October 2023 arXiv papers — page 5

Jarkko Peltomäki, Ivan Porres

We present the OGAN algorithm for automatic requirement falsification of cyber-physical systems. System inputs and outputs are represented as piecewise constant signals over time while requirements are expressed in signal temporal logic. OGAN can find inputs that are counterexamples for the correctness of a system revealing design, software, or hardware defe

Nadun Wijesinghe, Hadi Hemmati

Most enterprise applications use logging as a mechanism to diagnose anomalies, which could help with reducing system downtime. Anomaly detection using software execution logs has been explored in several prior studies, using both classical and deep neural network-based machine learning models. In recent years, the research has largely focused in using variat

Peng Gao, Yu Shen, Ming C. Lin

Collaborative decision-making is an essential capability for multi-robot systems, such as connected vehicles, to collaboratively control autonomous vehicles in accident-prone scenarios. Under limited communication bandwidth, capturing comprehensive situational awareness by integrating connected agents' observation is very challenging. In this paper, we propo

Weiqi Li, Fan Lyu, Fanhua Shang, Liang Wan

Real-world data is extremely imbalanced and presents a long-tailed distribution, resulting in models that are biased towards classes with sufficient samples and perform poorly on rare classes. Recent methods propose to rebalance classes but they undertake the seesaw dilemma (what is increasing performance on tail classes may decrease that of head classes, an

Sambrita Ghatak, Jai Sukhatme

The Boreal Summer Intraseasonal Oscillation (BSISO) is a pronounced mode of tropical variability. Here, we identify two types of BSISO events, one which propagates northward over South Asia (SA) from the equatorial Indian Ocean (EIO), and the other which doesn't. Contrasting their behaviour shows that northward propagation occurs in multiple stages after con

Daniel Alabi, Atinuke Adegbile, Lekan Afuye, Philip Abel

The majority of Nigerian high schoolers have little to no exposure to the basics of algorithms and programming. We believe this trajectory should change as programming offers these students, especially those from indigent backgrounds, an opportunity to learn profitable skills and ignite their passions for problem-solving and critical thinking. NaijaCoder is

Zhengyi Yang, Jiancan Wu, Yanchen Luo, Jizhi Zhang

Sequential recommendation is to predict the next item of interest for a user, based on her/his interaction history with previous items. In conventional sequential recommenders, a common approach is to model item sequences using discrete IDs, learning representations that encode sequential behaviors and reflect user preferences. Inspired by recent success in

Sahel Torkamani, Javad B. Ebrahimi, Parastoo Sadeghi, Rafael G. L. D'Oliveira

We present the notion of \emph{reasonable utility} for binary mechanisms, which applies to all utility functions in the literature. This notion induces a partial ordering on the performance of all binary differentially private (DP) mechanisms. DP mechanisms that are maximal elements of this ordering are optimal DP mechanisms for every reasonable utility. By

Fu Ouyang, Thomas Tao Yang

We propose two approaches to estimate semiparametric discrete choice models for bundles. Our first approach is a kernel-weighted rank estimator based on a matching-based identification strategy. We establish its complete asymptotic properties and prove the validity of the nonparametric bootstrap for inference. We then introduce a new multi-index least absolu

Jean-Philippe Soulié, Kiroubanand Sankaran, Valeria Founta, Karl Opsomer

Al$_x$Sc$_{1-x}$ thin films have been studied with compositions around Al$_3$Sc ($x$ = 0.75) for potential interconnect metallization applications. As-deposited 25 nm thick films were x-ray amorphous but crystallized at 190{\deg}C, followed by recrystallization at 440{\deg}C. After annealing at 500{\deg}C, 24 nm thick stoichiometric Al$_3$Sc showed a resisti

Elie Abdo, Ruimeng Hu, Quyuan Lin

We consider an electrodiffusion model that describes the intricate interplay of multiple ionic species with a two-dimensional, incompressible, viscous fluid subjected to stochastic additive noise. This system involves nonlocal nonlinear drift-diffusion Nernst-Planck equations for ionic species and stochastic Navier-Stokes equations for fluid motion under the

Giuseppe Toscani

Measuring inequalities in a multidimensional framework is a challenging problem which is common to most field of science and engineering. Nevertheless, despite the enormous amount of researches illustrating the fields of application of inequality indices, and of the Gini index in particular, very few consider the case of a multidimensional variable. In this

Vincent Guedj, Henri Guenancia, Ahmed Zeriahi

We establish upper bounds on the diameter of compact K\"ahler manifolds endowed with K\"ahler metrics whose volume form satisfies an Orlicz integrability condition. Our results extend previous estimates due to Fu-Guo-Song, Y.Li, and Guo-Phong-Song-Sturm. In particular, they do not involve any constraint on the vanishing of the volume form. Moreover, we show

J C Lopez Vieyra, A V Turbiner

One-dimensional 3-body Wolfes model with 2- and 3-body interactions also known as $G_2/I_6$-rational integrable model of the Hamiltonian reduction is exactly-solvable and superintegrable. Its Hamiltonian $H$ and two integrals ${\cal I}_{1}, {\cal I}_{2}$, which can be written as algebraic differential operators in two variables (with polynomial coefficients)

Ivano Basile, Giorgio Leone

The landscape of six-dimensional supergravities is dramatically constrained by the cancellation of gauge and gravitational anomalies, but the full extent of its implications has not been uncovered. We explore the cancellation of global anomalies of the Dai-Freed type in this setting with abelian and simply laced gauge groups, finding novel constraints. In pa

Issam Boukhris, Nicola Puletti, Christian Vonderach, Matteo Guasti

Taper equations are indispensable tools for characterizing the stem profile of trees, providing valuable insights for forest management, timber inventory, and optimal assortments allocation. The recent progress in Terrestrial Laser Scanning (TLS) has revolutionized forest inventory practices by enabling non-destructive data collection. In this study, four ta

Yohan Jo, Xinyan Zhao, Arijit Biswas, Nikoletta Basiou

While most task-oriented dialogues assume conversations between the agent and one user at a time, dialogue systems are increasingly expected to communicate with multiple users simultaneously who make decisions collaboratively. To facilitate development of such systems, we release the Multi-User MultiWOZ dataset: task-oriented dialogues among two users and on

Md Shajalal, Sebastian Denef, Md. Rezaul Karim, Alexander Boden

Recent technological advancements have led to a large number of patents in a diverse range of domains, making it challenging for human experts to analyze and manage. State-of-the-art methods for multi-label patent classification rely on deep neural networks (DNNs), which are complex and often considered black-boxes due to their opaque decision-making process

Elisa Nguyen, Evgenii Kortukov, Jean Y. Song, Seong Joon Oh

Explainable AI (XAI) aims to provide insight into opaque model reasoning to humans and as such is an interdisciplinary field by nature. In this paper, we interviewed 10 practitioners to understand the possible usability of training data attribution (TDA) explanations and to explore the design space of such an approach. We confirmed that training data quality

Alfredo V Clemente, Alessandro Nocente, Massimiliano Ruocco

A thorough regulation of building energy systems translates in relevant energy savings and in a better comfort for the occupants. Algorithms to predict the thermal state of a building on a certain time horizon with a good confidence are essential for the implementation of effective control systems. This work presents a global Transformer architecture for ind

Michael Färber, David Lamprecht

In this paper, we introduce Linked Papers With Code (LPWC), an RDF knowledge graph that provides comprehensive, current information about almost 400,000 machine learning publications. This includes the tasks addressed, the datasets utilized, the methods implemented, and the evaluations conducted, along with their results. Compared to its non-RDF-based counte

Seraj A. M. Mostafa, Md Z. Islam, Mohammad Z. Islam, Fairose Jeehan

Artificially intelligent chatbot, such as ChatGPT, represents a recent and powerful advancement in the AI domain. Users prefer them for obtaining quick and precise answers, avoiding the usual hassle of clicking through multiple links in traditional searches. ChatGPT's conversational approach makes it comfortable and accessible for finding answers quickly and

Alina Harbuzova, Ce Jin, Virginia Vassilevska Williams, Zixuan Xu

Roundtrip spanners are the analog of spanners in directed graphs, where the roundtrip metric is used as a notion of distance. Recent works have shown existential results of roundtrip spanners nearly matching the undirected case, but the time complexity for constructing roundtrip spanners is still widely open. This paper focuses on developing fast algorithms

Marcin Misiaszek, Nicola Rossi

The nature of the dark matter in the Universe is one of the hardest unsolved problems in modern physics. Indeed, on one hand, the overwhelming indirect evidence from astrophysics seems to leave no doubt about its existence; on the other hand, direct search experiments, especially those conducted with low background detectors in underground laboratories all o

Ashot Aleksian, Julian Tugaut

In this paper, we study McKean-Vlasov SDE living in $\mathbb{R}^d$ in the reversible case without assuming any type of convexity assumptions for confinement or interaction potentials. Kramers' type law for the exit-time from a domain of attraction is established. Namely, in the small-noise regime, the limit in probability of the first exit-time behaves expon

A. Seza Doğruöz, Sunayana Sitaram, Zheng-Xin Yong

Multilingualism is widespread around the world and code-switching (CSW) is a common practice among different language pairs/tuples across locations and regions. However, there is still not much progress in building successful CSW systems, despite the recent advances in Massive Multilingual Language Models (MMLMs). We investigate the reasons behind this setba

Haoyu Liu, Alec F. Diallo, Paul Patras

Embedding covert streams into a cover channel is a common approach to circumventing Internet censorship, due to censors' inability to examine encrypted information in otherwise permitted protocols (Skype, HTTPS, etc.). However, recent advances in machine learning (ML) enable detecting a range of anti-censorship systems by learning distinct statistical patter

Jaron J. R. Lee, Gopika Ajaykumar, Ilya Shpitser, Chien-Ming Huang

Quantitative methods in Human-Robot Interaction (HRI) research have primarily relied upon randomized, controlled experiments in laboratory settings. However, such experiments are not always feasible when external validity, ethical constraints, and ease of data collection are of concern. Furthermore, as consumer robots become increasingly available, increasin

Chen Tang, Frank Guerin, Chenghua Lin

The ACL Anthology is an online repository that serves as a comprehensive collection of publications in the field of natural language processing (NLP) and computational linguistics (CL). This paper presents a tool called ``ACL Anthology Helper''. It automates the process of parsing and downloading papers along with their meta-information, which are then store

Richard Marin, Carlos Baldo

In this study, we explore how quantum pathfinding algorithm called Gaussian Amplitude Amplification (GAA) can be used to solve the DNA sequencing problem. To do this, sequencing by hybridization was assumed wherein short fragments of the nucleic acids called oligonucleotides of length l were gathered and were then assembled. The process of reassembling the s

Michael Chiang, Austin Hopkins, Benjamin Loewe, M. Cristina Marchetti

Spatiotemporal patterns in multicellular systems are important to understanding tissue dynamics, for instance, during embryonic development and disease. Here, we use a multiphase field model to study numerically the behavior of a near-confluent monolayer of deformable cells with intercellular friction. Varying friction and cell motility drives a solid-liquid

Luigi Ranno, Yong Zen Tan, Chi Siang Ong, Xin Guo

Lead (Pb2+) toxification in society is one of the most concerning public health crisis that remains unaddressed. The exposure to Pb2+ poisoning leads to a multitude of enduring health issues, even at the part-per-billion scale (ppb). Yet, public action dwarfs its impact. Pb2+ poisoning is estimated to account for 1 million deaths per year globally, which is

Binghan Liu, Gary S. Grest, Shengfeng Cheng

Molecular dynamics simulations are used to demonstrate that a binary solvent can be used to stratify colloidal mixtures when the suspension is rapidly dried. The solvent consists of two components, one more volatile than the other. When evaporated at high rates, the more volatile component becomes depleted near the evaporation front and develops a negative c

Yolanne Yi Ran Lee

Partial differential equations (PDEs) are ubiquitous in the world around us, modelling phenomena from heat and sound to quantum systems. Recent advances in deep learning have resulted in the development of powerful neural solvers; however, while these methods have demonstrated state-of-the-art performance in both accuracy and computational efficiency, a sign

Zhanar Berikkyzy, Joe Miller, Elizabeth Sprangel, Shanise Walker

Given a graph $G$, an exact $r$-coloring of $G$ is a surjective function $c:V(G) \to [1,\dots,r]$. An arithmetic progression in $G$ of length $j$ with common difference $d$ is a set of vertices $\{v_1,\dots, v_j\}$ such that $dist(v_i,v_{i+1}) = d$ for $1\le i < j$. An arithmetic progression is rainbow if all of the vertices are colored distinctly. The fewes

Richard Montgomery, Matías Pavez-Signé, Jun Yan

For every $k\ge 2$ and $\Delta$, we prove that there exists a constant $C_{\Delta,k}$ such that the following holds. For every graph $H$ with $\chi(H)=k$ and every tree with at least $C_{\Delta,k}|H|$ vertices and maximum degree at most $\Delta$, the Ramsey number $R(T,H)$ is $(k-1)(|T|-1)+\sigma(H)$, where $\sigma(H)$ is the size of a smallest colour class

Lin Gui, Yuchao Jiang, Jingshu Wang

Combining dependent p-values poses a long-standing challenge in statistical inference, particularly when aggregating findings from multiple methods to enhance signal detection. Recently, p-value combination tests based on regularly varying-tailed distributions, such as the Cauchy combination test and harmonic mean p-value, have attracted attention for their

Sandra Carillo, Cornelia Schiebold

A couple of applications of B\"acklund transformations in the study of nonlinear evolution equations is here given. Specifically, we are concerned about third order nonlinear evolution equations. Our attention is focussed on one side, on proving a new invariance admitted by a third order nonlinear evolution equation and, on the other one, on the construction

Tom Coates, Alexander M. Kasprzyk, Sara Veneziale

Algebraic varieties are the geometric shapes defined by systems of polynomial equations; they are ubiquitous across mathematics and science. Amongst these algebraic varieties are Q-Fano varieties: positively curved shapes which have Q-factorial terminal singularities. Q-Fano varieties are of fundamental importance in geometry as they are "atomic pieces" of m

Mert Unsal, Ali Maatouk, Antonio De Domenico, Nicola Piovesan

As deep learning models become increasingly large, they pose significant challenges in heterogeneous devices environments. The size of deep learning models makes it difficult to deploy them on low-power or resource-constrained devices, leading to long inference times and high energy consumption. To address these challenges, we propose FlexTrain, a framework

Haoran Sun, Xiaohu Zhao, Yikun Lei, Shaolin Zhu

In this paper, we employ Singular Value Canonical Correlation Analysis (SVCCA) to analyze representations learnt in a multilingual end-to-end speech translation model trained over 22 languages. SVCCA enables us to estimate representational similarity across languages and layers, enhancing our understanding of the functionality of multilingual speech translat

Corinne Blondel, Guy Henniart, Shaun Stevens

Let $F$ be a non-archimedean local field of odd residual characteristic. We compute the Jordan set of a simple cuspidal representation of a symplectic group over $F$, using explicit computations of generators of the Hecke algebras of covers reflecting the parabolic induction under study. When $F$ is a $p$-adic field we obtain the Langlands parameter of the r

J. T. Schmelz, G. L. Verschuur, A. Escorza, A. Jorissen

Supernova explosions attributed to the unseen companion in several binary systems identified by the Third Gaia Data Release (Gaia DR3) may be responsible for a number of well-known and well-studied features in the radio sky, including the Low-Latitude-Intermediate-Velocity Arch and the North-Celestial-Pole Loop. Slices from the Longitude-Latitude-Velocity da

Zhengyi Yang, Jiancan Wu, Zhicai Wang, Xiang Wang

Sequential recommendation aims to recommend the next item that matches a user's interest, based on the sequence of items he/she interacted with before. Scrutinizing previous studies, we can summarize a common learning-to-classify paradigm -- given a positive item, a recommender model performs negative sampling to add negative items and learns to classify whe

Rustem Islamov, Mher Safaryan, Dan Alistarh

We analyze asynchronous-type algorithms for distributed SGD in the heterogeneous setting, where each worker has its own computation and communication speeds, as well as data distribution. In these algorithms, workers compute possibly stale and stochastic gradients associated with their local data at some iteration back in history and then return those gradie

Waleed Aziz, Colin Christopher, Chara Pantazi, Sebastian Walcher

We consider a three dimensional complex polynomial, or rational, vector field (equivalently, a two-form in three variables) which admits a Liouvillian first integral. We prove that there exists a first integral whose differential is the product of a rational 1-form with a Darboux function, or there exists a Darboux Jacobi multiplier. Moreover, we prove that

Daniel Beasley

In the past two decades, AB testing has proliferated to optimise products in digital domains. Traditional AB tests use fixed-horizon testing, determining the sample size of the experiment and continuing until the experiment has concluded. However, due to the feedback provided by modern data infrastructure, experimenters may take incorrect decisions based on

Kevin Ford, Mikhail R. Gabdullin

We show that for any polynomial $f$ from the integers to the integers, with positive leading coefficient and irreducible over the rationals, if $x$ is large enough then there is a string of $(\log x)(\log\log x)^{1/835}$ consecutive integers $n \in [1,x]$ for which $f(n)$ is composite. This improves a result of the first author, Konyagin, Maynard, Pomerance

Ziyao Liu, Yu Jiang, Jiyuan Shen, Minyi Peng

In recent years, the notion of ``the right to be forgotten" (RTBF) has become a crucial aspect of data privacy for digital trust and AI safety, requiring the provision of mechanisms that support the removal of personal data of individuals upon their requests. Consequently, machine unlearning (MU) has gained considerable attention which allows an ML model to

Steven Adriaensen, Herilalaina Rakotoarison, Samuel Müller, Frank Hutter

Learning curve extrapolation aims to predict model performance in later epochs of training, based on the performance in earlier epochs. In this work, we argue that, while the inherent uncertainty in the extrapolation of learning curves warrants a Bayesian approach, existing methods are (i) overly restrictive, and/or (ii) computationally expensive. We describ

Yuxin Ye, Wenming Yang, Yapeng Tian

Existing machine learning research has achieved promising results in monaural audio-visual separation (MAVS). However, most MAVS methods purely consider what the sound source is, not where it is located. This can be a problem in VR/AR scenarios, where listeners need to be able to distinguish between similar audio sources located in different directions. To a

Ignacio Salinas, Javier Cubillos Cornejo, Alexander Szameit, Pablo Solano

Photonic lattices enable experimental exploration of transport and localization phenomena, two of the mayor goals in physics and technology. In particular, the optical excitation of some lattice sites which evanescently couple to a lattice array emulates radiation processes into structured reservoirs, a fundamental subject in quantum optics. Moreover, the si

Michael Färber, Lazaros Tampakis

Artificial Intelligence (AI) is one of the most momentous technologies of our time. Thus, it is of major importance to know which stakeholders influence AI research. Besides researchers at universities and colleges, researchers in companies have hardly been considered in this context. In this article, we consider how the influence of companies on AI research

Björn Schembera, Frank Wübbeling, Hendrik Kleikamp, Christine Biedinger

In applied mathematics and related disciplines, the modeling-simulation-optimization workflow is a prominent scheme, with mathematical models and numerical algorithms playing a crucial role. For these types of mathematical research data, the Mathematical Research Data Initiative has developed, merged and implemented ontologies and knowledge graphs. This cont

Jin-Da Li, He Gao, Shunke Ai, Wei-Hua Lei

As the most energetic explosions in the Universe, gamma-ray bursts (GRBs) are commonly believed to be generated by relativistic jets. Recent observational evidence suggests that the jets producing GRBs are likely to have a structured nature. Some studies have suggested that non-axisymmetric structured jets may be formed through internal non-uniform magnetic

O. V. Pshyk, A. Vasylenko, P. Kuttel, B. Wicher

Traditionally, increasing compositional complexity and chemical diversity of high entropy alloy ceramics whilst maintaining a stable single-phase solid solution has been a primary design strategy for the development of new ceramics. However, only a handful have shown properties that justify the increased alloying content. Here, we unveil a groundbreaking str

Jorge Abreu-Vicente, Hannah Sonntag, Thomas Eidens, Cassie S. Mitchell

High throughput extraction and structured labeling of data from academic articles is critical to enable downstream machine learning applications and secondary analyses. We have embedded multimodal data curation into the academic publishing process to annotate segmented figure panels and captions. Natural language processing (NLP) was combined with human-in-t

Igor Kukavica, Wojciech Ożański, Marco Sammartino

We consider the incompressible Euler equation on an analytic domain $\Omega $ with nonhomogeneous boundary condition $u\cdot \mathsf{n} = \overline{u} \cdot \mathsf{n}$ on $\partial \Omega$, where $\overline{u}$ is a given divergence-free analytic vector field. We establish local well-posedness for $u$ in analytic spaces without any compatibility conditions

Hang Zhang, Ping Li

We study the phase transition phenomenon inherent in the shuffled (permuted) regression problem, which has found numerous applications in databases, privacy, data analysis, etc. In this study, we aim to precisely identify the locations of the phase transition points by leveraging techniques from message passing (MP). In our analysis, we first transform the p

Juan F. Hernández Cabrera, Javier De Miguel, Enrique Joven Álvarez, E. Hernández-Suárez

The axion is a long-postulated boson that can simultaneously solve two fundamental problems of modern physics: the charge-parity symmetry problem in the strong interaction and the enigma of dark matter. In this work we estimate, by means of Monte Carlo simulations, the sensitivity of the Dark-photons$\&$Axion-Like particles Interferometer (DALI), a new-gener

Zhengdi Yu, Shaoli Huang, Yongkang Cheng, Tolga Birdal

We present SignAvatars, the first large-scale, multi-prompt 3D sign language (SL) motion dataset designed to bridge the communication gap for Deaf and hard-of-hearing individuals. While there has been an exponentially growing number of research regarding digital communication, the majority of existing communication technologies primarily cater to spoken or w

Chao Feng, Alberto Huertas Celdran, Pedro Miguel Sanchez Sanchez, Lynn Zumtaugwald

Artificial intelligence (AI) increasingly influences critical decision-making across sectors. Federated Learning (FL), as a privacy-preserving collaborative AI paradigm, not only enhances data protection but also holds significant promise for intelligent network management, including distributed monitoring, adaptive control, and edge intelligence. Although t

Edward J. Thomas, Virginia N. Ciriano-Tejel, David F. Wise, Domenic Prete

Quantum computers are nearing the thousand qubit mark, with the current focus on scaling to improve computational performance. As quantum processors grow in complexity, new challenges arise such as the management of device variability and the interface with supporting electronics. Spin qubits in silicon quantum dots are poised to address these challenges wit

Antonio Casares, Marcin Pilipczuk, Michał Pilipczuk, Uéverton S. Souza

We give a simple proof that assuming the Exponential Time Hypothesis (ETH), determining the winner of a Rabin game cannot be done in time $2^{o(k \log k)} \cdot n^{O(1)}$, where $k$ is the number of pairs of vertex subsets involved in the winning condition and $n$ is the vertex count of the game graph. While this result follows from the lower bounds provided

Alena S. Kazmina, Ilia V. Zalivako, Alexander S. Borisenko, Nikita A. Nemkov

Scalable quantum computers hold the promise to solve hard computational problems, such as prime factorization, combinatorial optimization, simulation of many-body physics, and quantum chemistry. While being key to understanding many real-world phenomena, simulation of non-conservative quantum dynamics presents a challenge for unitary quantum computation. In

Arik Ermshaus, Patrick Schäfer, Ulf Leser

Ubiquitous sensors today emit high frequency streams of numerical measurements that reflect properties of human, animal, industrial, commercial, and natural processes. Shifts in such processes, e.g. caused by external events or internal state changes, manifest as changes in the recorded signals. The task of streaming time series segmentation (STSS) is to par

Takashi Nakayama, Yusuke Matsushita, Ken Sakayori, Ryosuke Sato

Automated verification of functional correctness of imperative programs with references (a.k.a. pointers) is challenging because of reference aliasing. Ownership types have recently been applied to address this issue, but the existing approaches were limited in that they are effective only for a class of programs whose reference usage follows a certain style

Yu-Ji Shi, Jun Zeng, Zhi-Fu Deng

We investigate the $K_1(1270)-K_1(1400)$ mixing caused by the flavor $SU(3)$ symmetry breaking. The mixing angle is expressed by a $K_{1A}\to K_{1B}$ matrix element induced by the operators that breaks flavor $SU(3)$ symmetry. The QCD contribution to this matrix element is assumed to be dominated and calculated by QCD sum rules. A three-point correlation fun

Tomás Vega Waichman, M. Luz Vercesi, Ariel A. Berardino, Maximiliano S. Beckel

Single-cell RNA sequencing (scRNA-seq) has transformed our ability to explore biological systems. Nevertheless, proficient expertise is essential for handling and interpreting the data. In this paper, we present scX, an R package built on the Shiny framework that streamlines the analysis, exploration, and visualization of single-cell experiments. With an int

Augustin Cosse

Motivated by recent results in the statistical physics of spin glasses, we study the recovery of a sparse vector $\mathbf{x}_0\in \mathbb{S}^{n-1}$, $\|\mathbf{x}_0\|_{\ell_0} = k<n$, from $m$ quadratic measurements of the form $ (1-\lambda)\langle \mathbf{A}_i, \mathbf{x}_0\mathbf{x}_0^T \rangle + \lambda \langle\mathbf{c}_i,\mathbf{x}_0 \rangle $ where $\m

Peixiang Huang, Songtao Zhang, Yulu Gan, Rui Xu

Deep learning in digital pathology brings intelligence and automation as substantial enhancements to pathological analysis, the gold standard of clinical diagnosis. However, multiple steps from tissue preparation to slide imaging introduce various image corruptions, making it difficult for deep neural network (DNN) models to achieve stable diagnostic results

Xi Lin, Xiaoyuan Zhang, Zhiyuan Yang, Qingfu Zhang

In the past few decades, many multiobjective evolutionary optimization algorithms (MOEAs) have been proposed to find a finite set of approximate Pareto solutions for a given problem in a single run, each with its own structure. However, in many real-world applications, it could be desirable to have structure constraints on the entire optimal solution set, wh

Benji Alwis

Over the past decade, the field of machine learning has experienced remarkable advancements. While image recognition systems have achieved impressive levels of accuracy, they continue to rely on extensive training datasets. Additionally, a significant challenge has emerged in the form of poor out-of-distribution performance, which necessitates retraining neu

Marcus Haywood-Alexander, Wei Liu, Kiran Bacsa, Zhilu Lai

The intersection of physics and machine learning has given rise to the physics-enhanced machine learning (PEML) paradigm, aiming to improve the capabilities and reduce the individual shortcomings of data- or physics-only methods. In this paper, the spectrum of physics-enhanced machine learning methods, expressed across the defining axes of physics and data,

Cenlin Duan, Jianlei Yang, Xiaolin He, Yingjie Qi

Processing-in-memory (PIM), as a novel computing paradigm, provides significant performance benefits from the aspect of effective data movement reduction. SRAM-based PIM has been demonstrated as one of the most promising candidates due to its endurance and compatibility. However, the integration density of SRAM-based PIM is much lower than other non-volatile

Jordi Castellví, Benedikt Stufler

We study random $k$-connected chordal graphs with bounded tree-width. Our main results are scaling limits and quenched local limits.

David Gabrielyan, Dmitry Volkov, Elizabeth Kozlova, Ansar Safin

Recently, canted antiferromagnets offer great potential for fundamental research and applications due to their unique properties. The presence of the Dzyaloshinskii-Moriya interaction leads to the existence of a weak ferromagnetic moment at room temperature. We study both theoretically and experimentally microwave spin pumping by the quasi-ferromagnetic mode

Shuixin Xiao, Yuanlong Wang, Daoyi Dong, Jun Zhang

Quantum process tomography (QPT) is a fundamental task to characterize the dynamics of quantum systems. In contrast to standard QPT, ancilla-assisted process tomography (AAPT) framework introduces an extra ancilla system such that a single input state is needed. In this paper, we extend the two-stage solution, a method originally designed for standard QPT, t

Zsuzsanna Dancso, Iva Halacheva, Guillaume Laplante-Anfossi, Marcy Robertson

We show that solutions to the Kashiwara-Vergne problem can be extended degree by degree. This can be used to simplify the computation of a class of Drinfel'd associators, which under the Alekseev-Torossian conjecture, may comprise all associators. We also give a proof that the associated graded Lie algebra of the Kashiwara-Vergne group is isomorphic to the g

Naoki Ono, Yusuke Matsui

Approximate Nearest Neighbor Search (ANNS) is the task of finding the database vector that is closest to a given query vector. Graph-based ANNS is the family of methods with the best balance of accuracy and speed for million-scale datasets. However, graph-based methods have the disadvantage of long index construction time. Recently, many researchers have imp

Vinicius Salem, Alison A. Silva, Fabiano M. Andrade

We introduce and analyze the entanglement properties of randomized hypergraph states, as an extended notion of the randomization procedure in the quantum logic gates for the usual graph states, recently proposed in the literature. The probabilities of applying imperfect generalized controlled-$Z$ gates simulate the noisy operations over the qubits. We obtain

I. D. Roberts, R. J. van Weeren, D. V. Lal, M. Sun

$Aims:$ We used the nearby Coma Cluster as a laboratory in order to probe the impact of ram pressure on star formation as well as to constrain the characteristic timescales and velocities for the stripping of the non-thermal ISM. $Methods:$ We used high-resolution ($6.5'' \approx 3\,\mathrm{kpc}$), multi-frequency ($144\,\mathrm{MHz} - 1.5\,\mathrm{GHz}$) ra

William E. Salazar, Omar Calderón-Losada, John H. Reina

Beam splitters (BSs) and optical parametric amplifiers (OPAs) can be described using Lie groups $SU(2)$ and $SU(1,1)$. Here, we show that the dynamical trajectories of these devices are connected via a Wick rotation on their respective group manifolds. This yields an exact amplitude-level duality between BSs of transmittance $\eta$ and OPAs of gain $g=1/\eta

Christian Basteck, Frank Huettner

We consider manipulations in the context of coalitional games, where a coalition aims to increase the total payoff of its members. An allocation rule is immune to coalitional manipulation if no coalition can benefit from internal reallocation of worth on the level of its subcoalitions (reallocation-proofness), and if no coalition benefits from a lower worth

Benji Alwis

This paper introduces a new approach for quickly adapting a multi-view visuomotor system for robots to varying camera configurations from the baseline setup. It utilises meta-learning to fine-tune the perceptual network while keeping the policy network fixed. Experimental results demonstrate a significant reduction in the number of new training episodes need

Anne S. M. Buckner, Tim Naylor, Clare L. Dobbs, Steven Rieder

Observations of clusters suffer from issues such as completeness, projection effects, resolving individual stars and extinction. As such, how accurate are measurements and conclusions are likely to be? Here, we take cluster simulations (Westerlund2- and Orion- type), synthetically observe them to obtain luminosities, accounting for extinction and the inheren

Sungjin Cheong, Wonho Jung, Yoon Seop Lim, Yong-Hwa Park

This paper proposes a thermal-infrared (TIR) remote target detection system for maritime rescue using deep learning and data augmentation. We established a self-collected TIR dataset consisting of multiple scenes imitating human rescue situations using a TIR camera (FLIR). Additionally, to address dataset scarcity and improve model robustness, a synthetic da

He-Feng Hsieh, Rubén Cabezón, Li-Ting Ma, Kuo-Chuan Pan

We present self-consistent three-dimensional core-collapse supernova simulations of a rotating $20M_\odot$ progenitor model with various initial angular velocities from $0.0$ to $4.0$ rad s$^{-1}$ using a smoothed particle hydrodynamics code, SPHYNX, and a grid-based hydrodynamics code, FLASH. We identify two strong gravitational-wave features, with peak fre

Yuxin Jiang, Yufei Wang, Xingshan Zeng, Wanjun Zhong

The ability to follow instructions is crucial for Large Language Models (LLMs) to handle various real-world applications. Existing benchmarks primarily focus on evaluating pure response quality, rather than assessing whether the response follows constraints stated in the instruction. To fill this research gap, in this paper, we propose FollowBench, a Multi-l

Nikolai Spuck, Matthias Schmid, Moritz Berger

In generalized regression models the effect of continuous covariates is commonly assumed to be linear. This assumption, however, may be too restrictive in applications and may lead to biased effect estimates and decreased predictive ability. While a multitude of alternatives for the flexible modeling of continuous covariates have been proposed, methods that

E. J. Ridley, M. Nicholl, C. A. Ward, P. K. Blanchard

We present a pan-chromatic study of AT2017bcc, a nuclear transient that was discovered in 2017 within the skymap of a reported burst-like gravitational wave candidate, G274296. It was initially classified as a superluminous supernova, and then reclassified as a candidate tidal disruption event. Its optical light curve has since shown ongoing variability with

Benji Alwis

This paper provides an overview of the intricate relationship between social dynamics, technological advancements, and pioneering figures in the fields of cybernetics and artificial intelligence. It explores the impact of collaboration and interpersonal relationships among key scientists, such as McCulloch, Wiener, Pitts, and Rosenblatt, on the development o

Yasser Zouzou, Onur Varol

Automated social media accounts, known as bots, are increasingly recognized as key tools for manipulative online activities. These activities can stem from coordination among several accounts and these automated campaigns can manipulate social network structure by following other accounts, amplifying their content, and posting messages to spam online discour

Antonio Ortiz, Lizardo Valencia Palomo, Victor Manuel Minjares Neriz

The ALICE collaboration recently reported the mean transverse momentum as a function of charged-particle multiplicity for different pp-collisions classes defined based on the "jettiness" of the event. The event "jettiness" is quantified using transverse spherocity that is measured at midpseudorapidity ($|\eta|<0.8$) considering charged particles with transve

M. Koussour, N. Myrzakulov, Alnadhief H. A. Alfedeel, E. I. Hassan

This paper is a parametrization of the equation of state (EoS) parameter of dark energy (DE), which is parameterized using Square-Root (SR) form i.e. $\omega _{SR}=\text{$\omega _{0}$}+\text{$\omega _{1}$}\frac{z}{\sqrt{z^{2}+1}}$, where $\omega _{0}$ and $\omega _{1}$ are free constants. This parametrization will be examined in the context of the recently s

L. K. Dewangan, N. K. Bhadari, A. K. Maity, C. Eswaraiah

To probe star formation processes, we present a multi-scale and multi-wavelength investigation of the `Snake' nebula/infrared dark cloud G11.11$-$0.12 (hereafter, G11; length $\sim$27 pc). Spitzer images hint at the presence of sub-filaments (in absorption), and reveal four infrared-dark hub-filament system (HFS) candidates (extent $<$ 6 pc) toward G11, wher

Elia Favarelli, Elisabetta Matricardi, Lorenzo Pucci, Enrico Paolini

In this work, we investigate the performance of a joint sensing and communication (JSC) network consisting of multiple base stations (BSs) that cooperate through a fusion center (FC) to exchange information about the sensed environment while concurrently establishing communication links with a set of user equipments (UEs). Each BS within the network operates

Stefan Schrott, Daniel Toneian

Strassen established that there exists a two step martingale with marginal distributions $\mu$, $\nu$ if and only if $\mu$, $\nu$ are in convex order. Recently Chon\'e-Gozlan-Kramarz obtained a transport characterization of the stochastic order defined by convex positively 1-homogeneous functions, in the spirit of Strassen's theorem under certain technical a

Devon R. Graham, Kevin Leyton-Brown, Tim Roughgarden

We present the first nontrivial procedure for configuring heuristic algorithms to maximize the utility provided to their end users while also offering theoretical guarantees about performance. Existing procedures seek configurations that minimize expected runtime. However, very recent theoretical work argues that expected runtime minimization fails to captur

Manuel V. Gnann, Anouk C. Wisse

We prove well-posedness, partial regularity, and stability of the thin-film equation $h_t + (m(h) h_{zzz})_z = 0$ with general mobility $m(h) = h^n$ and mobility exponent $n\in (1,\tfrac{3}{2})\cup (\tfrac{3}{2},3)$ in the regime of perfect wetting (zero contact angle). After a suitable coordinate transformation to fix the free boundary (the contact line whe