February 2024 arXiv papers — page 165

Hugo Lebeau, Florent Chatelain, Romain Couillet

This work presents a comprehensive understanding of the estimation of a planted low-rank signal from a general spiked tensor model near the computational threshold. Relying on standard tools from the theory of large random matrices, we characterize the large-dimensional spectral behavior of the unfoldings of the data tensor and exhibit relevant signal-to-noi

Bartosz Prokop, Nikita Frolov, Lendert Gelens

Many dynamical systems exhibit oscillatory behavior that can be modeled with differential equations. Recently, these equations have increasingly been derived through data-driven methods, including the transparent technique known as Sparse Identification of Nonlinear Dynamics (SINDy). This paper illustrates the importance of accurately determining the system'

Boao Kong, Shuchen Zhu, Songtao Lu, Xinmeng Huang

Stochastic bilevel optimization (SBO) is becoming increasingly essential in machine learning due to its versatility in handling nested structures. To address large-scale SBO, decentralized approaches have emerged as effective paradigms in which nodes communicate with immediate neighbors without a central server, thereby improving communication efficiency and

José Morano, Guilherme Aresta, Hrvoje Bogunović

The caliber and configuration of retinal blood vessels serve as important biomarkers for various diseases and medical conditions. A thorough analysis of the retinal vasculature requires the segmentation of the blood vessels and their classification into arteries and veins, typically performed on color fundus images obtained by retinography. However, manually

Maico H. W. Engelaar, Zengjie Zhang, Mircea Lazar, Sofie Haesaert

This paper concerns the risk-aware control of stochastic systems with temporal logic specifications dynamically assigned during runtime. Conventional risk-aware control typically assumes that all specifications are predefined and remain unchanged during runtime. In this paper, we propose a novel, provably correct model predictive control scheme for linear sy

Till Hofmann, Stefan Schupp, Gerhard Lakemeyer

Representing time is crucial for cyber-physical systems and has been studied extensively in the Situation Calculus. The most commonly used approach represents time by adding a real-valued fluent $\mathit{time}(a)$ that attaches a time point to each action and consequently to each situation. We show that in this approach, checking whether there is a reachable

Adrian-Gabriel Chifu, Sébastien Fournier

One of the challenges of natural language understanding is to deal with the subjectivity of sentences, which may express opinions and emotions that add layers of complexity and nuance. Sentiment analysis is a field that aims to extract and analyze these subjective elements from text, and it can be applied at different levels of granularity, such as document,

Shiyuan Yang, Liang Hou, Haibin Huang, Chongyang Ma

Recent text-to-video diffusion models have achieved impressive progress. In practice, users often desire the ability to control object motion and camera movement independently for customized video creation. However, current methods lack the focus on separately controlling object motion and camera movement in a decoupled manner, which limits the controllabili

Yang Jin, Zhicheng Sun, Kun Xu, Kun Xu

In light of recent advances in multimodal Large Language Models (LLMs), there is increasing attention to scaling them from image-text data to more informative real-world videos. Compared to static images, video poses unique challenges for effective large-scale pre-training due to the modeling of its spatiotemporal dynamics. In this paper, we address such lim

Sahil

In this work, we derive state-dependent uncertainty relations (uncertainty equalities) in which commutators of incompatible operators (not necessarily Hermitian) are explicitly present and state-independent uncertainty relations based on the Wigner-Yanase (-Dyson) skew information. We derive uncertainty equality based on standard deviation for incompatible o

Ran Ben-Basat, Yaniv Ben-Itzhak, Michael Mitzenmacher, Shay Vargaftik

Quantization is a fundamental optimization for many machine-learning use cases, including compressing gradients, model weights and activations, and datasets. The most accurate form of quantization is \emph{adaptive}, where the error is minimized with respect to a given input, rather than optimizing for the worst case. However, optimal adaptive quantization m

Philipp Karg, Adrian Kienzle, Jonas Kaub, Balint Varga

In this work, we analyze the applicability of Inverse Dynamic Game (IDG) methods based on the Minimum Principle (MP). The IDG method determines unknown cost functions in a single- or multi-agent setting from observed system trajectories by minimizing the so-called residual error, i.e. the extent to which the optimality conditions of the MP are violated with

Artem Bazhenov, Vladimir Berman, Sergei Satsevich, Olga Shalopanova

This paper introduces DogSurf - a newapproach of using quadruped robots to help visually impaired people navigate in real world. The presented method allows the quadruped robot to detect slippery surfaces, and to use audio and haptic feedback to inform the user when to stop. A state-of-the-art GRU-based neural network architecture with mean accuracy of 99.92

Lukas Lewark

A set L of links is introduced, containing positive braid links as well as arborescent positive Hopf plumbings. It is shown that for links in P, the leading and the second coefficient of the Alexander polynomial have opposite sign. It follows that certain satellite links, such as (n,1)-cables, are not in P.

Alec Helbling, Seongmin Lee, Polo Chau

Machine learning has enabled the development of powerful systems capable of editing images from natural language instructions. However, in many common scenarios it is difficult for users to specify precise image transformations with text alone. For example, in an image with several dogs, it is difficult to select a particular dog and move it to a precise loc

Gaoping Long, Hongguang Liu

In this article we propose a new construction of the spatial scalar curvature operator in (1+3)-dimensional LQG based on the twisted geometry. The starting point of the construction is to express the holonomy of the spin connection on a graph in terms of the twisted geometry variables, and we check that this expression reproduces the spin connection in terms

M. Naderibeni, M. J. T. Reinders, L. Wu, D. M. J. Tax

We leverage Physics-Informed Neural Networks (PINNs) to learn solution functions of parametric Navier-Stokes Equations (NSE). Our proposed approach results in a feasible optimization problem setup that bypasses PINNs' limitations in converging to solutions of highly nonlinear parametric-PDEs like NSE. We consider the parameter(s) of interest as inputs of PIN

Dusty Grundmeier, Jiří Lebl

Given a proper, rational map of balls, D'Angelo and Xiao introduced five natural groups encoding properties of the map. We study these groups using a recently discovered normal form for rational maps of balls. Using this normal form, we also provide several new groups associated to the map.

Timur Kenzhaev

We give a full and detailed solution of eighth Arnold's trivium problem. We find critical points of a smooth function on a given one-parametric two-dimensional surface with Lagrange multipliers method. Basic Morse theory and Poincare-Hopf theorem makes it possible to determine a genus of this surface and gives a beautiful training example of Morse surgery on

Claudio Corianò, Mario Cretì, Stefano Lionetti, Riccardo Tommasi

We investigate the general structure of the chiral anomaly $AVV/AAA$ and $(LLL, RRR)$ vertices, in the presence of chemical potentials in perturbation theory. The study finds application in anomalous transport, whenever chirally unbalanced matter is present, with propagating external currents that are classically conserved. Examples are topological materials

Sairam Sri Vatsavai, Venkata Sai Praneeth Karempudi, Oluwaseun Adewunmi Alo, Ishan Thakkar

Several microring resonator (MRR) based analog photonic architectures have been proposed to accelerate general matrix-matrix multiplications (GEMMs) in deep neural networks with exceptional throughput and energy efficiency. To implement GEMM functions, these MRR-based architectures, in general, manipulate optical signals in five different ways: (i) Splitting

Tim S. Lyon, Kees van Berkel

This paper provides a set of cut-free complete sequent-style calculi for deontic STIT ('See To It That') logics used to formally reason about choice-making, obligations, and norms in a multi-agent setting. We leverage these calculi to write a proof-search algorithm deciding deontic, multi-agent STIT logics with (un)limited choice and introduce a loop-checkin

Liming Jiang

Large Language Models (LLMs) have gained prominence in various applications, including security. This paper explores the utility of LLMs in scam detection, a critical aspect of cybersecurity. Unlike traditional applications, we propose a novel use case for LLMs to identify scams, such as phishing, advance fee fraud, and romance scams. We present notable secu

Xiongkuo Min, Huiyu Duan, Wei Sun, Yucheng Zhu

Perceptual video quality assessment plays a vital role in the field of video processing due to the existence of quality degradations introduced in various stages of video signal acquisition, compression, transmission and display. With the advancement of internet communication and cloud service technology, video content and traffic are growing exponentially,

Abdelhakim Benechehab, Albert Thomas, Giuseppe Paolo, Maurizio Filippone

In model-based reinforcement learning, most algorithms rely on simulating trajectories from one-step models of the dynamics learned on data. A critical challenge of this approach is the compounding of one-step prediction errors as the length of the trajectory grows. In this paper we tackle this issue by using a multi-step objective to train one-step models.

Robin Strässer, Manuel Schaller, Karl Worthmann, Julian Berberich

The Koopman operator serves as the theoretical backbone for machine learning of dynamical control systems, where the operator is heuristically approximated by extended dynamic mode decomposition (EDMD). In this paper, we propose SafEDMD, a novel stability- and feedback-oriented EDMD-based controller design framework. Our approach leverages a reliable surroga

Jérémy Berthomieu, Rafael Mohr

We describe a version of the FGLM algorithm that can be used to compute generic fibers of positive-dimensional polynomial ideals. It combines the FGLM algorithm with a Hensel lifting strategy. In analogy with Hensel lifting, we show that this algorithm has a complexity quasi-linear in the number of terms of certain $\mathfrak{m}$-adic expansions we compute.

Kaska Porayska-Pomsta

Current discourse surrounding Artificial Intelligence (AI) oscillates between hope and apprehension, painting a future where AI reshapes every facet of human life, including Education. This paper delves into the complexities of AI's role in Education, addressing the mixed messages that have both enthused and alarmed educators, policymakers, and the public. I

Martando Rath, Yu Chen, Guillaume Krieger, H. Sahib

We report scanning tunnelling microscopy (STM) and x-ray photoemission spectroscopy (XPS) measurements on uncapped and SrTiO3 (STO) capped NdNiO2 realized by pulsed-laser deposition and topotactic reduction process. We find that untreated NdNiO2 surfaces are insulating and contain Ni mostly in a nominal Ni2+ oxidation state. Room temperature STM shows signat

Michele Mastromattei, Fabio Massimo Zanzotto

Neural network pruning has become increasingly crucial due to the complexity of these models and their widespread use in various fields. Existing pruning algorithms often suffer from limitations such as architecture specificity, excessive complexity and reliance on demanding calculations, rendering them impractical for real-world applications. This paper int

Eduard Zamfir, Zongwei Wu, Nancy Mehta, Yulun Zhang

Reconstructing high-resolution (HR) images from low-resolution (LR) inputs poses a significant challenge in image super-resolution (SR). While recent approaches have demonstrated the efficacy of intricate operations customized for various objectives, the straightforward stacking of these disparate operations can result in a substantial computational burden,

Qingyuan Wu, Simon Sinong Zhan, Yixuan Wang, Yuhui Wang

Reinforcement learning (RL) is challenging in the common case of delays between events and their sensory perceptions. State-of-the-art (SOTA) state augmentation techniques either suffer from state space explosion or performance degeneration in stochastic environments. To address these challenges, we present a novel Auxiliary-Delayed Reinforcement Learning (A

Huynh Khanh

A class of parametric optimal control problems governed by semilinear parabolic equations with mixed pointwise constraints is investigated. The perturbations appear in the objective functional, the state equation and in mixed pointwise constraints. By analyzing regularity and establishing stability condition of Lagrange multipliers we prove that, if the stri

Zachary Porreca

Bride kidnapping is a form of forced marriage in which a woman is taken against her will and coerced into accepting marriage with her captor. Post-Soviet Kyrgyzstan has seen a large increase in the prominence of this practice alongside a revitalization of traditional values and culture. As part of this resurgence of Kyrgyz identity and culture, the central g

Binghui Xie, Yatao Bian, Kaiwen zhou, Yongqiang Chen

Learning neural subset selection tasks, such as compound selection in AI-aided drug discovery, have become increasingly pivotal across diverse applications. The existing methodologies in the field primarily concentrate on constructing models that capture the relationship between utility function values and subsets within their respective supersets. However,

Stefan Sylvius Wagner, Stefan Harmeling

In this paper we adopt a representation-centric perspective on exploration in reinforcement learning, viewing exploration fundamentally as a density estimation problem. We investigate the effectiveness of clustering representations for exploration in 3-D environments, based on the observation that the importance of pixel changes between transitions is less p

Kushal Tatariya, Heather Lent, Johannes Bjerva, Miryam de Lhoneux

Emotion classification is a challenging task in NLP due to the inherent idiosyncratic and subjective nature of linguistic expression, especially with code-mixed data. Pre-trained language models (PLMs) have achieved high performance for many tasks and languages, but it remains to be seen whether these models learn and are robust to the differences in emotion

Stijn T. de Vries, Laura Kley, Daniel Schindler

Golden Gate cloning has become a powerful and widely used DNA assembly method. Its modular nature and the reusability of standardized parts allow rapid construction of transcription units and multi-gene constructs. Importantly, its modular structure makes it compatible with laboratory automation, allowing for systematic and highly complex DNA assembly. Golde

Yannik Mahlau, Frederik Schubert, Bodo Rosenhahn

The combination of self-play and planning has achieved great successes in sequential games, for instance in Chess and Go. However, adapting algorithms such as AlphaZero to simultaneous games poses a new challenge. In these games, missing information about concurrent actions of other agents is a limiting factor as they may select different Nash equilibria or

Andrew Willis, Collin Hague, Artur Wolek, Kevin Brink

UAV missions often require specific geometric constraints to be satisfied between ground locations and the vehicle location. Such requirements are typical for contexts where line-of-sight must be maintained between the vehicle location and the ground control location and are also important in surveillance applications where the UAV wishes to be able to sense

Igor Arrieta, Martín Hötzel Escardó, Ayberk Tosun

Stone locales together with continuous maps form a coreflective subcategory of spectral locales and perfect maps. A proof in the internal language of an elementary topos was previously given by the second-named author. This proof can be easily translated to univalent type theory using resizing axioms. In this work, we show how to achieve such a translation w

Beatrice Pelloni, David A. Smith

We study Dirichlet-type problems for the simplest third-order linear dispersive PDE, often referred to as the Airy equation. Such problems have not been extensively studied, perhaps due to the complexity of the spectral structure of the spatial operator. Our specific interest is to determine whether the peculiar phenomenon of revivals, also known as Talbot e

Elias Rego, Sergio Romaña

In this work, we investigate diffeomorphisms whose positiveness of topological entropy is destroyed by singular suspensions. We show that this phenomenon is rare in the set of $C^1$-diffeomorphisms. Precisely, we prove that for an open and dense set of $C^1$-diffeomorphism positive topological entropy is preserved by singular suspensions, even for suspension

Duong Minh Le, Yang Chen, Alan Ritter, Wei Xu

Zero-shot cross-lingual transfer utilizing multilingual LLMs has become a popular learning paradigm for low-resource languages with no labeled training data. However, for NLP tasks that involve fine-grained predictions on words and phrases, the performance of zero-shot cross-lingual transfer learning lags far behind supervised fine-tuning methods. Therefore,

Tanha Miah, Hong Zhu

With the rapid advance of machine learning (ML) technology, large language models (LLMs) are increasingly explored as an intelligent tool to generate program code from natural language specifications. However, existing evaluations of LLMs have focused on their capabilities in comparison with humans. It is desirable to evaluate their usability when deciding o

Md Nadim Zafar, Adeeba Zaidi, Gauree Shanker

In this paper, we investigate the geometry of Clairaut anti-invariant Riemannnian maps whose base space are Sasakian manifolds. We obtain the necessary and sufficient conditions for a curve on a base manifold to be geodesic. We obtain conditions for an anti-invariant Riemannian map to be Clairaut. Further, we discuss the biharmonicity of such maps and constr

George Lazarides, Rinku Maji, Qaisar Shafi

We present a novel mechanism for producing topologically stable monopoles (TSMs) from the quantum mechanical decay of metastable cosmic strings in the early universe. In an $SO(10)$ model this mechanism yields TSMs that carry two units ($4\pi/e$) of Dirac magnetic charge as well as some color magnetic charge which is screened. For a dimensionless string tens

Mahmoud Alawashra, Martin Pohl

Relativistic pair beams produced in the cosmic voids by TeV gamma rays from blazars are expected to produce a detectable GeV-scale cascade that is missing in the observations. The suppression of this secondary cascade implies either the deflection of the pair beam by intergalactic magnetic fields or, alternatively, an energy loss of the beam due to the beam-

Amit Attia, Tomer Koren

We study the problem of parameter-free stochastic optimization, inquiring whether, and under what conditions, do fully parameter-free methods exist: these are methods that achieve convergence rates competitive with optimally tuned methods, without requiring significant knowledge of the true problem parameters. Existing parameter-free methods can only be cons

Yanzhou Wang, Lidia Al-Zogbi, Jiawei Liu, Lauren Shepard

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical s

Yanbo Wang, Jian Liang, Ran He

Gradient inversion attacks aim to reconstruct local training data from intermediate gradients exposed in the federated learning framework. Despite successful attacks, all previous methods, starting from reconstructing a single data point and then relaxing the single-image limit to batch level, are only tested under hard label constraints. Even for single-ima

Bruno Ventéjou, Iris Magniez--Papillon, Eric Bertin, Philippe Peyla

In open water, social fish gather to form schools, in which fish generally align with each other. In this work, we study how this social behavior evolves when perturbed by artificial obstacles. We measure the collective behavior of a group of zebrafish in the presence of a periodic array of pillars. When pillar density is low, the fish regroup with a typical

Florian Rössing, David Arutinov, Alessia Brignoli, Horst Fischer

On a very fundamental level, particle detectors share similar requirements for their read-out chain. This is reflected in the way that typical read-out solutions are developed, where a previous design is taken and modified to fit some changes in requirements. One of the two common approaches is the current-based read-out, where the waveform of the sensor out

Ilia V. Zalivako, Anastasiia S. Nikolaeva, Alexander S. Borisenko, Andrei E. Korolkov

We demonstrate a quantum processor based on a 3D linear Paul trap that uses $^{171}$Yb$^{+}$ ions with 8 individually controllable four-level qudits (ququarts), which is computationally equivalent to a 16-qubit quantum processor. The design of the developed ion trap provides high secular frequencies, low heating rate, which, together with individual addressi

Hsin-Yu Chen, Jose María Ezquiaga, Ish Gupta

Advancements in cosmology through next-generation ground-based gravitational wave observatories will bring in a paradigm shift. We explore the pivotal role that gravitational-wave standard sirens will play in inferring cosmological parameters with next-generation observatories, not only achieving exquisite precision but also opening up unprecedented redshift

Amin Parchami-Araghi, Moritz Böhle, Sukrut Rao, Bernt Schiele

Knowledge Distillation (KD) has proven effective for compressing large teacher models into smaller student models. While it is well known that student models can achieve similar accuracies as the teachers, it has also been shown that they nonetheless often do not learn the same function. It is, however, often highly desirable that the student's and teacher's

Amirreza Talebi, Sayed Pedram Haeri Boroujeni, Abolfazl Razi

This paper explores the critical domain of Revenue Management (RM) within Operations Research (OR), focusing on intricate pricing dynamics. Utilizing Mixed Integer Linear Programming (MILP) models, the study enhances revenue optimization by considering product prices as decision variables and emphasizing the interplay between demand and supply. Recent advanc

Zhi-Zhong Chen, Xu-Liang Chen, Peng-Fei Yang, Wei Chen

We have studied the mass spectra of the P-wave fully charm and fully bottom tetraquark states in the framework of QCD sum rules. We construct the interpolating currents by inserting the covariant derivative operator $\overset{ \leftrightarrow } { \mathcal D }_{ \mu }$ between the S-wave diquark and antidiquark fields. The excitation structures show that the

Saiful Khan, Scott Jones, Benjamin Bach, Jaehoon Cha

We present a method to create storytelling visualization with time series data. Many personal decisions nowadays rely on access to dynamic data regularly, as we have seen during the COVID-19 pandemic. It is thus desirable to construct storytelling visualization for dynamic data that is selected by an individual for a specific context. Because of the need to

Christopher J. Soelistyo, Alan R. Lowe

How can we find interpretable, domain-appropriate models of natural phenomena given some complex, raw data such as images? Can we use such models to derive scientific insight from the data? In this paper, we propose some methods for achieving this. In particular, we implement disentangled representation learning, sparse deep neural network training and symbo

Derin Cayir, Abbas Acar, Riccardo Lazzeretti, Marco Angelini

In this work, we present a device-centric analysis of security and privacy attacks and defenses on Extended Reality (XR) devices, highlighting the need for robust and privacy-aware security mechanisms. Based on our analysis, we present future research directions and propose design considerations to help ensure the security and privacy of XR devices.

Robert Gruhlke, Anthony Nouy, Philipp Trunschke

We consider the problem of optimising the expected value of a loss functional over a nonlinear model class of functions, assuming that we have only access to realisations of the gradient of the loss. This is a classical task in statistics, machine learning and physics-informed machine learning. A straightforward solution is to replace the exact objective wit

Chengchun Liu, Fanyang Mo

Infrared (IR) spectroscopy is a pivotal technique in chemical research for elucidating molecular structures and dynamics through vibrational and rotational transitions. However, the intricate molecular fingerprints characterized by unique vibrational and rotational patterns present substantial analytical challenges. Here, we present a machine learning approa

Rémi Prébet, Mohab Safey El Din, Éric Schost

A roadmap for an algebraic set $V$ defined by polynomials with coefficients in the field $\mathbb{Q}$ of rational numbers is an algebraic curve contained in $V$ whose intersection with all connected components of $V\cap\mathbb{R}^{n}$ is connected. These objects, introduced by Canny, can be used to answer connectivity queries over $V\cap \mathbb{R}^{n}$ prov

Petter Törnberg

Large Language Models (LLMs) have ushered in a new era of text annotation, as their ease-of-use, high accuracy, and relatively low costs have meant that their use has exploded in recent months. However, the rapid growth of the field has meant that LLM-based annotation has become something of an academic Wild West: the lack of established practices and standa

Anna L. Trella, Walter Dempsey, Asim H. Gazi, Ziping Xu

For the non-stationary multi-armed bandit (MAB) problem, many existing methods allow a general mechanism for the non-stationarity, but rely on a budget for the non-stationarity that is sub-linear to the total number of time steps $T$. In many real-world settings, however, the mechanism for the non-stationarity can be modeled, but there is no budget for the n

Jonathan Edwards, Alex Yakovlev, Simon O'Keefe

Clocks are a central part of many computing paradigms, and are mainly used to synchronise the delicate operation of switching, necessary to drive modern computational processes. Unfortunately, this synchronisation process is reaching a natural ``apocalypse''. No longer can clock scaling be used as a blunt tool to accelerate computation, we are up against the

Ana Paula Martins, María C. Vega-Hernández, Francisca Ribeiro Soares, Rosa Marina Afonso

The present study examines the factor structure of a Portuguese version of the Perceived Vulnerability to Disease Scale (PVD), designed to assess individual differences in chronic concerns about transmission of infectious diseases. Method: Data from a Portuguese convenience sample (n=1203), collected during the first Covid-19 pandemic lockdown. Results: the

Marilena Barnabei, Niccolò Castronuovo, Matteo Silimbani

We study groups generated by sets of pattern avoiding permutations. In the first part of the paper we prove some general results concerning the structure of such groups. In the second part we carry out a case-by-case analysis of groups generated by permutations avoiding few short patterns.

Qianyue He, Dongyu Du, Haitian Jiang, Xin Jin

Time-of-flight (ToF) devices have greatly propelled the advancement of various multi-modal perception applications. However, achieving accurate rendering of time-resolved information remains a challenge, particularly in scenes involving complex geometries, diverse materials and participating media. Existing ToF rendering works have demonstrated notable resul

Yuguang Chen, Tucker Jones, Ryan Sanders, Dario Fadda

In Chen et al., 2023 (C23; arXiv:2304.09898), we introduced a new method to directly measure temperature fluctuations and applied it to a nearby dwarf galaxy, Mrk 71, finding a temperature fluctuation parameter $t^2 = 0.008\pm 0.043$. This result is lower by $\sim 2\sigma$ than the value required to explain the abundance discrepancy (AD) in this object. In t

Lam Ngo, Huong Ha, Jeffrey Chan, Vu Nguyen

Bayesian Optimization (BO) is an effective method for finding the global optimum of expensive black-box functions. However, it is well known that applying BO to high-dimensional optimization problems is challenging. To address this issue, a promising solution is to use a local search strategy that partitions the search domain into local regions with high lik

Cristina Matache, Sam Lindley, Sean Moss, Sam Staton

Notions of computation can be modelled by monads. Algebraic effects offer a characterization of monads in terms of algebraic operations and equational axioms, where operations are basic programming features, such as reading or updating the state, and axioms specify observably equivalent expressions. However, many useful programming features depend on additio

E. Billaud, L. Balembois, J. Travesedo, M. Le Dantec

Counting the microwave photons emitted by an ensemble of electron spins when they relax radiatively has recently been introduced as a sensitive new method for electron paramagnetic resonance spectroscopy at millikelvin temperatures. Here, we apply this spin fluorescence method to a scheelite crystal of CaWO4, finding some known ($\mathrm{Er}^{3+}$, $\mathrm{

Ajay Chandra, Léonard Ferdinand

We show that the flow approach of Duch [Duc21] can be adapted to prove local well-posedness for the generalized Kardar-Parisi-Zhang equation. The key step is to extend the flow approach so that it can accommodate semi-linear equations involving smooth, non-polynomial, functions of the solution - this is accomplished by introducing coordinates for the flow bu

Viktor Bezborodov, Tyll Krueger, Cornelia Pokalyuk, Piotr Szymański

This study examines the effectiveness of regional lockdown strategies in mitigating pathogen spread across regional units, termed cities hereinafter. We develop simplified models to analyze infection spread across cities within a country during an epidemic wave. Isolation of a city is initiated when infection numbers within the city surpass defined threshold

Elad Levi, Eli Brosh, Matan Friedmann

Prompt engineering is a challenging and important task due to the high sensitivity of Large Language Models (LLMs) to the given prompt and the inherent ambiguity of a textual task instruction. Automatic prompt engineering is essential to achieve optimized performance from LLMs. Recent studies have demonstrated the capabilities of LLMs to automatically conduc

Jianchun Chu, Yaxiong Liu, Nicholas McCleerey

We establish $C^{1,1}$-regularity and uniqueness of the first eigenfunction of the complex Hessian operator on strongly $m$-pseudoconvex manifolds, along with a variational formula for the first eigenvalue. From these results, we derive a number of applications, including a bifurcation-type theorem and geometric bounds for the eigenvalue.

Laurent Freidel, Aldo Riello

In this paper, we provide a comprehensive study of asymptotically flat spacetime in even dimensions $d\geq 4$. We analyze the most general boundary condition and asymptotic symmetry compatible with Penrose's definition of asymptotic null infinity $\mathscr{I}$ through conformal compactification. Following Penrose's prescription and using a minimal version of

Claudio Bonanno, Claudio Bonati, Mario Papace, Davide Vadacchino

We study the $\theta$-dependence of the string tension and of the lightest glueball mass in four-dimensional $\mathrm{SU}(N)$ Yang-Mills theories. More precisely, we focus on the coefficients parametrizing the $\mathcal{O}(\theta^2)$ dependence of these quantities, which we investigate by means of numerical simulations of the lattice-discretized theory, carr

Muddasir Rahim, Thanh Luan Nguyen, Georges Kaddoum, Tri Nhu Do

Terahertz (THz) communication is envisioned as one of the candidate technologies for future wireless communications to enable achievable data rates of up to several terabits per second (Tbps). However, the high pathloss and molecular absorption in THz band communications often limit the transmission range. To overcome these limitations, this paper proposes i

Shuai Li, Xiaoyu Jiang, Xiaoguang Ma

Deep neural networks were significantly vulnerable to adversarial examples manipulated by malicious tiny perturbations. Although most conventional adversarial attacks ensured the visual imperceptibility between adversarial examples and corresponding raw images by minimizing their geometric distance, these constraints on geometric distance led to limited atta

Yuqian Fu, Yu Wang, Yixuan Pan, Lian Huai

This paper studies the challenging cross-domain few-shot object detection (CD-FSOD), aiming to develop an accurate object detector for novel domains with minimal labeled examples. While transformer-based open-set detectors, such as DE-ViT, show promise in traditional few-shot object detection, their generalization to CD-FSOD remains unclear: 1) can such open

Yixuan Wu, Kaiyuan Hu, Danny Z. Chen, Jian Wu

With the rapid advance of computer graphics and artificial intelligence technologies, the ways we interact with the world have undergone a transformative shift. Virtual Reality (VR) technology, aided by artificial intelligence (AI), has emerged as a dominant interaction media in multiple application areas, thanks to its advantage of providing users with imme

Florian Bridoux, Aymeric Picard Marchetto, Adrien Richard

A Boolean network is a function $f:\{0,1\}^n\to\{0,1\}^n$ from which several dynamics can be derived, depending on the context. The most classical ones are the synchronous and asynchronous dynamics. Both are digraphs on $\{0,1\}^n$, but the synchronous dynamics (which is identified with $f$) has an arc from $x$ to $f(x)$ while the asynchronous dynamics $\mat

Jianliang Qian, Timo Sprekeler, Hung V. Tran, Yifeng Yu

We study the optimal rate of convergence in periodic homogenization of the viscous Hamilton-Jacobi equation $u^\varepsilon_t + H(\frac{x}{\varepsilon},Du^\varepsilon) = \varepsilon \Delta u^\varepsilon$ in $\mathbb R^n\times (0,\infty)$ subject to a given initial datum. We prove that $\|u^\varepsilon-u\|_{L^\infty(\mathbb R^n \times [0,T])} \leq C(1+T) \sqrt

Alexander Ulanovskii, Ilya Zlotnikov

We introduce two families of generators (functions) $\mathcal{G}$ that consist of entire and meromorphic functions enjoying a certain periodicity property and contain the classical Gaussian and hyperbolic secant generators. Sharp results are proved on the density of separated sets that provide non-uniform sampling for the shift-invariant and quasi shift-inva

Matthias Pacé, Oleksandr Kovalenko, José Solano, Michel Hehn

Spintronic THz emitters, consisting of Ta/Co/Pt trilayers patterned into rectangles of lateral size in the 10 ${\mu}$m range, have been integrated in planar electromagnetic antennas of various types (dipole, bow-tie, spiral). Antenna dimensions and shapes have been optimized with the help of electromagnetic simulations so as to maximize antenna efficiency in

Lutz Straßburger

The news we consume influence our political opinions, and therefore whoever controls which news we read also controls our political opinions. Does this mean that the algorithms that select our news determine the outcomes of our elections?

Matias Heikkilä

We study the localization properties of bipartite channels, whose action on a subsystem yields a unitary channel. In particular we show that, under such channel, the subsystem must evolve independent of its environment. This point of view is another way to verify certain well-known conservation laws of quantum information in a generalized way. A no-go theore

Hans L. Bodlaender, Krisztina Szilágyi

The class XNLP consists of (parameterized) problems that can be solved nondeterministically in $f(k)n^{O(1)}$ time and $f(k)\log n$ space, where $n$ is the size of the input instance and $k$ the parameter. The class XALP consists of problems that can be solved in the above time and space with access to an additional stack. These two classes are a "natural ho

Mathieu Tanneau, Pascal Van Hentenryck

This paper presents Dual Lagrangian Learning (DLL), a principled learning methodology for dual conic optimization proxies. DLL leverages conic duality and the representation power of ML models to provide high-duality, dual-feasible solutions, and therefore valid Lagrangian dual bounds, for linear and nonlinear conic optimization problems. The paper introduce

Tixuan Tan, Aidan P. Reddy, Liang Fu, Trithep Devakul

We show that topological flat minibands can be engineered in a class of narrow gap semiconductor films using only an external electrostatic superlattice potential. We demonstrate that, for realistic material parameters, these bands are capable of hosting correlated topological phases such as integer and fractional quantum anomalous Hall states and composite

Umberto Martínez-Peñas

In this work, we provide four methods for constructing new maximum sum-rank distance (MSRD) codes. The first method, a variant of cartesian products, allows faster decoding than known MSRD codes of the same parameters. The other three methods allow us to extend or modify existing MSRD codes in order to obtain new explicit MSRD codes for sets of matrix sizes

R. Flores-Calderón, Owen Benton, Roderich Moessner

Classical spin liquids (CSLs) have proved to be a fruitful setting for the emergence of exotic gauge theories. Vacancy clusters in CSLs can introduce gauge charges into the system, and the resulting behavior in turn reveals the nature of the underlying theory. We study these effects for a series of CSLs on the honeycomb lattice. We find that dilution leads t

Yan Shu, Weichao Zeng, Zhenhang Li, Fangmin Zhao

Visual text, a pivotal element in both document and scene images, speaks volumes and attracts significant attention in the computer vision domain. Beyond visual text detection and recognition, the field of visual text processing has experienced a surge in research, driven by the advent of fundamental generative models. However, challenges persist due to the

Andi Peng, Andreea Bobu, Belinda Z. Li, Theodore R. Sumers

Learning from demonstrations is a common way for users to teach robots, but it is prone to spurious feature correlations. Recent work constructs state abstractions, i.e. visual representations containing task-relevant features, from language as a way to perform more generalizable learning. However, these abstractions also depend on a user's preference for wh

C. C. Montanari, P. Dimitriou, L. Marian, A. M. P. Mendez

We review the electronic stopping power data within the IAEA database, assessing the abundance and scarcity of available data as a function of energy and collisional systems. Our analysis includes an examination of the experimental values, their evolution in time, the dispersion among data, and trends of the more recent measurements. Additionally, we provide

Gunnar Behrens, Tom Beucler, Fernando Iglesias-Suarez, Sungduk Yu

Deep learning is a powerful tool to represent subgrid processes in climate models, but many application cases have so far used idealized settings and deterministic approaches. Here, we develop stochastic parameterizations with calibrated uncertainty quantification to learn subgrid convective and turbulent processes and surface radiative fluxes of a superpara

Can Wang, Dianbo Sui, Bolin Zhang, Xiaoyu Liu

Large Language Models (LLMs) have shown impressive abilities in solving various natural language processing tasks and are now widely offered as services. LLM services enable users to accomplish tasks without requiring specialized knowledge, simply by paying service providers. However, numerous providers offer various LLM services with variations in pricing,