February 2024 arXiv papers — page 158

Tuan Anh Dao, Lukas Lundgren, Murtazo Nazarov

Nonlinear conservation laws such as the system of ideal magnetohydrodynamics (MHD) equations may develop singularities over time. In these situations, viscous regularization is a common approach to regain regularity of the solution. In this paper, we present a new viscous flux to regularize the MHD equations which holds many attractive properties. In particu

Ian Gemp, Marc Lanctot, Luke Marris, Yiran Mao

The core is a central solution concept in cooperative game theory, defined as the set of feasible allocations or payments such that no subset of agents has incentive to break away and form their own subgroup or coalition. However, it has long been known that the core (and approximations, such as the least-core) are hard to compute. This limits our ability to

Simone Balloccu, Patrícia Schmidtová, Mateusz Lango, Ondřej Dušek

Natural Language Processing (NLP) research is increasingly focusing on the use of Large Language Models (LLMs), with some of the most popular ones being either fully or partially closed-source. The lack of access to model details, especially regarding training data, has repeatedly raised concerns about data contamination among researchers. Several attempts h

A. García Muñoz, P. Wolkenberg, A. Sánchez-Lavega, R. Hueso

Thermal radiation becomes a prominent feature in the continuum spectrum of Venus longwards of $\sim$3 $\mu$m. The emission is traceable to the upper cloud and haze layers in the planet's mesosphere. Venus' thermal radiation spectrum is punctuated by CO$_2$ bands of various strengths probing into different atmospheric depths. It is thus possible to invert mea

M E Dieckmann, L Palodhi, C Fegan, M Borghesi

Ablating a target with an ultraintense laser pulse can create a cloud of collisionless plasma. A density ramp forms, in which the plasma density decreases and the ion's mean speed increases with distance from the plasma source. Its width increases with time. Electrons lose energy in the ion's expansion direction, which gives them a temperature anisotropy. We

Miguel Ángel Domínguez-Ríos, Francisco Chicano, Enrique Alba

In multiobjective optimization, the result of an optimization algorithm is a set of efficient solutions from which the decision maker selects one. It is common that not all the efficient solutions can be computed in a short time and the search algorithm has to be stopped prematurely to analyze the solutions found so far. A set of efficient solutions that are

Sui Tao, Francisco Rowe, Hongyu Shan

Understanding the variability of people's travel patterns is key to transport planning and policy-making. However, to what extent daily transit use displays geographic and temporal variabilities, and what are the contributing factors have not been fully addressed. Drawing on smart card data in Beijing, China, this study seeks to address these deficits by ado

Lucas H. McCabe, Naoki Masuda, Shannon Casillas, Nathan Danneman

We present a nation-wide network analysis of non-fatal opioid-involved overdose journeys in the United States. Leveraging a unique proprietary dataset of Emergency Medical Services incidents, we construct a journey-to-overdose geospatial network capturing nearly half a million opioid-involved overdose events spanning 2018-2023. We analyze the structure and s

Tsunehiko Tanaka, Kenshi Abe, Kaito Ariu, Tetsuro Morimura

Traditional approaches in offline reinforcement learning aim to learn the optimal policy that maximizes the cumulative reward, also known as return. It is increasingly important to adjust the performance of AI agents to meet human requirements, for example, in applications like video games and education tools. Decision Transformer (DT) optimizes a policy tha

Luis Guijarro, Jose-Ramon Vidal, Vicent Pla

5G specifications promise a common and flexible-enough network infrastructure capable of satisfying diverse requirements of both current and future use cases. Two service types standardized in 5G are eMBB, without stringent delay guarantee, and URLLC, with stringent delay guarantee. We focus on a use case where data timeliness is the relevant quality paramet

Tennison Liu, Nicolás Astorga, Nabeel Seedat, Mihaela van der Schaar

Bayesian optimization (BO) is a powerful approach for optimizing complex and expensive-to-evaluate black-box functions. Its importance is underscored in many applications, notably including hyperparameter tuning, but its efficacy depends on efficiently balancing exploration and exploitation. While there has been substantial progress in BO methods, striking t

Massimo Bernaschi, Isidoro González-Adalid Pemartín, Víctor Martín-Mayor, Giorgio Parisi

We release a set of GPU programs for the study of the Quantum ($S=1/2$) Spin Glass on a square lattice, with binary couplings. The library contains two main codes: MCQSG (that carries out Monte Carlo simulations using both the Metropolis and the Parallel Tempering algorithms, for the problem formulated in the Trotter-Suzuki approximation), and EDQSG (that ob

Lei Xie, Fan Liu, Jiajin Luo, Shenghui Song

Sensing performance is typically evaluated by classical radar metrics, such as Cramer-Rao bound and signal-to-clutter-plus-noise ratio. The recent development of the integrated sensing and communication (ISAC) framework motivated the efforts to unify the performance metric for sensing and communication, where mutual information (MI) was proposed as a sensing

Francisco Chicano, Gabriela Ochoa, Darrell Whitley, Renato Tinós

An optimal recombination operator for two parent solutions provides the best solution among those that take the value for each variable from one of the parents (gene transmission property). If the solutions are bit strings, the offspring of an optimal recombination operator is optimal in the smallest hyperplane containing the two parent solutions. Exploring

Sarwar Khan

Deepfake technology has raised concerns about the authenticity of digital content, necessitating the development of effective detection methods. However, the widespread availability of deepfakes has given rise to a new challenge in the form of adversarial attacks. Adversaries can manipulate deepfake videos with small, imperceptible perturbations that can dec

Simone Magistri, Tomaso Trinci, Albin Soutif-Cormerais, Joost van de Weijer

Exemplar-Free Class Incremental Learning (EFCIL) aims to learn from a sequence of tasks without having access to previous task data. In this paper, we consider the challenging Cold Start scenario in which insufficient data is available in the first task to learn a high-quality backbone. This is especially challenging for EFCIL since it requires high plastici

Qiang Liu, Xiang Tao, Junfei Wu, Shu Wu

In this work, we investigate to use Large Language Models (LLMs) for rumor detection on social media. However, it is challenging for LLMs to reason over the entire propagation information on social media, which contains news contents and numerous comments, due to LLMs may not concentrate on key clues in the complex propagation information, and have trouble i

Olivier Jeunen, Aleksei Ustimenko

Online controlled experiments are a crucial tool to allow for confident decision-making in technology companies. A North Star metric is defined (such as long-term revenue or user retention), and system variants that statistically significantly improve on this metric in an A/B-test can be considered superior. North Star metrics are typically delayed and insen

Zijie Zhong, Yunhui Zhang, Ziyi Chang, Zengchang Qin

Node Importance Estimation (NIE) is crucial for integrating external information into Large Language Models through Retriever-Augmented Generation. Traditional methods, focusing on static, single-graph characteristics, lack adaptability to new graphs and user-specific requirements. CADReN, our proposed method, addresses these limitations by introducing a Con

Eva Lope-Oter, Aneta Wojnar

We explore gravity-independent equations of state for neutron stars, particularly focusing on twin stars. Examining four categories, we emphasize their behavior in both General Relativity and Palatini gravity. Additionally, we discuss a subcategory of type I, which, in the context of General Relativity, does not exhibit twin star phenomena, yet demonstrates

Francesco Carnazza, Federico Carollo, Sabine Andergassen, Georg Martius

We develop a machine learning algorithm to infer the emergent stochastic equation governing the evolution of an order parameter of a many-body system. We train our neural network to independently learn the directed force acting on the order parameter as well as an effective diffusive noise. We illustrate our approach using the classical Ising model endowed w

V. Ryzhii, C. Tang, T. Otsuji, M. Ryzhii

We analyze plasmonic oscillations in the coplanar graphene nanoribbon (GNR) structures induced by the applied terahertz (THz) signals and calculate the GNR impedance. The plasmonic oscillations in the CNR structures are associated with the electron and hole inductances and the lateral inter-CNR capacitance. A relatively low inter-GNR capacitance enables the

Louis Mozart Kamdem Teyou, Caglar Demir, Axel-Cyrille Ngonga Ngomo

Clifford algebras are a natural generalization of the real numbers, the complex numbers, and the quaternions. So far, solely Clifford algebras of the form $Cl_{p,q}$ (i.e., algebras without nilpotent base vectors) have been studied in the context of knowledge graph embeddings. We propose to consider nilpotent base vectors with a nilpotency index of two. In t

Kris Schroven, Vladimir Karas, Jiri Horak, Audrey Trova

We study the conditions for stability of electrically charged, non-conductive perfect fluid tori with respect to linear perturbations. To this end we employ Lagrangian perturbation formalism and we assume a system where the fluid orbits a central body. Gravitational field of the latter is described in the Newtonian framework. We first formulate the criteria

Ghazal Kalhor, Behnam Bahrak

Studying acquisitions offers invaluable insights into startup trends, aiding informed investment decisions for businesses. However, the scarcity of studies in this domain prompts our focus on shedding light in this area. Employing Crunchbase data, our study delves into the global network of company acquisitions using diverse network analysis techniques. Our

Feng-Yuan Liu, Y. Sophia Dai, Alain Omont, Daizhong Liu

Some high-z active galactic nuclei (AGNs) are found to reside in extreme star-forming galaxies, such as hyper-luminous infrared galaxies (HyLIRGs), with AGN-removed $L_{\rm{IR}}$ of $>10^{13} L_{\rm{\odot}}$. In this paper, we report NOEMA observations of six apparent starburst HyLIRGs associated with optical quasars at $z\sim2-3$ in the Stripe 82 field, to

Xin Kong, Shikun Liu, Xiaoyang Lyu, Marwan Taher

We introduce EscherNet, a multi-view conditioned diffusion model for view synthesis. EscherNet learns implicit and generative 3D representations coupled with a specialised camera positional encoding, allowing precise and continuous relative control of the camera transformation between an arbitrary number of reference and target views. EscherNet offers except

Volodymyr Braiman, Anatoliy Malyarenko, Yuliya Mishura, Yevheniia Anastasiia Rudyk

We consider two types of entropy, namely, Shannon and R\'{e}nyi entropies of the Poisson distribution, and establish their properties as the functions of intensity parameter. More precisely, we prove that both entropies increase with intensity. While for Shannon entropy the proof is comparatively simple, for R\'{e}nyi entropy, which depends on additional par

Efe Bozkir, Süleyman Özdel, Ka Hei Carrie Lau, Mengdi Wang

Advances in artificial intelligence and human-computer interaction will likely lead to extended reality (XR) becoming pervasive. While XR can provide users with interactive, engaging, and immersive experiences, non-player characters are often utilized in pre-scripted and conventional ways. This paper argues for using large language models (LLMs) in XR by emb

Zhuangkun Wei, Wenxiu Hu, Junqing Zhang, Weisi Guo

Reconfigurable intelligent surfaces (RIS) can both help and hinder the physical layer secret key generation (PL-SKG) of communications systems. Whilst a legitimate RIS can yield beneficial impacts, including increased channel randomness to enhance PL-SKG, a malicious RIS can poison legitimate channels and crack almost all existing PL-SKGs. In this work, we p

Hani Barhum, Muhammad A. Atrash, Inga Brice, Toms Salgals

Polymers, demonstrating distinctive optical properties alongside facile and mastered fabrication methods, have become increasingly important platforms for realizing a variety of nanophotonic devices. Enhancing these materials with additional functions might expand their range of multidisciplinary applications. Here, we demonstrate the temperature sensing pot

Mahyar Karimi, Kamyar Seyedkazem Viliyani

Employee's knowledge is an organization asset. Turnover may impose apparent and hidden costs and irreparable damages. To overcome and mitigate this risk, employee's condition should be monitored. Due to high complexity of analyzing well-being features, employee's turnover predicting can be delegated to machine learning techniques. In this paper, we discuss e

Brett Daley, Martha White, Marlos C. Machado

Multistep returns, such as $n$-step returns and $\lambda$-returns, are commonly used to improve the sample efficiency of reinforcement learning (RL) methods. The variance of the multistep returns becomes the limiting factor in their length; looking too far into the future increases variance and reverses the benefits of multistep learning. In our work, we dem

Marco Bondaschi, Michael Gastpar

Large language models (LLMs) have recently gained much popularity due to their surprising ability at generating human-like English sentences. LLMs are essentially predictors, estimating the probability of a sequence of words given the past. Therefore, it is natural to evaluate their performance from a universal prediction perspective. In order to do that fai

Dechuan Teng, Chunlin Lu, Xiao Xu, Wanxiang Che

Recently, Profile-based Spoken Language Understanding (SLU) has gained increasing attention, which aims to incorporate various types of supplementary profile information (i.e., Knowledge Graph, User Profile, Context Awareness) to eliminate the prevalent ambiguities in user utterances. However, existing approaches can only separately model different profile i

L. Scharenberg, F. Brunbauer, H. Danielson, Z. Fang

Micro-Pattern Gaseous Detectors (MPGDs) with resistive anode planes provide intrinsic discharge robustness while maintaining good spatial and time resolutions. Typically read out with 1D strips or pad structures, here the characterisation results of resistive anode plane MPGDs with 2D strip readout are presented. A uRWELL prototype is investigated in view of

Jongwoo Ko, Sungnyun Kim, Tianyi Chen, Se-Young Yun

Knowledge distillation (KD) is widely used for compressing a teacher model to a smaller student model, reducing its inference cost and memory footprint while preserving model capabilities. However, current KD methods for auto-regressive sequence models (e.g., large language models) suffer from missing a standardized objective function. Moreover, the recent u

Shuai Li, Chaoyi Chen, Haotian Zheng, Jiawei Wang

Data-driven predictive control promises model-free wave-dampening strategies for Connected and Autonomous Vehicles (CAVs) in mixed traffic flow. However, its performance relies on data quality, which suffers from unknown noise and disturbances. This paper introduces a Robust Data-EnablEd Predictive Leading Cruise Control (RDeeP-LCC) method based on reachabil

Kun Li, George Vosselman, Michael Ying Yang

Visual Question Answering (VQA) is a challenging task of predicting the answer to a question about the content of an image. Prior works directly evaluate the answering models by simply calculating the accuracy of predicted answers. However, the inner reasoning behind the predictions is disregarded in such a "black box" system, and we cannot ascertain the tru

Haruka Mori, Shin Sasaki

We study an analogue of the Drinfel'd double for algebroids associated with the $O(D,D+n)$ gauged double field theory (DFT). We show that algebroids defined by the twisted C-bracket in the gauged DFT are built out of a direct sum of three (twisted) Lie algebroids. They exhibit a "tripled", which we call the extended double, rather than the "doubled" structur

Benedikt V. Meylahn, Arnoud V. den Boer, Michel Mandjes

We study the interpersonal trust of a population of agents, asking whether chance may decide if a population ends up in a high trust or low trust state. We model this by a discrete time, random matching stochastic coordination game. Agents are endowed with an exponential smoothing learning rule about the behaviour of their neighbours. We find that, with prob

Manuel Muñoz Sánchez, Chris van der Ploeg, Robin Smit, Jos Elfring

Predicting the movement of other road users is beneficial for improving automated vehicle (AV) performance. However, the relationship between the time horizon associated with these predictions and AV performance remains unclear. Despite the existence of numerous trajectory prediction algorithms, no studies have been conducted on how varying prediction length

A. Arnal, J. Monterde

The affine space of all tensor product B\'ezier patches of degree nxn with prescribed main diagonal curves is determined. First, the pair of B\'ezier curves which can be diagonals of a B\'ezier patch is characterized. Besides prescribing the diagonal curves, other related problems are considered, those where boundary curves or tangent planes along boundary c

Nils Lommen, Éléanore Meyer, Jürgen Giesl

Recently, we showed how to use control-flow refinement (CFR) to improve automatic complexity analysis of integer programs. While up to now CFR was limited to classical programs, in this paper we extend CFR to probabilistic programs and show its soundness for complexity analysis. To demonstrate its benefits, we implemented our new CFR technique in our complex

Changyan Di, Jianyue Guan, Qingguo Zhou, Jingqiang Wang

Exploration of mechanisms underlying the emergence of collective cooperation remains a focal point in field of evolution of cooperation. Prevailing studies often neglect historical information, relying on the latest rewards as the primary criterion for individual decision-making-a method incongruent with human cognition and decision-making modes. This limita

Aditya Jayaraman, Andrey V. Danilov, Jonas Bylander, Sergey E. Kubatkin

Solid-state devices used for quantum computation and quantum sensing applications are adversely affected by loss and noise caused by spurious, charged two-level systems (TLS) and stray paramagnetic spins. These two sources of noise are interconnected, exacerbating the impact on circuit performance. We use an on-chip electron spin resonance (ESR) technique, w

Luis Sanz, Rafael Bravo de la Parra

In this work we address the analysis of discrete-time models of structured metapopulations subject to environmental stochasticity. Previous works on these models made use of the fact that migrations between the patches can be considered fast with respect to demography (maturation, survival, reproduction) in the population. It was assumed that, within each ti

Carolin Müller-Spitzer, Samira Ochs

This paper focuses on language change based on shifting social norms, in particular with regard to the debate on language and gender. It is a recurring argument in this debate that language develops "naturally" and that "severe interventions" - such as gender-inclusive language is often claimed to be - in the allegedly "organic" language system are inappropr

Mehdi Sattari, Hao Guo, Deniz Gündüz, Ashkan Panahi

Millimeter wave (mmWave) multiple-input-multi-output (MIMO) is now a reality with great potential for further improvement. We study full-duplex transmissions as an effective way to improve mmWave MIMO systems. Compared to half-duplex systems, full-duplex transmissions may offer higher data rates and lower latency. However, full-duplex transmission is hindere

Mononito Goswami, Konrad Szafer, Arjun Choudhry, Yifu Cai

We introduce MOMENT, a family of open-source foundation models for general-purpose time series analysis. Pre-training large models on time series data is challenging due to (1) the absence of a large and cohesive public time series repository, and (2) diverse time series characteristics which make multi-dataset training onerous. Additionally, (3) experimenta

K. Bakke, J. G. G. S. Ramos

We examine the spatial distribution of electric charges within an extended, non-conductive cylinder featuring an inner radius denoted as $r_{0}$. Our investigation unveils the emergence of a distinct modified attractive-inverse square potential, arising from the intricate interplay between the electric field and the induced electric dipole moment of a neutra

Alisson R. Cadore, Alexandre S. M. V. Ore, David Steinberg, Juan D. Zapata

Layered franckeite is a natural superlattice composed of two alternating layers of different compositions, SnS$_2$- and PbS-like. This creates incommensurability between the two species along the planes of the layers, resulting in spontaneous symmetry-break periodic ripples in the \textit{a}-axis orientation. Nevertheless, natural franckeite heterostructure

Andi Han, Bamdev Mishra, Pratik Jawanpuria, Akiko Takeda

Bilevel optimization has gained prominence in various applications. In this study, we introduce a framework for solving bilevel optimization problems, where the variables in both the lower and upper levels are constrained on Riemannian manifolds. We present several hypergradient estimation strategies on manifolds and analyze their estimation errors. Furtherm

Rune Højlund Marholt, Mads Givskov Senstius, Stefan Kragh Nielsen

The WKB approximation of geometrical optics is widely used in plasma physics, quantum mechanics and reduced wave modeling in general. However, it is well-known that the approximation breaks down at focal and turning points. In this work we present the first unsupervised numerical implementation of the recently developed metaplectic geometrical optics framewo

K. Bakke, J. G. G. S. Ramos

We obtain the analytical solutions to the Schr\"odinger equation for the attractive inverse-square potential in an induced electric dipole moment system under the influence of the harmonic oscillator. We show that bound states can exist when the electric field configuration brings a cut-off point that imposes a forbidden region for the neutral particle. Then

Chonghe Zhao, Yipeng Zhou, Shengli Zhang, Taotao Wang

In Ethereum, the ledger exchanges messages along an underlying Peer-to-Peer (P2P) network to reach consistency. Understanding the underlying network topology of Ethereum is crucial for network optimization, security and scalability. However, the accurate discovery of Ethereum network topology is non-trivial due to its deliberately designed security mechanism

Miguel Gayo Abeleira, Carlos Santos Pérez, Francisco Javier Rodríguez Sánchez, Pedro Martín Sánchez

This work proposes a geographically-based split of the community microgrids into clusters of members that tend to have similar consumption and generation profiles. Assuming a community microgrid divided into clusters, a two-layer architecture is developed to facilitate the greater penetration of distributed energy resources in an efficient way. The first lay

Tristan Benoist, Arnaud Hautecoeur, Clément Pellegrini

Quantum trajectories are Markov processes modeling the evolution of a quantum system subjected to repeated independent measurements. Inspired by the theory of random products of matrices, it has been shown that these Markov processes admit a unique invariant measure under a purification and an irreducibility assumptions. This paper is devoted to the spectral

Jia Zhou, Zhilan Wang, Jin Yan

An oriented graph is an orientation of a simple graph. In 2009, Keevash, K\"{u}hn and Osthus proved that every sufficiently large oriented graph $D$ of order $n$ with $(3n-4)/8$ is Hamiltonian. Later, Kelly, K\"{u}hn and Osthus showed that it is also pancyclic. Inspired by this, we show that for any given constant $t$ and positive integer partition $n = n_1

Spyridon Mouselinos, Henryk Michalewski, Mateusz Malinowski

Large Language Models (LLMs) demonstrate ever-increasing abilities in mathematical and algorithmic tasks, yet their geometric reasoning skills are underexplored. We investigate LLMs' abilities in constructive geometric problem-solving one of the most fundamental steps in the development of human mathematical reasoning. Our work reveals notable challenges tha

Haoyu Yang, Juanli Zhao, Qiankun Wang, Bin Liu

Single-atom catalysts (SACs) have emerged as frontiers for catalyzing chemical reactions, yet the diverse combinations of active elements and support materials, the nature of coordination environments, elude traditional methodologies in searching optimal SAC systems with superior catalytic performance. Herein, by integrating multi-branch Convolutional Neural

Qi Song, Jie Lou, Yan Chen

We find a novel chiral superfluid (CSF) phase in a one-dimensional Bose-Fermi Hubbard model with significant mass and density imbalance between the two species. In the CSF phase, bosons condensate at non-zero momentum $\pm 2\pi /L$ with chain length $L$. To capture the essential physics of this new phenomenon, we study an alternative simplified model that on

Veronica Arena, Samir Canning, Emily Clader, Richard Haburcak

We prove, for infinitely many values of $g$ and $n$, the existence of non-tautological algebraic cohomology classes on the moduli space $\mathcal{M}_{g,n}$ of smooth, genus-$g$, $n$-pointed curves. In particular, when $n=0$, our results show that there exist non-tautological algebraic cohomology classes on $\mathcal{M}_g$ for $g=12$ and all $g \geq 16$. Thes

Sota Kimura, Michiharu Wada, Hiromitsu Haba, Hironobu Ishiyama

We report the mass measurements of neutron-rich isotopes produced via spontaneous fission of ${}^{252}$Cf using a multi-reflection time-of-flight mass spectrograph. The mass of ${}^{155}$Ce is determined experimentally for the first time. A discrepancy between the experimental and literature values was found for the mass of ${}^{127}$Sb, which was previously

Shuxiong Zhang, Lianghui Luo

Given a supercritical branching random walk $\{Z_n\}_{n\geq 0}$ on $\mathbb{R}$, let $Z_n([y,\infty))$ be the number of particles located in $[y,\infty)\subset\mathbb{R}$ at generation $n$. Let $m$ be the mean of the offspring law of $\{Z_n\}_{n\geq 0}$ and $I(x)$ be the large deviation rate function of the underlying random walk of $\{Z_n\}_{n\geq 0}$. It i

Chukwudubem Umeano, Vincent E. Elfving, Oleksandr Kyriienko

Geometric quantum machine learning (GQML) aims to embed problem symmetries for learning efficient solving protocols. However, the question remains if (G)QML can be routinely used for constructing protocols with an exponential separation from classical analogs. In this Letter we consider Simon's problem for learning properties of Boolean functions, and show t

Carolin Müller-Spitzer, Samira Ochs, Alexander Koplenig, Jan-Oliver Rüdiger

Research on gender and language is tightly knitted to social debates on gender equality and non-discriminatory language use. Psycholinguistic scholars have made significant contributions in this field. However, corpus-based studies that investigate these matters within the context of language use are still rare. In our study, we address the question of how m

Yuriy Akimov, Aswin Alexander Eapen, Shiyang Zhu, Doris K. T. Ng

We develop a theoretical predictive model for an all-pass ring resonator that enables the most complete description of linear coupling regimes. The model is based on eigenmode decomposition of Maxwell's equations with full account of the confined and leaky modes, as opposed to the existing phenomenological methods restricted to the confined modes only. This

Heba Bou KaedBey, Mark van Hoeij, Man Cheung Tsui

We classify order $3$ linear difference operators over $\mathbb{C}(x)$ that are solvable in terms of lower order difference operators. To prove this result, we introduce the notion of absolute irreducibility for difference modules, and classify (for arbitrary order) modules that are irreducible but not absolutely irreducible.

Gil Geva, Olivier Warusfel, Shlomo Dubnov, Tammuz Dubnov

This paper introduces a new approach to sound source localization using head-related transfer function (HRTF) characteristics, which enable precise full-sphere localization from raw data. While previous research focused primarily on using extensive microphone arrays in the frontal plane, this arrangement often encountered limitations in accuracy and robustne

Beatriz Arregui-García, Antonio Longa, Quintino Francesco Lotito, Sandro Meloni

The analysis of complex and time-evolving interactions like social dynamics represents a current challenge for the science of complex systems. Temporal networks stand as a suitable tool to schematise such systems, encoding all the appearing interactions between pairs of individuals in discrete time. Over the years, network science has developed many measures

Miguel Gayo, Francisco Javier Rodríguez, Carlos Santos, Ying Wu

The growth of distributed renewable energy in the electrical grid presents challenges to its stability and quality. To address this at the local level, flexibility energy strategies emerge as an innovative technique. However, managing these strategies in residential areas becomes complex due to the unique characteristics of each prosumer. A major challenge l

Andrea Bonfanti, Giuseppe Bruno, Cristina Cipriani

The Neural Tangent Kernel (NTK) viewpoint is widely employed to analyze the training dynamics of overparameterized Physics-Informed Neural Networks (PINNs). However, unlike the case of linear Partial Differential Equations (PDEs), we show how the NTK perspective falls short in the nonlinear scenario. Specifically, we establish that the NTK yields a random ma

Marco Fedele

In this proceeding we will review the current theoretical status of rare $B$ decays. These decays are indeed excellent indirect probes for New Physics searches, and in the current situation where no new states have been directly observed at collider, they provide a fundamental and alternative approach in the quest for Physics beyond the Standard Model. We wi

Yiting Wu, Junyu Zhang

This paper considers the discounted criterion of nonzero-sum decentralized stochastic games with prospect players. The state and action spaces are finite. The state transition probability is nonstationary. Each player independently controls their own Markov chain. The subjective behavior of players is described by the prospect theory (PT). Compared to the av

Wei-Hua Luo, Xian-Ming Gu, Bruno Carpentieri, Jun Guo

We propose a Bernoulli-barycentric rational matrix collocation method for two-dimensional evolutionary partial differential equations (PDEs) with variable coefficients that combines Bernoulli polynomials with barycentric rational interpolations in time and space, respectively. The theoretical accuracy $O\left((2\pi)^{-N}+h_x^{d_x-1}+h_y^{d_y-1}\right)$ of ou

Jia-Fong Yeh, Kuo-Han Hung, Pang-Chi Lo, Chi-Ming Chung

We introduce a new task called Adaptable Error Detection (AED), which aims to identify behavior errors in few-shot imitation (FSI) policies based on visual observations in novel environments. The potential to cause serious damage to surrounding areas limits the application of FSI policies in real-world scenarios. Thus, a robust system is necessary to notify

Xixi Hu, Bo Liu, Xingchao Liu, Qiang Liu

Diffusion-based imitation learning improves Behavioral Cloning (BC) on multi-modal decision-making, but comes at the cost of significantly slower inference due to the recursion in the diffusion process. It urges us to design efficient policy generators while keeping the ability to generate diverse actions. To address this challenge, we propose AdaFlow, an im

David Lannes, Mathieu Rigal

This paper is devoted to the theoretical and numerical investigation of the initial boundary value problem for a system of equations used for the description of waves in coastal areas, namely, the Boussinesq-Abbott system in the presence of topography. We propose a procedure that allows one to handle very general linear or nonlinear boundary conditions. It c

Sandip Kumar Giri, Biswajit Sen

In the development of quantum technologies, nonclassical states have been playing a pivotal role, as quantum advantage cannot be obtained without appropriate utilization of nonclassicality. In the present work, we consider a hybrid coherent state (HCS), which is a coherent superposition of the single-photon-added coherent (SPAC) state and a coherent state (C

Bogdan-Vasile Matioc, Emilian Parau

We construct two-dimensional steady periodic hydroelastic waves with vorticity that propagate on water of finite depth under a deformable floating elastic plate which is modeled by using the special Cosserat theory of hyperelastic shells satisfying Kirchhoff's hypothesis. This is achieved by providing necessary and sufficient condition for local bifurcation

Zhixin Lu, Guo Meng, Tomasz Tyranowski, Alex Chankin

In this study, we consider a numerical implementation of the nonlinear Rosenbluth-Trubnikov collision operator for particle simulations in plasma physics in the framework of the finite element method (FEM). The relevant particle evolution equations are formulated as stochastic differential equations, both in the Stratonovich and It\^o forms, and are then sol

Satvik Golechha, James Dao

Mechanistic interpretability (MI) aims to understand AI models by reverse-engineering the exact algorithms neural networks learn. Most works in MI so far have studied behaviors and capabilities that are trivial and token-aligned. However, most capabilities important for safety and trust are not that trivial, which advocates for the study of hidden representa

Vittorio Somà, Thomas Duguet

The rich phenomenology of quantum many-body systems such as atomic nuclei is complex to interpret. Often, the behaviour (e.g. evolution with the number of constituents) of measurable/observable quantities such as binding or excitation energies can be best understood on the basis of a simplified picture involving auxiliary quantities that are not observable,

Sheikh Noman Shiddique, Ahnaf Tahmid Abir, Md Jayed Hossain, Mainul Hossain

In this article, we demonstrate CdSe-CuSbSe2-based double junction two-terminal tandem solar cells simulated with SCAPS-1D. The highest performance of the tandem cell has been confirmed by optimizing the electrical and optical properties of window, top absorber, CdSe (bandgap 1.7 eV), bottom absorber, CuSbSe2 (bandgap 1.08 eV) and back surface layers. In add

Denise S. Christovam, Martin Sundermann, Andrea Marino, Daisuke Takegami

We investigate the topological superconductor candidate UTe$_2$ using high-resolution valence-band resonant inelastic x-ray scattering at the U $M_{4,5}$-edges. We observe atomic-like low-energy excitations that support the correlated nature of this unconventional superconductor. These excitations originate from the U $5f^2$ configuration, which is unexpecte

Rajendra Singh Negi, Priyanka Iyer, Gerhard Gompper

Distance control in many-particle systems is a fundamental problem in nature. This becomes particularly relevant in systems of active agents, which can sense their environment and react by adjusting their direction of motion. We employ agent-based simulations to investigate the complex interplay between agent activity, characterized by P{\'e}clet number $Pe$

Nicolas Bousquet, Laurent Feuilloley, Sébastien Zeitoun

In this work, we provide an upper bound for global certification of graph homomorphism, a generalization of graph coloring. In certification, the nodes of a network should decide if the network satisfies a given property, thanks to small pieces of information called certificates. Here, there is only one global certificate which is shared by all the nodes, an

David Peer, Philemon Schöpf, Volckmar Nebendahl, Alexander Rietzler

Traditionally, discriminative models have been the predominant choice for tasks like document classification and information extraction. These models make predictions that fall into a limited number of predefined classes, facilitating a binary true or false evaluation and enabling the direct calculation of metrics such as the F1 score. However, recent advanc

Shengxin Zhuang, John Tanner, Yusen Wu, Du Q. Huynh

Quantum machine learning (QML) is one of the most promising applications of quantum computation. However, it is still unclear whether quantum advantages exist when the data is of a classical nature and the search for practical, real-world applications of QML remains active. In this work, we apply the well-studied quantum support vector machine (QSVM), a powe

Jose Cribeiro-Ramallo, Vadim Arzamasov, Klemens Böhm

Outlier generation is a popular technique used for solving important outlier detection tasks. Generating outliers with realistic behavior is challenging. Popular existing methods tend to disregard the 'multiple views' property of outliers in high-dimensional spaces. The only existing method accounting for this property falls short in efficiency and effective

Christian Horvat, Jean-Pascal Pfister

Diffusion models are generative models that have recently demonstrated impressive performances in terms of sampling quality and density estimation in high dimensions. They rely on a forward continuous diffusion process and a backward continuous denoising process, which can be described by a time-dependent vector field and is used as a generative model. In th

Peihan Lyu, Zhaoyu Ding, Masao Doi, Xingkun Man

A sheet of glassy polymers placed in a solvent shows swelling behaviors quite different from that of soft polymers (rubbers and gels). (1) Non-Fickian diffusion (called case II diffusion): As solvent permeates into the sample, a sharp front is created between the swollen part and the glassy part, and it moves toward the center at constant speed. (2) Nonmonot

Yunqing Bao, Bin Hu

This paper presents a novel algorithm for non-destructive damage detection for steel ropes in high-altitude environments (aerial ropeway). The algorithm comprises two key components: First, a segmentation model named RGBD-UNet is designed to accurately extract steel ropes from complex backgrounds. This model is equipped with the capability to process and com

Jules Olayé, Hala Bouzidi, Andrey Aristov, Antoine Barizien

The growth of a population is often modeled as branching process where each individual at the end of its life is replaced by a certain number of offspring. An example of these branching models is the Bellman-Harris process, where the lifetime of individuals is assumed to be independent and identically distributed. Here, we are interested in the estimation of

Ji-seong Chae, Jae-Hyuk Oh

We explore mathematical relationship between holographic Wilsonian renormalization group(HWRG) and stochastic quantization(SQ) motivated by the similarity of the monotonicity in RG flow with Langevin dynamics of non-equilibrium thermodynamics. We look at scalar field theory in AdS space with its generic mass, self-interaction, and boundary deformation in the

Mario A. V. Saucedo, Akash Patel, Akshit Saradagi, Christoforos Kanellakis

In this article, we propose the novel concept of Belief Scene Graphs, which are utility-driven extensions of partial 3D scene graphs, that enable efficient high-level task planning with partial information. We propose a graph-based learning methodology for the computation of belief (also referred to as expectation) on any given 3D scene graph, which is then

Alexis Ayme, Claire Boyer, Aymeric Dieuleveut, Erwan Scornet

Constant (naive) imputation is still widely used in practice as this is a first easy-to-use technique to deal with missing data. Yet, this simple method could be expected to induce a large bias for prediction purposes, as the imputed input may strongly differ from the true underlying data. However, recent works suggest that this bias is low in the context of

Raphaël Carpintero Perez, Sébastien da Veiga, Josselin Garnier, Brian Staber

Supervised learning has recently garnered significant attention in the field of computational physics due to its ability to effectively extract complex patterns for tasks like solving partial differential equations, or predicting material properties. Traditionally, such datasets consist of inputs given as meshes with a large number of nodes representing the

Benjamin Dayan, Marc Kaufmann, Ulysse Schaller

Recently there has been increased interest in fitting generative graph models to real-world networks. In particular, Bl\"asius et al. have proposed a framework for systematic evaluation of the expressivity of random graph models. We extend this framework to Geometric Inhomogeneous Random Graphs (GIRGs). This includes a family of graphs induced by non-metric