May 2022 arXiv papers — page 7

Yang Cai, Jaime Llorca, Antonia M. Tulino, Andreas F. Molisch

Distributed computing (cloud) networks, e.g., mobile edge computing (MEC), are playing an increasingly important role in the efficient hosting, running, and delivery of real-time stream-processing applications such as industrial automation, immersive video, and augmented reality. While such applications require timely processing of real-time streams that are

Yang Cai, Jaime Llorca, Antonia M. Tulino, Andreas F. Molisch

The timely delivery of resource-intensive and latency-sensitive services (e.g., industrial automation, augmented reality) over distributed computing networks (e.g., mobile edge computing) is drawing increasing attention. Motivated by the insufficiency of average delay performance guarantees provided by existing studies, we focus on the critical goal of deliv

Vilas Patwardhan

This is the first in a series of papers which deal with the development of novel methods for solving a system of linear algebraic equations with a time complexity lower than existing algorithms. The NxN system of linear equations, Ax = b, is often solved iteratively by minimizing the corresponding quadratic form using well known optimization techniques. The

Ge Zhang, Jia Wu, Jian Yang, Shan Xue

Graph-structured data consisting of objects (i.e., nodes) and relationships among objects (i.e., edges) are ubiquitous. Graph-level learning is a matter of studying a collection of graphs instead of a single graph. Traditional graph-level learning methods used to be the mainstream. However, with the increasing scale and complexity of graphs, Graph-level Neur

Hyunsoo Cho, JiSun Huh, Hayan Nam, Jaebum Sohn

In this paper, we construct a bijection from a set of bounded free Motzkin paths to a set of bounded Motzkin prefixes that induces a bijection from a set of bounded free Dyck paths to a set of bounded Dyck prefixes. We also give bijections between a set of bounded cornerless Motzkin paths and a set of $t$-core partitions, and a set of bounded cornerless symm

Li-Jen Chang, Yu-Cheng Liao, Chia-Hui Lin, Hwann-Tzong Chen

We present a self-trainable method, Mask2Hand, which learns to solve the challenging task of predicting 3D hand pose and shape from a 2D binary mask of hand silhouette/shadow without additional manually-annotated data. Given the intrinsic camera parameters and the parametric hand model in the camera space, we adopt the differentiable rendering technique to p

Shulei Cao, Michal Zajaček, Swayamtrupta Panda, Mary Loli Martínez-Aldama

We use 38 C IV quasar (QSO) reverberation-mapped (RM) observations, which span eight orders of magnitude in luminosity and the redshift range $0.001064 \leq z \leq 3.368$, to simultaneously constrain cosmological-model and QSO radius-luminosity ($R-L$) relation parameters in six cosmological models, using an improved technique that more correctly accounts fo

Takahiro Miura, Yukinao Akamatsu, Masayuki Asakawa, Yukana Kaida

We study the properties of the Lindbladian quantum mechanical evolution of quarkonia with non-Abelian charges (color-singlet and octet) in the quark-gluon plasma. We confirm that heavy quark recoils in the Lindblad equation correctly thermalize quarkonium colorful states within statistical errors from the simulation method. We also demonstrate that the Lindb

Shu'ang Li, Xuming Hu, Li Lin, Aiwei Liu

Natural Language Inference (NLI) is a growingly essential task in natural language understanding, which requires inferring the relationship between the sentence pairs (premise and hypothesis). Recently, low-resource natural language inference has gained increasing attention, due to significant savings in manual annotation costs and a better fit with real-wor

Eng Hock Lee, Vladimir Cherkassky

There has been growing interest in generalization performance of large multilayer neural networks that can be trained to achieve zero training error, while generalizing well on test data. This regime is known as 'second descent' and it appears to contradict the conventional view that optimal model complexity should reflect an optimal balance between underfit

Mostafa Rahmani, Anoop Deoras, Laurent Callot

This paper presents a novel, closed-form, and data/computation efficient online anomaly detection algorithm for time-series data. The proposed method, dubbed RPE, is a window-based method and in sharp contrast to the existing window-based methods, it is robust to the presence of anomalies in its window and it can distinguish the anomalies in time-stamp level

Morteza Alipourlangouri, Fei Chiang

Keys for graphs uses the topology and value constraints needed to uniquely identify entities in a graph database. They have been studied to support object identification, knowledge fusion, data deduplication, and social network reconciliation. In this paper, we present our algorithm to mine keys over graphs. Our algorithm discovers keys in a graph via freque

Pei Liu, Li Li, Yichun Yan, Mattia Fazzini

With over 500,000 commits and more than 700 contributors, the Android platform is undoubtedly one of the largest industrial-scale software projects. This project provides the Android API, and developers heavily rely on this API to develop their Android apps. Unfortunately, because the Android platform and its API evolve at an extremely rapid pace, app develo

Danny Harnik, Oded Naor, Effi Ofer, Or Ozery

Disk encryption today uses standard encryption methods that are length preserving and do not require storing any additional information with an encrypted disk sector. This significantly simplifies disk encryption management as the disk mapping does not change with encryption. On the other hand, it forces the encryption to be deterministic when data is being

Jingyi Zhang, Cheng Meng, Jun Yu, Mengrui Zhang

Subsampling methods aim to select a subsample as a surrogate for the observed sample. Such methods have been used pervasively in large-scale data analytics, active learning, and privacy-preserving analysis in recent decades. Instead of model-based methods, in this paper, we study model-free subsampling methods, which aim to identify a subsample that is not c

Xuan-Phi Nguyen, Shafiq Joty, Wu Kui, Ai Ti Aw

Numerous recent work on unsupervised machine translation (UMT) implies that competent unsupervised translations of low-resource and unrelated languages, such as Nepali or Sinhala, are only possible if the model is trained in a massive multilingual environment, where these low-resource languages are mixed with high-resource counterparts. Nonetheless, while th

Ripon Kumar Saha, A. M. Mahmud Chowdhury, Kyung-Sun Na, Gyu Deok Hwang

Purpose: Develop a deep learning-based automated method to segment meibomian glands (MG) and eyelids, quantitatively analyze the MG area and MG ratio, estimate the meiboscore, and remove specular reflections from infrared images. Methods: A total of 1600 meibography images were captured in a clinical setting. 1000 images were precisely annotated with multipl

Yuxiang Luo, Wei Ren

Let $G$ be a group and $R$ be a ring. We define the Gorenstein homological dimension of $G$ over $R$, denoted by ${\rm Ghd}_{R}G$, as the Gorenstein flat dimension of trivial $RG$-module $R$. It is proved that ${\rm Ghd}_SG \leq {\rm Ghd}_RG$ for any flat extension of commutative rings $R\rightarrow S$; in particular, ${\rm Ghd}_{R}G$ is a refinement of ${\r

Kashin Sugishita, Hiroki Mizutani, Shinya Hanaoka

The COVID-19 pandemic has had serious impacts on the airline industry. Ensuring that aviation policies in emergent situations both guarantee network connectivity and maintain competition among airlines is crucial in these circumstances. To this end, we aimed to understand the network dynamics of individual airlines. In this study, we quantitatively reveal th

Wenzhuo Yang, Jia Li, Caiming Xiong, Steven C. H. Hoi

Counterfactual explanation is an important Explainable AI technique to explain machine learning predictions. Despite being studied actively, existing optimization-based methods often assume that the underlying machine-learning model is differentiable and treat categorical attributes as continuous ones, which restricts their real-world applications when categ

Riku Kawase, Hiroyuki Kawashima, Hiromitsu Kato, Norio Tokuda

The longest spin coherence times for nitrogen-vacancy (NV) centers at room temperature have been achieved in phosphorus-doped n-type diamond. However, difficulty controlling impurity incorporation and the utilization of highly toxic phosphine gas in the chemical vapor deposition (CVD) technique pose problems for the growth of n-type diamond. In the present s

Zheng-Hao Liu, Qiang Li, Bi-Heng Liu, Yun-Feng Huang

Quantum contextuality is one of the most perplexing and peculiar features of quantum mechanics. Concisely, it refers to the observation that the result of a single measurement in quantum mechanics depends on the set of joint measurements actually performed. The study of contextuality has a long history at University of Science and Technology of China (USTC).

Ashish Singh Bais, Lokendra Singh Chouhan, Joseph Thomas Andrews

Integrating all optical components of an optical coherence tomography (OCT) device into a single chip is a non-trivial and a challenging job. The design and development of such a lab-on-a chip will be possible only via Micro-Opto-Electro-Mechanical System (MOEMS) technology. The reproducible and integrated optical device fabrication would reduce cost and siz

Anish Khazane, Julien Hoachuck, Krzysztof J. Gorgolewski, Russell A. Poldrack

Recent advancements in the field of magnetic resonance imaging (MRI) have enabled large-scale collaboration among clinicians and researchers for neuroimaging tasks. However, researchers are often forced to use outdated and slow software to anonymize MRI images for publication. These programs specifically perform expensive mathematical operations over 3D imag

Pei Liu, Mattia Fazzini, John Grundy, Li Li

To satisfy varying customer needs, device vendors and OS providers often rely on the open-source nature of the Android OS and offer customized versions of the Android OS. When a new version of the Android OS is released, device vendors and OS providers need to merge the changes from the Android OS into their customizations to account for its bug fixes, secur

Kisung Moon, Sunyoung Kwon

Self-supervised learning (SSL) is a method that learns the data representation by utilizing supervision inherent in the data. This learning method is in the spotlight in the drug field, lacking annotated data due to time-consuming and expensive experiments. SSL using enormous unlabeled data has shown excellent performance for molecular property prediction, b

Peter Sutor, Dehao Yuan, Douglas Summers-Stay, Cornelia Fermuller

Hyperdimensional Computing affords simple, yet powerful operations to create long Hyperdimensional Vectors (hypervectors) that can efficiently encode information, be used for learning, and are dynamic enough to be modified on the fly. In this paper, we explore the notion of using binary hypervectors to directly encode the final, classifying output signals of

Richard S. Garavuso

In this paper, we discuss various aspects of a class of A-twisted heterotic Landau-Ginzburg models on a Kaehler variety X. We provide a classification of the R-symmetries in these models which allow the A-twist to be implemented, focusing on the case in which the gauge bundle is either a deformation of the tangent bundle of X or a deformation of a sub-bundle

Yan Lin, Tianming Liu, Wei Liu, Zhigaoyuan Wang

Researchers have proposed kinds of malware detection methods to solve the explosive mobile security threats. We argue that the experiment results are inflated due to the research bias introduced by the variability of malware dataset. We explore the impact of bias in Android malware detection in three aspects, the method used to flag the ground truth, the dis

Hyeon Cho, Junyong Choi, Geonwoo Baek, Wonjun Hwang

Point-cloud based 3D object detectors recently have achieved remarkable progress. However, most studies are limited to the development of network architectures for improving only their accuracy without consideration of the computational efficiency. In this paper, we first propose an autoencoder-style framework comprising channel-wise compression and decompre

Jun Shi, Yuanming Zhang, Zheng Li, Xiangmin Han

Computer-aided diagnosis (CAD) can help pathologists improve diagnostic accuracy together with consistency and repeatability for cancers. However, the CAD models trained with the histopathological images only from a single center (hospital) generally suffer from the generalization problem due to the straining inconsistencies among different centers. In this

B. -W. Li, Q. -X. Mei, Y. -K. Wu, M. -L. Cai

Jaynes-Cummings-Hubbard (JCH) model is a fundamental many-body model for light-matter interaction. As a leading platform for quantum simulation, the trapped ion system has realized the JCH model for two to three ions. Here we report the quantum simulation of the JCH model using up to 32 ions. We verify the simulation results even for large ion numbers by eng

Xiaowen Tian, Nuria Gonzalez-Prelcic, Takayuki Shimizu

Low Earth Orbit (LEO) satellite communications (SatCom) are considered a promising solution to provide uninterrupted services in cellular networks. Line-of-sight (LoS) links between the LEO satellites and the ground users are, however, easily blocked in urban scenarios. In this paper, we propose to enable LEO SatCom in non-line-of-sight (NLoS) channels, as t

Zhi Zeng

We present a simple method for the complete analysis of maximally hyperentangled state in polarization and spatial-mode degrees of freedom assisted by the weak cross-Kerr nonlinearity. Our method not only can be used for two-photon hyperentangled Bell state analysis and three-photon hyperentangled Greenberger-Horne-Zeilinger (GHZ) state analysis, but also is

Mikiya Masuda, Takashi Sato

The solution of Shareshian-Wachs conjecture by Brosnan-Chow linked together the cohomology of regular semisimple Hessenberg varieties and graded chromatic symmetric functions on unit interval graphs. On the other hand, it is known that unicellular LLT polynomials have similar properties to graded chromatic symmetric functions. In this paper, we link together

Yuichiro Hidaka, Shunsuke C. Furuya, Atsushi Ueda, Yasuhiro Tada

We study a three-leg spin-1/2 ladder with geometrically frustrated interleg interactions. We call this model an anisotropic triangular-strip (ATS) model. We numerically and field-theoretically show that its ground state belongs to a gapless symmetry-protected topological (SPT) phase. The numerical approach is based on density-matrix renormalization group ana

Pengyu Hou, Fang Liu, Aihui Zhou

In this paper, a symmetrized two-scale finite element method is proposed for a class of partial differential equations with symmetric solutions. With this method, the finite element approximation on a fine tensor product grid is reduced to the finite element approximations on a much coarse grid and a univariant fine grid. It is shown by both theory and numer

Jinyong Hou, Jeremiah D. Deng, Stephen Cranefield, Xuejie Din

To successfully apply trained neural network models to new domains, powerful transfer learning solutions are essential. We propose to introduce a novel cross-domain latent modulation mechanism to a variational autoencoder framework so as to achieve effective transfer learning. Our key idea is to procure deep representations from one data domain and use it to

Takuto Nakamura, Toru Nakaya, Yoshiyuki Ohtsubo, Hiroki Sugihara

The electronic structure changes of SmS surfaces under potassium (K) doping are elucidated using synchrotron-based core-level photoelectron spectroscopy and angle-resolved photoelectron spectroscopy (ARPES). The Sm core-level and ARPES spectra indicate that the Sm mean valence of the surface increased from the nearly divalent to trivalent states, with increa

Masataka Shiozawa, Shigeyasu Uno

An analytical formula for electrical impedance between an adherent living cell and sensor substrate measured using a microelectrode is presented for the first time. Previously-reported formula has been applicable only for the case where many cells are on a large electrode. In contrast, our formula is valid even when a microelectrode smaller than the cell siz

Xiaowen Tian, Nuria Gonzalez-Prelcic, Robert W. Heath

Millimeter wave (MmWave) systems are vulnerable to blockages, which cause signal drop and link outage. One solution is to deploy reconfigurable intelligent surfaces (RISs) to add a strong non-line-of-sight path from the transmitter to receiver. To achieve the best performance, the location of the deployed RIS should be optimized for a given site, considering

Bowen Li, Jianfeng Lu, Ziang Yu

This work aims to numerically construct exactly commuting matrices close to given almost commuting ones, which is equivalent to the joint approximate diagonalization problem. We first prove that almost commuting matrices generically have approximate common eigenvectors that are almost orthogonal to each other. Based on this key observation, we propose a fast

Masoud Roudneshin, Kamran Ghaffari, Amir G. Aghdam

In this paper, a new numerical method to solve the forward kinematics (FK) of a parallel manipulator with three-limb spherical-prismatic-revolute (3SPR) structure is presented. Unlike the existing numerical approaches that rely on computation of the manipulator's Jacobian matrix and its inverse at each iteration, the proposed algorithm requires much less com

Jingxiang Sun, Xuan Wang, Yichun Shi, Lizhen Wang

Existing 3D-aware facial generation methods face a dilemma in quality versus editability: they either generate editable results in low resolution or high-quality ones with no editing flexibility. In this work, we propose a new approach that brings the best of both worlds together. Our system consists of three major components: (1) a 3D-semantics-aware genera

D. Moratuwage, B. -N. Vo, B. -T. Vo, C. Shim

If computational tractability were not an issue, multi-object estimation should integrate all measurements from multiple sensors across multiple scans. In this article, we propose an efficient numerical solution to the multi-scan multi-sensor multi-object estimation problem by computing the (labeled) multi-sensor multi-object posterior density. Minimizing th

Xianlong He, Kai Liao, Xuheng Ding, Lilan Yang

The recent rapid growth of the black hole (BH) catalog from gravitational waves (GWs), has allowed us to study the substructure of black hole mass function (BHMF) beyond the simplest Power-Law distribution. However, the BH masses inferred from binary BH merger events, may be systematically 'brightened' or 'dimmed' by the gravitational lensing effect. In this

Jason R. South

We prove the Goldbach Conjecture using p-adic analysis and algebraic methods, requiring no knowledge of prime gaps or distribution. To begin, we define the set of primes up to $a \in \mathbb{N}$ as $\mathcal{P}_a$. It will be shown that if a counter-example $2a$ exists, there exists a polynomial \[ \mathcal{G}_-(z) = \prod_{p_k \in \mathcal{P}_a} (z - p_k) -

Qi Zhang, Jie Zhou, Qin Chen, Qingchun Bai

Structured sentiment analysis, which aims to extract the complex semantic structures such as holders, expressions, targets, and polarities, has obtained widespread attention from both industry and academia. Unfortunately, the existing structured sentiment analysis datasets refer to a few languages and are relatively small, limiting neural network models' per

Avinash Madasu, Mauajama Firdaus, Asif Eqbal

Social chatbots have gained immense popularity, and their appeal lies not just in their capacity to respond to the diverse requests from users, but also in the ability to develop an emotional connection with users. To further develop and promote social chatbots, we need to concentrate on increasing user interaction and take into account both the intellectual

Xue-Ting Fang, Zheng-Qi Dai, Di Xiang, Shou-Long Chen

Ultracold spinor atoms in the weak and strong interaction regime have received extensive investigations, while the behavior in the intermediate regime is less understood. We numerically investigate ultracold spinor atomic ensembles of finite size in the intermediate interaction regime, and reveal the evolution of the eigenstates from the strong to the interm

Wei Xiong, Han Zhong, Chengshuai Shi, Cong Shen

Offline reinforcement learning (RL) aims at learning an optimal strategy using a pre-collected dataset without further interactions with the environment. While various algorithms have been proposed for offline RL in the previous literature, the minimax optimality has only been (nearly) established for tabular Markov decision processes (MDPs). In this paper,

Qi Zhang, Jie Zhou, Qin Chen, Qinchun Bai

Previous studies about event-level sentiment analysis (SA) usually model the event as a topic, a category or target terms, while the structured arguments (e.g., subject, object, time and location) that have potential effects on the sentiment are not well studied. In this paper, we redefine the task as structured event-level SA and propose an End-to-End Event

Jiace Sun, Lixue Cheng, Thomas F. Miller

This study extends the accurate and transferable molecular-orbital-based machine learning (MOB-ML) approach to modeling the contribution of electron correlation to dipole moments at the cost of Hartree-Fock computations. A molecular-orbital-based (MOB) pairwise decomposition of the correlation part of the dipole moment is applied, and these pair dipole momen

Bingqian Lin, Yi Zhu, Zicong Chen, Xiwen Liang

Vision-Language Navigation (VLN) is a challenging task that requires an embodied agent to perform action-level modality alignment, i.e., make instruction-asked actions sequentially in complex visual environments. Most existing VLN agents learn the instruction-path data directly and cannot sufficiently explore action-level alignment knowledge inside the multi

Jianheng Tang, Jiajin Li, Ziqi Gao, Jia Li

Graph Neural Networks (GNNs) are widely applied for graph anomaly detection. As one of the key components for GNN design is to select a tailored spectral filter, we take the first step towards analyzing anomalies via the lens of the graph spectrum. Our crucial observation is the existence of anomalies will lead to the `right-shift' phenomenon, that is, the s

Zehao Lin, Ye Xu, Ligang Hou, Dejian Liu

Using the cross-matched data of Gaia EDR3 and the 2MASS Point Source Catalog, a sample of RC stars with parallax accuracies better than 20% is identified and used to reveal the nearby spiral pattern traced by old stars. As shown in the overdensity distribution of RC stars, there is an arc-like feature extended from $l~\sim$ 90$^\circ$ to $\sim$ 243$^\circ$,

Ivan Mitrofanov

We prove that for a compact metric space the property of having finite covering dimension is equivalent to the existence of a total order with finite snake number.

Rui Ma, Yanqing Hu, Jin-Hua Zhao

To enhance robustness of complex networked systems, a simple method is introducing reinforced nodes which always function during failure propagation. A random scheme of node reinforcement can be considered as a benchmark for finding an optimal reinforcement solution. Yet there still lacks a systematic evaluation on how node reinforcement affects network stru

Jinane Bazzi, Jana Sweidan, Mohammed E. Fouda, Rouwaida Kanj

DNA pattern matching is essential for many widely used bioinformatics applications. Disease diagnosis is one of these applications, since analyzing changes in DNA sequences can increase our understanding of possible genetic diseases. The remarkable growth in the size of DNA datasets has resulted in challenges in discovering DNA patterns efficiently in terms

Yi Zhang, Mengjia Wu, Jie Lu

Interest in tracing the research interests of scientific researchers is rising, and particularly that of predicting a researcher's knowledge trajectories beyond their current foci into potential inter-/cross-/multi-disciplinary interactions. Hence, in this study, we present a method of diffusion-based network analytics for knowledge trajectory recommendation

Young-Ho Kim, Sungdong Kim, Minsuk Chang, Sang-Woo Lee

Current natural language interaction for self-tracking tools largely depends on bespoke implementation optimized for a specific tracking theme and data format, which is neither generalizable nor scalable to a tremendous design space of self-tracking. However, training machine learning models in the context of self-tracking is challenging due to the wide vari

Yi-Zheng Fan, Ya Yang, Chuan-Ming She, Jian Zheng

Let $\mathcal{H}$ be an $m$-uniform hypergraph, and let $\mathcal{A}(\mathcal{H})$ be the adjacency tensor of $\mathcal{H}$ which can be viewed as a system of homogeneous polynomials of degree $m-1$. Morozov and Shakirov generalized the traces of linear systems to nonlinear homogeneous polynomial systems and obtained explicit formulas for multidimensional re

Ziyuan Xia, Anchen Sun, Jingyi Xu, Yuanzhe Peng

This survey paper conducts a comprehensive analysis of the evolution and contemporary landscape of recommendation systems, which have been extensively incorporated across a myriad of web applications. It delves into the progression of personalized recommendation methodologies tailored for online products or services, organizing the array of recommendation te

Yiming Zeng, Jiarui Zhang, Ji Liu, Zhenhua Liu

Quantum networks are considered as a promising future platform for quantum information exchange and quantum applications, which have capabilities far beyond the traditional communication networks. Remote quantum entanglement is an essential component of a quantum network. How to efficiently design a multi-routing entanglement protocol is a fundamental yet ch

Keisuke Izumi, Tetsuya Shiromizu, Kenta Suzuki, Tadashi Takayanagi

In this paper we study various dynamical aspects of the AdS/BCFT correspondence in higher dimensions. We study properties of holographic stress energy tensor by analyzing the metric perturbation in the gravity dual. We also calculate the stress energy tensor for a locally excited state on a half plane in a free scalar CFT. Both of them satisfy a reflective b

Pei-Sen Li, Zenghu Li, Jian Wang, Xiaowen Zhou

We study the ergodic property of a continuous-state branching process with immigration and competition. The exponential ergodicity in a weighted total variation distance is proved under natural assumptions. The main theorem applies to subcritical, critical and supercritical branching mechanisms, including all those of stable types. The proof is based on the

Makoto Araya, Masaaki Harada, Koji Momihara

In 2013, Nebe and Villar gave a series of ternary self-dual codes of length $2(p+1)$ for a prime $p$ congruent to $5$ modulo $8$. As a consequence, the third ternary extremal self-dual code of length $60$ was found. We show that the ternary self-dual code contains codewords which form a Hadamard matrix of order $2(p+1)$ when $p$ is congruent to $5$ modulo $2

Thomas E. Gilsdorf

The lifting theorem of Valdivia concerning (pre) compact sets and convergent (respectively, Cauchy) sequences from a quasi-(LB) space to a metrizable, strictly barrelled space is extended to a strictly larger collection of range spaces. Specifically, we assume that the range space has a sequential web structure and do not require that it be metrizable, nor s

Xianjia Yu, Jorge Pena Queralta, Tomi Westerlund

The role of deep learning (DL) in robotics has significantly deepened over the last decade. Intelligent robotic systems today are highly connected systems that rely on DL for a variety of perception, control, and other tasks. At the same time, autonomous robots are being increasingly deployed as part of fleets, with collaboration among robots becoming a more

Peng Dai, Yiqiang Feng, Renliang Weng, Changshui Zhang

The recent trend in multiple object tracking (MOT) is heading towards leveraging deep learning to boost the tracking performance. In this paper, we propose a novel solution named TransSTAM, which leverages Transformer to effectively model both the appearance features of each object and the spatial-temporal relationships among objects. TransSTAM consists of t

Mintong Kang, Linyi Li, Maurice Weber, Yang Liu

Extensive efforts have been made to understand and improve the fairness of machine learning models based on observational metrics, especially in high-stakes domains such as medical insurance, education, and hiring decisions. However, there is a lack of certified fairness considering the end-to-end performance of an ML model. In this paper, we first formulate

Hui Zhao, Yu Yang, Naihuan Jing, Zhi-Xi Wang

We study entanglement and genuine entanglement of tripartite and four-partite quantum states by using Heisenberg-Weyl (HW) representation of density matrices. Based on the correlation tensors in HW representation, we present criteria to detect entanglement and genuine tripartite and four-partite entanglement. Detailed examples show that our method can detect

Xing Wang, Yuntian He

We design and implement a temporal convolutional network model to predict sepsis onset. Our model is trained on data extracted from MIMIC III database, based on a retrospective analysis of patients admitted to intensive care unit who did not fall under the definition of sepsis at the time of admission. Benchmarked with several machine learning models, our mo

Si Duc Quang

This paper has twofold. The first is to establish a second main theorem for meromorphic functions on the complex disc $\Delta (R_0)\subset\mathbb C$ with finite growth index and small functions, where the counting functions are truncated to level $1$ and the small term is more detailed estimated. The second is to prove a generalization and improvement of the

Shengchun Xiong, Xiangru Li, Caixiu Liao

This work proposes a Residual Recurrent Neural Network (RRNet) for synthetically extracting spectral information, and estimating stellar atmospheric parameters together with 15 chemical element abundances for medium-resolution spectra from Large Sky Area Multi-Object Fiber Spectroscopic Telescope (LAMOST). The RRNet consists of two fundamental modules: a res

Tim von Hahn, Chris K. Mechefske

Scientific research frequently involves the use of computational tools and methods. Providing thorough documentation, open-source code, and data -- the creation of reproducible computational research -- helps others understand a researcher's work. Here, we explore computational reproducibility, broadly, and from within the field of prognostics and health man

Arunchandar Vasan, Prasant Misra, Srinarayana Nagarathinam, Venkata Ramakrishna

Cyber-physical systems (CPS) are systems where a decision making (cyber/control) component is tightly integrated with a physical system (with sensing/actuation) to enable real-time monitoring and control. Recently, there has been significant research effort in viewing and optimizing physical infrastructure in built environments as CPS, even if the control ac

Jonathan R. Dietz, Boyang Jiang, Aaron M. Day, Sunil A. Bhave

We demonstrate direct, acoustically mediated spin control of naturally occurring negatively charged silicon monovacancies (V$_{Si}^-$) in a high quality factor Lateral Overtone Bulk Acoustic Resonator fabricated out of high purity semi-insulating 4H-Silicon Carbide. We compare the frequency response of silicon monovacancies to a radio-frequency magnetic driv

Jin Yun Guo, Yanping Hu, Deren Luo

In this paper, we introduce multi-layer quiver and show how to construct an $(n+1)$-slice algebras of infinite type from an $n$-slice algebra of infinite type using the bound quivers. This leads to constructing $(n+1)$-slice algebras of infinite type as matrix algebra and as tensor algebra of an $n$-slice algebra and equivalences of their module categories a

Nicola Di Vittorio

The notion of $\textbf{Gray}$-category, a semi-strict $3$-category in which the middle four interchange is weakened to an isomorphism, is central in the study of three-dimensional category theory. In this context it is common practice to use $2$-dimensional pasting diagrams to express composites of $2$-cells, however there is no thorough treatment in the lit

Yuzhe Zhang, Hong Zhang

Financial named entity recognition (FinNER) from literature is a challenging task in the field of financial text information extraction, which aims to extract a large amount of financial knowledge from unstructured texts. It is widely accepted to use sequence tagging frameworks to implement FinNER tasks. However, such sequence tagging models cannot fully tak

David R. Leibrandt, Sergey G. Porsev, Charles Cheung, Marianna S. Safronova

We propose a many-ion optical atomic clock based on three-dimensional Coulomb crystals of order one thousand Sn$^{2+}$ ions confined in a linear RF Paul trap. Sn$^{2+}$ has a unique combination of features that is not available in previously considered ions: a $^1$S$_0$ $\leftrightarrow$ $^3$P$_0$ clock transition between two states with zero electronic and

Aaron Ray, Alyssa Pierson, Hai Zhu, Javier Alonso-Mora

We address the problem of assigning a team of drones to autonomously capture a set desired shots of a dynamic target in the presence of obstacles. We present a two-stage planning pipeline that generates offline an assignment of drone to shots and locally optimizes online the viewpoint. Given desired shot parameters, the high-level planner uses a visibility h

Ze Zheng, Lei Xu, Lujun Huang, Daria Smirnova

To date, second-harmonic generation (SHG) at nanoscale has been concentrated on employing high-refractive-index nanostructures, owing to the strong field confinement at deep subwavelength scales based on optically resonant effects. However, low-index nanostructures generally exhibit weaker resonant effects and lower field confinement. To address this issue,

Hiroki Fukuda, Akira Nagakubo, Shigeyoshi Usami, Masashi Ikeda

We acoustically measured the energy level for thermally activated conduction (TAC) in high-resistivity Fe-doped GaN using the non-contacting antenna-transmission acoustic-resonance method. The acoustic attenuation takes a maximum at a specific temperature, where the TAC is accelerated with the help of phonon energy. The Debye type relaxation is thus observed

Mert Yuksekgonul, Maggie Wang, James Zou

Concept Bottleneck Models (CBMs) map the inputs onto a set of interpretable concepts (``the bottleneck'') and use the concepts to make predictions. A concept bottleneck enhances interpretability since it can be investigated to understand what concepts the model "sees" in an input and which of these concepts are deemed important. However, CBMs are restrictive

Minh Huynh Nguyen, Nghi D. Q. Bui, Truong Son Hy, Long Tran-Thanh

We propose a novel method for code summarization utilizing Heterogeneous Code Representations (HCRs) and our specially designed HierarchyNet. HCRs effectively capture essential code features at lexical, syntactic, and semantic levels by abstracting coarse-grained code elements and incorporating fine-grained program elements in a hierarchical structure. Our H

Masaya Norimoto, Ryuhei Mori, Naoki Ishikawa

In this paper, we propose a quantum algorithm that supports a real-valued higher-order unconstrained binary optimization (HUBO) problem. This algorithm is based on the Grover adaptive search that originally supported HUBO with integer coefficients. Next, as an application example, we formulate multiple-input multiple-output maximum likelihood detection as a

Ala-Eddine Benrazek, Zineddine Kouahla, Brahim Farou, Hamid Seridi

This paper addresses the issue of the real-time tracking quality of moving objects in large-scale video surveillance systems. During the tracking process, the system assigns an identifier or label to each tracked object to distinguish it from other objects. In such a mission, it is essential to keep this identifier for the same objects, whatever the area, th

Hyeonsu B. Kang, Xin Qian, Tom Hope, Dafna Shahaf

Analogies have been central to creative problem-solving throughout the history of science and technology. As the number of scientific papers continues to increase exponentially, there is a growing opportunity for finding diverse solutions to existing problems. However, realizing this potential requires the development of a means for searching through a large

Pengfei Huang, Georgios Kydonakis, Hao Sun, Lutian Zhao

The nonabelian Hodge correspondence for vector bundles over noncompact curves is adequately described by implementing a weighted filtration on the objects involved. In order to establish a full correspondence between a Dolbeault and a de Rham space for a general complex reductive group $G$, we introduce torsors given by parahoric group schemes in the sense o

Hui Zhang, Minbo Yang, Jianjun Zhang, Xuexiu Zhong

In this paper, we consider the Hamiltonian elliptic system in dimension two\begin{equation}\label{1.5}\aligned \left\{ \begin{array}{lll} -\epsilon^2\Delta u+V(x)u=g(v)\ & \text{in}\quad \mathbb{R}^2,\\ -\epsilon^2\Delta v+V(x)v=f(u)\ & \text{in}\quad \mathbb{R}^2, \end{array}\right.\endaligned \end{equation} where $V\in C(\mathbb{R}^2)$ has local minimum po

Aaron Ray, Alyssa Pierson, Daniela Rus

We apply a novel framework for decomposing and reasoning about free space in an environment to a multi-agent persistent monitoring problem. Our decomposition method represents free space as a collection of ellipsoids associated with a weighted connectivity graph. The same ellipsoids used for reasoning about connectivity and distance during high level plannin

Masashi Tsuge, Akira Kouchi, Naoki Watanabe

Chemical processes on the surface of icy grains play an important role in the chemical evolution in molecular clouds. In particular, reactions involving non-energetic hydrogen atoms accreted from the gaseous phase have been extensively studied. These reactions are believed to effectively proceed only on the surface of the icy grains; thus, molecules embedded

Gi-Sang Cheon, Tamás Forgács, Arnauld Mesinga Mwafise, Khang Tran

We present combinatorial and analytical results concerning a Sheffer sequence with an exponential generating function of the form $G(s,z)=e^{czs+\alpha z^{2}+\beta z^{4}}$, where $\alpha, \beta, c \in \mathbb{R}$ with $\beta<0$ and $c\neq 0$. We demonstrate that the zeros of all polynomials in such a Sheffer sequence are either real, or purely imaginary. Add

Guo-Li Wang, Qiang Li, Tianhong Wang, Tai-Fu Feng

Using an almost complete relativistic method based on the Bethe-Salpeter equation, we study the mixing angle $\theta$, the mass splitting $\bigtriangleup M$, the strong decay widths $\Gamma(D^{({\prime})}_1)$ and the weak production rates $Br(B\to D^{({\prime})}_1\ell\nu_{\ell})$ of the $D_1(2420)$ and $D_1^{\prime}(2430)$. We find there is the strong cancel

Peng Zheng, Huazhu Fu, Deng-Ping Fan, Qi Fan

In this paper, we present a novel end-to-end group collaborative learning network, termed GCoNet+, which can effectively and efficiently (250 fps) identify co-salient objects in natural scenes. The proposed GCoNet+ achieves the new state-of-the-art performance for co-salient object detection (CoSOD) through mining consensus representations based on the follo

Bao-Quan Huang, Tong Liu

Jet precessions are widely involved in astrophysical phenomena from galaxies to X-ray binaries and gamma-ray bursts (GRBs). Polarization presents a unique probe of the magnetic fields in GRB jets. The precession of GRBs relativistic jets will change the geometry within the observable emitting region of the jet, which can potentially affect the polarization o

Hasan Mahmood, R. A. Reid-Edwards

We consider deformations of CFTs from the perspective of parallel transport in moduli space. In particular, we show how the deformations of individual operators can be computed and we also explore how these ideas can be extended to more general QFTs lacking conformal symmetry. We explore how to write one theory in terms of operators defined in a nearby theor

Jiachen T. Wang, Ruoxi Jia

Data valuation has wide use cases in machine learning, including improving data quality and creating economic incentives for data sharing. This paper studies the robustness of data valuation to noisy model performance scores. Particularly, we find that the inherent randomness of the widely used stochastic gradient descent can cause existing data value notion