April 2024 arXiv papers — page 135

Niels Jakob Laustsen, Pedro Tradacete

Motivated by the construction of the free Banach lattice generated by a Banach space, we introduce and study several vector and Banach lattices of positively homogeneous functions defined on the dual of a Banach space $E$. The relations between these lattices allow us to give multiple characterizations of when the underlying Banach space $E$ is finite-dimens

A. C. Aguilar, N. Brito, M. N. Ferreira, J. Papavassiliou

The Batalin-Vilkovisky function is a central component in the modern formulation of the background field method and the physical applications derived from it. In the present work we report on novel lattice results for this particular quantity, obtained by capitalizing on its equality with the Kugo-Ojima function in the Landau gauge. The results of the lattic

Wenhao Wang, Lulu Zhou, Aviv Yaish, Fan Zhang

Zero-knowledge proofs (ZKPs) are computationally demanding to generate. Their importance for applications like ZK-Rollups has prompted some to outsource ZKP generation to a market of specialized provers. However, existing market designs either do not fit the ZKP setting or lack formal description and analysis. In this work, we propose a formal ZKP market mod

Vladimir Damljanovic

Increased interest in physics of graphene and other two-dimensional materials boosted investigations of band structure near nodal points and lines. In contrast, group theoretical explanation of simple bands (that do not touch other bands), is sporadically present in the literature. This paper presents electronic dispersions up to forth order in momentum, nea

Anagh Malik, Noah Juravsky, Ryan Po, Gordon Wetzstein

We present an imaging and neural rendering technique that seeks to synthesize videos of light propagating through a scene from novel, moving camera viewpoints. Our approach relies on a new ultrafast imaging setup to capture a first-of-its kind, multi-viewpoint video dataset with picosecond-level temporal resolution. Combined with this dataset, we introduce a

Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi

Graphs are a natural representation for systems based on relations between connected entities. Combinatorial optimization problems, which arise when considering an objective function related to a process of interest on discrete structures, are often challenging due to the rapid growth of the solution space. The trial-and-error paradigm of Reinforcement Learn

A. V. Dodin, S. A. Potanin, M. A. Burlak, D. V. Cheryasov

A strong global magnetic field of young low-mass stars and a high accretion rate are the necessary conditions for the formation of collimated outflows (jets) from these objects. But it is still unclear whether these conditions are also sufficient. We aim to check whether BP Tau, an actively accreting young star with a strong magnetic field, has a jet. We car

Satyajith Bommana Boyana, Thomas Lewis, Sijing Liu, Yi Zhang

In this paper, we propose and analyze a numerically stable and convergent scheme for a convection-diffusion-reaction equation in the convection-dominated regime. Discontinuous Galerkin (DG) methods are considered since standard finite element methods for the convection-dominated equation cause spurious oscillations. We choose to follow a novel DG finite elem

Teodora Dobos, Martin Bichler, Johannes Knörr

The European day-ahead electricity market is split into multiple bidding zones with a uniform price. The increase in renewables leads to a growing number of interventions in the generation of energy sources and increasing redispatch costs. To ensure efficient congestion management, the EU Commission mandated a Bidding Zone Review (BZR) to reevaluate the conf

Ipek Baris Schlicht, Defne Altiok, Maryanne Taouk, Lucie Flek

This paper addresses debiasing in news editing and evaluates the effectiveness of conversational Large Language Models in this task. We designed an evaluation checklist tailored to news editors' perspectives, obtained generated texts from three popular conversational models using a subset of a publicly available dataset in media bias, and evaluated the texts

Alexander J. Gordon, Annette M. N. Ferguson, Robert G. Mann

Tidal features are a key observable prediction of the hierarchical model of galaxy formation and contain a wealth of information about the properties and history of a galaxy. Modern wide-field surveys such as LSST and Euclid will revolutionise the study of tidal features. However, the volume of data will prohibit visual inspection to identify features, there

Jiayi Shen, Cheems Wang, Zehao Xiao, Nanne Van Noord

This paper proposes \textit{GO4Align}, a multi-task optimization approach that tackles task imbalance by explicitly aligning the optimization across tasks. To achieve this, we design an adaptive group risk minimization strategy, comprising two techniques in implementation: (i) dynamical group assignment, which clusters similar tasks based on task interaction

Diego Goldsztajn, Sem C. Borst, Johan S. H. van Leeuwaarden

We consider a model inspired by compatibility constraints that arise between tasks and servers in data centers, cloud computing systems and content delivery networks. The constraints are represented by a bipartite graph or network that interconnects dispatchers with compatible servers. Each dispatcher receives tasks over time and sends every task to a compat

Cailean Osborne

Governments are increasingly funding open source software (OSS) development to support software security, digital sovereignty, and national competitiveness in science and innovation, amongst others. However, little is known about how OSS developers evaluate the relative benefits and drawbacks of governmental funding for OSS. This study explores this question

Bochao Zou, Zizheng Guo, Xiaocheng Hu, Huimin Ma

Remote photoplethysmography (rPPG) is a method for non-contact measurement of physiological signals from facial videos, holding great potential in various applications such as healthcare, affective computing, and anti-spoofing. Existing deep learning methods struggle to address two core issues of rPPG simultaneously: understanding the periodic pattern of rPP

Jesse Thorner

Let $\chi$ be an idele class character over a number field $F$, and let $\pi,\pi'$ be non-dihedral twist-inequivalent cuspidal automorphic representations of $\mathrm{GL}_2(\mathbb{A}_F)$. We prove that if $m,n\geq 0$ are integers, $m+n\geq 1$, $F$ is totally real, $\chi$ corresponds with a ray class character, and $\pi,\pi'$ correspond with primitive non-CM

Raúl Miñán, Javier Gallardo, Álvaro Ciudad, Alexis Molina

This work introduces GeoDirDock (GDD), a novel approach to molecular docking that enhances the accuracy and physical plausibility of ligand docking predictions. GDD guides the denoising process of a diffusion model along geodesic paths within multiple spaces representing translational, rotational, and torsional degrees of freedom. Our method leverages expert

Chonghua Wang, Haodong Duan, Songyang Zhang, Dahua Lin

Recently, the large language model (LLM) community has shown increasing interest in enhancing LLMs' capability to handle extremely long documents. As various long-text techniques and model architectures emerge, the precise and detailed evaluation of models' long-text capabilities has become increasingly important. Existing long-text evaluation benchmarks, su

Zhenhailong Wang, Joy Hsu, Xingyao Wang, Kuan-Hao Huang

Despite significant advancements, large multimodal models (LMMs) still struggle to bridge the gap between low-level visual perception -- focusing on shapes, sizes, and layouts -- and high-level language reasoning, such as semantics and logic. This limitation is evident in tasks that require precise visual perception, like comparing geometric properties or so

Tomasz Kacprzak, Stavros Samothrakitis, Camilla Buhl Larsen, Jaromír Kopeček

Laue tomography experiments retrieve the positions and orientations of crystal grains in a polycrystalline samples from diffraction patterns recorded at multiple viewing angles. The use of a broad wavelength spectrum beam can greatly reduce the experimental time, but poses a difficult challenge for the indexing of diffraction peaks in polycrystalline samples

Vladimir Gladshtein, Qiyuan Zhao, Willow Ahrens, Saman Amarasinghe

Arrays are a fundamental abstraction to represent collections of data. It is often possible to exploit structural properties of the data stored in an array (e.g., repetition or sparsity) to develop a specialised representation optimised for space efficiency. Formally reasoning about correctness of manipulations with such structured data is challenging, as th

Valery V. Ryzhikov

In 1949 V.A. Rokhlin introduced into ergodic theory the k-fold mixing and puzzled the mathematical community with the problem of the mismatch of these invariants. Here's what Rokhlin wrote: "The proposed work arose from the author's attempts to solve the well-known spectral problem of the theory of dynamical systems: are there any metrically different dynami

Basile Gallet

We consider two-dimensional flows above topography, revisiting the selective decay (or minimum-enstrophy) hypothesis of Bretherton and Haidvogel. We derive a 'condensed branch' of solutions to the variational problem where a domain-scale condensate coexists with a flow at the (smaller) scale of the topography. The condensate arises through a supercritical bi

Jiayi Pan, Yichi Zhang, Nicholas Tomlin, Yifei Zhou

We show that domain-general automatic evaluators can significantly improve the performance of agents for web navigation and device control. We experiment with multiple evaluation models that trade off between inference cost, modularity of design, and accuracy. We validate the performance of these models in several popular benchmarks for digital agents, findi

Tewodros Amdeberhan, Ajit Singh

A cubic partition consists of partition pairs $(\lambda,\mu)$ such that $\vert\lambda\vert+\vert\mu\vert=n$ where $\mu$ involves only even integers but no restriction is placed on $\lambda$. This paper initiates the notion of generalized cubic partitions and will prove a number of new congruences akin to the classical Ramanujan-type. The tools emphasize thre

Pelin Ozgul, Marie-Christine Fregin, Michael Stops, Simon Janssen

Artificial Intelligence (AI) will change human work by taking over specific job tasks, but there is a debate which tasks are susceptible to automation, and whether AI will augment or replace workers and affect wages. By combining data on job tasks with a measure of AI susceptibility, we show that more highly skilled workers are more susceptible to AI automat

Eric Auerbach, Yong Cai, Ahnaf Rafi

This paper studies regression discontinuity designs (RDD) when linear-in-means spillovers occur between units that are close in their running variable. We show that the RDD estimand depends on the ratio of two terms: (1) the radius over which spillovers occur and (2) the choice of bandwidth used for the local linear regression. RDD estimates direct treatment

Rohan Sarkar, Avinash Kak

We add one more invariance - the state invariance - to the more commonly used other invariances for learning object representations for recognition and retrieval. By state invariance, we mean robust with respect to changes in the structural form of the objects, such as when an umbrella is folded, or when an item of clothing is tossed on the floor. In this wo

Zihang Song, Osvaldo Simeone, Bipin Rajendran

In-context learning (ICL), a property demonstrated by transformer-based sequence models, refers to the automatic inference of an input-output mapping based on examples of the mapping provided as context. ICL requires no explicit learning, i.e., no explicit updates of model weights, directly mapping context and new input to the new output. Prior work has prov

Caroline Laber-Smith, Eve Armstrong, A. Baha Balantekin, Elizabeth K. Jones

Understanding the physics of the deep solar interior, and the more exotic environs of core-collapse supernovae (CCSN) and binary neutron-star (NS) mergers, is of keen interest in many avenues of research. To date, this physics is based largely on simulations via forward integration. While these simulations provide valuable constraints, it could be insightful

John Ihnotic

The development of composable systems architecture marks a significant shift in resource allocation and utilization within data centers. This paper presents a composable architecture scaling up to 32 GPUs on a single node, addressing the technical challenges encountered and the innovative solutions implemented. This design introduces a flexible and dynamic r

Thomas L. Lee, Sigrid Passano Hellan, Linus Ericsson, Elliot J. Crowley

In continual learning (CL) -- where a learner trains on a stream of data -- standard hyperparameter optimisation (HPO) cannot be applied, as a learner does not have access to all of the data at the same time. This has prompted the development of CL-specific HPO frameworks. The most popular way to tune hyperparameters in CL is to repeatedly train over the who

David P. Herzog, Jonathan C. Mattingly

When studying out-of-equilibrium systems, one often excites the dynamics in some degrees of freedom while removing the excitation in others through damping. In order for the system to converge to a statistical steady state, the dynamics must transfer the energy from the excited modes to the dissipative directions. The precise mechanisms underlying this trans

Caleidgh Bayer, Robert J. Smith, Malcolm I. Heywood

The navigation of complex labyrinths with tens of rooms under visual partially observable state is typically addressed using recurrent deep reinforcement learning architectures. In this work, we show that navigation can be achieved through the emergent evolution of a simple Braitentberg-style heuristic that structures the interaction between agent and labyri

Hirotaka Tashiro

Following the analogies between knots and primes, 3-manifolds and number rings in arithmetic topology, we show a topological analogue of the Hasse norm principle for finite cyclic coverings of 3-manifolds, which was originally stated for finite cyclic extensions of number fields.

Mirco Guerrini, Giuseppe Pagliara, Alessandro Drago, Andrea Lavagno

At the extreme densities reached in the core of neutron stars, it is possible that quark deconfined matter is produced. The formation of this new phase of strongly interacting matter is likely to occur via a first-order phase transition for the typical temperatures reached in astrophysical processes. The first seeds of quark matter would then form through a

Xavier Cabre, Gyula Csató, Albert Mas

We consider nonconstant periodic constrained minimizers of semilinear elliptic equations for integro-differential operators in $\mathbb{R}$. We prove that, after an appropriate translation, each of them is necessarily an even function which is decreasing in half its period. In particular, it has only two critical points in half its period, the absolute maxim

Rajendra Purohit, K R Chowdhary, S D Purohit

The parallel and distributed processing are becoming de facto industry standard, and a large part of the current research is targeted on how to make computing scalable and distributed, dynamically, without allocating the resources on permanent basis. The present article focuses on the study and performance of distributed and parallel algorithms their file sy

Beomseok Kang, Harshit Kumar, Minah Lee, Biswadeep Chakraborty

Locally interacting dynamical systems, such as epidemic spread, rumor propagation through crowd, and forest fire, exhibit complex global dynamics originated from local, relatively simple, and often stochastic interactions between dynamic elements. Their temporal evolution is often driven by transitions between a finite number of discrete states. Despite sign

David Morselli, Marcello E. Delitala, Adrianne L. Jenner, Federico Frascoli

Oncolytic virotherapy, utilizing genetically modified viruses to combat cancer and trigger anti-cancer immune responses, has garnered significant attention in recent years. In our previous work arXiv:2305.12386, we developed a stochastic agent-based model elucidating the spatial dynamics of infected and uninfected cells within solid tumours. Building upon th

Giovanni Girardi

We consider the Cauchy problem for a class of non-linear evolution equations in the form \[L(\partial_t,\partial_x) u=F(\partial_t^\ell u), \quad (t,x)\in [0,\infty)\times \mathbb{R}^n;\] here, $L(\partial_t,\partial_x)$ is a linear partial differential operator with constant coefficients, of order $m\geq 1$ with respect to the time variable $t$, and $\ell$

Rina Foygel Barber

The aim of this paper is to establish Hoeffding and Bernstein type concentration inequalities for weighted sums of exchangeable random variables. A special case is the i.i.d. setting, where random variables are sampled independently from some distribution (and are therefore exchangeable). In contrast to the existing literature on this problem, our results pr

U Vaes

The aim of this note is to revisit propagation of chaos for a Langevin-type interacting particle system used for sampling probability measures. The interacting particle system we consider coincides, in the setting of a log-quadratic target distribution, with the ensemble Kalman sampler, for which propagation of chaos was first proved by Ding and Li. Like the

Patrick Cabau, Fernand Pelletier

This paper offers an adaptation to the convenient setting of finite dimensional Nambu-Poisson structures. In particular, for partial Nambu structures, we look for those whose classical geometrical results in finite dimension can be extended to this infinite dimensional context. Finally, we are interested in the projective and direct limits of such structures

Weronika Hryniewska-Guzik, Jakub Bilski, Bartosz Chrostowski, Jakub Drak Sbahi

Robust and highly accurate lung segmentation in X-rays is crucial in medical imaging. This study evaluates deep learning solutions for this task, ranking existing methods and analyzing their performance under diverse image modifications. Out of 61 analyzed papers, only nine offered implementation or pre-trained models, enabling assessment of three prominent

Abel Jansma

A new class of quantum states is introduced by demanding that the computational measurement statistics approach the Boltzmann distribution of higher-order strongly coupled Ising models. The states, referred to as $n$-coupled states, are superpositions of even or odd parity $n$-qubit states, generalize Bell states, and form an orthonormal basis for the $n$-qu

Maximilian Dreyer, Erblina Purelku, Johanna Vielhaben, Wojciech Samek

The field of mechanistic interpretability aims to study the role of individual neurons in Deep Neural Networks. Single neurons, however, have the capability to act polysemantically and encode for multiple (unrelated) features, which renders their interpretation difficult. We present a method for disentangling polysemanticity of any Deep Neural Network by dec

Daniel Enright, Yecheng Xiang, Hyunjong Choi, Hyoseung Kim

This paper proposes a Priority-driven Accelerator Access Management (PAAM) framework for multi-process robotic applications built on top of the Robot Operating System (ROS) 2 middleware platform. The framework addresses the issue of predictable execution of time- and safety-critical callback chains that require hardware accelerators such as GPUs and TPUs. PA

Xiaoyu Liu, Yuxiang Wei, Ming Liu, Xianhui Lin

Human visual imagination usually begins with analogies or rough sketches. For example, given an image with a girl playing guitar before a building, one may analogously imagine how it seems like if Iron Man playing guitar before Pyramid in Egypt. Nonetheless, visual condition may not be precisely aligned with the imaginary result indicated by text prompt, and

Matin Tavayef, Shohreh Abdolrahimi, Ivan Booth, Hari Kunduri

We construct a family of local static, vacuum five-dimensional solutions with two commuting spatial isometries describing a black hole with a $S^3$ horizon and a 2-cycle `bubble' in the domain of outer communications. The solutions are obtained by adding distortions to an asymptotically flat seed solution. We show that the conical singularities in the undist

Ting Bao, Runzhang Xu, He Li, Xiaoxun Gong

Moir\'e-twisted materials have garnered significant research interest due to their distinctive properties and intriguing physics. However, conducting first-principles studies on such materials faces challenges, notably the formidable computational cost associated with simulating ultra-large twisted structures. This obstacle impedes the construction of a twis

Zihan Fang, Zheng Lin, Zhe Chen, Xianhao Chen

Recently, there has been a surge in the development of advanced intelligent generative content (AIGC), especially large language models (LLMs). However, for many downstream tasks, it is necessary to fine-tune LLMs using private data. While federated learning offers a promising privacy-preserving solution to LLM fine-tuning, the substantial size of an LLM, co

Enrique Fita Sanmartín, Christoph Schnörr, Fred A. Hamprecht

Spanning trees are an important primitive in many data analysis tasks, when a data set needs to be summarized in terms of its "skeleton", or when a tree-shaped graph over all observations is required for downstream processing. Popular definitions of spanning trees include the minimum spanning tree and the optimum distance spanning tree, a.k.a. the minimum ro

Norm Levenberg, Mayuresh Londhe

A classical result of Fekete gives necessary conditions on a compact set in the complex plane so that it contains infinitely many sets of conjugate algebraic integers. For such sets, we demonstrate the existence of a sequence of algebraic integers such that most of their conjugates eventually lie near the set, while maintaining a bound on heights. Finally, w

Wen Yin

Several attempts to solve the cosmological constant problem, which concerns the value of the cosmological constant being extremely smaller than the Standard Model mass scales, have introduced a scalar field with a very flat potential that can be approximated as linear around any given position. The scalar field scans the cosmological constant in such a way t

Zihan Wang, Siyang Song, Cheng Luo, Songhe Deng

Human facial action units (AUs) are mutually related in a hierarchical manner, as not only they are associated with each other in both spatial and temporal domains but also AUs located in the same/close facial regions show stronger relationships than those of different facial regions. While none of existing approach thoroughly model such hierarchical inter-d

Gerhard Hellstern, Jörg Hettel, Bettina Just

Quantum computing is an exciting field with high disruptive potential, but very difficult to access. For this reason, many approaches to teaching quantum computing are being developed worldwide. This always raises questions about the didactic concept, the content actually taught, and how to measure the success of the teaching concept. In 2022 and 2023, the a

Yash Mehan, Kumaraditya Gupta, Rohit Jayanti, Anirudh Govil

Robotic tasks such as planning and navigation require a hierarchical semantic understanding of a scene, which could include multiple floors and rooms. Current methods primarily focus on object segmentation for 3D scene understanding. However, such methods struggle to segment out topological regions like "kitchen" in the scene. In this work, we introduce a tw

Maximilian Berbig

The main limitation for preinflationary breaking of Peccei-Quinn (PQ) symmetry is the upper bound on the Hubble rate during inflation from axion isocurvature fluctuations. This leads to a tension between high scale inflation and QCD axions with grand unified theory (GUT) scale decay constants, which reduces the potential for a detection of tensor modes at ne

Dima Grigoriev

For a tropical prevariety $V\subset \RR^n$ (being a finite union of rational polyhedra) we define a tropical Hilbert function $TH_V(k)$ to be the maximal number of tropically independent on $V$ among tropical monomials with degrees at most $k$. In case $\dim V=1$ we define the tropical degree as $$degT (V):=\lim_{k\to \infty} \frac{TH_V(k)}{k}$$ \noindent an

Keneth Garcia-Cifuentes, Rosa L. Becerra, Fabio De Colle

Gamma-ray burst (GRBs) are the brightest events in the universe. For decades, astrophysicists have known about their cosmological nature. Every year, space missions such as Fermi and SWIFT detect hundreds of them. In spite of this large sample, GRBs show a complex taxonomy in the first seconds after their appearance, which makes it very difficult to find sim

Dimitrios Michail, Lefki-Ioanna Panagiotou, Charalampos Davalas, Ioannis Prapas

With climate change expected to exacerbate fire weather conditions, the accurate anticipation of wildfires on a global scale becomes increasingly crucial for disaster mitigation. In this study, we utilize SeasFire, a comprehensive global wildfire dataset with climate, vegetation, oceanic indices, and human-related variables, to enable seasonal wildfire forec

Misaki Ozawa, Nina Javerzat

R\'enyi entropy is a one-parameter generalization of Shannon entropy, which has been used in various fields of physics. Despite its wide applicability, the physical interpretations of the R\'enyi entropy are not widely known. In this paper, we discuss some basic properties of the R\'enyi entropy relevant to physics, in particular statistical mechanics, and i

José Cecílio, Alan Oliveira de Sá, André Souto

With the proliferation of Internet of Things (IoT) devices, ensuring secure communications has become imperative. Due to their low cost and embedded nature, many of these devices operate with computational and energy constraints, neglecting the potential security vulnerabilities that they may bring. This work-in-progress is focused on designing secure commun

Yuhan Huang, Ferris Prima Nugraha, Siyuan Jin, Yichi Zhang

Quadratic unconstrained binary optimization (QUBO) tasks are very important in chemistry, finance, job scheduling, and so on, which can be represented using graph structures, with the variables as nodes and the interaction between them as edges. Variational quantum algorithms, especially the Quantum Approximate Optimization Algorithm (QAOA) and its variants,

Mallikharjuna Chinnapadamala, Charul Rajput, B. Sundar Rajan

In the original coded caching model introduced by Maddah-Ali and Niesen in 2014, the server starts broadcasting only after it receives demands from all the users. So, all the users must be active during the delivery phase. In this work, we consider a coded caching model called hotplug coded caching in which some of the users are offline during the delivery p

Rex Chen, Ruiyi Wang, Fei Fang, Norman Sadeh

Decision aids based on artificial intelligence (AI) induce a wide range of outcomes when they are deployed in uncertain environments. In this paper, we investigate how users' trust in recommendations from an AI decision aid is impacted over time by designs that expose uncertainty in predicted outcomes. Unlike previous work, we focus on gig driving - a real-w

Alexander Y. Chen, Yajie Yuan, Dominic Bernardi

We write down the force-free electrodynamics (FFE) equations in dipole coordinates, and solve for normal modes corresponding to Alfv\'enic perturbations in the magnetosphere of a neutron star. We show that a single Alfv\'en wave propagating on dipole field lines spontaneously sources a fast magnetosonic (fms) wave at the next order in the perturbation expans

Filip Granqvist, Congzheng Song, Áine Cahill, Rogier van Dalen

Federated learning (FL) is an emerging machine learning (ML) training paradigm where clients own their data and collaborate to train a global model, without revealing any data to the server and other participants. Researchers commonly perform experiments in a simulation environment to quickly iterate on ideas. However, existing open-source tools do not offer

Fan Yang, Jianfeng Zhang, Yichun Shi, Bowen Chen

Benefiting from the rapid development of 2D diffusion models, 3D content generation has witnessed significant progress. One promising solution is to finetune the pre-trained 2D diffusion models to produce multi-view images and then reconstruct them into 3D assets via feed-forward sparse-view reconstruction models. However, limited by the 3D inconsistency in

Olaf Müller

This article suggests the definition of "Lorentzian space" weakening the notion of Lorentzian length spaces just as much that it allows for a functor from the category of strongly causal Lorentzian manifolds to the corresponding category of Lorentzian spaces, and considers three problems in the context of maximal Cauchy developments of Lorentzian spa

Petteri Kaski, Mateusz Michałek

The exponent $\sigma(T)$ of a tensor $T\in\mathbb{F}^d\otimes\mathbb{F}^d\otimes\mathbb{F}^d$ over a field $\mathbb{F}$ captures the base of the exponential growth rate of the tensor rank of $T$ under Kronecker powers. Tensor exponents are fundamental from the standpoint of algorithms and computational complexity theory; for example, the exponent $\omega$ of

Laetitia P. Bettmann, Michael J. Kewming, Gabriel T. Landi, John Goold

We introduce a numerical method to sample the distributions of charge, heat, and entropy production in open quantum systems coupled strongly to macroscopic reservoirs, with both temporal and energy resolution and beyond the linear-response regime. Our method exploits the mesoscopic-leads formulation, where macroscopic reservoirs are modeled by a finite colle

Ta-Ying Cheng, Prafull Sharma, Andrew Markham, Niki Trigoni

We propose ZeST, a method for zero-shot material transfer to an object in the input image given a material exemplar image. ZeST leverages existing diffusion adapters to extract implicit material representation from the exemplar image. This representation is used to transfer the material using pre-trained inpainting diffusion model on the object in the input

Xu Ding, ZhiMing Song, ChuanJun Wang, KaiFan Ji

Contact binary may be the progenitor of a red nova that eventually produces a merger event and have a cut-off period around 0.2 days. Therefore, a large number of contact binaries is needed to search for the progenitor of red novae and to study the characteristics of short-period contact binaries. In this paper, we employ the Phoebe program to generate a lar

Manav Mishra, Hritik Bana, Saswata Sarkar, Sujeevraja Sanjeevi

This article presents a deep reinforcement learning-based approach to tackle a persistent surveillance mission requiring a single unmanned aerial vehicle initially stationed at a depot with fuel or time-of-flight constraints to repeatedly visit a set of targets with equal priority. Owing to the vehicle's fuel or time-of-flight constraints, the vehicle must b

Kayla Duskin, Joseph S. Schafer, Jevin D. West, Emma S. Spiro

The presence of political misinformation and ideological echo chambers on social media platforms is concerning given the important role that these sites play in the public's exposure to news and current events. Algorithmic systems employed on these platforms are presumed to play a role in these phenomena, but little is known about their mechanisms and effect

Christian Marius Lillelund, Martin Magris, Christian Fischer Pedersen

Variational Inference (VI) is a commonly used technique for approximate Bayesian inference and uncertainty estimation in deep learning models, yet it comes at a computational cost, as it doubles the number of trainable parameters to represent uncertainty. This rapidly becomes challenging in high-dimensional settings and motivates the use of alternative techn

Ahmet Unutulmaz, Cem Ünsalan

In this paper, we present a general reconfigurable multiple-valued logic circuit. The proposed architecture is based on threshold logic gate and is compatible with binary logic, which allows a designer to easily integrate multiple valued logic with binary logic. We also present a methodology to design sequential circuits.

Kai Ma

Both collider searches and direct detections are promising approaches to probe fermionic dark matter. In this paper, we study signatures of four-fermion contact operators involving a dark fermion, an electron, and a quark pair. We show that the mono-electron production channel at hadron colliders can provide strong constraints. Associated productions of a ch

Wei Xu, Derek Freeman DeSantis, Xihaier Luo, Avish Parmar

Learning a continuous and reliable representation of physical fields from sparse sampling is challenging and it affects diverse scientific disciplines. In a recent work, we present a novel model called MMGN (Multiplicative and Modulated Gabor Network) with implicit neural networks. In this work, we design additional studies leveraging explainability methods

Matthew Fickus, Enrique Gomez-Leos, Joseph W. Iverson

Every equi-isoclinic tight fusion frame (EITFF) is a type of optimal code in a Grassmannian, consisting of subspaces of a finite-dimensional Hilbert space for which the smallest principal angle between any pair of them is as large as possible. EITFFs yield dictionaries with minimal block coherence and so are ideal for certain types of compressed sensing. By

Zahra Keyshams, Khachatur Aghavardovich Khachatryan, Monire Mikaeili Nia

The class of nonlinear integral equations on the positive half-line with a monotone operator of Hammerstein type is studied. With various partial representations of the corresponding kernel and nonlinearity, this class of equations has applications in the dynamic theory of $p$-adic strings, in the kinetic theory of gases, in the theory of radiation transfer

F. Kaplan, M. E. Harding, C. Seiler, F. Weigend

We present the formalism and implementation of quasi-particle self-consistent GW (qsGW) and eigenvalue only quasi-particle self-consistent GW (evGW) adapted to standard quantum chemistry packages. Our implementation is benchmarked against high-level quantum chemistry computations (coupled-cluster theory) and experimental results using a representative set of

Naoki Tsukamoto, Ryotaro Kase

The Event Horizon Telescope (EHT) Collaboration observed ring images called the shadows of M87* and Sagittarius~A* (Sgr~A*), which are supermassive objects in M87 and our galaxy, respectively, and their general relativistic magnetohydrodynamic simulations of black holes imply that the observed rings are formed by the gravitational lensing of synchrotron radi

Kunal Garg, Songyuan Zhang, Jacob Arkin, Chuchu Fan

Connected multi-agent robotic systems (MRS) are prone to deadlocks in an obstacle environment where the robots can get stuck away from their desired locations under a smooth low-level control policy. Without an external intervention, often in terms of a high-level command, a low-level control policy cannot resolve such deadlocks. Utilizing the generalizabili

José D. V. Arbañil, César H. Lenzi, Juan M. Z. Pretel, César O. V. Flores

In this work we study, within the framework of Cowling approximation, the effect of the electric charge on the gravitational wave frequency of fluid oscillation modes of strange quark stars. For this purpose, the dense matter of the stellar fluid is described by the MIT bag model equation of state (EoS), while for the electric charge profile, we consider tha

Luca Gioacchini, Giuseppe Siracusano, Davide Sanvito, Kiril Gashteovski

The advances made by Large Language Models (LLMs) have led to the pursuit of LLM agents that can solve intricate, multi-step reasoning tasks. As with any research pursuit, benchmarking and evaluation are key corner stones to efficient and reliable progress. However, existing benchmarks are often narrow and simply compute overall task success. To face these i

Alan Frieze, Aditya Raut

Let $G^r_{n,p}$ denote the $r$th power of the random graph $G_{n,p}$, where $p=c/n$ for a positive constant $c$. We prove that w.h.p. the maximum degree $\Delta\left(G^r_{n,p}\right)\sim \frac{\log n}{\log_{(r+1)}n}$. Here $\log_{(k)}n$ indicates the repeated application of the log-function $k$ times. So, for example, $\log_{(3)}n=\log\log\log n$.

Arghyadeep Basu, Enrico Garaldi, Benedetta Ciardi

Recently, models of the quasar luminosity function (QLF) rooted on large observational compilations have been produced that, unlike their predecessors, feature a smooth evolution with time. This bypasses the need to assume an ionizing emissivity evolution when simulating helium reionization with observations-based QLF, thus yielding more robust constraints.

Hao Xu

We establish a correspondence between modules and spans of algebras within a general monoidal 2-category $\mathfrak{C}$. Specifically, for an algebra $A$ in $\mathfrak{C}$, we construct a normalized lax 3-functor from the 2-category of $A$-modules to the 3-category of 2-spans of algebras in $\mathfrak{C}$ under $A$. This framework unifies and generalizes the

Hongyu Cai, Arjun Arunasalam, Leo Y. Lin, Antonio Bianchi

Large language models (LLMs) have become increasingly integrated with various applications. To ensure that LLMs do not generate unsafe responses, they are aligned with safeguards that specify what content is restricted. However, such alignment can be bypassed to produce prohibited content using a technique commonly referred to as jailbreak. Different systems

Yitao Xu, Ehsan Pajouheshgar, Sabine Süsstrunk

Neural Cellular Automata (NCA) models are trainable variations of traditional Cellular Automata (CA). Emergent motion in the patterns created by NCA has been successfully applied to synthesize dynamic textures. However, the conditions required for an NCA to display dynamic patterns remain unexplored. Here, we investigate the relationship between the NCA arch

Shiven Sinha, Ameya Prabhu, Ponnurangam Kumaraguru, Siddharth Bhat

Proving geometric theorems constitutes a hallmark of visual reasoning combining both intuitive and logical skills. Therefore, automated theorem proving of Olympiad-level geometry problems is considered a notable milestone in human-level automated reasoning. The introduction of AlphaGeometry, a neuro-symbolic model trained with 100 million synthetic samples,

M. Namvarpour, A. Razi

Large Language Models (LLMs) have emerged as powerful tools in various research domains. This article examines their potential through a literature review and firsthand experimentation. While LLMs offer benefits like cost-effectiveness and efficiency, challenges such as prompt tuning, biases, and subjectivity must be addressed. The study presents insights fr

Ayman Chaouki, Jesse Read, Albert Bifet

Decision Trees are prominent prediction models for interpretable Machine Learning. They have been thoroughly researched, mostly in the batch setting with a fixed labelled dataset, leading to popular algorithms such as C4.5, ID3 and CART. Unfortunately, these methods are of heuristic nature, they rely on greedy splits offering no guarantees of global optimali

N. Tuning, S. Bachmann, A. Pellegrino, U. Uwer

During the detector construction phase between 2004 and 2006, it was discovered that the LHCb Outer Tracker (OT) detector suffered from gain loss after irradiation in the laboratory at moderate intensities. Under irradiation an insulating layer was formed on the anode wire. The aging was caused by contamination of the counting gas due to outgassing of the gl

Egor Gorbachev, Tomasz Kociumaka

The edit distance of two strings is the minimum number of insertions, deletions, and substitutions needed to transform one string into the other. The textbook algorithm determines the edit distance of length-$n$ strings in $O(n^2)$ time, which is optimal up to subpolynomial factors under Orthogonal Vectors Hypothesis. In the bounded version of the problem, p

Maximilian Witte, Fabricio Rodrigues Lapolli, Philip Freese, Sebastian Götschel

Using the nonlinear shallow water equations as benchmark, we demonstrate that a simulation with the ICON-O ocean model with a 20km resolution that is frequently corrected by a U-net-type neural network can achieve discretization errors of a simulation with 10km resolution. The network, originally developed for image-based super-resolution in post-processing,

Ming-Zhu Liu, Ya-Wen Pan, Zhi-Wei Liu, Tian-Wei Wu

In the past two decades, a plethora of hadronic states beyond the conventional quark model of $q\bar{q}$ mesons and $qqq$ baryons have been observed experimentally, which motivated extensive studies to understand their nature and the non-perturbative strong interaction. Since most of these exotic states are near the mass thresholds of a pair of conventional