May 2024 arXiv papers — page 140

Julian Dörfler, Benito van der Zander, Markus Bläser, Maciej Liskiewicz

The framework of Pearl's Causal Hierarchy (PCH) formalizes three types of reasoning: probabilistic (i.e. purely observational), interventional, and counterfactual, that reflect the progressive sophistication of human thought regarding causation. We investigate the computational complexity aspects of reasoning in this framework focusing mainly on satisfiabili

Andrzej Kozlowski, Kohhei Yamaguchi

For any field $\Bbb F$ and positive integers $m,n,d$ with $(m,n)\not= (1,1)$, Farb and Wolfson defined the certain affine varieties ${\rm Poly}^{d,m}_n(\Bbb F)$ as generalizations of spaces first studied by Arnold, Vassiliev, Segal and others. As a natural generalization of this, for each fan $\Sigma$ and $r$-tuple $D=(d_1,\cdots ,d_r)$ of positive integers,

Saksham Sahai Srivastava, Ashutosh Gandhi

Large language models have been proven to be capable of handling complex linguistic and cognitive tasks. Therefore their usage has been extended to tasks requiring logical reasoning ability such as Mathematics. In this paper, we propose a prompting technique called MathDivide that breaks down the mathematical problem into simpler subproblems. Each of the sub

Jaume Anguera Peris, Joakim Jaldén

Poisson point processes provide a versatile framework for modeling the distributions of random points in space. When the space is partitioned into cells, each associated with a single generating point from the Poisson process, there appears a geometric structure known as Poisson Voronoi tessellation. These tessellations find applications in various fields su

Andrea Grisafi, Mathieu Salanne

A crucial aspect in the simulation of electrochemical interfaces consists in treating the distribution of electronic charge of electrode materials that are put in contact with an electrolyte solution. Recently, it has been shown how a machine-learning method that specifically targets the electronic charge density, also known as SALTED, can be used to predict

Felix J. Dorfner, Janis L. Vahldiek, Leonhard Donle, Andrei Zhukov

Purpose: To examine whether incorporating anatomical awareness into a deep learning model can improve generalizability and enable prediction of disease progression. Methods: This retrospective multicenter study included conventional pelvic radiographs of 4 different patient cohorts focusing on axial spondyloarthritis (axSpA) collected at university and commu

Akira Kamatsuka, Koki Kazama, Takahiro Yoshida

The problem of computing $\alpha$-capacity for $\alpha>1$ is equivalent to that of computing the correct decoding exponent. Various algorithms for computing them have been proposed, such as Arimoto and Jitsumatsu--Oohama algorithm. In this study, we propose a novel alternating optimization algorithm for computing the $\alpha$-capacity for $\alpha>1$ based on

Samuel Gill, Daniel Bayliss, Solène Ulmer-Moll, Peter J. Wheatley

Discovering transiting exoplanets with relatively long orbital periods ($>$10 days) is crucial to facilitate the study of cool exoplanet atmospheres ($T_{\rm eq} < 700 K$) and to understand exoplanet formation and inward migration further out than typical transiting exoplanets. In order to discover these longer period transiting exoplanets, long-term photome

Abela Kevin, Chetcuti Emmanuel

We study uO convergence on infinitely distributive lattices, extending key properties known from Riesz spaces. We show that order continuity of uO convergence characterizes infinite distributivity. We examine O-adherence and uO adherence of sublattices and ideals, proving that the uO and O closures of a sublattice coincide and form a sublattice, and that the

Jacob Cooper, Ethan Leventhal

Let $G$ be a simple graph with binomial edge ideal $J_G$. We prove how to calculate the multidegree of $J_G$ based on combinatorial properties of $G$. In particular, we study the set $S_{\min}(G)$ defined as the collection of subsets of vertices whose prime ideals have minimum codimension. We provide results which assist in determining $S_{\min}(G)$, then ca

Amar Ali-Bey, Brahim Chaib-draa, Philippe Giguère

In visual place recognition, accurately identifying and matching images of locations under varying environmental conditions and viewpoints remains a significant challenge. In this paper, we introduce a new technique, called Bag-of-Queries (BoQ), which learns a set of global queries designed to capture universal place-specific attributes. Unlike existing meth

Çağrı Çöltekin, Matyáš Kopp, Katja Meden, Vaidas Morkevicius

We introduce a dataset on political orientation and power position identification. The dataset is derived from ParlaMint, a set of comparable corpora of transcribed parliamentary speeches from 29 national and regional parliaments. We introduce the dataset, provide the reasoning behind some of the choices during its creation, present statistics on the dataset

Ankit Kumar

Due to the weakness of gravitational coupling, all quantum experiments up to date in which gravity plays a role utilized the field of the Earth. Since this field undergoes practically undetectable back-action from quantum particles, it effectively admits a classical description as a fixed background Newtonian field or spacetime. This argument strongly motiva

Maya M. Durvasula, Sabri Eyuboglu, David M. Ritzwoller

We develop a method for assigning high-quality labels to unstructured text. This method is based on fine-tuning an efficient, open-source language model with data extracted from a large, proprietary language model. We apply this method to construct a census of published clinical trials. With these data, we revisit a literature that contends that pharmaceutic

A. Zhadyranova, M. Koussour, S. Bekkhozhayev

In this work, we investigate the dynamics of bouncing cosmologies within the framework of Weyl-type $f(Q,T)$ gravity. Here, $Q$ represents the non-metricity of the space-time, is determined by the vector field $w_\mu$, while $T$ represents the trace of the matter energy-momentum tensor. Our objective is to explore the feasibility of avoiding the Big Bang sin

Shambel Sahlu, Álvaro de la Cruz-Dombriz, Amare Abebe

We take into account redshift-space distortion measurements to investigate the growth of cosmological large-scale structures within the framework of modified symmetric teleparallel $f(Q)$ gravity. After comparing the predictions of the $f(Q)$-gravity expansion history with OHD and SNIa Pantheon+ sample datasets and constraining the pertinent cosmological par

Meenakshi L., Bhaskar Sen Gupta

We investigate the effect of annealed disorder on the mechanical properties and plasticity of a modeled amorphous solid by introducing a small fraction of heavy impurities into the material which resembles real experimental systems. The disorder being mobile, the total degrees of freedom and the potential energy landscape of the pure system are preserved in

Antonio Malpica-Morales, Miguel A. Durán-Olivencia, Serafim Kalliadasis

Accurate prediction of electricity day-ahead prices is essential in competitive electricity markets. Although stationary electricity-price forecasting techniques have received considerable attention, research on non-stationary methods is comparatively scarce, despite the common prevalence of non-stationary features in electricity markets. Specifically, exist

Lampis Alevizos, Lalit Bhakuni, Stefan Jaschke

This paper introduces a value-driven cybersecurity innovation framework for the transportation and infrastructure sectors, as opposed to the traditional market-centric approaches that have dominated the field. Recontextualizing innovation categories into sustaining, incremental, disruptive, and transformative, we aim to foster a culture of self-innovation wi

A. Zhadyranova, M. Koussour, S. Bekkhozhayev, V. Zhumabekova

Understanding the evolution of dark energy poses a significant challenge in modern cosmology, as it is responsible for the universe's accelerated expansion. In this study, we focus on a specific $f(T)$ cosmological model and analyze its behavior using observational data, including 31 data points from the CC dataset, 1048 points from the Pantheon SNe Ia sampl

Mark Pollicott, Daofei Zhang

In this article, we give explicit conditions for compact group extensions of hyperbolic flows (including geodesic flows on negatively curved manifolds) to exhibit quantifiable rates of mixing (or decay of correlations) with respect to the natural probability measures, which are locally the product of a Gibbs measure for a H\"older potential and the Haar meas

Nilasis Chaudhuri, Eduard Feireisl, Ewelina Zatorska, Boguslaw Zegarlinski

We consider the Navier-Stokes-Fourier-Poisson system driven by an inhomogeneous temperature distribution on the boundary of an exterior fluid domain. We impose the finite mass constraint, positive far field condition for the temperature as well as the no--slip boundary conditions for the velocity. The existence of global--in--time weak solutions and the weak

Sushant Gautam, Mehdi Houshmand Sarkhoosh, Jan Held, Cise Midoglu

The application of Automatic Speech Recognition (ASR) technology in soccer offers numerous opportunities for sports analytics. Specifically, extracting audio commentaries with ASR provides valuable insights into the events of the game, and opens the door to several downstream applications such as automatic highlight generation. This paper presents SoccerNet-

Magnús M. Halldórsson, Yannic Maus, Saku Peltonen

The constructive Lov\'{a}sz Local Lemma has become a central tool for designing efficient distributed algorithms. While it has been extensively studied in the classic LOCAL model that uses unlimited bandwidth, much less is known in the bandwidth-restricted CONGEST model. In this paper, we present bandwidth- and time-efficient algorithms for various subclasse

Mahesh Deshwal, Apoorva Chawla

In this paper cum technical report, we present PHUDGE A fine tuned Phi3 model that achieved SOTA results in 4 tasks as Feedback Test, Feedback OOD, MT Human, Preference Test surpassing each and every existing model in latency and throughput. It shows very strong correlation not only with GPT4 but with Human annotators too in unseen data as well as in both ab

Philippe Nadeau

Notation conventions for rigid transformations are as diverse as they are fundamental to the field of robotics. A well-defined convention that is practical, consistent and unambiguous is essential for the clear communication of ideas and to foster collaboration between researchers. This work presents an analysis of conventions used in state-of-the-art roboti

Hector Simon, Lucas Caron, Romaric Journet, Viviane Cotte

Non-Gaussian states of light, such as GKP states, are essential resources for optical continuous-variable quantum computing. The ability to efficiently produce these states would open up tremendous prospects for quantum technologies in general and fault-tolerant quantum computing in particular. This letter demonstrates a versatile method using a quantum memo

Matthew Gazzard, Helen Hicks, Isibor Kennedy Ihianle, Jordan J. Bird

Blackgrass (Alopecurus myosuroides) is a competitive weed that has wide-ranging impacts on food security by reducing crop yields and increasing cultivation costs. In addition to the financial burden on agriculture, the application of herbicides as a preventive to blackgrass can negatively affect access to clean water and sanitation. The WeedScout project int

Yuichiro Terasaki, Kohei Nakajima

A continuous one-dimensional map with period three includes all periods. This raises the following question: Can we obtain any types of periodic orbits solely by learning three data points? In this paper, we report the answer to be yes. Considering a random neural network in its thermodynamic limit, we first show that almost all learned periods are unstable,

Ofir Ben Shoham, Nadav Rappoport

We present MedConceptsQA, a dedicated open source benchmark for medical concepts question answering. The benchmark comprises of questions of various medical concepts across different vocabularies: diagnoses, procedures, and drugs. The questions are categorized into three levels of difficulty: easy, medium, and hard. We conducted evaluations of the benchmark

Y. Angarita, M. J. F. Versteeg, M. Haverkorn, A. Marchal

Angular dispersion functions are typically used to estimate the fluctuations in polarization angle around the mean magnetic field orientation in dense regions, such as molecular clouds. The technique provides accurate turbulent to regular magnetic field ratios, $\langle B_t^2\rangle^{1/2}/B_{pos}$, which are often underestimated by the classic Davis-Chandras

Jiarui Wang, Huiyu Duan, Guangtao Zhai, Xiongkuo Min

Artificial Intelligence Generated Content (AIGC) has grown rapidly in recent years, among which AI-based image generation has gained widespread attention due to its efficient and imaginative image creation ability. However, AI-generated Images (AIGIs) may not satisfy human preferences due to their unique distortions, which highlights the necessity to underst

Laurin Köhler-Schindler, Aurelio L. Sulser

On a locally finite, infinite tree $T$, let $p_c(T)$ denote the critical probability for Bernoulli percolation. We prove that every positively associated, finite-range dependent percolation model on $T$ with marginals $p > p_c(T)$ must percolate. Among finite-range dependent models on trees, positive association is thus a favourable property for percolation

Remi Genet, Hugo Inzirillo

Recurrent Neural Networks (RNNs) have revolutionized many areas of machine learning, particularly in natural language and data sequence processing. Long Short-Term Memory (LSTM) has demonstrated its ability to capture long-term dependencies in sequential data. Inspired by the Kolmogorov-Arnold Networks (KANs) a promising alternatives to Multi-Layer Perceptro

Yadong Zhang, Pranav M Karve, Sankaran Mahadevan

This article investigates the ability of graph neural networks (GNNs) to identify risky conditions in a power grid over the subsequent few hours, without explicit, high-resolution information regarding future generator on/off status (grid topology) or power dispatch decisions. The GNNs are trained using supervised learning, to predict the power grid's aggreg

Ananya Hazarika, Mehdi Rahmati

The emerging paradigm of Non-Conventional Internet of Things (NC IoT), which is focused on the usefulness of information as opposed to the notion of high volume data collection and transmission, will be an important and dominant part of human life in the near future. This paper proposes a novel semantic-based approach for addressing the unique challenges pos

M. J. Martins

In this paper we investigate a correspondence among spin and vertex models with the same number of local states on the square lattice with toroidal boundary conditions. We argue that the partition functions of an arbitrary $n$-state spin model and of a certain specific $n$-state vertex model coincide for finite lattice sizes. The equivalent vertex model has

Eduardo C. Garrido-Merchán

The hypothesis of conscious machines has been debated since the invention of the notion of artificial intelligence, powered by the assumption that the computational intelligence achieved by a system is the cause of the emergence of phenomenal consciousness in that system as an epiphenomenon or as a consequence of the behavioral or internal complexity of the

Mannque Rho

The long-standing puzzle of the quenched $g_A$ in nuclei is shown to have an extremely simple resolution in a renormalization-group (RG) treatment of a hidden local symmetric (HLS) and scale-symmetric (HSS) chiral Lagrangian. It is shown that the Landau-Migdal fixed-point approximation in nuclear matter (or $V_{lowk}$ in finite nuclei) in RG approach to stro

Fatema Tuj Johora Faria, Mukaffi Bin Moin, Pronay Debnath, Asif Iftekher Fahim

Early detection of vision-threatening conditions such as diabetic retinopathy, glaucoma, and age-related macular degeneration depends on retinal fundus image analysis, but manual assessment is slow and expert-dependent. Automated convolutional neural networks classify fundus images accurately yet act as black boxes, and existing retinal vessel segmentation m

Gal Beniamini, Nati Linial, Adi Shraibman

A graph is called Rank-Ramsey if (i) Its clique number is small, and (ii) The adjacency matrix of its complement has small rank. We initiate a systematic study of such graphs. Our main motivation is that their constructions, as well as proofs of their non-existence, are intimately related to the famous log-rank conjecture from the field of communication comp

Kongyang Chen, Zeming Xu, Bing Mi

With the widespread application of machine learning technology in recent years, the demand for training data has increased significantly, leading to the emergence of research areas such as data trading. The work in this field is still in the developmental stage. Different buyers have varying degrees of demand for various types of data, and auctions play a ro

Dr Corrie Green, Dr Yang Jiang, Dr John Isaacs, Dr Michael Heron

Comparative to conventional 2D interaction methods, virtual reality (VR) demonstrates an opportunity for unique interface and interaction design decisions. Currently, this poses a challenge when developing an accessible VR experience as existing interaction techniques may not be usable by all users. It was discovered that many traditional 2D interface intera

Paolo Padoan, Liubin Pan, Veli-Matti Pelkonen, Troels Haugboelle

Protoplanetary disks are traditionally described as finite mass reservoirs left over by the gravitational collapse of the protostellar core, a view that strongly constrains both disk evolution and planet formation models. We propose a different scenario where protoplanetary disks of pre-main sequence stars are primarily assembled by Bondi-Hoyle accretion fro

Nishant Saurabh, Pradeep Mantha, Florian J. Kiwit, Shantenu Jha

With the increasing maturity and scale of quantum hardware and its integration into HPC systems, there is a need to develop robust techniques for developing, characterizing, and benchmarking quantum-HPC applications and middleware systems. This requires a better understanding of interaction, coupling, and common execution patterns between quantum and classic

Kan Xu, Hamsa Bastani, Surbhi Goel, Osbert Bastani

We study the stochastic bandit problem with ReLU neural network structure. We show that a $\tilde{O}(\sqrt{T})$ regret guarantee is achievable by considering bandits with one-layer ReLU neural networks; to the best of our knowledge, our work is the first to achieve such a guarantee. In this specific setting, we propose an OFU-ReLU algorithm that can achieve

Jonathan L. Feng, Alec Hewitt, Felix Kling, Daniel La Rocco

Heavy neutral leptons (HNLs) are motivated by attempts to explain neutrino masses and dark matter. If their masses are in the MeV to several GeV range, HNLs are light enough to be copiously produced at collider and accelerator facilities, but also heavy enough to decay to visible particles on length scales that can be observed in particle detectors. Previous

Alexander Schuett

Synchrotron light source storage rings aim to maintain a continuous beam current without observable beam motion during injection. One element that paves the way to this target is the non-linear kicker (NLK). The field distribution it generates poses challenges for optimising the topping-up operation. Within this study, a reinforcement learning agent was deve

Oscar Dominguez, Yinqin Li, Sergey Tikhonov, Dachun Yang

We extend the affine inequalities on $\mathbb{R}^n$ for Sobolev functions in $W^{s,p}$ with $1 \leq p < n/s$ obtained recently by Haddad-Ludwig [16, 17] to the remaining range $p \geq n/s$. For each value of $s$, our results are stronger than affine Moser-Trudinger and Morrey inequalities. As a byproduct, we establish the analog of the classical $L^p$ Bourga

Tobias K. S. Ritschel, John Wyller

Time delays are ubiquitous in industry and nature, and they significantly affect both transient dynamics and stability properties. Consequently, it is often necessary to identify and account for the delays when, e.g., designing a model-based control strategy. However, identifying delays in differential equations is not straightforward and requires specialize

Franco Bagnoli, Michele Baia, Tommaso Matteuzzi

We investigate elementary cellular automata (ECA) from the point of view of (discrete) dynamical systems. By studying small lattice sizes, we obtain the complete phase space of all minimal ECA, and, starting from a maximal entropy distribution (all configurations equiprobable), we show how the dynamics affects this distribution. We then investigate how a van

Carter Blair, Ben Armstrong, Kate Larson

Continual learning aims to enable machine learning models to continually learn from a shifting data distribution without forgetting what has already been learned. Such shifting distributions can be broken into disjoint subsets of related examples; by training each member of an ensemble on a different subset it is possible for the ensemble as a whole to achie

Amirhossein Shahrokhi, Mahmood Ahmadi

The Internet of Things has affected all aspects of daily life, and the number of IoT devices is increasing day by day. According to forecasts, the number of Internet of Things devices will reach one trillion devices by 2035. The increase in the number of devices connected to the Internet will cause various concerns. One of the most important concerns is the

Abdul Wadud, Fatemeh Golpayegani, Nima Afraz

The advent of Open Radio Access Network (RAN) has revolutionized the field of RAN by introducing elements of native support of intelligence and openness into the next generation of mobile network infrastructure. Open RAN paves the way for standardized interfaces and enables the integration of network applications from diverse vendors, thereby enhancing netwo

Segun Taofeek Aroyehun, Almog Simchon, Fabio Carrella, Jana Lasser

Pursuit of honest and truthful decision-making is crucial for governance and accountability in democracies. However, people sometimes take different perspectives of what it means to be honest and how to pursue truthfulness. Here we explore a continuum of perspectives from evidence-based reasoning, rooted in ascertainable facts and data, at one end, to intuit

Leonardo F. Cavenaghi, Lino Grama, Ludmil Katzarkov

In arXiv:2404.19088, we initiated a program linking birational invariants with smooth ones and offering new interpretations of classical invariants, such as the Kervaire-Milnor invariants. Here, we rely on the profound geometric reasoning provided by Lupercio and Uribe in the early 00s to establish a connection between Chen-Ruan cohomology and several $G$-bi

Matthias Raphael Stock

The Jiangmen Underground Neutrino Observatory (JUNO) is a multi-purpose neutrino experiment currently under construction in China. It is located 52.5km away from two nuclear power plants in a newly constructed 700-m-deep underground laboratory. JUNO will be the largest liquid scintillator (LS) detector in the world comprising 20 kt of ultrapure LS filled in

Ken Kato, Annabelle Purnomo, Christopher Cochrane, Raeid Saqur

The quantitative analysis of political ideological positions is a difficult task. In the past, various literature focused on parliamentary voting data of politicians, party manifestos and parliamentary speech to estimate political disagreement and polarization in various political systems. However previous methods of quantitative political analysis suffered

Siyou Lin, Zhe Li, Zhaoqi Su, Zerong Zheng

Animatable clothing transfer, aiming at dressing and animating garments across characters, is a challenging problem. Most human avatar works entangle the representations of the human body and clothing together, which leads to difficulties for virtual try-on across identities. What's worse, the entangled representations usually fail to exactly track the slidi

Ananya Hazarika, Mehdi Rahmati

In the evolving era of Unmanned Aerial Vehicles (UAVs), the emphasis has moved from mere data collection to strategically obtaining timely and relevant data within the Internet of Drones (IoDs) ecosystem. However, the unpredictable conditions in dynamic IoDs pose safety challenges for drones. Addressing this, our approach introduces a multi-UAV framework usi

Zixin Wang, Kongyang Chen

Machine unlearning is a complex process that necessitates the model to diminish the influence of the training data while keeping the loss of accuracy to a minimum. Despite the numerous studies on machine unlearning in recent years, the majority of them have primarily focused on supervised learning models, leaving research on contrastive learning models relat

Mayank Bakshi, Sara Ghasvarianjahromi, Yauhen Yakimenka, Allison Beemer

We introduce the paradigm of validated decentralized learning for undirected networks with heterogeneous data and possible adversarial infiltration. We require (a) convergence to a global empirical loss minimizer when adversaries are absent, and (b) either detection of adversarial presence of convergence to an admissible consensus irrespective of the adversa

Alireza Ataei

In this work, we derive a sharp condition on the mass of the initial data for the global existence of the Chern-Simons-Schr\"odinger equation. As a corollary, we prove that if the strength of interaction is less than the Bogomolny bound, then, for a large enough mass of initial data, there exists a globally defined solution. On the other hand, for the intera

Wenjie Wang, Honghui Bao, Xinyu Lin, Jizhi Zhang

Utilizing powerful Large Language Models (LLMs) for generative recommendation has attracted much attention. Nevertheless, a crucial challenge is transforming recommendation data into the language space of LLMs through effective item tokenization. Current approaches, such as ID, textual, and codebook-based identifiers, exhibit shortcomings in encoding semanti

Yikang Xiao, Wenbin Lin

We investigate the scattering of gravitino wave in a Schwarzschild gravitational field. Employing the simulating wave function method within the framework of perturbative techniques, we derive the differential scattering cross section for the gravitino wave in the long-wavelength limit. It is proven that the cross section of gravitino wave follows the same s

M. M. Al Chawa, R. Tetzlaff, D. Bedau, J. W. Reiner

This paper presents a novel compact delay model of Ovonic Threshold Switch (OTS) devices that works efficiently for circuit simulations. The internal state variable of the two terminal devices is estimated using a delay system that uses a few electrical components related to a suggested equivalent circuit of the device. Finally, we tested the proposed model

Milad Beikbabaei, Michael Lindemann, Mohammad Heidari Kapourchali, Ali Mehrizi-Sani

100% inverter-based renewable units are becoming more prevalent, introducing new challenges in the protection of microgrids that incorporate these resources. This is particularly due to low fault currents and bidirectional flows. Previous work has studied the protection of microgrids with high penetration of inverter-interfaced distributed generators; howeve

Yang Jin, Jun Lv, Shuqiang Jiang, Cewu Lu

Generating robot demonstrations through simulation is widely recognized as an effective way to scale up robot data. Previous work often trained reinforcement learning agents to generate expert policies, but this approach lacks sample efficiency. Recently, a line of work has attempted to generate robot demonstrations via differentiable simulation, which is pr

Yanqiao Deng, Minda Ma, Nan Zhou, Zhili Ma

Assessing the emissions of plug-in hybrid electric vehicle (PHEV) operations is crucial for accelerating the carbon-neutral transition in the passenger car sector. This study is the first to adopt a bottom-up model to measure the real-world energy use and carbon dioxide emissions of China's top twenty selling PHEV models across different regions from 2020 to

Ayşe Berkman, Alexandre Borovik

We give a review of one of the lines in development of the theory of groups of finite Morley rank. These groups naturally appear in model theory as model-theoretic analogues of Galois groups, therefore their actions and their role as permutation groups is of primary interest. We restrict our story to the study of connected groups of finite Morley rank $G$ ac

Zhenyang Li, Zilong Chen, Feifan Qu, Mingqing Wang

In NeRF-aided editing tasks, object movement presents difficulties in supervision generation due to the introduction of variability in object positions. Moreover, the removal operations of certain scene objects often lead to empty regions, presenting challenges for NeRF models in inpainting them effectively. We propose an implicit ray transformation strategy

Thomas Mildner, Gian-Luca Savino, Susanne Putze, Rainer Malaka

Social networking services (SNS) have become integral to modern life to create and maintain meaningful relationships. Nevertheless, their historic growth of features has led to labyrinthine user interfaces (UIs) that often result in frustration among users - for instance, when trying to control privacy-related settings. This paper aims to mitigate labyrinthi

M. A. Prosnikov, M. Bal, R. V. Pisarev, P. C. M. Christianen

The observation of strongly coupled lattice and spin dynamics in altermagnet CoF$_2$ is reported. On the background of the expected spin-phonon interaction leading to renormalization of all phonons at the Neel temperature an additional strong coupling between one-magnon excitation and the lowest frequency Raman-active phonon of B1g symmetry was observed and

L. T. Yang, S. K. Liu, Q. Yue, K. J. Kang

We present the first limit on $g_{A\gamma}$ coupling constant using the Bragg-Primakoff conversion based on an exposure of 1107.5 kg days of data from the CDEX-1B experiment at the China Jinping Underground Laboratory. The data are consistent with the null signal hypothesis, and no excess signals are observed. Limits of the coupling $g_{A\gamma}<2.08\times10

Keigo Oyama, Yui Hayashi, Shigeo Kuwamoto, Shun Katakami

Small-angle scattering (SAS) techniques, which utilize neutrons and X-rays, are employed in various scientific fields, including materials science, biochemistry, and polymer physics. During the analysis of SAS data, model parameters that contain information about the sample are estimated by fitting the observational data to a model of sample. Previous resear

Wolfgang Woess

Euclidean branching Brownian motion (BBM) has been intensively studied during many decades by renowned researchers. BBM on hyperbolic space has received less attention. A profound study of Lalley and Sellke (1997) provided insight on the recurrent, resp. transient regimes of BBM on the Poincare' disk. In particular, they determined the Hausdorff dimension of

Hashem Hamedivafa

We study the structure and evolution of the horizontal proper motions in a regular sunspot penumbra, very close to the solar disc center, in active region NOAA 11092 using a 48 min time sequences of blue continuum images recorded by Hinode/SOT in 2010 August 3. We apply local correlation tracking (LCT). The penumbra shows a slow (fast) flow field with an ave

Victor F. Los

A novel approach to accounting for the influence of initial system-bath correlations on the dynamics of an open quantum system, based on the conventional projection operator technique, is suggested. To avoid the difficulties of treating the initial correlations, the conventional Nakajima-Zwanzig inhomogeneous generalized master equations (GMEs) for a system'

Jaeyun Moon, Zhiting Tian

Thermal transport properties of amorphous carbon has attracted increasing attention due to its extreme thermal properties: It has been reported to have among the highest thermal conductivity for bulk amorphous solids up to $\sim$ 37 Wm\textsuperscript{-1}K\textsuperscript{-1}, comparable to crystalline sapphire ($\alpha$-Al\textsubscript{2}O\textsubscript{3}

Emanuel Juliano

We show that if all the eigenvalues of a tree are integers, then it does not contain a subdivided edge with 7 vertices.

Hongyu Li, Matteo Nerini, Shanpu Shen, Bruno Clerckx

This work investigates the modeling and optimization of beyond diagonal reconfigurable intelligent surface (BD-RIS), which generalizes conventional RIS with diagonal phase shift matrices and provides additional flexibility for manipulating wireless channels, in wideband communication systems. Specifically, we start from the signal modeling of the BD-RIS-aide

Sayan Goswami, Sourav Kanti Patra

We establish and fully characterize the multidimensional extension of the Stronger Central Sets Theorem. Additionally, we develop a polynomial generalization of this result. Our approach utilizes tools from the Algebra of the Stone-\v{C}ech compactification of discrete semigroups. Several applications of these results are also discussed.

Rajat Saxena, Bruce L. McNaughton

Continual learning (CL) refers to an agent's capability to learn from a continuous stream of data and transfer knowledge without forgetting old information. One crucial aspect of CL is forward transfer, i.e., improved and faster learning on a new task by leveraging information from prior knowledge. While this ability comes naturally to biological brains, it

Weilin Chen, Clifford Lam

Most factor modelling research in vector or matrix-valued time series assume all factors are pervasive/strong and leave weaker factors and their corresponding series to the noise. Weaker factors can in fact be important to a group of observed variables, for instance a sector factor in a large portfolio of stocks may only affect particular sectors, but can be

Jing Xu, Wentao Shi, Sheng Ren, Lijuan Zhang

Effective monitoring of unusual transportation behaviors, such as wrong-way cycling (i.e., riding a bicycle or e-bike against designated traffic flow), is crucial for optimizing law enforcement deployment and traffic planning. However, accurately recording all wrong-way cycling events is both unnecessary and infeasible in resource-constrained environments, a

Rajveer Jat, Daanish Padha

We forecast a single time series using a high-dimensional set of predictors. When these predictors share common underlying dynamics, an approximate latent factor model provides a powerful characterization of their co-movements Bai(2003). These latent factors succinctly summarize the data and can also be used for prediction, alleviating the curse of dimension

Xinquan Wang, Fenghao Zhu, Chongwen Huang, Ahmed Alhammadi

Millimeter-wave (mmWave) multiple-input multiple-output (MIMO) communication with the advanced beamforming technologies is a key enabler to meet the growing demands of future mobile communication. However, the dynamic nature of cellular channels in large-scale urban mmWave MIMO communication scenarios brings substantial challenges, particularly in terms of c

Alan Chodos, Fred Cooper

We recently introduced a particular nonlinear generalization of quantum mechanics which has the property that it is exactly solvable in terms of the eigenvalues and eigenfunctions of the Hamiltonian of the usual linear quantum mechanics problem. In this paper we suggest that the two components of the wave function represent the system described by the Hamilt

Masane Fuchi, Tomohiro Takagi

Generating images from text has become easier because of the scaling of diffusion models and advancements in the field of vision and language. These models are trained using vast amounts of data from the Internet. Hence, they often contain undesirable content such as copyrighted material. As it is challenging to remove such data and retrain the models, metho

Antoine Jallon, Pierre Recho, Jocelyn Étienne

Contractile biopolymer networks, such as the actomyosin meshwork of animal cells, are ubiquitous in living organisms. The active gel theory, which provides the thermodynamic framework for these materials, has been mostly used in conjunction with the assumption that the microstructure of the biopolymer network is based on rigid rods. However, experimentally,

Sean Pinkney

We propose a Cholesky factor parameterization of correlation matrices that facilitates a priori restrictions on the correlation matrix. It is a smooth and differentiable transform that allows additional boundary constraints on the correlation values. Our particular motivation is random sampling under positivity constraints on the space of correlation matrice

Saaketh Koundinya Gundavarapu, Arushi Arora, Shreya Agarwal

We present SLIP (SAM+CLIP), an enhanced architecture for zero-shot object segmentation. SLIP combines the Segment Anything Model (SAM) \cite{kirillov2023segment} with the Contrastive Language-Image Pretraining (CLIP) \cite{radford2021learning}. By incorporating text prompts into SAM using CLIP, SLIP enables object segmentation without prior training on speci

Mingkai Jia, Qingwen Zhang, Bowen Yang, Jin Wu

Global point clouds that correctly represent the static environment features can facilitate accurate localization and robust path planning. However, dynamic objects introduce undesired ghost tracks that are mixed up with the static environment. Existing dynamic removal methods normally fail to balance the performance in computational efficiency and accuracy.

Tian Xie, Xueru Zhang

As machine learning (ML) models are increasingly used in social domains to make consequential decisions about humans, they often have the power to reshape data distributions. Humans, as strategic agents, continuously adapt their behaviors in response to the learning system. As populations change dynamically, ML systems may need frequent updates to ensure hig

Alexey Tikhonov

This paper presents the Character Decision Points Detection (CHADPOD) task, a task of identification of points within narratives where characters make decisions that may significantly influence the story's direction. We propose a novel dataset based on CYOA-like games graphs to be used as a benchmark for such a task. We provide a comparative analysis of diff

Zhenqiao Cheng, Nanxi Li, Jianchi Zhu, Chongjun Ouyang

This article investigates the integration of movable antennas (MAs) into multicast communication systems. By discretizing the motion of the MAs, a novel MA-assisted multicast transmission architecture is formulated. An alternating optimization (AO) algorithm based on successive convex approximation is proposed to optimize the transmit beamforming and antenna

Alexey Tikhonov, Pavel Shtykovskiy

In this paper, we explore the generation of one-liner jokes through multi-step reasoning. Our work involved reconstructing the process behind creating humorous one-liners and developing a working prototype for humor generation. We conducted comprehensive experiments with human participants to evaluate our approach, comparing it with human-created jokes, zero

Tenri Jinno, Takayuki R. Saitoh, Yoko Funato, Junichiro Makino

Recent exoplanet observations have revealed a diversity of exoplanetary systems, which suggests the ubiquity of radial planetary migration. One powerful known mechanism of planetary migration is planetesimal-driven migration (PDM), which can let planets undergo significant migration through gravitational scattering with planetesimals. In this series of paper

Justin K. Miller, Tristram J. Alexander

Clustering short text is a difficult problem, due to the low word co-occurrence between short text documents. This work shows that large language models (LLMs) can overcome the limitations of traditional clustering approaches by generating embeddings that capture the semantic nuances of short text. In this study clusters are found in the embedding space usin

Weitao Chen, Olivier Giraud, Jiangbin Gong, Gabriel Lemarié

Due to their analytical tractability, random matrix ensembles serve as robust platforms for exploring exotic phenomena in systems that are computationally demanding. Building on a companion letter [arXiv:2312.17481], this paper investigates two random matrix ensembles tailored to capture the critical behavior of the Anderson transition in infinite dimension,