December 2023 arXiv papers — page 99

Quoc-Huy Trinh, Minh-Van Nguyen, Phuoc-Thao Vo Thi

Polyp segmentation, a contentious issue in medical imaging, has seen numerous proposed methods aimed at improving the quality of segmented masks. While current state-of-the-art techniques yield impressive results, the size and computational cost of these models create challenges for practical industry applications. To address this challenge, we present KDAS,

Irving Solis, James Motes, Mike Qin, Marco Morales

This work presents Adaptive Robot Coordination (ARC), a novel hybrid framework for multi-robot motion planning (MRMP) that employs local subproblems to resolve inter-robot conflicts. ARC creates subproblems centered around conflicts, and the solutions represent the robot motions required to resolve these conflicts. The use of subproblems enables an inexpensi

Shaojin Ding, David Qiu, David Rim, Yanzhang He

End-to-end automatic speech recognition (ASR) models have seen revolutionary quality gains with the recent development of large-scale universal speech models (USM). However, deploying these massive USMs is extremely expensive due to the enormous memory usage and computational cost. Therefore, model compression is an important research topic to fit USM-based

Michał J. Pacholski, Ashley M. Cook

Topological phases stabilized by crystalline point group symmetry protection are a large class of symmetry-protected topological phases subjected to considerable experimental scrutiny. Here, we show that the canonical three-dimensional (3D) crystalline topological insulator protected by time-reversal symmetry $\mathcal{T}$ and four-fold rotation symmetry $\m

Mohammad Koosha, Nicholas Mastronarde

The rapid growth of active wireless communications technologies has fostered research on spectrum coexistence worldwide. One idea that is gaining attention is sharing frequency bands solely devoted to passive applications, such as passive remote sensing. One such option is the 27 MHz L-band spectrum from 1.400 to 1.427 GHz. Active wireless transmissions are

Giovanni Luca Marchetti, Christopher Hillar, Danica Kragic, Sophia Sanborn

In this work, we formally prove that, under certain conditions, if a neural network is invariant to a finite group then its weights recover the Fourier transform on that group. This provides a mathematical explanation for the emergence of Fourier features -- a ubiquitous phenomenon in both biological and artificial learning systems. The results hold even for

Raghav Thakar, Rajat Agrawal, Sujit PB

This paper presents a novel conflict resolution strategy for autonomous surface vehicles (ASVs) to safely navigate and avoid collisions in a multi-vessel environment at sea. Collisions between two or more marine vessels must be avoided by following the International Regulations for Preventing Collisions at Sea (COLREGs). We propose strategy a two-phase strat

Mykola Lavreniuk, Shariq Farooq Bhat, Matthias Müller, Peter Wonka

This work presents the network architecture EVP (Enhanced Visual Perception). EVP builds on the previous work VPD which paved the way to use the Stable Diffusion network for computer vision tasks. We propose two major enhancements. First, we develop the Inverse Multi-Attentive Feature Refinement (IMAFR) module which enhances feature learning capabilities by

M. Bonesini, R. Benocci, R. Bertoni, A. Abba

Large area LaBr3:Ce detectors with a SiPM array readout have been developed for the FAMU experiment at RAL. The aim was to have a good energy resolution for low energy X-rays detection (around 100 keV) and good timing properties of the signal pulse. Sixteen 1" detectors and twelve 1/2" detectors have been presently installed in the FAMU experiment and are ta

Naotaka Kajino, Mathav Murugan

We study the boundary trace processes of reflected diffusions on uniform domains. We obtain stable-like heat kernel estimates for such a boundary trace process when the diffusion on the underlying ambient space satisfies sub-Gaussian heat kernel estimates. Our arguments rely on new results of independent interest such as sharp two-sided estimates and the vol

Tahereh Toosi

Neural systems, artificial and biological, show similar representations of inputs when optimized to perform similar tasks. In visual systems optimized for tasks similar to object recognition, we propose that representation similarities arise from the constraints imposed by the development of abstractions in the representation across the processing stages. To

Redmond McNamara

We provide a counterexample to a conjecture of Hildebrand which states that if $\S$ has positive lower density and is stable i.e. for all $d$, $n$ is in $\mathcal{S}$ if and only if $dn$ is in $\mathcal{S}$ except on a set of density $0$ then $\mathcal{S} \cap (\mathcal{S}+1) \cap (\mathcal{S}+2)$ has positive lower density and in particular is nonempty. We

Mariam Guizani, Zixuan Feng, Emily Judith Arteaga, Luis Cañas-Díaz

Managing open-source software (OSS) projects requires managing communities of contributors. In particular, it is essential for project leaders to understand their community's diversity and turnover. We present CommunityTapestry, a dynamic real-time community dashboard, which presents key diversity and turnover signals that we identified from the literature a

Matías Leizerovich, Susana J. Landau, Claudia G. Scóccola

We present a comprehensive analysis of statistical tools for evaluating tensions in cosmological parameter estimates arising from distinct datasets. Focusing on the unresolved Hubble constant ($H_0$) tension, we explore the Pantheon Plus + SH0ES (PPS) compilation, which includes low-redshift Cepheid data from the SH0ES collaboration, along with the latest re

Duyu Chen, Michael A. Klatt, Glenn H. Fredrickson

Disordered hyperuniform (DHU) systems are recently discovered exotic states of matter, where (normalized) large-scale density fluctuations are completely suppressed as in crystals, even though the systems are isotropic and lack conventional long-range order. Despite recent success, realizing such systems using bottom-up approaches remains challenging. Here,

Kathrin Hanauer, Martin P. Seybold, Julian Unterweger

Representing a polygon using a set of simple shapes has numerous applications in different use-case scenarios. We consider the problem of covering the interior of a rectilinear polygon with holes by a set of area-weighted, axis-aligned rectangles such that the total weight of the rectangles in the cover is minimized. Already the unit-weight case is known to

Victor Parque, Tomoyuki Miyashita

Minimal and efficient graph representations are key to store, communicate, and sample the search space of graphs and networks while meeting user-defined criteria. In this paper, we investigate the feasibility of gradient-free optimization heuristics based on Differential Evolution to search for minimal integer representations of undirected graphs. The class

Bingcong Li, Shuai Zheng, Parameswaran Raman, Anshumali Shrivastava

On-device memory concerns in distributed deep learning have become severe due to (i) the growth of model size in multi-GPU training, and (ii) the wide adoption of deep neural networks for federated learning on IoT devices which have limited storage. In such settings, communication efficient optimization methods are attractive alternatives, however they still

Alessandro Gianola, Marco Montali, Sarah Winkler

Real-world processes operate on objects that are inter-dependent. To accurately reflect the nature of such processes, object-centric process mining techniques are needed, notably conformance checking. However, while the object-centric perspective has recently gained traction, few concrete process mining techniques have been presented so far. Moreover, existi

Amirhossein Afsharrad, Sanjay Lall

This paper addresses the challenge of a particular class of noisy state observations in Markov Decision Processes (MDPs), a common issue in various real-world applications. We focus on modeling this uncertainty through a confusion matrix that captures the probabilities of misidentifying the true state. Our primary goal is to estimate the inherent measurement

Thomas Brilland, Guillaume Matheron, Laetitia Leduc, Yukihide Nakada

This article presents a new methodology for extracting intervals when a home is vacant from low-frequency electricity consumption data. The approach combines multiple algorithms, including change point detection, classification, period detection, and periodic spikes retrieval. It shows encouraging results on both simulated and real consumption curves. This a

Sebastian Grieninger, Kazuki Ikeda, Dmitri E. Kharzeev

The recently introduced concept of timelike entanglement entropy has sparked a lot of interest. Unlike the traditional spacelike entanglement entropy, timelike entanglement entropy involves tracing over a timelike subsystem. In this work, we propose an extension of timelike entanglement entropy to Euclidean space ("temporal entanglement entropy"), and relate

Marc Rigter, Jun Yamada, Ingmar Posner

World models are a powerful tool for developing intelligent agents. By predicting the outcome of a sequence of actions, world models enable policies to be optimised via on-policy reinforcement learning (RL) using synthetic data, i.e. in "in imagination". Existing world models are autoregressive in that they interleave predicting the next state with sampling

Xingli Fang, Richard Bradford, Jung-Eun Kim

We propose a cooperative training framework for deep neural network architectures that enables the runtime network depths to change to satisfy dynamic computing resource requirements. In our framework, the number of layers participating in computation can be chosen dynamically to meet performance-cost trade-offs at inference runtime. Our method trains two Te

Zijian Liu, Zhengyuan Zhou

In the past several years, the last-iterate convergence of the Stochastic Gradient Descent (SGD) algorithm has triggered people's interest due to its good performance in practice but lack of theoretical understanding. For Lipschitz convex functions, different works have established the optimal $O(\log(1/\delta)\log T/\sqrt{T})$ or $O(\sqrt{\log(1/\delta)/T})

Lingxiao Wang

Two-phase sampling designs are frequently employed in epidemiological studies and large-scale health surveys. In such designs, certain variables are exclusively collected within a second-phase random subsample of the initial first-phase sample, often due to factors such as high costs, response burden, or constraints on data collection or measurement assessme

T. Vaessen, J. van Roestel

Context. Double eclipsing binaries are gravitationally bound quadruple systems in a 2+2 configuration where both of the binaries are eclipsing. These systems are interesting objects to better understand stellar formation, to investigate the dynamical interaction between the two binary systems or to study certain stages of stellar evolution. Aims. With this w

Marc-André Zöller, Marius Lindauer, Marco F. Huber

In today's data-driven landscape, time series forecasting is pivotal in decision-making across various sectors. Yet, the proliferation of more diverse time series data, coupled with the expanding landscape of available forecasting methods, poses significant challenges for forecasters. To meet the growing demand for efficient forecasting, we introduce auto-sk

Alastair Craw, Ryo Yamagishi

For any quiver $Q$ and dimension vector $v$, Le Bruyn-Procesi proved that the invariant ring for the action of the change of basis group on the space of representations $\text{Rep}(Q,v)$ is generated by the traces of matrix products associated to cycles in the quiver. Lusztig generalised this to allow for vertices where the group acts trivially. Here we prov

Johannes H. Weber

Minimally doubled fermions realize one degenerate pair of Dirac fermions on the lattice. Similarities to staggered fermions exist, namely, spin and taste degrees of freedom become intertwined, and a remnant, non-singlet chiral symmetry and ultralocality are maintained. However, charge conjugation, isotropy and some space-time reflection symmetries are broken

Daniela Cadamuro

The Tomita-Takesaki modular operator for local algebras plays an important role in quantum field theory, and more recently in the study of relative entropy. However, the explicit expression of this operator, except for the case of wedges, is difficult to describe mathematically. We have obtained instead numerical results for the form of the modular Hamiltoni

Abhinau K. Venkataramanan, Cosmin Stejerean, Ioannis Katsavounidis, Alan C. Bovik

Recent years have seen steady growth in the popularity and availability of High Dynamic Range (HDR) content, particularly videos, streamed over the internet. As a result, assessing the subjective quality of HDR videos, which are generally subjected to compression, is of increasing importance. In particular, we target the task of full-reference quality assess

Victor Parque, Aiki Nakamura, Tomoyuki Miyashita

Power modules with excellent inductance and temperature metrics are significant to meet the rising sophistication of energy demand in new technologies. In this paper, we use a surrogate-based approach to render optimal layouts of power modules with feasible and attractive inductance-temperature ratios at low computational budget. In particular, we use the cl

Emmanuel Calvet, Bertrand Reulet, Jean Rouat

Reservoir Computing (RC) is a paradigm in artificial intelligence where a recurrent neural network (RNN) is used to process temporal data, leveraging the inherent dynamical properties of the reservoir to perform complex computations. In the realm of RC, the excitatory-inhibitory balance b has been shown to be pivotal for driving the dynamics and performance

Mariana Frank, Benjamin Fuks, Adil Jueid, Stefano Moretti

We explore the potential of the Large Hadron Collider (LHC) in detecting a signal originating from the production of a heavy $SU(2)_R$ charged gauge boson that then decays into a top-bottom quark pair via the mediation of a right-handed neutrino, $p p \to W_R \to N_R \ell \to (\ell' t b)\ell$. Such a channel, that we study in the context of the minimal Left-

Dong Li, Ruoming Jin, Bin Ren

Inspired by the success of contrastive learning, we systematically examine recommendation losses, including listwise (softmax), pairwise (BPR), and pointwise (MSE and CCL) losses. In this endeavor, we introduce InfoNCE+, an optimized generalization of InfoNCE with balance coefficients, and highlight its performance advantages, particularly when aligned with

Qihong Lu, Tan T. Nguyen, Qiong Zhang, Uri Hasson

It has been proposed that, when processing a stream of events, humans divide their experiences in terms of inferred latent causes (LCs) to support context-dependent learning. However, when shared structure is present across contexts, it is still unclear how the "splitting" of LCs and learning of shared structure can be simultaneously achieved. Here, we prese

Ridvan Ozdemir, Junshan Lin

In this work, we examine the topological phases of the spring-mass lattices when the spatial inversion symmetry of the system is broken and prove the existence of edge modes when two lattices with different topological phases are glued together. In particular, for the one-dimensional lattice consisting of an infinite array of masses connected by springs, we

Ruoming Jin, Dong Li

In this paper, we perform a systemic examination of the recommendation losses, including listwise (softmax), pairwise(BPR), and pointwise (mean-squared error, MSE, and Cosine Contrastive Loss, CCL) losses through the lens of contrastive learning. We introduce and study both debiased InfoNCE and mutual information neural estimator (MINE), for the first time,

Luigi Brugnano, Gianmarco Gurioli, Felice Iavernaro

In this paper we consider the numerical solution of fractional terminal value problems (FDE-TVPs). In particular, the proposed procedure uses a Newton-type iteration which is particularly efficient when coupled with a recently-introduced step-by-step procedure for solving fractional initial value problems (FDE-IVPs), able to produce spectrally accurate solut

Kelly Maggs, Celia Hacker, Bastian Rieck

Geometric deep learning extends deep learning to incorporate information about the geometry and topology data, especially in complex domains like graphs. Despite the popularity of message passing in this field, it has limitations such as the need for graph rewiring, ambiguity in interpreting data, and over-smoothing. In this paper, we take a different approa

Raghav Goyal, Wan-Cyuan Fan, Mennatullah Siam, Leonid Sigal

Video Object Segmentation (VOS) has emerged as an increasingly important problem with availability of larger datasets and more complex and realistic settings, which involve long videos with global motion (e.g, in egocentric settings), depicting small objects undergoing both rigid and non-rigid (including state) deformations. While a number of recent approach

Austin G. Nixon, Matthieu Chalifour, Marc R. Bourgeois, Michael Sanchez

Recent advancements in abilities to create and manipulate the electron's transverse wave function within the transmission electron microscope (TEM) and scanning TEM (STEM) have enabled vectorially-resolved electron energy loss (EEL) and gain (EEG) measurements of nanoscale and quantum material responses using pre- and post-selected free electron states. This

Victor Hugo Pereira Rodrigues, Tiago Roux Oliveira, Liu Hsu, Mamadou Diagne

This paper proposes an event-triggered control scheme for multivariable extremum seeking of static maps. Both static and dynamic triggering conditions are developed. Integrating Lyapunov and averaging theories for discontinuous systems, a systematic design procedure and stability analysis are developed. Both event-based methods enable one to achieve an asymp

Juan Carlos Perdomo

Algorithmic predictions are increasingly used to inform the allocations of goods and interventions in the public sphere. In these domains, predictions serve as a means to an end. They provide stakeholders with insights into likelihood of future events as a means to improve decision making quality, and enhance social welfare. However, if maximizing welfare is

Adam Zahir, Milan Groshev, Kiril Antevski, Carlos J. Bernardos

The stringent low-latency, high reliability, availability and resilience requirements of 6G use cases will present challenges to cloud providers. Currently, cloud providers lack simple, efficient, and secure implementation of provisioning solutions that meet these challenges. Multi-cloud federation is a promising approach. In this paper, we evaluate the appl

Pooja Kulkarni, Rucha Kulkarni, Ruta Mehta

We study the problem of fairly dividing indivisible goods among a set of agents under the fairness notion of Any Price Share (APS). APS is known to dominate the widely studied Maximin share (MMS). Since an exact APS allocation may not exist, the focus has traditionally been on the computation of approximate APS allocations. Babaioff et al. studied the proble

Aurora Ireland, Stefano Profumo, Jordan Scharnhorst

The spectra of gravitational waves from black hole evaporation generically peak at frequencies of order the Hawking temperature, making this signal ultra-high frequency for primordial black holes evaporating in the early universe. This motivates us to consider small black holes in theories with large extra dimensions, for which the peak frequency can be lowe

Siddhant Gautam, Angqi Li, Saiprasad Ravishankar

There has been much recent interest in adapting undersampled trajectories in MRI based on training data. In this work, we propose a novel patient-adaptive MRI sampling algorithm based on grouping scans within a training set. Scan-adaptive sampling patterns are optimized together with an image reconstruction network for the training scans. The training optimi

Louisiane Devaud, Bernhard Rauer, Simon Mauras, Stefan Rotter

Speckle patterns are inherent features of coherent light propagation through complex media. As a result of interference, they are sensitive to multiple experimental parameters such as the configuration of disorder or the propagating wavelength. Recent developments in wavefront shaping have made it possible to control speckle pattern statistics and correlatio

Mohammed Moutand

Let $(X,\bar x)$ be a pointed connected noetherian scheme. In this note, we give characterizations for the vanishing of the second \'etale homotopy group $\pi^{\rm \'et}_2(X,\bar x)$ in terms of splitting profinite-\'etale covers of $X$, and by means of universal covering spaces of the Artin-Mazur-Friedlander \'etale homotopy type $Et(X)$. In particular, thi

Chandra Chekuri, Pooja Kulkarni, Rucha Kulkarni, Ruta Mehta

We study fair distribution of a collection of m indivisible goods among a group of n agents, using the widely recognized fairness principles of Maximin Share (MMS) and Any Price Share (APS). These principles have undergone thorough investigation within the context of additive valuations. We explore these notions for valuations that extend beyond additivity.

Anderson Gomes Vieira, Marcelo Lopes Pereira Júnior, Vincent Meunier, Eduardo Costa Girão

A rectangular graphyne sheet is composed of units similar to phenyl rings that are linked by acetylenic chains, as in hexagonal $\gamma$-graphyne. This system is organized over a rectangular lattice similar to that of the recently synthesized biphenylene network. We investigate the stability of this sheet from different perspectives and study its electronic

Abhinav Anand, Kenneth R. Brown

Efficiently calculating the low-lying eigenvalues of Hamiltonians, written as sums of Pauli operators, is a fundamental challenge in quantum computing. While various methods have been proposed to reduce the complexity of quantum circuits for this task, there remains room for further improvement. In this article, we introduce a new circuit design using commut

David Barnhill, John Cobb, Matthew Faust

Maximum likelihood estimation (MLE) is a fundamental problem in statistics. Characteristics of the MLE problem for discrete algebraic statistical models are reflected in the geometry of the $\textit{likelihood correspondence}$, a variety that ties together data and their maximum likelihood estimators. We construct this ideal for the large class of toric mode

Alon Zabatani, Shay Kreymer, Tamir Bendory

Multi-target detection (MTD) is the problem of estimating an image from a large, noisy measurement that contains randomly translated and rotated copies of the image. Motivated by the single-particle cryo-electron microscopy technology, we design data-driven diffusion priors for the MTD problem, derived from score-based stochastic differential equations model

Giovanna Lazzari Miotto, Javier Lopez-Gomez

Compared to LHC Run 1 and Run 2, future HEP experiments, e.g., at the HL-LHC, will increase the volume of generated data by an order of magnitude. In order to sustain the expected analysis throughput, ROOT's RNTuple I/O subsystem has been engineered to overcome the bottlenecks of the TTree I/O subsystem, focusing also on a compact data format, asynchronous a

Nikita Virin

We present a description for the automorphism groups of Du Val del Pezzo surfaces whose automorphism groups are infinite.

Yacine Ali-Haïmoud, Suroor Seher Gandhi, Tristan L. Smith

Elastic scattering of dark matter (DM) particles with baryons induce cosmological signals that may be detectable with modern or future telescopes. For DM-baryon scattering cross sections scaling with negative powers of relative velocity, $\sigma_{\chi b}(v) \propto v^{-2}, v^{-4}$, such interactions introduce a momentum-exchange rate that is nonlinear in DM-

A. N. Grekov, N. A. Grekov, E. N. Sychev

The article discusses methods for measuring the speed of sound, scattering, attenuation and absorption of sound in liquids, on the basis of which the structural diagrams of modern devices have been developed. In real conditions, acoustic measurement schemes are not ideal and depending on specific structure may give different results. The technical and metrol

Veysel Kocaman, Hasham Ul Haq, David Talby

Recent research advances achieve human-level accuracy for de-identifying free-text clinical notes on research datasets, but gaps remain in reproducing this in large real-world settings. This paper summarizes lessons learned from building a system used to de-identify over one billion real clinical notes, in a fully automated way, that was independently certif

Robin Netzorg, Ajil Jalal, Luna McNulty, Gopala Krishna Anumanchipalli

Perceptual modification of voice is an elusive goal. While non-experts can modify an image or sentence perceptually with available tools, it is not clear how to similarly modify speech along perceptual axes. Voice conversion does make it possible to convert one voice to another, but these modifications are handled by black box models, and the specifics of wh

Andrzej Kałuża, Paweł M. Morkisz, Bartłomiej Mulewicz, Paweł Przybyłowicz

We present a novel deep learning method for estimating time-dependent parameters in Markov processes through discrete sampling. Departing from conventional machine learning, our approach reframes parameter approximation as an optimization problem using the maximum likelihood approach. Experimental validation focuses on parameter estimation in multivariate re

Kristina F. Chang, Daniel M. B. Lesko, Carter Mashburn, Peter Chang

Dual-comb spectroscopy in the ultraviolet (UV) and visible would enable broad bandwidth electronic spectroscopy with unprecedented frequency resolution. However, there are significant challenges in generation, detection and processing of dual-comb data that have restricted its progress in this spectral region. In this work, we leverage robust 1550 nm few-cyc

Tomas Sikorsky, Andrzej Pelczar, Stephan Schneider, Thorsten Schumm

We present a new approach to Nuclear Quadrupole Resonance (NQR)/Nuclear Magnetic Resonance (NMR) spectroscopy, the Damp-Enhanced Superregenerative Nuclear Spin Analyser (DESSA). This system integrates Superregenerative principles with pulsed sample excitation and detection, offering significant advancements over traditional Super-Regenerative Receivers (SRRs

Jordi De Jonghe, Michał D. Kuczyński

To predict the immediate evolution of a plasma system, one needs to identify the nature of the dominant instability. In this work, a neural network is employed to address a non-binary classification problem of instabilities in astrophysical jets, whose natural oscillations and instabilities are quantified with the magnetohydrodynamic spectroscopy code Legola

Bingbing Hu, Evangelos Kosinas, Adam Polak

The problem of designing connectivity oracles supporting vertex failures is one of the basic data structures problems for undirected graphs. It is already well understood: previous works [Duan--Pettie STOC'10; Long--Saranurak FOCS'22] achieve query time linear in the number of failed vertices, and it is conditionally optimal as long as we require preprocessi

Joshua Wang

We propose a PnP algorithm for a camera constrained to two-dimensional motion (applicable, for instance, to many wheeled robotics platforms). Leveraging this assumption allows accuracy and performance improvements over 3D PnP algorithms due to the reduction in search space dimensionality. It also reduces the incidence of ambiguous pose estimates (as, in most

Shivali Agrawal, Len van Deurzen, Jimy Encomendero, Joseph E. Dill

Ultrawide bandgap heterojunction p-n diodes with polarization-induced AlGaN p-type layers are demonstrated using plasma-assisted molecular beam epitaxy on bulk AlN substrates. Current-voltage characteristics show a turn on voltage of $V_{\text{bi}}\approx5.5$ V, a minimum room temperature ideality factor of $\eta\approx 1.63$, and more than 12 orders of curr

Walter R. L. Lambrecht

LiGa$_5$O$_8$ in the spinel type structure is investigated as a potential ultra-wide-band-gap semiconductor. The band structure is determined using the quasiparticle self-consistent $GW$ method and the optical properties are calculated at the Bethe Salpeter Equation level including electron-hole interaction effects. The optical gap including exciton effects

Michael Law, Peter Bühlmann, Ya'acov Ritov

We consider the problem of statistical inference on parameters of a target population when auxiliary observations are available from related populations. We propose a flexible empirical Bayes approach that can be applied on top of any asymptotically linear estimator to incorporate information from related populations when constructing confidence regions. The

Quentin Bertrand, Juan Duque, Emilio Calvano, Gauthier Gidel

A growing body of computational studies shows that simple machine learning agents converge to cooperative behaviors in social dilemmas, such as collusive price-setting in oligopoly markets, raising questions about what drives this outcome. In this work, we provide theoretical foundations for this phenomenon in the context of self-play multi-agent Q-learners

G. Camacho-Ciurana, P. Lee, N. Arsenov, A. Kovács

The cross-correlation of cosmic voids with the lensing convergence ($\kappa$) map of the CMB fluctuations offers a powerful tool to refine our understanding of the dark sector in the consensus cosmological model. Our principal aim is to compare the lensing signature of our galaxy data set with simulations based on the concordance model and characterize the r

Bradford Garcia, Matthew P. Young

We prove an asymptotic formula for the second moment of central values of Dirichlet $L$-functions restricted to a coset. More specifically, consider a coset of the subgroup of characters modulo $d$ inside the full group of characters modulo $q$. Suppose that $\nu_p(d) \geq \nu_p(q)/2$ for all primes $p$ dividing $q$. In this range, we obtain an asymptotic fo

Yucheng Fu, Amanda Howard, Chao Zeng, Yunxiang Chen

Aqueous organic redox flow batteries (AORFBs) have gained popularity in renewable energy storage due to their low cost, environmental friendliness and scalability. The rapid discovery of aqueous soluble organic (ASO) redox-active materials necessitates efficient machine learning surrogates for predicting battery performance. The physics-guided continual lear

P. Zhevandrov, A. Merzon, M. I. Romero Rodríguez, J. E. De la Paz Méndez

Exact solutions describing trapped modes in a plane quantum waveguide with a small rigid obstacle are constructed in the form of convergent series in powers of the small parameter characterizing the smallness of the obstacle. The terms of this series are expressed through the solution of the exterior Neumann problem for the Laplace equation describing the fl

Manu Goyal, Laura J. Tafe, James X. Feng, Kristen E. Muller

Endometrial cancer, the fourth most common cancer in females in the United States, with the lifetime risk for developing this disease is approximately 2.8% in women. Precise histologic evaluation and molecular classification of endometrial cancer is important for effective patient management and determining the best treatment modalities. This study introduce

Joshua Eby, Patrick J. Fox, Graham D. Kribs

Inelastic dark matter with moderate splittings, $\mathcal{O}({\rm few} \; {\rm to} \; 150)$ keV, can upscatter to an excited state in the Earth, with the excited state subsequently decaying, leaving a distinctive monoenergetic photon signal in large underground detectors. The photon signal can exhibit sidereal-daily modulation, providing excellent separation

Yu Hao, Weiteng Chen, Ziqiao Zhou, Weidong Cui

Static analysis, the process of examining code without executing it, is crucial for identifying software issues. Yet, static analysis is hampered by its complexity and the need for customization for different targets. Traditional static analysis tools require extensive human effort and are often limited to specific target programs and programming languages.

Xin-Yue Zhang, Thomas K. M. Graham, Hyeonhu Bae, Yu-Xuan Wang

Two dimensional (2D) semiconductors present unique opportunities to intertwine optical and magnetic functionalities and to tune these performances through defects and dopants. Here, we integrate exciton pumping into a quantum sensing protocol on nitrogen-vacancy centers in diamond to image the optically-induced transient stray fields in few-layer, antiferrom

Faizuddin Ahmed, Abdelmalek Bouzenada

In this paper, we conduct a comprehensive exploration of the relativistic quantum dynamics of spin-0 scalar particles, as described by the Duffin-Kemmer-Petiau (DKP) equation, within the framework of a magnetic space-time. Our focus is on the Bonnor-Melvin-Lambda (BML) solution, a four-dimensional magnetic universe characterized by a magnetic field that vari

Mike Guidry, Lianao Wu, Fletcher Williams

We review the physics of monolayer graphene in a strong magnetic field, with emphasis on highly collective states that emerge from the weakly interacting system because of correlations (emergent states). After reviewing the general properties of graphene and of electrons in a magnetic field, we give a brief introduction to the integer quantum Hall effect (IQ

Maciej Demianowicz, Kajetan Vogtt, Remigiusz Augusiak

We introduce a class of entangled subspaces: completely entangled subspaces of entanglement depth $k$ ($k$-CESs). These are subspaces of multipartite Hilbert spaces containing only pure states with an entanglement depth of at least $k$. We present an efficient construction of $k$-CESs of any achievable dimensionality in any multipartite scenario. Further, we

P. Bodewei, E. Şaşıoğlu, N. F. Hinsche, I. Mertig

Cold metals have recently gained attention as a promising platform for innovative devices, such as tunnel diodes with negative differential resistance (NDR) and field-effect transistors with subthreshold swings below the thermionic limit. Recently discovered two-dimensional (2D) MA$_2$Z$_4$ (M = Ti, Zr, Hf, Nb, Ta; A = Si, Ge; Z = N, P) compounds exhibit bot

Anirban Roy, Nicholas Battaglia

Line intensity mapping (LIM) serves as a potent probe in astrophysics, relying on the statistical analysis of integrated spectral line emissions originating from distant star-forming galaxies. While LIM observations hold the promise of achieving a broad spectrum of scientific objectives, a significant hurdle for future experiments lies in distinguishing the

Leonard Wossnig, Norbert Furtmann, Andrew Buchanan, Sandeep Kumar

Over the past 40 years, the discovery and development of therapeutic antibodies to treat disease has become common practice. However, as therapeutic antibody constructs are becoming more sophisticated (e.g., multi-specifics), conventional approaches to optimisation are increasingly inefficient. Machine learning (ML) promises to open up an in silico route to

Ryan P. Creedon, Huy Q. Nguyen, W. A. Strauss

A Stokes wave is a traveling free-surface periodic water wave that is constant in the direction transverse to the direction of propagation. In 1981 McLean discovered via numerical methods that Stokes waves at infinite depth are unstable with respect to transverse perturbations of the initial data. Even for a Stokes wave that has very small amplitude $\vareps

Wiem Khlifi, Siddarth Singh, Omayma Mahjoub, Ruan de Kock

Cooperative multi-agent reinforcement learning (MARL) has made substantial strides in addressing the distributed decision-making challenges. However, as multi-agent systems grow in complexity, gaining a comprehensive understanding of their behaviour becomes increasingly challenging. Conventionally, tracking team rewards over time has served as a pragmatic me

Natalia Ożegalska-Łukasik, Szymon Łukasik

In the contemporary interconnected world, the concept of cultural responsibility occupies paramount importance. As the lines between nations become less distinct, it is incumbent upon individuals, communities, and institutions to assume the responsibility of safeguarding and valuing the landscape of diverse cultures that constitute our global society. This p

Omayma Mahjoub, Ruan de Kock, Siddarth Singh, Wiem Khlifi

Measuring the contribution of individual agents is challenging in cooperative multi-agent reinforcement learning (MARL). In cooperative MARL, team performance is typically inferred from a single shared global reward. Arguably, among the best current approaches to effectively measure individual agent contributions is to use Shapley values. However, calculatin

Ed Bennett, Jack Holligan, Deog Ki Hong, Jong-Wan Lee

We report the findings of our extensive study of the spectra of flavoured mesons in lattice gauge theories with symplectic gauge group and fermion matter content treated in the quenched approximation. For the $Sp(4)$, $Sp(6)$, and $Sp(8)$ gauge groups, the (Dirac) fermions transform in either the fundamental, or the 2-index, antisymmetric or symmetric, repre

Frederick B. Davies, Sarah E. I. Bosman, Prakash Gaikwad, Fahad Nasir

The variations in Ly$\alpha$ forest opacity observed at $z>5.3$ between lines of sight to different background quasars are too strong to be caused by fluctuations in the density field alone. The leading hypothesis for the cause of this excess variance is a late, ongoing reionization process at redshifts below six. Another model proposes strong ionizing backg

Siddarth Singh, Omayma Mahjoub, Ruan de Kock, Wiem Khlifi

Establishing sound experimental standards and rigour is important in any growing field of research. Deep Multi-Agent Reinforcement Learning (MARL) is one such nascent field. Although exciting progress has been made, MARL has recently come under scrutiny for replicability issues and a lack of standardised evaluation methodology, specifically in the cooperativ

Yi Tan, Brenden Roberts, Nathanan Tantivasadakarn, Beni Yoshida

We explore a deep connection between fracton order and product codes. In particular, we propose and analyze conditions on classical seed codes which lead to fracton order in the resulting quantum product codes. Depending on the properties of the input codes, product codes can realize either Type-I or Type-II fracton models, in both nonlocal and local constru

Ahmed Abdeljawad, Thomas Dittrich

Approximation capabilities of shallow neural networks (SNNs) form an integral part in understanding the properties of deep neural networks (DNNs). In the study of these approximation capabilities some very popular classes of target functions are the so-called spectral Barron spaces. This spaces are of special interest when it comes to the approximation of pa

Sam J. Beckers, Colin M. Poppelaars, Veronica S. Ulibarrena, Tjarda C. N. Boekholt

Located at the core of the Galactic Centre, the S-star cluster serves as a remarkable illustration of chaos in dynamical systems. The long-term chaotic behaviour of this system can be studied with gravitational $N$-body simulations. By applying a small perturbation to the initial position of star S5, we can compare the evolution of this system to its unpertu

Shivangi Aneja, Justus Thies, Angela Dai, Matthias Nießner

We introduce FaceTalk, a novel generative approach designed for synthesizing high-fidelity 3D motion sequences of talking human heads from input audio signal. To capture the expressive, detailed nature of human heads, including hair, ears, and finer-scale eye movements, we propose to couple speech signal with the latent space of neural parametric head models

Vasilis Mpisketzis, Raphaël Duqué, Antonios Nathanail, Alejandro Cruz-Osorio

Binary neutron stars mergers widely accepted as potential progenitors of short gamma-ray bursts. After the remnant of the merger has collapsed to a black hole, a jet is powered and may breakout from the the matter expelled during the collision and the subsequent wind emission. The interaction of the jet with the ejecta may affect its dynamics and the resulti

Joachim Kopp, Toby Opferkuch

A neutron star harbors of order $10^{56}$ electrons in its core, and almost the same number of muons, with muon decay prohibited by Pauli blocking. However, as macroscopic properties of the star such as its mass, rotational velocity, or magnetic field evolve over time, the equilibrium lepton abundances (dictated by the weak interactions) change as well. Scen

Kazem Bitaghsir Fadafan, Giacomo Cacciapaglia

The Swampland program, which looks for low energy theories consistent with quantum gravity, has led to the introduction of a dark dimension stemming from the cosmological constant. We show that the same argument leads to the emergence of the electroweak scale, once the dark dimension is realised in a warped background. A second warped extra dimension at the