to the disordered (dirty) limit. Here, we provide a magnetotransport study of the dirty metamagnetic system calcium-doped strontium ruthenate. Fermi liquid properties persist across the metamagnetic transition, but with an unusually strong variation of the Kadowaki-Woods ratio. This is revealed by a strong decoupling of inelastic electron scattering and electronic mass inferred from density-of-state probes. We discuss this Fermi liquid behavior in terms of a magnetic field tunable van Hove singularity in the presence of disorder. More generally, we show how dimensionality and disorder control the fate of transport properties across metamagnetic transitions.

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preprint2020arXiv

A Computational Investigation on Denominalization

Language has been a dynamic system and word meanings always have been changed over times. Every time a novel concept or sense is introduced, we need to assign it a word to express it. Also, some changes have happened because the result of a change can be more desirable for humans, or cognitively easier to be used by humans. Finding the patterns of these changes is interesting and can reveal some facts about human cognitive evolution. As we have enough resources for studying this problem, it is a good idea to work on the problem through computational modeling, and that can make the work easier and possible to be studied on large scale. In this work, we want to study the nouns which have been used as verbs after some years of their emergence as nouns and find some commonalities among these nouns. In other words, we are interested in finding what potential requirements are essential for this change.

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preprint2020arXiv

A Real-time Automatic Validation System for Optical Transients detected by GWAC

The ground-based wide-angle camera array (GWAC) generates millions of single frame alerts per night. After the complicated and elaborate filters by multiple methods, a couple of dozens of candidates are still needed to be confirmed by follow-up observations in real-time. In order to free scientists from the complex and high-intensity follow-up tasks, we developed a Real-time Automatic transient Validation System (RAVS), and introduce here its system architecture, data processing flow, database schema, automatic follow-up control flow, and mobile message notification solution. This system is capable of automatically carrying out all operations in real-time without human intervention, including the validation of transient candidates, the adaptive light-curve sampling for identified targets in multi-band, and the pushing of observation results to the mobile client. The running of RAVS shows that an M-type stellar flare event can be well sampled by RAVS without a significant loss of the details, while the observing time is only less than one-third of the time coverage. Because the control logic of RAVS is designed to be independent of the telescope hardware, RAVS can be conveniently transplanted to other telescopes, especially the follow-up system of SVOM. Some future improvements are presented for the adaptive light-curve sampling, after taking into account both the brightness of sources and the evolution trends of the corresponding light-curves.

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preprint2020arXiv

An algorithm of selection of meteor candidates in GWAC system

With its large field of view, GWAC can record hundreds of meteors every day. These meteors are valuable treasures for some meteor research groups. It is therefore very important to accurately find all of these meteors. To address the challenge of precisely distinguishing meteors from other elongated objects in a GWAC-like sky survey system, we design and implement a meteor candidate recognition algorithm, including the recognizing and morphology analysis of the light curves of the meteor candidates. Although the algorithm may filter out some real meteors, it can provide a sample of meteor with high confidence. After processing the images of Mini-GWAC taken in two months, we detect 109,000 elongated objects in which more than 90 percent of objects are not meteor. Among the elongated objects, about 5.9% objects are identified as meteors with high confidence, after the filters based upon an existence in a single frame, a single peak in the light curves, and a slow variation of the light curves.

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preprint2020arXiv

Application of Pre-training Models in Named Entity Recognition

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

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preprint2020arXiv

Contextualized moral inference

Developing moral awareness in intelligent systems has shifted from a topic of philosophical inquiry to a critical and practical issue in artificial intelligence over the past decades. However, automated inference of everyday moral situations remains an under-explored problem. We present a text-based approach that predicts people&#39;s intuitive judgment of moral vignettes. Our methodology builds on recent work in contextualized language models and textual inference of moral sentiment. We show that a contextualized representation offers a substantial advantage over alternative representations based on word embeddings and emotion sentiment in inferring human moral judgment, evaluated and reflected in three independent datasets from moral psychology. We discuss the promise and limitations of our approach toward automated textual moral reasoning.

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preprint2020arXiv

Corpus of Chinese Dynastic Histories: Gender Analysis over Two Millennia

Chinese dynastic histories form a large continuous linguistic space of approximately 2000 years, from the 3rd century BCE to the 18th century CE. The histories are documented in Classical (Literary) Chinese in a corpus of over 20 million characters, suitable for the computational analysis of historical lexicon and semantic change. However, there is no freely available open-source corpus of these histories, making Classical Chinese low-resource. This project introduces a new open-source corpus of twenty-four dynastic histories covered by Creative Commons license. An original list of Classical Chinese gender-specific terms was developed as a case study for analyzing the historical linguistic use of male and female terms. The study demonstrates considerable stability in the usage of these terms, with dominance of male terms. Exploration of word meanings uses keyword analysis of focus corpora created for genderspecific terms. This method yields meaningful semantic representations that can be used for future studies of diachronic semantics.

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preprint2020arXiv

Gate field effects on the topological insulator BiSbTeSe2 interface

Interfaces between two topological insulators are of fundamental interest in condensed matter physics. Inspired by experimental efforts, we study interfacial processes between two slabs of BiSbTeSe2 (BSTS) via first principles calculations. Topological surface states are absent for the BSTS interface at its equilibrium separation, but our calculations show that they appear if the inter-slab distance is greater than 6 Ang. More importantly, we find that topological interface states can be preserved by inserting two or more layers of hexagonal boron nitride between the two BSTS slabs. In experiments, the electric current tunneling through the interface is insensitive to back gate voltage when the bias voltage is small. Using a first-principles based method that allows us to simulate gate field, we show that at low bias the extra charge induced by a gate voltage resides on the surface that is closest to the gate electrode, leaving the interface almost undoped. This provides clues to understand the origin of the observed insensitivity of transport properties to back voltage at low bias. Our study resolves a few questions raised in experiment, which does not yet offer a clear correlation between microscopic physics and transport data. We provide a road map for the design of vertical tunneling junctions involving the interface between two topological insulators.

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preprint2020arXiv

Improving probability selecting based weights for Satisfiability Problem

The Boolean Satisfiability problem (SAT) is important on artificial intelligence community and the impact of its solving on complex problems. Recently, great breakthroughs have been made respectively on stochastic local search (SLS) algorithms for uniform random k-SAT resulting in several state-of-the-art SLS algorithms Score2SAT, YalSAT, ProbSAT, CScoreSAT and on a hybrid algorithm for hard random SAT (HRS) resulting in one state-of-the-art hybrid algorithm SparrowToRiss. However, there is no an algorithm which can effectively solve both uniform random k-SAT and HRS. In this paper, we present a new SLS algorithm named SelectNTS for uniform random k-SAT and HRS. SelectNTS is an improved probability selecting based local search algorithm for SAT problem. The core of SelectNTS relies on new clause and variable selection heuristics. The new clause selection heuristic uses a new clause weighting scheme and a biased random walk. The new variable selection heuristic uses a probability selecting strategy with the variation of CC strategy based on a new variable weighting scheme. Extensive experimental results on the well-known random benchmarks instances from the SAT Competitions in 2017 and 2018, and on randomly generated problems, show that our algorithm outperforms state-of-the-art random SAT algorithms, and our SelectNTS can effectively solve both uniform random k-SAT and HRS.

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preprint2020arXiv

Multi-Feature Discrete Collaborative Filtering for Fast Cold-start Recommendation

Hashing is an effective technique to address the large-scale recommendation problem, due to its high computation and storage efficiency on calculating the user preferences on items. However, existing hashing-based recommendation methods still suffer from two important problems: 1) Their recommendation process mainly relies on the user-item interactions and single specific content feature. When the interaction history or the content feature is unavailable (the cold-start problem), their performance will be seriously deteriorated. 2) Existing methods learn the hash codes with relaxed optimization or adopt discrete coordinate descent to directly solve binary hash codes, which results in significant quantization loss or consumes considerable computation time. In this paper, we propose a fast cold-start recommendation method, called Multi-Feature Discrete Collaborative Filtering (MFDCF), to solve these problems. Specifically, a low-rank self-weighted multi-feature fusion module is designed to adaptively project the multiple content features into binary yet informative hash codes by fully exploiting their complementarity. Additionally, we develop a fast discrete optimization algorithm to directly compute the binary hash codes with simple operations. Experiments on two public recommendation datasets demonstrate that MFDCF outperforms the state-of-the-arts on various aspects.

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preprint2020arXiv

Text-based inference of moral sentiment change

We present a text-based framework for investigating moral sentiment change of the public via longitudinal corpora. Our framework is based on the premise that language use can inform people&#39;s moral perception toward right or wrong, and we build our methodology by exploring moral biases learned from diachronic word embeddings. We demonstrate how a parameter-free model supports inference of historical shifts in moral sentiment toward concepts such as slavery and democracy over centuries at three incremental levels: moral relevance, moral polarity, and fine-grained moral dimensions. We apply this methodology to visualizing moral time courses of individual concepts and analyzing the relations between psycholinguistic variables and rates of moral sentiment change at scale. Our work offers opportunities for applying natural language processing toward characterizing moral sentiment change in society.

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preprint2020arXiv

The darkweb: a social network anomaly

We analyse the darkweb and find its structure is unusual. For example, $ \sim 87 \%$ of darkweb sites \emph{never} link to another site. To call the darkweb a &#34;web&#34; is thus a misnomer -- it&#39;s better described as a set of largely isolated dark silos. As we show through a detailed comparison to the World Wide Web (www), this siloed structure is highly dissimilar to other social networks and indicates the social behavior of darkweb users is much different to that of www users. We show a generalized preferential attachment model can partially explain the strange topology of the darkweb, but an understanding of the anomalous behavior of its users remains out of reach. Our results are relevant to network scientists, social scientists, and other researchers interested in the social interactions of large numbers of agents.

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preprint2020arXiv

The extended Gaia-PS1-SDSS (GPS1+) proper motion catalog

The GPS1 catalog was released in 2017. It delivered precise proper motions for around 350 million sources across three-fourths of the sky down to a magnitude of $r\sim20$\,mag. In this study, we present GPS1+ the extension GPS1 catalog down to $r\sim22.5$\,mag, based on {\it Gaia} DR2, PS1, SDSS and 2MASS astrometry. The GPS1+ totally provides proper motions for $\sim$400 million sources with a characteristic systematic error of less than 0.1\masyr. This catalog is divided into two sub-samples, i.e., the primary and secondary parts. The primary $\sim$264 million sources have either or both of the {\it Gaia} and SDSS astrometry, with a typical precision of 2.0-5.0 \masyr. In this part, $\sim$160 million sources have {\it Gaia} proper motions, we provide another new proper motion for each of them by building a Bayesian model. Relative to {\it Gaia}&#39;s values, the precision is improved by $\sim$0.1\,dex on average at the faint end; $\sim$50 million sources are the objects whose proper motions are missing in {\it Gaia} DR2, we provide their proper motion with a precision of $\sim$4.5\masyr; the remaining $\sim$54 million faint sources are beyond {\it Gaia} detecting capability, we provide their proper motions for the first time with a precision of 7.0 \masyr. However, the secondary $\sim$136 million sources only have PS1 astrometry, the average precision is worse than 15.0 \masyr. All the proper motions have been validated using QSOs and the existing {\it Gaia} proper motions. The catalog will be released on-line and available via the VO-TAP Service, or via the National Astronomical Data Center serviced by China-VO: https://nadc.china-vo.org/data/data/gps1p/f.

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preprint2020arXiv

The Typology of Polysemy: A Multilingual Distributional Framework

Lexical semantic typology has identified important cross-linguistic generalizations about the variation and commonalities in polysemy patterns---how languages package up meanings into words. Recent computational research has enabled investigation of lexical semantics at a much larger scale, but little work has explored lexical typology across semantic domains, nor the factors that influence cross-linguistic similarities. We present a novel computational framework that quantifies semantic affinity, the cross-linguistic similarity of lexical semantics for a concept. Our approach defines a common multilingual semantic space that enables a direct comparison of the lexical expression of concepts across languages. We validate our framework against empirical findings on lexical semantic typology at both the concept and domain levels. Our results reveal an intricate interaction between semantic domains and extra-linguistic factors, beyond language phylogeny, that co-shape the typology of polysemy across languages.

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preprint2020arXiv

To schedule or not to schedule: when no-scheduling can beat the best-known flow scheduling algorithm in datacenter networks

Conventional wisdom for minimizing the average flow completion time (AFCT) in the datacenter network (DCN), where flow sizes are highly variable, would suggest scheduling every individual flow. However, we show that considering scheduling delay (including scheduler&#39;s computational and communication delays), serving most of the flows without any scheduling and only in first-come-first-served (FCFS) manner significantly improves their performance even when it is compared to the shortest remaining processing time (SRPT)-known as optimum algorithm when scheduling delay is zero. To do so, we only require to have two coarse classes of flows categorized based on flows&#39; sizes (1st-class including flows smaller than a threshold, H, and 2nd-class including others) and serve 1st-class flows always before serving 2nd-class ones. To show that, we take SRPT scheduling algorithm accompanied by the global knowledge of flows, formulate impact of scheduling delay on its performance, and prove that for any flow size distribution and network load (<1), there is always a threshold, H, which guarantees 1st-class flows achieve lower AFCT under FCFS compared to SRPT. Our numerically calculated results and extensive flow-level simulations show that on average, more than 90% of flows could be in 1st-class and consequently do not require any scheduling.

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preprint2020arXiv

Word class flexibility: A deep contextualized approach

Word class flexibility refers to the phenomenon whereby a single word form is used across different grammatical categories. Extensive work in linguistic typology has sought to characterize word class flexibility across languages, but quantifying this phenomenon accurately and at scale has been fraught with difficulties. We propose a principled methodology to explore regularity in word class flexibility. Our method builds on recent work in contextualized word embeddings to quantify semantic shift between word classes (e.g., noun-to-verb, verb-to-noun), and we apply this method to 37 languages. We find that contextualized embeddings not only capture human judgment of class variation within words in English, but also uncover shared tendencies in class flexibility across languages. Specifically, we find greater semantic variation when flexible lemmas are used in their dominant word class, supporting the view that word class flexibility is a directional process. Our work highlights the utility of deep contextualized models in linguistic typology.

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preprint2019arXiv

Fourier-based Rotation-invariant Feature Boosting: An Efficient Framework for Geospatial Object Detection

Geospatial object detection of remote sensing imagery has been attracting an increasing interest in recent years, due to the rapid development in spaceborne imaging. Most of previously proposed object detectors are very sensitive to object deformations, such as scaling and rotation. To this end, we propose a novel and efficient framework for geospatial object detection in this letter, called Fourier-based rotation-invariant feature boosting (FRIFB). A Fourier-based rotation-invariant feature is first generated in polar coordinate. Then, the extracted features can be further structurally refined using aggregate channel features. This leads to a faster feature computation and more robust feature representation, which is good fitting for the coming boosting learning. Finally, in the test phase, we achieve a fast pyramid feature extraction by estimating a scale factor instead of directly collecting all features from image pyramid. Extensive experiments are conducted on two subsets of NWPU VHR-10 dataset, demonstrating the superiority and effectiveness of the FRIFB compared to previous state-of-the-art methods.

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preprint2019arXiv

In-network Congestion-aware Load Balancing at Transport Layer

Load balancing at transport layer is an important function in data centers, content delivery networks, and mobile networks, where per-connection consistency (PCC) has to be met for optimal performance. Cloud-native L4 load balancers are commonly deployed as virtual network functions (VNFs) and are a critical forwarding element in modern cloud infrastructure. We identify load imbalance among service instances as the main cause of additional processing delay caused by transport-layer load balancers. Existing transport-layer load balancers rely on one of two methods: host-level traffic redirection, which may add as much as 12.48% additional traffic to underlying networks, or connection tracking, which consumes a considerable amount of memory in load balancers. Both of these methods result in inefficient usage of network resources. We propose the in-network congestion-aware load Balancer (INCAB) to achieve even load distribution among service instances and optimal network resources usage in addition to meeting the PCC requirement. We show that INCAB is capable of identifying and monitoring each instance&#39;s most-utilized resource and can improve the load distribution among all service instances. INCAB utilizes a Bloom filter and an ultra-compact connection table for in-network flow distribution. Furthermore, it does not rely on end hosts for traffic redirection. Our flow level simulations show that INCAB improves flows&#39; average completion time by 31.97% compared to stateless solutions.

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