May 2023 arXiv papers — page 162

Yushen Wei, Yang Liu, Hong Yan, Guanbin Li

Existing methods for video question answering (VideoQA) often suffer from spurious correlations between different modalities, leading to a failure in identifying the dominant visual evidence and the intended question. Moreover, these methods function as black boxes, making it difficult to interpret the visual scene during the QA process. In this paper, to di

Galit Cohen, Jonah B. Haber, Jeffrey B. Neaton, Diana Y. Qiu

Non-radiative exciton relaxation processes are critical for energy transduction efficiencies in optoelectronic materials, but how these processes are connected to the underlying crystal structure and its associated electron, exciton, and phonon band structures is poorly understood. Here, we present a first-principles approach to explore exciton relaxation pa

Roberto Gorrieri

Place bisimilarity is a behavioral equivalence for finite Petri nets, proposed in \cite{ABS91} and proved decidable in \cite{Gor21}. In this paper we propose an extension to finite Petri nets with silent moves of the place bisimulation idea, yielding {\em branching} place bisimilarity $\approx_p$, following the intuition of branching bisimilarity \cite{vGW96

Solveig S. Aamlid, Graham H. J. Johnstone, Sam Mugiraneza, Mohamed Oudah

The prediction of new high entropy oxides (HEOs) remains a profound challenge due to their inherent chemical complexity. In this work, we combine experimental and computational methods to search for new HEOs in the tetravalent $A$O$_2$ family, using exclusively $d^0$ and $d^{10}$ cations, and to explain the observed phase stability of the $\alpha$-PbO$_2$ st

Augustin Skopal

Accretion of mass onto a white dwarf (WD) in a binary system can lead to stellar explosions. If a WD accretes from stellar wind of a distant evolved giant in a symbiotic binary, it can undergo occasional outbursts in which it brightens by several magnitudes, produces a low- and high-velocity mass-outflow, and, in some cases, ejects bipolar jets. In this pape

Noah F. Q. Yuan

We propose a new type of supercurrent diode effect on the surface of a superconductor with surface states under in-plane magnetic fields. Surface supercurrent diode effect can lead to a perfect supercurrent diode in a considerably wide range of fields. For comparison, the conventional supercurrent diode effect due to the spin-orbit coupling in a two-dimensio

Hyungryul Baik, Juhun Baik

We investigate the behavior of itinerary sequence of each point of the Julia set of $z\mapsto z^2 + c$ when the parameter $c$ in the shift locus is allowed to pass through points in the bifurcation locus $\mathcal{P}_2$, which we call ``narrow", first proposed by Dierk Schleicher in \cite{schleicher2017internal}. We first show the combinatoric and geometric

Pitawelayalage Dasun Dileepa Pitawela, Gamage Upeksha Ganegoda

Data mining focuses on discovering interesting, non-trivial and meaningful information from large datasets. Data clustering is one of the unsupervised and descriptive data mining task which group data based on similarity features and physically stored together. As a partitioning clustering method, K-means is widely used due to its simplicity and easiness of

Jie Dong

Digital transformation challenges financial management while reducing costs and increasing efficiency for enterprises in various countries. Identifying the transmission paths of enterprise financial risks in the context of digital transformation is an urgent problem to be solved. This paper constructs a system of influencing factors of corporate financial ri

C. Liu, Joshua T. Y. Tse, H. C. Ong

Periodic nanoparticle arrays can support surface lattice resonances (SLRs), which arise from the hybridization between localized surface plasmons (LSPs) and diffractive Rayleigh anomalies (RAs). In contrast to LSPs, SLRs enjoy a much higher quality (Q) factor. As the Q factor depends on many system parameters, a good understanding of them is essential for op

Agus Sudjianto, Aijun Zhang, Zebin Yang, Yu Su

PiML (read $\pi$-ML, /`pai`em`el/) is an integrated and open-access Python toolbox for interpretable machine learning model development and model diagnostics. It is designed with machine learning workflows in both low-code and high-code modes, including data pipeline, model training and tuning, model interpretation and explanation, and model diagnostics and

Yi Cheng, Haochao Ying, Renjun Hu, Jinhong Wang

Image ordinal regression has been mainly studied along the line of exploiting the order of categories. However, the issues of class imbalance and category overlap that are very common in ordinal regression were largely overlooked. As a result, the performance on minority categories is often unsatisfactory. In this paper, we propose a novel framework called C

Mingwei Xu, Ronghui Quan, Yunjia Yao

The Magnetic Sail is a space propulsion system that utilizes the interaction between solar wind particles and an artificial dipole magnetic field generated by a spacecraft's coil to produce thrust without the need for additional plasma or propellant. To reduce the size of the sail while improving the efficiency of capturing solar wind, a new type of rotating

Jiaxin Zheng, Xueyi Huang, Junjie Wang

For any integer $k\geq 2$, a spanning $k$-ended tree is a spanning tree with at most $k$ leaves. In this paper, we provide a tight spectral radius condition for the existence of a spanning $k$-ended tree in $t$-connected graphs, which generalizes a result of Ao, Liu and Yuan (2023).

Yuan Meng, Xiao-Mei Kuang, Xi-Jing Wang, Jian-Pin Wu

Dynamical Chern-Simons (dCS) gravity has been attracting plenty of attentions due to the fact that it is a parity-violating modified theory of gravity that corresponds to a well-posed effective field theory in weak coupling approximation. In particular, a rotating black hole in dCS gravity is in contrast to the general relativistic counterparts. In this pape

Sarthak Mittal, Oleksii Hrinchuk, Oleksii Kuchaiev

In this work, we provide a recipe for training machine translation models in a limited resource setting by leveraging synthetic target data generated using a large pre-trained model. We show that consistently across different benchmarks in bilingual, multilingual, and speech translation setups, training models on synthetic targets outperforms training on the

Yi C. Huang

Given a generator of a bounded analytic semigroup on a Hilbert space, we show that the corresponding forward maximal regularity operator commutes with the backward. In particular, for self-adjoint generators the images under the two maximal regularity operators have equal unweighted Hilbert space norms.

Xiaoyu Yang, Lijian Xu, Simon Yu, Qing Xia

Segmentation of the coronary artery is an important task for the quantitative analysis of coronary computed tomography angiography (CCTA) images and is being stimulated by the field of deep learning. However, the complex structures with tiny and narrow branches of the coronary artery bring it a great challenge. Coupled with the medical image limitations of l

Zhiqiang Yuan, Yiling Lou, Mingwei Liu, Shiji Ding

Unit testing is essential in detecting bugs in functionally-discrete program units. Manually writing high-quality unit tests is time-consuming and laborious. Although traditional techniques can generate tests with reasonable coverage, they exhibit low readability and cannot be directly adopted by developers. Recent work has shown the large potential of large

Yu-Ming Zhang, Jun-Wei Hsieh, Chun-Chieh Lee, Kuo-Chin Fan

Various hand-designed CNN architectures have been developed, such as VGG, ResNet, DenseNet, etc., and achieve State-of-the-Art (SoTA) levels on different tasks. Neural Architecture Search (NAS) now focuses on automatically finding the best CNN architecture to handle the above tasks. However, the verification of a searched architecture is very time-consuming

Siyu Li, Kailun Yang, Hao Shi, Jiaming Zhang

A semantic map of the road scene, covering fundamental road elements, is an essential ingredient in autonomous driving systems. It provides important perception foundations for positioning and planning when rendered in the Bird's-Eye-View (BEV). Currently, the prior knowledge of hypothetical depth can guide the learning of translating front perspective views

JuAe Song

We define tropical rational function semifields $\overline{\boldsymbol{T}(X_1, \ldots, X_n)}$ and prove that a tropical curve $\varGamma$ is realized (except for points at infinity) as the congruence variety $V \subset \boldsymbol{R}^n$ associated with a congruence on $\overline{\boldsymbol{T}(X_1, \ldots, X_n)}$ by giving a specific map $\varGamma \to V$. A

Wenhai Wan, Xinrui Wang, Ming-Kun Xie, Shao-Yuan Li

Learning from noisy data has attracted much attention, where most methods focus on closed-set label noise. However, a more common scenario in the real world is the presence of both open-set and closed-set noise. Existing methods typically identify and handle these two types of label noise separately by designing a specific strategy for each type. However, in

Linfeng Ding, Ranran Lu, Lianjun Wang, Qiuju Zheng

Indentation has been widely used for investigating the mechanical behavior of glasses. However, how the various microscopic properties (such as atomic structure and mechanics) of glass evolve from the immediate contact with the indenter to the far-field regions, and how these observables are correlated to each other remain largely unknown. Here, using large-

Sixia Li, Shogo Okada

Multimodal sentiment analysis is an important area for understanding the user's internal states. Deep learning methods were effective, but the problem of poor interpretability has gradually gained attention. Previous works have attempted to use attention weights or vector distributions to provide interpretability. However, their explanations were not intuiti

Xin-Chun Li, Yang Yang, De-Chuan Zhan

We consider a real-world scenario in which a newly-established pilot project needs to make inferences for newly-collected data with the help of other parties under privacy protection policies. Current federated learning (FL) paradigms are devoted to solving the data heterogeneity problem without considering the to-be-inferred data. We propose a novel learnin

Yiqun Duan, Jinzhao Zhou, Zhen Wang, Yu-Cheng Chang

The differences in brain dynamics across human subjects, commonly referred to as human artifacts, have long been a challenge in the field, severely limiting the generalizability of brain dynamics recognition models. Traditional methods for human artifact removal typically employ spectrum filtering or blind source separation, based on simple prior distributio

Kaori Yamaguchi, Hiraku Nozawa

A statistic on a statistical model is sufficient if it has no information loss, namely, the Fisher metric of the induced model coincides with that of the original model due to Kullback and Ay-Jost-L\^e-Schwachh\"ofer. We introduce a quantitatively weak version of sufficient statistics such that the Fisher metric of the induced model is bi-Lipschitz equivalen

Ronghang Chen, Shi-Yao Hou, Cong Guo, Guanru Feng

Optimization problems are prevalent in various fields, and the gradient-based gradient descent algorithm is a widely adopted optimization method. However, in classical computing, computing the numerical gradient for a function with $d$ variables necessitates at least $d+1$ function evaluations, resulting in a computational complexity of $O(d)$. As the number

Devender Garg, Manju, Shubhrangshu Dasgupta, Asoka Biswas

It is often conjectured that quantum synchronisation and entanglement are two independent properties which two coupled quantum systems may not exhibit at the same time. However, as both these properties can be understood in terms of the second order moments of a set of conjugate quadratures, there may exist specific conditions for simultaneous existence of e

Qiqi Ren, Omid Abbasi, Gunes Karabulut Kurt, Halim Yanikomeroglu

Distributed computing enables Internet of vehicle (IoV) services by collaboratively utilizing the computing resources from the network edge and the vehicles. However, the computing interruption issue caused by frequent edge network handoffs, and a severe shortage of computing resources are two problems in providing IoV services. High altitude platform statio

Sheng Yan, Yang Liu, Haoqiang Wang, Xin Du

Cross-modal retrieval of image-text and video-text is a prominent research area in computer vision and natural language processing. However, there has been insufficient attention given to cross-modal retrieval between human motion and text, despite its wide-ranging applicability. To address this gap, we utilize a concise yet effective dual-unimodal transform

Taichi Kato, Rod Stubbings

We found an active state lasting for ~200 d in the AM CVn star NSV 1440 in 2022. During this state, the object reached a magnitude of 16.5, 2.0-2.5 mag above quiescence, and showed a number of superposed normal outbursts. Such an active state was probably brought either by an enhanced mass-transfer from the secondary or increased quiescent viscosity of the a

Jiaxi Nie

The $r$-expansion of a $k$-uniform hypergraph $H$, denoted by $H^{(+r)}$, is an $r$-uniform hypergraph obtained by enlarging each $k$-edge of $H$ with a set of $r-k$ vertices of degree one. The random Tur\'an number $\mathrm{ex}(G^r_{n,p},H)$ is the maximum number of edges in an $H$-free subgraph of $G^r_{n,p}$, where $G^r_{n,p}$ is the Erd\H{o}s-R\'enyi ran

Filippo Disanto, Michael Fuchs, Chun-Yen Huang, Ariel R. Paningbatan

Given a gene-tree labeled topology $G$ and a species tree $S$, the "ancestral configurations" at an internal node $k$ of $S$ represent the combinatorially different sets of gene lineages that can be present at $k$ when all possible realizations of $G$ in $S$ are considered. Ancestral configurations have been introduced as a data structure for evaluating the

M. A. Mabrok, Ilyasse Aksikas, Nader Meskin

Nonlinear Negative Imaginary (NI) systems arise in various engineering applications, such as controlling flexible structures and air vehicles. However, unlike linear NI systems, their theory is not well-developed. In this paper, we propose a data-driven method for learning a lifted linear NI dynamics that approximates a nonlinear dynamical system using the K

Peng Wang, Yong Liang Guan, Lipo Wang, Peng Cheng

This paper addresses the blind recovery of the parity check matrix of an (n,k) linear block code over noisy channels by proposing a fast recovery scheme consisting of 3 parts. Firstly, this scheme performs initial error position detection among the received codewords and selects the desirable codewords. Then, this scheme conducts Gaussian elimination (GE) on

D. Karinkuzhi, S. Van Eck, S. Goriely, L. Siess

A sample of 895 s-process-rich candidates has been found among the 454180 giant stars surveyed by LAMOST at low spectral resolution (R~1800). In a previous study, taking advantage of the higher resolution (R~86 000) offered by the the HERMES-Mercator spectrograph, we performed the re-analysis of 15 among the brightest stars of this sample. Among these 15 pro

Chaehwa Jeong, Juhyeok Lee, Hyesung Jo, Jaewhan Oh

In the early 2000s, low dimensional systems were predicted to have topologically nontrivial polar structures, such as vortices or skyrmions, depending on mechanical or electrical boundary conditions. A few variants of these structures have been experimentally observed in thin film model systems, where they are engineered by balancing electrostatic charge and

Nitsan Davidor, Yair Binyamin, Tamar Hayuni, Ilana Nisky

Epidural analgesia involves injection of anesthetics into the epidural space, using a Touhy needle to proceed through the layers in the epidural region and a "loss of resistance" (LOR) syringe to sense the environment stiffness. The anesthesiologist's case experience is one of the leading causes of accidental dural puncture and failed epidural - the two most

Xijun Wang, Aggelos K. Katsaggelos

Weakly-supervised temporal action localization aims to identify and localize the action instances in the untrimmed videos with only video-level action labels. When humans watch videos, we can adapt our abstract-level knowledge about actions in different video scenarios and detect whether some actions are occurring. In this paper, we mimic how humans do and b

P. Chainakun, N. Nakhonthong, W. Luangtip, A. J. Young

The Granger causality is an econometric test for determining whether one time series is useful for forecasting another one with a certain Granger lag. Here, the light curves in the 0.3-1 keV (reflection dominated, soft) and 1.2-5 keV (continuum dominated, hard) bands of Active Galactic Nuclei (AGNs) are produced, and the Granger lags are estimated and compar

Yi C. Huang

We give a conceptually simple and essentially one-dimensional approach to Rellich inequality in Euclidean space $\mathbb{R}^n$. In particular, we show that the radial part and the spherical part of the standard Laplacian form an angle in $[0,\pi/2]$ when $n\geq4$, a property known in the works of Evans-Lewis, Machihara-Ozawa-Wadade, and Bez-Machihara-Ozawa.

Gangqiang Liu, Andrew Lingenfelter, Vidul R. Joshi, Nicholas E. Frattini

We present a way to achieve fully directional, quantum-limited phase-preserving amplification in a four-port, four-mode superconducting Josephson circuit by utilizing interference between six parametric processes that couple all four modes. Full directionality, defined as the reverse isolation surpassing forward gain between the matched input and output port

Nghia Hieu Nguyen, Duong T. D. Vo, Kiet Van Nguyen, Ngan Luu-Thuy Nguyen

In recent years, visual question answering (VQA) has attracted attention from the research community because of its highly potential applications (such as virtual assistance on intelligent cars, assistant devices for blind people, or information retrieval from document images using natural language as queries) and challenge. The VQA task requires methods tha

Yanhang Zhang, Zhifan Li, Shixiang Liu, Jianxin Yin

In this paper, we focus our attention on the high-dimensional double sparse linear regression, that is, a combination of element-wise and group-wise sparsity. To address this problem, we propose an IHT-style (iterative hard thresholding) procedure that dynamically updates the threshold at each step. We establish the matching upper and lower bounds for parame

Kuicai Dong, Aixin Sun, Jung-Jae Kim, Xiaoli Li

Open Information Extraction (OIE) aims to extract factual relational tuples from open-domain sentences. Downstream tasks use the extracted OIE tuples as facts, without examining the certainty of these facts. However, uncertainty/speculation is a common linguistic phenomenon. Existing studies on speculation detection are defined at sentence level, but even if

Jinghao Xin, Jinwoo Kim, Zhi Li, Ning Li

Deep Reinforcement Learning (DRL) has exhibited efficacy in resolving the Local Path Planning (LPP) problem. However, such application in the real world is immensely limited due to the deficient training efficiency and generalization capability of DRL. To alleviate these two issues, a solution named Color is proposed, which consists of an Actor-Sharer-Learne

Huijun Zhang, Feng Liu, Yilong Han

Characterizing the local structural evolution is an essential step in understanding the nature of glass transition. In this work, we probe the evolution of Voronoi cell geometry in simple glass models, and find that the individual particle cages deform anisotropically in supercooled liquid and isotropically in glass. We introduce an anisotropy parameter $k$

Meiqiao Zhang, Fengming Dong

In 2013, Ku and Wong showed that for any partitions $\mu$ and $\mu'$ of a positive integer $n$ with the same first part $u$ and the lexicographic order $\mu\triangleleft \mu'$, the eigenvalues $\xi_{\mu}$ and $\xi_{\mu'}$ of the derangement graph $\Gamma_n$ have the property $|\xi_{\mu}|\le |\xi_{\mu'}|$, where the equality holds if and only if $u=3$ and all

Anastasia Razdaibiedina, Alexander Brechalov

Learning semantically meaningful representations from scientific documents can facilitate academic literature search and improve performance of recommendation systems. Pre-trained language models have been shown to learn rich textual representations, yet they cannot provide powerful document-level representations for scientific articles. We propose MIReAD, a

Sameh Gana

This paper focuses on the study of Sturm-Liouville eigenvalue problems. In the classical Chebyshev collocation method, the Sturm-Liouville problem is discretized to a generalized eigenvalue problem where the functions represent interpolants in suitably rescaled Chebyshev points. We are concerned with the computation of high-order eigenvalues of Sturm-Liouvil

Shengfang Zhai, Yinpeng Dong, Qingni Shen, Shi Pu

With the help of conditioning mechanisms, the state-of-the-art diffusion models have achieved tremendous success in guided image generation, particularly in text-to-image synthesis. To gain a better understanding of the training process and potential risks of text-to-image synthesis, we perform a systematic investigation of backdoor attack on text-to-image d

Venkat Nemani, Luca Biggio, Xun Huan, Zhen Hu

On top of machine learning models, uncertainty quantification (UQ) functions as an essential layer of safety assurance that could lead to more principled decision making by enabling sound risk assessment and management. The safety and reliability improvement of ML models empowered by UQ has the potential to significantly facilitate the broad adoption of ML s

Lin Liu, Xinbo Wang, Yuhao Wang

Treatment effect estimation under unconfoundedness is a fundamental task in causal inference. In response to the challenge of analyzing high-dimensional datasets collected in substantive fields such as epidemiology, genetics, economics, and social sciences, various methods for treatment effect estimation with high-dimensional nuisance parameters (the outcome

Masahico Saito, Emanuele Zappala

Braided algebras are associative algebras endowed with a Yang-Baxter operator that satisfies certain compatibility conditions involving the multiplication. Along with Hochschild cohomology of algebras, there is also a notion of Yang-Baxter cohomology, which is associated to any Yang-Baxter operator. In this article, we introduce and study a cohomology theory

Mohannad Ibrahim, Nicholas T. Bronn, Gregory T. Byrd

In this paper, we propose an ansatz approximation approach for variational quantum algorithms (VQAs) that uses one of the hardware's main attributes, its crosstalk behavior, as its main approximation driver. By utilizing crosstalk-adaptive scheduling, we are able to apply a circuit-level approximation/optimization to our ansatz. Our design procedure involves

Ngoc Cuong Nguyen

We prove that the regularity of the extremal function of a compact subset of a compact K\"ahler manifold is a local property, and that the continuity and H\"older continuity are equivalent to classical notions of the local $L$-regularity and the locally H\"older continuous property in pluripolential theory. As a consequence we give an effective characterizat

Lin Huang, Weisheng Li, Yujuan Tan, Linlin Shen

In this study, we examine the associations between channel features and convolutional kernels during the processes of feature purification and gradient backpropagation, with a focus on the forward and backward propagation within the network. Consequently, we propose a method called Dense Channel Compression for Feature Spatial Solidification. Drawing upon th

Shuai Yuan, Zuhuang Chen, Sergei Prokhorenko, Yousra Nahas

Polar skyrmions are topologically stable, swirling polarization textures with particle-like characteristics, which hold promise for next-generation, nanoscale logic and memory. While understanding of how to create ordered polar skyrmion lattice structures and how such structure respond to applied electric fields, temperature, and film thickness remains elusi

Yusuke Kobayashi, Ryoga Mahara, Souta Sakamoto

One of the most important topics in discrete fair division is whether an EFX allocation exists for any instance. Although the existence of EFX allocations is a standing open problem for both goods and chores, the understanding of the existence of EFX allocations for chores is less established compared to goods. We study the existence of EFX allocation for ch

Feifei Miao, Liguang Wang, Wei Yuan

We characterize Cuntz-Nica-Pimsner algebras for compactly aligned product systems over quasi-lattice ordered groupoids. We show that the full cross sectional $C^*$-algebras of Fell bundles of Morita equivalence bimodules are isomorphic to the related Cuntz-Nica-Pimsner algebras under certain conditions.

Doanh C. Bui, Nghia Hieu Nguyen, Khang Nguyen

Image Captioning is one of the vision-language tasks that still interest the research community worldwide in the 2020s. MS-COCO Caption benchmark is commonly used to evaluate the performance of advanced captioning models, although it was published in 2015. Recent captioning models trained on the MS-COCO Caption dataset only have good performance in language

Y. Dabaghian

We propose a mechanism enabling the appearance of border cells -- neurons firing at the boundaries of the navigated enclosures. The approach is based on the recent discovery of discrete complex analysis on a triangular lattice, which allows constructing discrete epitomes of complex-analytic functions and making use of their inherent ability to attain maximal

A. M. Encinas, Samir Mondal, K. C. Sivakumar

In the setting of real square matrices, it is known that, if $A$ is a singular irreducible $M$-matrix, then the only nonnegative vector that belongs to the range space of $A$ is the zero vector. In this paper, we prove an analogue of this result for the Lyapunov and the Stein operators.

Yaolong Shen

We develop a bar involution and canonical basis for every morphism space of the oriented skein category through a diagrammatic approach. In particular, our construction gives rise to Kazhdan-Lusztig type bases on quantized walled Brauer algebras.

Mukesh Gautam, Rakib Hossain, Mohammad MansourLakouraj, Narayan Bhusal

Federal Energy Regulatory Commission (FERC) Orders 841 and 2222 have recommended that distributed energy resources (DERs) should participate in energy and reserve markets; therefore, a mechanism needs to be developed to facilitate DERs' participation at the distribution level. Although the available reserve from a single distribution system may not be suffic

Wenrui Hao, Sun Lee, Young Ju Lee

The use of nonlinear PDEs has led to significant advancements in various fields, such as physics, biology, ecology, and quantum mechanics. However, finding multiple solutions for nonlinear PDEs can be a challenging task, especially when suitable initial guesses are difficult to obtain. In this paper, we introduce a novel approach called the Companion-Based M

Kegang Wang, Yantao Wei, Jiankai Tang, Yuntao Wang

In recent years, due to the widespread use of internet videos, remote photoplethysmography (rPPG) has gained more and more attention in the fields of affective computing. Restoring blood volume pulse (BVP) signals from facial videos is a challenging task that involves a series of preprocessing, image algorithms, and postprocessing to restore waveforms. Not o

Feilong Chen, Minglun Han, Haozhi Zhao, Qingyang Zhang

Large language models (LLMs) have demonstrated remarkable language abilities. GPT-4, based on advanced LLMs, exhibits extraordinary multimodal capabilities beyond previous visual language models. We attribute this to the use of more advanced LLMs compared with previous multimodal models. Unfortunately, the model architecture and training strategies of GPT-4

Ke Wan, Zuo Zhang, Zhiquan Chen

Dynamic traffic assignment and vehicle route guidance have been important problems in ITS for some time. This paper proposes a new model for VRGS, which takes into consideration of the information propagation, user selection and information reaction. Parameter p is then defined as the updating weight for computing cost of traffic based on a distributive lear

Grant P. Strimel, Yi Xie, Brian King, Martin Radfar

Streaming speech recognition architectures are employed for low-latency, real-time applications. Such architectures are often characterized by their causality. Causal architectures emit tokens at each frame, relying only on current and past signal, while non-causal models are exposed to a window of future frames at each step to increase predictive accuracy.

Yuhan Li, Xiaoqiang Ji

In this paper, a novel Koopman-type inverse operator for linear time-invariant non-minimum phase systems with stochastic disturbances is proposed. This operator employs functions of the desired output to directly calculate the input. Furthermore, it can be applied as a data-driven approach for systems with unknown parameters yet a known relative degree, whic

Felipe A. Asenjo, Sergio A. Hojman, Braulio M. Villegas-Martínez, Héctor M. Moya-Cessa

A medium with specific anisotropic refractive indices can induce a supersymmetric behavior in the propagation of polarized electromagnetic waves, in an analogue fashion to a quantum mechanical system. The polarizations of the wave are the ones which behave as superpartners from each other. For this to happen, the anisotropy of the medium must be transverse t

Xinwen Zhang, Chaoyi Zhang, Dongnan Liu, Qianbi Yu

The adversarial methods showed advanced performance by producing synthetic images to mitigate the domain shift, a common problem due to the hardship of acquiring labelled data in medical field. Most existing studies focus on modifying the network architecture, but little has worked on the GAN training strategy. In this work, we propose SynthMix, an add-on mo

Jaswanthi Mandalapu, Krishna Jagannathan, Avhishek Chatterjee, Andrew Thangaraj

We consider a queue-channel model that captures the waiting time-dependent degradation of information bits as they wait to be transmitted. Such a scenario arises naturally in quantum communications, where quantum bits tend to decohere rapidly. Trailing the capacity results obtained recently for certain queue-channels, this paper aims to construct practical c

David Noever, Matt Ciolino

The research creates a professional certification survey to test large language models and evaluate their employable skills. It compares the performance of two AI models, GPT-3 and Turbo-GPT3.5, on a benchmark dataset of 1149 professional certifications, emphasizing vocational readiness rather than academic performance. GPT-3 achieved a passing score (>70% c

Ismail Bustany, Andrew B. Kahng, Ioannis Koutis, Bodhisatta Pramanik

State-of-the-art hypergraph partitioners follow the multilevel paradigm that constructs multiple levels of progressively coarser hypergraphs that are used to drive cut refinement on each level of the hierarchy. Multilevel partitioners are subject to two limitations: (i) hypergraph coarsening processes rely on local neighborhood structure without fully consid

Jeffrey Chen, Scott E. Fahlman

We present Score, a rule engine designed and implemented for the Scone knowledge base system. Scone is a knowledge base system designed for storing and manipulating rich representations of general knowledge in symbolic form. It represents knowledge in the form of nodes and links in a network structure, and it can perform basic inference about the relationshi

Frederik Benirschke, Carlos A. Serván

Pulling back complex structures along a branched covering induces a holomorphic isometric embedding of Teichm\"uller spaces. We show that for dimension at least $2$, all isometric embeddings arise from branched coverings. This generalizes a theorem of Royden. As a consequence we obtain that totally geodesic submanifolds of Teichm\"uller space, which are isom

Rodrigo Veiga, Markus Endler, Valeria de Paiva

Blockchains provide a mechanism through which mutually distrustful remote parties can reach consensus on the state of a ledger of information. With the great acceleration with which this space is developed, the demand for those seeking to learn about blockchain also grows. Being a technical subject, it can be quite intimidating to start learning. For this re

Boning Zhang, Dongzhu Liu, Osvaldo Simeone, Guangxu Zhu

The recent development of scalable Bayesian inference methods has renewed interest in the adoption of Bayesian learning as an alternative to conventional frequentist learning that offers improved model calibration via uncertainty quantification. Recently, federated averaging Langevin dynamics (FALD) was introduced as a variant of federated averaging that can

Pengyu Yan, Saleem Ahmed, David Doermann

As a prerequisite of chart data extraction, the accurate detection of chart basic elements is essential and mandatory. In contrast to object detection in the general image domain, chart element detection relies heavily on context information as charts are highly structured data visualization formats. To address this, we propose a novel method CACHED, which s

Efstratios Chatzoglou, Georgios Karopoulos, Georgios Kambourakis, Zisis Tsiatsikas

Being on a mushrooming spree since at least 2013, malware can take a large toll on any system. In a perpetual cat-and-mouse chase with defenders, malware writers constantly conjure new methods to hide their code so as to evade detection by security products. In this context, focusing on the MS Windows platform, this work contributes a comprehensive empirical

Lezhi Tan, Jianfeng Lu

Sampling a probability distribution with known likelihood is a fundamental task in computational science and engineering. Aiming at multimodality, we propose a new sampling method that takes advantage of both birth-death process and exploration component. The main idea of this method is look before you leap. We keep two sets of samplers, one at warmer temper

Yifei Chen, Zhan Yu, Chenghong Zhu, Xin Wang

The rapid advancement of quantum computing has led to an extensive demand for effective techniques to extract classical information from quantum systems, particularly in fields like quantum machine learning and quantum chemistry. However, quantum systems are inherently susceptible to noises, which adversely corrupt the information encoded in quantum systems.

Maximillian Chen, Xiao Yu, Weiyan Shi, Urvi Awasthi

Mixed-initiative dialogue tasks involve repeated exchanges of information and conversational control. Conversational agents gain control by generating responses that follow particular dialogue intents or strategies, prescribed by a policy planner. The standard approach has been fine-tuning pre-trained language models to perform generation conditioned on thes

Kiwan Maeng, Chuan Guo, Sanjay Kariyappa, G. Edward Suh

Privacy-preserving instance encoding aims to encode raw data as feature vectors without revealing their privacy-sensitive information. When designed properly, these encodings can be used for downstream ML applications such as training and inference with limited privacy risk. However, the vast majority of existing instance encoding schemes are based on heuris

Kai Jun Chen, Lok Him Lai, Zi Iun Lai

Mahjong is a complex game with an intractably large state space with extremely sparse rewards, which poses challenges to develop an agent to play Mahjong. To overcome this, the ShangTing function was adopted as a reward shaping function. This was combined with a forward-search algorithm to create an agent capable of completing a winning hand in Single-player

Domingos Djinja, Sergei Silvestrov, Alex Behakanira Tumwesigye

Representations of polynomial covariance type commutation relations by linear integral operators on $L_p$ over measures spaces are investigated. Necessary and sufficient conditions for integral operators to satisfy polynomial covariance type commutation relations are obtained in terms of their kernels. For important classes of polynomial covariance commutati

Eric R. Cohn, Zirui Song, Jose R. Zubizarreta

Gun violence is a major source of injury and death in the United States. However, relatively little is known about the effects of firearm injuries on survivors and their family members and how these effects vary across subpopulations. To study these questions and, more generally, to address a gap in the causal inference literature, we present a framework for

Wei Dai, Hejie Cui, Xuan Kan, Ying Guo

Brain networks, graphical models such as those constructed from MRI, have been widely used in pathological prediction and analysis of brain functions. Within the complex brain system, differences in neuronal connection strengths parcellate the brain into various functional modules (network communities), which are critical for brain analysis. However, identif

Mevin B Hooten, Michael R Schwob, Devin S Johnson, Jacob S Ivan

Methods for population estimation and inference have evolved over the past decade to allow for the incorporation of spatial information when using capture-recapture study designs. Traditional approaches to specifying spatial capture-recapture (SCR) models often rely on an individual-based detection function that decays as a detection location is farther from

Xiaoran Ma, Wensheng Guo, Mengyang Gu, Len Usvyat

Some patients with COVID-19 show changes in signs and symptoms such as temperature and oxygen saturation days before being positively tested for SARS-CoV-2, while others remain asymptomatic. It is important to identify these subgroups and to understand what biological and clinical predictors are related to these subgroups. This information will provide insig

Michael Baggaley, Ifaz Haider, Olivia Bruce, Arash Khassetarash

A fatigue-failure process is hypothesized to govern the development of tibial stress fractures, where bone damage is highly dependent on the peak strain magnitude. To date, much of the work examining tibial strains during running has ignored uphill and downhill running despite the prevalence of this terrain. This study examined the sensitivity of tibial stra

Richard Ostertág

Cryptographic algorithms and protocols often need unique random numbers as parameters (e.g. nonces). Failure to satisfy this requirement lead to vulnerable implementation and can result in security breach. We show how linear types and static type checking can be used to enforce the correct generation of a new unique random number for each function invocation

Carsten H. Chong, Viktor Todorov

We propose model-free (nonparametric) estimators of the volatility of volatility and leverage effect using high-frequency observations of short-dated options. At each point in time, we integrate available options into estimates of the conditional characteristic function of the price increment until the options' expiration and we use these estimates to recove

Nolan Samboy

We investigate the long-range, two-body interactions between rubidium and potassium atoms in highly excited ($n=70$) Rydberg states. After establishing properly symmetrized asymptotic basis states, we diagonalize an interaction Hamiltonian consisting of the standard Coulombic potential expansion and atomic fine structure to calculate electronic potential ene

George Adam, Benjamin Haibe-Kains, Anna Goldenberg

Deployed machine learning models should be updated to take advantage of a larger sample size to improve performance, as more data is gathered over time. Unfortunately, even when model updates improve aggregate metrics such as accuracy, they can lead to errors on samples that were correctly predicted by the previous model causing per-sample regression in perf

Hernan Ceferino Vazquez

Artificial Intelligence (AI) has been rapidly advancing and has demonstrated its ability to perform a wide range of cognitive tasks, including language processing, visual recognition, and decision-making. Part of this progress is due to LLMs (Large Language Models) like those of the GPT (Generative Pre-Trained Transformers) family. These models are capable o