April 2024 arXiv papers — page 163

Pedro Afonso Fernandes

Economic forecasting is concerned with the estimation of some variable like gross domestic product (GDP) in the next period given a set of variables that describes the current situation or state of the economy, including industrial production, retail trade turnover or economic confidence. Neuro-dynamic programming (NDP) provides tools to deal with forecastin

Zijie Wu, Chaohui Yu, Yanqin Jiang, Chenjie Cao

Recent advances in 2D/3D generative models enable the generation of dynamic 3D objects from a single-view video. Existing approaches utilize score distillation sampling to form the dynamic scene as dynamic NeRF or dense 3D Gaussians. However, these methods struggle to strike a balance among reference view alignment, spatio-temporal consistency, and motion fi

Suddhasattwa Das

One of the prime motivation for topology was Homotopy theory, which captures the general idea of a continuous transformation between two entities, which may be spaces or maps. In later decades, an algebraic formulation of topology was discovered with the development of Homology theory. Some of the deepest results in topology are about the connections between

Jasper Geldenbott, Karen Leung

Humans have a remarkable ability to fluently engage in joint collision avoidance in crowded navigation tasks despite the complexities and uncertainties inherent in human behavior. Underlying these interactions is a mutual understanding that (i) individuals are prosocial, that is, there is equitable responsibility in avoiding collisions, and (ii) individuals

Len van Deurzen, Eungkyun Kim, Naomi Pieczulewski, Zexuan Zhang

Unlike non-polar semiconductors such as silicon, the broken inversion symmetry of the wide bandgap semiconductor gallium nitride leads to a large electronic polarization along a unique crystal axis. This makes the two surfaces of the semiconductor wafer perpendicular to the polar axis dramatically different in their physical and chemical properties. In the l

Bradley P. Allen, Fina Polat, Paul Groth

We describe the University of Amsterdam Intelligent Data Engineering Lab team's entry for the SemEval-2024 Task 6 competition. The SHROOM-INDElab system builds on previous work on using prompt programming and in-context learning with large language models (LLMs) to build classifiers for hallucination detection, and extends that work through the incorporation

Jagannath Sutradhar, Jonathan Ruhman, Avraham Klein

The electron-electron and electron-phonon coupling in complex materials can be more complicated than simple density-density interactions, involving intertwined dynamics of spin, charge, and spatial symmetries. This motivates studying universal models with complex interactions, and studying whether in this case BCS-type singlet pairing is still the ``natural'

Kimberly A. Weaver, Jenna M. Cann, Lynne Valencic, Ryan W. Pfeifle

NGC 4945 contains a well-known heavily obscured active galactic nucleus (AGN) at its core, with prior reports of strong nuclear and off-nuclear neutral Fe K$\alpha$ emission due to the AGN activity. We report the discovery of very extended Fe K$\alpha$ emission with the XMM-Newton EPIC pn in a $\sim5$ kpc by $\sim10$ kpc region that is misaligned with the pl

Lars Ankile, Anthony Simeonov, Idan Shenfeld, Pulkit Agrawal

While learning from demonstrations is powerful for acquiring visuomotor policies, high-performance imitation without large demonstration datasets remains challenging for tasks requiring precise, long-horizon manipulation. This paper proposes a pipeline for improving imitation learning performance with a small human demonstration budget. We apply our approach

Isidora Araya Day, Sebastian Miles, Hugo K. Kerstens, Daniel Varjas

A common technique in the study of complex quantum-mechanical systems is to reduce the number of degrees of freedom in the Hamiltonian by using quasi-degenerate perturbation theory. While the Schrieffer--Wolff transformation achieves this and constructs an effective Hamiltonian, its scaling is suboptimal, it is limited to two subspaces, and implementing it e

Sebastián Roca-Jerat, Marcos Rubín-Osanz, Mark D. Jenkins, Agustín Camón

We explore the competition between light-mediated and intrinsic matter-matter interactions in waveguide quantum electrodynamics. For this, we couple a superconducting transmission line to a model magnetic material, made of organic free radical molecules with a spin $S=1/2$ and a $g_{S}$ factor very close to that of a free electron. The microwave transmission

Kyle A. Oman, Alexander H. Riley

In the conventional approach to decomposing a rotation curve into a set of contributions from mass model components, the measurements of the rotation curve at different radii are taken to be independent. It is clear, however, that radial correlations are present in such data, for instance (but not only) because the orbital speed depends on the mass distribut

Isaac H. Kim, Xiang Li, Ting-Chun Lin, John McGreevy

In a physical system with conformal symmetry, observables depend on cross-ratios, measures of distance invariant under global conformal transformations (conformal geometry for short). We identify a quantum information-theoretic mechanism by which the conformal geometry emerges at the gapless edge of a 2+1D quantum many-body system with a bulk energy gap. We

Alex Davey, Oscar J. C. Dias, David Sola Gil

Christodoulou's formulation of Strong Cosmic Censorship (SCC) holds true for Kerr-de Sitter black holes. On the other hand, Reissner-Nordstr\"om-de Sitter black holes violate SCC. We do a detailed scan of the parameter space of Kerr-Newman-de Sitter black holes between these two limiting families, to identify the boundary that marks the transition between so

Arian J. Stolk, Kian L. van der Enden, Marie-Christine Slater, Ingmar te Raa-Derckx

A key challenge towards future quantum internet technology is connecting quantum processors at metropolitan scale. Here, we report on heralded entanglement between two independently operated quantum network nodes separated by 10km. The two nodes hosting diamond spin qubits are linked with a midpoint station via 25km of deployed optical fiber. We minimize the

Maximilian Häberle, Nadine Neumayer, Andrea Bellini, Mattia Libralato

Omega Centauri ($\omega$ Cen) is the most massive globular cluster of the Milky Way. It is thought to be the nucleus of an accreted dwarf galaxy because of its high mass and its complex stellar populations. To decipher its formation history and study its dynamics, we created the most comprehensive kinematic catalog for its inner region, by analyzing both arc

Lorenzo Mirasola, Paola Leaci, Pia Astone, Luca D'Onofrio

We present a novel semicoherent targeted search method for continuous gravitational waves (CWs) emitted by pulsars in binary systems. The method is based on a custom optimization of the coherence time, which is tailored according to the orbital parameters and their uncertainties, as provided by electromagnetic observations. While rotating pulsars are expecte

Tatsuya Akiba, Selah McIntyre, Ann-Marie Madigan

The surfaces of many white dwarfs are polluted by metals, implying a recent accretion event. The tidal disruption of planetesimals is a viable source of white dwarf pollution and offers a unique window into the composition of exoplanet systems. The question of how planetary material enters the tidal disruption radius of the white dwarf is currently unresolve

Tyler Corbett, Jay Desai, O. J. P. Eboli, M. C. Gonzalez-Garcia

We present the basis of dimension-eight operators associated with universal theories. We first derive a complete list of independent dimension-eight operators formed with the Standard Model bosonic fields characteristic of such universal new physics scenarios. Without imposing C or P symmetries the basis contains 175 operators -- that is, the assumption of U

Kevin B. Burdge, Kareem El-Badry, Erin Kara, Claude Canizares

Evidence suggests that when compact objects such as black holes and neutron stars form, they may receive a ``natal kick,'' where the stellar remnant gains momentum. Observational evidence for neutron star kicks is substantial, yet limited for black hole natal kicks, and some proposed black hole formation scenarios result in very small kicks. Here, we report

Diogo L. Pires, Mark Broom

Community organization permeates both social and biological complex systems. To study its interplay with behavior emergence, we model mobile structured populations with multiplayer interactions. We derive general analytical methods for evolutionary dynamics under high home fidelity when populations self-organize into networks of asymptotically isolated commu

Adam Tropper

We show that in supersymmetric theories, knowing the soft theorem for a single particle in a supermultiplet allows one to immediately determine soft theorems for the remainder of the supermultiplet. While soft theorems in supersymmetric theories have a rich history, they have only been chronicled for specific examples due to the fact that they are usually de

Thomas W. Grimm, Arno Hoefnagels, Mick van Vliet

Cosmological correlators capture the spatial fluctuations imprinted during the earliest episodes of the universe. While they are generally very non-trivial functions of the kinematic variables, they are known to arise as solutions to special sets of differential equations. In this work we use this fact to uncover the underlying tame structure for such correl

Rui Li, Tobias Fischer, Mattia Segu, Marc Pollefeys

Recovering the 3D scene geometry from a single view is a fundamental yet ill-posed problem in computer vision. While classical depth estimation methods infer only a 2.5D scene representation limited to the image plane, recent approaches based on radiance fields reconstruct a full 3D representation. However, these methods still struggle with occluded regions

Anwesa Choudhuri, Girish Chowdhary, Alexander G. Schwing

We propose the new task 'open-world video instance segmentation and captioning'. It requires to detect, segment, track and describe with rich captions never before seen objects. This challenging task can be addressed by developing "abstractors" which connect a vision model and a language foundation model. Concretely, we connect a multi-scale visual feature e

Hanzhe Hu, Zhizhuo Zhou, Varun Jampani, Shubham Tulsiani

We present MVD-Fusion: a method for single-view 3D inference via generative modeling of multi-view-consistent RGB-D images. While recent methods pursuing 3D inference advocate learning novel-view generative models, these generations are not 3D-consistent and require a distillation process to generate a 3D output. We instead cast the task of 3D inference as d

Nanoom Lee, Yacine Ali-Haimoud

Weak magnetic fields must have existed in the early Universe, as they were sourced by the cross product of electron density and temperature gradients through the Biermann-battery mechanism. In this paper we calculate the magnetic fields generated at cosmic dawn by a variety of small-scale primordial perturbations, carefully computing the evolution of electro

Zhongkai Wu, Ziyu Wan, Jing Zhang, Jing Liao

NeRF (Neural Radiance Fields) has demonstrated tremendous potential in novel view synthesis and 3D reconstruction, but its performance is sensitive to input image quality, which struggles to achieve high-fidelity rendering when provided with low-quality sparse input viewpoints. Previous methods for NeRF restoration are tailored for specific degradation type,

Dongzhi Jiang, Guanglu Song, Xiaoshi Wu, Renrui Zhang

Diffusion models have demonstrated great success in the field of text-to-image generation. However, alleviating the misalignment between the text prompts and images is still challenging. The root reason behind the misalignment has not been extensively investigated. We observe that the misalignment is caused by inadequate token attention activation. We furthe

Xinyang Han, Zelin Gao, Angjoo Kanazawa, Shubham Goel

Humans can infer 3D structure from 2D images of an object based on past experience and improve their 3D understanding as they see more images. Inspired by this behavior, we introduce SAP3D, a system for 3D reconstruction and novel view synthesis from an arbitrary number of unposed images. Given a few unposed images of an object, we adapt a pre-trained view-c

Chris Akers, Ronak M. Soni, Annie Y. Wei

Holographic tensor networks model AdS/CFT, but so far they have been limited by involving only systems that are very different from gravity. Unfortunately, we cannot straightforwardly discretize gravity to incorporate it, because that would break diffeomorphism invariance. In this note, we explore a resolution. In low dimensions gravity can be written as a t

Francis Engelmann, Fabian Manhardt, Michael Niemeyer, Keisuke Tateno

Large visual-language models (VLMs), like CLIP, enable open-set image segmentation to segment arbitrary concepts from an image in a zero-shot manner. This goes beyond the traditional closed-set assumption, i.e., where models can only segment classes from a pre-defined training set. More recently, first works on open-set segmentation in 3D scenes have appeare

Ashleigh Adams, Colin Defant, Jessica Striker

Inspired by recent work on refraction billiards in dynamics, we introduce a notion of refraction for combinatorial billiards. This allows us to define a generalization of toric promotion that we call toric promotion with reflections and refractions, which is a dynamical system defined via a graph $G$ whose edges are partitioned into a set of reflection edges

Hanyu Lai, Xiao Liu, Iat Long Iong, Shuntian Yao

Large language models (LLMs) have fueled many intelligent web agents, but most existing ones perform far from satisfying in real-world web navigation tasks due to three factors: (1) the complexity of HTML text data (2) versatility of actions on webpages, and (3) task difficulty due to the open-domain nature of the web. In light of these challenges, we develo

Darioush Kevian, Usman Syed, Xingang Guo, Aaron Havens

In this paper, we explore the capabilities of state-of-the-art large language models (LLMs) such as GPT-4, Claude 3 Opus, and Gemini 1.0 Ultra in solving undergraduate-level control problems. Controls provides an interesting case study for LLM reasoning due to its combination of mathematical theory and engineering design. We introduce ControlBench, a benchma

Arnab Sen Sharma, David Atkinson, David Bau

We investigate the mechanisms of factual recall in the Mamba state space model. Our work is inspired by previous findings in autoregressive transformer language models suggesting that their knowledge recall is localized to particular modules at specific token locations; we therefore ask whether factual recall in Mamba can be similarly localized. To investiga

Shuting He, Henghui Ding

Referring video segmentation relies on natural language expressions to identify and segment objects, often emphasizing motion clues. Previous works treat a sentence as a whole and directly perform identification at the video-level, mixing up static image-level cues with temporal motion cues. However, image-level features cannot well comprehend motion cues in

Vishaal Udandarao, Ameya Prabhu, Adhiraj Ghosh, Yash Sharma

Web-crawled pretraining datasets underlie the impressive "zero-shot" evaluation performance of multimodal models, such as CLIP for classification/retrieval and Stable-Diffusion for image generation. However, it is unclear how meaningful the notion of "zero-shot" generalization is for such multimodal models, as it is not known to what extent their pretraining

Alexander Zlokapa, Rolando D. Somma

We consider the task of simulating time evolution under a Hamiltonian $H$ within its low-energy subspace. Assuming access to a block-encoding of $H'=(H-E)/\lambda$ for some $E \in \mathbb R$, the goal is to implement an $\epsilon$-approximation to $e^{-itH}$ when the initial state is confined to the subspace corresponding to eigenvalues $[-1, -1+\Delta/\lamb

G. D'Ambrosio, A. M. Iyer, F. Mahmoudi, S. Neshatpour

The rare kaon decay $K^+ \to \pi^+\ell^+\ell^-$ offers insights into Standard Model (SM) physics and beyond. Driven by vector form factor in the SM, it can also probe non-standard contributions. In this letter we study the scalar contribution, $f_S$. Using differential decay width and Forward-Backward Asymmetry we propose a simultaneous fit to vector and sca

Yiming Zhang, Zhe Wang, Xinjie Li, Yunchen Yuan

Human body restoration plays a vital role in various applications related to the human body. Despite recent advances in general image restoration using generative models, their performance in human body restoration remains mediocre, often resulting in foreground and background blending, over-smoothing surface textures, missing accessories, and distorted limb

Harrison Grodin, Robert Harper

Amortized analysis is a cost analysis technique for data structures in which cost is studied in aggregate: rather than considering the maximum cost of a single operation, one bounds the total cost encountered throughout a session. Traditionally, amortized analysis has been phrased inductively, quantifying over finite sequences of operations. Connecting to pr

Corby Rosset, Ching-An Cheng, Arindam Mitra, Michael Santacroce

This paper studies post-training large language models (LLMs) using preference feedback from a powerful oracle to help a model iteratively improve over itself. The typical approach for post-training LLMs involves Reinforcement Learning from Human Feedback (RLHF), which traditionally separates reward learning and subsequent policy optimization. However, such

Roxana Peña-Mendieta, Ania Mesa-Rodríguez, Ernesto Estevez-Rams, Daniel Estevez-Moya

Any continuous curve in a higher dimensional space can be considered a trajectory that can be parameterized by a single variable, usually taken as time. It is well known that a continuous curve can have a fractional dimensionality, which can be estimated using already standard algorithms. However, characterizing a trajectory from an entropic perspective is f

Kenny De Commer, Stephen T. Moore

We prove a version of Sylvester's law of inertia for the Reflection Equation Algebra (=REA). We will only be concerned with the REA constructed from the $R$-matrix associated to the standard $q$-deformation of $GL(N,\mathbb{C})$. For $q$ positive, this particular REA comes equipped with a natural $*$-structure, by which it can be viewed as a $q$-deformation

Botsz Huang, You-Ting Huang, Jan-Chi Yang, Tse-Ming Chen

The high-order Hall effects, which go beyond the ordinary, unlock more possibilities of electronic transport properties and functionalities. Pioneer works focus on the manufacture of complex nanostructures with low lattice symmetry to produce them. In this paper, we theoretically show that such high-order Hall effects can alternatively be generated by curvin

Jun Zhan, Yuhao Gu, Xianxin Wu, Jiangping Hu

The recent observation of high-T$_c$ superconductivity in the bilayer nickelate La$_3$Ni$_2$O$_7$ under pressure has garnered significant interests. While researches have predominantly focused on the role of electron-electron interactions in the superconducting mechanism, the impact of electron-phonon coupling (EPC) has remained elusive. In this work, we per

Ziru Liu, Shuchang Liu, Zijian Zhang, Qingpeng Cai

In the landscape of Recommender System (RS) applications, reinforcement learning (RL) has recently emerged as a powerful tool, primarily due to its proficiency in optimizing long-term rewards. Nevertheless, it suffers from instability in the learning process, stemming from the intricate interactions among bootstrapping, off-policy training, and function appr

Christian Hallas, Grace K. Li, Nathaniel B. Vilas, Paige Robichaud

We demonstrate a blue-detuned magneto-optical trap (MOT) of a polyatomic molecule, calcium monohydroxide (CaOH). We identify a novel MOT frequency configuration that produces high spatial compression of the molecular cloud. This high compression MOT achieves a cloud radius of $59(5)~\mu\text{m}$ and a peak density of $8(2) \times 10^8~\text{cm}^{-3}$, the hi

Ziyao Zeng, Daniel Wang, Fengyu Yang, Hyoungseob Park

Three-dimensional (3D) reconstruction from a single image is an ill-posed problem with inherent ambiguities, i.e. scale. Predicting a 3D scene from text description(s) is similarly ill-posed, i.e. spatial arrangements of objects described. We investigate the question of whether two inherently ambiguous modalities can be used in conjunction to produce metric-

Kairui Ding, Boyuan Chen, Ruihai Wu, Yuyang Li

Robotic manipulation with two-finger grippers is challenged by objects lacking distinct graspable features. Traditional pre-grasping methods, which typically involve repositioning objects or utilizing external aids like table edges, are limited in their adaptability across different object categories and environments. To overcome these limitations, we introd

Nicola De Nitti, Stefano Lisini, Antonio Segatti, Roman Taranets

In this paper, we discuss existence and finite speed of propagation for the solutions to an initial-boundary value problem for a family of fractional thin-film equations in a bounded domain in $\mathbb{R}^d$. The nonlocal operator we consider is the spectral fractional Laplacian with Neumann boundary conditions. In the case of a ``strong slippage'' regime wi

Bahri Batuhan Bilecen, Yigit Yalin, Ning Yu, Aysegul Dundar

Generative Adversarial Networks (GANs) have emerged as powerful tools for high-quality image generation and real image editing by manipulating their latent spaces. Recent advancements in GANs include 3D-aware models such as EG3D, which feature efficient triplane-based architectures capable of reconstructing 3D geometry from single images. However, limited at

Dario Padovano, Alessio Carpegna, Alessandro Savino, Stefano Di Carlo

One of today's main concerns is to bring Artificial Intelligence power to embedded systems for edge applications. The hardware resources and power consumption required by state-of-the-art models are incompatible with the constrained environments observed in edge systems, such as IoT nodes and wearable devices. Spiking Neural Networks (SNNs) can represent a s

Minh Pham, Kelly O. Marshall, Chinmay Hegde, Niv Cohen

With the rapid growth of text-to-image models, a variety of techniques have been suggested to prevent undesirable image generations. Yet, these methods often only protect against specific user prompts and have been shown to allow unsafe generations with other inputs. Here we focus on unconditionally erasing a concept from a text-to-image model rather than co

Chao-Hsiang Sheu, Mikhail Shifman

We discuss various questions which emerge in connection with the Lie-algebraic deformation of $\mathbb{CP}^1$ sigma model in two dimensions. First we supersymmetrize the original model endowing it with the minimal ${\cal N}=(0,2)$ and extended ${\cal N}=(2,2)$ supersymmetries. Then we derive the general hypercurrent anomaly in the both cases. In the latter c

Christopher S. Timperley, Gijs van der Hoorn, André Santos, Harshavardhan Deshpande

As robotic systems such as autonomous cars and delivery drones assume greater roles and responsibilities within society, the likelihood and impact of catastrophic software failure within those systems is increased.To aid researchers in the development of new methods to measure and assure the safety and quality of robotics software, we systematically curated

Joshua Lackman

We canonically quantize a Poisson manifold to a Lie 2-groupoid, complete with a quantization map, and show that it relates geometric and deformation quantization: the perturbative expansion in $\hbar$ of the (formal) convolution of two quantized functions yields Kontsevich's star product. Meanwhile, we can push forward this quantization map (by integrating o

Stephane Dartois, Benjamin McKenna

The injective norm is a natural generalization to tensors of the operator norm of a matrix. In quantum information, the injective norm is one important measure of genuine multipartite entanglement of quantum states, where it is known as the geometric entanglement. In this paper, we give a high-probability upper bound on the injective norm of real and complex

Brian Lester, Jaehoon Lee, Alex Alemi, Jeffrey Pennington

In this paper, we explore the idea of training large language models (LLMs) over highly compressed text. While standard subword tokenizers compress text by a small factor, neural text compressors can achieve much higher rates of compression. If it were possible to train LLMs directly over neurally compressed text, this would confer advantages in training and

Andrew Pocklington, Aashish A. Clerk

We demonstrate a surprising connection between pure steady state entanglement and relaxation timescales in an extremely broad class of Markovian open systems, where two (possibly many-body) systems $A$ and $B$ interact locally with a common dissipative environment. This setup also encompases a broad class of adaptive quantum dynamics based on continuous meas

Bradley P. Allen, Filip Ilievski

Knowledge engineering is the process of creating and maintaining knowledge-producing systems. Throughout the history of computer science and AI, knowledge engineering workflows have been widely used given the importance of high-quality knowledge for reliable intelligent agents. Meanwhile, the scope of knowledge engineering, as apparent from its target tasks

Angus Nicolson, Lisa Schut, J. Alison Noble, Yarin Gal

Concept-based explanations translate the internal representations of deep learning models into a language that humans are familiar with: concepts. One popular method for finding concepts is Concept Activation Vectors (CAVs), which are learnt using a probe dataset of concept exemplars. In this work, we investigate three properties of CAVs: (1) inconsistency a

Marco Bronzini, Carlo Nicolini, Bruno Lepri, Jacopo Staiano

Large Language Models (LLMs) demonstrate an impressive capacity to recall a vast range of factual knowledge. However, understanding their underlying reasoning and internal mechanisms in exploiting this knowledge remains a key research area. This work unveils the factual information an LLM represents internally for sentence-level claim verification. We propos

Wenshan Wu, Shaoguang Mao, Yadong Zhang, Yan Xia

Large language models (LLMs) have exhibited impressive performance in language comprehension and various reasoning tasks. However, their abilities in spatial reasoning, a crucial aspect of human cognition, remain relatively unexplored. Human possess a remarkable ability to create mental images of unseen objects and actions through a process known as the Mind

S. Juneau, R. Canning, D. M. Alexander, R. Pucha

The Dark Energy Spectroscopic Instrument (DESI) cosmology survey includes a Bright Galaxy Survey (BGS) which will yield spectra for over ten million bright galaxies (r<20.2 AB mag). The resulting sample will be valuable for both cosmological and astrophysical studies. However, the star/galaxy separation criterion implemented in the nominal BGS target selecti

Rinon Gal, Or Lichter, Elad Richardson, Or Patashnik

Recent advancements in diffusion models have introduced fast sampling methods that can effectively produce high-quality images in just one or a few denoising steps. Interestingly, when these are distilled from existing diffusion models, they often maintain alignment with the original model, retaining similar outputs for similar prompts and seeds. These prope

Mingrui Jing, Chengkai Zhu, Xin Wang

Circuit knitting, a method for connecting quantum circuits across multiple processors to simulate nonlocal quantum operations, is a promising approach for distributed quantum computing. While various techniques have been developed for circuit knitting, we uncover fundamental limitations to the scalability of this technology. We prove that the sampling overhe

Haozhe Luo, Ziyu Zhou, Corentin Royer, Anjany Sekuboyina

Vision-language pre-training for chest X-rays has made significant strides, primarily by utilizing paired radiographs and radiology reports. However, existing approaches often face challenges in encoding medical knowledge effectively. While radiology reports provide insights into the current disease manifestation, medical definitions (as used by contemporary

Andrew Lavin

Since the breakthrough performance of AlexNet in 2012, convolutional neural networks (convnets) have grown into extremely powerful vision models. Deep learning researchers have used convnets to perform vision tasks with accuracy that was unachievable a decade ago. Confronted with the immense computation that convnets use, deep learning researchers also becam

Daniele Dell'Erba, Arthur Dumas, Sven Schewe

While discounted payoff games and classic games that reduce to them, like parity and mean-payoff games, are symmetric, their solutions are not. We have taken a fresh view on the properties that optimal solutions need to have, and devised a novel way to converge to them, which is entirely symmetric. We achieve this by building a constraint system that uses ev

Alexander Brudnyi, Amol Sasane

Let $\mathscr O_u$ be the algebra of holomorphic functions on ${\bf C}_+:=\{s\in{\bf C}:\text{Re }s>0\}$ that are limits of Dirichlet series $D=\sum_{n=1}^\infty a_n n^{-s}$, $s\in \bf{C}_+$, that converge uniformly on proper half-planes of $\bf{C}_+$. We study algebraic-topological properties of subalgebras of $\mathscr O_u$: the Banach algebras $\mathscr W

Mohammadmehdi Ataei, Hyunmin Cheong, Daniele Grandi, Ye Wang

Requirements elicitation, a critical, yet time-consuming and challenging step in product development, often fails to capture the full spectrum of user needs. This may lead to products that fall short of expectations. This paper introduces a novel framework that leverages Large Language Models (LLMs) to automate and enhance the requirements elicitation proces

P. Yanes-Thomas, R. Gutiérrez-Jáuregui, P. Barberis-Blostein, D. Sahagún-Sánchez

Microscopic models based on multilevel atoms are central to optimizing non-linear optical responses and the coherent control of light. These models are traditionally based on single-atom effects that are parametrically extrapolated to include collective effects, such as an enhanced response or propagation within atomic media. In this work we present a system

Gaurav Parthasarathy, Thibault Dardinier, Benjamin Bonneau, Peter Müller

Automated program verifiers are typically implemented using an intermediate verification language (IVL), such as Boogie or Why3. A verifier front-end translates the input program and specification into an IVL program, while the back-end generates proof obligations for the IVL program and employs an SMT solver to discharge them. Soundness of such verifiers th

Jeongmin Bae, Seoha Kim, Youngsik Yun, Hahyun Lee

As 3D Gaussian Splatting (3DGS) provides fast and high-quality novel view synthesis, it is a natural extension to deform a canonical 3DGS to multiple frames for representing a dynamic scene. However, previous works fail to accurately reconstruct complex dynamic scenes. We attribute the failure to the design of the deformation field, which is built as a coord

Frederick Choi, Charlotte Lambert, Vinay Koshy, Sowmya Pratipati

Much of the research in online moderation focuses on punitive actions. However, emerging research has shown that positive reinforcement is effective at encouraging desirable behavior on online platforms. We extend this research by studying the "creator heart" feature on YouTube, quantifying their primary effects on comments that receive hearts and on videos

Qianning Wang, Chenglin Wang, Zhixin Lai, Yucheng Zhou

The classification of insect pests is a critical task in agricultural technology, vital for ensuring food security and environmental sustainability. However, the complexity of pest identification, due to factors like high camouflage and species diversity, poses significant obstacles. Existing methods struggle with the fine-grained feature extraction needed t

Oksana Pichugina, Yingcong Tan, Christopher Beck

With the advances in customized hardware for quantum annealing and digital/CMOS Annealing, Quadratic Unconstrained Binary Optimization (QUBO) models have received growing attention in the optimization literature. Motivated by an existing general-purpose approach that derives QUBO models from binary linear programs (BLP), we propose a novel Multilevel Constra

Nguyen Thi Hong Phuong, Trinh Tuan, Lai Tien Minh

Derived from the results in [Giang et al.: \emph{Convolutions for the Fourier transforms with geometric variables and applications}, Math. Nachr. 283(12) (2010), 1758--1770], in this paper, we devoted to studying the boundedness properties for the Fourier-cosine convolution weighted by a Gaussian function of the form $\gamma =\exp(-\frac{1}{2}y^2)$ via Young

Longxu Dou, Qian Liu, Guangtao Zeng, Jia Guo

We present Sailor, a family of open language models ranging from 0.5B to 7B parameters, tailored for South-East Asian (SEA) languages. These models are continually pre-trained from Qwen1.5, a great language model for multilingual use cases. From Qwen1.5, Sailor models accept 200B to 400B tokens, primarily covering the languages of English, Chinese, Vietnames

Anuj Kankani, Sean T. McWilliams

The Boundary-to-Bound (B2B) correspondence, which connects orbital and radiative observables between bound and unbound orbits, has recently been introduced and demonstrated in the perturbative regime. We produce a large number of numerical relativity simulations of bound and unbound encounters between two nonspinning equal mass black holes in order to test t

S M Rakib Hasan, Aakar Dhakal, Ms. Ayesha Siddiqua, Mohammad Mominur Rahman

Music plays a huge part in shaping peoples' psychology and behavioral patterns. This paper investigates the connection between national anthems and different global indices with computational music analysis and statistical correlation analysis. We analyze national anthem musical data to determine whether certain musical characteristics are associated with pe

Aniruddha Nrusimha, Mayank Mishra, Naigang Wang, Dan Alistarh

We consider the problem of accurate quantization for language models, where both the weights and activations are uniformly quantized to 4 bits per parameter, the lowest bitwidth format natively supported by GPU hardware. In this context, the key challenge is activation quantization: it is known that language models contain outlier channels whose values on av

Shuo Sun, Rajan Udwani, Zuo-Jun Max Shen

We consider assortment and inventory planning problems with dynamic stockout-based substitution effects, and without replenishment, in two different settings: (1) Customers can see all available products when they arrive, a typical scenario in physical stores. (2) The seller can choose to offer a subset of available products to each customer, which is more c

Nour-eddine Toutlini, Abdelaziz Beljadid, Azzeddine Soulaïmani

In this study, a novel semi-implicit second-order temporal scheme combined with the finite element method for space discretization is proposed to solve the coupled system of infiltration and solute transport in unsaturated porous media. The Richards equation is used to describe unsaturated flow, while the advection-dispersion equation (ADE) is used for model

Ryo Kamoi, Sarkar Snigdha Sarathi Das, Renze Lou, Jihyun Janice Ahn

With Large Language Models (LLMs) being widely used across various tasks, detecting errors in their responses is increasingly crucial. However, little research has been conducted on error detection of LLM responses. Collecting error annotations on LLM responses is challenging due to the subjective nature of many NLP tasks, and thus previous research focuses

Luigi Ferraro, W. Frank Moore

Let $(R,\mathfrak{m},\Bbbk)$ be a regular local ring of dimension 3. Let $I$ be a Gorenstein ideal of $R$ of grade 3. It follows from a result of Buchsbaum and Eisenbud that there is a skew-symmetric matrix of odd size such that $I$ is generated by the sub-maximal pfaffians of this matrix. Let $J$ be the ideal obtained by multiplying some of the pfaffian gen

Andrea Bevilacqua, Jerzy Kowalski-Glikman, Wojciech Wislicki

In this paper we consider description of kaon -- anti-kaon interference in the context of a theory with deformed $\cal CPT$ symmetry. In the case of such theoretical models, deviations from the standard $\cal CPT$ invariance is related to the momentum carried by the particles; in particular the rest masses of particles and antiparticles are equal. We find th

Oscar Loaiza-Brito, Víctor M. López-Ramos

This mini-course, conducted at the XI School on Geometric, Algebraic, and Topological Methods in Quantum Field Theory held in Villa de Leyva, Colombia, provides an overview of the interconnection between generalized symmetries and cohomology. It is designed for advanced undergraduate students with a background in physics or mathematics. Additionally, we desc

Ankan Mullick, Mukur Gupta, Pawan Goyal

Biomedical queries have become increasingly prevalent in web searches, reflecting the growing interest in accessing biomedical literature. Despite recent research on large-language models (LLMs) motivated by endeavours to attain generalized intelligence, their efficacy in replacing task and domain-specific natural language understanding approaches remains qu

Wiesław Kubiś, Franz-Viktor Kuhlmann

We study spherical completeness of ball spaces and its stability under expansions. We give some criteria for ball spaces that guarantee that spherical completeness is preserved when the ball space is closed under unions of chains. This applies in particular to the spaces of closed ultrametric balls in ultrametric spaces with linearly ordered value sets, or m

Yannick Molinghen, Raphaël Avalos, Mark Van Achter, Ann Nowé

We introduce the Laser Learning Environment (LLE), a collaborative multi-agent reinforcement learning environment in which coordination is central. In LLE, agents depend on each other to make progress (interdependence), must jointly take specific sequences of actions to succeed (perfect coordination), and accomplishing those joint actions does not yield any

Hainan Xu, Zhehuai Chen, Fei Jia, Boris Ginsburg

This paper proposes Transducers with Pronunciation-aware Embeddings (PET). Unlike conventional Transducers where the decoder embeddings for different tokens are trained independently, the PET model's decoder embedding incorporates shared components for text tokens with the same or similar pronunciations. With experiments conducted in multiple datasets in Man

Zhou Jie, Xiao Chao, Peng Bo, Liu Zhen

Aircraft target detection in SAR images is a challenging task due to the discrete scattering points and severe background clutter interference. Currently, methods with convolution-based or transformer-based paradigms cannot adequately address these issues. In this letter, we explore diffusion models for SAR image aircraft target detection for the first time

Andrea Bisoffi, Dominiek M. Steeman, Claudio De Persis

We consider the problem of designing a controller for an unknown bilinear system using only noisy input-states data points generated by it. The controller should achieve regulation to a given state setpoint and provide a guaranteed basin of attraction. Determining the equilibrium input to achieve that setpoint is not trivial in a data-based setting and we pr

Isaac C. D. Lenton, Felix Pertl, Lubuna Shafeek, Scott R. Waitukaitis

Scanning Kelvin probe microscopy (SKPM) is a powerful technique for investigating the electrostatic properties of material surfaces, enabling the imaging of variations in work function, topology, surface charge density, or combinations thereof. Regardless of the underlying signal source, SKPM results in a voltage image which is spatially distorted due to the

Zhengxuan Wu, Aryaman Arora, Zheng Wang, Atticus Geiger

Parameter-efficient finetuning (PEFT) methods seek to adapt large neural models via updates to a small number of weights. However, much prior interpretability work has shown that representations encode rich semantic information, suggesting that editing representations might be a more powerful alternative. We pursue this hypothesis by developing a family of R

Orcun Yildiz, Dmitriy Morozov, Arnur Nigmetov, Bogdan Nicolae

In situ approaches can accelerate the pace of scientific discoveries by allowing scientists to perform data analysis at simulation time. Current in situ workflow systems, however, face challenges in handling the growing complexity and diverse computational requirements of scientific tasks. In this work, we present Wilkins, an in situ workflow system that is

Kailin Li, Jingbo Wang, Lixin Yang, Cewu Lu

Generating natural human grasps necessitates consideration of not just object geometry but also semantic information. Solely depending on object shape for grasp generation confines the applications of prior methods in downstream tasks. This paper presents a novel semantic-based grasp generation method, termed SemGrasp, which generates a static human grasp po