April 2023 arXiv papers — page 150

Jingwen Zhang, Junjie Shen, Yeting Liu, Dennis W. Hong

Generating dynamic jumping motions on legged robots remains a challenging control problem as the full flight phase and large landing impact are expected. Compared to quadrupedal robots or other multi-legged robots, bipedal robots place higher requirements for the control strategy given a much smaller footprint. To solve this problem, a novel heuristic landin

Hiroyuki Tajima, Hajime Moriya, Wataru Horiuchi, Eiji Nakano

We show that strong spin-triplet neutron-proton interaction causes polaronic protons to occur in neutron matter at subnuclear densities and nonzero temperature. As the neutron density increases, proton spectra exhibit a smooth crossover from a bare impurity to a repulsive polaron branch; this branch coexists with an attractive polaron branch. With the neutro

Zichun Wang, Ying Fu, Ji Liu, Yulun Zhang

Despite the significant results on synthetic noise under simplified assumptions, most self-supervised denoising methods fail under real noise due to the strong spatial noise correlation, including the advanced self-supervised blind-spot networks (BSNs). For recent methods targeting real-world denoising, they either suffer from ignoring this spatial correlati

Joachim Jelisiejew, Kristian Ranestad, Frank-Olaf Schreyer

We discuss the space $VPS(Q,H)$ of ideals with Hilbert function $H=(1,n,n, \ldots)$ that are apolar to a full rank quadric $Q$. We prove that its components of saturated ideals are closely related to the locus of Gorenstein algebras and to the Slip component in border apolarity. We also point out an important error in~[RS] and provide the necessary correctio

Adolfo Ballester-Bolinches, Ramón Esteban-Romero, Leonid A. Kurdachenko, Vicent Pérez-Calabuig

We present a construction of left braces of right nilpotency class at most two based on suitable actions of an abelian group on itself with an invariance condition. This construction allows us to recover the construction of a free right nilpotent one-generated left brace of class two.

Zihao Zhao, Xiaodong Ge, Zhihong Shen

In graph data applications, data is primarily maintained using two models: RDF (Resource Description Framework) and property graph. The property graph model is widely adopted by industry, leading to property graph databases generally outperforming RDF databases in graph traversal query performance. However, users often prefer SPARQL as their query language,

Tianyu Liu, Somabha Mukherjee, Rahul Biswas

The $k$-tensor Ising model is an exponential family on a $p$-dimensional binary hypercube for modeling dependent binary data, where the sufficient statistic consists of all $k$-fold products of the observations, and the parameter is an unknown $k$-fold tensor, designed to capture higher-order interactions between the binary variables. In this paper, we descr

Ait Sadi Nassima, Chemlal Rezki

In this paper we are interested in the dynamical properties of a two dimensional cellular automaton that model the language shift in Algeria. We will study among other properties the surjectivity and the existence of equicontinuity points for the cellular automaton. Using computer simulation we obtained some properties of the bassin of attraction of some fix

Yuming Qin, Xiuqing Wang, Junchen Liu

In this paper, we prove local existence and uniqueness for the 2D Prandtl-Hartmann regine in weighted Sobolev spaces. Our proof is based on using uniform estimates of the regularized parabolic equation and maximal principle under the Oleinik's monotonicity assumption. Compared with Nash-Moser iteration in \cite{[5]}, the data do not require high regularity.

Takahiro Misawa, Joji Nasu, Yukitoshi Motome

The Kitaev model offers a platform for quantum spin liquids (QSLs) with fractional excitations, itinerant Majorana fermions and localized fluxes. Since these fractional excitations could be utilized for quantum computing, how to create, observe, and control them through the spin degree of freedom is a central issue. Here, we study dynamical spin transport in

Nomvelo Karabo Sibisi

This paper combines probability theory and fractional calculus to derive a novel integral representation of the three-parameter Mittag-Leffler function or Prabhakar function, where the three parameters are combinations of four base parameters. The fundamental concept is the Riemann-Liouville fractional integral of the one-sided stable density, conditioned on

Won Sang Chung, Ilyas Haouam, Hassan Hassanabadi

In this paper, quantum mechanics on a circle with finite number of {\alpha}-uniformly distributed points is discussed. The angle operator and translation operator are defined. Using discrete angle representation, two types of discrete angular momentum operators and Hermitian Hamiltonian on a circle with d {\alpha}-distributed discrete angles are constructed.

Huitong Yang, Xuyang Bai, Xinge Zhu, Yuexin Ma

Current on-board chips usually have different computing power, which means multiple training processes are needed for adapting the same learning-based algorithm to different chips, costing huge computing resources. The situation becomes even worse for 3D perception methods with large models. Previous vision-centric 3D perception approaches are trained with r

Ali Kashif Bashir, Nancy Victor, Sweta Bhattacharya, Thien Huynh-The

Recent technological advancements have considerately improved healthcare systems to provide various intelligent healthcare services and improve the quality of life. Federated learning (FL), a new branch of artificial intelligence (AI), opens opportunities to deal with privacy issues in healthcare systems and exploit data and computing resources available at

Yuchen Yang, Tong Wu

In this paper, on the basis of defining the spectral Einstein functional associated with the Dirac operator for manifolds with boundary, we prove Kastler-Kalau-Walze type theorem for the spectral Einstein functional associated with the Dirac operator on low-dimensional manifolds with boundary.

Eduard Feireisl, Piotr Gwiazda, Young-Sam Kwon, Agnieszka Świerczewska-Gwiazda

We propose a new concept of weak solution to the equations of compressible magnetohydrodynamics driven by large boundary data. The system of the underlying field equations is solvable globally in time in the out of equilibrium regime characteristic for turbulence. The weak solutions comply with the weak-strong uniqueness principle; they coincide with the cla

Samuel Albanie, Liliane Momeni, João F. Henriques

Driven by recent advances AI, we passengers are entering a golden age of scientific discovery. But golden for whom? Confronting our insecurity that others may beat us to the most acclaimed breakthroughs of the era, we propose a novel solution to the long-standing personal credit assignment problem to ensure that it is golden for us. At the heart of our appro

Halil Faruk Karagoz, Gulcin Baykal, Irem Arikan Eksi, Gozde Unal

The problem of text-guided image generation is a complex task in Computer Vision, with various applications, including creating visually appealing artwork and realistic product images. One popular solution widely used for this task is the diffusion model, a generative model that generates images through an iterative process. Although diffusion models have de

Hao-Hao Peng, Xin-Li Sheng, Shi Pu, Qun Wang

In Wigner function approach with relaxation time approximation, we calculate electric and magnetic conductivities of a fermion system in the strong magnetic field. The linear response has been calculated to the perturbation of electromagnetic fields on the background constant magnetic field. The Wigner function is separated into an equilibrium part in the ba

Dong Xie, Chunling Xu

Exceptional point (EP) denotes the non-Hermitian degeneracy, in which both eigenvalues and eigenstates become identical. By the conventional local Markovian master equation, EP can be constructed by parity-time (PT) or anti-PT symmetry in a system composed of coupled subsystems. However, the coupling between two systems makes the conventional local Markovian

Min Han, Jiangming Kan, Gongping Yang, Xinghui Li

In random sample consensus (RANSAC), the problem of ellipsoid fitting can be formulated as a problem of minimization of point-to-model distance, which is realized by maximizing model score. Hence, the performance of ellipsoid fitting is affected by distance metric. In this paper, we proposed a novel distance metric called the axial distance, which is convert

AliAkbar Alijani

Let $G$ be a locally compact abelian (LCA) group. We denote by $G_{op}$, the intersection of all open pure subgroups of $G$, which we call the $OP$ subgroup of $G$. In this paper, we prove that the $OP$ subgroup of a torsion-free LCA group $G$ is a non-zero pure subgroup of $G$.

Chandrashekar Murugeshan, Robert Dzudzar, Ryan Bagge, Tamsyn O'Beirne

We present a new high-resolution neutral atomic hydrogen (HI) survey of ring galaxies using the Australia Telescope Compact Array (ATCA). We target a sample of 24 ring galaxies from the Buta (1995) Southern Ring Galaxy Survey Catalogue in order to study the origin of resonance-, collisional- and interaction-driven ring galaxies. In this work, we present an o

Yang Shao, Hirokazu Atsumori, Tadayuki Matsumura, Kanako Esaki

Wealth distribution is a complex and critical aspect of any society. Information exchange is considered to have played a role in shaping wealth distribution patterns, but the specific dynamic mechanism is still unclear. In this research, we used simulation-based methods to investigate the impact of different modes of information exchange on wealth distributi

David Carl, Corinne Emmenegger, Peter Bühlmann, Zijian Guo

TSCI implements treatment effect estimation from observational data under invalid instruments in the R statistical computing environment. Existing instrumental variable approaches rely on arguably strong and untestable identification assumptions, which limits their practical application. TSCI does not require the classical instrumental variable identificatio

Sara A. Webb, Simon R. Goode

With the volume and availability of astronomical data growing rapidly, astronomers will soon rely on the use of machine learning algorithms in their daily work. This proceeding aims to give an overview of what machine learning is and delve into the many different types of learning algorithms and examine two astronomical use cases. Machine learning has opened

Wenbing Jiang, Junfeng Chen, Xiaoyu Liu, Zhengqi Niu

The quantum excitations of macroscopic surface acoustic waves (SAWs) have been tailored to control, communicate and transduce stationary and flying quantum states. However, the limited lifetime of this hybrid quantum systems remains critical obstacles to extend their applications in quantum information processing. Here we present the potentials of thin film

Hung Quoc To, Nghi D. Q. Bui, Jin Guo, Tien N. Nguyen

Pre-trained language models for code (PLMCs) have gained attention in recent research. These models are pre-trained on large-scale datasets using multi-modal objectives. However, fine-tuning them requires extensive supervision and is limited by the size of the dataset provided. We aim to improve this issue by proposing a simple data augmentation framework. O

Monika Baloda

Financial market volatility is a crucial factor for investment planning, option pricing, and financial market regulation, and technology is widely recognized as a key driver of economic growth. In this project, we investigate the co-movement of technology and non-technology sectors over the last two decades. We identify a decoupling phenomenon in the levels

YueXiao, Xiaojun Zhang

Evolving networks are more widely existed in real world than static networks, and studying their statistical characteristics is vital to recognize and explore them further. But for the networks with nodes preferential deletion, there are few researches due to the lack of effective methods. In this article, we propose an extended SPR (ESPR) for these preferen

Yuzhou Tian, Xiuli Cen

The present paper is devoted to investigating the two-dimensional real Jacobian conjecture. This conjecture claims that if $F=\left(f,g\right):\mathbb{R}^2\rightarrow \mathbb{R}^2$ is a polynomial map with $\det DF\left(x,y\right)\ne0$ for all $\left(x,y\right)\in\mathbb{R}^2$, then $F$ is globally injective. With the help of the Newton diagram, we provide a

Oleg Aristov

It is shown by the author in [J. Lie Theory 29:4, 1045-1070, 2019] that for every connected linear complex Lie group the algebra of polynomials (regular functions) is dense in the algebra of holomorphic functions of exponential type. However, the argument is quite involved. Here we present a short proof.

Weinan Lin

We consider a theory of noncommutative Gr\"obner bases on decreasingly filtered algebras whose associated graded algebras are commutative. We transfer many algorithms that use commutative Gr\"obner bases to this context. As an important application, we implement very efficient algorithms to compute the Ext groups over the Steenrod algebra $\mathscr{A}$ at th

Claudio Bravo

Let $\mathcal{C}$ be a smooth, projective and geometrically integral curve defined over a finite field $\mathbb{F}$. Let $A$ be the ring of function of $\mathcal{C}$ that are regular outside a closed point $P$ and let $k=\mathrm{Quot}(A)$. Let $\mathcal{G}=\mathrm{SU}(3)$ be the non-split group-scheme defined from an (isotropic) hermitian form in three varia

Handan Akulker, Erdal Aydin

The energy sector has become priority around the world with developing technology and increasing power and energy demand. That all sources for energy production are not renewable increases greenhouse gas emissions and causes global warming. Turkey's dependence on foreign sources in terms of vital energy resources and the suboptimality of the energy distribut

Matej Smid, Jindrich Dunik

The ability to adapt to changing conditions is a key feature of a successful autonomous system. In this work, we use the Recursive Gaussian Processes (RGP) for identification of the quadrotor air drag model online, without the need of training data. The identified drag model then augments a physics-based model of the quadrotor dynamics, which allows more acc

Aristotelis Ballas, Christos Diou

In the past decade, deep convolutional neural networks have achieved significant success in image classification and ranking and have therefore found numerous applications in multimedia content retrieval. Still, these models suffer from performance degradation when neural networks are tested on out-of-distribution scenarios or on data originating from previo

Juan Terven, Diana Cordova-Esparza

YOLO has become a central real-time object detection system for robotics, driverless cars, and video monitoring applications. We present a comprehensive analysis of YOLO's evolution, examining the innovations and contributions in each iteration from the original YOLO up to YOLOv8, YOLO-NAS, and YOLO with Transformers. We start by describing the standard metr

Roberto Amoroso, Davide Morelli, Marcella Cornia, Lorenzo Baraldi

Recent advancements in diffusion models have enabled the generation of realistic deepfakes from textual prompts in natural language. While these models have numerous benefits across various sectors, they have also raised concerns about the potential misuse of fake images and cast new pressures on fake image detection. In this work, we pioneer a systematic st

Samira Kabri, Tim Roith, Daniel Tenbrinck, Martin Burger

In this paper we investigate the use of Fourier Neural Operators (FNOs) for image classification in comparison to standard Convolutional Neural Networks (CNNs). Neural operators are a discretization-invariant generalization of neural networks to approximate operators between infinite dimensional function spaces. FNOs - which are neural operators with a speci

Sohan Ghosh

The $p^\infty$-fine Selmer group of an elliptic curve $E$ over a global field is a subgroup of the classical $p^\infty$-Selmer group. Coates and Sujatha discovered that the structure of the fine Selmer group of $E$ over certain $p$-adic Lie extensions of a number field is intricately related to some deep questions in classical Iwasawa theory. Inspired by a c

Cheng Chen, Yueming Lyu, Ivor W. Tsang

To ensure that the data collected from human subjects is entrusted with a secret, rival labels are introduced to conceal the information provided by the participants on purpose. The corresponding learning task can be formulated as a noisy partial-label learning problem. However, conventional partial-label learning (PLL) methods are still vulnerable to the hi

Yuki Noguchi, Takayuki Yamada

In this paper, we propose a level set-based shape optimization method for acoustic wave propagation problems with a deformable structure. First, we propose a mathematical model for acoustic wave propagation with a deformed structure based on coordinate transformation and the Eulerian approach. Next, we formulate the shape optimization problem and perform sen

Behnaz Lajmiri, Behroz Bidabad, Mehdi Rafie-Rad, Yadollah Aryanejad-Keshavarzi

There are two definitions of Einstein-Finsler spaces introduced by Akbar-Zadeh, which we will show is equal along the integral curves of $I$-invariant projective vector fields. The sub-algebra of the $C$-projective vector fields, leaving the $H$-curvature invariant, has been studied extensively. Here we show on a closed Finsler space with negative definite R

Lu Huo, Jiahao Xia, Leijie Zhang, Haimin Zhang

Existing multiple modality fusion methods, such as concatenation, summation, and encoder-decoder-based fusion, have recently been employed to combine modality characteristics of Hyperspectral Image (HSI) and Light Detection And Ranging (LiDAR). However, these methods consider the relationship of HSI-LiDAR signals from limited perspectives. More specifically,

Erik Habbestad, Sergey Neshveyev

We apply Majid's transmutation procedure to Hopf algebra maps $H \to \mathbb C[T]$, where $T$ is a compact abelian group, and explain how this construction gives rise to braided Hopf algebras over quotients of $T$ by subgroups that are cocentral in $H$. This allows us to unify and generalize a number of recent constructions of braided compact quantum gro

Xavier Pic, Eva Gil San Antonio, Melpomeni Dimopoulou, Marc Antonini

Over the past years, the ever-growing trend on data storage demand, more specifically for "cold" data (i.e. rarely accessed), has motivated research for alternative systems of data storage. Because of its biochemical characteristics, synthetic DNA molecules are considered as potential candidates for a new storage paradigm. Because of this trend, several codi

P. Udvarhelyi, T. Clua-Provost, A. Durand, J. Li

The boron-vacancy spin defect ($\text{V}_\text{B}^{-}$) in hexagonal boron nitride (hBN) has a great potential as a quantum sensor in a two-dimensional material that can directly probe various external perturbations in atomic-scale proximity to the quantum sensing layer. Here, we apply first principles calculations to determine the coupling of the $\text{V}_

D. Asrorov, U. Bekbaev, I. Rakhimov

In the paper, we consider the class of so-called endo-commutative algebras. From the identity imposed to specify this class, one can easily see that the product in this class preserves the square of elements. We give a complete classification of this class, up to isomorphism, over any basic field in dimension two. This elementary and self-contained expositio

Lukas Gonon, Lyudmila Grigoryeva, Juan-Pablo Ortega

Reservoir computing approximation and generalization bounds are proved for a new concept class of input/output systems that extends the so-called generalized Barron functionals to a dynamic context. This new class is characterized by the readouts with a certain integral representation built on infinite-dimensional state-space systems. It is shown that this c

Samidh Pal

Purpose: The objective of this research was to show the response of the potential reduction of excess capacity in terms of capital intensity to the growth rate of labor productivity in the manufacturing industrial sector. Design/Methodology/Approach: The research was carried out in 2019 in 55 groups of Indian manufacturing industry within six major Indian in

Scott Pesme, Nicolas Flammarion

In this paper we fully describe the trajectory of gradient flow over diagonal linear networks in the limit of vanishing initialisation. We show that the limiting flow successively jumps from a saddle of the training loss to another until reaching the minimum $\ell_1$-norm solution. This saddle-to-saddle dynamics translates to an incremental learning process

Joseph Paul Cohen, Rupert Brooks, Sovann En, Evan Zucker

This study evaluates the effect of counterfactual explanations on the interpretation of chest X-rays. We conduct a reader study with two radiologists assessing 240 chest X-ray predictions to rate their confidence that the model's prediction is correct using a 5 point scale. Half of the predictions are false positives. Each prediction is explained twice, once

Ramtin Hosseini, Li Zhang, Bhanu Garg, Pengtao Xie

The integration of machine learning models in various real-world applications is becoming more prevalent to assist humans in their daily decision-making tasks as a result of recent advancements in this field. However, it has been discovered that there is a tradeoff between the accuracy and fairness of these decision-making tasks. In some cases, these AI syst

Bo Yan, Cheng Yang, Chuan Shi, Yong Fang

The explosive growth of cyber attacks nowadays, such as malware, spam, and intrusions, caused severe consequences on society. Securing cyberspace has become an utmost concern for organizations and governments. Traditional Machine Learning (ML) based methods are extensively used in detecting cyber threats, but they hardly model the correlations between real-w

Carlo Ciaccia, Roy Haller, Asbjørn C. C. Drachmann, Tyler Lindemann

The Josephson diode (JD) is a non-reciprocal circuit element that supports a larger critical current in one direction compared to the other. This effect has gained a growing interest because of promising applications in superconducting electronic circuits with low power consumption. Some implementations of a JD rely on breaking the inversion symmetry in the

Iva Bojic, Josef Halim, Verena Suharman, Sreeja Tar

Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality datasets are needed for general-purpose Large Language Models (LLMs) training, as well as for domain-specific models, which are usually small in size as it is costly to engage a l

Bo Yan, Cheng Yang, Chuan Shi, Jiawei Liu

Abnormal event detection, which refers to mining unusual interactions among involved entities, plays an important role in many real applications. Previous works mostly over-simplify this task as detecting abnormal pair-wise interactions. However, real-world events may contain multi-typed attributed entities and complex interactions among them, which forms an

Zuleika Ferre, Patricia Triunfo, José-Ignacio Antón

Abundant evidence has tracked the labour market and health assimilation of immigrants, including static analyses of differences in how foreign-born and native-born residents consume health care services. However, we know much less about how migrants' patterns of health care usage evolve with time of residence, especially in countries providing universal or q

Mustafa Sencer Aydın, Igor Kukavica, Mohammed Ziane

We address the long time behavior of the Boussinesq system coupling the Navier-Stokes equations driven by density with the non-diffusive equation for the density. We construct solutions of the system justifying previously obtained a priori bounds.

Behroz Bidabad, Faranak Sedighi

Lagrange introduced the notion of Schwarzian derivative and Thurston discovered its mysterious properties playing a role similar to that of curvature on Riemannian manifolds. Here we continue our studies on the development of the Schwarzian derivative on Finsler manifolds. First, we obtain an integrability condition for the M\"{o}bius equations. Then we obta

Simge Küçükyavuz, Qimeng Yu

Submodularity is an important concept in integer and combinatorial optimization. A classical submodular set function models the utility of selecting homogenous items from a single ground set, and such selections can be represented by binary variables. In practice, many problem contexts involve choosing heterogenous items from more than one ground set or sele

Zohreh Fathia, Behroz Bidabad

In the present work, we study the optimal control paths in the Zermelo navigation problem from the geometric and differential equations point of view rather than the optimal control point of view, where the latter has been carried out in our recent work. Here, we obtain the precise form of the system of ODE where the solutions are optimal trajectories of Zer

Pingchuan Ma, Rui Ding, Shuai Wang, Shi Han

Exploring data is crucial in data analysis, as it helps users understand and interpret the data more effectively. However, performing effective data exploration requires in-depth knowledge of the dataset and expertise in data analysis techniques. Not being familiar with either can create obstacles that make the process time-consuming and overwhelming for dat

Aqsa Ashraf Makhdomi, Iqra Altaf Gillani

In recent years, ridesharing platforms have become a prominent mode of transportation for the residents of urban areas. As a fundamental problem, route recommendation for these platforms is vital for their sustenance. The works done in this direction have recommended routes with higher passenger demand. Despite the existing works, statistics have suggested t

Clémentine Helfenstein-Didier, Amira Dhouib, Florent Favre, Jonathan Pascal

VEs are typically limited to visual and auditory cues; however, recent results show that multiple sensory modalities increase the immersion. In this study, an experimental protocol is proposed to recreate multiple tactile, in particular thermal, sensations in VR. The aim is twofold: (1) studying the performance of different devices for creating warm and cold

Wei Lin, Xun Xue

The possibility of distinguishing Dirac and Majorana fermions by cosmic torsion in the spatial-flat FRW spacetime is discussed. The scattering amplitudes of two types of fermions deviate from each other by the vector part of torsion in non-minimal coupling case. The scattering of massive fermions by cosmic torsion leads to a shift of final state energy distr

Simon Foucart, Chunyang Liao, Nate Veldt

Learning a smooth graph signal from partially observed data is a well-studied task in graph-based machine learning. We consider this task from the perspective of optimal recovery, a mathematical framework for learning a function from observational data that adopts a worst-case perspective tied to model assumptions on the function to be learned. Earlier work

Yoav Yair, Melody Korman, Colin Price, Eytan Stibbe

The ILAN-ES (Imaging of Lightning And Nocturnal Emissions from Space) experiment was conducted by Israeli astronaut Eytan Stibbe in April 2022 as part of the Axiom Space company AX-1 private mission to the International Space Station, in the framework of Rakia, a set of experiments selected for flight by the Ramon Foundation and the Israeli Space Agency. The

Mohammad Nadim, Wonjun Lee, David Akopian

One of the most elusive types of malware in recent times that pose significant challenges in the computer security system is the kernel-level rootkits. The kernel-level rootkits can hide its presence and malicious activities by modifying the kernel control flow, by hooking in the kernel space, or by manipulating the kernel objects. As kernel-level rootkits c

S. M. Stishov

Melting of a quantum system of hard spheres has been considered in the case when the effects of Bose and Fermi statistics can be neglected. It has been found that the quantum melting line always differs from the classical line with exception for T=0, P=0, where the both lines crossed. It is shown that the classical limit is not reachable at any finite temper

Mohammed Saeed, Nicola De Cao, Paolo Papotti

In many use-cases, information is stored in text but not available in structured data. However, extracting data from natural language text to precisely fit a schema, and thus enable querying, is a challenging task. With the rise of pre-trained Large Language Models (LLMs), there is now an effective solution to store and use information extracted from massive

Bo-Kyeong Kim, Jaemin Kang, Daeun Seo, Hancheol Park

Virtual humans have gained considerable attention in numerous industries, e.g., entertainment and e-commerce. As a core technology, synthesizing photorealistic face frames from target speech and facial identity has been actively studied with generative adversarial networks. Despite remarkable results of modern talking-face generation models, they often entai

Akihiko Inoue, Junho Yang

We derive sharp approximation error bounds for inverse block Toeplitz matrices associated with multivariate long-memory stationary processes. The error bounds are evaluated for both column and row sums. These results are used to prove the strong convergence of the solutions of general block Toeplitz systems. A crucial part of the proof is to bound sums consi

Nathan M. Dunfield, Stavros Garoufalidis, Seokbeom Yoon

In earlier work of two of the authors, two 1-loop polynomial invariants of cusped 3-manifolds were constructed using combinatorial data of ideal triangulations, and conjectured to be equal to the $\mathbb{C}^2$ and the $\mathbb{C}^3$-torsion polynomials. Here, we prove this conjecture for layered triangulations of fibered 3-manifolds with toroidal boundary,

Wonseong Kim

This study investigates the impact of negative words on sentiment analysis and its effect on the South Korean stock market index, KOSPI200. The research analyzes a dataset of 45,723 South Korean daily economic news articles using Word2Vec, cosine similarity, and an expanded lexicon. The findings suggest that incorporating negative words significantly increas

Haiping Wang, Yuan Liu, Zhen Dong, Yulan Guo

In this paper, we present a new method for the multiview registration of point cloud. Previous multiview registration methods rely on exhaustive pairwise registration to construct a densely-connected pose graph and apply Iteratively Reweighted Least Square (IRLS) on the pose graph to compute the scan poses. However, constructing a densely-connected graph is

Yifeng Wang, Luyang Luo, Mingxiang Wu, Qiong Wang

Collecting annotations from multiple independent sources could mitigate the impact of potential noises and biases from a single source, which is a common practice in medical image segmentation. Learning segmentation networks from multi-source annotations remains a challenge due to the uncertainties brought by the variance of annotations and the quality of im

P. A. Brooksbank, J. F. Maglione, E. A. O'Brien, J. B. Wilson

Within a category $\mathtt{C}$, having objects $\mathtt{C}_0$, it may be instructive to know not only that two objects are non-isomorphic, but also how far from being isomorphic they are. We introduce pseudo-metrics $d:\mathtt{C}_0 \times \mathtt{C}_0 \to [0,\infty]$ with the property that $x\cong y$ implies $d(x,y)=0$. We also give a canonical construction

Weikang Wan, Haoran Geng, Yun Liu, Zikang Shan

We propose a novel, object-agnostic method for learning a universal policy for dexterous object grasping from realistic point cloud observations and proprioceptive information under a table-top setting, namely UniDexGrasp++. To address the challenge of learning the vision-based policy across thousands of object instances, we propose Geometry-aware Curriculum

Huaiyuan Yang, Jiaxi Zeng, Yuelin Shao, Yuanfeng Xu

Quantum spin-hall insulator (QSHI) processes nontrivial topology. We notice that the electronic structures of some particular QSHIs are favorable for realization of excitonic insulators (EIs). Using first-principles many-body perturbation theory ($GW$+BSE) and $k \cdot p$ model, we show that high-temperature ($T$) topological EIs with unlike spin can exist i

Shinya Hosokawa, Koji Yoshida

Previously reported inelastic x-ray scattering spectra of a typical van der Waals molecular liquid CCl4 were reanalyzed by using a generalized Langevin formalism with a memory function including a thermal and two viscoelastic relaxation processes together with a simple sparse modeling. The obtained excitations of longitudinal acoustic phonons show a largely

Mohamed Karim Belaid, Dorra El Mekki, Maximilian Rabus, Eyke Hüllermeier

With the rapid growth of data availability and usage, quantifying the added value of each training data point has become a crucial process in the field of artificial intelligence. The Shapley values have been recognized as an effective method for data valuation, enabling efficient training set summarization, acquisition, and outlier removal. In this paper, w

Prashant K. Jha, Timothy Breitzman, Kaushik Dayal

We consider electrostatic interactions in two classes of nanostructures embedded in a three dimensional space: (1) helical nanotubes, and (2) thin films with uniform bending (i.e., constant mean curvature). Starting from the atomic scale with a discrete distribution of dipoles, we obtain the continuum limit of the electrostatic energy; the continuum energy d

Qinglin Ouyang, Wei Wang, Meng Zhai, Guo Chen

We report the transit observations of the ultra hot Jupiter WASP-121b using the Goodman High Throughput Spectrograph (GHTS) at the 4-meter ground-based telescope Southern Astrophysical Research Telescope (SOAR), covering the wavelength range $502-900$ nm. By dividing the target and reference star into 19 spectroscopic passbands and applying differential spec

Yibo Yan, Seth Frey, Amy Zhang, Vladimir Filkov

Open-source Software (OSS) has become a valuable resource in both industry and academia over the last few decades. Despite the innovative structures they develop to support the projects, OSS projects and their communities have complex needs and face risks such as getting abandoned. To manage the internal social dynamics and community evolution, OSS developer

Chen Fan, Christos Thrampoulidis, Mark Schmidt

Modern machine learning models are often over-parameterized and as a result they can interpolate the training data. Under such a scenario, we study the convergence properties of a sampling-without-replacement variant of stochastic gradient descent (SGD) known as random reshuffling (RR). Unlike SGD that samples data with replacement at every iteration, RR cho

Martin Hansen

This paper is a sharp and focussed exploration of the Fibonacci substitution and the mathematical entity it gives rise to, the Fibonacci word. Our investigations are both of an algebraic and a geometric nature. Indeed, it is the combination of the two that gives this paper its overall character. The work is in four parts. Chapter 1 is a brisk tour of necessa

Patrik Puchert, Poonam Poonam, Christian van Onzenoodt, Timo Ropinski

Large Language Models (LLMs) have revolutionized natural language processing and demonstrated impressive capabilities in various tasks. Unfortunately, they are prone to hallucinations, where the model exposes incorrect or false information in its responses, which renders diligent evaluation approaches mandatory. While LLM performance in specific knowledge fi

Haonan Zhang

Building energy retrofits have been identified as key to realizing climate mitigation goals in Canada. This study aims to provide a roadmap for existing mid-rise building retrofits in order to understand the required capital investment, energy savings, energy cost savings, and carbon footprint for mid-rise residential buildings in Canada. This study employed

Ganesh Ghimire, Rajesh Kumar Ulaganathan, Agnes Tempez, Oleksii Ilchenko

Molybdenum disulfide (MoS$_2$) nanoribbons have attracted increased interest due to their properties which can be tailored by tuning their dimensions. Herein, we demonstrate the growth of highly crystalline quasi-one-dimensional (1D)MoS$_2$ nanoribbons and aligned 3D triangular crystals with predominantly 3R or 2H stacking orientation. The synthesis method r

Mitchell M. Shen, Zoltan Sternovsky, David M. Malaspina

Electric field instruments carried by spacecraft (SC) are complementary to dedicated dust detectors by registering transient voltage perturbations caused by impact-generated plasma. The signal waveform contains information about the interaction between the impact-generated plasma cloud and the elements of SC-antenna system. The variability of antenna signals

Mitchell M. Shen, Zoltan Sternovsky, Mihály Horányi, Hsiang-Wen Hsu

Space missions often carry antenna instruments that are sensitive to dust impacts, however, the understanding of signal generation mechanisms remained incomplete. A signal generation model in an analytical form is presented that provides a good agreement with laboratory measurements. The model is based on the direct and induced charging of the spacecraft fro

Mitchell M. Shen, Zoltan Sternovsky, Alessandro Garzelli, David M. Malaspina

Dust impacts on spacecraft are commonly detected by antenna instruments as transient voltage perturbations. The signal waveform is generated by the interaction between the impact-generated plasma cloud and the elements of the antenna-spacecraft system. A general electrostatic model is presented that includes the two key elements of the interaction, namely th

Avinab Saha, Sandeep Mishra, Alan C. Bovik

Automatic Perceptual Image Quality Assessment is a challenging problem that impacts billions of internet, and social media users daily. To advance research in this field, we propose a Mixture of Experts approach to train two separate encoders to learn high-level content and low-level image quality features in an unsupervised setting. The unique novelty of ou

Sangmin Woo, So-Yeong Jeon, Jinyoung Park, Minji Son

We introduce Sketch-based Video Object Localization (SVOL), a new task aimed at localizing spatio-temporal object boxes in video queried by the input sketch. We first outline the challenges in the SVOL task and build the Sketch-Video Attention Network (SVANet) with the following design principles: (i) to consider temporal information of video and bridge the

Daisuke Ikegami, Nam Trang

We show that assuming $\mathsf{ZF}+\mathsf{AD}^+ +$ "$V = \mathrm{L} \bigl(\wp (\mathbb{R})\bigr)$", any poset which increases $\Theta$ does not preserve the truth of $\mathsf{AD}$. We also show that in $\mathsf{ZF} + \mathsf{AD}$, any non-trivial poset on $\mathbb{R}$ does not preserve the truth of $\mathsf{AD}$. This answers the question of Chan and Jackso

Ali Abkar

We study certain weighted Bergman and weighted Besov spaces of holomorphic functions in the polydisk and in the unit ball. We seek Mergelyan-type conditions on the non-radial weight function to guarantee that the dilations of a given function tend to the same function in norm; in particular, we seek conditions on the non-radial weights to ensure that the ana

Warda Benarab, Zahir Belhadi

In this paper, we apply the generalized integration constants method in field theory to quantize Maxwell and the Klein-Gordon free fields. The study is performed in both position and momentum spaces, to obtain equal-time Dirac brackets among the fields and their conjugate momenta. The idea is to obtain the brackets near the initial instant using the Taylor p

Evan Patterson

Structured and decorated cospans are broadly applicable frameworks for building bicategories or double categories of open systems. We streamline and generalize these frameworks using central concepts of double category theory. We show that, under mild hypotheses, double categories of structured cospans are cocartesian (have finite double-categorical coproduc