October 2023 arXiv papers — page 80

Zhejun Zhang, Alexander Liniger, Christos Sakaridis, Fisher Yu

The real-world deployment of an autonomous driving system requires its components to run on-board and in real-time, including the motion prediction module that predicts the future trajectories of surrounding traffic participants. Existing agent-centric methods have demonstrated outstanding performance on public benchmarks. However, they suffer from high comp

Aritra Dutta, El Houcine Bergou, Soumia Boucherouite, Nicklas Werge

Stochastic gradient descent (SGD) and its variants are the main workhorses for solving large-scale optimization problems with nonconvex objective functions. Although the convergence of SGDs in the (strongly) convex case is well-understood, their convergence for nonconvex functions stands on weak mathematical foundations. Most existing studies on the nonconve

Gregorio Carullo, Marina De Amicis

We study nonlinear evolutions of binary black hole mergers, uncovering power-law contributions generated by the long-range behaviour of the highly-curved dynamical spacetime. The result is achieved by exploiting the strong increase of the tail relevance due to binary eccentricity, recently observed in perturbative evolutions of a small-mass-ratio binary unde

Thomas Decker, Michael Lebacher, Volker Tresp

Deep Learning has already been successfully applied to analyze industrial sensor data in a variety of relevant use cases. However, the opaque nature of many well-performing methods poses a major obstacle for real-world deployment. Explainable AI (XAI) and especially feature attribution techniques promise to enable insights about how such models form their de

Amine Bahayou

We study Lie bialgebra structures on \emph{flat metric Lie algebras}, that is, Lie algebras $(\mathfrak{g},\langle\cdot,\cdot\rangle)$ whose associated left-invariant Riemannian metric on the simply connected Lie group $G$ has zero curvature. By Milnor's structure theorem, such $\mathfrak{g}$ splits orthogonally as \[\mathfrak{g}=\mathfrak{a}\oplus\mathfrak{

Abid Khan, Bryan K. Clark, Norm M. Tubman

The variational quantum eigensolver (VQE) is a promising algorithm for demonstrating quantum advantage in the noisy intermediate-scale quantum (NISQ) era. However, optimizing VQE from random initial starting parameters is challenging due to a variety of issues including barren plateaus, optimization in the presence of noise, and slow convergence. While simul

Wenwen Si, Sangdon Park, Insup Lee, Edgar Dobriban

Prediction sets capture uncertainty by predicting sets of labels rather than individual labels, enabling downstream decisions to conservatively account for all plausible outcomes. Conformal inference algorithms construct prediction sets guaranteed to contain the true label with high probability. These guarantees fail to hold in the face of distribution shift

Pranjal Aggarwal, Aman Madaan, Ankit Anand, Srividya Pranavi Potharaju

Large language models (LLMs) are now available from cloud API providers in various sizes and configurations. While this diversity offers a broad spectrum of choices, effectively leveraging the options to optimize computational cost and performance remains challenging. In this work, we present Automix, an approach that strategically routes queries to larger L

Eric Mitchell, Rafael Rafailov, Archit Sharma, Chelsea Finn

Widely used language models (LMs) are typically built by scaling up a two-stage training pipeline: a pre-training stage that uses a very large, diverse dataset of text and a fine-tuning (sometimes, 'alignment') stage that uses targeted examples or other specifications of desired behaviors. While it has been hypothesized that knowledge and skills come from pr

Charles Yang, Julian May-Mann, Ziyan Zhu, Trithep Devakul

We systematically explore the structural and electronic properties of twisted trilayer graphene systems. In general, these systems are characterized by two twist angles, which lead to two incommensurate moir\'{e} periods. We show that lattice relaxation results in the formation of domains of periodic single-moir\'{e} structures only for twist angles close to

Xueliang Zhao, Xinting Huang, Wei Bi, Lingpeng Kong

Large Language Models (LLMs) have driven substantial progress in artificial intelligence in recent years, exhibiting impressive capabilities across a wide range of tasks, including mathematical problem-solving. Inspired by the success of subgoal-based methods, we propose a novel framework called \textbf{SE}quential sub\textbf{G}oal \textbf{O}ptimization (SEG

Andreas Weingartner

We derive asymptotic estimates for distribution functions related to the Schinzel-Szekeres function. These results are then used in three different applications: the longest simple path in the divisor graph, a problem of Erd\H{o}s about a sum of reciprocals, and the small sieve of Erd\H{o}s and Ruzsa.

Samuel Goldstein, Oliver H. E. Philcox, J. Colin Hill, Angelo Esposito

We introduce a non-perturbative method to constrain the amplitude of local-type primordial non-Gaussianity ($f_{\rm NL}$) using squeezed configurations of the CMB lensing convergence and cosmic shear bispectra. First, we use cosmological consistency relations to derive a model for the squeezed limit of angular auto- and cross-bispectra of lensing convergence

Makram Chahine, Roya Firoozi, Wei Xiao, Mac Schwager

Game-theoretic motion planners are a powerful tool for the control of interactive multi-agent robot systems. Indeed, contrary to predict-then-plan paradigms, game-theoretic planners do not ignore the interactive nature of the problem, and simultaneously predict the behaviour of other agents while considering change in one's policy. This, however, comes at th

Aliakbar Moradi Marjaneh, Vakhid A. Gani, Azam Ghaani, Kurosh Javidan

We study various properties of topological solitons (kinks) of a field-theoretic model with a polynomial potential of the twelfth degree. This model is remarkable in that it has several topological sectors, in which kinks have different masses. We obtain asymptotic estimates for the kink-antikink and antikink-kink interaction forces. We also study numericall

David T. Hoffmann, Simon Schrodi, Jelena Bratulić, Nadine Behrmann

In this work, we study rapid improvements of the training loss in transformers when being confronted with multi-step decision tasks. We found that transformers struggle to learn the intermediate task and both training and validation loss saturate for hundreds of epochs. When transformers finally learn the intermediate task, they do this rapidly and unexpecte

Rui Yang, Han Zhong, Jiawei Xu, Amy Zhang

Offline reinforcement learning (RL) presents a promising approach for learning reinforced policies from offline datasets without the need for costly or unsafe interactions with the environment. However, datasets collected by humans in real-world environments are often noisy and may even be maliciously corrupted, which can significantly degrade the performanc

Taewon Park, Hubert S. Stokowski, Vahid Ansari, Samuel Gyger

Quantum optical technologies promise advances in sensing, computing, and communication. A key resource is squeezed light, where quantum noise is redistributed between optical quadratures. We introduce a monolithic, chip-scale platform that exploits the $\chi^{(2)}$ nonlinearity of a thin-film lithium niobate (TFLN) resonator device to efficiently generate sq

Sangho Suh, Meng Chen, Bryan Min, Toby Jia-Jun Li

Thanks to their generative capabilities, large language models (LLMs) have become an invaluable tool for creative processes. These models have the capacity to produce hundreds and thousands of visual and textual outputs, offering abundant inspiration for creative endeavors. But are we harnessing their full potential? We argue that current interaction paradig

Amey P. Pasarkar, Adji Bousso Dieng

Measuring diversity accurately is important for many scientific fields, including machine learning (ML), ecology, and chemistry. The Vendi Score was introduced as a generic similarity-based diversity metric that extends the Hill number of order q=1 by leveraging ideas from quantum statistical mechanics. Contrary to many diversity metrics in ecology, the Vend

Asier Alonso-Bardaji, David Brizuela

We study covariant models for vacuum spherical gravity within a canonical setting. Starting from a general ansatz, we derive the most general family of Hamiltonian constraints that are quadratic in first-order and linear in second-order spatial derivatives of the triad variables, and obey certain specific covariance conditions. These conditions ensure that t

Jasmine Parsons, Anatoly Spitkovsky, Arno Vanthieghem

Particle-in-cell (PIC) simulations have shown that relativistic collisionless shocks mediated by the Weibel instability accelerate about 1% of incoming particles, while the majority are transmitted through the shock and become thermalized. The microphysical processes that determine whether an incoming particle will be transmitted or reflected are poorly unde

Jeffrey C. Lagarias, Wijit Yangjit

This paper presents an extension of Bhargava's theory of factorials associated to any nonempty subset $S$ of $\mathbb{Z}$. Bhargava's factorials $k!_S$ are invariants, constructed using the notion of $p$-orderings of $S$ where $p$ is a prime. This paper defines $b$-orderings of any nonempty subset $S$ of $\mathbb{Z}$ for all integers $b\ge2$, as well as "ext

Félix Parraud, Kevin Schnelli

In this paper we study multi-matrix models whose potentials are perturbations of the quadratic potential associated with independent GUE random matrices. More precisely, we compute the free energy and the expectation of the trace of polynomials evaluated in those matrices. We prove an asymptotic expansion in the inverse of the matrix dimension to any order.

Aynur Bulut, Manh Khang Huynh, Stan Palasek

We establish new non-uniqueness results for the forced inviscid surface quasi-geostrophic equation, via an alternating formulation of convex integration techniques. Our results imply non-uniquenesss in the class of weak solutions with $|\nabla|^{-1}\theta\in C_tC_x^\alpha$, for any $\alpha<1$.

Victor Falgas-Ravry, Eero Räty, István Tomon

Consider the partially ordered set on $[t]^n:=\{0,\dots,t-1\}^n$ equipped with the natural coordinate-wise ordering. Let $A(t,n)$ denote the number of antichains of this poset. The quantity $A(t,n)$ has a number of combinatorial interpretations: it is precisely the number of $(n-1)$-dimensional partitions with entries from $\{0,\dots,t\}$, and by a result of

Chunyi Sun, Junlin Han, Weijian Deng, Xinlong Wang

In the pursuit of efficient automated content creation, procedural generation, leveraging modifiable parameters and rule-based systems, emerges as a promising approach. Nonetheless, it could be a demanding endeavor, given its intricate nature necessitating a deep understanding of rules, algorithms, and parameters. To reduce workload, we introduce 3D-GPT, a f

Hao Deng, Chuping Li, Yuxuan Wu, Lin Sun

The ground-state superfluid behavior of ultracold atomic Fermi gases with a short-range attractive interaction in a quasi-two-dimensional Lieb lattice is studied using BCS mean-field theory, within the context of BCS-BEC crossover. We find that the flat band leads to nontrivial exotic effects. As the Fermi level enters the flat band, both the pairing gap and

Amrita Banerjee, Biswajit Pandey, Anindita Nandi

We analyze a volume-limited sample from the Sloan Digital Sky Survey (SDSS) to compare the spatial clustering and physical properties of active galactic nuclei (AGN) and star-forming galaxies (SFG) at fixed stellar mass. We find no statistically significant difference in clustering strength or local density between AGN and SFG. However, after matching their

Franz Nowak, Anej Svete, Li Du, Ryan Cotterell

This work investigates the computational expressivity of language models (LMs) based on recurrent neural networks (RNNs). Siegelmann and Sontag (1992) famously showed that RNNs with rational weights and hidden states and unbounded computation time are Turing complete. However, LMs define weightings over strings in addition to just (unweighted) language membe

Rishi Bommasani, Kevin Klyman, Shayne Longpre, Sayash Kapoor

Foundation models have rapidly permeated society, catalyzing a wave of generative AI applications spanning enterprise and consumer-facing contexts. While the societal impact of foundation models is growing, transparency is on the decline, mirroring the opacity that has plagued past digital technologies (e.g. social media). Reversing this trend is essential:

J. V. Fontana, C. Cuttle, D. Pihler-Puzovic, A. L. Hazel

Using experiments and a depth-averaged numerical model, we study instabilities of two-phase flows in a Hele-Shaw channel with an elastic upper boundary and a non-uniform cross-section prescribed by initial collapse. Experimentally, we find increasingly complex and unsteady modes of air-finger propagation as the dimensionless bubble speed, Ca, and level of co

Giuseppe Arnone, Florinda Capone, Jacopo A. Gianfrani

The aim of this paper is to investigate the onset of penetrative convection in a Darcy-Brinkmann porous medium under the hypothesis of local therma non-equilibrium. For the problem at stake, the strong form of the principle of exchange of stabilities has been proved, i.e. convective motions can occur only through a secondary stationary motion. We perform lin

Morgan G. Blevins, Svetlana V. Boriskina

There is a need for compact, dynamically tunable nonreciprocal optical elements to enable on-chip-compatible optical isolators and more efficient radiative energy transfer systems. Plasmon Fizeau drag, the drag of electrical current on propagating surface plasmon polaritons, has been proposed to induce nonreciprocal surface modes to enable one-way energy tra

Xinrui Ye, Yanzan Sun, Dingzhu Wen, Guanjin Pan

Cooperative inference in Mobile Edge Computing (MEC), achieved by deploying partitioned Deep Neural Network (DNN) models between resource-constrained user equipments (UEs) and edge servers (ESs), has emerged as a promising paradigm. Firstly, we consider scenarios of continuous Artificial Intelligence (AI) task arrivals, like the object detection for video st

Yi Zhou, Jose Camacho-Collados, Danushka Bollegala

Various types of social biases have been reported with pretrained Masked Language Models (MLMs) in prior work. However, multiple underlying factors are associated with an MLM such as its model size, size of the training data, training objectives, the domain from which pretraining data is sampled, tokenization, and languages present in the pretrained corpora,

Andrew Craig, Claudette Robinson

We show that all Sugihara monoids can be represented as algebras of binary relations, with the monoid operation given by relational composition. Moreover, the binary relations are weakening relations. The first step is to obtain an explicit relational representation of all finite odd Sugihara chains. Our construction mimics that of Maddux (2010), where a rel

Daniil Tiapkin, Nikita Morozov, Alexey Naumov, Dmitry Vetrov

The recently proposed generative flow networks (GFlowNets) are a method of training a policy to sample compositional discrete objects with probabilities proportional to a given reward via a sequence of actions. GFlowNets exploit the sequential nature of the problem, drawing parallels with reinforcement learning (RL). Our work extends the connection between R

Jiajie Guo, Fengxiao Sun, Qiongyi He, Matteo Fadel

We analyse the conditional states in which one part of a split spin-squeezed state is left, upon performing a collective spin measurement on the other part. For appropriate measurement directions and outcomes, we see the possibility of obtaining states with high quantum Fisher information, even reaching the Heisenberg limit. This allows us to propose a metro

Chengjie Fu, Shao-Jiang Wang

Recent attempts to fully resolve the Hubble tension from early dark energy models seem to favor a primordial Harrison-Zeldovich universe with its scalar spectrum being extremely scale invariant. Restoring the Harrison-Zeldovich spectrum within the single-field inflationary paradigm appears to be infeasible, turning to the multifield approach from either curv

Yecheng Jason Ma, William Liang, Guanzhi Wang, De-An Huang

Large Language Models (LLMs) have excelled as high-level semantic planners for sequential decision-making tasks. However, harnessing them to learn complex low-level manipulation tasks, such as dexterous pen spinning, remains an open problem. We bridge this fundamental gap and present Eureka, a human-level reward design algorithm powered by LLMs. Eureka explo

Enrico Arbarello

Let $|L_g|$, be the genus $g$ du Val linear system on a Halphen surface $Y$ of index $k$. We prove that the Clifford index $cliff(C)$ is constant on smooth curves $C\in |L_g|$. Let $\gamma(C)$ be the gonality of $C$. When $cliff(C)<\lfloor{\frac{g-1}{2}}\rfloor$ (the relevant case), we show that $\gamma(C)=cliff(C)+2=k$, and that the gonality is realized by

Xavier Puig, Eric Undersander, Andrew Szot, Mikael Dallaire Cote

We present Habitat 3.0: a simulation platform for studying collaborative human-robot tasks in home environments. Habitat 3.0 offers contributions across three dimensions: (1) Accurate humanoid simulation: addressing challenges in modeling complex deformable bodies and diversity in appearance and motion, all while ensuring high simulation speed. (2) Human-in-

Paulo Soares, Adarsh Pyarelal, Kobus Barnard

We develop a probabilistic graphical model (PGM) for artificially intelligent (AI) agents to infer human beliefs during a simulated urban search and rescue (USAR) scenario executed in a Minecraft environment with a team of three players. The PGM approach makes observable states and actions explicit, as well as beliefs and intentions grounded by evidence abou

Richard Willis, Yali Du, Joel Z Leibo, Michael Luck

Social dilemmas present a significant challenge in multi-agent cooperation because individuals are incentivised to behave in ways that undermine socially optimal outcomes. Consequently, self-interested agents often avoid collective behaviour. In response, we formalise social dilemmas and introduce a novel metric, the general self-interest level, to quantify

Robert Boltje, Hatice Mutlu

The goal of a series of papers is to define $G$-actions on various $A$-fibered structures, where $G$ is a finite group and $A$ is an abelian group. One prominent such example is the $A$-fibered Burnside ring. If $A=\mathbb{C}^\times$, it is also called the ring of monomial representations (introduced by Dress in \cite{Dress1971}) and is the natural home for

José Gil-Férez, Peter Jipsen, Melissa Sugimoto

We show that every locally integral involutive partially ordered semigroup (ipo-semigroup) $\mathbf A = (A,\le, \cdot, \sim,-)$, and in particular every locally integral involutive semiring, decomposes in a unique way into a family $\{\mathbf A_p : p\in A^+\}$ of integral ipo-monoids, which we call its integral components. In the semiring case, the integral

Germán Stefanich

We develop the theory of exact completions of regular $\infty$-categories, and show that the $\infty$-categorical exact completion (resp. hypercompletion) of an abelian category recovers the connective half of its bounded (resp. unbounded) derived $\infty$-category. Along the way, we prove that a finitely complete $\infty$-category is exact and additive if a

Yaroslav Balytskyi, Nataliia Kalashnyk, Inna Hubenko, Alina Balytska

The combination of Deep Learning techniques and Raman spectroscopy shows great potential offering precise and prompt identification of pathogenic bacteria in clinical settings. However, the traditional closed-set classification approaches assume that all test samples belong to one of the known pathogens, and their applicability is limited since the clinical

Yagmur Yigit, Bahadir Bal, Aytac Karameseoglu, Trung Q. Duong

Existing distributed denial of service attack (DDoS) solutions cannot handle highly aggregated data rates; thus, they are unsuitable for Internet service provider (ISP) core networks. This article proposes a digital twin-enabled intelligent DDoS detection mechanism using an online learning method for autonomous systems. Our contributions are three-fold: we f

Jintong Wu, Daniel R. Mason, Fredric Granberg

We demonstrate a practical way to perform decomposition of the elasto-plastic deformation directly from atomistic simulation snapshots. Through molecular dynamics simulations on a large single crystal, we elucidate the intricate process of converting plastic strain, atomic strain, and rigid rotation during irradiation. Our study highlights how prismatic disl

Laura Donnay

We review the role that infinite-dimensional symmetries arising at the boundary of asymptotically flat spacetimes play in the context of the celestial holography program. Once recast into the language of conformal field theory, asymptotic symmetries provide key constraints on the sought-for celestial dual to quantum gravity in flat spacetimes.

Juan Rocamonde, Victoriano Montesinos, Elvis Nava, Ethan Perez

Reinforcement learning (RL) requires either manually specifying a reward function, which is often infeasible, or learning a reward model from a large amount of human feedback, which is often very expensive. We study a more sample-efficient alternative: using pretrained vision-language models (VLMs) as zero-shot reward models (RMs) to specify tasks via natura

Sulin Liu, Peter J. Ramadge, Ryan P. Adams

We introduce marginalization models (MAMs), a new family of generative models for high-dimensional discrete data. They offer scalable and flexible generative modeling by explicitly modeling all induced marginal distributions. Marginalization models enable fast approximation of arbitrary marginal probabilities with a single forward pass of the neural network,

Joseph R. Masiero, Dar W. Dahlen, Amy K. Mainzer, William F. Bottke

The Near Earth Object Surveyor mission has a requirement to find two-thirds of the potentially hazardous asteroids larger than 140 meters in size. In order to determine the mission's expected progress toward this goal during design and testing, as well as the actual progress during the survey, a simulation tool has been developed to act as a consistent and q

A. K. Mainzer, Joseph R. Masiero, Paul A. Abell, J. M. Bauer

The Near-Earth Object (NEO) Surveyor mission is a NASA observatory designed to discover and characterize near-Earth asteroids and comets. The mission's primary objective is to find the majority of objects large enough to cause severe regional impact damage ($>$140 m in effective spherical diameter) within its five-year baseline survey. Operating at the Sun-E

Felix Willenborg, Dennis Philipp, Claus Lämmerzahl

Spacetime perturbations due to scalar, vector, and tensor fields on a fixed background geometry can be described in the framework of Teukolsky's equation. In this work, wave scattering is treated analytically, using the Green's function method and solutions to the separated radial and angular differential equations in combination with a partial wave techniqu

Narayan Kandel, Melanie Lambert

With the maturity of deep learning, its use is emerging in every field. Also, as different types of GPUs are becoming more available in the markets, it creates a difficult decision for users. How can users select GPUs to achieve optimal performance for a specific task? Analysis of GPU architecture is well studied, but existing works that benchmark GPUs do no

Alexander Steinhoff, Edith Wietek, Matthias Florian, Tommy Schulz

Exciton-exciton interactions are key to understanding non-linear optical and transport phenomena in van der Waals heterobilayers, which emerged as versatile platforms to study correlated electronic states. We present a combined theory-experiment study of excitonic many-body effects based on first-principle band structures and Coulomb interaction matrix eleme

Daniel Soskin, Prateek Kumar Vishwakarma

We show that the partial sums of the long Pl\"ucker relations for pairs of weakly separated Pl\"ucker coordinates oscillate around $0$ on the totally nonnegative part of the Grassmannian. Our result generalizes the classical oscillating inequalities by Gantmacher--Krein (1941) and recent results on totally nonnegative matrix inequalities by Fallat--Vishwakar

Gauhar Abbas

We discuss a standard hierarchical VEVs model which predicts the leptonic mixing angles in terms of the Cabibbo angle, and masses of strange and charm quarks as $\sin \theta_{12}^\ell \geq 1 - 2 \sin \theta_{12}$, $\sin \theta_{23}^\ell \geq 1 - \sin \theta_{12}$, and $\sin \theta_{13}^\ell \geq \sin \theta_{12} - \frac{m_s}{m_c}$ for the normal mass orderin

Emre Horsanali, Yagmur Yigit, Gokhan Secinti, Aytac Karameseoglu

As the current detection solutions of distributed denial of service attacks (DDoS) need additional infrastructures to handle high aggregate data rates, they are not suitable for sensor networks or the Internet of Things. Besides, the security architecture of software-defined sensor networks needs to pay attention to the vulnerabilities of both software-defin

Nick Fischer, Piotr Kaliciak, Adam Polak

As one of the three main pillars of fine-grained complexity theory, the 3SUM problem explains the hardness of many diverse polynomial-time problems via fine-grained reductions. Many of these reductions are either directly based on or heavily inspired by P\u{a}tra\c{s}cu's framework involving additive hashing and are thus randomized. Some selected reductions

Yasin Findik, Paul Robinette, Kshitij Jerath, S. Reza Ahmadzadeh

Effective coordination and cooperation among agents are crucial for accomplishing individual or shared objectives in multi-agent systems. In many real-world multi-agent systems, agents possess varying abilities and constraints, making it necessary to prioritize agents based on their specific properties to ensure successful coordination and cooperation within

Joshua Brakensiek, Neng Huang, Uri Zwick

Austrin showed that the approximation ratio $\beta\approx 0.94016567$ obtained by the MAX 2-SAT approximation algorithm of Lewin, Livnat and Zwick (LLZ) is optimal modulo the Unique Games Conjecture (UGC) and modulo a Simplicity Conjecture that states that the worst performance of the algorithm is obtained on so called simple configurations. We prove Austrin

Yasin Findik, Hamid Osooli, Paul Robinette, Kshitij Jerath

Relational networks within a team play a critical role in the performance of many real-world multi-robot systems. To successfully accomplish tasks that require cooperation and coordination, different agents (e.g., robots) necessitate different priorities based on their positioning within the team. Yet, many of the existing multi-robot cooperation algorithms

Yasin Findik, Paul Robinette, Kshitij Jerath, S. Reza Ahmadzadeh

Cooperative multi-agent reinforcement learning (MARL) approaches tackle the challenge of finding effective multi-agent cooperation strategies for accomplishing individual or shared objectives in multi-agent teams. In real-world scenarios, however, agents may encounter unforeseen failures due to constraints like battery depletion or mechanical issues. Existin

Dariusz Piekarz

It is shown that the constant $c_{d,3}$ in von Neumann's inequality for d-tuples of commutative and row contractive $3\times3$ matrices, as proved by Hartz, Richter, and Shalit in [2], is independent of the size of the d-tuple. A numerical estimation of the constant is provided.

F. Chiaffredo, L. Fatibene, M. Ferraris, E. Ricossa

A covariant, global, variational framework for perturbations in field theories is presented. Perturbations are obtained as vertical vector fields on the configuration bundle and they drag, exactly, solution into solutions. The flow of a perturbation drags solutions into solutions and the dragged perturbed solutions can be expanded in a series with respect to

Richard J. Anslow, Amy Bonsor, Paul B. Rimmer

In this work we consider the potential of cometary impacts to deliver complex organic molecules and the prebiotic building blocks required for life to rocky exoplanets. Numerical experiments have demonstrated that for these molecules to survive, impacts at very low velocities are required. This work shows that for comets scattered from beyond the snow-line i

Yi-Hong Chen, Bao-Zong Wang, Ting-Fung Jeffrey Poon, Xin-Chi Zhou

The Kitaev chiral spin liquid has captured widespread interest in recent decades because of its intrinsic non-Abelian excitations, yet the experimental realization is challenging. Here we propose to realize and detect Kitaev chiral spin liquid in a deformed honeycomb array of Rydberg atoms. Through a novel laser-assisted dipole-dipole interaction mechanism t

Oriane Siméoni, Éloi Zablocki, Spyros Gidaris, Gilles Puy

The recent enthusiasm for open-world vision systems show the high interest of the community to perform perception tasks outside of the closed-vocabulary benchmark setups which have been so popular until now. Being able to discover objects in images/videos without knowing in advance what objects populate the dataset is an exciting prospect. But how to find ob

S. Monsurrò, C. Perugia, F. Raimondi

This paper deals with an elliptic problem with a nonlinear lower order term set in an open bounded cylinder of $R^N$, $N\geq 2$, divided into two connected components by an imperfect rough interface. More precisely, we assume that at the interface the flux is continuous and proportional, via a nonlinear rule, to the jump of the solution. According to the amp

Nina Begus

The paper proposes a framework that combines behavioral and computational experiments employing fictional prompts as a novel tool for investigating cultural artifacts and social biases in storytelling both by humans and generative AI. The study analyzes 250 stories authored by crowdworkers in June 2019 and 80 stories generated by GPT-3.5 and GPT-4 in March 2

Jordan C. Rozum, Colin Campbell, Eli Newby, Fatemeh Sadat Fatemi Nasrollahi

Interacting biological systems at all organizational levels display emergent behavior. Modeling these systems is made challenging by the number and variety of biological components and interactions (from molecules in gene regulatory networks to species in ecological networks) and the often-incomplete state of system knowledge (e.g., the unknown values of kin

Masahiro Suzuki, Shomu Furuta, Yusuke Fukazawa

We explain the methodology used to create the data submitted to HuMob Challenge, a data analysis competition for human mobility prediction. We adopted a personalized model to predict the individual's movement trajectory from their data, instead of predicting from the overall movement, based on the hypothesis that human movement is unique to each person. We d

Alicja Wróbel, Karolina Źróbek, Marie-Monique Schaper, Paulina Zguda

As child-robot interactions become more and more common in daily life environment, it is important to examine how robot's errors influence children's behavior. We explored how a robot's unexpected behaviors affect child-robot interactions during two workshops on active reading: one in a modern art museum and one in a school. We observed the behavior and atti

Joshua Brakensiek, Manik Dhar, Sivakanth Gopi, Zihan Zhang

The recently-emerging field of higher order MDS codes has sought to unify a number of concepts in coding theory. Such areas captured by higher order MDS codes include maximally recoverable (MR) tensor codes, codes with optimal list-decoding guarantees, and codes with constrained generator matrices (as in the GM-MDS theorem). By proving these equivalences, Br

Masoud Makrehchi, Dell Zhang, Alina Petrova, John Armour

This is the Proceedings of the 3rd International Workshop on Mining and Learning in the Legal Domain (MLLD-23) which took place in conjunction with the 32nd ACM International Conference on Information and Knowledge Management (CIKM-2023) at the University of Birmingham, Birmingham, UK on Sunday 22nd October 2023.

Paul Thévenin

We consider here multitype Bienaym\'e--Galton--Watson trees, under the conditioning that the numbers of vertices of given type satisfy some linear relations. We prove that, under some smoothness conditions on the offspring distribution $\mathbf{\zeta}$, there exists a critical offspring distribution $\tilde{\mathbf{\zeta}}$ such that the trees with offspring

Stanley Rabinowitz, Ercole Suppa

We study properties of certain circles associated with a triangle. Each circle is inside the triangle, tangent to two sides of the triangle, and externally tangent to the arc of a circle erected internally on the third side.

S. Zamora, Ángeles I. Díaz

Circumnuclear star forming regions (CNSFR) are massive clusters found close to galactic nuclei. These entities give us an excellent opportunity to study star formation in environments with high metallicity and to relate it with active galactic nuclei. Our principal aim is to derive the physical properties and dynamical masses of the CNSFRs in the two rings o

Heitor Casasola, Guilherme Delfino, Pedro R. S. Gomes, Paula F. Bienzobaz

In this work, we study unconventional anisotropic topologically ordered phases in $3d$ that manifest type-II fractonic physics along submanifolds. While they behave as usual topological order along a preferred spatial direction, their physics along perpendicular planes is dictated by the presence of fractal subsystem symmetries, completely restricting the mo

Changhao Li, Boning Li, Omar Amer, Ruslan Shaydulin

Distributed quantum computing is a promising computational paradigm for performing computations that are beyond the reach of individual quantum devices. Privacy in distributed quantum computing is critical for maintaining confidentiality and protecting the data in the presence of untrusted computing nodes. In this work, we introduce novel blind quantum machi

Songbo Hu, Han Zhou, Moy Yuan, Milan Gritta

Achieving robust language technologies that can perform well across the world's many languages is a central goal of multilingual NLP. In this work, we take stock of and empirically analyse task performance disparities that exist between multilingual task-oriented dialogue (ToD) systems. We first define new quantitative measures of absolute and relative equiv

Anders Martinsson, Raphael Steiner

Let $r$ be any positive integer. We prove that for every sufficiently large $k$ there exists a $k$-chromatic vertex-critical graph $G$ such that $\chi(G-R)=k$ for every set $R \subseteq E(G)$ with $|R|\le r$. This partially solves a problem posed by Erd\H{o}s in 1985, who asked whether the above statement holds for $k \ge 4$.

Christopher Candelora, Hong Li, Muxian Xu, Brenden R. Ortiz

A wide array of unusual phenomena has recently been uncovered in kagome solids. The charge density wave (CDW) state in the kagome superconductor AV3Sb5 in particular intrigued the community -- the CDW phase appears to break the time-reversal symmetry despite the absence of spin magnetism, which has been tied to exotic orbital loop currents possibly intertwin

Shyan Akmal

Connectivity (or equivalently, unweighted maximum flow) is an important measure in graph theory and combinatorial optimization. Given a graph $G$ with vertices $s$ and $t$, the connectivity $\lambda(s,t)$ from $s$ to $t$ is defined to be the maximum number of edge-disjoint paths from $s$ to $t$ in $G$. Much research has gone into designing fast algorithms fo

Joshua Brakensiek, Manik Dhar, Sivakanth Gopi

The GM-MDS theorem, conjectured by Dau-Song-Dong-Yuen and proved by Lovett and Yildiz-Hassibi, shows that the generator matrices of Reed-Solomon codes can attain every possible configuration of zeros for an MDS code. The recently emerging theory of higher order MDS codes has connected the GM-MDS theorem to other important properties of Reed-Solomon codes, in

Karthik Subramanian, Saurav Singh, Justin Namba, Jamison Heard

Many attempts have been made at estimating discrete emotions (calmness, anxiety, boredom, surprise, anger) and continuous emotional measures commonly used in psychology, namely `valence' (The pleasantness of the emotion being displayed) and `arousal' (The intensity of the emotion being displayed). Existing methods to estimate arousal and valence rely on lear

GP Benham, O Devauchelle, SJ Thomson

A theory is presented for wave-driven propulsion of floating bodies driven into oscillation at the fluid interface. By coupling the equations of motion of the body to a quasi-potential flow model of the fluid, we derive expressions for the drift speed and propulsive thrust of the body which in turn are shown to be consistent with global momentum conservation

Evan Habbershaw, Ryan S. Glasby, Jeffrey R. Haack, Cory D. Hauck

Multi-species BGK models describe the dynamics of rarefied gases with constituent particles of different elements or compounds with potentially non-trivial velocity distributions. In this paper, moment equations for the bulk velocities, energies, and temperatures of a spatially homogeneous multi-species BGK model are examined. A key challenge in analyzing th

Enrique Matos Alfonso, Giorgos Stamou

In this paper, we explore conjunctive query rewriting, focusing on queries containing universally quantified negation within the framework of disjunctive existential rules. We address the undecidability of the existence of a finite and complete UCQ-rewriting and the identification of finite unification sets (fus) of rules. We introduce new rule classes, conn

Antonio C. Costa, Tosif Ahamed, David Jordan, Greg J. Stephens

How do we capture the breadth of behavior in animal movement, from rapid body twitches to aging? Using high-resolution videos of the nematode worm $C. elegans$, we show that a single dynamics connects posture-scale fluctuations with trajectory diffusion, and longer-lived behavioral states. We take short posture sequences as an instantaneous behavioral measur

Steven Winter, Omar Melikechi, David B. Dunson

Gibbs posteriors are proportional to a prior distribution multiplied by an exponentiated loss function, with a key tuning parameter weighting information in the loss relative to the prior and providing a control of posterior uncertainty. Gibbs posteriors provide a principled framework for likelihood-free Bayesian inference, but in many situations, including

Jianshu Cao, Eli Pollak

The central topic of this letter is to show that light-matter hybridization not only gives rise to novel dynamic responses but can also modify intermolecular interactions and induce new structural order. Using the van der Waals (vdW) system in an optical cavity as an example, we predict the effects of interference and collectivity in cavity-induced many-body

Yagmur Yigit, Omer Kemal Kinaci, Trung Q. Duong, Berk Canberk

The idea of next-generation ports has become more apparent in the last ten years in response to the challenge posed by the rising demand for efficiency and the ever-increasing volume of goods. In this new era of intelligent infrastructure and facilities, it is evident that cyber-security has recently received the most significant attention from the seaport a

Claudio Hernández-López, Paul Baconnier, Corentin Coulais, Olivier Dauchot

Active solids such as cell collectives, colloidal clusters, and active metamaterials exhibit diverse collective phenomena, ranging from rigid body motion to shape-changing mechanisms. The nonlinear dynamics of such active materials remains however poorly understood when they host zero-energy deformation modes and when noise is present. Here, we show that str

Behnam Pourhassan, Xiaoping Shi, Salman Sajad Wani, Saif-Al-Khawari

One way to test quantum gravitational corrections is through black hole physics. In this paper, We investigate the scales at which quantum gravitational corrections can be detected in a black hole using information theory. This is done by calculating the Kullback-Leibler divergence for the probability distributions obtained from the Parikh-Wilczek formalism.

Peibei Cao, Rafal K. Mantiuk, Kede Ma

High dynamic range (HDR) rendering has the ability to faithfully reproduce the wide luminance ranges in natural scenes, but how to accurately assess the rendering quality is relatively underexplored. Existing quality models are mostly designed for low dynamic range (LDR) images, and do not align well with human perception of HDR image quality. To fill this g