April 2024 arXiv papers — page 177

Mario E. Gomez, Smaragda Lola, Qaisar Shafi, Cem Salih Un

We present a class of supersymmetric (SUSY) GUT models that can explain the apparent discrepancy between the SM predictions and experimental values of muon g-2 while providing testable signals for lepton flavor violation in charged lepton decays. Moreover, these models predict LSP neutralino abundance that is compatible with the Planck dark matter bounds. We

Ramachandran Anantharaman, Virendra Sule

This paper discusses the observability of nonlinear Dynamical Systems over Finite Fields (DSFF) through the Koopman operator framework. In this work, given a nonlinear DSFF, we construct a linear system of the smallest dimension, called the Linear Output Realization (LOR), which can generate all the output sequences of the original nonlinear system through p

Parham Abed Azad, Hamid Beigy

The rapid expansion of texts' volume and diversity presents formidable challenges in multi-domain settings. These challenges are also visible in the Persian name entity recognition (NER) settings. Traditional approaches, either employing a unified model for multiple domains or individual models for each domain, frequently pose significant limitations. Single

Jason Kaye, Hugo U. R. Strand, Nils Wentzell

We introduce cppdlr, a C++ library implementing the discrete Lehmann representation (DLR) of functions in imaginary time and Matsubara frequency, such as Green's functions and self-energies. The DLR is based on a low-rank approximation of the analytic continuation kernel, and yields a compact and explicit basis consisting of exponentials in imaginary time an

Yijia Li, Joel Leja, Benjamin D. Johnson, Sandro Tacchella

JWST is producing high-quality rest-frame optical and UV spectra of faint galaxies at $z>4$ for the first time, challenging models of galaxy and stellar populations. One galaxy recently observed at $z=5.943$, GS9422, has nebular line and UV continuum emission that appears to require a high ionizing photon production efficiency. This has been explained with a

Hongjun Choi, Sinjini Mitra, Jason Brodksy, Ruben Glatt

In this work, we explore the use of deep learning techniques to learn how nuclear cross sections change as we add or remove protons and neutrons. As a proof of principle, we focus on the neutron-induced reactions in the fast energy regime. Our approach follows a two-stage learning framework. First, we apply representation learning to encode cross section dat

Xavier Calmet, Stephen D. H. Hsu

We discuss recent applications of Euclidean path integrals to the black hole information problem. In calculations with replica wormholes as the next-to-leading order correction to the Gibbons-Hawking saddlepoint, the radiation density matrix approaches a pure state at late times, following the Page curve. We compare unitary evaporation of black holes (in rea

Sandeep Chataut, Tuyen Do, Bichar Dip Shrestha Gurung, Shiva Aryal

Keywords play a crucial role in bridging the gap between human understanding and machine processing of textual data. They are essential to data enrichment because they form the basis for detailed annotations that provide a more insightful and in-depth view of the underlying data. Keyword/domain driven term extraction is a pivotal task in natural language pro

Andrew Fiori

In his 1979 paper Samuel Wagstaff studied the problem of bounding the first prime in an arithmetic progression. In this paper we update a number of his computations using advances in hardware. Based on this we refine his conjecture on Primes in Arithmetic Progression and provide further numerical evidence in support of it. For instance we conjecture that for

Raju Baral, Amanda V. Haglund, Jue Liu, Alexander I. Kolesnikov

Two-dimensional (2D) layered materials, whether in bulk form or reduced to just a single layer, have potential applications in spintronics and capacity for advanced quantum phenomena. A prerequisite for harnessing these opportunities lies in gaining a comprehensive understanding of the spin behavior in 2D materials. The low dimensionality motivates an unders

Yongqiang Wang, Angelia Nedic

We address differential privacy for fully distributed optimization subject to a shared inequality constraint. By co-designing the distributed optimization mechanism and the differential-privacy noise injection mechanism, we propose the first distributed constrained optimization algorithm that can ensure both provable convergence to a global optimal solution

Vladimir Novikov, Jing Ping Wang

We propose a novel approach to tackle integrability problem for evolutionary differential-difference equations (D$\Delta$Es) on free associative algebras, also referred to as nonabelian D$\Delta$Es. This approach enables us to derive necessary integrability conditions, determine the integrability of a given equation, and make progress in the classification o

Matteo Marchi, Stefano Soatto, Pratik Chaudhari, Paulo Tabuada

Improvement and adoption of generative machine learning models is rapidly accelerating, as exemplified by the popularity of LLMs (Large Language Models) for text, and diffusion models for image generation. As generative models become widespread, data they generate is incorporated into shared content through the public web. This opens the question of what hap

Vishnunandan L. N. Venkatesh, Byung-Cheol Min

Learning from Demonstration (LfD) is a promising approach to enable Multi-Robot Systems (MRS) to acquire complex skills and behaviors. However, the intricate interactions and coordination challenges in MRS pose significant hurdles for effective LfD. In this paper, we present a novel LfD framework specifically designed for MRS, which leverages visual demonstr

Zhewei Sun, Qian Hu, Rahul Gupta, Richard Zemel

Recent advancement in large language models (LLMs) has offered a strong potential for natural language systems to process informal language. A representative form of informal language is slang, used commonly in daily conversations and online social media. To date, slang has not been comprehensively evaluated in LLMs due partly to the absence of a carefully d

Cameron Davies

We study a two parameter family of energy minimization problems for interaction energies $\mathcal{E}_{\alpha,\beta}$ with attractive-repulsive potential $W_{\alpha,\beta}$. We develop a concavity principle, which allows us to provide a lower bound on $\mathcal{E}_{\alpha,\beta}$ if there exist $\beta_0<\beta<\beta_1$ with minimizers of $\mathcal{E}_{\alpha,

M. Cristina Rabello Soares, Sarbani Basu, Richard Bogart

The gradient of rotation in the near-surface shear layer (NSSL) of the Sun provides valuable insights into the dynamics associated with the solar activity cycle and the dynamo. Results obtained with global oscillation mode-splittings lack resolution near the surface, prompting the use of the local helioseismic ring-diagram method. While the Helioseismic and

Brian K. Tran, Ben S. Southworth, Melvin Leok

We investigate the geometric structure of adjoint systems associated with evolutionary partial differential equations at the fully continuous, semi-discrete, and fully discrete levels and the relations between these levels. We show that the adjoint system associated with an evolutionary partial differential equation has an infinite-dimensional Hamiltonian st

Benedikt Holm, Gabriel Jouan, Emil Hardarson, Sigríður Sigurðardottir

Polysomnographic recordings are essential for diagnosing many sleep disorders, yet their detailed analysis presents considerable challenges. With the rise of machine learning methodologies, researchers have created various algorithms to automatically score and extract clinically relevant features from polysomnography, but less research has been devoted to ho

Bowen Wen

Future SAE Level 4 and Level 5 autonomous vehicles will require novel applications of localization, perception, control and artificial intelligence technology in order to offer innovative and disruptive solutions to current mobility problems. Accurate localization is essential for self driving vehicle navigation in GPS inaccessible environments. This thesis

Tobias Schnabel, Jennifer Neville

In many modern LLM applications, such as retrieval augmented generation, prompts have become programs themselves. In these settings, prompt programs are repeatedly called with different user queries or data instances. A big practical challenge is optimizing such prompt programs. Recent work has mostly focused on either simple prompt programs or assumed that

Vishnunandan L. N. Venkatesh, Byung-Cheol Min

Incorporating language comprehension into robotic operations unlocks significant advancements in robotics, but also presents distinct challenges, particularly in executing spatially oriented tasks like pattern formation. This paper introduces ZeroCAP, a novel system that integrates large language models with multi-robot systems for zero-shot context aware pa

Chase Stokes, Chelsea Sanker, Bridget Cogley, Vidya Setlur

Understanding and communicating data uncertainty is crucial for making informed decisions in sectors like finance and healthcare. Previous work has explored how to express uncertainty in various modes. For example, uncertainty can be expressed visually with quantile dot plots or linguistically with hedge words and prosody. Our research aims to systematically

David J. Meer, Isabela Galoustian, Julio Gabriel de Falco Manuel, Eric R. Weeks

Circles of a single size can pack together densely in a hexagonal lattice, but adding in size variety disrupts the order of those packings. We conduct simulations which generate dense random packings of circles with specified size distributions, and measure the area fraction in each case. While the size distributions can be arbitrary, we find that for a wide

Sohee Kwon, Yuhang Liu, Hang Chi, Gen Yin

Cr$_2$Te$_3$ is a ferromagnetic, quasi-two-dimensional layered material with perpendicular magnetic anisotropy, strong spin-orbit coupling, and non-trivial band topology. The non-trivial topology results in an intrinsic anomalous Hall conductivity (AHC) that switches sign under filling and biaxial strain. Thin films can exhibit half metallicity. Using densit

Philippe Formont, Hugo Jeannin, Pablo Piantanida, Ismail Ben Ayed

Few-shot learning has recently attracted significant interest in drug discovery, with a recent, fast-growing literature mostly involving convoluted meta-learning strategies. We revisit the more straightforward fine-tuning approach for molecular data, and propose a regularized quadratic-probe loss based on the the Mahalanobis distance. We design a dedicated b

Wentao Li, Rosabeth White, Dennis Prangle

Composite likelihood provides approximate inference when the full likelihood is intractable and sub-likelihood functions of marginal events can be evaluated relatively easily. It has been successfully applied for many complex models. However, its wider application is limited by two issues. First, weight selection of marginal likelihood can have a significant

Leonardo Pereira Costa de Cruz, Joan Torregrossa, Miguel Berdugo, Josep Sardanyés

In drylands, water availability determines plant population densities and whether they cooperate via facilitation or compete. When water scarcity intensifies, plant densities decrease and competition for water surpasses the benefits of soil improvement by facilitator plants, involving an abrupt shift from facilitation to competition. Here, we model this faci

Hongxue Liang, Alessandro Montinaro

In this paper, we provide a complete classification of $2$-$(v,k,2)$ design admitting a flag-transitive automorphism group of affine type with the only exception of the semilinear $1$-dimensional group. Alongside this analysis we provide a construction of seven new families of such flag-transitive $2$-designs, two of them infinite, and some of them involve r

Christopher O'Neill, Vadim Ponomarenko, Eric Ren

Fix $t\in [1,\infty]$. Let $S$ be an atomic commutative semigroup and, for all $x\in S$, let $\mathscr{L}_t(S):=\{\|f\|_t:f\in Z(x)\}$ be the "$t$-length set" of $x$ (using the standard $l_p$-space definition of $\|\cdot\|_t$). The $t$-Delta set of $x$ (denoted $\Delta_t(S)$) is the set of gaps between consecutive elements of $\mathscr{L}_t(S)$; the Delta se

Michael Dinzinger, Florian Heß, Michael Granitzer

The groundbreaking advancements around generative AI have recently caused a wave of concern culminating in a row of lawsuits, including high-profile actions against Stability AI and OpenAI. This situation of legal uncertainty has sparked a broad discussion on the rights of content creators and publishers to protect their intellectual property on the web. Eur

Stefanie Klisch, Dylan Gilbert, Emma Breaux, Aliyah Dalier

A well-rounded introduction to work in a STEM laboratory is vital to scientific education. Besides the ability to use available instrumentation for sample characterization, students should also be imparted knowledge in the steps of instrument development and construction. These concepts can be taught using the example of lipid vesicle preparation via extrusi

András Mészáros

Let $T_n$ be a $2$-dimensional determinantal hypertree on $n$ vertices. Kahle and Newman conjectured that the $p$-torsion of $H_1(T_n,\mathbb{Z})$ asymptotically follows the Cohen-Lenstra distribution. For $p=2$, we disprove this conjecture by showing that given a positive integer $h$, for all large enough $n$, we have \[\mathbb{P}(\dim H_1(T_n,\mathbb{F}_2)

Laura Caputo, Viet-Hung Nguyen, Jean-Christophe Charlier

Graphene has been widely studied for various applications due to its outstanding electrical and mechanical properties. However, its potential in thermoelectric applications has been limited by a low Seebeck coefficient and high thermal conductivity. Efforts to enhance its thermoelectric properties have involved the usage of carbon-based nanoribbons, strain e

Giuseppe Cardone, Willi Jäger, Jean Louis Woukeng

We derive, through the deterministic homogenization theory in thin domains, a new model consisting of Hele-Shaw equation with memory coupled with the convective Cahn-Hilliard equation. The obtained system, which models in particular tumor growth, is then analyzed and we prove its well-posedness in dimension 2. To achieve our goal, we develop and use the new

David Herel, Tomas Mikolov

In various fields of knowledge creation, including science, new ideas often build on pre-existing information. In this work, we explore this concept within the context of language models. Specifically, we explore the potential of self-training models on their own outputs, akin to how humans learn and build on their previous thoughts and actions. While this a

Mengjie Zhao, Cees Taal, Stephan Baggerohr, Olga Fink

Accurate bearing load monitoring is essential for their Prognostics and Health Management (PHM), enabling damage assessment, wear prediction, and proactive maintenance. While bearing sensors are typically placed on the bearing housing, direct load monitoring requires sensors inside the bearing itself. Recently introduced sensor rollers enable direct bearing

S. Zamora, A. I. Díaz, Roberto Terlevich, Elena Terlevich

In this third paper of a series, we study the kinematics of the ionised gas and stars, calculating the dynamical masses of the circumnuclear star-forming regions in the ring of of the face-on spiral NGC 7742. We have used high spectral resolution data from the MEGARA instrument attached to the Gran Telescopio Canarias (GTC) to measure the kinematical compone

Robert Bryant, Luis Florit, Wolfgang Ziller

We classify curvature homogeneous hypersurfaces in S^4 and H^4. In higher dimesnsion one only has the FKM examples and an isolate one by Tsukada of a hypersurface in H^5. Besides some simple examples, we show that there exists an isolated hypersurface with a circle of symmetries and and a one parameter family admitting no continuous symmetries. Outside the s

Delio Jaramillo-Velez

Given a hypergraph $\mathcal{H}$, we introduce a new class of evaluation toric codes called edge codes derived from $\mathcal{H}$. We analyze these codes, focusing on determining their basic parameters. We provide estimations for the minimum distance, particularly in scenarios involving $d$-uniform clutters. Additionally, we demonstrate that these codes exhi

Xin Huang, Weipeng Zhuo, Minh Phu Vuong, Shiju Li

Recently, distributed GNN training frameworks, such as DistDGL and PyG, have been developed to enable training GNN models on large graphs by leveraging multiple GPUs in a distributed manner. Despite these advances, their memory requirements are still excessively high, thereby hindering GNN training on large graphs using commodity workstations. In this paper,

Siqi Li, Zhen Zhang, Shawn Alverson, David Cesar

Electron beam shaping allows the control of the temporal properties of x-ray free-electron laser pulses from femtosecond to attosecond timescales. Here we demonstrate the use of a laser heater to shape electron bunches and enable the generation of attosecond x-ray pulses. We demonstrate that this method can be applied in a selective way, shaping a targeted s

Eranda Somathilake, Bhathiya Rathnayake, Mamadou Diagne

In this paper, we expand recently introduced observer-based periodic event-triggered control (PETC) and self-triggered control (STC) schemes for reaction-diffusion PDEs to boundary control of $2\times2$ coupled hyperbolic PDEs in canonical form and with anti-collocated measurement and actuation processes. The class of problem under study governs transport ph

Peter A. Boyle, Felix Erben, Jonathan M. Flynn, Nicolas Garron

We present non-perturbative results for beyond the standard model kaon mixing matrix elements in the isospin symmetric limit ($m_u=m_d$) of QCD, including a complete estimate of all dominant sources of systematic error. Our results are obtained from numerical simulations of lattice QCD with $N_f = 2+1$ flavours of dynamical domain wall fermions. For the firs

Hans Christianson, John Toth

For sequences of quantum ergodic eigenfunctions, we define the quantum flux norm associated to a codimension $1$ submanifold $\Sigma$ of a non-degenerate energy surface. We prove restrictions of eigenfunctions to $\Sigma$, realized using the quantum flux norm, are quantum ergodic. We compare this result to known results from \cite{CTZ} in the case of Euclide

Ioanna D. Stamou

We investigate the large curvature perturbations which can lead to the formation of primordial black holes (PBHs) in the context of no-scale supergravity. Our study does not depend on any exotic scenario, such as scalar potentials with inflection points or bulks, and aims to avoid the fine-tuning of model parameters to achieve the formation of PBHs. This for

Faraz Lotfi, Farnoosh Faraji, Nikhil Kakodkar, Travis Manderson

This paper explores leveraging large language models for map-free off-road navigation using generative AI, reducing the need for traditional data collection and annotation. We propose a method where a robot receives verbal instructions, converted to text through Whisper, and a large language model (LLM) model extracts landmarks, preferred terrains, and cruci

Jianhang Chen, R. J. Ivison, M. Zwaan, Celine Peroux

Deep, unbiased surveys are essential to decipher the cosmic evolution of galaxies. The submillimetre (submm) and millimetre (mm) windows complement the UV/optical waveband and are key to revealing the cold and dusty Universe. Traditional ways of conducting deep surveys resort to either lensed fields or target small areas for ultra-long integrations. These su

Homaira Huda Shomee, Zhu Wang, Sathya N. Ravi, Sourav Medya

Recent advances in Pretrained Language Models (PLMs) and Large Language Models (LLMs) have demonstrated transformative capabilities across diverse domains. The field of patent analysis and innovation is not an exception, where natural language processing (NLP) techniques presents opportunities to streamline and enhance important tasks -- such as patent class

Xirui Zhang, Bhargav Rallabandi

The interaction between deformable surfaces and oscillatory driving is known to yield complex secondary time-averaged flows due to inertial and elastic nonlinearities. Here, we revisit the problem of oscillatory flow in a cylindrical tube with a deformable wall, and analyze it under a long-wave }theory for small deformations, but for arbitrary Womersley numb

Sekar Kulandaivel, Wenjuan Lu, Brandon Barry, Jorge Guajardo

In the automotive security sector, the absence of a testing platform that is configurable, practical, and user-friendly presents considerable challenges. These difficulties are compounded by the intricate design of vehicle systems, the rapid evolution of attack vectors, and the absence of standardized testing methodologies. We propose a next-generation testi

Tiberiu-Ioan Szatmari, Abhishek Cauligi

Multi-agent robotic exploration stands to play an important role in space exploration as the next generation of robotic systems ventures to far-flung environments. A key challenge in this new paradigm will be to effectively share and utilize the vast amount of data generated onboard while operating in bandwidth-constrained regimes typical of space missions.

Balázs Patkós, Miloš Stojaković, Jelena Stratijev, Máté Vizer

We study the following game version of the generalized graph Tur\'an problem. For two fixed graphs F and H, two players, Max and Mini, alternately claim unclaimed edges of the complete graph Kn such that the graph G of the claimed edges must remain F-free throughout the game. The game ends when no further edges can be claimed, i.e. when G becomes F-saturated

Mehmet Ergezer, Phat Duong, Christian Green, Tommy Nguyen

This paper presents a novel universal perturbation method for generating robust multi-view adversarial examples in 3D object recognition. Unlike conventional attacks limited to single views, our approach operates on multiple 2D images, offering a practical and scalable solution for enhancing model scalability and robustness. This generalizable method bridges

Dongliang Jing, Lin Lin, Andrew W. Eckford

Cooperative molecular communication (MC) is a promising technology for facilitating communication between nanomachines in the Internet of Bio-Nano Things (IoBNT) field. However, the performance of IoBNT is limited by the availability of energy for cooperative MC. This paper presents a novel transmitter design scheme that utilizes molecule movement between re

Yunshi Huang, Fereshteh Shakeri, Jose Dolz, Malik Boudiaf

In a recent, strongly emergent literature on few-shot CLIP adaptation, Linear Probe (LP) has been often reported as a weak baseline. This has motivated intensive research building convoluted prompt learning or feature adaptation strategies. In this work, we propose and examine from convex-optimization perspectives a generalization of the standard LP baseline

Apan Dastider, Hao Fang, Mingjie Lin

Dexterous manipulation, particularly adept coordinating and grasping, constitutes a fundamental and indispensable capability for robots, facilitating the emulation of human-like behaviors. Integrating this capability into robots empowers them to supplement and even supplant humans in undertaking increasingly intricate tasks in both daily life and industrial

Nathaniel Dyrkton, Paul Gustafson, Harlan Campbell

Non-representative surveys are commonly used and widely available but suffer from selection bias that generally cannot be entirely eliminated using weighting techniques. Instead, we propose a Bayesian method to synthesize longitudinal representative unbiased surveys with non-representative biased surveys by estimating the degree of selection bias over time.

Rudolf Herdt, Maximilian Schmidt, Daniel Otero Baguer, Peter Maaß

In this work, we investigate methods to reduce the noise in deep saliency maps coming from convolutional downsampling. Those methods make the investigated models more interpretable for gradient-based saliency maps, computed in hidden layers. We evaluate the faithfulness of those methods using insertion and deletion metrics, finding that saliency maps compute

T. Rogers, A. Courtoy

In this comment, we reassess the underlying physics of the number sum rule for dihadron fragmentation functions. We will argue that, currently, there are no settled constraints on what constitutes a valid number density interpretation for multihadron fragmentation functions. Imposing overly restrictive criteria might lead to misinterpretating the data. Most

A. Paetznick, M. P. da Silva, C. Ryan-Anderson, J. M. Bello-Rivas

The promise of quantum computers hinges on the ability to scale to large system sizes, e.g., to run quantum computations consisting of more than 100 million operations fault-tolerantly. This in turn requires suppressing errors to levels inversely proportional to the size of the computation. As a step towards this ambitious goal, we present experiments on a t

Ben Zindorf, Sougato Bose

We present an implementation of multi-controlled quantum gates which provides significant reductions of cost compared to state-of-the-art methods. The operator applied on the target qubit is a unitary, special unitary, or the Pauli X operator (Multi-Controlled Toffoli), and requires one clean ancilla, no ancilla, and one dirty ancilla, respectively. We gener

Robert Lilow, Punyakoti Ganeshaiah Veena, Adi Nusser

We reconstruct the 3D matter density and peculiar velocity fields in the local Universe up to a distance of 200$\,h^{-1}\,$Mpc from the Two-Micron All-Sky Redshift Survey (2MRS), using a neural network (NN). We employed an NN with a U-net autoencoder architecture and a weighted mean squared error loss function trained separately to output either the density

Alisson Andrey Puska, Luiz Adolpho Baroni, Roberto Pereira

This paper presents a systematic literature review in Computer Science that provide an overview of the initiatives related to digital misinformation. This is an exploratory study that covers research from 1993 to 2020, focusing on the investigation of the phenomenon of misinformation. The review consists of 788 studies from SCOPUS, IEEE, and ACM digital libr

Seraphina Yong, Leo Cui, Evan Suma Rosenberg, Svetlana Yarosh

Close relationships are irreplaceable social resources, yet prone to high-risk conflict. Building on findings from the fields of HCI, virtual reality, and behavioral therapy, we evaluate the unexplored potential of retrospective VR-embodied perspective-taking to fundamentally influence conflict resolution in close others. We develop a biographically-accurate

Alexander Thomasian

OLTP has stringent performance requirements defined by Service Level Agreements. Transaction response time is used to determine the maximum throughout in benchmarks. Capacity planning tools for OLTP performance are based on queueing network models for hardware resources and database lock contention has a secondary effect on performance. With ever increasing

Fengwei Xu, Ke Wang, Tie Liu, Lei Zhu

Leveraging the high resolution, high sensitivity, and wide frequency coverage of the Atacama Large Millimeter/submillimeter Array (ALMA), the QUARKS survey, standing for "Querying Underlying mechanisms of massive star formation with ALMA-Resolved gas Kinematics and Structures", is observing 139 massive star-forming clumps at ALMA Band 6 ($\lambda\sim$ 1.3 mm

Tiago Cruz, René Marczinzik

We generalise a theorem of Tachikawa about reflexive Auslander-Reiten sequences. We apply this to give a new characterisation of the dominant dimension of gendo-symmetric algebras. We also generalise a formula due to Reiten about the dominant dimension of an algebra $A$ and grades of torsion $A$-modules.

Georgy Tyukin

Large Language Models are growing in size, and we expect them to continue to do so, as larger models train quicker. However, this increase in size will severely impact inference costs. Therefore model compression is important, to retain the performance of larger models, but with a reduced cost of running them. In this thesis we explore the methods of model c

Gregory M. Dickinson

The steady flow of social-media cases toward the Supreme Court shows a nation reworking its fundamental relationship with technology. The cases raise a host of questions ranging from difficult to impossible: how to nurture a vibrant public square when a few tech giants dominate the flow of information, how social media can be at the same time free from confo

Alexander Shen

The exposition in Euclid's Elements contains an obvious gap (seemingly unnoticed by most commentators): he often compares not just angles, but *groups* of angles, and at the same time he avoids summing angles (and considering angles greater than $\pi$), and does not say what such a comparison of groups could mean. We discuss the problem and suggest a possibl

Laurent Pierre, Bernard Bernu, Laura Messio

This work presents an algorithm for calculating high temperature series expansions (HTSE) of Heisenberg spin models with spin $S=1/2$ in the thermodynamic limit. This algorithm accounts for the presence of a magnetic field. The paper begins with a comprehensive introduction to HTSE and then focuses on identifying the bottlenecks that limit the computation of

Arkadiusz Lipiecki, Bartosz Uniejewski, Rafał Weron

Operational decisions relying on predictive distributions of electricity prices can result in significantly higher profits compared to those based solely on point forecasts. However, the majority of models developed in both academic and industrial settings provide only point predictions. To address this, we examine three postprocessing methods for converting

Amanul Haque, Munindar P. Singh

We investigate the effectiveness of ChatGPT in extracting norms from contracts. Norms provide a natural way to engineer multiagent systems by capturing how to govern the interactions between two or more autonomous parties. We extract norms of commitment, prohibition, authorization, and power, along with associated norm elements (the parties involved, anteced

Fuhao Ji, Auralee Edelen, Ryan Roussel, Xiaozhe Shen

Ultrafast electron diffraction using MeV energy beams(MeV-UED) has enabled unprecedented scientific opportunities in the study of ultrafast structural dynamics in a variety of gas, liquid and solid state systems. Broad scientific applications usually pose different requirements for electron probe properties. Due to the complex, nonlinear and correlated natur

Ghurumuruhan Ganesan

In this paper, we study Dirac-type theorems for an inhomogenous random graph (G) whose edge probabilities are not necessarily all the same. We obtain sufficient conditions for the existence of Hamiltonian paths and perfect matchings, in terms of the \emph{sum} of edge probabilities. For edge probability assignments with two-sided bounds, we use P\'osa rotati

Seth Redfield, Natasha Batalha, Björn Benneke, Beth Biller

This STScI Working Group (WG) was charged with soliciting community feedback and evaluating the strategic planning for exoplanet science with JWST and HST given the high quality of exoplanet observations, the significantly lengthened mission lifetime for JWST, and the pronounced expansion of the field over the last decade. We were charged with identifying ke

Ghurumuruhan Ganesan

In this paper we study the problem of tracking the mean of a piecewise stationary sequence of independent random variables. First we consider the case where the transition times are known and show that a direct running average performs the tracking in short time and with high accuracy. We then use a single valued weighted running average with a tunable param

Andrew G. Curtis, Mark Yim, Michael Rubenstein

Despite their growing popularity, swarms of robots remain limited by the operating time of each individual. We present algorithms which allow a human to sculpt a swarm of robots into a shape that persists in space perpetually, independent of onboard energy constraints such as batteries. Robots generate a path through a shape such that robots cycle in and out

Corentin Bodart, Ruiwen Dong

The Tits alternative states that a finitely generated matrix group either contains a nonabelian free subgroup $F_2$, or it is virtually solvable. This paper considers two decision problems in virtually solvable matrix groups: the Identity Problem (does a given finitely generated subsemigroup contain the identity matrix?), and the Group Problem (is a given fi

James Oswald, Kavitha Srinivas, Harsha Kokel, Junkyu Lee

Developing domain models is one of the few remaining places that require manual human labor in AI planning. Thus, in order to make planning more accessible, it is desirable to automate the process of domain model generation. To this end, we investigate if large language models (LLMs) can be used to generate planning domain models from simple textual descript

Youshaa Murhij, Dmitry Yudin

The task of motion prediction is pivotal for autonomous driving systems, providing crucial data to choose a vehicle behavior strategy within its surroundings. Existing motion prediction techniques primarily focus on predicting the future trajectory of each agent in the scene individually, utilizing its past trajectory data. In this paper, we introduce an end

Ghurumuruhan Ganesan

In this paper, we study classification and regression error bounds for inhomogenous data that are independent but not necessarily identically distributed. First, we consider classification of data in the presence of non-stationary noise and establish ergodic type sufficient conditions that guarantee the achievability of the Bayes error bound, using universal

Nataliia Kholodna, Sahib Julka, Mohammad Khodadadi, Muhammed Nurullah Gumus

Low-resource languages face significant barriers in AI development due to limited linguistic resources and expertise for data labeling, rendering them rare and costly. The scarcity of data and the absence of preexisting tools exacerbate these challenges, especially since these languages may not be adequately represented in various NLP datasets. To address th

Michal Benes, Miroslav Kolar, Daniel Sevcovic

We investigate the motion of closed smooth curves that evolve in space $\mathbb{R}^3$. The governing evolutionary equation for the evolution of the curve is accompanied by a parabolic equation for the scalar quantity evaluated over the evolving curve. We apply the direct Lagrangian approach to describe the flow of 3D curves, resulting in a system of degenera

Jyoti Bisht, Somenath Jalal, Brijesh Kumar

The effect of Hubbard and Kondo interactions on the edge states in the half-filled Su-Schrieffer-Heeger chain of electrons is investigated by studying the behaviour of charge quasiparticles using Kumar representation and density matrix renormalization group method. For any finite dimerization of hopping, by increasing the Hubbard interaction, the edge states

David Raposo, Sam Ritter, Blake Richards, Timothy Lillicrap

Transformer-based language models spread FLOPs uniformly across input sequences. In this work we demonstrate that transformers can instead learn to dynamically allocate FLOPs (or compute) to specific positions in a sequence, optimising the allocation along the sequence for different layers across the model depth. Our method enforces a total compute budget by

Fangzhou Mu, Sicheng Mo, Yin Li

Temporal grounding of text descriptions in videos is a central problem in vision-language learning and video understanding. Existing methods often prioritize accuracy over scalability -- they have been optimized for grounding only a few text queries within short videos, and fail to scale up to long videos with hundreds of queries. In this paper, we study the

Tomoki Asaka, Katsunori Yoshimatsu, Kai Schneider

A wavelet-based machine learning method is proposed for predicting the time evolution of homogeneous isotropic turbulence where vortex tubes are preserved. Three-dimensional convolutional neural networks and long short-term memory are trained with a time series of direct numerical simulation (DNS) data of homogeneous isotropic turbulence at the Taylor micros

Gurusha Juneja, Subhabrata Dutta, Tanmoy Chakraborty

Despite demonstrating emergent reasoning abilities, Large Language Models (LLMS) often lose track of complex, multi-step reasoning. Existing studies show that providing guidance via decomposing the original question into multiple subproblems elicits more robustness in LLM reasoning -- a decomposer generates the subproblems, and a solver solves each of these

Ari Karchmer

Recently, multimodal machine learning has enjoyed huge empirical success (e.g. GPT-4). Motivated to develop theoretical justification for this empirical success, Lu (NeurIPS '23, ALT '24) introduces a theory of multimodal learning, and considers possible \textit{separations} between theoretical models of multimodal and unimodal learning. In particular, Lu (A

Théo Pinet

Fix a finite-dimensional simple Lie algebra $\mathfrak{g}$ and let $\mathfrak{g}_J\subseteq\mathfrak{g}$ be a Lie subalgebra coming from a Dynkin diagram inclusion. Then, the corresponding restriction functor is not essentially surjective on finite-dimensional simple $\mathfrak{g}_J$-modules. In this article, we study Finkelberg-Tsymbaliuk's shifted quantum

Junghyun Koo, Gordon Wichern, Francois G. Germain, Sameer Khurana

We introduce Self-Monitored Inference-Time INtervention (SMITIN), an approach for controlling an autoregressive generative music transformer using classifier probes. These simple logistic regression probes are trained on the output of each attention head in the transformer using a small dataset of audio examples both exhibiting and missing a specific musical

Francisco-Javier Soto, Ana I. Gómez, Domingo Gómez-Pérez

Gaussian random number generators attract a widespread interest due to their applications in several fields. Important requirements include easy implementation, tail accuracy, and, finally, a flat spectrum. In this work, we study the applicability of uniform pseudorandom binary generators in combination with the Central Limit Theorem to propose an easy to im

T. Nony, R. Galván-Madrid, N. Brouillet, G. Suárez

We aim to identify and characterize cores in the high-mass proto-cluster W49, determine their evolutionary stages and measure the associated lifetimes. We built a catalog of 129 cores extracted from an ALMA 1.3 mm continuum image at 0.26" (2900 au) angular resolution. The association between cores and Hyper/Ultra Compact HII (H/UC HII) regions was establishe

Yushen Li, Jinpeng Wang, Tao Dai, Jieming Zhu

Predicting click-through rates (CTR) is a fundamental task for Web applications, where a key issue is to devise effective models for feature interactions. Current methodologies predominantly concentrate on modeling feature interactions within an individual sample, while overlooking the potential cross-sample relationships that can serve as a reference contex

Shadi Matinizadeh, Noah Pacik-Nelson, Ioannis Polykretis, Krupa Tishbi

We introduce QUANTISENC, a fully configurable open-source software-defined digital quantized spiking neural core architecture to advance research in neuromorphic computing. QUANTISENC is designed hierarchically using a bottom-up methodology with multiple neurons in each layer and multiple layers in each core. The number of layers and neurons per layer can be

Gabriele Pichierri, Bertram Bitsch, Elena Lega

In a previous paper we showed that, like the migration speed, the eccentricity damping efficiency is modulated linearly by the depth of the partial gap a planet carves in the disk surface density profile, resulting in less efficient $e$-damping compared to the prescription commonly used in population synthesis works. Here, we extend our analysis to 3D, refin

Spyridon Kakaroumpas, Thu Hien Nguyen, Dimitris Vardakis

We investigate matrix-weighted bounds for the sublinear non-kernel operators considered by F. Bernicot, D. Frey, and S. Petermichl. We extend their result to sublinear operators acting upon vector-valued functions. First, we dominate these operators by bilinear convex body sparse forms, adapting a recent general principle due to T. Hyt\"onen. Then we use thi

Giovanni Minuto, Dario Melegari, Simone Caletti, Paolo Solinas

We present a detailed numerical study of an alternative approach, named Quantum Non-Demolition Measurement (QNDM), to efficiently estimate the gradients or the Hessians of a quantum observable. This is a key step and a resource-demanding task when we want to minimize the cost function associated with a quantum observable. In our detailed analysis, we account