April 2024 arXiv papers — page 12

Seyed Soroush Karimi Madahi, Gargya Gokhale, Marie-Sophie Verwee, Bert Claessens

A continuous rise in the penetration of renewable energy sources, along with the use of the single imbalance pricing, provides a new opportunity for balance responsible parties to reduce their cost through energy arbitrage in the imbalance settlement mechanism. Model-free reinforcement learning (RL) methods are an appropriate choice for solving the energy ar

Zhiyuan Li, Yanhui Zhou, Hao Wei, Chenyang Ge

Image compression at extremely low bitrates (below 0.1 bits per pixel (bpp)) is a significant challenge due to substantial information loss. In this work, we propose a novel two-stage extreme image compression framework that exploits the powerful generative capability of pre-trained diffusion models to achieve realistic image reconstruction at extremely low

Jiyu Chen, Francesco Petocchi, Viktor Christiansson, Philipp Werner

Pump-probe experiments with femtosecond time resolution allow to disentangle the electronic dynamics from the lattice response and thus provide valuable insights into the non-equilibrium behavior of correlated materials. In Cr-doped V$_2$O$_3$, a multi-orbital Mott-Hubbard material which has been intensively investigated for decades, time-resolved experiment

Ian Tillman, Thirupathaiah Vasantam, Don Towsley, Kaushik P. Seshadreesan

Quantum repeaters are necessary to fully realize the capabilities of the emerging quantum internet, especially applications involving distributing entanglement across long distances. A more general notion of this can be called a quantum switch, which connects to many users and can act as a repeater to create end-to-end entanglement between different subsets

Chen-Da Liu, Simone Santini

In many social networks, one publishes information that one wants to reveal (e.g., the photograph of some friends) together with information that may lead to privacy breaches (e.g., the name of these people). One might want to hide this sensitive information by encrypting it and sharing the decryption key only with trusted people, but this might not be enoug

Xuebin Ren, Shusen Yang, Cong Zhao, Julie McCann

Federated learning (FL) has great potential for large-scale machine learning (ML) without exposing raw data.Differential privacy (DP) is the de facto standard of privacy protection with provable guarantees.Advances in ML suggest that DP would be a perfect fit for FL with comprehensive privacy preservation. Hence, extensive efforts have been devoted to achiev

Alessandro Abate, Sergiy Bogomolov, Alec Edwards, Kostiantyn Potomkin

We present a novel technique for online safety verification of autonomous systems, which performs reachability analysis efficiently for both bounded and unbounded horizons by employing neural barrier certificates. Our approach uses barrier certificates given by parameterized neural networks that depend on a given initial set, unsafe sets, and time horizon. S

Sebastian Bruch, Franco Maria Nardini, Cosimo Rulli, Rossano Venturini

Learned sparse representations form an attractive class of contextual embeddings for text retrieval. That is so because they are effective models of relevance and are interpretable by design. Despite their apparent compatibility with inverted indexes, however, retrieval over sparse embeddings remains challenging. That is due to the distributional differences

Manuel Hohmann, Christian Pfeifer, Fabian Wagner

We study the weak equivalence principle in the context of modified dispersion relations, a prevalent approach to quantum gravity phenomenology. We find that generic modified dispersion relations violate the weak equivalence principle. The acceleration in general depends on the mass of the test body, unless the Hamiltonian is either two-homogeneous in the tes

Wondimagegnhue Tsegaye Tufa, Ilia Markov, Piek Vossen

Cross-lingual transfer has become an effective way of transferring knowledge between languages. In this paper, we explore an often overlooked aspect in this domain: the influence of the source language of a language model on language transfer performance. We consider a case where the target language and its script are not part of the pre-trained model. We co

C. Poole, T. M. Graham, M. A. Perlin, M. Otten

We propose an implementation of bivariate bicycle codes (Nature {\bf 627}, 778 (2024)) based on long-range Rydberg gates between stationary neutral atom qubits. An optimized layout of data and ancilla qubits reduces the maximum Euclidean communication distance needed for non-local parity check operators. An optimized Rydberg gate pulse design enables $\sf CZ

Peter Beelen, Maria Montanucci, Lara Vicino

In this article we continue the work started in arXiv:2303.00376v1, explicitly determining the Weierstrass semigroup at any place and the full automorphism group of a known $\mathbb{F}_{q^2}$-maximal function field $Y_3$ having the third largest genus, for $q \equiv 1 \pmod 3$. This function field arises as a Galois subfield of the Hermitian function field,

Nathan Huetsch, Javier Mariño Villadamigo, Alexander Shmakov, Sascha Diefenbacher

Recent innovations from machine learning allow for data unfolding, without binning and including correlations across many dimensions. We describe a set of known, upgraded, and new methods for ML-based unfolding. The performance of these approaches are evaluated on the same two datasets. We find that all techniques are capable of accurately reproducing the pa

Richard J. Mathar

The manuscript studies configurations of non-overlapping non-bonding dominoes on finite rectangular boards of unit squares characterized by row and column number. The non-bonding dominoes are defined here by the requirement that any domino on the board shares at most one point (one of its four corner points) with any other domino, but no edge. With the Trans

David Nesvorny, David Vokrouhlicky, Frank Shelly, Rogerio Deienno

Our previous model (NEOMOD2) for the orbital and absolute magnitude distribution of Near Earth Objects (NEOs) was calibrated on the Catalina Sky Survey observations between 2013 and 2022. Here we extend NEOMOD2 to include visible albedo information from the Wide-Field Infrared Survey Explorer. The debiased albedo distribution of NEOs can be approximated by t

Leonardo Balart, Grigoris Panotopoulos, Ángel Rincón

We discuss some thermodynamic properties as well as the stability of a quantum Schwarzschild black hole, comparing the results with those obtained within a bumblebee gravity model. In particular, the Hawking temperature, $T_H$, the entropy, $S$, the heat capacity, $C$, and the Gibbs free energy, $G$, are computed for both cases. In addition to that, we compu

Cyril Labbé, Benoît Laslier, Fabio Toninelli, Lorenzo Zambotti

We present a general black box theorem that ensures convergence of a sequence of stationary Markov processes, provided a few assumptions are satisfied. This theorem relies on a control of the resolvents of the sequence of Markov processes, and on a suitable characterization of the resolvents of the limit. One major advantage of this approach is that it circu

Célia Biane, Greg Hampikian, Sergey Kirgizov, Khaydar Nurligareev

An endhered (end-adhered) pattern is a subset of arcs in matchings, such that the corresponding starting points are consecutive and the same holds for the ending points. Such patterns are in one-to-one correspondence with the permutations. We focus on the occurrence frequency of such patterns in matchings and native (real-world) RNA structures with pseudokno

Vishal Purohit, Wenxin Jiang, Akshath R. Ravikiran, James C. Davis

This paper undertakes the task of replicating the MaskFormer model a universal image segmentation model originally developed using the PyTorch framework, within the TensorFlow ecosystem, specifically optimized for execution on Tensor Processing Units (TPUs). Our implementation exploits the modular constructs available within the TensorFlow Model Garden (TFMG

Huijun Xing, Xuhui Zhang, Shuo Chang, Jinke Ren

Signal detection and modulation classification are two crucial tasks in various wireless communication systems. Different from prior works that investigate them independently, this paper studies the joint signal detection and automatic modulation classification (AMC) by considering a realistic and complex scenario, in which multiple signals with different mo

Giovane Avancini, Nathan Shauer, Francisco T. Orlandini, Paulo Cesar A. Lucci

This contribution introduces the idea of refinement patterns for the generation of optimal meshes in the context of the Finite Element Method. The main idea is to generate a library of possible patterns on which elements can be refined and use this library to inform an h adaptive code on how to handle complex refinements in regions of interest. There are no

Robbert Beerten, Vida Ranjbar, Andrea P. Guevara, Hazem Sallouha

Cell-Free Massive MIMO (CF mMIMO) has emerged as a potential enabler for future networks. It has been shown that these networks are much more energy-efficient than classical cellular systems when they are serving users at peak capacity. However, these CF mMIMO networks are designed for peak traffic loads, and when this is not the case, they are significantly

Rolando Fernandez, Garrett Warnell, Derrik E. Asher, Peter Stone

In multi-agent reinforcement learning (MARL), coordination plays a crucial role in enhancing agents' performance beyond what they could achieve through cooperation alone. The interdependence of agents' actions, coupled with the need for communication, leads to a domain where effective coordination is crucial. In this paper, we introduce and define $\textit{M

Eugene Yang, Suraj Nair, Dawn Lawrie, James Mayfield

Probabilistic Structured Queries (PSQ) is a cross-language information retrieval (CLIR) method that uses translation probabilities statistically derived from aligned corpora. PSQ is a strong baseline for efficient CLIR using sparse indexing. It is, therefore, useful as the first stage in a cascaded neural CLIR system whose second stage is more effective but

Pat Verga, Sebastian Hofstatter, Sophia Althammer, Yixuan Su

As Large Language Models (LLMs) have become more advanced, they have outpaced our abilities to accurately evaluate their quality. Not only is finding data to adequately probe particular model properties difficult, but evaluating the correctness of a model's freeform generation alone is a challenge. To address this, many evaluations now rely on using LLMs the

Filippo Bonchi, Alessandro Di Giorgio, Davide Trotta

Fo-bicategories are a categorification of Peirce's calculus of relations. Notably, their laws provide a proof system for first-order logic that is both purely equational and complete. This paper illustrates a correspondence between fo-bicategories and Lawvere's hyperdoctrines. To streamline our proof, we introduce peircean bicategories, which offer a more su

David de Laat, Nando M. Leijenhorst, Willem H. H. de Muinck Keizer

We prove that the $D_4$ root system (the set of vertices of the regular $24$-cell) is the unique optimal kissing configuration in $\mathbb R^4$, and is an optimal spherical code. For this, we use semidefinite programming to compute an exact optimal solution to the second level of the Lasserre hierarchy. We also improve the upper bound for the kissing number

Andrea Nóvoa, Nicolas Noiray, James R. Dawson, Luca Magri

When they occur, azimuthal thermoacoustic oscillations can detrimentally affect the safe operation of gas turbines and aeroengines. We develop a real-time digital twin of azimuthal thermoacoustics of a hydrogen-based annular combustor. The digital twin seamlessly combines two sources of information about the system (i) a physics-based low-order model; and (i

Jihun Yum

We prove that a proper holomorphic local isometry between bounded domains with respect to the Bergman metrics is necessarily a biholomorphism. The proof relies on a new method grounded in Information Geometry theories.

Dmitrii Korzh, Elvir Karimov, Mikhail Pautov, Oleg Y. Rogov

Speaker recognition technology is applied to various tasks, from personal virtual assistants to secure access systems. However, the robustness of these systems against adversarial attacks, particularly to additive perturbations, remains a significant challenge. In this paper, we pioneer applying robustness certification techniques to speaker recognition, ini

William Dubois, Matěj Boxan, Johann Laconte, François Pomerleau

In this paper, we present a field report of the mapping of the Athabasca Glacier, using a custom-made lidar-inertial mapping platform. With the increasing autonomy of robotics, a wider spectrum of applications emerges. Among these, the surveying of environmental areas presents arduous and hazardous challenges for human operators. Leveraging automated platfor

Innes Mackay

A recent combination of measurements performed by the LHCb collaboration determined that $\gamma=(63.8^{+3.5}_{-3.7})^\circ$. The fit combined the results of $\gamma$ and charm measurements, which resulted in precision improvements for the strong-phase difference between $D^0 \to K^-\pi^+$ and $D^0 \to K^+\pi^-$ decays, $\delta_{K\pi}^{D}$, and the $D^0-\bar

B. Guo, F. Yan, L. Nortmann, D. Cont

Ultrahot Jupiters are a type of gaseous exoplanet that orbit extremely close to their host star, resulting in significantly high equilibrium temperatures. In recent years, high-resolution emission spectroscopy has been broadly employed in observing the atmospheres of ultrahot Jupiters. We used the CARMENES spectrograph to observe the high-resolution spectra

Reza G. Shirazi, Benedikt M. Schoenauer, Peter Schmitteckert, Michael Marthaler

We introduce analysis of orbital parities as a concept and a tool for understanding radicals. Based on fundamental reduced one- and two-electron density matrices, our approach allows us to evaluate a total measure of radical character and provides spin-like orbitals to visualize real excess spin or odd electron distribution of singlet polyradicals. Finding s

P. M. Aronow, Haoge Chang, Patrick Lopatto

We consider the problem of generating confidence sets in randomized experiments with noncompliance. We show that a refinement of a randomization-based procedure proposed by Imbens and Rosenbaum (2005) has desirable properties. Namely, we show that using a studentized Anderson--Rubin-type statistic as a test statistic yields confidence sets that are finite-sa

Paolo Rissone, Marc Rico-Pasto, Steve Smith, Felix Ritort

DNA hybridization is a fundamental reaction with wide-ranging applications in biotechnology. The nearest-neighbor (NN) model provides the most reliable description of the energetics of duplex formation. Most DNA thermodynamics studies have been done in melting experiments in bulk, of limited resolution due to ensemble averaging. In contrast, single-molecule

Paul Thagard

Explanatory inference is the creation and evaluation of hypotheses that provide explanations, and is sometimes known as abduction or abductive inference. Generative AI is a new set of artificial intelligence models based on novel algorithms for generating text, images, and sounds. This paper proposes a set of benchmarks for assessing the ability of AI progra

Tessa Masis, Brendan O'Connor

Geo-entity linking is the task of linking a location mention to the real-world geographic location. In this paper we explore the challenging task of geo-entity linking for noisy, multilingual social media data. There are few open-source multilingual geo-entity linking tools available and existing ones are often rule-based, which break easily in social media

Akash Awasthi, Safwan Ahmad, Bryant Le, Hien Van Nguyen

In the realm of chest X-ray (CXR) image analysis, radiologists meticulously examine various regions, documenting their observations in reports. The prevalence of errors in CXR diagnoses, particularly among inexperienced radiologists and hospital residents, underscores the importance of understanding radiologists' intentions and the corresponding regions of i

Annalisa De Bonis

Recent papers initiated the study of a generalization of group testing where the potentially contaminated sets are the members of a given hypergraph F=(V,E). This generalization finds application in contexts where contaminations can be conditioned by some kinds of social and geographical clusterings. The paper focuses on few-stage group testing algorithms, i

Mohammad Ali Labbaf-Khaniki, Mohammad Manthouri, Amin Hajizadeh

Designing a robust controller for Modular Multilevel Converters (MMCs) is crucial to ensure stability and optimal dynamic performance under various operating conditions, including faulty and disturbed scenarios. The primary objective of controlling grid-connected MMCs (GC-MMCs) is to accurately track real and reactive power references while maintaining excel

Andrea Virtuoso, Edoardo Milotti

The analysis of gravitational-wave (GW) signals is one of the most challenging application areas of signal processing. Wavelet transforms are specially helpful in detecting and analyzing GW transients and several analysis pipelines are based on these transforms, both continuous and discrete. While discrete wavelet transforms have distinct advantages in terms

Gabriel Turinici

Physics-informed neural networks (PINN) is a extremely powerful paradigm used to solve equations encountered in scientific computing applications. An important part of the procedure is the minimization of the equation residual which includes, when the equation is time-dependent, a time sampling. It was argued in the literature that the sampling need not be u

Yifan Cui, Jan Hannig, Paul Edlefsen

R. A. Fisher introduced the fiducial distribution as a potential replacement for the Bayesian posterior distribution in the 1930s. During the past century, fiducial approaches have been explored in various parametric and nonparametric settings. However, to the best of our knowledge, no fiducial inference has been developed in the realm of semiparametric stat

Roxanne He, Jackie Lok

We show that the Potts model on a graph can be approximated by a sequence of independent and identically distributed spins in terms of Wasserstein distance at high temperatures. We prove a similar result for the Curie--Weiss--Potts model on the complete graph, conditioned on being close enough to any of its equilibrium macrostates, in the low-temperature reg

Hanqiao Zhang, Joy D. Xiuyao Yang

The COVID-19 pandemic has disrupted traditional academic collaboration patterns, offering a unique opportunity to analyze the influence of peer effects and collaboration dynamics on research productivity. Using a novel network dataset, this paper investigates the role of peer effects on the productivity of economists, as measured by their publication count,

Adam Walton, Anne Ghesquière, Benjamin Varcoe

Secret key exchange relies on the creation of correlated signals, serving as the raw resource for secure communication. Thermal states, exhibit Hanbury Brown and Twiss correlations, which offer a promising avenue for generating such signals. In this paper, we present an experimental implementation of a central broadcast thermal state quantum key distribution

Lubomir Pentchev, Eugene Chudakov

We perform analysis of the near-threshold $J/\psi $ photoproduction data off the proton based on two theoretical approaches, GPD [1] and holographic [2], that represent the differential cross sections as powers of the skewness parameter with coefficients that depend only on the momentum transfer $t$. This allows to separate kinematically the corresponding co

Nils Breitmar, Matthew C. Harding, Hanqiao Zhang

Despite the earlier claim of "Death of Distance", recent studies revealed that geographical proximity still greatly influences link formation in online social networks. However, it is unclear how physical distances are intertwined with users' online behaviors in a virtual world. We study the role of spatial dependence on a global online social network with a

Sarah Marzen

We propose a new computational-level objective function for theoretical biology and theoretical neuroscience that combines: reinforcement learning, the study of learning with feedback via rewards; rate-distortion theory, a branch of information theory that deals with compressing signals to retain relevant information; and computational mechanics, the study o

M. L. Schneider, E. M. Jué, M. R. Pufall, K. Segall

Reinforcement learning algorithms are used in a wide range of applications, from gaming and robotics to autonomous vehicles. In this paper we describe a set of reinforcement learning-based local weight update rules and their implementation in superconducting hardware. Using SPICE circuit simulations, we implement a small-scale neural network with a learning

Ahmed Elhussein, Gamze Gursoy

Federated Learning is increasingly used in domains such as healthcare to facilitate collaborative model training without data-sharing. However, datasets located in different sites are often non-identically distributed, leading to degradation of model performance in FL. Most existing methods for assessing these distribution shifts are limited by being dataset

Gustaw Opiełka, Jessica Loke, Steven Scholte

Deep learning algorithms lack human-interpretable accounts of how they transform raw visual input into a robust semantic understanding, which impedes comparisons between different architectures, training objectives, and the human brain. In this work, we take inspiration from neuroscience and employ representational approaches to shed light on how neural netw

Jianhong Zhao, Yongwang Zhao, Peisen Yao, Fanlang Zeng

Complex safety-critical systems require multiple models for a comprehensive description, resulting in error-prone development and laborious verification. Bidirectional transformation (BX) is an approach to automatically synchronizing these models. However, existing BX frameworks lack formal verification to enforce these models' consistency rigorously. This p

Koki Ho, Yuri Shimane, Masafumi Isaji

Recent growing complexity in space missions has led to an active research field of space logistics and mission design. This research field leverages the key ideas and methods used to handle complex terrestrial logistics to tackle space logistics design problems. A typical goal in space logistics is to optimize the commodity flow to satisfy some mission objec

Fanghui Liu, Leello Dadi, Volkan Cevher

Recent studies show that a reproducing kernel Hilbert space (RKHS) is not a suitable space to model functions by neural networks as the curse of dimensionality (CoD) cannot be evaded when trying to approximate even a single ReLU neuron (Bach, 2017). In this paper, we study a suitable function space for over-parameterized two-layer neural networks with bounde

M. Frau, P. S. Tarabunga, M. Collura, M. Dalmonte

In this paper, we investigate the relationship between entanglement and non-stabilizerness (also known as magic) in matrix product states (MPSs). We study the relation between magic and the bond dimension used to approximate the ground state of a many-body system in two different contexts: full state of magic and mutual magic (the non-stabilizer analogue of

Markus Clemens, Marvin-Lucas Henkel, Fotios Kasolis, Michael Günther

Electromagneto-quasistatic (EMQS) field formulations are often dubbed as Darwin-type field formulations which approximate the Maxwell equations by neglecting radiation effects while modelling resistive, capacitive, and inductive effects. A common feature of EMQS field models is the Darwin-Amp\'ere equation formulated with the magnetic vector potential and th

Patrick Haller, Jonas Golde, Alan Akbik

Recent advancements in large language models (LLMs) have showcased their exceptional abilities across various tasks, such as code generation, problem-solving and reasoning. Existing benchmarks evaluate tasks in isolation, yet the extent to which LLMs can understand prose-style tasks, identify the underlying problems, and then generate appropriate code soluti

Targyn A. Nauryz, Stanislav N. Kharin, Adriana C. Briozzo, Julieta Bollati

In this study we develop a mathematical model that describe the behavior of electromagnetic fields and heat transfer in closed electrical contacts that arises when instantaneous explosion of the micro-asperity which involves vaporization zone and liquid, solid zones where temperature is defined by a generalized heat equation with Thomson effect. This model a

Clemens Stilianu, Christina Graf, Markus Huemer, Clemens Diwoky

Purpose: To employ optimal control for the numerical design of CEST saturation pulses to maximize contrast and stability against $B_0$ inhomogeneities. Theory and Methods: We applied an optimal control framework for the design pulse shapes for CEST saturation pulse trains. The cost functional minimized both the pulse energy and the discrepancy between the co

Thomas Rochefort-Beaudoin, Aurelian Vadean, Sofiane Achiche, Niels Aage

This paper introduces YOLOv8-TO, a novel approach for reverse engineering of topology-optimized structures into interpretable geometric parameters using the YOLOv8 instance segmentation model. Density-based topology optimization methods require post-processing to convert the optimal density distribution into a parametric representation for design exploration

Jose Luis Ponton, Eduard Pujol, Andreas Aristidou, Carlos Andujar

High-quality motion reconstruction that follows the user's movements can be achieved by high-end mocap systems with many sensors. However, obtaining such animation quality with fewer input devices is gaining popularity as it brings mocap closer to the general public. The main challenges include the loss of end-effector accuracy in learning-based approaches,

Yulan Qing, Wenyuan Yang

In this paper, we show that for a proper statistically convex-cocompact action on a proper geodesic metric space, the sublinearly Morse boundary has full Patterson-Sullivan measure in the horofunction boundary.

Aurélien Alfonsi, Ahmed Kebaier, Jérôme Lelong

This paper develops a new dual approach to compute the hedging portfolio of a Bermudan option and its initial value. It gives a "purely dual" algorithm following the spirit of Rogers (2010) in the sense that it only relies on the dual pricing formula. The key is to rewrite the dual formula as an excess reward representation and to combine it with a strict co

Hanxiao Tan

Although point cloud models have gained significant improvements in prediction accuracy over recent years, their trustworthiness is still not sufficiently investigated. In terms of global explainability, Activation Maximization (AM) techniques in the image domain are not directly transplantable due to the special structure of the point cloud models. Existing

Juraj Vladika, Stephen Meisenbacher, Martina Preis, Alexandra Klymenko

In recent years, the field of Legal Tech has risen in prevalence, as the Natural Language Processing (NLP) and legal disciplines have combined forces to digitalize legal processes. Amidst the steady flow of research solutions stemming from the NLP domain, the study of use cases has fallen behind, leading to a number of innovative technical methods without a

Liyuan Wang, Yan Jin, Zhen Chen, Jinlin Wu

The vision-language pre-training has enabled deep models to make a huge step forward in generalizing across unseen domains. The recent learning method based on the vision-language pre-training model is a great tool for domain generalization and can solve this problem to a large extent. However, there are still some issues that an advancement still suffers fr

Chao Li, Xia Zhao

The Minkowski problem of harmonic measures was first studied by Jerison [19]. Recently, Akman and Mukherjee [1] studied the Minkowski problem corresponding to $p$-harmonic measures on convex domains and generalized Jerison's results. In this paper, we prove the existence of the smooth solution to the Minkowski problem for the $p$-harmonic measure by method o

Jianhong Zhao, Jinhui Kang, Yongwang Zhao

CIRCT, an open-source EDA framework akin to LLVM for software, is a foundation for various hardware description languages. Despite its crucial role, CIRCT's lack of formal semantics challenges necessary rigorous hardware verification. Thus, this paper introduces K-CIRCT, the first formal semantics in {\K} for a substantial CIRCT subset adequate for simulatin

Shereen Elsayed, Ahmed Rashed, Lars Schmidt-Thieme

In the context of recommendation systems, addressing multi-behavioral user interactions has become vital for understanding the evolving user behavior. Recent models utilize techniques like graph neural networks and attention mechanisms for modeling diverse behaviors, but capturing sequential patterns in historical interactions remains challenging. To tackle

Bahar Radmehr, Adish Singla, Tanja Käser

There has been a growing interest in developing learner models to enhance learning and teaching experiences in educational environments. However, existing works have primarily focused on structured environments relying on meticulously crafted representations of tasks, thereby limiting the agent's ability to generalize skills across tasks. In this paper, we a

Michele Aleandri, Felix Fritz, Stefano Moretti

In coalitional games, a player $i$ is regarded as strictly more desirable than player $j$ if substituting $j$ with $i$ within any coalition leads to a strict augmentation in the value of certain coalitions, while preserving the value of the others. We adopt a property-driven approach to 'integrate' the notion of the desirability relation into a total relatio

Mike Zhang

[Abridged Abstract] Recent technological advances underscore labor market dynamics, yielding significant consequences for employment prospects and increasing job vacancy data across platforms and languages. Aggregating such data holds potential for valuable insights into labor market demands, new skills emergence, and facilitating job matching for various st

Hong Yi Huang, Cai Heng Li, Yi Lin Xie

Let $G\leqslant\mathrm{Sym}(\Omega)$ be a finite transitive permutation group with point stabiliser $H$. We say that a subgroup $K$ of $G$ is a fixer if every element of $K$ has fixed points, and we say that $K$ is large if $|K| \geqslant |H|$. There is a special interest in studying large fixers due to connections with Erd\H{o}s-Ko-Rado type problems. In th

Papiya Bhattacharjee, Anthony W. Hager, Warren Wm. McGovern, Brian Wynne

$\bf{W}^*$ is the category of the archimedean l-groups with distinguished strong order unit and unit-preserving l-group homomorphisms. For $G \in \bf{W}^*$, we have the canonical compact space $YG$, and Yosida representation $G \leq C(YG)$, thus, for $g \in G$, the cozero-set coz(g) in $YG$. The ideals at issue in $G$ include the principal ideals and polars,

Guglielmo Lami, Tobias Haug, Jacopo De Nardis

Nonstabilizerness, or `magic', is a critical quantum resource that, together with entanglement, characterizes the non-classical complexity of quantum states. Here, we address the problem of quantifying the average nonstabilizerness of random Matrix Product States (RMPS). RMPS represent a generalization of random product states featuring bounded entanglement

Peter Mortimer, Mirko Maehlisch

Perception is an essential component of pipelines in field robotics. In this survey, we quantitatively compare publicly available datasets available in unstructured outdoor environments. We focus on datasets for common perception tasks in field robotics. Our survey categorizes and compares available research datasets. This survey also reports on relevant dat

Quentin Le Houérou, Ludovic Levy Patey

A left-variable word over an alphabet~$A$ is a word over~$A \cup \{\star\}$ whose first letter is the distinguished symbol~$\star$ standing for a placeholder. The Ordered Variable Word theorem ($\mathsf{OVW}$), also known as Carlson-Simpson's theorem, is a tree partition theorem, stating that for every finite alphabet~$A$ and every finite coloring of the wor

Guoqing Liu, Jiajun Mo, Zeyi Lu, Qinghang Zhang

This work systematically investigates the spin glass behavior of the double perovskite Ca2FeReO6. Building on previous studies, we have developed a formula to quantify the ions distribution at B-site, incorporating the next-nearest neighbor interactions. Employing molecular field theory and Monte Carlo simulations, the influence of various arrangements of tw

Shanle Yao, Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi

Video Anomaly Detection (VAD) identifies unusual activities in video streams, a key technology with broad applications ranging from surveillance to healthcare. Tackling VAD in real-life settings poses significant challenges due to the dynamic nature of human actions, environmental variations, and domain shifts. Many research initiatives neglect these complex

Hongyi Zhu, Jia-Hong Huang, Stevan Rudinac, Evangelos Kanoulas

Image search stands as a pivotal task in multimedia and computer vision, finding applications across diverse domains, ranging from internet search to medical diagnostics. Conventional image search systems operate by accepting textual or visual queries, retrieving the top-relevant candidate results from the database. However, prevalent methods often rely on s

Paul Pu Liang

Building multisensory AI systems that learn from multiple sensory inputs such as text, speech, video, real-world sensors, wearable devices, and medical data holds great promise for impact in many scientific areas with practical benefits, such as in supporting human health and well-being, enabling multimedia content processing, and enhancing real-world autono

Nurun Nesha

In this article, we study the existence of $\eta\in W_0^{1,\infty}(\Omega;\mathbb R^n)$ satisfying $$\textrm{curl} \ \eta\in E \textrm{ a.e. in }\Omega,$$ where $n\in \mathbb N, \Omega\subseteq \mathbb R^n$ is open, bounded and $E\subseteq \Lambda^2.$

Vimal Palanivelrajan, Joaquín E. Drut

We explore a generalization of nonrelativistic fermionic statistics that interpolates between bosons and fermions, in which up to $K$ particles may occupy a single-particle state. We show that it can be mapped exactly to $K$ flavors of fermions with imaginary polarization. In particular, for $K\!=\!2$, we use such a mapping to derive the virial coefficients

Mike W. Peel, Siegfried Eggl, Meredith Rawls, Michelle Dadighat

SatHub is one of the four hubs of the IAU Centre for the Protection of the Dark and Quiet Sky from Satellite Constellation Interference (CPS). It focuses on observations, data analysis, software, and training materials to improve our understanding of the impact of satellite constellations on astronomy and observers worldwide. As a preface to more in-depth IA

Nick E. Mavromatos, Panagiotis Dorlis, Sotirios-Neilos Vlachos

We discuss the role of torsion in string theory on inducing pseudoscalar degrees of freedom (axions), which in turn couple to (gravitational) Chern-Simons (CS) anomalous terms. Such interactions can induce inflation, of running vacuum type, not requiring external inflaton fields, through condensation of the anomalous terms as a consequence of primordial chir

P. G. Heighway, S. Singh, M. G. Gorman, D. McGonegle

The prototypical $\alpha\to\omega$ phase transition in zirconium is an ideal test-bed for our understanding of polymorphism under extreme loading conditions. After half a century of study, a consensus had emerged that the transition is realized via one of two distinct displacive mechanisms, depending on the nature of the compression path. However, recent dyn

Artem Abzaliev, Humberto Pérez Espinosa, Rada Mihalcea

Similar to humans, animals make extensive use of verbal and non-verbal forms of communication, including a large range of audio signals. In this paper, we address dog vocalizations and explore the use of self-supervised speech representation models pre-trained on human speech to address dog bark classification tasks that find parallels in human-centered task

Dimitris Bertsimas, Yu Ma

Developing an integrated many-to-many framework leveraging multimodal data for multiple tasks is crucial to unifying healthcare applications ranging from diagnoses to operations. In resource-constrained hospital environments, a scalable and unified machine learning framework that improves previous forecast performances could improve hospital operations and s

Lotte Blank, Anne Driemel

The fine-grained complexity of computing the Fr\'echet distance has been a topic of much recent work, starting with the quadratic SETH-based conditional lower bound by Bringmann from 2014. Subsequent work established largely the same complexity lower bounds for the Fr\'echet distance in 1D. However, the imbalanced case, which was shown by Bringmann to be tig

Quentin Le Houérou, Ludovic Levy Patey, Keita Yokoyama

In this article, we prove that Ramsey's theorem for pairs and two colors is a $\forall \Pi^0_4$ conservative extension of $\mathsf{RCA}_0 + \mathsf{B}\Sigma^0_2$, where a $\forall \Pi^0_4$ formula consists of a universal quantifier over sets followed by a $\Pi^0_4$ formula. The proof is an improvement of a result by Patey and Yokoyama and a step towards the

Stefano Battaglia, Max Rossmannek, Vladimir V. Rybkin, Ivano Tavernelli

We developed a general framework for hybrid quantum-classical computing of molecular and periodic embedding approaches based on an orbital space separation of the fragment and environment degrees of freedom. We demonstrate its potential by presenting a specific implementation of periodic range-separated DFT coupled to a quantum circuit ansatz, whereby the va

Luca Deck, Astrid Schomäcker, Timo Speith, Jakob Schöffer

The widespread use of artificial intelligence (AI) systems across various domains is increasingly surfacing issues related to algorithmic fairness, especially in high-stakes scenarios. Thus, critical considerations of how fairness in AI systems might be improved -- and what measures are available to aid this process -- are overdue. Many researchers and polic

Dmitriy Kunisky, Cristopher Moore, Alexander S. Wein

Many problems in high-dimensional statistics appear to have a statistical-computational gap: a range of values of the signal-to-noise ratio where inference is information-theoretically possible, but (conjecturally) computationally intractable. A canonical such problem is Tensor PCA, where we observe a tensor $Y$ consisting of a rank-one signal plus Gaussian

Alon Duvall, M. Ali Al-Radhawi, Dhruv D. Jatkar, Eduardo Sontag

We establish a new relationship between monotonicity and contractivity and use this connection to describe a new general class of weakly contractive reaction networks. The new class is characterized by the stoichiometry matrix of the reaction network admitting a precise matrix factorization that can be verified computationally. Reaction networks in this clas

Pierfrancesco Di Cintio, Federico Re, Caterina Chiari

Aims. We explore the dynamical friction on a test mass in gravitational systems in the Quasi linear formulation of Modified Newtonian Dynamics (QuMOND). Methods. Exploiting the quasi linearity of QuMOND we derive a simple expression for the dynamical friction in akin to its Newtonian counterpart in the standard Chandrasekhar derivation. Moreover, adopting a

Ghazal Alinezhad Noghre, Armin Danesh Pazho, Hamed Tabkhi

Video Anomaly Detection (VAD) represents a challenging and prominent research task within computer vision. In recent years, Pose-based Video Anomaly Detection (PAD) has drawn considerable attention from the research community due to several inherent advantages over pixel-based approaches despite the occasional suboptimal performance. Specifically, PAD is cha

Wenyang Liu, Ganggang Xu, Jianqing Fan, Xuening Zhu

While the Vector Autoregression (VAR) model has received extensive attention for modelling complex time series, quantile VAR analysis remains relatively underexplored for high-dimensional time series data. To address this disparity, we introduce a two-way grouped network quantile (TGNQ) autoregression model for time series collected on large-scale networks,

Jianping Zhou, Junhao Li, Guanjie Zheng, Yunqiang Zhu

In this paper, we propose a multi-view visualization technology for spatio-temporal knowledge graph(STKG), which utilizes three distinct perspectives: knowledge tree, knowledge net, and knowledge map, to facilitate a comprehensive analysis of the STKG. The knowledge tree enables the visualization of hierarchical interrelation within the STKG, while the knowl