July 2023 arXiv papers — page 99

Pavel S. Gevorgyan, A. A. Nazaryan

Orbits and bi-invariant subsets of binary $G$-spaces are studied. The problem of the distributivity of a binary action of a group $G$ on a space $X$, which was posed in 2016 by one of the authors, is solved.

Yashar Deldjoo

Our research investigates the potential of Large-scale Language Models (LLMs), specifically OpenAI's GPT, in credit risk assessment-a binary classification task. Our findings suggest that LLMs, when directed by judiciously designed prompts and supplemented with domain-specific knowledge, can parallel the performance of traditional Machine Learning (ML) model

Valeria Zelina Reyna Ortiz, Maciej Rybczynski, Zbigniew Wlodarczyk

With large volumes of data available at LHC, it has possible to study the multiplicity distributions. It is interesting as well to check how well event generators can describes the properties and the behavior of multi-particle production processes. In this paper, we analyse the oscillatory behavior of modified combinants in proton-proton collisions at centre

Nicolò Cesa-Bianchi, Tommaso Cesari, Roberto Colomboni, Federico Fusco

We study the problem of regret minimization for a single bidder in a sequence of first-price auctions where the bidder discovers the item's value only if the auction is won. Our main contribution is a complete characterization, up to logarithmic factors, of the minimax regret in terms of the auction's \emph{transparency}, which controls the amount of informa

Mariana-Iuliana Georgescu

Pathological anomalies exhibit diverse appearances in medical imaging, making it difficult to collect and annotate a representative amount of data required to train deep learning models in a supervised setting. Therefore, in this work, we tackle anomaly detection in medical images training our framework using only healthy samples. We propose to use the Maske

Rama Mishra, Tumpa Mahato

We review the polynomial parameterization of classical knots and prove the analogous results for long $2$ knots. We also construct polynomial parameterizations for certain classes of knotted spheres (such as spun and twist spun of the classical knots).

Miaoxin Liu, Xiao-Dong Li, Alvin J. K. Chua

The accuracy of Bayesian inference can be negatively affected by the use of inaccurate forward models. In the case of gravitational-wave inference, accurate but computationally expensive waveform models are sometimes substituted with faster but approximate ones. The model error introduced by this substitution can be mitigated in various ways, one of which is

Anna Kaczmarska

Extending the Higgs sector by introducing additional scalar fields to account for the electroweak symmetry breaking, can provide solutions to some of the questions the Standard Model fails to answer. Introducing additional scalar fields leads to extra Higgs like particles, which can be either neutral or charged. These proceedings present some recent direct s

Takashi Nishimura

There is a widespread method to represent the envelope when a given hyperplane family creates an envelope. However, one sometimes encounters cases when the widespread method fails to represent the desired envelope precisely, and is confused. At the same time, one wants to find a correct method to draw the envelope precisely. In this article, focused on strai

Shimin Zhang, Qu Yang, Chenxiang Ma, Jibin Wu

The identification of sensory cues associated with potential opportunities and dangers is frequently complicated by unrelated events that separate useful cues by long delays. As a result, it remains a challenging task for state-of-the-art spiking neural networks (SNNs) to identify long-term temporal dependencies since bridging the temporal gap necessitates a

Wei Qi

We study the Aharonov-Bohm (AB) caging effect in rhombic exciton-polariton lattices, with the Rashba-Dresselhaus spin-orbit coupling (RDSOC) in acting a synthetic gauge field. The effective magnetic flux through each plaquette is controlled by the orientation of the RDSOC and geometry of the rhombic lattice. The results show that the interplay of lattice geo

Igor E. Shparlinski, Laurence P. Wijaya

Let $\vartheta(m)$ is number of nonzero coefficients in the $m$-th cyclotomic polynomial. For real $\gamma > 0$ and $x \ge 2$ we define $$H_{\gamma}(x)=\#\left\{m:~m=pq \le x, \ p<q\text{ primes }, \ \vartheta(m)\le m^{1/2+\gamma}\right\}, $$ and show that for any fixed $\eta> 0$, uniformly over $\gamma$ with $$9/20+\eta \le \gamma\le 1/2 -\eta, $$ we have a

Gang Liu, Zhihuan Zhou, Yuhao Mu, Lixin Xu

In this paper, we investigate the interaction between early dark energy (EDE) and scalar field dark matter, proposing a coupled scalar fields model to address the Hubble tension and $S_8$ tension. While the EDE model successfully alleviates the Hubble tension, it exacerbates the $S_8$ tension. To mitigate the negative impact of EDE, we introduce the interact

Milad Tatar Mamaghani, Xiangyun Zhou, Nan Yang, A. Lee Swindlehurst

Short-packet communication (SPC) and unmanned aerial vehicles (UAVs) are anticipated to play crucial roles in the development of 5G-and-beyond wireless networks and the Internet of Things (IoT). In this paper, we propose a secure SPC system, where a UAV serves as a mobile decode-and-forward (DF) relay, periodically receiving and relaying small data packets f

E. Aristidi, B. Carry, K. Minker, J. -L. Prieur

We present new speckle measurements of the position of Linus, the satellite of the asteroid (22) Kalliope, obtained at the 1m C2PU-Epsilon telescope on the Plateau de Calern, France. Observations were made in the visible domain with the speckle camera PISCO. We obtained 122 measurements in February-March 2022 and April 2023, with a mean uncertainty close to

Alfredo Fiorentino, Enrico Drigo, Stefano Baroni, Paolo Pegolo

The time-honored Allen-Feldman theory of heat transport in glasses is generally assumed to predict a finite value for the thermal conductivity, even if it neglects the anharmonic broadening of vibrational normal modes. We demonstrate that the harmonic approximation predicts that the bulk lattice thermal conductivity of harmonic solids inevitably diverges at

Shahine Bouabid, Dino Sejdinovic, Duncan Watson-Parris

Emulators, or reduced complexity climate models, are surrogate Earth system models that produce projections of key climate quantities with minimal computational resources. Using time-series modelling or more advanced machine learning techniques, data-driven emulators have emerged as a promising avenue of research, producing spatially resolved climate respons

Annika Reinke, Georg Grab, Lena Maier-Hein

While clinical trials are the state-of-the-art methods to assess the effect of new medication in a comparative manner, benchmarking in the field of medical image analysis is performed by so-called challenges. Recently, comprehensive analysis of multiple biomedical image analysis challenges revealed large discrepancies between the impact of challenges and qua

Fabian Allmendinger, Benjamin Brauneis, Werner Heil, Ulrich Schmidt

We report on the performance of a Magnetically Shielded Room (MSR) intended for next level $^3$He/$^{129}$Xe co-magnetometer experiments which require improved magnetic conditions. The MSR consists of three layers of Mu-metal with a thickness of 3 mm each, and one additional highly conductive copper-coated aluminum layer with a thickness of 10 mm. It has a c

Ignacio Parellada-Serrano, Mario Pérez-Escribano, Carlos Molero, Pablo Padilla

This work employs a new approach to analyze coupled-resonator circuits to design and manufacture a fully metallic dual polarization frequency selective surface (FSS). The proposed filtering structure is composed of a series of unit cells with resonators fundamentally coupled along the z-direction and then repeated periodically in the xy-plane. The fully meta

Alan Dow, Klaas Pieter Hart, Jan van Mill, Hans Vermeer

We investigate closed copies of~$\mathbb{N}$ in powers of~$\mathbb{R}$ with respect to $C^*$- and $C$-embedding. We show that $\mathbb{R}^{\omega_1}$ contains closed copies of~$\mathbb{N}$ that are not $C^*$-embedded.

Shiri Chechik, Shay Mozes, Oren Weimann

We show how to assign labels of size $\tilde O(1)$ to the vertices of a directed planar graph $G$, such that from the labels of any three vertices $s,t,f$ we can deduce in $\tilde O(1)$ time whether $t$ is reachable from $s$ in the graph $G\setminus \{f\}$. Previously it was only known how to achieve $\tilde O(1)$ queries using a centralized $\tilde O(n)$ si

Junjie Wang, Yuchao Huang, Chunyang Chen, Zhe Liu

Pre-trained large language models (LLMs) have recently emerged as a breakthrough technology in natural language processing and artificial intelligence, with the ability to handle large-scale datasets and exhibit remarkable performance across a wide range of tasks. Meanwhile, software testing is a crucial undertaking that serves as a cornerstone for ensuring

Yongzhe Ma, Hongcheng Ni, Yang Li, Feng He

Photon is a concept that does not apply at the instantaneous level when light is described by classical electromagnetic fields. Exploiting the dynamical rotational symmetry of circularly or elliptically polarized classical light pulses, however, we demonstrate the existence of instantaneous quasiphotons down to the subcycle level. We illustrate the concept o

Zhechen Li, Ao Liu, Lirong Xia, Yongzhi Cao

In this paper, we investigate tradeoffs among differential privacy (DP) and several important voting axioms: Pareto efficiency, SD-efficiency, PC-efficiency, Condorcet consistency, and Condorcet loser criterion. We provide upper and lower bounds on the two-way tradeoffs between DP and each axiom. We also provide upper and lower bounds on three-way tradeoffs

Ziyue Jiang, Jinglin Liu, Yi Ren, Jinzheng He

Zero-shot text-to-speech (TTS) aims to synthesize voices with unseen speech prompts, which significantly reduces the data and computation requirements for voice cloning by skipping the fine-tuning process. However, the prompting mechanisms of zero-shot TTS still face challenges in the following aspects: 1) previous works of zero-shot TTS are typically traine

Jean-François Grosjean, Antoine Lemenant, Rémy Mougenot

This paper is devoted to prove that any domain satisfying a $(\delta_0,r_0)-$capacity condition of first order is automatically $(m,p)-$stable for all $m\geqslant 1$ and $p\geqslant 1$, and for any dimension $N\geqslant 1$. In particular, this includes regular enough domains such as $\mathscr{C}^1-$domains, Lipchitz domains, Reifenberg flat domains, but is w

Hadrien Brochet, Bruno Salvy

Creative telescoping is an algorithmic method initiated by Zeilberger to compute definite sums by synthesizing summands that telescope, called certificates. We describe a creative telescoping algorithm that computes telescopers for definite sums of D-finite functions as well as the associated certificates in a compact form. The algorithm relies on a discrete

Jose Luis Ponton, Víctor Ceballos, Lesly Acosta, Alejandro Ríos

In the era of the metaverse, self-avatars are gaining popularity, as they can enhance presence and provide embodiment when a user is immersed in Virtual Reality. They are also very important in collaborative Virtual Reality to improve communication through gestures. Whether we are using a complex motion capture solution or a few trackers with inverse kinemat

Pawel Sznajder, Piotr Zdybel, Lujia Liu, Maria L. Ekiel-Jezewska

We analyze the three-dimensional buckling of an elastic filament in a shear flow of a viscous fluid at low Reynolds number and high Peclet number. We apply the Euler-Bernoulli beam (elastica) theoretical model. We show the universal character of the full 3D spectral problem for the small perturbation of the thin filament from a straight position of arbitrary

Qiulei Dong, Jiayin Sun, Mengyu Gao

Open-set image recognition is a challenging topic in computer vision. Most of the existing works in literature focus on learning more discriminative features from the input images, however, they are usually insensitive to the high- or low-frequency components in features, resulting in a decreasing performance on fine-grained image recognition. To address thi

Ethan Ackelsberg, Florian K. Richter, Or Shalom

Let $(G/\Gamma,R_a)$ be an ergodic $k$-step nilsystem for $k\geq 2$. We adapt an argument of Parry to show that $L^2(G/\Gamma)$ decomposes as a sum of a subspace with discrete spectrum and a subspace of Lebesgue spectrum with infinite multiplicity. In particular, we generalize a result previously established by Host, Kra and Maass for $2$-step nilsystems and

Muhammad Shoaib Farooq, Fatima Ahmed

A blockchain-based framework for distributed agile software testing life cycle is an innovative approach that uses blockchain technology to optimize the software testing process. Previously, various methods were employed to address communication and collaboration challenges in software testing, but they were deficient in aspects such as trust, traceability,

Jonah T. Hansen, Samuel Wade, Michael J. Ireland, Tony D. Travouillon

In the past few years, there has been a resurgence in studies towards space-based optical/infrared interferometry, particularly with the vision to use the technique to discover and characterise temperate Earth-like exoplanets around solar analogues. One of the key technological leaps needed to make such a mission feasible is demonstrating that formation flyi

Kittithat Krongchon, Tawfiqur Rakib, Shivesh Pathak, Elif Ertekin

An uncertainty in studying twisted bilayer graphene (TBG) is the minimum energy geometry, which strongly affects the electronic structure. The minimum energy geometry is determined by the potential energy surface, which is dominated by van der Waals (vdW) interactions. In this work, large-scale diffusion quantum Monte Carlo (QMC) simulations are performed to

Yoshiki Hiruta, Kenta Ishimoto

From bacteria and sperm cells to artificial microrobots, self-propelled microscopic objects at low Reynolds numbers often perceive fluctuating mechanical and chemical stimuli and contact exterior wall boundaries both in nature and the laboratory. In this study, we theoretically investigate the fundamental features of microswimmers by focusing on their recipr

Guram Bezhanishvili, Nick Bezhanishvili, Tommaso Moraschini

A classic result in modal logic, known as the Blok Dichotomy Theorem, states that the degree of incompleteness of a normal extension of the basic modal logic $\sf K$ is $1$ or $2^{\aleph_0}$. It is a long-standing open problem whether Blok Dichotomy holds for normal extensions of other prominent modal logics (such as $\sf S4$ or $\sf K4$) or for extensions o

P. S. Muraev, D. N. Maksimov, A. R. Kolovsky

We analyse the stationary current of Bose particles across the Bose-Hubbard chain connected to a battery, focusing on the effect of inter-particle interactions. It is shown that the current magnitude drastically decreases as the strength of inter-particle interactions exceeds the critical value which marks the transition to quantum chaos in the Bose-Hubbard

Alec J. M. Thomson, David McConnell, Emil Lenc, Timothy J Galvin

The Australian SKA Pathfinder (ASKAP) radio telescope has carried out a survey of the entire Southern Sky at 887.5MHz. The wide area, high angular resolution, and broad bandwidth provided by the low-band Rapid ASKAP Continuum Survey (RACS-low) allow the production of a next-generation rotation measure (RM) grid across the entire Southern Sky. Here we introdu

Buyang Li, Zongze Yang, Zhi Zhou

In contrast with the diffusion equation which smoothens the initial data to $C^\infty$ for $t>0$ (away from the corners/edges of the domain), the subdiffusion equation only exhibits limited spatial regularity. As a result, one generally cannot expect high-order accuracy in space in solving the subdiffusion equation with nonsmooth initial data. In this paper,

Alessandro Flaborea, Luca Collorone, Guido D'Amely, Stefano D'Arrigo

Anomalies are rare and anomaly detection is often therefore framed as One-Class Classification (OCC), i.e. trained solely on normalcy. Leading OCC techniques constrain the latent representations of normal motions to limited volumes and detect as abnormal anything outside, which accounts satisfactorily for the openset'ness of anomalies. But normalcy shares th

Dongjin Park

We consider the defocusing energy-critical inhomogeneous nonlinear Schr\"{o}dinger equation (INLS) $iu_t + \Delta u = |x|^{-b}|u|^{k}u$ in $\mathbb{R} \times \mathbb{R}^{n}$ where $n \geq 3$, $0<b<\min(2, n/2)$, and $k=(4-2b)/(n-2)$. We show that for every spherically symmetric initial data $\phi \in H^1(\mathbb{R}^n)$, or preferably $\dot{H}^1(\mathbb{R}^n)

R. Cavoretto, F. Dell'Accio, A. De Rossi, F. Di Tommaso

We construct cubature methods on scattered data via resampling on the support of known algebraic cubature formulas, by different kinds of adaptive interpolation (polynomial, RBF, PUM). This approach gives a promising alternative to other recent methods, such as direct meshless cubature by RBF or least-squares cubature formulas.

L. Benfatto, C. Castellani, G. Seibold

We present a detailed theoretical investigation of the linear and non-linear optical response in a model system for a disordered d-wave superconductor, showing that for both quantities the gap symmetry considerably changes the paradigm of the optical response based on the conventional s-wave case. For what concerns the linear response our findings agree with

A. Bazzi, A. Zanella, G. Cecchini, B. M. Masini

Short-range wireless technologies will enable vehicles to communicate and coordinate their actions, thus improving people's safety and traffic efficiency. Whereas IEEE 802.11p (and related standards) had been the only practical solution for years, in 2016 a new option was introduced with Release 14 of long term evolution (LTE), which includes new features to

Jiarui Wang, Thushara Abhayapala, Jihui Aimee Zhang, Prasanga Samarasinghe

Acoustic velocity vectors (AVVs) are related to the human's perception of sound at low frequencies and are widely used in Ambisonics. This paper proposes a spatial sound field reproduction algorithm called velocity matching, which reproduces the AVVs in the spherical listening region by matching the AVVs' spherical harmonic coefficients. Using the sound fiel

Nan Xue, Bin Tan, Yuxi Xiao, Liang Dong

This paper studies the problem of structured 3D reconstruction using wireframes that consist of line segments and junctions, focusing on the computation of structured boundary geometries of scenes. Instead of leveraging matching-based solutions from 2D wireframes (or line segments) for 3D wireframe reconstruction as done in prior arts, we present NEAT, a ren

Zitha Sasindran, Harsha Yelchuri, T. V. Prabhakar

Federated learning (FL) has evolved as a prominent method for edge devices to cooperatively create a unified prediction model while securing their sensitive training data local to the device. Despite the existence of numerous research frameworks for simulating FL algorithms, they do not facilitate comprehensive deployment for automatic speech recognition tas

Hodaka Kurokawa, Keidai Wakamatsu, Shintaro Nakazato, Toshiharu Makino

The coherent control of the orbital state is crucial for color centers in diamonds for realizing extremely low-power manipulation. Here, we propose the neutrally charged nitrogen-vacancy center, NV$^0$, as an ideal system for orbital control through electric fields. We estimate electric susceptibility in the ground state of NV$^0$ to be comparable to that in

Guanlin Li, Kangjie Chen, Yuan Xu, Han Qiu

Adversarial training is an important topic in robust deep learning, but the community lacks attention to its practical usage. In this paper, we aim to resolve a real-world challenge, i.e., training a model on an imbalanced and noisy dataset to achieve high clean accuracy and adversarial robustness, with our proposed Omnipotent Adversarial Training (OAT) stra

Maria Biernacka, Paweł Butkiewicz, Konrad J. Kapcia, Wojciech Olszewski

The electrical polarization switching on stoichiometric GaFeO$_{3}$ single crystal was measured, and a new model of atomic displacements responsible for the polarization reverse was proposed. The widely adapted mechanism of polarization switching in GaFeO$_{3}$ can be applied to stoichiometric, perfectly ordered crystals. However, the grown single crystals,

Zhenxing Ming, Julie Stephany Berrio, Mao Shan, Eduardo Nebot

For smart vehicles driving through signalised intersections, it is crucial to determine whether the vehicle has right of way given the state of the traffic lights. To address this issue, camera based sensors can be used to determine whether the vehicle has permission to proceed straight, turn left or turn right. This paper proposes a novel end to end interse

Navneet Roshan, Rahul Pandit

Focal arrhythmias, which arise from delayed afterdepolarizations (DADs), are observed in various pathophysiological heart conditions; these can lead to sudden cardiac death. A clear understanding of the electrophysiological factors of cardiac myocytes, which lead to DADs, can suggest pharmacological targets that can eliminate DAD-induced arrhythmias. Therefo

Alexander Haluszczynski, Daniel Köglmayr, Christoph Räth

Controlling nonlinear dynamical systems using machine learning allows to not only drive systems into simple behavior like periodicity but also to more complex arbitrary dynamics. For this, it is crucial that a machine learning system can be trained to reproduce the target dynamics sufficiently well. On the example of forcing a chaotic parametrization of the

Rami Ahmad, Mark David Groves, Dag Nilsson

We present a Lyapunov centre theorem for an antisymplectically reversible Hamiltonian system exhibiting a nondegenerate $1:1$ or $1:-1$ semisimple resonance as a detuning parameter is varied. The system can be finite- or infinite dimensional (and quasilinear) and have a non-constant symplectic structure. We allow the origin to be a "trivial" eigenvalue arisi

Guanlin Li, Guowen Xu, Tianwei Zhang

In this paper, we study adversarial training on datasets that obey the long-tailed distribution, which is practical but rarely explored in previous works. Compared with conventional adversarial training on balanced datasets, this process falls into the dilemma of generating uneven adversarial examples (AEs) and an unbalanced feature embedding space, causing

Elias X. Huber, Benjamin Y. L. Tan, Paul R. Griffin, Dimitris G. Angelakis

We extend the qubit-efficient encoding presented in [Tan et al., Quantum 5, 454 (2021)] and apply it to instances of the financial transaction settlement problem constructed from data provided by a regulated financial exchange. Our methods are directly applicable to any QUBO problem with linear inequality constraints. Our extension of previously proposed met

Sándor J Kovács, Behrouz Taji

In this paper we are making the first step toward answering a question posed by Steven Zucker at a JAMI conference at Johns Hopkins University in 1996, organized by Vyacheslav Shokurov.

J. Gomez Enri, S. Vignudelli, G. Quartly, C. Gommenginger

This letter examines waveform data from the Envisat RA-2 as it passes regularly over Pianosa (a 10-km 2 island in the northwestern Mediterranean). Forty-six repeat passes were analyzed, with most showing a reduction in signal upon passing over the island, with weak early returns corresponding to the reflections from land. Intriguingly, one third of cases sho

Manos Kirtas, Nikolaos Passalis, Anastasios Tefas

Even nowadays, where Deep Learning (DL) has achieved state-of-the-art performance in a wide range of research domains, accelerating training and building robust DL models remains a challenging task. To this end, generations of researchers have pursued to develop robust methods for training DL architectures that can be less sensitive to weight distributions,

ARIANNA Collaboration, A. Anker, P. Baldi, S. W. Barwick

The ARIANNA experiment is an Askaryan radio detector designed to measure high-energy neutrino induced cascades within the Antarctic ice. Ultra-high-energy neutrinos above $10^{16}$ eV have an extremely low flux, so experimental data captured at trigger level need to be classified correctly to retain more neutrino signal. We first describe two new physics-bas

Neng Dong, Liyan Zhang, Shuanglin Yan, Hao Tang

Occlusion perturbation presents a significant challenge in person re-identification (re-ID), and existing methods that rely on external visual cues require additional computational resources and only consider the issue of missing information caused by occlusion. In this paper, we propose a simple yet effective framework, termed Erasing, Transforming, and Noi

Kirill Goncharuk, Yuri Feldman, Oz Oshri

We study the emergence of fluid flow in a closed chamber that is driven by dynamical deformations of an elastic sheet. The sheet is compressed between the sidewalls of the chamber and partitions it into two separate parts, each of which is initially filled with an inviscid fluid. When fluid exchange is allowed between the two compartments of the chamber, the

Marius Durea, Elena-Andreea Florea

We discuss, on finite and infinite dimensional normed vector spaces, some versions of Radstr\"{o}m cancellation law (or lemma) that are suited for applications to set optimization problems. In this sense, we call our results "conic" variants of the celebrated result of Radstr\"{o}m, since they involve the presence of an ordering cone on the underlying space.

Xu Zhang, Fan Ni, Guan-Nan Dong, Aichun Zhu

Most existing methods for text-based person retrieval focus on text-to-image person retrieval. Nevertheless, due to the lack of dynamic information provided by isolated frames, the performance is hampered when the person is obscured or variable motion details are missed in isolated frames. To overcome this, we propose a novel Text-to-Video Person Retrieval (

A. Aldarias, J. Gomez-Enri, I. Laiz, B. Tejedor

Altimetry data of two and a half years (June 2016-November 2018) of Sentinel 3A SRAL were validated at the sampling frequency of 80 Hz. The study areas are three coastal sites in Spain: Huelva (HU) (Gulf of Cadiz), Barcelona (BA), and Bilbao (BI). Two tracks were selected in each site: one ascending and one descending. Data were validated using in situ tide

Timofey Lomonosov

In this paper, an algebraic modification of the method of undetermined coefficients for solving nonhomogeneous linear stationary difference equations for quasipolynomial right-hand sides is proposed. Although the classical method of undetermined coefficients is well-known in both differential equations and difference equations case, its application in the di

Chia-Yuan Chang, Yu-Neng Chuang, Guanchu Wang, Mengnan Du

Domain generalization aims to learn a generalization model that can perform well on unseen test domains by only training on limited source domains. However, existing domain generalization approaches often bring in prediction-irrelevant noise or require the collection of domain labels. To address these challenges, we consider the domain generalization problem

Max Deppert, Matthias Kaul, Matthias Mnich

One of the most studied extensions of the famous Traveling Salesperson Problem (TSP) is the {\sc Multiple TSP}: a set of $m\geq 1$ salespersons collectively traverses a set of $n$ cities by $m$ non-trivial tours, to minimize the total length of their tours. This problem can also be considered to be a variant of {\sc Uncapacitated Vehicle Routing} where the o

Burak Ozbagci

We prove a simple necessary and sufficient condition for a two-bridge knot K(p,q) to be quasipositive, based on the continued fraction expansion of p/q. As an application, coupled with some classification results in contact and symplectic topology, we give a new proof of the fact that smoothly slice two-bridge knots are non-quasipositive. Another proof of th

Wei Kang, Liang Xu, Hong Zhou

We propose a novel learning-based surrogate data assimilation (DA) model for efficient state estimation in a limited area. Our model employs a feedforward neural network for online computation, eliminating the need for integrating high-dimensional limited-area models. This approach offers significant computational advantages over traditional DA algorithms. F

Linfeng Liu, Junyan Lyu, Siyu Liu, Xiaoying Tang

The prediction of mild cognitive impairment (MCI) conversion to Alzheimer's disease (AD) is important for early treatment to prevent or slow the progression of AD. To accurately predict the MCI conversion to stable MCI or progressive MCI, we propose Triformer, a novel transformer-based framework with three specialized transformers to incorporate multi-model

Weidong Huang, Jiaming Ji, Chunhe Xia, Borong Zhang

The deployment of Reinforcement Learning (RL) in real-world applications is constrained by its failure to satisfy safety criteria. Existing Safe Reinforcement Learning (SafeRL) methods, which rely on cost functions to enforce safety, often fail to achieve zero-cost performance in complex scenarios, especially vision-only tasks. These limitations are primaril

Hermann Kroll, Christin Katharina Kreutz, Mirjam Cuper, Bill Matthias Thang

Digital libraries oftentimes provide access to historical newspaper archives via keyword-based search. Historical figures and their roles are particularly interesting cognitive access points in historical research. Structuring and clustering news articles would allow more sophisticated access for users to explore such information. However, real-world limitat

Tomasz Kociumaka, Anish Mukherjee, Barna Saha

The edit distance is a fundamental measure of sequence similarity, defined as the minimum number of character insertions, deletions, and substitutions needed to transform one string into the other. Given two strings of length at most $n$, simple dynamic programming computes their edit distance exactly in $O(n^2)$ time, which is also the best possible (up to

Xiaotie Deng, Hangxin Gan, Ningyuan Li, Weian Li

We introduce a game model called "customer attraction game" to demonstrate the competition among online content providers. In this model, customers exhibit interest in various topics. Each content provider selects one topic and benefits from the attracted customers. We investigate both symmetric and asymmetric settings involving agents and customers. In the

Fong Yew Leong, Dax Enshan Koh, Wei-Bin Ewe, Jian Feng Kong

We assess the use of variational quantum imaginary time evolution for solving partial differential equations. Our results demonstrate that real-amplitude ansaetze with full circular entangling layers lead to higher-fidelity solutions compared to those with partial or linear entangling layers. To efficiently encode impulse functions, we propose a graphical ma

Jingjing Xue, Min Liu, Sheng Sun, Yuwei Wang

Federated Learning (FL) emerges as a distributed machine learning paradigm without end-user data transmission, effectively avoiding privacy leakage. Participating devices in FL are usually bandwidth-constrained, and the uplink is much slower than the downlink in wireless networks, which causes a severe uplink communication bottleneck. A prominent direction t

Pei Chen, Soumajyoti Sarkar, Leonard Lausen, Balasubramaniam Srinivasan

Language models pretrained on large collections of tabular data have demonstrated their effectiveness in several downstream tasks. However, many of these models do not take into account the row/column permutation invariances, hierarchical structure, etc. that exist in tabular data. To alleviate these limitations, we propose HYTREL, a tabular language model,

Zhen Zhang, Guanhua Zhang, Bairu Hou, Wenqi Fan

Although large language models (LLMs) have achieved great success in vast real-world applications, their vulnerabilities towards noisy inputs have significantly limited their uses, especially in high-stake environments. In these contexts, it is crucial to ensure that every prediction made by large language models is stable, i.e., LLM predictions should be co

Yoshihito Kuno, Takahiro Orito, Ikuo Ichinose

Information scrambling is nowadays one of the most important topics in various fields of research. Measurement-only circuit (MoC) exhibits specific information scrambling dynamics, depending on the types of projective measurements and their mutual anti-commutativity. The spatial range of the projective measurements in MoCs gives significant influences on cir

Meng Wang, Shunsuke Mori, Xiuzhen Yu, Masahiro Sawada

The interface composed of magnets and strong spin-orbit coupling (SOC) materials forms an important platform for spintronic devices and intriguing magnetic phenomena, such as the chiral spin textures and magnetic proximity effect (MPE). The interface exchange interaction and Dzyaloshinskii-Moriya interaction (DMI) have been discussed in a wide range of heter

Jingna Qiu, Frauke Wilm, Mathias Öttl, Maja Schlereth

The process of annotating histological gigapixel-sized whole slide images (WSIs) at the pixel level for the purpose of training a supervised segmentation model is time-consuming. Region-based active learning (AL) involves training the model on a limited number of annotated image regions instead of requesting annotations of the entire images. These annotation

Olukorede Fakorede, Ashutosh Kumar Nirala, Modeste Atsague, Jin Tian

Adversarial Training (AT) has been found to substantially improve the robustness of deep learning classifiers against adversarial attacks. AT involves obtaining robustness by including adversarial examples in training a classifier. Most variants of AT algorithms treat every training example equally. However, recent works have shown that better performance is

Ryosuke Korekata, Motonari Kambara, Yu Yoshida, Shintaro Ishikawa

This paper describes a domestic service robot (DSR) that fetches everyday objects and carries them to specified destinations according to free-form natural language instructions. Given an instruction such as "Move the bottle on the left side of the plate to the empty chair," the DSR is expected to identify the bottle and the chair from multiple candidates in

Bruno Bouchard, Xiaolu Tan, Jixin Wang

We provide an It\^o's formula for $C^1$-functionals of flows of conditional marginal distributions of continuous semimartingales. This is based on the notion of weak Dirichlet process, and extends the $C^1$-It\^o's formula in Gozzi and Russo (2006) to this context. As the first application, we study a class of McKean-Vlasov optimal control problems, and esta

Liang Wang, Nan Yang, Furu Wei

Large language models (LLMs) have demonstrated their ability to learn in-context, allowing them to perform various tasks based on a few input-output examples. However, the effectiveness of in-context learning is heavily reliant on the quality of the selected examples. In this paper, we propose a novel framework to iteratively train dense retrievers that can

Junhui Fan, Hubing Xiao, Wenxin Yang, Lixia Zhang

The studies and constraints on the emission region are crucial to the blazar radiation mechanism. Yet the previous works mainly focus on individual sources. In this work, we make use of the largest and the latest spectral energy distribution (SED) fitting results in the literature to statistically study the blazar emission region property in the framework of

Wangjun Jiang, Dingyou Ma, Zhiqing Wei, Zhiyong Feng

Recent advances in wireless communication with the enormous demands of sensing ability have given rise to the integrated sensing and communication (ISAC) technology, among which passive sensing plays an important role. The main challenge of passive sensing is how to achieve high sensing performance in the condition of communication demodulation errors. In th

Daocheng Fu, Xin Li, Licheng Wen, Min Dou

In this paper, we explore the potential of using a large language model (LLM) to understand the driving environment in a human-like manner and analyze its ability to reason, interpret, and memorize when facing complex scenarios. We argue that traditional optimization-based and modular autonomous driving (AD) systems face inherent performance limitations when

Arkabrata Ghosh

In this article, I study and solve the exponential Diophantine equation $M_p^{x} + (M_q + 1)^{y}= (lz)^2$ where $M_p$ and $M_q$ are Mersenne primes, $l$ is a prime number, and $x,y$, and $z$ are non-negative integers. Several illustrations are presented as well as cases where no solution of the given Diophantine equation is present.

Shahriar Golchin, Mihai Surdeanu, Nazgol Tavabi, Ata Kiapour

We propose a novel task-agnostic in-domain pre-training method that sits between generic pre-training and fine-tuning. Our approach selectively masks in-domain keywords, i.e., words that provide a compact representation of the target domain. We identify such keywords using KeyBERT (Grootendorst, 2020). We evaluate our approach using six different settings: t

Rui Wang, Tigran A. Sedrakyan, Baigeng Wang, Lingjie Du

Correlation and frustration play essential roles in physics, giving rise to novel quantum phases [1-6]. A typical frustrated system is correlated bosons on moat bands, which could host topological orders with long-range quantum entanglement [4]. However, the realization of moat-band physics is still challenging. Here, we explore moat-band phenomena in shallo

Kai-Hong Zhuang, Yue-Yue Chen, Yan-Fei Li, Karen Z. Hatsagortsyan

Generation of ultrarelativistic polarized leptons during interaction of an ultrarelativistic electron beam with a counterpropagating ultraintense laser pulse is investigated in the quantum radiation-dominated domain. While the symmetry of the laser field tends to average the radiative polarization of leptons to zero, we demonstrate the feasibility of sizable

Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler

Accelerating the discovery of novel and more effective therapeutics is an important pharmaceutical problem in which deep learning is playing an increasingly significant role. However, real-world drug discovery tasks are often characterized by a scarcity of labeled data and significant covariate shift$\unicode{x2013}\unicode{x2013}$a setting that poses a chal

Madeline Howell, Simon W. Campbell, Dennis Stello, Gayandhi M. De Silva

Asteroseismology provides a new avenue for accurately measuring the masses of evolved globular cluster (GC) stars through the detection of their solar-like oscillations. We present the first detections of solar-like oscillations in 47 red giant branch (RGB) and early asymptotic giant branch (EAGB) stars in the metal-poor GC M80; only the second ever with mea

Yuki Oyama

This study performs an attribute-level analysis of the global and local path preferences of network travelers. To this end, a reward decomposition approach is proposed and integrated into a link-based recursive (Markovian) path choice model. The approach decomposes the instantaneous reward function associated with each state-action pair into the global utili

Omprokash Das, Jihao Liu, Roktim Mascharak

We prove the ACC for lc thresholds and the global ACC for algebraically integrable foliations and provide applications.

A. Calini, C. L. Ellisor, C. M. Schober, E. Smith

We investigate a higher order nonlinear Schr\"odinger equation with linear damping and weak viscosity, recently proposed as a model for deep water waves exhibiting frequency downshifting. Through analysis and numerical simulations, we discuss how the viscosity affects the linear stability of the Stokes wave solution, enhances rogue wave formation, and leads

Md. Mahfuzur Rahman, Vince D. Calhoun, Sergey M. Plis

Deep learning (DL) models have been popular due to their ability to learn directly from the raw data in an end-to-end paradigm, alleviating the concern of a separate error-prone feature extraction phase. Recent DL-based neuroimaging studies have also witnessed a noticeable performance advancement over traditional machine learning algorithms. But the challeng