May 2023 arXiv papers — page 181

Alessio Mazzetto, Eli Upfal

We develop and analyze a general technique for learning with an unknown distribution drift. Given a sequence of independent observations from the last $T$ steps of a drifting distribution, our algorithm agnostically learns a family of functions with respect to the current distribution at time $T$. Unlike previous work, our technique does not require prior kn

Stefan Kramer, Mattia Cerrato, Jannis Brugger, Sašo Džeroski

The paper surveys automated scientific discovery, from equation discovery and symbolic regression to autonomous discovery systems and agents. It discusses the individual approaches from a "big picture" perspective and in context, but also discusses open issues and recent topics like the various roles of deep neural networks in this area, aiding in the discov

Cesar Ceballos, Clément Chenevière

Given a lattice path $\nu$, the $\nu$-Tamari lattice and the $\nu$-Dyck lattice are two natural examples of partial order structures on the set of lattice paths that lie weakly above $\nu$. In this paper, we introduce a more general family of lattices, called alt $\nu$-Tamari lattices, which contains these two examples as particular cases. Unexpectedly, we s

Ravhi S Kumar, Han Liu, Quan Li, Yuming Xiao

The strongly correlated material FeSi exhibits several unusual thermal, magnetic, and structural properties under varying pressure-temperature (P-T) conditions. It is a potential thermoelectric alloy and a materials of several geological implications as a possible constituent at the Earth's core mantle boundary (CMB). The phase transition behavior and la

Konstantinos E. Nikolakakis, Amin Karbasi, Dionysis Kalogerias

We establish matching upper and lower generalization error bounds for mini-batch Gradient Descent (GD) training with either deterministic or stochastic, data-independent, but otherwise arbitrary batch selection rules. We consider smooth Lipschitz-convex/nonconvex/strongly-convex loss functions, and show that classical upper bounds for Stochastic GD (SGD) als

Thomas Gauthier, Johan Taflin, Gabriel Vigny

An endomorphism $f:\mathbb{P}^k\to\mathbb{P}^k$ of degree $d\geq2$ is said to be postcritically finite (or PCF) if its critical set $\mathrm{Crit}(f)$ is preperiodic, i.e. if there are integers $m>n\geq0$ such that $f^m(\mathrm{Crit}(f))\subseteq f^n(\mathrm{Crit}(f))$. When $k\geq2$, it was conjectured by Ingram, Ramadas and Silverman that, in the space $\m

Lovro Dulibić, James Gratrex, Blaženka Melić, Ivan Nišandžić

We present updated predictions for lifetimes of doubly charmed baryons, within the heavy quark expansion, including available NLO $\alpha_s$ contributions and newly-computed terms in the $1/m_c$ series. Our improved results confirm the expected hierarchy $$\tau(\Xi_{cc}^{+}) < \tau(\Omega_{cc}^{+}) < \tau(\Xi_{cc}^{++}) \,, $$ while the predicted lifetime $\

Miguel Bosch-Calvo, Fabrizio Grandoni, Afrouz Jabal Ameli

The 2-Vertex-Connected Spanning Subgraph problem (2VCSS) is among the most basic NP-hard (Survivable) Network Design problems: we are given an (unweighted) undirected graph $G$. Our goal is to find a spanning subgraph $S$ of $G$ with the minimum number of edges which is $2$-vertex-connected, namely $S$ remains connected after the deletion of an arbitrary nod

Lingyu Gao, Debanjan Ghosh, Kevin Gimpel

Pretrained language models have improved zero-shot text classification by allowing the transfer of semantic knowledge from the training data in order to classify among specific label sets in downstream tasks. We propose a simple way to further improve zero-shot accuracies with minimal effort. We curate small finetuning datasets intended to describe the label

A. Hamidi, k. Mohamed-Pour, M. Yousefi

This paper introduces a novel technique called &#34;Forged Channel,&#34; which aims to comprehensively represent EEG signals in order to achieve accurate classification of Parkinson&#39;s disease. The forged channel method prepares EEG signals in a manner that allows a deep learning model to effectively perceive all EEG channels within a single input. By emp

Daniel Yang, Levi Cai, Stewart Jamieson, Yogesh Girdhar

Coral reefs are fast-changing and complex ecosystems that are crucial to monitor and study. Biological hotspot detection can help coral reef managers prioritize limited resources for monitoring and intervention tasks. Here, we explore the use of autonomous underwater vehicles (AUVs) with cameras, coupled with visual detectors and photogrammetry, to map and i

Louis Soares

Let $\Gamma$ be a Schottky subgroup of $\mathrm{SL}_2(\mathbb{Z})$ and let $X=\Gamma\backslash \mathbb{H}^2$ be the associated hyperbolic surface. Conditional on the generalized Riemann hypothesis for quadratic $L$-functions, we establish a uniform and explicit spectral gap for the Laplacian on the Hecke congruence covers $ X_0(p) = \Gamma_0(p)\backslash \ma

Célia Griffet, Tobias Haas, Nicolas J. Cerf

We present several measurement schemes for accessing separability criteria for continuous-variable bipartite quantum systems. Starting from moments of the bosonic mode operators, criteria suitable to witness entanglement are expressed in terms of multimode spin observables via the Jordan Schwinger map. These observables are typically defined over a few repli

Rob Procter, Miguel Arana-Catania, Yulan He, Maria Liakata

Social media and user-generated content (UGC) have become increasingly important features of journalistic work in a number of different ways. However, the growth of misinformation means that news organisations have had devote more and more resources to determining its veracity and to publishing corrections if it is found to be misleading. In this work, we pr

N. Cavanagh, K. Fleck, M. J. V. Streeter, E. Gerstmayr

We report on the first experimental characterisation of a gamma-ray spectrometer designed to spectrally resolve high-flux photon beams with energies in the GeV range. The spectrometer has been experimentally characterised using a bremsstrahlung source obtained at the Apollon laser facility during the interaction of laser-wakefield accelerated electron beams

John Giorgi, Augustin Toma, Ronald Xie, Sondra S. Chen

This paper describes our submission to the MEDIQA-Chat 2023 shared task for automatic clinical note generation from doctor-patient conversations. We report results for two approaches: the first fine-tunes a pre-trained language model (PLM) on the shared task data, and the second uses few-shot in-context learning (ICL) with a large language model (LLM). Both

Siyu Jiang, Fa Peng Huang, Chong Sheng Li

Motivated by current status of dark matter (DM) search, a new type of DM production mechanism is proposed based on thedynamical process of a strong first-order phase transition in the early universe, namely, the filtered DM mechanism. We study the hydrodynamic effects on the DM relic density. By detailed calculations, we demonstrate that the hydrodynamic mod

Zhi-Hua Zhou

Conventional theoretical machine learning studies generally assume explicitly or implicitly that there are enough or even infinitely supplied computational resources. In real practice, however, computational resources are usually limited, and the performance of machine learning depends not only on how many data have been received, but also on how many data c

Alfredo Spuri, Danilo Nikolić, Subrata Chakraborty, Maya Klang

The combination of a superconductor with a magnetically inhomogeneous material has been established as an efficient mechanism for the generation of long-ranged spin-polarized (spin-triplet) Cooper pairs. Evidence for this mechanism, however, has been established based on studies done on three-dimensional systems, where the strong bonds existing at the interf

Elena Sofia Ruzzetti, Federico Ranaldi, Felicia Logozzo, Michele Mastromattei

The impressive achievements of transformers force NLP researchers to delve into how these models represent the underlying structure of natural language. In this paper, we propose a novel standpoint to investigate the above issue: using typological similarities among languages to observe how their respective monolingual models encode structural information. W

Eric Mayor

In the last decades, important progress has been achieved in the understanding of the neurotrophic effects of intermittent fasting (IF), caloric restriction (CR) and exercise. Improved neuroprotection, synaptic plasticity and adult neurogenesis (NSPAN) are essential examples of these neurotrophic effects. The importance in this respect of the metabolic switc

Tonghang Han, Zhengguang Lu, Giovanni Scuri, Jiho Sung

Rhombohedral stacked multilayer graphene is an ideal platform to search for correlated electron phenomena, due to its pair of flat bands touching at zero energy and further tunability by an electric field. Furthermore, its valley-dependent Berry phase at zero energy points to possible topological states when the pseudospin symmetry is broken by electron corr

Saeed Hadadan, Geng Lin, Jan Novák, Fabrice Rousselle

Inverse rendering methods that account for global illumination are becoming more popular, but current methods require evaluating and automatically differentiating millions of path integrals by tracing multiple light bounces, which remains expensive and prone to noise. Instead, this paper proposes a radiometric prior as a simple alternative to building comple

Shun Hatano, Masami Ouchi, Hiroya Umeda, Kimihiko Nakajima

We present ionizing spectra estimated at 13.6--100 eV for ten dwarf galaxies with strong high ionization lines of He {\sc {ii}}$λ$4686 and [Ne {\sc{v}}]$λ$3426 ([Ne {\sc{iv}}]$λ$2424) at $z=0$ ($z=8$) that are identified in our Keck/LRIS spectroscopy and the literature (the JWST ERO program). With the flux ratios of these high ionization lines and $>10$ low-

James Liang, Tianfei Zhou, Dongfang Liu, Wenguan Wang

We present CLUSTSEG, a general, transformer-based framework that tackles different image segmentation tasks (i.e., superpixel, semantic, instance, and panoptic) through a unified neural clustering scheme. Regarding queries as cluster centers, CLUSTSEG is innovative in two aspects:1) cluster centers are initialized in heterogeneous ways so as to pointedly add

Shunsuke Imai, Lei Qin, Takahide Yanagi

We consider a panel data analysis to examine the heterogeneity in treatment effects with respect to groups, periods, and a pre-treatment covariate of interest in the staggered difference-in-differences setting of Callaway and Sant'Anna (2021). Under standard identification conditions, a doubly robust estimand conditional on the covariate identifies the group

Sunhao Dai, Ninglu Shao, Haiyuan Zhao, Weijie Yu

The debut of ChatGPT has recently attracted the attention of the natural language processing (NLP) community and beyond. Existing studies have demonstrated that ChatGPT shows significant improvement in a range of downstream NLP tasks, but the capabilities and limitations of ChatGPT in terms of recommendations remain unclear. In this study, we aim to conduct

Xu Yang, Jiawei Peng, Zihua Wang, Haiyang Xu

We propose to Transform Scene Graphs (TSG) into more descriptive captions. In TSG, we apply multi-head attention (MHA) to design the Graph Neural Network (GNN) for embedding scene graphs. After embedding, different graph embeddings contain diverse specific knowledge for generating the words with different part-of-speech, e.g., object/attribute embedding is g

Haoran Xu, Maha Elbayad, Kenton Murray, Jean Maillard

Mixture-of-experts (MoE) models that employ sparse activation have demonstrated effectiveness in significantly increasing the number of parameters while maintaining low computational requirements per token. However, recent studies have established that MoE models are inherently parameter-inefficient as the improvement in performance diminishes with an increa

Jack Morgan Davies

We discuss a notion of uniqueness up to $n$-homotopy and study examples from stable homotopy theory. In particular, we show that the $q$-expansion map from elliptic cohomology to topological $K$-theory is unique up to $3$-homotopy, away from the prime $2$, and that upon taking $p$-completions and $\mathbf{F}_p^\times$-homotopy fixed points, this map is uniqu

Gideon Yoffe, Axel Bühler, Nachum Dershowitz, Israel Finkelstein

We present a pipeline for a statistical textual exploration, offering a stylometry-based explanation and statistical validation of a hypothesized partition of a text. Given a parameterization of the text, our pipeline: (1) detects literary features yielding the optimal overlap between the hypothesized and unsupervised partitions, (2) performs a hypothesis-te

Katharina Brazda, Martin Kružík, Fabian Rupp, Ulisse Stefanelli

We propose a sharp-interface model for a hyperelastic material consisting of two phases. In this model, phase interfaces are treated in the deformed configuration, resulting in a fully Eulerian interfacial energy. In order to penalize large curvature of the interface, we include a geometric term featuring a curvature varifold. Equilibrium solutions are prove

Tian Xia, Jia Liu, Abdel Lisser

This paper considers the distributionally robust chance constrained Markov decision process with random reward and ambiguous reward distribution. We consider individual and joint chance constraint cases with Kullback-Leibler divergence based ambiguity sets centered at elliptical distributions or elliptical mixture distributions, respectively. We derive tract

Haihao Lu, Jinwen Yang

We present a unified viewpoint of proximal point method (PPM), primal-dual hybrid gradient (PDHG) and alternating direction method of multipliers (ADMM) for solving convex-concave primal-dual problems. This viewpoint shows the equivalence of these three algorithms upto a norm change, and it leads to a four-line simple proof of their $\mathcal O(1/k)$ ergodic

Chao Du, Tianbo Li, Tianyu Pang, Shuicheng Yan

Sliced-Wasserstein Flow (SWF) is a promising approach to nonparametric generative modeling but has not been widely adopted due to its suboptimal generative quality and lack of conditional modeling capabilities. In this work, we make two major contributions to bridging this gap. First, based on a pleasant observation that (under certain conditions) the SWF of

Hao Dai

To perform reliable quantum computation, quantum error correction is indispensable. In certain cases, continuous covariance symmetry of the physical system can make exact error correction impossible. In this work we study the approximate error correction and covariance symmetry from the information-theoretic perspective. For general encoding and noise channe

Chang Liu, Hiromasa Tamaki, Tomoyasu Yokoyama, Kensuke Wakasugi

Stable or metastable crystal structures of assembled atoms can be predicted by finding the global or local minima of the energy surface within a broad space of atomic configurations. Generally, this requires repeated first-principles energy calculations, which is often impractical for large crystalline systems. Here, we present significant progress toward so

Zhaoyuan He, Changhan Ge, Wangyang Li, Lili Qiu

4K videos are becoming increasingly popular. However, despite advances in wireless technology, streaming 4K videos over mmWave to multiple users is facing significant challenges arising from directional communication, unpredictable channel fluctuation and high bandwidth requirements. This paper develops a novel 4K layered video multicast system. We (i) devel

Geoffroy Caillat-Grenier

Expander graphs, due to their mixing properties, are useful in many algorithms and combinatorial constructions. One can produce an expander graph with high probability by taking a random graph (e.g., the union of $d$ random bijections for a bipartite graph of degree $d$). This construction is much simpler than all known explicit constructions of expanders an

Frederik Ravn Klausen, Asbjørn Bækgaard Lauritsen

We introduce a stochastic cellular automaton as a model for culture and border formation. The model can be conceptualized as a game where the expansion rate of cultures is quantified in terms of their area and perimeter in such a way that approximately geometrically round cultures get a competitive advantage. We first analyse the model with periodic boundary

Fred Philippy, Siwen Guo, Shohreh Haddadan

Prior research has investigated the impact of various linguistic features on cross-lingual transfer performance. In this study, we investigate the manner in which this effect can be mapped onto the representation space. While past studies have focused on the impact on cross-lingual alignment in multilingual language models during fine-tuning, this study exam

Livio Ciorciaro, Tomasz Smolenski, Ivan Morera, Natasha Kiper

Magnetic properties of materials ranging from conventional ferromagnetic metals to strongly correlated materials such as cuprates originate from Coulomb exchange interactions. The existence of alternate mechanisms for magnetism that could naturally facilitate electrical control have been discussed theoretically but an experimental demonstration in an extende

Alexis Marchand

The relative Gromov seminorm is a finer invariant than stable commutator length where a relative homology class is fixed. We show a duality result between bounded cohomology and the relative Gromov seminorm, analogously to Bavard duality for scl. We give an application to computations of scl in graphs of groups. We also explain how our duality result can be

Volodymyr Sydorskyi, Igor Krashenyi, Denis Sakva, Oleksandr Zarichkovyi

We present a new method for functional tissue unit segmentation at the cellular level, which utilizes the latest deep learning semantic segmentation approaches together with domain adaptation and semi-supervised learning techniques. This approach allows for minimizing the domain gap, class imbalance, and captures settings influence between HPA and HubMAP dat

Iván López-Espejo, Santi Prieto, Alfonso Ortega, Eduardo Lleida

Despite the maturity of modern speaker verification technology, its performance still significantly degrades when facing non-neutrally-phonated (e.g., shouted and whispered) speech. To address this issue, in this paper, we propose a new speaker embedding compensation method based on a minimum mean square error (MMSE) estimator. This method models the joint d

Ali Hojjat, Janek Haberer, Olaf Landsiedel

Progressive compression allows images to start loading as low-resolution versions, becoming clearer as more data is received. This increases user experience when, for example, network connections are slow. Today, most approaches for image compression, both classical and learned ones, are designed to be non-progressive. This paper introduces ProgDTD, a traini

Henry A. Lockwood, Emily S. Carrington, Suzanne M. Fielding

We predict a phenomenon of catastrophic material failure arising suddenly within an amorphous material, with an extremely long delay time since the material was last deformed. By simulating a mesoscopic soft glassy rheology model in one dimension (1D), a mesoscopic elastoplastic model in 1D and 2D, and a continuum fluidity model in 1D, we demonstrate the bas

Fabio Hellmann, Silvan Mertes, Mohamed Benouis, Alexander Hustinx

In recent years, the increasing availability of personal data has raised concerns regarding privacy and security. One of the critical processes to address these concerns is data anonymization, which aims to protect individual privacy and prevent the release of sensitive information. This research focuses on the importance of face anonymization. Therefore, we

Pantea Nadimi Goki, Stella Civelli, Emanuele Parente, Roberto Caldelli

In this work, the concept of optical identification (OI) based on physical unclonable functions is introduced for the first time, to our knowledge, in optical communication systems and networks. The OI assigns an optical fingerprint and the corresponding digital representation to each sub-system of the network and estimates its reliability in different measu

Yuval Harduf, Eyal Setter, Izhak Bucher

This paper is concerned with identifying the instantaneous modal parameters of forced oscillatory systems with response-dependent generalized inertia (mass, inductance, or equivalent) based on their measured dynamics. An identification method is proposed, which is a variation of the &#34;FORCEVIB&#34; method. The method utilizes analytic signal representatio

Kazuya Mameda

From quantum field theory, we derive the chiral kinetic theory involving nonlinear quantum corrections coupled with spacetime-dependent electromagnetic fields and fluid velocity gradients. An equilibrium Wigner function determined by the kinetic equation verifies the nondissipativeness of the charge induced by the magneto-vortical coupling. We reveal that th

Jan Goedgebeur, Edita Máčajová, Jarne Renders

An edge $e$ of a graph $G$ is called deletable for some orientation $o$ if the restriction of $o$ to $G-e$ is a strong orientation. Inspired by a problem of Frank, in 2021 H\"orsch and Szigeti proposed a new parameter for $3$-edge-connected graphs, called the Frank number, which refines $k$-edge-connectivity. The Frank number is defined as the minimum number

Ryan M. L. McFadden, Md Asaduzzaman, Tobias Junginger

In a recent Letter by Romanenko et al., the authors used low-energy muon spin rotation (LE-$μ$SR) to measure the Meissner screening profile in cutouts from Nb superconducting radio frequency (SRF) cavities, systematically comparing how different surface treatments affect the screening properties of the elemental type-II superconductor. They reported a &#34;s

Matteo Bettini, Ajay Shankar, Amanda Prorok

Evolutionary science provides evidence that diversity confers resilience in natural systems. Yet, traditional multi-agent reinforcement learning techniques commonly enforce homogeneity to increase training sample efficiency. When a system of learning agents is not constrained to homogeneous policies, individuals may develop diverse behaviors, resulting in em

Robert Hartmann, Guru S. Yerragolam, Roberto Verzicco, Detlef Lohse

The heat transport in rotating Rayleigh-Bénard convection (RBC) can be significantly enhanced for moderate rotation, i.e., for an intermediate range of Rossby numbers $Ro$, compared to the non-rotating case. At Rayleigh numbers $Ra\lesssim5\cdot10^8$, the largest heat transport enhancement (HTE) is achieved when the thicknesses of kinetic and thermal boundar

Nicolas Folastre, Junhao Cao, Gozde Oney, Sunkyu Park

The technique known as 4D-STEM has recently emerged as a powerful tool for the local characterization of crystalline structures in materials, such as cathode materials for Li-ion batteries or perovskite materials for photovoltaics. However, the use of new detectors optimized for electron diffraction patterns and other advanced techniques requires constant ad

M. Ould-Elhkim, C. Moutou, J-F. Donati, É. Artigau

Context: Recent advances in the development of precise radial velocity (RV) instruments in the near-infrared (nIR) domain, such as SPIRou, have facilitated the study of M-type stars to more effectively characterize planetary systems. However, the nIR presents unique challenges in exoplanet detection due to various sources of planet-independent signals which

Jonathan C. Betts, Carsten van de Bruck, Christian Arnold, Baojiu Li

In General Relativity approximations based on the spherical collapse model such as Press--Schechter theory and its extensions are able to predict the number of objects of a certain mass in a given volume. In this paper we use a machine learning algorithm to test whether such approximations hold in screened modified gravity theories. To this end, we train ran

Luiz Maia, Gerhard Fohler

The Logical Execution Time (LET) model has been gaining industrial attention because of its timing and data-flow deterministic characteristics, which simplify the computation of end-to-end latencies of multi-rate cause-effect chains at the cost of pessimistic latencies. In this paper, we propose a novel method to reduce the pessimism in the latencies introdu

Yong Cao, Xianzhi Li, Huiwen Liu, Wen Dai

Knowledge base question answering (KBQA) is a challenging task that aims to retrieve correct answers from large-scale knowledge bases. Existing attempts primarily focus on entity representation and final answer reasoning, which results in limited supervision for this task. Moreover, the relations, which empirically determine the reasoning path selection, are

Turgay Pamuklu, Aisha Syed, W. Sean Kennedy, Melike Erol-Kantarci

Having unmanned aerial vehicles (UAVs) with edge computing capability hover over smart farmlands supports Internet of Things (IoT) devices with low processing capacity and power to accomplish their deadline-sensitive tasks efficiently and economically. In this work, we propose a graph neural network-based reinforcement learning solution to optimize the task

Michele Focchi, Francesco Roscia, Claudio Semini

The architecture of a robotics software framework tremendously influences the effort and time it takes for end users to test new concepts in a simulation environment and to control real hardware. Many years of activity in the field allowed us to sort out crucial requirements for a framework tailored for robotics: modularity and extensibility, source code reu

Gayatri Singh, Kavita Dorai, Arvind

We experimentally demonstrate the transfer of an unknown single-qubit state from Alice to Bob via a two-step discrete-time quantum random walk on a cycle with four vertices on a four-qubit nuclear magnetic resonance quantum processor. The qubits with Alice and Bob are used as coin qubits and the walk is carried out on in a two-qubit `Gaming Arena'. In this s

Zhen Wan, Fei Cheng, Zhuoyuan Mao, Qianying Liu

In spite of the potential for ground-breaking achievements offered by large language models (LLMs) (e.g., GPT-3), they still lag significantly behind fully-supervised baselines (e.g., fine-tuned BERT) in relation extraction (RE). This is due to the two major shortcomings of LLMs in RE: (1) low relevance regarding entity and relation in retrieved demonstratio

Gerhard Rein

We review stability and instability results for self-gravitating matter distributions, where the matter model is a collisionless gas as described by the Vlasov equation. The focus is on the general relativistic situation, i.e., on steady states of the Einstein-Vlasov system and their stability properties. In order to put things into perspective we include th

Abhinav Suresh, Vishal Varma, Priya Batra, T S Mahesh

The quantum adiabatic method, which maintains populations in their instantaneous eigenstates throughout the state evolution, is an established and often a preferred choice for state preparation and manipulation. Though it minimizes the driving cost significantly, its slow speed is a severe limitation in noisy intermediate-scale quantum (NISQ) era technologie

Metin Gurses, Tahsin Cagri Sisman, Bayram Tekin

We are interested in the charged dust solutions of the Einstein field equations in stationary and axially symmetric spacetimes; and inquire if the naked singularities of the Israel-Wilson-Perjes (IWP) metrics can be removed. The answer is negative in four dimensions. We examine whether this negative result can be avoided by adding scalar or dilaton fields. W

Maximilian Mattern, Jasmin Jarecki, Jon Ander Arregi, Vojtěch Uhlíř

We use ultrafast x-ray diffraction (UXRD) and the polar time-resolved magneto-optical Kerr effect (tr-MOKE) to study the laser-induced metamagnetic phase transition in two FeRh films with thicknesses below and above the optical penetration depth. In the thin film, we identify an intrinsic timescale for the light-induced nucleation of ferromagnetic (FM) domai

Arman Rahbar, Ziyu Ye, Yuxin Chen, Morteza Haghir Chehreghani

Constructing decision trees online is a classical machine learning problem. Existing works often assume that features are readily available for each incoming data point. However, in many real world applications, both feature values and the labels are unknown a priori and can only be obtained at a cost. For example, in medical diagnosis, doctors have to choos

Quinten Mortier, Ming-Hao Li, Jutho Haegeman, Nick Bultinck

We extend the study of finite-entanglement scaling from one-dimensional gapless models to two-dimensional systems with a Fermi surface. In particular, we show that the entanglement entropy of a contractible spatial region with linear size $L$ scales as $S\sim L\log[ξf(L/ξ)]$ in the optimal tensor network, and hence area-law entangled, state approximation to

Kai Jeggle, David Neubauer, Gustau Camps-Valls, Ulrike Lohmann

Cirrus clouds are key modulators of Earth&#39;s climate. Their dependencies on meteorological and aerosol conditions are among the largest uncertainties in global climate models. This work uses three years of satellite and reanalysis data to study the link between cirrus drivers and cloud properties. We use a gradient-boosted machine learning model and a Lon

Xin Cai, Yaxin Bi, Peter Nicholl, Roy Sterritt

Satellite Image Time Series (SITS) representation learning is complex due to high spatiotemporal resolutions, irregular acquisition times, and intricate spatiotemporal interactions. These challenges result in specialized neural network architectures tailored for SITS analysis. The field has witnessed promising results achieved by pioneering researchers, but

Simon Gardner, Derek I. Glazier, Kenneth Livingston, Dzmitry Maneuski

The design of pixel-based tracking detectors for a Low Q2 Tagger in the Far Backward region of the ePIC detector at EIC is presented. The physics case is outlined, together with estimates of rates and resolutions, and the current design based on Timepix4 technology is introduced.

Chen Zhu, Liang Du, Hong Chen, Shuang Zhao

Click-Through Rate (CTR) prediction is a pivotal task in product and content recommendation, where learning effective feature embeddings is of great significance. However, traditional methods typically learn fixed feature representations without dynamically refining feature representations according to the context information, leading to suboptimal performan

Kuria Watanabe, Masami Ouchi, Kimihiko Nakajima, Yuki Isobe

We present gas-phase elemental abundance ratios of 7 local extremely metal-poor galaxies (EMPGs) including our new Keck/LRIS spectroscopy determinations together with 33 JWST $z\sim 4-10$ star-forming galaxies in the literature, and compare chemical evolution models. We develop chemical evolution models with the yields of core-collapse supernovae (CCSNe), Ty

Bashar Saleh

Given a simply connected space $X$, there are several, a priori different, algebraic groups whose groups of $\mathbb Q$-points are isomorphic to the group of homotopy classes of homotopy automorphisms of the rationalization of $X$. We will show that two of these different algebraic groups are isomorphic using the theory of operadic Koszul duality. As a by-pr

Lisa Steyer, Almond Stöcker, Sonja Greven

We propose regression models for curve-valued responses in two or more dimensions, where only the image but not the parametrization of the curves is of interest. Examples of such data are handwritten letters, movement paths or outlines of objects. In the square-root-velocity framework, a parametrization invariant distance for curves is obtained as the quotie

Josiah Smith, Yusef Alimam, Geetika Vedula, Murat Torlak

In this paper, we develop a novel super-resolution algorithm for near-field synthetic-aperture radar (SAR) under irregular scanning geometries. As fifth-generation (5G) millimeter-wave (mmWave) devices are becoming increasingly affordable and available, high-resolution SAR imaging is feasible for end-user applications and non-laboratory environments. Emergin

Xiaoyang Chen, Yanjiang Liu, Ben He, Le Sun

The Differentiable Search Index (DSI) is a novel information retrieval (IR) framework that utilizes a differentiable function to generate a sorted list of document identifiers in response to a given query. However, due to the black-box nature of the end-to-end neural architecture, it remains to be understood to what extent DSI possesses the basic indexing an

Louis Quilley, Valérie de Lapparent

We measure the scaling relations of the bulges and disks of the EFIGI galaxies in the nearby Universe versus morphology, using bulge and disk decomposition of SDSS gri images with SourceXtractor++. The Kormendy (1977) relation between effective surface brightness and effective radius of E galaxies extends to the bulges of types S0 to Sb, whereas fainter and

Yuliia Kazmina, Eelke M. Heemskerk, Eszter Bokanyi, Frank W. Takes

We propose a social network-aware approach to studying socio-economic segregation. The key question that we address is whether patterns of segregation are more pronounced in social networks than the common spatial neighborhood-focused manifestations of segregation. We, therefore, conduct a population-scale social network analysis to study socio-economic segr

Ezra Waxman, Nadav Yesha

On the circle of radius $R$ centred at the origin, consider a ``thin'' sector about the fixed line $y = \alpha x$ with edges given by the lines $y = (\alpha \pm \epsilon) x$, where $\epsilon = \epsilon_R \rightarrow 0$ as $ R \to \infty $. We establish an asymptotic count for $S_{\alpha}(\epsilon,R)$, the number of integer lattice points lying in such a sect

Takehiro Ito, Naonori Kakimura, Naoyuki Kamiyama, Yusuke Kobayashi

The qubit routing problem, also known as the swap minimization problem, is a (classical) combinatorial optimization problem that arises in the design of compilers of quantum programs. We study the qubit routing problem from the viewpoint of theoretical computer science, while most of the existing studies investigated the practical aspects. We concentrate on

Muhammad Burhan Hafez, Tilman Immisch, Tom Weber, Stefan Wermter

Deep Reinforcement Learning agents often suffer from catastrophic forgetting, forgetting previously found solutions in parts of the input space when training on new data. Replay Memories are a common solution to the problem, decorrelating and shuffling old and new training samples. They naively store state transitions as they come in, without regard for redu

Arjun S. Lakshmipathy, Nicole Feng, Yu Xi Lee, Moshe Mahler

Posing high-contact interactions is challenging and time-consuming, with hand-object interactions being especially difficult due to the large number of degrees of freedom (DOF) of the hand and the fact that humans are experts at judging hand poses. This paper addresses this challenge by elevating contact areas to first-class primitives. We provide \textit{en

D. Zavazieva, L. Moleri, A. Jash, G. Sela

We present a new design and assembly procedure of a large-area gas-avalanche Resistive-Plate WELL (RPWELL) detector. A $50\times50 ~\mathrm{cm^2}$ prototype was tested in $\mathrm{80 ~GeV/c}$ muon beam at CERN-SPS, presenting improved performances compared to previous ones: MIP detection efficiency over 96\% with 3\% uniformity across the entire detector are

Marco Fellous-Asiani, Moein Naseri, Chandan Datta, Alexander Streltsov

In this work, we consider biased-noise qubits affected only by bit-flip errors, which is motivated by existing systems of stabilized cat qubits. This property allows us to design a class of noisy Hadamard-tests involving entangling and certain non-Clifford gates, which can be conducted reliably with only a polynomial overhead in algorithm repetitions. On the

Alexander P Browning, Adrianne L Jenner, Ruth E Baker, Philip K Maini

Many physical and biological systems rely on the progression of material through multiple independent stages. In viral replication, for example, virions enter a cell to undergo a complex process comprising several disparate stages before the eventual accumulation and release of replicated virions. While such systems may have some control over the internal dy

Palak Patel, Mohit Sharma, Sarika Maitra Bhattacharyya

In polydisperse systems, describing the structure and any structural order parameter (SOP) is not trivial as it varies with the number of species we use to describe the system, M . Depending on the degree of polydispersity, there is an optimum value of M = M0 where we show that the mutual information of the system increases. However, surprisingly the correla

Artur Nicolau, Odí Soler i Gibert

A sharp version of the Central Limit Theorem for linear combinations of iterates of an inner function is proved. The authors previously showed this result assuming a suboptimal condition on the coefficients of the linear combination. Here we explain a variation of the original argument which leads to the sharp result. We also review the steps of the proof as

Yogesh Darmwal, Ketan Rajawat

This work puts forth low-complexity Riemannian subspace descent algorithms for the minimization of functions over the symmetric positive definite (SPD) manifold. Different from the existing Riemannian gradient descent variants, the proposed approach utilizes carefully chosen subspaces that allow the update to be written as a product of the Cholesky factor of

Leonel Gruñeiro, Miguel Alvarado, Alfredo Levy Yeyati, Liliana Arrachea

We study two-terminal configurations in junctions between a topological superconducting wire with spin-orbit coupling and magnetic field, and an ordinary conductor with an embedded quantum dot. One of the signatures of the Majorana zero modes in the topological phase is a quantization of the zero-bias conductance at $G(V=0)=2e^2/h$. However, the finite size

Zoltán Halasi, Károly Podoski, László Pyber, Endre Szabó

There are several results in the literature concerning $p$-groups $G$ with a maximal elementary abelian normal subgroup of rank $k$ due to Thompson, Mann and others. Following an idea of Sambale we obtain bounds for the number of generators etc. of a $2$-group $G$ in terms of $k$, which were previously known only for $p>2$. We also prove a theorem that is ne

Di Wang, Jing Zhang, Bo Du, Minqiang Xu

The success of the Segment Anything Model (SAM) demonstrates the significance of data-centric machine learning. However, due to the difficulties and high costs associated with annotating Remote Sensing (RS) images, a large amount of valuable RS data remains unlabeled, particularly at the pixel level. In this study, we leverage SAM and existing RS object dete

Nitay Calderon, Subhabrata Mukherjee, Roi Reichart, Amir Kantor

Modern Natural Language Generation (NLG) models come with massive computational and storage requirements. In this work, we study the potential of compressing them, which is crucial for real-world applications serving millions of users. We focus on Knowledge Distillation (KD) techniques, in which a small student model learns to imitate a large teacher model,

Hisashi Kotegawa, Yoshiki Kuwata, Vu Thi Ngoc Huyen, Yuki Arai

Specific antiferromagnetic (AF) spin configurations generate large anomalous Hall effects (AHEs) even at zero magnetic field through nonvanishing Berry curvature in momentum space. In addition to restrictions on AF structures, suitable control of AF domains is essential to observe this effect without cancellations among its domains; therefore, compatible mat

Ding Li, Yongqiang Tang, Zhizhong Zhang, Wensheng Zhang

Self-supervised skeleton-based action recognition enjoys a rapid growth along with the development of contrastive learning. The existing methods rely on imposing invariance to augmentations of 3D skeleton within a single data stream, which merely leverages the easy positive pairs and limits the ability to explore the complicated movement patterns. In this pa

Mohit Lal Bera, Jessica O. de Almeida, Marlena Dziurawiec, Marcin Płodzień

Su-Schrieffer-Heeger (SSH) chains are paradigmatic examples of 1D topological insulators hosting zero-energy edge modes when the bulk of the system has a non-zero topological winding invariant. Recently, high-harmonic spectroscopy has been suggested as a tool for detecting the topological phase. Specifically, it has been shown that when the SSH chain is coup

Laurent Bartholdi, Roman Mikhailov

We examine the complexity of the ``Texas Hold'em'' variant of poker from a topological perspective. We show that there exists a natural simplicial complex governing the multi-way winning probabilities between various hands, and that this simplicial complex contains $4$-dimensional spheres as induced subcomplexes. We deduce that evaluating the strength of a p

Kiran Purohit, Soumi Das, Sourangshu Bhattacharya, Santu Rana

Federated Learning systems are increasingly subjected to a multitude of model poisoning attacks from clients. Among these, edge-case attacks that target a small fraction of the input space are nearly impossible to detect using existing defenses, leading to a high attack success rate. We propose an effective defense using an external defense dataset, which pr