February 2024 arXiv papers — page 42

G. Canezin, L. M. S. Margato, A. Morozov, A. Blanco

Recently we have proposed a new concept of a thermal neutron detector based on resistive plate chambers and 10B4C solid neutron converters, enabling to readout with high resolution in both the 3D position of neutron capture and the neutron time of flight (ToF). In this paper, we report the results of the first beam tests conducted with a new neutron RPC dete

Taewan Kim, Behçet Açıkmeşe

Funnel synthesis refers to a procedure for synthesizing a time-varying controlled invariant set and an associated control law around a nominal trajectory. The computation of the funnel involves solving a continuous-time differential equation or inequality, ensuring the invariance of the funnel. Previous approaches often compromise the invariance property of

Peng Guo

In present work, a relativistic relation that connects the difference of interacting and non-interacting integrated two-particle correlation functions in finite volume to infinite volume scattering phase shift through an integral is derived. We show that the difference of integrated finite volume correlation functions converge rapidly to its infinite volume

Ziheng Jiang, Haibin Lin, Yinmin Zhong, Qi Huang

We present the design, implementation and engineering experience in building and deploying MegaScale, a production system for training large language models (LLMs) at the scale of more than 10,000 GPUs. Training LLMs at this scale brings unprecedented challenges to training efficiency and stability. We take a full-stack approach that co-designs the algorithm

Jonathan Smucker, Jesus Pérez-Ríos

We propose an array of ultracold polar molecules as a platform to study alignment transport between molecules. We envision a polar molecule being aligned with an intense off-resonant laser field whose alignment migrates to a nearby molecule due to dipole-dipole interactions. Our results show that the transport of the alignment is due to a complex interplay b

Ruofan Wang, Prakruthi Prabhakar, Gaurav Srivastava, Tianqi Wang

In the realm of recommender systems, the ubiquitous adoption of deep neural networks has emerged as a dominant paradigm for modeling diverse business objectives. As user bases continue to expand, the necessity of personalization and frequent model updates have assumed paramount significance to ensure the delivery of relevant and refreshed experiences to a di

Muralikrishnna G. Sethuraman, Faramarz Fekri

Causal learning is a fundamental problem in statistics and science, offering insights into predicting the effects of unseen treatments on a system. Despite recent advances in this topic, most existing causal discovery algorithms operate under two key assumptions: (i) the underlying graph is acyclic, and (ii) the available data is complete. These assumptions

Esma Dirican Erdal

Let $M$ be a $3$-manifold with connected non-vacuos boundary which is not spherical. Assume that $N$ is another $3$-manifold with vacuous boundary and $N^{\ast}$ is the $3$-manifold obtained by removing from $N$ the interior of a $3$-cell. In the present paper, we find a relationship between the multiplicative property of the twisted Reidemeister torsion and

Joyce Zhou, Yijia Dai, Thorsten Joachims

Most conventional recommendation methods (e.g., matrix factorization) represent user profiles as high-dimensional vectors. Unfortunately, these vectors lack interpretability and steerability, and often perform poorly in cold-start settings. To address these shortcomings, we explore the use of user profiles that are represented as human-readable text. We prop

Ian Colombo, Pietro Minazzi, Emiliano Bonera, Fabio Pezzoli

We design germanium-based higher-order topological cavities for terahertz applications by breaking the symmetry of a two-dimensional photonic crystal following the Su-Schrieffer-Heeger model. Calculations demonstrate the parity inversion of the electric field in differently deformed unit cells. The interface between domains of opposite topology presents edge

Joshua Cooper, Gabrielle Tauscheck

Graham and Pollak showed in 1971 that the determinant of a tree's distance matrix depends only on its number of vertices, and, in particular, it is always nonzero. The Steiner distance of a collection of $k$ vertices in a graph is the fewest number of edges in any connected subgraph containing those vertices; for $k=2$, this reduces to the ordinary definitio

Tao Wang, Shiying Xiao, Jun Yan

Economic structure comparisons between China and Japan have long captivated development economists. To delve deeper into their sectoral differences from 1995 to 2018, we used the annual input-output tables (IOTs) of both nations to construct weighted and directed input-output networks (IONs). This facilitated deeper network analyses. Strength distributions u

Arindam Fadikar, Abby Stevens, Nicholson Collier, Kok Ben Toh

Sequential Monte Carlo (SMC) algorithms represent a suite of robust computational methodologies utilized for state estimation and parameter inference within dynamical systems, particularly in real-time or online environments where data arrives sequentially over time. In this research endeavor, we propose an integrated framework that combines a stochastic epi

Shoaib Khalid, Anderson Janotti, Bharat Medasani

Like in any other semiconductor, point defects in transition-metal dichalcogenides (TMDs) are expected to strongly impact their electronic and optical properties. However, identifying defects in these layered two-dimensional materials has been quite challenging with controversial conclusions despite the extensive literature in the past decade. Using first-pr

Zheyu Zhang

Reinforcement Learning (RL), one of the core paradigms in machine learning, learns to make decisions based on real-world experiences. This approach has significantly advanced AI applications across various domains, notably in smart grid optimization and smart home automation. However, the proliferation of RL in these critical sectors has also exposed them to

Mercè Roig, Andreas Kreisel, Yue Yu, Brian M. Andersen

Altermagnets feature vanishing net magnetization, like antiferromagnets, but exhibit time-reversal symmetry breaking and momentum-dependent spin-split band structures. Motivated by the prevalence of altermagnetic materials with non-symmorphic symmetry-dictated band degeneracies, we provide realistic minimal models for altermagnetism by constructing tight-bin

Stefan Frei, Erik Burman, Edward R Johnson

This paper discusses the effect of rotation on the boundary layer in high Reynolds number flow over a ridge using a numerical method based on stabilised finite elements that captures steady solutions up to Reynolds number of order $10^6$. The results are validated against boundary layer computations in shallow flows and for deep flows against experimental ob

Russell L. Graf, Andrew Wetzel, Matthew A. Bellardini, Jeremy Bailin

Spatial patterns of stellar elemental abundances encode rich information about a galaxy's formation history. We analyze the radial, vertical, and azimuthal variations of metals in stars, both today and at formation, in the FIRE-2 cosmological simulations of Milky Way (MW)-mass galaxies, and we compare with the MW. The radial gradient today is steeper (more n

Artem Vysogorets, Achintya Gopal

Fine-tuning Large Language Models (LLMs) is now a common approach for text classification in a wide range of applications. When labeled documents are scarce, active learning helps save annotation efforts but requires retraining of massive models on each acquisition iteration. We drastically expedite this process by using pretrained representations of LLMs wi

Deborah Kent, Emilie Aebischer, Stuart Neave

This article presents a source list to support departments in creating more equitable, diverse, and inclusive mathematics classrooms. Included are relevant general-interest books, as well as articles and books presenting research results about how matters of inequity, exclusion, and homogeneity surface in educational contexts. We also present sources more sp

Giacomo Albi, Sara Bicego, Michael Herty, Yuyang Huang

Feedback control synthesis for large-scale particle systems is reviewed in the framework of model predictive control (MPC). The high-dimensional character of collective dynamics hampers the performance of traditional MPC algorithms based on fast online dynamic optimization at every time step. Two alternatives to MPC are proposed. First, the use of supervised

Tejas Srinivasan, Jack Hessel, Tanmay Gupta, Bill Yuchen Lin

Selective prediction minimizes incorrect predictions from vision-language models (VLMs) by allowing them to abstain from answering when uncertain. However, when deploying a vision-language system with low tolerance for inaccurate predictions, selective prediction may be over-cautious and abstain too frequently, even on many correct predictions. We introduce

Thomas P. Novak, Donna L. Hoffman

This article proposes a method to uncover opportunities for exploitation and exploration from consumer IoT interaction data. We develop a unique decomposition of cosine similarity that quantifies exploitation through functional similarity of interactions, exploration through cross-capacity similarity of counterfactual interactions, and differentiation of the

Jason M. H. Beedle, Li-Jen Chen, Jason R. Shuster, Harsha Gurram

On April 24th, 2023, a CME event caused the solar wind to become sub-Alfvenic, leading to the development of an Alfven Wing configuration in the Earth's Magnetosphere. Alfven Wings have previously been observed as cavities of low flow in Jupiter's magnetosphere, but the observing satellites did not have the ability to directly measure the Alfven Wings' curre

Anjie Liu, Jinglang W. Sun, Anh Ngo, Ademide O. Mabadeje

Establishing accurate field development parameters to optimize long-term oil production takes time and effort due to the complexity of oil well development, and the uncertainty in estimating long-term well production. Traditionally, oil and gas companies use simulation software that are inherently computationally expensive to forecast production. Thus, machi

Hongkang Li, Meng Wang, Songtao Lu, Xiaodong Cui

Transformer-based large language models have displayed impressive in-context learning capabilities, where a pre-trained model can handle new tasks without fine-tuning by simply augmenting the query with some input-output examples from that task. Despite the empirical success, the mechanics of how to train a Transformer to achieve ICL and the corresponding IC

Claudia D. Alvarado, Eduardo Chiumiento

We study the action of Bogoliubov transformations on admissible generalized one-particle density matrices arising in Hartree-Fock-Bogoliubov theory. We show that the orbits of this action are reductive homogeneous spaces, and we give several equivalences that characterize when they are embedded submanifolds of natural ambient spaces. We use Lie theoretic arg

Ghulam Jilani Quadri, Arran Zeyu Wang, Zhehao Wang, Jennifer Adorno

Designers often create visualizations to achieve specific high-level analytical or communication goals. These goals require people to naturally extract complex, contextualized, and interconnected patterns in data. While limited prior work has studied general high-level interpretation, prevailing perceptual studies of visualization effectiveness primarily foc

Long Kiu Chung, Wonsuhk Jung, Chuizheng Kong, Shreyas Kousik

Autonomous mobile robots must maintain safety, but should not sacrifice performance, leading to the classical reach-avoid problem: find a trajectory that is guaranteed to reach a goal and avoid obstacles. This paper addresses the near danger case, also known as a narrow gap, where the agent starts near the goal, but must navigate through tight obstacles that

Hrad Ghoukasian, Shahab Asoodeh

In this work, we investigate binary classification under the constraints of both differential privacy and fairness. We first propose an algorithm based on the decoupling technique for learning a classifier with only fairness guarantee. This algorithm takes in classifiers trained on different demographic groups and generates a single classifier satisfying sta

Kaihong Zhang, Caitlyn H. Yin, Feng Liang, Jingbo Liu

We study the asymptotic error of score-based diffusion model sampling in large-sample scenarios from a non-parametric statistics perspective. We show that a kernel-based score estimator achieves an optimal mean square error of $\widetilde{O}\left(n^{-1} t^{-\frac{d+2}{2}}(t^{\frac{d}{2}} \vee 1)\right)$ for the score function of $p_0*\mathcal{N}(0,t\boldsymb

Jonah J. Glunt, Jacob A. Siefert, Andrew F. Thompson, Herschel C. Pangborn

Nonlinear expressions are often approximated by piecewise affine (PWA) functions to simplify analysis or reduce computational costs. To reduce computational complexity, multivariate functions can be represented as compositions of functions with one or two inputs, which can be approximated individually. This paper provides efficient methods to generate PWA ap

Yichuan Bai, Lynna Chu

We consider the problem of estimating the number of clusters (k) in a dataset. We propose a non-parametric approach to the problem that utilizes similarity graphs to construct a robust statistic that effectively captures similarity information among observations. This graph-based statistic is applicable to datasets of any dimension, is computationally effici

Nicholas P. Gaba, Carlos E. V. de Moura, Rajat Majumder, Alexander Yu. Sokolov

Accurate simulations of transient X-ray photoelectron spectra (XPS) provide unique opportunities to bridge the gap between theory and experiment in understanding the photoactivated dynamics in molecules and materials. However, simulating X-ray photoelectron spectra along a photochemical reaction pathway is challenging as it requires accurate description of e

David Torpey, Richard Klein

Often, applications of self-supervised learning to 3D medical data opt to use 3D variants of successful 2D network architectures. Although promising approaches, they are significantly more computationally demanding to train, and thus reduce the widespread applicability of these methods away from those with modest computational resources. Thus, in this paper,

Mohammad Hossein Alizadeh, Alireza Youhannaee Zanjani

We introduce and study the notion of (e,y)-conjugate for a proper and e-convex function in locally convex spaces, which is an extension of the concept of the conjugate. The mutual relationships between the concepts of (e,y)-conjugacy and e-subdifferential are presented. Moreover, some applications of these notions in optimization are established.

Li-Li Ye, Cheng-Zhen Wang, Ying-Cheng Lai

Experimentally feasible methods to determine the Berry phase, a fundamental quantity characterizing a quantum material, are often needed in applications. We develop an approach to detecting the Berry phase by using a class of two-dimensional (2D) Dirac materials with a flat band, the $\alpha$-$\mathcal{T}_3$ lattices. The properties of this class of quantum

Meilani Wibowo-Teale, Bang C. Huynh, Andrew M. Wibowo-Teale, Frank De Proft

[Abridged] The extension of conceptual DFT to include external fields in chemical systems is utilised to investigate the effects of strong magnetic fields on the electronic charge distribution and its consequences on the reactivity of $\pi$-systems. Formaldehyde, H$_2$CO, is considered as a prototypical example and current-DFT calculations are used to evalua

Jintao Jiang, Yingbo Gao, Mohammad Zeineldeen, Zoltan Tuske

In this paper, alternating weak triphone/BPE alignment supervision is proposed to improve end-to-end model training. Towards this end, triphone and BPE alignments are extracted using a pre-existing hybrid ASR system. Then, regularization effect is obtained by cross-entropy based intermediate auxiliary losses computed on such alignments at a mid-layer represe

Francisco Gancedo, Rafael Granero-Belinchón, Elena Salguero

In this paper we establish the global-in-time well-posedness for an arbitrary $C^{1+\gamma}$, $0<\gamma<1$, initial internal wave for the free boundary gravity Stokes system in two dimensions. This classical well-posedness result is complemented with a weak solvability result in the case of $C^\gamma$ or Lipschitz interfaces. Furthermore, we also propose and

Zhe Jiao, Wantao Jia, Weiqiu Zhu

The aim of this work is to develop a deep learning method for solving high-dimensional stochastic control problems based on the Hamilton--Jacobi--Bellman (HJB) equation and physics-informed learning. Our approach is to parameterize the feedback control and the value function using a decoupled neural network with multiple outputs. We train this network by usi

Zeyuan Zhang, Tanmay Laud, Zihang He, Xiaojie Chen

We present a new Python toolkit called RecWizard for Conversational Recommender Systems (CRS). RecWizard offers support for development of models and interactive user interface, drawing from the best practices of the Huggingface ecosystems. CRS with RecWizard are modular, portable, interactive and Large Language Models (LLMs)-friendly, to streamline the lear

Taeho Ryu, Rosalba Perna, Matteo Cantiello

Nuclear star clusters (NSCs), made up of a dense concentrations of stars and the compact objects they leave behind, are ubiquitous in the central regions of galaxies, surrounding the central supermassive black hole (SMBH). Close interactions between stars and stellar-mass black holes (sBH) lead to tidal disruption events (TDEs). We uncover an interesting new

Eftekhar Hossain, Sanjeev Kumar Sinha, Naman Bansal, Alex Knipper

One of the most important yet onerous tasks in the academic peer-reviewing process is composing meta-reviews, which involves assimilating diverse opinions from multiple expert peers, formulating one's self-judgment as a senior expert, and then summarizing all these perspectives into a concise holistic overview to make an overall recommendation. This process

Agrima Seth, Sanchit Ahuja, Kalika Bali, Sunayana Sitaram

Generative models are increasingly being used in various applications, such as text generation, commonsense reasoning, and question-answering. To be effective globally, these models must be aware of and account for local socio-cultural contexts, making it necessary to have benchmarks to evaluate the models for their cultural familiarity. Since the training d

Vuko Vukcevic, Robert Keser

The paper provides a mathematical model and a tool for the focused investing strategy as advocated by Buffett, Munger, and others from this investment community. The approach presented here assumes that the investor's role is to think about probabilities of different outcomes for a set of businesses. Based on these assumptions, the tool calculates the optima

Cheng Long, Adrian Barbu

Shape modeling is a challenging task with many potential applications in computer vision and medical imaging. There are many shape modeling methods in the literature, each with its advantages and applications. However, many shape modeling methods have difficulties handling shapes that have missing pieces or outliers. In this regard, this paper introduces sha

Jieren Deng, Aaron Palmer, Rigel Mahmood, Ethan Rathbun

Achieving resiliency against adversarial attacks is necessary prior to deploying neural network classifiers in domains where misclassification incurs substantial costs, e.g., self-driving cars or medical imaging. Recent work has demonstrated that robustness can be transferred from an adversarially trained teacher to a student model using knowledge distillati

Laura Battaglia, Timothy Christensen, Stephen Hansen, Szymon Sacher

Researchers now routinely use AI or other machine learning methods to estimate latent variables of economic interest, then plug-in the estimates as covariates in a regression. We show both theoretically and empirically that naively treating AI/ML-generated variables as "data" leads to biased estimates and invalid inference. To restore valid inference, we pro

Nikola Zubić, Mathias Gehrig, Davide Scaramuzza

Today, state-of-the-art deep neural networks that process event-camera data first convert a temporal window of events into dense, grid-like input representations. As such, they exhibit poor generalizability when deployed at higher inference frequencies (i.e., smaller temporal windows) than the ones they were trained on. We address this challenge by introduci

Jiachen Yao, Nina Hagemann, Qiaojie Xiong, Jianxu Chen

Vascular networks play a crucial role in understanding brain functionalities. Brain integrity and function, neuronal activity and plasticity, which are crucial for learning, are actively modulated by their local environments, specifically vascular networks. With recent developments in high-resolution 3D light-sheet microscopy imaging together with tissue pro

Yichen Xie, Hongge Chen, Gregory P. Meyer, Yong Jae Lee

Due to the lack of depth cues in images, multi-frame inputs are important for the success of vision-based perception, prediction, and planning in autonomous driving. Observations from different angles enable the recovery of 3D object states from 2D image inputs if we can identify the same instance in different input frames. However, the dynamic nature of aut

Andrew Novick, Diana Cai, Quan Nguyen, Roman Garnett

Active learning is a valuable tool for efficiently exploring complex spaces, finding a variety of uses in materials science. However, the determination of convex hulls for phase diagrams does not neatly fit into traditional active learning approaches due to their global nature. Specifically, the thermodynamic stability of a material is not simply a function

Michael A. Fedderke, David E. Kaplan, Anubhav Mathur, Surjeet Rajendran

The tentative identification of approximately ten relativistic anti-helium cosmic-ray events at AMS-02 would, if confirmed, challenge our understanding of the astrophysical synthesis of heavy anti-nuclei. We propose a novel scenario for the enhanced production of such anti-nuclei that is triggered by isolated, catastrophic injections of large quantities of e

Xiao Zhan, Rao Fu, Daniel Ritchie

We present CharacterMixer, a system for blending two rigged 3D characters with different mesh and skeleton topologies while maintaining a rig throughout interpolation. CharacterMixer also enables interpolation during motion for such characters, a novel feature. Interpolation is an important shape editing operation, but prior methods have limitations when app

Xinjie Li

This paper explores the challenge of procedure planning in instructional videos, which involves creating goal-directed plans based on visual start and goal observations from videos. Previous research has tackled this problem with gradually weaker training supervision, from heavy intermediate visual observations or language instructions to task class supervis

ShengYun Peng, Seongmin Lee, Xiaojing Wang, Rajarajeswari Balasubramaniyan

Table structure recognition (TSR) aims to convert tabular images into a machine-readable format. Although hybrid convolutional neural network (CNN)-transformer architecture is widely used in existing approaches, linear projection transformer has outperformed the hybrid architecture in numerous vision tasks due to its simplicity and efficiency. However, exist

Bharti Bhoy, Paul Stevenson

This study presents a simulated quantum computing approach for the investigation into the shell-model energy levels of $^{58}$Ni through the application of the variational eigensolver (VQE) method in combination with a problem-specific ansatz. The primary objective is to achieve a fully accurate low-lying energy spectrum of $^{58}$Ni. The chosen isotope, $^{

Márcio S. Gomes-Filho, Pablo de Castro, Danilo B. Liarte, Fernando A. Oliveira

The Kardar-Parisi-Zhang (KPZ) equation describes a wide range of growth-like phenomena, with applications in physics, chemistry and biology. There are three central questions in the study of KPZ growth: the determination of height probability distributions; the search for ever more precise universal growth exponents; and the apparent absence of a fluctuation

Sebastián Alfaro, P. A. González, Diego Olmos, Eleftherios Papantonopoulos

In this work we explore the propagation of massive scalar fields on some wormhole backgrounds. On one side, we consider the Bronnikov-Ellis wormhole solution and wormhole geometries with a non-constant redshift function by introducing a gravitational mass $M$, which goes over into the Bronnikov-Ellis wormhole when the gravitational mass parameter vanishes. W

Ricardo Correa da Silva, Johannes Grosse, Gandalf Lechner

KMS states on $\mathbb{Z}_2$-crossed products of unital $C^*$-algebras $\mathcal{A}$ are characterized in terms of KMS states and twisted KMS functionals of $\mathcal{A}$. These functionals are shown to describe the extensions of KMS states $\omega$ on $\mathcal{A}$ to the crossed product $\mathcal{A} \rtimes \mathbb{Z}_2$ and can also be characterized by th

S. Samuroff, A. Campos, A. Porredon, J. Blazek

In cosmological analyses it is common to combine different types of measurement from the same survey. In this paper we use simulated DES Y3 and LSST Y1 data to explore differences in sensitivity to intrinsic alignments (IA) between cosmic shear and galaxy-galaxy lensing. We generate mock shear, galaxy-galaxy lensing and galaxy clustering data, contaminated w

Shihong Ling, Yue Wan, Xiaowei Jia, Na Du

The rapid evolution of automated vehicles (AVs) has the potential to provide safer, more efficient, and comfortable travel options. However, these systems face challenges regarding reliability in complex driving scenarios. Recent explainable AV architectures neglect crucial information related to inherent uncertainties while providing explanations for action

Ankita Bhaumik, Ning Sa, Gregorios Katsios, Tomek Strzalkowski

Social media platforms are popular tools for disseminating targeted information during major public events like elections or pandemics. Systematic analysis of the message traffic can provide valuable insights into prevailing opinions and social dynamics among different segments of the population. We are specifically interested in influence spread, and in par

Vinu Sankar Sadasivan, Shoumik Saha, Gaurang Sriramanan, Priyatham Kattakinda

In this paper, we introduce a novel class of fast, beam search-based adversarial attack (BEAST) for Language Models (LMs). BEAST employs interpretable parameters, enabling attackers to balance between attack speed, success rate, and the readability of adversarial prompts. The computational efficiency of BEAST facilitates us to investigate its applications on

Frank Cwitkowitz, Zhiyao Duan

Multi-pitch estimation is a decades-long research problem involving the detection of pitch activity associated with concurrent musical events within multi-instrument mixtures. Supervised learning techniques have demonstrated solid performance on more narrow characterizations of the task, but suffer from limitations concerning the shortage of large-scale and

Chaim Even-Zohar, Tsviqa Lakrec, Matteo Parisi, Ran Tessler

The amplituhedron is a mathematical object which was introduced to provide a geometric origin of scattering amplitudes in $\mathcal{N}=4$ super Yang Mills theory. It generalizes \emph{cyclic polytopes} and the \emph{positive Grassmannian}, and has a very rich combinatorics with connections to cluster algebras. In this article we provide a series of results a

Seohong Park, Tobias Kreiman, Sergey Levine

Unsupervised and self-supervised objectives, such as next token prediction, have enabled pre-training generalist models from large amounts of unlabeled data. In reinforcement learning (RL), however, finding a truly general and scalable unsupervised pre-training objective for generalist policies from offline data remains a major open question. While a number

Rajeev V. Rikhye, Aaron Loh, Grace Eunhae Hong, Preeti Singh

Recently, there has been great progress in the ability of artificial intelligence (AI) algorithms to classify dermatological conditions from clinical photographs. However, little is known about the robustness of these algorithms in real-world settings where several factors can lead to a loss of generalizability. Understanding and overcoming these limitations

A. G. McLean, N. J. C. Spooner, T. Crane, C. Eldridge

Low pressure gaseous Time Projection Chambers (TPCs) are a viable technology for directional Dark Matter (DM) searches and have the potential for exploring the parameter space below the neutrino fog. Gases like CF4 are advantageous because they contain flourine which is predicted to have heightened elastic scattering rates with a possible Weakly Interacting

Dario Fiore Mosca, Cesare Franchini, Leonid V. Pourovskii

The origin of the "hidden" quadrupolar and unconventional magnetic low-temperature orders observed in the spin-orbit double perovskite Ba$_2$MgReO$_6$ defies explanation through standard experimental and theoretical techniques. Here we address this problem by deriving and solving an ab initio low-temperature effective Hamiltonian including inter-site electro

Khachatur G. Nazaryan, Ivan Ridkokasha, Marios H. Michael, Eugene Demler

We discuss terahertz pump-probe experiments in hBN from the perspective of Floquet optical materials and photonic time crystals. Anisotropic nature of this material results in a large separation of frequencies of in-plane and out-of-plane optical phonons and leads to several branches of phonon-polariton excitations. We consider a slab of finite thickness pum

Hector Parra De Freitas

Non-supersymmetric heterotic strings share various properties with their supersymmetric counterparts. Torus compactifications of the latter live in a component of the moduli space of string vacua with 16 supercharges, and various asymmetric orbifolds thereof realize vacua in other components, exhibiting qualitative differences such as rank reduction. We set

Parian Haghighat, Denisa G'andara, Lulu Kang, Hadis Anahideh

Predictive analytics is widely used in various domains, including education, to inform decision-making and improve outcomes. However, many predictive models are proprietary and inaccessible for evaluation or modification by researchers and practitioners, limiting their accountability and ethical design. Moreover, predictive models are often opaque and incomp

Collin Beaudoin, Koustubh Phalak, Swaroop Ghosh

Quantum circuit transformation aims to produce equivalent circuits while optimizing for various aspects such as circuit depth, gate count, and compatibility with modern Noisy Intermediate Scale Quantum (NISQ) devices. There are two techniques for circuit transformation. The first is a rule-based approach that greedily cancels out pairs of gates that equate t

Loïc Herviou, Frédéric Mila

We numerically investigate the guiding center stucture factors of several states in the Read-Rezayi family. Using exact diagonalizations on the torus and density matrix renormalization group on an infinite cylinder, we test a conjecture proposed in [Can et al, Phys. Rev. Lett. 113 (2014)] for the $\nu = \frac{3}{5}$ and $\nu = \frac{4}{6}$ Read-Rezayi states

Uesli Alushi, Wojciech Górecki, Simone Felicetti, Roberto Di Candia

We compare critical quantum sensing to passive quantum strategies to perform frequency estimation, in the case of single-mode quadratic Hamiltonians. We show that, while in the unitary case both strategies achieve precision scaling quadratic with the number of photons, in the presence of dissipation this is true only for critical strategies. We also establis

Sebastian Bieringer, Gregor Kasieczka, Jan Kieseler, Mathias Trabs

In recent years, neural network-based classification has been used to improve data analysis at collider experiments. While this strategy proves to be hugely successful, the underlying models are not commonly shared with the public and rely on experiment-internal data as well as full detector simulations. We show a concrete implementation of a newly proposed

Florent Renaud, Oscar Agertz, Alessandro B. Romeo

The clumpy nature of gas-rich galaxies at cosmic noon raises the question of universality of the scaling relations and average properties of the star-forming structures. Using controlled simulations of disk galaxies and varying only the gas fraction, we show that the influence of the galactic environments (large-scale turbulence, tides, shear) contributes, t

Federico Roccati, Dario Cilluffo

Giant artificial atoms are promising and flexible building blocks for the implementation of analog quantum simulators. They are realized via a multi-local pattern of couplings of two-level systems to a waveguide, or to a two-dimensional photonic bath. A hallmark of giant-atom physics is their non-Markovian character in the form of self-coherent feedback, lea

William Ortolá Leonard, Zachary Slepian, Jiamin Hou

The field of cosmology is entering an epoch of unparalleled wealth of observational data thanks to galaxy surveys such as DESI, Euclid, and Roman. Therefore, it is essential to have a firm theoretical basis that allows the effective analysis of the data. With this purpose, we compute the nonlinear, gravitationally-induced connected galaxy 4-point correlation

German Barquero, Sergio Escalera, Cristina Palmero

Conditional human motion generation is an important topic with many applications in virtual reality, gaming, and robotics. While prior works have focused on generating motion guided by text, music, or scenes, these typically result in isolated motions confined to short durations. Instead, we address the generation of long, continuous sequences guided by a se

Ahmed Imtiaz Humayun, Randall Balestriero, Richard Baraniuk

Grokking, or delayed generalization, is a phenomenon where generalization in a deep neural network (DNN) occurs long after achieving near zero training error. Previous studies have reported the occurrence of grokking in specific controlled settings, such as DNNs initialized with large-norm parameters or transformers trained on algorithmic datasets. We demons

Anna Velichko, Annalisa De Cia, Christina Konstantopoulou, Cédric Ledoux

The chemical evolution of distant galaxies cannot be assessed from observations of individual stars, in contrast to the case of nearby galaxies. On the other hand, the study of the interstellar medium (ISM) offers an alternative way to reveal important properties of the chemical evolution of distant galaxies. The chemical enrichment of the ISM is produced by

Haruki Shimizu, Atsushi Ueda

In this study, we explore the geometric construction of the Klein bottle and the real projective plane ($\mathrm{RP}^2$) within the framework of tensor networks, focusing on the implementation of crosscap and rainbow boundaries. Previous investigations have applied boundary matrix product state techniques to study these boundaries. We introduce an approach t

Jianguo Zhang, Tian Lan, Rithesh Murthy, Zhiwei Liu

Autonomous agents powered by large language models (LLMs) have garnered significant research attention. However, fully harnessing the potential of LLMs for agent-based tasks presents inherent challenges due to the heterogeneous nature of diverse data sources featuring multi-turn trajectories. In this paper, we introduce \textbf{AgentOhana} as a comprehensive

Yuejiang Liu, Alexandre Alahi

Steering the behavior of a strong model pre-trained on internet-scale data can be difficult due to the scarcity of competent supervisors. Recent studies reveal that, despite supervisory noises, a strong student model may surpass its weak teacher when fine-tuned on specific objectives. Yet, the effectiveness of such weak-to-strong generalization remains limit

Chun-Hsiao Yeh, Ta-Ying Cheng, He-Yen Hsieh, Chuan-En Lin

Recent text-to-image diffusion models are able to learn and synthesize images containing novel, personalized concepts (e.g., their own pets or specific items) with just a few examples for training. This paper tackles two interconnected issues within this realm of personalizing text-to-image diffusion models. First, current personalization techniques fail to

Adam Coffman, Yifei Pan, Yuan Zhang

We establish a unique continuation property for solutions of the differential inequality $|\nabla u|\leq V|u|$, where $V$ is locally $L^n$ integrable on a domain in $\mathbb R^n$. A stronger uniqueness result is obtained if in addition the solutions are locally Lipschitz. One application is a finite order vanishing property in the $L^2$ sense for the exponen

Carlos García Meixide, David Ríos Insua

We introduce a new predictive mechanism that operates in the presence of hidden confounding across distributionally diverse data sources while ensuring consistent estimation of causal parameters-despite their recognized suboptimality for prediction in the literature. Our method is based on a novel estimand that captures the dependence structure between respo

Rachael L. Cohn, Christopher A. Petroff, Virginia E. McGhee, John A. Marohn

We used broadband local dielectric spectroscopy (BLDS), an electric force microscopy technique, to make non-contact measurements of conductivity in the dark and under illumination of PM6:Y6 and Y6 prepared on ITO and PEDOT:PSS/ITO. Over a range of illumination intensities, BLDS spectra were acquired and fit to an impedance model of the tip-sample interaction

Md Asaduzzaman, Ryan M. L. McFadden, Edward Thoeng, Robert E. Laxdal

We report measurements of the first-flux-penetration field in surface-treated and coated Nb samples using muon spin rotation ($\mu$SR). Using thin Ag foils as energy moderators for the implanted muon spin-probes, we "profile" the vortex penetration field $\mu_{0} H_{\mathrm{vp}}$ at sub-surface depths on the order of $\sim 10$ $\mathrm{\mu}$m to $\sim 100$ $

Zach D. Merino, Bohdan Khromets, Jonathan Baugh

In this study, we address challenges in designing quantum information processors based on electron spin qubits in electrostatically-defined quantum dots (QDs). Numerical calculations of charge stability diagrams are presented for a realistic double QD device geometry. These methods generaize to linear QD arrays, and are based on determining the effective par

Ying Wu, Garvit Arora, Xuan Mei

Forecasting the loss given default (LGD) for defaulted Commercial Real Estate (CRE) loans poses a significant challenge due to the extended resolution and workout time associated with such defaults, particularly in CCAR and CECL framework where the utilization of post-default information, including macroeconomic variables (MEVs) such as unemployment (UER) an

Qiaoyu Tang, Jiawei Chen, Zhuoqun Li, Bowen Yu

The rise of large language models (LLMs) has significantly transformed both the construction and application of information retrieval (IR) systems. However, current interactions between IR systems and LLMs remain limited, with LLMs merely serving as part of components within IR systems, and IR systems being constructed independently of LLMs. This separated a

Lars Ferencz, Stefan Höche, Judith Katzy, Frank Siegert

Top-quark pair production in association with two b-jets is computed at next-to-leading order QCD precision, including effects of the b-quark mass, and matched to a $t\bar{t}$+jets simulation in a variable flavor number scheme. The Monte Carlo realization of this method, called fusing, consistently embeds the four-flavor calculation in a particle-level event

Daniel Alba-Cuellar

The LC method described in this work seeks to approximate the roots of polynomial equations in one variable. This book allows you to explore the LC method, which uses geometric structures of Lines L and Circumferences C in the plane of complex numbers, based on polynomial coefficients. These structures depend on the inclination angle of a line with fixed poi

Dominik Francoeur, Rostislav Grigorchuk, Paul-Henry Leemann, Tatiana Nagnibeda

We describe the block structure of finitely generated subgroups of branch groups with the so-called subgroup induction property, including the first Grigorchuk group $\mathcal{G}$ and the torsion GGS groups.

İlke Çanakçı, Francesca Fedele, Ana Garcia Elsener, Khrystyna Serhiyenko

One can explicitly compute the generators of a surface cluster algebra either combinatorially, through dimer covers of snake graphs, or homologically, through the CC-map applied to indecomposable modules over the appropriate algebra. Recent work by Musiker, Ovenhouse and Zhang used Penner and Zeitlin's decorated super Teichm{\"u}ller theory to define a super

Shenglai Zeng, Jiankun Zhang, Pengfei He, Yue Xing

Retrieval-augmented generation (RAG) is a powerful technique to facilitate language model with proprietary and private data, where data privacy is a pivotal concern. Whereas extensive research has demonstrated the privacy risks of large language models (LLMs), the RAG technique could potentially reshape the inherent behaviors of LLM generation, posing new pr