October 2023 arXiv papers — page 41

Calvin Osinga, Benedikt Diemer, Francisco Villaescusa-Navarro, Elena D'Onghia

Intensity mapping experiments are beginning to measure the spatial distribution of neutral atomic hydrogen (HI) to constrain cosmological parameters and the large-scale distribution of matter. However, models of the behaviour of HI as a tracer of matter are complicated by galaxy evolution. In this work, we examine the clustering of HI in relation to galaxy c

Mackenna L. Wood, Andrew W. Mann, Madyson G. Barber, Jonathan L. Bush

The dispersed remnants of stellar nurseries, stellar associations provide unparalleled samples of coeval stars critical for studies of stellar and planetary formation and evolution. The Carina Stellar Association is one of the closest stellar associations to Earth, and yet measurements of its age have varied from 13 to 45 Myr. We aim to update the age of Car

Sudhakantha Girmohanta, Yuichiro Nakai, Yoshihiro Shigekami, Kohsaku Tobioka

The dilaton $\phi$ is a pseudo-Nambu-Goldstone boson associated with the spontaneous breaking of scale invariance in a nearly conformal theory, and couples to the trace of the stress-energy tensor. We analyze experimental constraints on a light dilaton with mass in the MeV-GeV range from rare meson decays. New model-independent inclusive bounds for the $b \t

Haruki Watanabe, Hosho Katsura, Jong Yeon Lee

The Hohenberg--Mermin--Wagner theorem states that there is no spontaneous breaking of continuous symmetries in spatial dimensions $d\leq2$ at finite temperature. At zero temperature, the classical/quantum mapping further implies the absence of continuous symmetry breaking in one dimension, which is also known as Coleman's theorem in the context of relativist

Damien Gagnier, Ondrej Pejcha

During common envelope evolution, an initially weak magnetic field may undergo amplification by interacting with spiral density waves and turbulence generated in the stellar envelope by the inspiralling companion. Using 3D magnetohydrodynamical simulations on adaptively refined spherical grids with excised central regions, we studied the amplification of mag

Thomas Wevers, Taeho Ryu

This chapter provides an overview of tidal disruption events, aiming to provide an overview of both the theoretical and the observational state of the field, with the overarching goal of introducing them as tools to indirectly observe massive black holes in the Universe. We start by introducing the relevant theoretical concepts, physical scales and timescale

Sheng-Jie Huang, Meng Cheng

Topological holography is a holographic principle that describes the generalized global symmetry of a local quantum system in terms of a topological order in one higher dimension. This framework separates the topological data from the local dynamics of a theory and provides a unified description of the symmetry and duality in gapped and gapless phases of mat

Elba Alonso-Monsalve, David I. Kaiser

We describe a realistic mechanism whereby black holes with significant QCD color charge could have formed during the early universe. Primordial black holes (PBHs) could make up a significant fraction of the dark matter if they formed well before the QCD confinement transition. Such PBHs would form by absorbing unconfined quarks and gluons, and hence could ac

Bin Xu, Wei Xue

We present a semiclassical non-perturbative approach for calculating the preheating process at the end of inflation. Our method involves integrating out the decayed particles within the path integral framework and subsequently determining world-line instanton solutions in the effective action. This enables us to obtain the effective action of the inflaton, w

Mainak Mukhopadhyay, Shigeo S. Kimura, Kohta Murase

The next generation gravitational wave (GW) detectors -- Einstein Telescope (ET) and Cosmic Explorer (CE) will have distance horizons up to $\mathcal{O}(10)$ Gpc for detecting binary neutron star (BNS) mergers. This will make them ideal for triggering high-energy neutrino searches from BNS mergers at the next generation neutrino detectors, such as IceCube-Ge

A. Botteon, F. Gastaldello, J. A. ZuHone, M. Balboni

We report on the detection of diffuse radio emission with peculiar morphology in the central region of the galaxy cluster Abell 2657. The most striking feature identified in our 144 MHz LOFAR image is a bifurcated radio arc that extends for a projected size of 150-200 kpc. From the analysis of XMM-Newton data, we find clear evidence of gas sloshing in the cl

G. Columba, E. Rigliaco, R. Gratton, D. Mesa

Context. The study of protoplanetary disks is fundamental to understand their evolution and interaction with the surrounding environment, and to constrain planet formation mechanisms. Aims. We aim at characterising the young binary system HD 34700 A, which shows a wealth of structures. Methods. Taking advantage of the high-contrast imaging instruments SPHERE

Ruizhi Liu, Ho Tat Lam, Han Ma, Liujun Zou

We analyze the internal symmetries and their anomalies in the Kitaev spin-$S$ models. Importantly, these models have a lattice version of a $\mathbb{Z}_2$ 1-form symmetry, denoted by $\mathbb{Z}_2^{[1]}$. There is also an ordinary 0-form $\mathbb{Z}_2^{(x)}\times\mathbb{Z}_2^{(y)}\times\mathbb{Z}_2^T$ symmetry, where $\mathbb{Z}_2^{(x)}\times\mathbb{Z}_2^{(y

Qianxu Wang, Haotong Zhang, Congyue Deng, Yang You

Humans demonstrate remarkable skill in transferring manipulation abilities across objects of varying shapes, poses, and appearances, a capability rooted in their understanding of semantic correspondences between different instances. To equip robots with a similar high-level comprehension, we present SparseDFF, a novel DFF for 3D scenes utilizing large 2D vis

Zizhao Zhang, Yi Yang, Lutong Zou, He Wen

Benefiting from high-quality datasets and standardized evaluation metrics, machine learning (ML) has achieved sustained progress and widespread applications. However, while applying machine learning to relational databases (RDBs), the absence of a well-established benchmark remains a significant obstacle to the development of ML. To address this issue, we in

Shih-yang Liu, Zechun Liu, Xijie Huang, Pingcheng Dong

We propose LLM-FP4 for quantizing both weights and activations in large language models (LLMs) down to 4-bit floating-point values, in a post-training manner. Existing post-training quantization (PTQ) solutions are primarily integer-based and struggle with bit widths below 8 bits. Compared to integer quantization, floating-point (FP) quantization is more fle

Quentin Bouniot, Romaric Audigier, Angélique Loesch, Amaury Habrard

The use of pretrained deep neural networks represents an attractive way to achieve strong results with few data available. When specialized in dense problems such as object detection, learning local rather than global information in images has proven to be more efficient. However, for unsupervised pretraining, the popular contrastive learning requires a larg

Aaron Lou, Chenlin Meng, Stefano Ermon

Despite their groundbreaking performance for many generative modeling tasks, diffusion models have fallen short on discrete data domains such as natural language. Crucially, standard diffusion models rely on the well-established theory of score matching, but efforts to generalize this to discrete structures have not yielded the same empirical gains. In this

Lukas Fischer, Andreas M. Menzel

Amongst the various fascinating types of material behavior featured by magnetic gels and elastomers are magnetostrictive effects. That is, deformations in shape or changes in volume are induced from outside by external magnetic fields. Application of the materials as soft actuators is therefore conceivable. Mostly, straight contraction or extension of the ma

Aarush Gupta, Junli Cao, Chaoyang Wang, Ju Hu

Real-time novel-view image synthesis on mobile devices is prohibitive due to the limited computational power and storage. Using volumetric rendering methods, such as NeRF and its derivatives, on mobile devices is not suitable due to the high computational cost of volumetric rendering. On the other hand, recent advances in neural light field representations h

Guangcong Wang, Peng Wang, Zhaoxi Chen, Wenping Wang

Neural Radiance Field (NeRF) has achieved substantial progress in novel view synthesis given multi-view images. Recently, some works have attempted to train a NeRF from a single image with 3D priors. They mainly focus on a limited field of view with a few occlusions, which greatly limits their scalability to real-world 360-degree panoramic scenarios with lar

Zipei Nie

We prove that the alternating surgeries on flat fully augmented chainmail links yield total L-spaces. We also study the non-left-orderability of surgeries on the connected sum with an L-space knot using order detection.

Christian Doberstein, Peter Binev

We introduce a new approach to the numerical simulation of Scanning Transmission Electron Microscopy images. The Lattice Multislice Algorithm (LMA) takes advantage of the fact that electron waves passing through the specimen have limited bandwidth and therefore can be approximated very well by a low-dimensional linear space spanned by translations of a well-

Nicklas Hansen, Hao Su, Xiaolong Wang

TD-MPC is a model-based reinforcement learning (RL) algorithm that performs local trajectory optimization in the latent space of a learned implicit (decoder-free) world model. In this work, we present TD-MPC2: a series of improvements upon the TD-MPC algorithm. We demonstrate that TD-MPC2 improves significantly over baselines across 104 online RL tasks spann

Chien-Chung Huang, François Sellier

Matroid intersection is a classical optimization problem where, given two matroids over the same ground set, the goal is to find the largest common independent set. In this paper, we show that there exists a certain "sparsifer": a subset of elements, of size $O(|S^{opt}| \cdot 1/\varepsilon)$, where $S^{opt}$ denotes the optimal solution, that is guaranteed

Eloy Peña-Asensio, Josep M. Trigo-Rodríguez, Pau Grèbol-Tomàs, David Regordosa-Avellana

In recent decades, the use of optical detection systems for meteor studies has increased dramatically, resulting in huge amounts of data being analyzed. Automated meteor detection tools are essential for studying the continuous meteoroid incoming flux, recovering fresh meteorites, and achieving a better understanding of our Solar System. Concerning meteor de

Aaron Gokaslan, A. Feder Cooper, Jasmine Collins, Landan Seguin

We assemble a dataset of Creative-Commons-licensed (CC) images, which we use to train a set of open diffusion models that are qualitatively competitive with Stable Diffusion 2 (SD2). This task presents two challenges: (1) high-resolution CC images lack the captions necessary to train text-to-image generative models; (2) CC images are relatively scarce. In tu

Ágnes Baran, Sándor Baran

Although by now the ensemble-based probabilistic forecasting is the most advanced approach to weather prediction, ensemble forecasts still might suffer from lack of calibration and/or display systematic bias, thus require some post-processing to improve their forecast skill. Here we focus on visibility, which quantity plays a crucial role e.g. in aviation an

Jose Alonso Carpio, Nick Ekanger, Mukul Bhattacharya, Kohta Murase

Rapidly rotating and strongly magnetized protoneutron stars (PNSs) created in core-collapse supernovae can drive relativistic magnetized winds. Ions and neutrons can be co-accelerated while they remain coupled through elastic collisions. We investigate the nucleosynthesis and subsequent nuclear disintegration, and find that relativistic neutrons can be gener

Xuming Hu, Junzhe Chen, Aiwei Liu, Shiao Meng

How can we better extract entities and relations from text? Using multimodal extraction with images and text obtains more signals for entities and relations, and aligns them through graphs or hierarchical fusion, aiding in extraction. Despite attempts at various fusions, previous works have overlooked many unlabeled image-caption pairs, such as NewsCLIPing.

J. E. Proctor, K. Trachenko

Physics-based first-principles pressure-volume-temperature equations of state (EOS) exist for solids and gases but not for liquids due to the long-standing fundamental problems involved in liquid theory. Current EOS models that are applicable to liquids and supercritical fluids at liquid-like density under conditions relevant to planetary interiors and indus

Ana Inés Gómez de Castro, Raúl de la Fuente Marcos, Ada Canet, Leire Beitia-Antero

In this work, we examine the list of 63 candidates to T Tauri star (TTS) in the TAMC identified by their ultraviolet (UV) and infrared colours (IR) measured from data obtained by the Galaxy Evolution Explorer all sky survey (GALEX-AIS) and the Two Microns All Sky Survey (2MASS), respectively. The objective of this work is twofold: evaluate whether they are p

Masahiro Kato, Masaaki Imaizumi

In causal inference about two treatments, Conditional Average Treatment Effects (CATEs) play an important role as a quantity representing an individualized causal effect, defined as a difference between the expected outcomes of the two treatments conditioned on covariates. This study assumes two linear regression models between a potential outcome and covari

Jingxiang Sun, Bo Zhang, Ruizhi Shao, Lizhen Wang

We present DreamCraft3D, a hierarchical 3D content generation method that produces high-fidelity and coherent 3D objects. We tackle the problem by leveraging a 2D reference image to guide the stages of geometry sculpting and texture boosting. A central focus of this work is to address the consistency issue that existing works encounter. To sculpt geometries

Georg Arnold, Thomas Werner, Rishabh Sahu, Lucky N. Kapoor

The rapid development of superconducting quantum hardware is expected to run into significant I/O restrictions due to the need for large-scale error correction in a cryogenic environment. Classical data centers rely on fiber-optic interconnects to remove similar networking bottlenecks and to allow for reconfigurable, software-defined infrastructures. In the

Martin Treiber, Ankit Anil Chaudhari

Recently, a fully two-dimensional microscopic traffic flow model for lane-free vehicular traffic flow has been proposed [Physica A, 509, pp. 1-11 (2018)]. In this contribution, we generalize this model to describe any kind of human-driven directed flow including lane-based vehicular flow, lane-free mixed traffic, bicycle traffic, and pedestrian flow. The pro

Jacopo Fiore, Niccolò Sellati, Francesco Gabriele, Claudio Castellani

Josephson plasmons in layered superconductors represent a natural source of optical non-linearity, thanks to their intrinsically anharmonic nature. Here we derive the selection rules behind non-linear plasmonics showing its dependence on plasmonic branches hidden to other spectroscopies, like RIXS. We benchmark our results for the case of layered cuprates, s

Timo Knippenberg, Ashreya Jayaram, Thomas Speck, Clemens Bechinger

We investigate the structural and dynamic properties of active Brownian particles (APs) confined within a soft annulus-shaped channel. Depending on the strength of the confinement and the P\'eclet number, we observe a novel re-entrant behavior that is not present in unconfined systems. Our findings are substantiated by numerical simulations and analytical co

Jared D. Fisher, Colin Montague

Many enthusiasts and experts publish forecasts of the order players are drafted into professional sports leagues, known as mock drafts. Using a novel dataset of mock drafts for the National Basketball Association (NBA), we analyze authors' mock draft accuracy over time and ask how we can reasonably use information from multiple authors. To measure how accura

W. M. T. D. Wijesundara, T. D. Wanigathunga, M. N. C. Waas, R. T. Hithanadura

The agriculture sector requires a lot of labor and resources. Hence, farmers are constantly being pressed for technology and automation to be cost-effective. In this context, autonomous robots can play a very important role in carrying out agricultural tasks such as spraying, sowing, inspection, and even harvesting. This paper presents one such autonomous ro

Stephy Jose, Alberto Rosso, Kabir Ramola

We investigate the effect of initial conditions on the fluctuations of the integrated density current across the origin ($x=0$) up to a given time $t$ in a one-dimensional system of non-interacting run-and-tumble particles. Each particle has initial probabilities $f^+$ and $f^-$ to move with an initial velocity $+v$ and $-v$ respectively, where $v>0$. We der

Yongxin Zhou, Fabien Ringeval, François Portet

This study investigates the ability of GPT models (ChatGPT, GPT-4 and GPT-4o) to generate dialogue summaries that adhere to human guidelines. Our evaluation involved experimenting with various prompts to guide the models in complying with guidelines on two datasets: DialogSum (English social conversations) and DECODA (French call center interactions). Human

Yongxin Shi, Dezhi Peng, Wenhui Liao, Zening Lin

This paper presents a comprehensive evaluation of the Optical Character Recognition (OCR) capabilities of the recently released GPT-4V(ision), a Large Multimodal Model (LMM). We assess the model's performance across a range of OCR tasks, including scene text recognition, handwritten text recognition, handwritten mathematical expression recognition, table str

Raghavendra Ramachandra, Sushma Venkatesh

Biometric verification systems are deployed in various security-based access-control applications that require user-friendly and reliable person verification. Among the different biometric characteristics, fingervein biometrics have been extensively studied owing to their reliable verification performance. Furthermore, fingervein patterns reside inside the s

Thorben Tröbst, Vijay V Vazirani

The Hylland-Zeckhauser gave a classic pricing-based mechanism (HZ) for a one-sided matching market; it yields allocations satisfying Pareto optimality and envy-freeness (Hylland and Zeckhauser, 1979), and the mechanism is incentive compatible in the large (He et al., 2018). They also studied the exchange extension of HZ and gave an example showing that it ma

Yuhki Hosoya

In this paper, we study the continuity of expected utility functions, and derive a necessary and sufficient condition for a weak order on the space of simple probabilities to have a continuous expected utility function. We also verify that almost the same condition is necessary and sufficient for a weak order on the space of probabilities with compact-suppor

Jurgen Dijkema, Xiao Xue, Patrick Harvey-Collard, Maximilian Rimbach-Russ

Direct interactions between quantum particles naturally fall off with distance. For future-proof qubit architectures, however, it is important to avail of interaction mechanisms on different length scales. In this work, we utilize a superconducting resonator to facilitate a coherent interaction between two semiconductor spin qubits 250 $\mu$m apart. This sep

Adrian Rojas-Campos, Lukas Stelz, Pascal Nieters

Highly-interconnected societies difficult to model the spread of infectious diseases such as COVID-19. Single-region SIR models fail to account for incoming forces of infection and expanding them to a large number of interacting regions involves many assumptions that do not hold in the real world. We propose using Universal Differential Equations (UDEs) to c

Saiteja Utpala, Alex Gu, Pin Yu Chen

Recently, code language models have achieved notable advancements in addressing a diverse array of essential code comprehension and generation tasks. Yet, the field lacks a comprehensive deep dive and understanding of the code embeddings of multilingual code models. In this paper, we present a comprehensive study on multilingual code embeddings, focusing on

Nima Shoghi, Adeesh Kolluru, John R. Kitchin, Zachary W. Ulissi

Foundation models have been transformational in machine learning fields such as natural language processing and computer vision. Similar success in atomic property prediction has been limited due to the challenges of training effective models across multiple chemical domains. To address this, we introduce Joint Multi-domain Pre-training (JMP), a supervised p

Justin Dallant, Frederik Haagensen, Riko Jacob, László Kozma

A saddlepoint of an $n \times n$ matrix $A$ is an entry of $A$ that is a maximum in its row and a minimum in its column. Knuth (1968) gave several different algorithms for finding a saddlepoint. The worst-case running time of these algorithms is $\Theta(n^2)$, and Llewellyn, Tovey, and Trick (1988) showed that this cannot be improved, as in the worst case al

Théo Simon

In this paper, we reassess the ability of the acoustic early dark energy (ADE) and axion-like early dark energy (EDE) models to resolve the Hubble tension in light of the new Pantheon+ and S$H_0$ES data on the one hand, and the BOSS LRG and eBOSS QSO data, analyzed under the effective field theory of large-scale structures (ETFofLSS) on the other hand. We fi

Andrew Laeuger, Brian Seymour, Yanbei Chen, Hang Yu

There may exist stellar-mass binary black holes (BBH) which merge while orbiting nearby a supermassive black hole (SMBH). In such a triple system, the SMBH will modulate the gravitational waveform of the BBH through orbital Doppler shift and de Sitter precession of the angular momentum. Future space-based GW observatories focused on the milli- and decihertz

A. R. Balasubramanian, Rupak Majumdar, Ramanathan S. Thinniyam, Georg Zetzsche

Pushdown Vector Addition Systems with States (PVASS) consist of finitely many control states, a pushdown stack, and a set of counters that can be incremented and decremented, but not tested for zero. Whether the reachability problem is decidable for PVASS is a long-standing open problem. We consider continuous PVASS, which are PVASS with a continuous semanti

Emanuele Pompei, Ylea Vlamidis, Letizia Ferbel, Valentina Zannier

We demonstrate the first successful functionalization of epitaxial three-dimensional graphene with metal nanoparticles. The functionalization is obtained by immersing the 3D graphene in a nanoparticle colloidal solution. This method is versatile and here is demonstrated for gold and palladium, but can be extended to other types and shapes of nanoparticles. W

Pan Guo, Hong-Bo Jin, Cong-Feng Qiao, Yue-Liang Wu

Quickly localizing the identified white dwarf (WD) binaries is the basic requirement for the space-based gravitational wave (GW) detection. In fact, the amplitude of GW signals are modulated by the periodic motion of GW detectors on the solar orbit. The intensity of the observed signals is enhanced according to the observation time beyond a year to enhance a

Elias Frantar, Dan Alistarh

Mixture-of-Experts (MoE) architectures offer a general solution to the high inference costs of large language models (LLMs) via sparse routing, bringing faster and more accurate models, at the cost of massive parameter counts. For example, the SwitchTransformer-c2048 model has 1.6 trillion parameters, requiring 3.2TB of accelerator memory to run efficiently,

Daniel Saragih, Atsuhiro Hibi, Pascal Tyrrell

Medical image analysis has become a prominent area where machine learning has been applied. However, high quality, publicly available data is limited either due to patient privacy laws or the time and cost required for experts to annotate images. In this retrospective study, we designed and evaluated a pipeline to generate synthetic labeled polyp images for

Benoit Dagallier

We consider the weakly asymmetric simple exclusion process on a ring, driven out of equilibrium by tilting the dynamics so as to enforce a macroscopic current of particles on a large time interval. In this current-biased dynamics, the tilt by the current makes the dynamics non-local, non homogeneous and induces long-range correlations. We compute the correla

Lingda Li, Thomas Flynn, Adolfy Hoisie

Performance modeling is an essential tool in many areas, including performance characterization/optimization, design space exploration, and resource allocation problems, to name a few. However, existing performance modeling approaches have limitations, such as high computational cost for discrete-event simulators, narrow flexibility of hardware emulators, or

Haoxiang Ma, Chongyang Shi, Shuo Han, Michael R. Dorothy

Covert planning refers to a class of constrained planning problems where an agent aims to accomplish a task with minimal information leaked to a passive observer to avoid detection. However, existing methods of covert planning often consider deterministic environments or do not exploit the observer's imperfect information. This paper studies how covert plann

Zhendong Chu, Ruiyi Zhang, Tong Yu, Rajiv Jain

To achieve state-of-the-art performance, one still needs to train NER models on large-scale, high-quality annotated data, an asset that is both costly and time-intensive to accumulate. In contrast, real-world applications often resort to massive low-quality labeled data through non-expert annotators via crowdsourcing and external knowledge bases via distant

Weijia Shi, Anirudh Ajith, Mengzhou Xia, Yangsibo Huang

Although large language models (LLMs) are widely deployed, the data used to train them is rarely disclosed. Given the incredible scale of this data, up to trillions of tokens, it is all but certain that it includes potentially problematic text such as copyrighted materials, personally identifiable information, and test data for widely reported reference benc

Peter Mortimer, Raphael Hagmanns, Miguel Granero, Thorsten Luettel

The potential for deploying autonomous systems can be significantly increased by improving the perception and interpretation of the environment. However, the development of deep learning-based techniques for autonomous systems in unstructured outdoor environments poses challenges due to limited data availability for training and testing. To address this gap,

Shayne Longpre, Robert Mahari, Anthony Chen, Naana Obeng-Marnu

The race to train language models on vast, diverse, and inconsistently documented datasets has raised pressing concerns about the legal and ethical risks for practitioners. To remedy these practices threatening data transparency and understanding, we convene a multi-disciplinary effort between legal and machine learning experts to systematically audit and tr

Tomás Sousa, Deaglan J. Bartlett, Harry Desmond, Pedro G. Ferreira

Inflation is a highly favoured theory for the early Universe. It is compatible with current observations of the cosmic microwave background and large scale structure and is a driver in the quest to detect primordial gravitational waves. It is also, given the current quality of the data, highly under-determined with a large number of candidate implementations

Vivek Maurya, Haimeng Zhang, Daria Kowsari, Andre Kuo

We present a superconducting circuit device that provides active, on-demand, tunable dissipation on a target mode of the electromagnetic field. Our device is based on a tunable "dissipator" that can be made lossy when tuned into resonance with a broadband filter mode. When driven parametrically, this dissipator induces loss on any mode coupled to it with ene

Arkady Kurnosov, Lucas J. Fernández-Alcázar, Alba Y. Ramos, Boris Shapiro

Recent experimental developments in multimode nonlinear photonic circuits (MMNPC), have motivated the development of an optical thermodynamic theory that describes the equilibrium properties of an initial beam excitation. However, a non-equilibrium transport theory for these systems, when they are in contact with thermal reservoirs, is still {\it terra incog

Kangxian Xie, Siyu Huang, Sebastian Andres Cajas Ordonez, Hanspeter Pfister

Deep-learning models have been successful in biomedical image segmentation. To generalize for real-world deployment, test-time augmentation (TTA) methods are often used to transform the test image into different versions that are hopefully closer to the training domain. Unfortunately, due to the vast diversity of instance scale and image styles, many augment

Miguel Biron-Lattes, Nikola Surjanovic, Saifuddin Syed, Trevor Campbell

Selecting the step size for the Metropolis-adjusted Langevin algorithm (MALA) is necessary in order to obtain satisfactory performance. However, finding an adequate step size for an arbitrary target distribution can be a difficult task and even the best step size can perform poorly in specific regions of the space when the target distribution is sufficiently

Morris Alper, Hadar Averbuch-Elor

Although the mapping between sound and meaning in human language is assumed to be largely arbitrary, research in cognitive science has shown that there are non-trivial correlations between particular sounds and meanings across languages and demographic groups, a phenomenon known as sound symbolism. Among the many dimensions of meaning, sound symbolism is par

Wen Huang, Song Shao, Rongzhong Xiao

In this paper, we study the pointwise convergence of centain continuous-time polynomial ergodic averages. Our approach is based on the topological models of measurable flows. One of the main results of this paper is as follows: Let $a\in \mathbb{R}$, $Q\in \mathbb{R}[t]$ with $\text{deg}\ Q\ge 2$. Let $(X,\mathcal{X},\mu, (T^{t})_{t\in \mathbb{R}})$ and $(X,

Jongheon Jeong, Jinwoo Shin

Along with recent diffusion models, randomized smoothing has become one of a few tangible approaches that offers adversarial robustness to models at scale, e.g., those of large pre-trained models. Specifically, one can perform randomized smoothing on any classifier via a simple "denoise-and-classify" pipeline, so-called denoised smoothing, given that an accu

Maggie Mahmood, Hamideh Talafian, Devyn Shafer, Morten Lundsgaard

In teacher professional development (PD), grouping teachers with varying levels of experience can be a productive and empowering way to stimulate the exchange and co-generation of content and pedagogical knowledge. However, less experienced teachers can face socio-emotional risks when engaging in collaborative science content reasoning tasks with more experi

Eshant English, Matthias Kirchler, Christoph Lippert

Normalising flows are generative models that transform a complex density into a simpler density through the use of bijective transformations enabling both density estimation and data generation from a single model. %However, the requirement for bijectivity imposes the use of specialised architectures. In the context of image modelling, the predominant choice

Devleena Das, Vivek Khetan

Recent advances have led to the availability of many pre-trained language models (PLMs); however, a question that remains is how much data is truly needed to fine-tune PLMs for downstream tasks? In this work, we introduce DEFT-UCS, a data-efficient fine-tuning framework that leverages unsupervised core-set selection to identify a smaller, representative data

Bruno Murta, Joaquín Fernández-Rossier

The widespread use of the noninteracting ground state as the initial state for the digital quantum simulation of the Fermi-Hubbard model is largely due to the scarcity of alternative easy-to-prepare approximations to the exact ground state in the literature. Exploiting the fact that the spin-$\frac{1}{2}$ Heisenberg model is the effective low-energy theory o

Hichame Amal, Saïd Asserda, Mohamed Barloub

In this paper, we introduce $m$-subharmonic functions in quaternionic space $\mathbb{H}^{n}$, we define the quaternionic Hessian operator and solve the homogeneous Dirichlet problem for the quaternionic Hessian equation on the unit ball with continuous boundary data.

Leonid Positselski

Let $\kappa$ be a regular cardinal, $\lambda<\kappa$ be a smaller infinite cardinal, and $\mathsf K$ be a $\kappa$-accessible category where colimits of $\lambda$-indexed chains exist. We show that various category-theoretic constructions applied to $\mathsf K$, such as the inserter and the equifier, produce $\kappa$-accessible categories $\mathsf E$ again,

Kejiang Qian, Lingjun Mao, Xin Liang, Yimin Ding

In urban planning, land use readjustment plays a pivotal role in aligning land use configurations with the current demands for sustainable urban development. However, present-day urban planning practices face two main issues. Firstly, land use decisions are predominantly dependent on human experts. Besides, while resident engagement in urban planning can pro

Rebecca Spiecker, Martin Spiecker, Adyasha Biswal, Mykola Shcherbinin

We introduce a new X-ray imaging technique to facilitate propagation-based phase contrast of large, centimeter-sized samples. The diffracted X-ray wavefield behind the sample is demagnified by asymmetric Bragg crystal optics, thereby virtually increasing the propagation distance and thus enhancing the image contrast. We demonstrate the significant increase i

Supravat Dey, Gladys Massiera, Estelle Pitard

Large groups of active cilia collectively beat in a fluid medium as metachronal waves, essential for some microorganisms motility and for flow generation in mucociliary clearance. Several models can predict the emergence of metachronal waves, but what controls the properties of metachronal waves is still unclear. Here, we investigate numerically a simple mod

Cormac Toher, Stefano Curtarolo

Many different types of phases can form within alloys, from highly-ordered intermetallic compounds, to structurally-ordered but chemically-disordered solid solutions, and structurally-disordered (i.e. amorphous) metallic glasses. The different types of phases display very different properties, so predicting phase formation is important for understanding how

Tridib Banerjee, Sergey Danilov, Knut Klingbeil

Expressions for local discrete variance decay (DVD) rates are directly derived from discrete tracer equations without any assumptions on discrete fluxes of the second moment. Spurious mixing (SM) associated with numerical implementations of scalar advection and diffusion is thus estimated. The new framework is shown to avoid the need for second-moment flux d

Ivan Dimitrov, Cole Gigliotti, Etan Ossip, Charles Paquette

The main result of this paper is a recursive description of all decompositions \[ \Delta^+ = \Phi_1 \sqcup \Phi_2 \sqcup \dots \sqcup \Phi_k \] of the positive roots $\Delta^+$ of an arbitrary root system $\Delta$ into a disjoint union of inversion sets. Such decompositions play a central role in geometric invariant theory (GIT) in connection with studying t

Sandra Di Rocco, Lukas Gustafsson, Luca Sodomaco

We introduce a theory of relative tangency for projective algebraic varieties. The dual variety $X_Z^\vee$ of a variety $X$ relative to a subvariety $Z$ is the set of hyperplanes tangent to $X$ at a point of $Z$. We also introduce the concept of polar classes of $X$ relative to $Z$. We explore the duality of varieties of low rank matrices relative to special

Rasim Volga Ovali, Shakir Ullah, Mehmet Gunay, Mehmet Emre Tasgin

A local measurement extracts work as a backaction, e.g., in a system of two entangled cavities: first cavity, $a$, comprises a piston and the measurement is carried out on the second cavity, $b$. When no one makes a measurement on the cavity $b$, i.e., it is simply placed in vacuum; environmental monitoring results in the coherent states as the einselected p

Yameng Liu, Jianhua Zhang, Yuxiang Zhang, Huiwen Gong

Integrated Sensing and Communication (ISAC) is a promising technology in 6G systems. The existing 3D Geometry-Based Stochastic Model (GBSM), as standardized for 5G systems, addresses solely communication channels and lacks consideration of the integration with sensing channel. Therefore, this letter extends 3D GBSM to support ISAC research, with a particular

Samuel L. Smith, Andrew Brock, Leonard Berrada, Soham De

Many researchers believe that ConvNets perform well on small or moderately sized datasets, but are not competitive with Vision Transformers when given access to datasets on the web-scale. We challenge this belief by evaluating a performant ConvNet architecture pre-trained on JFT-4B, a large labelled dataset of images often used for training foundation models

Gabriel Mukobi, Peter Chatain, Su Fong, Robert Windesheim

While large language models demonstrate remarkable capabilities, they often present challenges in terms of safety, alignment with human values, and stability during training. Here, we focus on two prevalent methods used to align these models, Supervised Fine-Tuning (SFT) and Reinforcement Learning from Human Feedback (RLHF). SFT is simple and robust, powerin

Neta Elad, Oded Padon, Sharon Shoham

First-order logic, and quantifiers in particular, are widely used in deductive verification. Quantifiers are essential for describing systems with unbounded domains, but prove difficult for automated solvers. Significant effort has been dedicated to finding quantifier instantiations that establish unsatisfiability, thus ensuring validity of a system's verifi

Bhavuk Singhal, Ashim Gupta, Shivasankaran V P, Amrith Krishna

Identifying intents from dialogue utterances forms an integral component of task-oriented dialogue systems. Intent-related tasks are typically formulated either as a classification task, where the utterances are classified into predefined categories or as a clustering task when new and previously unknown intent categories need to be discovered from these utt

Andrea Calvagna, Arabinda Ghosh, Sadegh Soudjani

We present a method, which incorporates knowledge awareness into the symbolic computation of discrete controllers for reactive cyber physical systems, to improve decision making about the unknown operating environment under uncertain/incomplete inputs. Assuming an abstract model of the system and the environment, we translate the knowledge awareness of the o

Jing Ye, J. Margueron, Niu Li, W. Z. Jiang

The meaningful correlations between the zero-sound modes and the stiffness of the nuclear equation of state (EOS) are uncovered in nuclear matter with the relativistic mean-field theory. It is demonstrated that the high-density zero-sound modes merely exist in models with the stiff EOS. While the stiff EOS can be softened by including {\omega}-meson self-int

Henri Darmon, Michele Fornea

A $p$-arithmetic subgroup of $\mathrm{SL}_2(\mathbb{Q})$ like the Ihara group $\Gamma := \mathrm{SL}_2(\mathbb{Z}[1/p])$ acts by M\"obius transformations on the Poincar\'e upper half plane $\mathcal{H}$ and on Drinfeld's $p$-adic upper half plane $\mathcal{H}_p := \mathbb{P}_1(\mathbb{C}_p)\setminus\mathbb{P}_1(\mathbb{Q}_p)$. The diagonal action of $\Gamma$

Seock-Hwan Noh, Seungpyo Lee, Banseok Shin, Sehun Park

Recognizing the explosive increase in the use of AI-based applications, several industrial companies developed custom ASICs (e.g., Google TPU, IBM RaPiD, Intel NNP-I/NNP-T) and constructed a hyperscale cloud infrastructure with them. These ASICs perform operations of the inference or training process of AI models which are requested by users. Since the AI mo

Guglielmo Fucci, César Romaniega Sancho

In this work we analyze the spectral zeta function for massless scalar fields propagating in a $D$-dimensional flat space under the influence of a shell potential. The shell potential is defined in terms of the two-interval self-adjoint extensions of the Hamiltonian describing the dynamics of the scalar field. After performing the necessary analytic continua

Hariharan Ravishankar, Rohan Patil, Vikram Melapudi, Harsh Suthar

In this paper, we present SonoSAMTrack - that combines a promptable foundational model for segmenting objects of interest on ultrasound images called SonoSAM, with a state-of-the art contour tracking model to propagate segmentations on 2D+t and 3D ultrasound datasets. Fine-tuned and tested exclusively on a rich, diverse set of objects from $\approx200$k ultr

Yinghui He, Yufan Wu, Yilin Jia, Rada Mihalcea

Theory of Mind (ToM) is the ability to reason about one's own and others' mental states. ToM plays a critical role in the development of intelligence, language understanding, and cognitive processes. While previous work has primarily focused on first and second-order ToM, we explore higher-order ToM, which involves recursive reasoning on others' beliefs. We

Asmar Nadeem, Adrian Hilton, Robert Dawes, Graham Thomas

In the context of Audio Visual Question Answering (AVQA) tasks, the audio visual modalities could be learnt on three levels: 1) Spatial, 2) Temporal, and 3) Semantic. Existing AVQA methods suffer from two major shortcomings; the audio-visual (AV) information passing through the network isn't aligned on Spatial and Temporal levels; and, inter-modal (audio and