October 2024 arXiv papers — page 34

Marharyta Domnich, Julius Välja, Rasmus Moorits Veski, Giacomo Magnifico

As machine learning models evolve, maintaining transparency demands more human-centric explainable AI techniques. Counterfactual explanations, with roots in human reasoning, identify the minimal input changes needed to obtain a given output and, hence, are crucial for supporting decision-making. Despite their importance, the evaluation of these explanations

Zhihao Zhao, Junjie Yang, Shahrooz Faghihroohi, Yinzheng Zhao

The utilization of longitudinal datasets for glaucoma progression prediction offers a compelling approach to support early therapeutic interventions. Predominant methodologies in this domain have primarily focused on the direct prediction of glaucoma stage labels from longitudinal datasets. However, such methods may not adequately encapsulate the nuanced dev

R. Arnau, J. M. Calabuig, L. M. García Raffi, E. A. Sánchez Pérez

Consider a finite directed graph without cycles in which the arrows are weighted. We present an algorithm for the computation of a new distance, called path-length-weighted distance, which has proven useful for graph analysis in the context of fraud detection. The idea is that the new distance explicitly takes into account the size of the paths in the calcul

Tuwe Löfström, Fatima Rabia Yapicioglu, Alessandra Stramiglio, Helena Löfström

This paper introduces Fast Calibrated Explanations, a method designed for generating rapid, uncertainty-aware explanations for machine learning models. By incorporating perturbation techniques from ConformaSight - a global explanation framework - into the core elements of Calibrated Explanations (CE), we achieve significant speedups. These core elements incl

Yang Tan, Ruilin Wang, Banghao Wu, Liang Hong

Enzyme engineering enables the modification of wild-type proteins to meet industrial and research demands by enhancing catalytic activity, stability, binding affinities, and other properties. The emergence of deep learning methods for protein modeling has demonstrated superior results at lower costs compared to traditional approaches such as directed evoluti

Catherine Ikae, Mascha Kurpicz-Briki

Studying bias detection and mitigation methods in natural language processing and the particular case of machine translation is highly relevant, as societal stereotypes might be reflected or reinforced by these systems. In this paper, we analyze the state-of-the-art with a particular focus on European and African languages. We show how the majority of the wo

Abhinav Anand, Kenneth R. Brown

In this work, we explore a new approach to designing both algorithms and error detection codes for preparing approximate ground states of molecules. We propose a classical algorithm to find the optimal stabilizer state by using excitations of the Hartree-Fock state, followed by constructing quantum error-detection codes based on this stabilizer state using c

Aadil Oufkir, Mario Berta

We study relaxations of entanglement-assisted quantum channel coding and establish that non-signaling assistance and a natural semi-definite programming relaxation\, -- \,termed meta-converse\, -- \,are equivalent in terms of success probabilities. We then present a rounding procedure that transforms any non-signaling-assisted strategy into an entanglement-a

Salah-Eddine Chorfi, Alemdar Hasanov, Roberto Morales

In this paper, we study an inverse problem of identifying two spatial-temporal source terms in the Schr\"odinger equation with dynamic boundary conditions from the final time overdetermination. We adopt a weak solution approach to solve the inverse source problem. By analyzing the associated Tikhonov functional, we prove a gradient formula of the functional

Qianjun Zheng, H. Y. Yuan, Yunshan Cao, Peng Yan

Significant progress has been made for the emerging concept of magnon frequency comb (MFC) but mainly in the classical region. The quantum property of the comb structure is yet to be explored. Here we theoretically investigate the quantum fluctuations of frequency combs and demonstrate the continuous-variable quantum entanglement and Einstein-Podolsky-Rosen

Yuly Billig, Henrique Rocha

We study the growth of representations of the Lie algebra of vector fields on the affine space that admit a compatible action of the polynomial algebra. We establish the Bernstein inequality for these representations, enabling us to focus on modules with minimal growth, known as holonomic modules. We show that simple holonomic modules are isomorphic to the t

Wasiq Khan, Luke K. Topham, Peter Atherton, Raghad Al-Shabandar

Education is being transformed by rapid advances in Artificial Intelligence (AI), including emerging Generative Artificial Intelligence (GAI). Such technology can significantly support academics and students by automating monotonous tasks and making personalised suggestions. However, despite the potential of the technology, there are significant concerns reg

Ziyang Zheng, Haipeng Jing, Canyu Rui, Askar Hamdulla

Text-To-SQL (T2S) conversion based on large language models (LLMs) has found a wide range of applications, by leveraging the capabilities of LLMs in interpreting the query intent expressed in natural language. Existing research focuses on suitable representations for data schema and/or questions, task-specific instructions and representative examples, and co

Jian Chen, Liping Tang, Xinmin Yang

Over the past two decades, descent methods have received substantial attention within the multiobjective optimization field. Nonetheless, both theoretical analyses and empirical evidence reveal that existing first-order methods for multiobjective optimization converge slowly, even for well-conditioned problems, due to the objective imbalances. To address thi

Sifat Ut Taki, Arthi Padmanabhan, Spyridon Mastorakis

Edge AI computing boxes are a new class of computing devices that are aimed to revolutionize the AI industry. These compact and robust hardware units bring the power of AI processing directly to the source of data--on the edge of the network. On the other hand, on-demand serverless inference services are becoming more and more popular as they minimize the in

Jun Bai, Yiliao Song, Di Wu, Atul Sajjanhar

One-Shot Federated Learning (OSFL) restricts communication between the server and clients to a single round, significantly reducing communication costs and minimizing privacy leakage risks compared to traditional Federated Learning (FL), which requires multiple rounds of communication. However, existing OSFL frameworks remain vulnerable to distributional het

Kang-Sin Choi

We show that the renormalized loop corrections to the scalar mass are suppressed if the field in the loop is heavy. Here, treating the renormalized mass is important, as it is the only observable. This means that the physics of a low-energy scalar field is insensitive to that of ultraviolet.

Nico Meyer, Julian Berberich, Christopher Mutschler, Daniel D. Scherer

Quantum machine learning leverages quantum computing to enhance accuracy and reduce model complexity compared to classical approaches, promising significant advancements in various fields. Within this domain, quantum reinforcement learning has garnered attention, often realized using variational quantum circuits to approximate the policy function. This paper

Dandan Fan, Xiaofeng Gu, Huiqiu Lin

Biregular bipartite graphs have been proven to have similar edge distributions to random bipartite graphs and thus have nice pseudorandomness and expansion properties. Thus it is quite desirable to find a biregular bipartite spanning subgraph in a given bipartite graph. In fact, a theorem of Ore implies a structural characterization of such subgraphs in bipa

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

Measurements of $CP$ observables and the CKM angle $\gamma$ are performed in $B^{\pm} \to D K^*(892)^{\pm}$ decays, where $D$ represents a superposition of $D^0$ and $\overline{D}{}^0$ states, using the LHCb dataset collected during Run 1 (2011-2012) and Run 2 (2015-2018). A study of this channel is presented with the $D$ meson reconstructed in two-body fina

Gaowei Cao, Gui-Qiang G. Chen, Xiaozhou Yang

We are concerned with a new solution formula and its applications to the analysis of properties of entropy solutions of the Cauchy problem for one-dimensional scalar hyperbolic conservation laws, wherein the flux functions exhibit convexity degeneracy and the initial data are in $L^\infty$. We first introduce/validate the novel formula for entropy solutions

Joao Pereira, Vasco Lopes, David Semedo, Joao Neves

The growing demand for surveillance in public spaces presents significant challenges due to the shortage of human resources. Current AI-based video surveillance systems heavily rely on core computer vision models that require extensive finetuning, which is particularly difficult in surveillance settings due to limited datasets and difficult setting (viewpoin

Shuai Wu, Sophie Leanza, Lu Lu, Yilong Chang

Vascular diseases such as thrombosis, atherosclerosis, and aneurysm, which can lead to blockage of blood flow or blood vessel rupture, are common and life-threatening. Conventional minimally invasive treatments utilize catheters, or long tubes, to guide small devices or therapeutic agents to targeted regions for intervention. Unfortunately, catheters suffer

Chi Ding, Qingchao Zhang, Ge Wang, Xiaojing Ye

Inverse problems arise in many applications, especially tomographic imaging. We develop a Learned Alternating Minimization Algorithm (LAMA) to solve such problems via two-block optimization by synergizing data-driven and classical techniques with proven convergence. LAMA is naturally induced by a variational model with learnable regularizers in both data and

Leonardo Perotti, Lech A. Grzelak, Cornelis W. Oosterlee

Prepayment risk embedded in fixed-rate mortgages forms a significant fraction of a financial institution's exposure, and it receives particular attention because of the magnitude of the underlying market. The embedded prepayment option (EPO) bears the same interest rate risk as an exotic interest rate swap (IRS) with a suitable stochastic notional. We invest

Yi Zheng, Zehao Li, Peng Jiang, Yijie Peng

We study the dynamic pricing and replenishment problems under inconsistent decision frequencies. Different from the traditional demand assumption, the discreteness of demand and the parameter within the Poisson distribution as a function of price introduce complexity into analyzing the problem property. We demonstrate the concavity of the single-period profi

Naga Venkata Sai Raviteja Chappa, Khoa Luu

Group Activity Recognition (GAR) remains challenging in computer vision due to the complex nature of multi-agent interactions. This paper introduces LiGAR, a LIDAR-Guided Hierarchical Transformer for Multi-Modal Group Activity Recognition. LiGAR leverages LiDAR data as a structural backbone to guide the processing of visual and textual information, enabling

Ya-Wei Eileen Lin, Ronald R. Coifman, Gal Mishne, Ronen Talmon

Finding meaningful distances between high-dimensional data samples is an important scientific task. To this end, we propose a new tree-Wasserstein distance (TWD) for high-dimensional data with two key aspects. First, our TWD is specifically designed for data with a latent feature hierarchy, i.e., the features lie in a hierarchical space, in contrast to the u

Enric Solé-Farré

Nearly K\"ahler and Einstein structures admit a variational characterization, where the second variation is associated with a strongly elliptic operator. This allows us to associate a Morse-like index to each structure. Our study focuses on how these indices behave under the assumption that the nearly K\"ahler structure admits a cohomogeneity one action. Spe

Michel F. C. Haddad, Martin Huber, José Eduardo Medina-Reyes, Lucas Z. Zhang

We propose a difference-in-differences (DiD) framework designed for time-varying continuous treatments across multiple periods. Specifically, we estimate the average treatment effect on the treated (ATET) by comparing distinct non-zero treatment intensities. Identification rests on a conditional parallel trends assumption that accounts for observed covariate

Anubha Goel, Henri Hansen, Juho Kanniainen

Building on topological data analysis and expert knowledge, this study introduces a Mapper-based approach to cluster agents based on their tendency to be influenced by information spread. The context of our paper is financial markets with an aim to identify agents trading opportunistically on insider information while minimizing false positives, a critical c

Enric Solé-Farré

We investigate generalisations of Hitchin's functionals, whose critical points correspond to nearly K\"ahler and nearly parallel $G_2$-structures. Our focus is on the gradient flow of these functionals and the spectral decomposition of their Hessians with respect to natural indefinite inner products. We introduce a Morse-like index for these functionals, ter

Christina Brech, Jörg Brendle, Márcio Telles

How many permutations are needed so that every infinite-coinfinite set of natural numbers with asymptotic density can be rearranged to no longer have the same density? We prove that the density number $\mathfrak{dd}$, which answers this question, is equal to the least size of a non-meager set of reals, $\mathsf{non} (\mathcal{M})$. The same argument shows th

Sandhya Aneja, Nagender Aneja

Browser fingerprinting is the identification of a browser through the network traffic captured during communication between the browser and server. This can be done using the HTTP protocol, browser extensions, and other methods. This paper discusses browser fingerprinting using the HTTPS over TLS 1.3 protocol. The study observed that different browsers use a

Desta Haileselassie Hagos, Hassan El Alami, Danda B. Rawat

Artificial Intelligence (AI) techniques, particularly machine learning techniques, are rapidly transforming tactical operations by augmenting human decision-making capabilities. This paper explores AI-driven Human-Autonomy Teaming (HAT) as a transformative approach, focusing on how it empowers human decision-making in complex environments. While trust and ex

Yizun Lin, Zhao-Rong Lai, Cheng Li

The Sharpe ratio is an important and widely-used risk-adjusted return in financial engineering. In modern portfolio management, one may require an m-sparse (no more than m active assets) portfolio to save managerial and financial costs. However, few existing methods can optimize the Sharpe ratio with the m-sparse constraint, due to the nonconvexity and the c

Marina Cano Amoros, Nadine Nettelmann, Nicola Tosi, Philipp Baumeister

Demixing properties of planetary major constituents influence the interior structure and evolution of planets. Comparing experimental and computational data on the miscibility of hydrogen and water to adiabatic profiles suggests phase separation between these components occurs in the ice giants Uranus and Neptune. We aim to predict the atmospheric water abun

Ina Dormuth, Carolin Herrmann, Frank Konietschke, Markus Pauly

When comparing multiple groups in clinical trials, we are not only interested in whether there is a difference between any groups but rather the location. Such research questions lead to testing multiple individual hypotheses. To control the familywise error rate (FWER), we must apply some corrections or introduce tests that control the FWER by design. In th

Marianne Akian, Stéphane Gaubert, Loïc Marchesini

We consider a new class of repeated zero-sum games in which the payoff is the escape rate of a switched dynamical system, where at every stage, the transition is given by a nonexpansive operator depending on the actions of both players. This generalizes to the two-player (and non-linear) case the notion of joint spectral radius of a family of matrices. We sh

E. V. Mardyban, D. F. Bayramov, A. Rahmatinejad, A. K. Azhibekov

The probabilities to form an $\alpha$-particle on the surface of heavy nuclei (spectroscopic factors) in the states of different angular momenta are calculated within the dinuclear system (DNS) approach. It is shown that this dependence is determined not only by the energies of the excited states of the daughter nucleus, as is suggested by the Boltzmann dist

V. E. Kuzmichev, V. V. Kuzmichev

An analogy between non-relativistic quantum mechanics in the Madelung formulation and quantum geometrodynamics in the case of the maximally symmetric space is drawn. The equations equivalent to the continuity equation and the hydrodynamic Euler equation describing the evolution of the velocity introduced for the case of hypothetical fluid flow characterizing

Ivan Rosas-Soto

In the present article, we study the integral aspects of the Fourier transform of an abelian variety $A$ over a field $k$, using \'etale motivic cohomology, following the ideas and theory given by Moonen, Polishchuk and later by Beckman and de Gaay Fortman. We prove that there exists a PD-structure over the positive degree part of the \'etale Chow ring $\tex

Emanuel Milman, Amir Yehudayoff

The Blaschke-Santal\'o inequality states that the volume product $|K| \cdot |K^{o}|$ of a symmetric convex body $K \subset \mathbb{R}^n$ is maximized by the standard Euclidean unit-ball. Cordero-Erausquin asked whether the inequality remains true for all even log-concave measures. We briefly survey the literature around this question and provide details for

Sarah Sohana, William Pourmajidi, John Steinbacher, Andriy Miranskyy

Cloud computing is essential for modern enterprises, requiring robust tools to monitor and manage Large-Scale Cloud Systems (LCS). Traditional monitoring tools often miss critical insights due to the complexity and volume of LCS telemetry data. This paper presents CloudHeatMap, a novel heatmap-based visualization tool for near-real-time monitoring of LCS hea

Junlong Chen, Jens Grubert, Per Ola Kristensson

Selection of occluded objects is a challenging problem in virtual reality, even more so if multiple objects are involved. With the advent of new artificial intelligence technologies, we explore the possibility of leveraging large language models to assist multi-object selection tasks in virtual reality via a multimodal speech and raycast interaction techniqu

Katsushi Kagaya, Tomoyuki Nakano, Ryo Nakayama

One of goals in soft robotics is to achive spontaneous behavior like real organisms. To gain a clue to achieve this, we examined the long (16-hour) spontaneous exploratory locomotion of snails. The active forager snail, Tegula nigerrima, from an intertidal rocky shore was selected to test the general hypothesis that nervous systems are inherently near a crit

Yang Yu, Tian-Peng Tang, Lei Feng

The singlet scalar dark matter (DM) model is a minimal extension of the Standard Model (SM). This model features only two free parameters: the singlet scalar mass $m_S$ and the quartic coupling $a_2$ between the singlet scalar and the SM Higgs doublet. Previous studies have suggested that only the resonant region remains viable under combined constraints. In

Wenda Li, Huijie Zhang, Qing Qu

The widespread use of AI-generated content from diffusion models has raised significant concerns regarding misinformation and copyright infringement. Watermarking is a crucial technique for identifying these AI-generated images and preventing their misuse. In this paper, we introduce Shallow Diffuse, a new watermarking technique that embeds robust and invisi

René Wittmann, Iman Abdoli, Abhinav Sharma, Joseph M. Brader

We derive a version of the so-called "best Fokker-Planck approximation" (BFPA) to describe the spatial properties of interacting active Ornstein-Uhlenbeck particles (AOUPs) in arbitrary spatial dimensions. In doing so, we also take into account the odd-diffusive contribution of the Lorentz force acting on a charged particle in a spatially dependent magnetic

Jiyao Wang, Xiao Yang, Zhenyu Wang, Ximeng Wei

Road safety remains a critical challenge worldwide, with approximately 1.35 million fatalities annually attributed to traffic accidents, often due to human errors. As we advance towards higher levels of vehicle automation, challenges still exist, as driving with automation can cognitively over-demand drivers if they engage in non-driving-related tasks (NDRTs

Shangde Gao, Yichao Fu, Ke Liu, Hongxia Xu

Recently, many foundation models for medical image analysis such as MedSAM, SwinUNETR have been released and proven to be useful in multiple tasks. However, considering the inherent heterogeneity and inhomogeneity of real-world medical data, directly applying these models to specific medical image segmentation tasks often leads to negative domain shift effec

Ilinka Dimitrova, Vítor H. Fernandes, Jörg Koppitz

In this paper, we consider the monoids of all partial endomorphisms, of all partial weak endomorphisms, of all injective partial endomorphisms, of all partial strong endomorphisms and of all partial strong weak endomorphisms of a star graph with a finite number of vertices. Our main objective is to exhibit a presentation for each of them.

Honglin Mu, Han He, Yuxin Zhou, Yunlong Feng

Large language model (LLM) safety is a critical issue, with numerous studies employing red team testing to enhance model security. Among these, jailbreak methods explore potential vulnerabilities by crafting malicious prompts that induce model outputs contrary to safety alignments. Existing black-box jailbreak methods often rely on model feedback, repeatedly

R. Arnau, E. A. Sánchez Pérez, S. Sanjuan

The extension of the concept of $p-$summability for linear operators to the context of Lipschitz operators on metric spaces has been extensively studied in recent years. This research primarily uses the linearization of the metric space $M$ afforded by the associated Arens-Eells space, along with the duality between $M$ and the metric dual space $M^\#$ defin

Benjamin Schiffer, Lucas Janson

Many practical applications of online reinforcement learning require the satisfaction of safety constraints while learning about the unknown environment. In this work, we establish theoretical foundations for reinforcement learning with safety constraints by studying the canonical problem of Linear Quadratic Regulator learning with unknown dynamics, but with

Filippo Giuliani, Raffaele Scandone

In this paper we prove the existence of solutions to the quantum Euler equations on $\mathbb{T}^d$, $d\geqslant 2$, with almost constant mass density, displaying energy transfers to high Fourier modes and polynomially fast-in-time growth of Sobolev norms above the finite-energy level. These solutions are uniformly far from vacuum, suggesting that weak turbul

Ge Yang, Changyi He, Jinyang Guo, Jianyu Wu

Although large language models (LLMs) have demonstrated their strong intelligence ability, the high demand for computation and storage hinders their practical application. To this end, many model compression techniques are proposed to increase the efficiency of LLMs. However, current researches only validate their methods on limited models, datasets, metrics

Alexander Wieser, Johannes Lachner, Martin Martschini, Dorian Zok

The detection of low abundances of $^{135}$Cs in environmental samples is of significant interest in different fields of environmental sciences, especially in combination with its shorter-lived sister isotope $^{137}$Cs. The method of Ion-Laser InterAction Mass Spectrometry (ILIAMS) for barium separation at the Vienna Environmental Research Accelerator (VERA

T. Majumder, M. V. Pruzhinskaya, E. E. O. Ishida, K. L. Malanchev

The advent of large astronomical surveys has made available large and complex data sets. However, the process of discovery and interpretation of each potentially new astronomical source is, many times, still handcrafted. In this context, machine learning algorithms have emerged as a powerful tool to mine large data sets and lower the burden on the domain exp

Alicja Polanska, Thibeau Wouters, Peter T. H. Pang, Kaze K. W. Wong

We present an accelerated pipeline, based on high-performance computing techniques and normalizing flows, for joint Bayesian parameter estimation and model selection and demonstrate its efficiency in gravitational wave astrophysics. We integrate the Jim inference toolkit, a normalizing flow-enhanced Markov chain Monte Carlo (MCMC) sampler, with the learned h

Shinjan Mandal, Indrajit Maity, H R Krishnamurthy, Manish Jain

The introduction of a twist between two layers of two-dimensional materials has opened up a new and exciting field of research known as twistronics. In these systems, the phonon dispersions show significant renormalization and enhanced electron-phonon interactions as a function of the twist angle. However, the large system size of the resulting moir\'e patte

Alexander M. Semenov, Sergey R. Usmanov, Aleksey K. Fedorov

Practically relevant problems of quadratic optimization often contain multidimensional arrays of variables interconnected by linear constraints, such as equalities and inequalities. The values of each variable depend on its specific meaning and can be binary, integer, discrete, and continuous. These circumstances make it technically difficult to reduce the o

Hiroki Matsutani, Masaaki Kondo, Kazuki Sunaga, Radu Marculescu

This paper proposes Skip2-LoRA as a lightweight fine-tuning method for deep neural networks to address the gap between pre-trained and deployed models. In our approach, trainable LoRA (low-rank adaptation) adapters are inserted between the last layer and every other layer to enhance the network expressive power while keeping the backward computation cost low

Zhi Wen Soi, Chenrui Fan, Aditya Shankar, Abele Mălan

Distributed time series data presents a challenge for federated learning, as clients often possess different feature sets and have misaligned time steps. Existing federated time series models are limited by the assumption of perfect temporal or feature alignment across clients. In this paper, we propose FedTDD, a novel federated time series diffusion model t

Eitan Farchi, Shmulik Froimovich, Rami Katan, Orna Raz

LLMs can be used in a variety of code related tasks such as translating from one programming language to another, implementing natural language requirements and code summarization. Artifacts generated by state of the art LLM technology are expected to be useful in the sense that a user will be able to use the LLM generated artifact after a small number of ea

Xiaoqi Ling, Cheng Cai, Demin Kong, Zhisheng Wei

Computational protein design (CPD) refers to the use of computational methods to design proteins. Traditional methods relying on energy functions and heuristic algorithms for sequence design are inefficient and do not meet the demands of the big data era in biomolecules, with their accuracy limited by the energy functions and search algorithms. Existing deep

Maxime Wagner, Tilmann Wurzbacher

We show the equivalence of five different conditions on a classical field $\psi$ with values in a restricted multicotangent bundle to be a solution of the field equations, notably in terms of the Hamilton-Volterra equations, the principle of least action and several conditions based on the contraction of the multi-vector tangent to $\psi$ with canonical diff

Meiqi Chen, Fandong Meng, Yingxue Zhang, Yan Zhang

Large language models (LLMs) have shown great promise in machine translation, but they still struggle with contextually dependent terms, such as new or domain-specific words. This leads to inconsistencies and errors that are difficult to address. Existing solutions often depend on manual identification of such terms, which is impractical given the complexity

Jieyi Bi, Yining Ma, Jianan Zhou, Wen Song

Vehicle Routing Problems (VRPs) can model many real-world scenarios and often involve complex constraints. While recent neural methods excel in constructing solutions based on feasibility masking, they struggle with handling complex constraints, especially when obtaining the masking itself is NP-hard. In this paper, we propose a novel Proactive Infeasibility

S. A. Saakyan

Rapidly oscillating, inhomogeneous electromagnetic field from laser exert a force that repels charged particles from regions of high light intensity. We propose and analyze a flat-bottomed hollow-beam ponderomotive optical trap for an ultracold neutral plasma (UNP), driven by a high-power CO$_2$ laser. Molecular dynamics simulations show that the plasma and

Carla Boix-Constant, Andrey Rybakov, Clara Miranda-Pérez, Gabriel Martínez-Carracedo

Twisting two-dimensional van der Waals magnets allows the formation and control of different spin-textures, as skyrmions or magnetic domains. Beyond the rotation angle, different spin reversal processes can be engineered by increasing the number of magnetic layers forming the twisted van der Waals heterostructure. Here, we consider pristine monolayers and bi

Thomas Michael Keller, Zachary Martin, Alexa Renner, Gabriel Roca

For a finite group $G$, the vertices of the prime graph $\Gamma(G)$ are the primes that divide $|G|$, and two vertices $p$ and $q$ are connected by an edge if there is an element of order $pq$ in $G$. Prime graphs of solvable groups have been classified, and prime graphs of groups whose noncyclic composition factors are isomorphic to a single nonabelian simp

Daiki Ueda, Kazuhiro Tatsumi

Relative entropy is a non-negative quantity and offers a powerful means of achieving a unified understanding of fundamental properties in physics, including the second law of thermodynamics and positivity bounds on effective field theories (EFTs). We analyze the relative entropy in scalar field theories and show that the non-negativity of relative entropy is

Vladimir Arkhipkin, Viacheslav Vasilev, Andrei Filatov, Igor Pavlov

Text-to-image (T2I) diffusion models are popular for introducing image manipulation methods, such as editing, image fusion, inpainting, etc. At the same time, image-to-video (I2V) and text-to-video (T2V) models are also built on top of T2I models. We present Kandinsky 3, a novel T2I model based on latent diffusion, achieving a high level of quality and photo

Yutong Cheng, Osama Bajaber, Saimon Amanuel Tsegai, Dawn Song

Textual descriptions in cyber threat intelligence (CTI) reports, such as security articles and news, are rich sources of knowledge about cyber threats, crucial for organizations to stay informed about the rapidly evolving threat landscape. However, current CTI knowledge extraction methods lack flexibility and generalizability, often resulting in inaccurate a

Xiaoxu Feng, Takato Horii, Takayuki Nagai

Mobile manipulators require coordinated control between navigation and manipulation to accomplish tasks. Typically, coordinated mobile manipulation behaviors have base navigation to approach the goal followed by arm manipulation to reach the desired pose. Selecting the embodiment between the base and arm can be determined based on reachability. Previous meth

Pablo Encarnacion, Albert Feijoo, Angels Ramos

We have calculated the femtoscopic correlation functions of meson-baryon pairs in the strangeness $S=-1$ sector, employing a unitarized chiral interaction model up to next-to-leading order. We will show preliminary results for the $\pi^-\Lambda$ correlation function, which is presently under analysis by the ALICE@LHC collaboration. We will also demonstrate t

Aleksander Doan, Thomas Walpuski

Motivated by counting pseudo-holomorphic curves in symplectic Calabi-Yau $3$-folds, this article studies a chamber structure in the space of real Cauchy-Riemann operators on a Riemann surface, and constructs three chambered invariants associated with such operators: $n_{\mathrm{Bl}}$, $n_{1,2}$, $n_{2,1}$. The first of these invariants is defined by counting

Chenhao Ma, Zixu Zhao

Considering environment-induced interatomic interaction, we study the entanglement dynamics of two uniformly accelerated atoms that interact with fluctuating massless scalar fields in the Minkowski vacuum in the presence of a reflecting boundary. The two atoms are initially prepared in a state such that one is in the ground state and the other is in the exci

Kaizhe Chen, Chunyang Hu, Shiping Liu, Heng Zhang

Amply regular graphs are graphs with local distance-regularity constraints. In this paper, we prove a weaker version of a conjecture proposed by Qiao, Park, and Koolen on diameter bounds of amply regular graphs and make new progress on Terwilliger's conjecture on finiteness of amply regular graphs. Terwilliger's conjecture can be considered as a natural exte

Florian Eichin, Carolin M. Schuster, Georg Groh, Michael A. Hedderich

Topic modeling is a key method in text analysis, but existing approaches fail to efficiently scale to large datasets or are limited by assuming one topic per document. Overcoming these limitations, we introduce Semantic Component Analysis (SCA), a topic modeling technique that discovers multiple topics per sample by introducing a decomposition step to the cl

Moreno Pintore, Bruno Després

Deriving sharp and computable upper bounds of the Lipschitz constant of deep neural networks is crucial to formally guarantee the robustness of neural-network based models. We analyse three existing upper bounds written for the $l^2$ norm. We highlight the importance of working with the $l^1$ and $l^\infty$ norms and we propose two novel bounds for both feed

Tu Trinh, Mohamad H. Danesh, Nguyen X. Khanh, Benjamin Plaut

While reinforcement learning (RL) agents often perform well during training, they can struggle with distribution shift in real-world deployments. One particularly severe risk of distribution shift is goal misgeneralization, where the agent learns a proxy goal that coincides with the true goal during training but not during deployment. In this paper, we explo

Pradeep Kumar Kumawat, Subhajit Barman, Bibhas Ranjan Majhi

We study the prospect of the equivalence principle at the quantum regime by investigating the transition probabilities of a two-level atomic detector in different scenarios. In particular, two specific set-ups are considered. ($i$) $Without~a~boundary$: In one scenario the atom is in uniform acceleration and interacting with Minkowski field modes. While in t

Maciej Paszyński

We show that assuming the availability of the processor with variable precision arithmetic, we can compute matrix-by-matrix multiplications in $O(N^2log_2N)$ computational complexity. We replace the standard matrix-by-matrix multiplications $\begin{bmatrix} A_{11} & A_{12} \\ A_{21} & A_{22}\end{bmatrix}\begin{bmatrix} B_{11} & B_{12} \\ B_{21} & B_{22}\end{

Caroline Davis, Malavika Mukundan, Danny Stoll, Giulio Tiozzo

We describe a family $\textrm{Cyc}_p(\mathcal{F})$ of marked cycle curves that parameterize the cycles of period $p$ of a given family $\mathcal{F}$ of dynamical systems. We produce algorithms to compute a canonical cell decomposition for the marked cycle curves over the family $\textrm{Per}_1(0)$ of quadratic polynomials as well as over the family $\textrm{

Yuli Liu, Min Liu, Xiaojing Liu

Transformer-based approaches have demonstrated remarkable success in various sequence-based tasks. However, traditional self-attention models may not sufficiently capture the intricate dependencies within items in sequential recommendation scenarios. This is due to the lack of explicit emphasis on attention weights, which play a critical role in allocating a

Majid Yazdani-Kachoei, Krzysztof Sacha, Boris A. Malomed

Research on time crystals concerns the spontaneous breaking of translational symmetry in time, as well as the realization of phenomena and phases known from solid-state physics in the time domain. Periodically driven systems of massive particles are widely used in these studies. In the present work, we consider a photonic system and demonstrate that stable n

Patrik Haslum, Augusto B. Corrêa

In AI planning, it is common to distinguish between planning domains and problem instances, where a "domain" is generally understood as a set of related problem instances. This distinction is important, for example, in generalised planning, which aims to find a single, general plan or policy that solves all instances of a given domain. In PDDL, domains and p

Yi-Lun Du, Xiaojian Song, Xi Luo

We introduce a novel deep learning framework based on Long Short-Term Memory (LSTM) networks to predict galactic cosmic-ray spectra on a one-day-ahead basis by leveraging historical solar activity data, overcoming limitations inherent in traditional transport models. By flexibly incorporating multiple solar parameters, such as the heliospheric magnetic field

Guglielmo Feltrin, Alessandro Fonda, Andrea Sfecci

We provide a new version of the Poincar\'e-Birkhoff theorem for possibly multivalued successor maps associated with planar non-autonomous Hamiltonian systems. As an application, we prove the existence of periodic and subharmonic solutions of the scalar second order equation $\ddot x + \lambda g(t,x) = 0$, for $\lambda>0$ sufficiently small, with $g(t,x)$ hav

John Atanbori

Recognising animals based on distinctive body patterns, such as stripes, spots, or other markings, in night images is a complex task in computer vision. Existing methods for detecting animals in images often rely on colour information, which is not always available in night images, posing a challenge for pattern recognition in such conditions. Nevertheless,

Simone Piaggesi, André Panisson, Megha Khosla

Node representations, or embeddings, are low-dimensional vectors that capture node properties, typically learned through unsupervised structural similarity objectives or supervised tasks. While recent efforts have focused on explaining graph model decisions, the interpretability of unsupervised node embeddings remains underexplored. To bridge this gap, we in

Mengke Li, Ye Liu, Yang Lu, Yiqun Zhang

Long-tail learning has garnered widespread attention and achieved significant progress in recent times. However, even with pre-trained prior knowledge, models still exhibit weaker generalization performance on tail classes. The promising Sharpness-Aware Minimization (SAM) can effectively improve the generalization capability of models by seeking out flat min

Gibran Gomez, Kevin van Liebergen, Davide Sanvito, Giuseppe Siracusano

Cryptocurrency abuse reporting services are a valuable data source about abusive blockchain addresses, prevalent types of cryptocurrency abuse, and their financial impact on victims. However, they may suffer data pollution due to their crowd-sourced nature. This work analyzes the extent and impact of data pollution in cryptocurrency abuse reporting services

Kazuma Obata, Tatsuya Aoki, Takato Horii, Tadahiro Taniguchi

This study proposes LiP-LLM: integrating linear programming and dependency graph with large language models (LLMs) for multi-robot task planning. In order for multiple robots to perform tasks more efficiently, it is necessary to manage the precedence dependencies between tasks. Although multi-robot decentralized and centralized task planners using LLMs have

Anh Xuan Do, Debdip Ganguly, Nguyen Lam, Guozhen Lu

We establish a general scale-dependent Poincar\'{e}-Hardy type identity involving a vector field on the hyperbolic space. By choosing suitable parameter, potential and vector field in this identity, we can recover, as well as derive new versions of and substantially improve several Poincar\'{e} type, Hardy type and Poincar\'{e}-Hardy type inequalities in the

Wei Zhang, Diego F. Torres, C. R. García, J. Li

Context. Pulsar wind nebulae (PWNe) are a source of very high energy radiation that can reach up to tera-electron volts and even peta-electron volts. Our work uses the pulsar tree, a graph theory tool recently presented to analyze the pulsar population and select candidates of interest. Aims. We aim to discover detectable PWNe. We also aim to test to what ex

Shuaihang Yuan, Halil Utku Unlu, Hao Huang, Congcong Wen

In this paper, we present a novel method for reliable frontier selection in Zero-Shot Object Goal Navigation (ZS-OGN), enhancing robotic navigation systems with foundation models to improve commonsense reasoning in indoor environments. Our approach introduces a multi-expert decision framework to address the nonsensical or irrelevant reasoning often seen in f

Chansup Byun, Albert Reuther, Julie Mullen, LaToya Anderson

The increasing use and cost of high performance computing (HPC) requires new easy-to-use tools to enable HPC users and HPC systems engineers to transparently understand the utilization of resources. The MIT Lincoln Laboratory Supercomputing Center (LLSC) has developed a simple command, LLload, to monitor and characterize HPC workloads. LLload plays an import