April 2023 arXiv papers — page 133

Xuetong Li, Xuening Zhu, Hansheng Wang

Large-scale rare events data are commonly encountered in practice. To tackle the massive rare events data, we propose a novel distributed estimation method for logistic regression in a distributed system. For a distributed framework, we face the following two challenges. The first challenge is how to distribute the data. In this regard, two different distrib

Friedrich Götze, Andrei Yu. Zaitsev

Let $X_1,\ldots,X_n$ be independent identically distributed random variables. In this paper we study the behavior of concentration functions of weighted sums $\sum_{k=1}^{n}X_ka_k $ with respect to the arithmetic structure of coefficients~$a_k$ in the context of the Littlewood--Offord problem. In our recent papers, we discussed the relations between the inve

Tomohiro Harada, Takaaki Ishii, Takuya Katagiri, Norihiro Tanahashi

We study hairy black holes in Einstein-Maxwell-complex scalar theory in four-dimensional asymptotically global anti-de Sitter (AdS) spacetime when the Robin boundary conditions are imposed on the scalar field. This setup is dual to the double trace deformation of strongly interacting field theory on $R \times S^2$ by charged scalar operators. We identify the

Xiwu Yang, Xiaodong Cheng, Yuansheng Yang

Let $H$, $T$ and $C_n$ be a graph, a tree and a cycle of order $n$, respectively. Let $H^{(i)}$ be the complete join of $H$ and an empty graph on $i$ vertices. Then the Cartesian product $H\Box T$ of $H$ and $T$ can be obtained by applying zip product on $H^{(i)}$ and the graph produced by zip product repeatedly. Let $\textrm{cr}_{\Sigma}(H)$ denote the cros

Gustav Grund Pihlgren, Fredrik Sandin, Marcus Liwicki

The concept of image similarity is ambiguous, and images can be similar in one context and not in another. This ambiguity motivates the creation of metrics for specific contexts. This work explores the ability of deep perceptual similarity (DPS) metrics to adapt to a given context. DPS metrics use the deep features of neural networks for comparing images. Th

Nele Albers, Mark A. Neerincx, Willem-Paul Brinkman

Despite their prevalence in eHealth applications for behavior change, persuasive messages tend to have small effects on behavior. Conditions or states (e.g., confidence, knowledge, motivation) and characteristics (e.g., gender, age, personality) of persuadees are two promising components for more effective algorithms for choosing persuasive messages. However

Shoukai Xu, Jiangchao Yao, Ran Luo, Shuhai Zhang

Vision foundation models exhibit impressive power, benefiting from the extremely large model capacity and broad training data. However, in practice, downstream scenarios may only support a small model due to the limited computational resources or efficiency considerations. Moreover, the data used for pretraining foundation models are usually invisible and ve

Debbie Lim, Joao F. Doriguello, Patrick Rebentrost

Optimization theory has been widely studied in academia and finds a large variety of applications in industry. The different optimization models in their discrete and/or continuous settings have catered to a rich source of research problems. Robust convex optimization is a branch of optimization theory in which the variables or parameters involved have a cer

Tung Mai, Alexander Munteanu, Cameron Musco, Anup B. Rao

We study oblivious sketching for $k$-sparse linear regression under various loss functions such as an $\ell_p$ norm, or from a broad class of hinge-like loss functions, which includes the logistic and ReLU losses. We show that for sparse $\ell_2$ norm regression, there is a distribution over oblivious sketches with $\Theta(k\log(d/k)/\varepsilon^2)$ rows, wh

Jeongwoo Kim, Eun-Sun Cho, Joon-Young Paik

Malware detection on binary executables provides a high availability to even binaries which are not disassembled or decompiled. However, a binary-level approach could cause ambiguity problems. In this paper, we propose a new feature engineering technique that use minimal knowledge about the internal layout on a binary. The proposed feature avoids the ambigui

Caroline Bauzet, Kerstin Schmitz, Aleksandra Zimmermann

We address an original approach for the convergence analysis of a finite-volume scheme for the approximation of a stochastic diffusion-convection equation with multiplicative noise in a bounded domain of $\mathbb{R}^d$ (with $d=2$ or $3$) and with homogeneous Neumann boundary conditions. The idea behind our approach is to avoid using the stochastic compactne

Maaike Los, Zoé Christoff, Davide Grossi

The popularity of an opinion in one's direct circles is not necessarily a good indicator of its popularity in one's entire community. Network structures make local information about global properties of the group potentially inaccurate, and the way a social network is wired constrains what kind of information distortion can actually occur. In this paper, we

D. Jezierski, K. Koteras, M. Domanski, P. Połczynski

High-temperature solid-state reaction between orthorhombic AgF2 and monoclinic CuF2 (y = 0.15, 0.3, 0.4, 0.5) in a fluorine atmosphere resulted in coexisting solid solutions of Cu-poor orthorhombic and Cu-rich monoclinic phases with stoichiometry Ag1-xCuxF2. Based on X-ray powder diffraction analyses, the mutual solubility in the orthorhombic phase (AgF2 dop

Ruiling Zheng, Tianlong Ma, Xian'an Jin

For a graph $G=(V,E)$ and $v_{i}\in V$, denote by $d_{v_{i}}$ (or $d_{i}$ for short) the degree of vertex $v_{i}$. The $p$-Sombor matrix $\textbf{S}_{\textbf{p}}(G)$ ($p\neq0$) of a graph $G$ is a square matrix, where the $(i,j)$-entry is equal to $\displaystyle (d_{i}^{p}+d_{j}^{p})^{\frac{1}{p}}$ if the vertices $v_{i}$ and $v_{j}$ are adjacent, and 0 othe

Soham Sen, Sunandan Gangopadhyay

In this paper we have considered a quantized and linearly polarized gravitational wave interacting with a gravitational wave detector (interferometer detector) in the generalized uncertainty principle (GUP) framework. Following the analysis in Phys. Rev. Lett. 127 (2021) 081602 (https://link.aps.org/doi/10.1103/PhysRevLett.127.081602), we consider a quantize

Shahira Abousamra, Rajarsi Gupta, Tahsin Kurc, Dimitris Samaras

In digital pathology, the spatial context of cells is important for cell classification, cancer diagnosis and prognosis. To model such complex cell context, however, is challenging. Cells form different mixtures, lineages, clusters and holes. To model such structural patterns in a learnable fashion, we introduce several mathematical tools from spatial statis

Beomjun Choi, Pei-Ken Hung

We investigate slowly converging solutions for non-linear evolution equations of elliptic or parabolic type. These equations arise from the study of isolated singularities in geometric variational problems. Slowly converging solutions have previously been constructed assuming the Adams-Simon positivity condition. In this study, we identify a necessary condit

Chao Zhao, Chunli Jiang, Junhao Cai, Michael Yu Wang

This paper tackles the task of singulating and grasping paper-like deformable objects. We refer to such tasks as paper-flipping. In contrast to manipulating deformable objects that lack compression strength (such as shirts and ropes), minor variations in the physical properties of the paper-like deformable objects significantly impact the results, making man

Chao Zhao, Chunli Jiang, Junhao Cai, Hongyu Yu

Humans excel in grasping objects through diverse and robust policies, many of which are so probabilistically rare that exploration-based learning methods hardly observe and learn. Inspired by the human learning process, we propose a method to extract and exploit latent intents from demonstrations, and then learn diverse and robust grasping policies through s

Chao Zhao, Shuai Yuan, Chunli Jiang, Junhao Cai

This letter introduces ERRA, an embodied learning architecture that enables robots to jointly obtain three fundamental capabilities (reasoning, planning, and interaction) for solving long-horizon language-conditioned manipulation tasks. ERRA is based on tightly-coupled probabilistic inferences at two granularity levels. Coarse-resolution inference is formula

Yang Zheng, Oles Andrienko, Yonglei Zhao, Minwoo Park

Regular object detection methods output rectangle bounding boxes, which are unable to accurately describe the actual object shapes. Instance segmentation methods output pixel-level labels, which are computationally expensive for real-time applications. Therefore, a polygon representation is needed to achieve precise shape alignment, while retaining low compu

Tingyi Zhou, Bahram Jalali

Time stretch instruments have been exceptionally successful in discovering single-shot ultrafast phenomena such as optical rogue waves and have led to record-speed microscopy, spectroscopy, lidar, etc. These instruments encode the ultrafast events into the spectrum of a femtosecond pulse and then dilate the time scale of the data using group velocity dispers

TianYi Zhang, CaiHua Wan, XiuFeng Han

Spin-orbit torque (SOT) is a candidate technique in next generation magnetic random-access memory (MRAM). Recently, experiments show that some material with low-symmetric crystalline or magnetic structures can generate anomalous SOT that has an out-of-plane component, which is crucial in switching perpendicular magnetization of adjacent ferromagnetic (FM) la

Jiwoo Hong, Yejin Cho, Jaemin Jung, Jiyoung Han

We address an important gap in detecting political bias in news articles. Previous works that perform document classification can be influenced by the writing style of each news outlet, leading to overfitting and limited generalizability. Our approach overcomes this limitation by considering both the sentence-level semantics and the document-level rhetorical

Philipp Straubinger, Laura Caspari, Gordon Fraser

Learning to program has become common in schools, higher education and individual learning. Although testing is an important aspect of programming, it is often neglected in education due to a perceived lack of time and knowledge, or simply because testing is considered less important or fun. To make testing more engaging, we therefore introduce Code Critters

Erdal Bayram, Çiğdem Bektaş, Yavuz Altın

The present study introduces the notions of statistical convergence of order $\alpha$ and strong $p-$ Ces\`{a}ro summability of order $\alpha$ in partial metric spaces. Also, we examine the inclusion relations between these concepts. In addition, we introduce the notion of $\lambda -$% statistical convergence of order $\alpha$ in partial metric spaces while

Chihaya Jibiki

We study left orderings on countably generated groups. In particular, we construct left orderings of inductive limits of amalgamated free products by using isolated left orderings of the groups appearing in the inductive system. Moreover, we show that they are no longer isolated orderings.

Eunseok Oh, Kyung-Hwan Jin, Han Woong Yeom

Interplay of crystal symmetry, strong spin$-$orbit coupling (SOC), and many-body interactions in low dimensional materials provides a fertile ground for the discovery of unconventional electronic and magnetic properties and versatile functionalities. Two-dimensional (2D) allotropes of group 15 elements are appealing due to their structures and controllabilit

Ayako Itaba

Let $A=\mathcal{A}(E,\sigma)$ be a $3$-dimensional quantum polynomial algebra where $E$ is $\mathbb{P}^{2}$ or a cubic divisor in $\mathbb{P}^{2}$, and $\sigma\in \mathrm{Aut}_{k}E$. Artin-Tate-Van den Bergh proved that $A$ is finite over its center if and only if the order $|\sigma|$ of $\sigma$ is finite. As a categorical analogy of their result, the autho

Neelesh Mungoli

This research paper provides a comprehensive analysis of Bitcoin, delving into its evolution, adoption, and potential future implications. As the pioneering cryptocurrency, Bitcoin has sparked significant interest and debate in recent years, challenging traditional financial systems and introducing the world to the power of blockchain technology. This paper

Yao-Ting Lin

In this work, we study the phase estimation problem. We show an alternative, simpler and self-contained proof of query lower bounds. Technically, compared to the previous proofs [NW99, Bes05], our proof is considerably elementary. Specifically, our proof consists of basic linear algebra without using the knowledge of Boolean function analysis and adversary m

Peter Dixon, A. Pavan, Jason Vander Woude, N. V. Vinodchandran

We investigate replicable learning algorithms. Ideally, we would like to design algorithms that output the same canonical model over multiple runs, even when different runs observe a different set of samples from the unknown data distribution. In general, such a strong notion of replicability is not achievable. Thus we consider two feasible notions of replic

Jinhao Li, Changlong Wang, Hao Wang

Wind energy has been rapidly gaining popularity as a means for combating climate change. However, the variable nature of wind generation can undermine system reliability and lead to wind curtailment, causing substantial economic losses to wind power producers. Battery energy storage systems (BESS) that serve as onsite backup sources are among the solutions t

Pham Hoang Hiep

In this paper, we combine tools from pluripotential theory and commutative algebra to study singularity invariants of plurisubharmonic functions. We establish several relationships between the singularity invariants of plurisubharmonic functions and those of holomorphic functions. These results yield a sharp lower bound for the log canonical threshold of a p

Yuchen Wang

In this paper, we obtain uniformly rotating vorticity with sufficiently large angular velocity in the unit disk. The solution consists of either a small nearly-ellipse vortex patch which is highly concentrated near the origin or a $2+1$ configuration in which the another two vortical components are very close to the boundary of fluid domain. The total vortic

Alexandra K. P. Yip, Radostin Kurtev, David J. Pinfield, Federico Marocco

Disintegrating multiple systems have been previously discovered from kinematic studies of the $\it Hipparcos$ catalogue. They are presumably the result of dynamical encounters taking place in the Galactic disk between single/multiple systems. In this paper, we aim to expand the search for such systems, to study their properties, as well as to characterize po

Liviu Aolaritei, Nicolas Lanzetti, Florian Dörfler

We study stochastic dynamical systems in settings where only partial statistical information about the noise is available, e.g., in the form of a limited number of noise realizations. Such systems are particularly challenging to analyze and control, primarily due to an absence of a distributional uncertainty model which: (1) is expressive enough to capture p

Pedro Sandoval-Segura, Jonas Geiping, Tom Goldstein

Recently developed text-to-image diffusion models make it easy to edit or create high-quality images. Their ease of use has raised concerns about the potential for malicious editing or deepfake creation. Imperceptible perturbations have been proposed as a means of protecting images from malicious editing by preventing diffusion models from generating realist

Zihao Wang, Ali Ahmadvand, Jason Choi, Payam Karisani

Open-domain conversational search (ODCS) aims to provide valuable, up-to-date information, while maintaining natural conversations to help users refine and ultimately answer information needs. However, creating an effective and robust ODCS agent is challenging. In this paper, we present a fully functional ODCS system, Ericson, which includes state-of-the-art

K. Mahesh Krishna

Let $(\{f_j\}_{j=1}^n, \{\tau_j\}_{j=1}^n)$ and $(\{g_k\}_{k=1}^m, \{\omega_k\}_{k=1}^m)$ be p-Schauder frames for a finite dimensional Banach space $\mathcal{X}$. Then for every $x \in \mathcal{X}\setminus\{0\}$, we show that \begin{align} (1) \quad \|\theta_f x\|_0^\frac{1}{p}\|\theta_g x\|_0^\frac{1}{q} \geq \frac{1}{\displaystyle\max_{1\leq j\leq n, 1\le

Aditya Khele, Canchen Jiang, Hao Wang

As the number of electric vehicles (EVs) continues to grow, there is an increasing need for smart charging strategies. This paper exploits the vehicle-to-vehicle (V2V) concept to leverage EVs' diverse charging patterns and unlock the value of flexibility by enabling energy transfer among EVs. We formulate a cost minimization problem for an EV charging statio

Swaroop Georgy Zachariah, Mohd. Arshad, Ashok Kumar Pathak

We propose a new bivariate symmetric copula with positive and negative dependence properties. The main features of the proposed copula are its simple mathematical structure, wider dependence range compared to FGM copula and its generalizations, and no lower and upper tail dependence. The maximum range of Spearman's Rho of the proposed copula is [-0.5866,0.58

Shrabani Das, Arpita Sarkhel, Rajat Kanti Nath

In this paper we compute first and second Zagreb indices of commuting and non-commuting graphs of finite groups and determine several classes of finite groups such that their commuting and non-commuting graphs satisfy Hansen-Vuki\v{c}evi\'c conjecture.

Nelvin Tan, Ramji Venkataramanan

We study the problem of regression in a generalized linear model (GLM) with multiple signals and latent variables. This model, which we call a matrix GLM, covers many widely studied problems in statistical learning, including mixed linear regression, max-affine regression, and mixture-of-experts. In mixed linear regression, each observation comes from one of

Mickaël D. Chekroun, Honghu Liu

Formulas for Galerkin-Koornwinder (GK) approximations of delay differential equations are summarized. The functional analysis ingredients (semigroups, operators, etc.) are intentionally omitted to focus instead on the formulas required to perform GK approximations in practice.

Li Wang, Chengyuan Li, Long Wang, Chenyu He

The detection of split main sequences (MSs) associated with young clusters ($\lesssim$600 Myr) has caught lots of attention. A prevailing scenario is that a bimodality of stellar rotation distribution drives the MS bifurcation. Nevertheless, the origin of the stellar rotation dichotomy remains unclear. Hypotheses involving tidally-locked binaries or blue str

Yan Hao Ling, Jonathan Scarlett

We study error exponents for the problem of low-rate communication over a directed graph, where each edge in the graph represents a noisy communication channel, and there is a single source and destination. We derive maxflow-based achievability and converse bounds on the error exponent that match when there are two messages and all channels satisfy a symmetr

Junheum Park, Jintae Kim, Chang-Su Kim

A novel 4K video frame interpolator based on bilateral transformer (BiFormer) is proposed in this paper, which performs three steps: global motion estimation, local motion refinement, and frame synthesis. First, in global motion estimation, we predict symmetric bilateral motion fields at a coarse scale. To this end, we propose BiFormer, the first transformer

Eugene Zhang

In this paper, we present a new axiomatic system that is a minimal axiomatization of Boolean algebras. Furthermore, the symmetric difference is shown to be algebraically analogous to the modular difference of two numbers. Finally, a new method to prove equivalent relations in a Boolean algebra is also given.

Rohan Ghosh, Mehul Motani

Most entropy measures depend on the spread of the probability distribution over the sample space $\mathcal{X}$, and the maximum entropy achievable scales proportionately with the sample space cardinality $|\mathcal{X}|$. For a finite $|\mathcal{X}|$, this yields robust entropy measures which satisfy many important properties, such as invariance to bijections

Michael Weiss, Paolo Tonella

Recent decades have seen the rise of large-scale Deep Neural Networks (DNNs) to achieve human-competitive performance in a variety of artificial intelligence tasks. Often consisting of hundreds of millions, if not hundreds of billion parameters, these DNNs are too large to be deployed to, or efficiently run on resource-constrained devices such as mobile phon

Fengyi Shen, Akhil Gurram, Ziyuan Liu, He Wang

Domain adaptive semantic segmentation methods commonly utilize stage-wise training, consisting of a warm-up and a self-training stage. However, this popular approach still faces several challenges in each stage: for warm-up, the widely adopted adversarial training often results in limited performance gain, due to blind feature alignment; for self-training, f

Tomoya Nishida, Takashi Endo, Yohei Kawaguchi

Semi-supervised anomaly detection~(SSAD) is a task where normal data and a limited number of anomalous data are available for training. In practical situations, SSAD methods suffer adapting to domain shifts, since anomalous data are unlikely to be available for the target domain in the training phase. To solve this problem, we propose a domain adaptation met

Aysu Ismayilova, Muhammad Ismayilov

In this paper we consider a new class of RBF (Radial Basis Function) neural networks, in which smoothing factors are replaced with shifts. We prove under certain conditions on the activation function that these networks are capable of approximating any continuous multivariate function on any compact subset of the $d$-dimensional Euclidean space. For RBF netw

Keita Takizawa, Hideki Asada

This paper extends the de-Sitter/anti-de Sitter (dS/AdS) background method based on the optical metric for gravitational lens [Phys. Rev. D 105, 084022 (2022)] to a static optical constant-curvature (SOCC) background. It is shown that the exact lens equation on the SOCC background can be written in the same form as that for either Minkowski, dS or AdS backgr

Evan M. Askanazi, Emanuel A. Lazar, Ilya Grinberg

Progress in the application of machine learning (ML) methods to materials design is hindered by the lack of understanding of the reliability of ML predictions, in particular for the application of ML to small data sets often found in materials science. Using ML prediction for transparent conductor oxide formation energy and band gap, dilute solute diffusion,

Ricardo Vidrio, Daniel Vincent, Benjamin Bachman, Cesar Saucedo

The efficacy of oxygen (O) surface terminations on diamond is an important factor for the performance and stability for diamond-based quantum sensors and electronics. Given the wide breadth of O-termination techniques, it can be difficult to discern which method would yield the highest and most consistent O coverage. Furthermore, the interpretation of surfac

Zilong Zhang, Zhibin Zhao, Xingwu Zhang, Chuang Sun

Industrial anomaly detection (IAD) is crucial for automating industrial quality inspection. The diversity of the datasets is the foundation for developing comprehensive IAD algorithms. Existing IAD datasets focus on the diversity of data categories, overlooking the diversity of domains within the same data category. In this paper, to bridge this gap, we prop

Chrameh Fru Mbah, Junwei Wang, Silvan Englisch, Praveen Bommineni

Bifurcations in kinetic pathways decide the evolution of a system. An example is crystallization, in which the thermodynamically stable polymorph may not form due to kinetic hindrance. Here, we use confined self-assembly to investigate the interplay of thermodynamics and kinetics in the crystallization pathways of finite clusters. We report the observation o

Binbin Feng, Jun Li, Jianhua Xu

Sketch-based image retrieval, which aims to use sketches as queries to retrieve images containing the same query instance, receives increasing attention in recent years. Although dramatic progress has been made in sketch retrieval, few efforts are devoted to logo sketch retrieval which is still hindered by the following challenges: Firstly, logo sketch retri

Tong Xie, Yuwei Wan, Wei Huang, Yufei Zhou

The amount of data has growing significance in exploring cutting-edge materials and a number of datasets have been generated either by hand or automated approaches. However, the materials science field struggles to effectively utilize the abundance of data, especially in applied disciplines where materials are evaluated based on device performance rather tha

Yuichi Asahiro, Masafumi Yamashita

This paper investigates a swarm of autonomous mobile robots in the Euclidean plane, under the semi-synchronous ($\cal SSYNC$) scheduler. Each robot has a target function to determine a destination point from the robots' positions. All robots in the swarm take the same target function conventionally. We allow the robots to take different target functions, and

Zhanyu Wang, Lingqiao Liu, Lei Wang, Luping Zhou

In clinical scenarios, multi-specialist consultation could significantly benefit the diagnosis, especially for intricate cases. This inspires us to explore a "multi-expert joint diagnosis" mechanism to upgrade the existing "single expert" framework commonly seen in the current literature. To this end, we propose METransformer, a method to realize this idea w

Longyue Wang, Chenyang Lyu, Tianbo Ji, Zhirui Zhang

Large language models (LLMs) such as ChatGPT can produce coherent, cohesive, relevant, and fluent answers for various natural language processing (NLP) tasks. Taking document-level machine translation (MT) as a testbed, this paper provides an in-depth evaluation of LLMs' ability on discourse modeling. The study focuses on three aspects: 1) Effects of Context

M. S. Angelo, J. F. C. Santos, F. F. S. Maia, W. J. B. Corradi

Most stars in our Galaxy form in stellar aggregates, which can become long-lived structures called open clusters (OCs). Along their dynamical evolution, their gradual depletion leave some imprints on their structure. In this work, we employed astrometric, photometric and spectroscopic data from the \textit{Gaia} DR3 catalogue to uniformly characterize a samp

A. Ravishankar Rao, Subrata Garai, Soumyabrata Dey, Hang Peng

With calls for increasing transparency, governments are releasing greater amounts of data in multiple domains including finance, education and healthcare. The efficient exploratory analysis of healthcare data constitutes a significant challenge. Key concerns in public health include the quick identification and analysis of trends, and the detection of outlie

Jan van den Brand, Zhao Song, Tianyi Zhou

Large language models (LLMs) have made fundamental changes in human life. The attention scheme is one of the key components over all the LLMs, such as BERT, GPT-1, Transformers, GPT-2, 3, 3.5 and 4. Inspired by previous theoretical study of static version of the attention multiplication problem [Zandieh, Han, Daliri, and Karbasi arXiv 2023, Alman and Song ar

Qiuyu Ren, Shengtong Zhang

We provide a shorter proof to Defant and Kravitz's theorem (arXiv:2201.03461, Theorem 1.2) on the length of Hitomezashi loops modulo 8.

Jifan Yu, Mengying Lu, Qingyang Zhong, Zijun Yao

Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education. Although the recent attempts from knowledge tracing and cognitive diagnosis propose several promising directions for improving the usability and effectiveness of current models, the existing

Han Yan

In this paper, we investigate the relationship between pinch point singularities observed in energy- and momentum-resolved spectroscopy and topologically non-trivial gapless points. We show that these singularities are a universal signature, and that the Berry flux encoded must be $n\pi$ for an $n-$fold pinch point under suitable symmetry protection. Our res

Han Yan, Judit Romhányi, Andreas Thomasen, Nic Shannon

"Half moons", distinctive crescent patterns in the dynamical structure factor, have been identified in inelastic neutron scattering experiments for a wide range of frustrated magnets. In an earlier paper [H. Yan et al., Phys. Rev. B 98, 140402(R) (2018)] we have shown how these features are linked to the local constraints realized in classical spin liquids.

Osman Tursun, Simon Denman, Sridha Sridharan, Clinton Fookes

Heatmaps are widely used to interpret deep neural networks, particularly for computer vision tasks, and the heatmap-based explainable AI (XAI) techniques are a well-researched topic. However, most studies concentrate on enhancing the quality of the generated heatmap or discovering alternate heatmap generation techniques, and little effort has been devoted to

Vladimir V. Mazurenko, Ilia A. Iakovlev, Oleg M. Sotnikov, Mikhail I. Katsnelson

In this review we discuss the latest results concerning development of the machine learning algorithms for characterization of the magnetic skyrmions that are topologically-protected magnetic textures originated from the Dzyaloshinskii-Moriya interaction that competes Heisenberg isotropic exchange in ferromagnets. We show that for classical spin systems ther

Ivan Cherednik

We begin with modular form periods, a focal point of several Yuri Manin's works. The similarity is discussed between the corresponding zeta-polynomials and superpolynomials of algebraic links, closely related to Khovanov-Rozansky polynomials. We focus on DAHA superpolynomials and motivic ones, defined via compactified Jacobians of plane curve singularities a

Tianyu Zhu, Bryce Ferenczi, Pulak Purkait, Tom Drummond

Rotated bounding boxes drastically reduce output ambiguity of elongated objects, making it superior to axis-aligned bounding boxes. Despite the effectiveness, rotated detectors are not widely employed. Annotating rotated bounding boxes is such a laborious process that they are not provided in many detection datasets where axis-aligned annotations are used in

Bowen Jing, Ezra Erives, Peter Pao-Huang, Gabriele Corso

Protein structure prediction has reached revolutionary levels of accuracy on single structures, yet distributional modeling paradigms are needed to capture the conformational ensembles and flexibility that underlie biological function. Towards this goal, we develop EigenFold, a diffusion generative modeling framework for sampling a distribution of structures

Hiroyuki Sato, Yuya Yamakawa, Kensuke Aihara

For optimization problems on Riemannian manifolds, many types of globally convergent algorithms have been proposed, and they are often equipped with the Riemannian version of the Armijo line search for global convergence. Such existing methods need to compute the value of a retraction mapping regarding the search direction several times at each iteration; th

Zhensen Fu, Zhaoxiang Qi, Shilong Liao, Xiyan Peng

The China Space Station Telescope (CSST) will enter a low Earth orbit around 2024 and operate for 10 years, with seven of those years devoted to surveying the area of the median-to-high Galactic latitude and median-to-high Ecliptic latitude of the sky. To maximize the scientific output of CSST, it is important to optimize the survey schedule. We aim to evalu

Sungmin Kang, Bei Chen, Shin Yoo, Jian-Guang Lou

Automated debugging techniques have the potential to reduce developer effort in debugging, and have matured enough to be adopted by industry. However, one critical issue with existing techniques is that, while developers want rationales for the provided automatic debugging results, existing techniques are ill-suited to provide them, as their deduction proces

Takashi Akamatsu, Koki Satsukawa, Yuki Oyama

Schedule-based transit assignment describes congestion in public transport services by modeling the interactions of passenger behavior in a time-space network built directly on a transit schedule. This study investigates the theoretical properties of scheduled-based Markovian transit assignment with boarding queues. When queues exist at a station, passenger

Greg Bodwin, Gary Hoppenworth

For a graph $G$, a $D$-diameter-reducing exact hopset is a small set of additional edges $H$ that, when added to $G$, maintains its graph metric but guarantees that all node pairs have a shortest path in $G \cup H$ using at most $D$ edges. A shortcut set is the analogous concept for reachability. These objects have been studied since the early '90s due to ap

Chao Wang

Multi-turn compositional image generation (M-CIG) is a challenging task that aims to iteratively manipulate a reference image given a modification text. While most of the existing methods for M-CIG are based on generative adversarial networks (GANs), recent advances in image generation have demonstrated the superiority of diffusion models over GANs. In this

A. Ravishankar Rao, Subrata Garai, Soumyabrata Dey, Hang Peng

Due to rapidly rising healthcare costs worldwide, there is significant interest in controlling them. An important aspect concerns price transparency, as preliminary efforts have demonstrated that patients will shop for lower costs, driving efficiency. This requires the data to be made available, and models that can predict healthcare costs for a wide range o

Mercy Nyamewaa Asiedu, Awa Dieng, Abigail Oppong, Maria Nagawa

With growing machine learning (ML) applications in healthcare, there have been calls for fairness in ML to understand and mitigate ethical concerns these systems may pose. Fairness has implications for global health in Africa, which already has inequitable power imbalances between the Global North and South. This paper seeks to explore fairness for global he

A. Ravishankar Rao, Daniel Clarke, Subrata Garai, Soumyabrata Dey

The interactive exploration of large and evolving datasets is challenging as relationships between underlying variables may not be fully understood. There may be hidden trends and patterns in the data that are worthy of further exploration and analysis. We present a system that methodically explores multiple combinations of variables using a searchlight tech

Sabah Al-Fedaghi

The complexity of software-based systems has increased significantly, especially with regards to capturing requirements along with dependencies among requirements. A conceptual model is a way of thinking about and making sense of the real world s complexities. In this paper, we focused on two approaches in this context: (a) multiple models applied to the sam

Chenyan Wu, Dolzodmaa Davaasuren, Tal Shafir, Rachelle Tsachor

Body movements carry important information about a person's emotions or mental state and are essential in daily communication. Enhancing the ability of machines to understand emotions expressed through body language can improve the communication of assistive robots with children and elderly users, provide psychiatric professionals with quantitative diagnosti

Apoorv Singh

Vision-based Transformer have shown huge application in the perception module of autonomous driving in terms of predicting accurate 3D bounding boxes, owing to their strong capability in modeling long-range dependencies between the visual features. However Transformers, initially designed for language models, have mostly focused on the performance accuracy,

Amir Jamali, Amirhossein Ranjbar, Ali Bozorgi-Amiri

Bottlenecks are one of the controversial issues in manufacturing companies. Managers and designers attempt to manage this trouble to improve efficiency in different ways. For example, expanding capacity is a prevalent solution to get rid of bottlenecks. In this paper, a color production line is chosen, which faces several challenges in its production line. T

Ko Arimatsu, Kohji Tsumura, Fumihiko Usui, Jun-ichi Watanabe

We have conducted numerical simulations to reproduce the observed optical energy profile of the 15 October 2021 (UT) impact flash on Jupiter, which was the largest and the most well-observed flash event detected by ground-based movie observations. The observed long-duration ($\sim 5.5~{\rm s}$) optical emission can be reproduced by an impact of an object wit

Wayne M. Witzel, Warren D. Craft, Robert Carr, Deepak Kapur

The general-purpose interactive theorem-proving assistant called Prove-It was used to verify the Quantum Phase Estimation (QPE) algorithm, specifically claims about its outcome probabilities. Prove-It is unique in its ability to express sophisticated mathematical statements, including statements about quantum circuits, integrated firmly within its formal the

Yuan Gao, Ruili Wang, Feng Hou

The recently released ChatGPT has demonstrated surprising abilities in natural language understanding and natural language generation. Machine translation relies heavily on the abilities of language understanding and generation. Thus, in this paper, we explore how to assist machine translation with ChatGPT. We adopt several translation prompts on a wide rang

Yi Zhu, Mohamed Imoussaïne-Aïkous, Carolyn Côté-Lussier, Tiago H. Falk

With advances seen in deep learning, voice-based applications are burgeoning, ranging from personal assistants, affective computing, to remote disease diagnostics. As the voice contains both linguistic and para-linguistic information (e.g., vocal pitch, intonation, speech rate, loudness), there is growing interest in voice anonymization to preserve speaker p

Sebastian Jaimungal, Yuri F. Saporito, Max O. Souza, Yuri Thamsten

This article explores the optimisation of trading strategies in Constant Function Market Makers (CFMMs) and centralised exchanges. We develop a model that accounts for the interaction between these two markets, estimating the conditional dependence between variables using the concept of conditional elicitability. Furthermore, we pose an optimal execution pro

Linkai Zhao, Q. Chen

We propose a scheme to have zero field magnetic resonance spectroscopy based on a nitrogen-vacancy center and investigate the new applications in which magnetic bias field might disturb the system under investigation. Continual driving with circularly polarized microwave fields is used to selectively address one spin state. The proposed method is applied for

Shinichi Saito

Photons are elementary particles of lights, which have both spin and orbital angular momentum as internal degrees of freedom. Nature of spin is known as polarisation, which is widely used for sunglasses, liquid-crystal displays, digital-coherent communications, while orbital angular momentum is useful for optical tweezers, laser-patterning, and quantum optic

Bo Yu, Lesiqi Yin, Peng Wang, Cheng Gong

The emergence of metamaterial has provided an unprecedented ability to manipulate electromagnetic waves, especially in the terahertz band where there is a lack of natural response materials. However, most metamaterials are fixed single function due to the fixed structure at the beginning of design. The paper reports a reconfigurable multi-functional terahert

Gabriel Lima, Nina Grgić-Hlača, Meeyoung Cha

Artificial intelligence (AI) systems can cause harm to people. This research examines how individuals react to such harm through the lens of blame. Building upon research suggesting that people blame AI systems, we investigated how several factors influence people's reactive attitudes towards machines, designers, and users. The results of three studies (N =

Aeris El Asslouj, Ella Atkins, Hossein Rastgoftar

This paper develops a high-density air corridor traffic flow model for Uncrewed Aircraft System (UAS) operation in urban low altitude airspace. To maximize throughput with safe separation guarantees, we define an airspace spatiotemporal planning problem. For the spatial planning, we propose a multi-floor UAS coordination structure divided into a finite numbe

Timofey V. Maximov, Igor V. Bondarev, Igor L. Kurbakov, Yurii E. Lozovik

We develop a theoretical model for a tunable coherent terahertz radiation source based on the long-lived Bose condensate of photons. In the device we propose, the original photon pumping is performed incoherently by a blackbody radiation emitter. The photons thus produced Bose-condense by the inelastic relaxation on a two-dimensional electron gas in a perpen