October 2023 arXiv papers — page 141

Antonio Sanna, Tiago F. T. Cerqueira, Yue-Wen Fang, Ion Errea

The primary challenge in the field of high-temperature superconductivity in hydrides is to achieve a superconducting state at ambient pressure rather than the extreme pressures that have been required in experiments so far. Here, we propose a family of compounds, of composition Mg$_2$XH$_6$ with X$=$Rh, Ir, Pd, or Pt, that achieves this goal. These materials

Yu Zhou, Yunqiu Han, Hanyu Zhou, Yulun Wu

Recent advances in general purpose pre-trained language models have shown great potential in commonsense reasoning. However, current works still perform poorly on standard commonsense reasoning benchmarks including the Com2Sense Dataset. We argue that this is due to a disconnect with current cutting-edge machine learning methods. In this work, we aim to brid

T. J. Davidge

The properties of the interacting, eccentric orbit binary V1507 Cyg (HD187399) are examined with spectra that cover wavelengths from 0.63 to 0.68um. The spectrum of the brightest star is very similar to that of the B8 I star Beta Ori, although with absorption lines that show sub-structure consistent with a varying tidal field. The bulk of the Halpha emission

Luis H. Carnevale, Panagiotis E. Theodorakis

MARTINI is a popular coarse-grained force-field that is mainly used in molecular dynamics (MD) simulations. It is based on the ``Lego'' approach where intermolecular interactions between coarse-grained beads representing chemical units of different polarity are obtained through water--octanol partition coefficients. This enables the simulation of a wide rang

Qikang Zhang

Digital life, a form of life generated by computer programs or artificial intelligence systems, it possesses self-awareness, thinking abilities, emotions, and subjective consciousness. Achieving it involves complex neural networks, multi-modal sensory integration [1, 2], feedback mechanisms, and self-referential processing [3]. Injecting prior knowledge into

The Viet Bui, Tien Mai, Thanh Hong Nguyen

This paper concerns imitation learning (IL) (i.e, the problem of learning to mimic expert behaviors from demonstrations) in cooperative multi-agent systems. The learning problem under consideration poses several challenges, characterized by high-dimensional state and action spaces and intricate inter-agent dependencies. In a single-agent setting, IL has prov

Jaganmohan Chandrasekaran, Tyler Cody, Nicola McCarthy, Erin Lanus

Machine learning (ML) - based software systems are rapidly gaining adoption across various domains, making it increasingly essential to ensure they perform as intended. This report presents best practices for the Test and Evaluation (T&E) of ML-enabled software systems across its lifecycle. We categorize the lifecycle of ML-enabled software systems into thre

Hirokazu Maruoka

In this paper, it is demonstrated that there is a parallelism between the relational interpretation of Rovelli and the interpretation of soft matter based on intermediate asymptotics. The general interpretation of physics strongly assumes the duality of the observer and the world, and the uniqueness of the world though the relational interpretation suggested

Martin Bojowald, Erick I. Duque

In its canonical formulation, general relativity is subject to gauge transformations that are equivalent to space-time coordinate changes of general covariance only when the gauge generators, given by the Hamiltonian and diffeomorphism constraints, vanish. Since the specific form taken by Poisson brackets of the constraints and of the gauge transformations a

Janka Biznárová, Amr Osman, Emil Rehnman, Lert Chayanun

We demonstrate aluminum-on-silicon planar transmon qubits with time-averaged ${T_1}$ energy relaxation times of up to ${270\,\mu s}$, corresponding to Q = 5 million, and a highest observed value of ${501\,\mu s}$. We use materials analysis techniques and numerical simulations to investigate the dominant sources of energy loss, and devise and demonstrate a st

Vikas Soni, Kinsuk Acharyya

Variations in atmospheric elemental nitrogen can considerably affect the abundance of major nitrogen-bearing species such as NH$_3$ and HCN. Also, due to vertical mixing and photochemistry, their abundance deviates from the thermochemical equilibrium. The goal of this study is to understand the effect of atmospheric metallicity on the composition of NH$_3$,

Md Ali Muntaha, Sourav Chatterjee, Sophie Blondel, Larry Aagesen

In this work, we quantify the impact of grain boundary (GB) and surface diffusion on fission gas bubble evolution and fission gas release in UO$_2$ nuclear fuel using simulations with a hybrid phase field/cluster dynamics model. We begin with a comprehensive literature review of uranium vacancy and xenon atom diffusivity in UO$_2$ through the bulk, along GBs

Siddhant Agarwal, Ishan Durugkar, Peter Stone, Amy Zhang

Goal-Conditioned Reinforcement Learning (RL) problems often have access to sparse rewards where the agent receives a reward signal only when it has achieved the goal, making policy optimization a difficult problem. Several works augment this sparse reward with a learned dense reward function, but this can lead to sub-optimal policies if the reward is misalig

Stefan Stojanovic, Yassir Jedra, Alexandre Proutiere

We study matrix estimation problems arising in reinforcement learning (RL) with low-rank structure. In low-rank bandits, the matrix to be recovered specifies the expected arm rewards, and for low-rank Markov Decision Processes (MDPs), it may for example characterize the transition kernel of the MDP. In both cases, each entry of the matrix carries important i

Domenico Zito, Tan Doan Nhut

This paper reports an inductorless transimpedance amplifier (TIA) with very compact size and adequate performance for spin qubit readout operations in monolithic quantum processors. The TIA has been designed and fabricated in a 22nm FDSOI CMOS foundry technology commercially available. The measurement results show a transimpedance gain of 103 dB{\Omega} with

Ilya Volkov, Nikita Ustimenko, Danil Kornovan, Roman Savelev

The optically trapped ensembles of atoms provide a versatile platform for storing and coherent manipulation of quantum information. However, efficient realization of quantum information processing requires long-lived quantum states protected from the decoherence e.g. via spontaneous emission. Here, we theoretically study collective dipolar oscillations in fi

C. Ricardo Constante-Amores, Alec J. Linot, Michael D. Graham

Data-driven approximations of the Koopman operator are promising for predicting the time evolution of systems characterized by complex dynamics. Among these methods, the approach known as extended dynamic mode decomposition with dictionary learning (EDMD-DL) has garnered significant attention. Here we present a modification of EDMD-DL that concurrently deter

Shotaro Yagishita, Shummin Nakayama

This paper proposes new proximal Newton-type methods with a diagonal metric for solving composite optimization problems whose objective function is the sum of a twice continuously differentiable function and a proper closed directionally differentiable function. Although proximal Newton-type methods using diagonal metrics have been shown to be superior to th

Sergio Fabiani, Fiamma Capitanio, Rosario Iaria, Juri Poutanen

We report on the coordinated observations of the neutron star low-mass X-ray binary (NS-LMXB) \gx in X-rays (IXPE, NICER, Nustar and INTEGRAL), optical (REM and LCO), near-infrared (REM), mid-infrared (VLT VISIR), and radio (ATCA). This Z-source was observed by \IXPE twice in March-April 2023 (Obs. 1 and 2). In the radio band, the source was detected, but on

Aaron Anderson

We develop a theory of generically stable and smooth Keisler measures in NIP metric theories, generalizing the case of classical logic. Using smooth extensions, we verify that fundamental properties of (Borel)-definable measures and the Morley product hold in the NIP metric setting. With these results, we prove that as in discrete logic, generic stability ca

Keiran Paster, Marco Dos Santos, Zhangir Azerbayev, Jimmy Ba

There is growing evidence that pretraining on high quality, carefully thought-out tokens such as code or mathematics plays an important role in improving the reasoning abilities of large language models. For example, Minerva, a PaLM model finetuned on billions of tokens of mathematical documents from arXiv and the web, reported dramatically improved performa

Jun Yin, Shiyin Shen, Lei Hao

The metallicity enrichment history (MEH) of a galaxy is determined by its star formation history (SFH) and the gas cycling process. In this paper, we construct a chemical evolution model that is regulated by the SFH of the system. In this SFH-regulated model, the evolution of all other variables, including the MEH, can be determined by the SFH. We test this

Ariyan Javanpeykar, Steven Lu, Ruiran Sun, Kang Zuo

We establish a finiteness result for pointed maps to the base space $U$ of a smooth projective family of varieties with maximal variation in moduli. For its proof, we establish the rigidity of pointed maps to a (not necessarily compact) variety which is hyperbolic modulo a proper closed subset. Together with Viehweg's hyperbolicity conjecture on the bigness

Nicholas Layden, Alan Coley, Dipanjan Dey

We investigate spherically symmetric static and dynamical Brans-Dicke theory exact solutions using invariants and, in particular, the Newman Penrose formalism utilizing Cartan scalars. The GR limit of these solutions is examined through the use of Cartan invariants via the Cartan-Karlhede algorithm and is additionally supported by analysis of scalar polynomi

Bingjie Wang, Joel Leja, Hakim Atek, Ivo Labbe

The era of the James Webb Space Telescope ushers stellar population models into uncharted territories, particularly at the high-redshift frontier. In a companion paper, we apply the \texttt{Prospector} Bayesian framework to jointly infer galaxy redshifts and stellar population properties from broad-band photometry as part of the UNCOVER survey. Here we prese

Diego Marques, Marcelo Oliveira, Pavel Trojovsk\' y

In this paper, among other things, we explicit a $G_{\delta}$-dense set of Liouville numbers, for which the triple power tower of any of its elements is a transcendental number.

R. Mosayebi, H. Kia, A. Kianpour Raki

The paper introduces Supervised Embedding and Clustering Anomaly Detection (SEMC-AD), a method designed to efficiently identify faulty alarm logs in a mobile network and alleviate the challenges of manual monitoring caused by the growing volume of alarm logs. SEMC-AD employs a supervised embedding approach based on deep neural networks, utilizing historical

Allison Woodruff, Renee Shelby, Patrick Gage Kelley, Steven Rousso-Schindler

Generative AI is expected to have transformative effects in multiple knowledge industries. To better understand how knowledge workers expect generative AI may affect their industries in the future, we conducted participatory research workshops for seven different industries, with a total of 54 participants across three US cities. We describe participants' ex

Jacob Chmura, Hasham Burhani, Xiao Qi Shi

Sparse reward environments are known to be challenging for reinforcement learning agents. In such environments, efficient and scalable exploration is crucial. Exploration is a means by which an agent gains information about the environment. We expand on this topic and propose a new intrinsic reward that systemically quantifies exploratory behavior and promot

S. Hudan, H. Desilets, Rohit Kumar, R. T. deSouza

Fusion enhancement for neutron-rich isotopes of oxygen on carbon nuclei was probed. To measure the fusion cross-section a $^{20}$O beam accelerated to E$_{lab}$/A=2.7 MeV bombarded the active-target detector MuSIC@Indiana with a fill gas of CH$_4$. Examination of the average fusion cross-section over the interval 12 MeV $\leq$E$_{c.m.}$$\leq$ 17 MeV for $^{1

Robert G. Felsted, Jaehun Chun, Gregory K. Schenter, Alexander B. Bard

Rigorous understanding of the self-assembly of colloidal nanocrystals is crucial to the development of tailored nanostructured materials. Despite extensive studies, a mechanistic understanding of self-assembly under non-equilibrium driven by an external field remains an ongoing challenge. We demonstrate self-assembly by optical tweezers imposing an external

Junsheng Zhou, Jinsheng Wang, Baorui Ma, Yu-Shen Liu

Scaling up representations for images or text has been extensively investigated in the past few years and has led to revolutions in learning vision and language. However, scalable representation for 3D objects and scenes is relatively unexplored. In this work, we present Uni3D, a 3D foundation model to explore the unified 3D representation at scale. Uni3D us

S G Bishop, J K Cannon, H B Yagci, R N Clark

Solid-state quantum light sources are being intensively investigated for applications in quantum technology. A key challenge is to extract light from host materials with high refractive index, where efficiency is limited by refraction and total internal reflection. Here we show that an index-matched solid immersion lens can, if placed sufficiently close to t

Christopher A. Choquette-Choo, Krishnamurthy Dvijotham, Krishna Pillutla, Arun Ganesh

Differentially private learning algorithms inject noise into the learning process. While the most common private learning algorithm, DP-SGD, adds independent Gaussian noise in each iteration, recent work on matrix factorization mechanisms has shown empirically that introducing correlations in the noise can greatly improve their utility. We characterize the a

Carlos E. Jimenez, John Yang, Alexander Wettig, Shunyu Yao

Language models have outpaced our ability to evaluate them effectively, but for their future development it is essential to study the frontier of their capabilities. We find real-world software engineering to be a rich, sustainable, and challenging testbed for evaluating the next generation of language models. To this end, we introduce SWE-bench, an evaluati

Christopher C. Hogan, Jason Murphy

We consider the dynamics of a boosted soliton evolving under the cubic NLS with an external potential. We show that for sufficiently large velocities, the soliton is effectively transmitted through the potential. This result extends work of Holmer, Marzuola, and Zworski, who considered the case of a delta potential with no bound states, and work of Datchev a

Luca Griguolo, Jacopo Papalini, Lorenzo Russo, Domenico Seminara

Significant progresses have been made recently in understanding the spectral form factor of Jackiw-Teitelboim gravity, particularly at late times where non-perturbative effects are expected to play a dominant role. By focusing on a peculiar regime of large time and fixed temperature, called $\tau$-scaling limit, it was found that it is possible to analytical

Federico Girotti, Alfred Godley, Mădălin Guţă

We revisit the problem of estimating an unknown parameter of a pure quantum state, and investigate `null-measurement' strategies in which the experimenter aims to measure in a basis that contains a vector close to the true system state. Such strategies are known to approach the quantum Fisher information for models where the quantum Cram\'{e}r-Rao bound is a

Jordi Hernández

We classify special self-birational transformations of the smooth quadric threefold and fourfold, $Q^3$ and $Q^4$. It turns out that there is only one such example in each dimension. In the case of $Q^3$, it is given by the linear system of quadrics passing through a rational normal quartic curve. In the case of $Q^4$, it is given by the linear system of cub

Wonseok Kang, Jaehyun Kim, Jiseong Chung, Seungwon Choi

We propose a novel approach to Graduated Non-Convexity (GNC) and demonstrate its efficacy through its application in robust pose graph optimization, a key component in SLAM backends. Traditional GNC methods often rely on heuristic methods for GNC schedule, updating control parameter {\mu} for escalating the non-convexity. In contrast, our approach leverages

Francis M. Tyers, Nicholas Howell

In this demo paper we present OmniLingo, an architecture for distributing data for listening- and speaking-based language learning applications and a demonstration client built using the architecture. The architecture is based on the Interplanetary Filesystem (IPFS) and puts at the forefront user sovereignty over data.

Qizhi Pei, Kaiyuan Gao, Lijun Wu, Jinhua Zhu

Modeling the interaction between proteins and ligands and accurately predicting their binding structures is a critical yet challenging task in drug discovery. Recent advancements in deep learning have shown promise in addressing this challenge, with sampling-based and regression-based methods emerging as two prominent approaches. However, these methods have

Xiao Wang, Yuansen Zhang, Tianze Chen, Songyang Gao

Aligned large language models (LLMs) demonstrate exceptional capabilities in task-solving, following instructions, and ensuring safety. However, the continual learning aspect of these aligned LLMs has been largely overlooked. Existing continual learning benchmarks lack sufficient challenge for leading aligned LLMs, owing to both their simplicity and the mode

Florence Fauquant-Millet

Let $\mathfrak p$ be a proper parabolic subalgebra of a simple Lie algebra $\mathfrak g$. Writing $\mathfrak p=\mathfrak r\oplus \mathfrak m$, with $\mathfrak r$ being the Levi factor of $\mathfrak p$ and $\mathfrak m$ the nilpotent radical of $\mathfrak p$, we may consider the semi-direct product $\tilde\mathfrak p=\mathfrak r\ltimes(\mathfrak m)^a$ where $

Isidoros Iakovidis, Nicola Arcozzi

Random forests are notable learning algorithms first introduced by Breinman in 2001, they are widely used for classification and regression tasks and their mathematical properties are under ongoing research. We consider a specific class of random forest algorithms related to kernel methods, the so-called KeRF (Kernel Random Forests.) In particular, we invest

Tinka Bruneau, Michael F. Whittaker

A brief history of planar aperiodic tile sets is presented, starting from the Domino Problem proposed by Hao Wang in 1961. We provide highlights that led to the discovery of the Taylor--Socolar aperiodic monotile in 2010 and the Hat and Spectre aperiodic monotiles in 2023. The Spectre tile is an amazingly simple monotile; a single tile whose translated and r

Mohannad Alhanahnah, Philipp Schubert, Thomas Reps, Somesh Jha

Researchers have recently devised tools for debloating software and detecting configuration errors. Several of these tools rely on the observation that programs are composed of an initialization phase followed by a main-computation phase. Users of these tools are required to manually annotate the boundary that separates these phases, a task that can be time-

A. Covelo-Paz, N. Themeßl, F. Espinoza-Rojas, S. Hekker

Context. Stars that are members of stellar clusters are assumed to be formed at the same time and place from material with the same initial chemical composition. These additional constraints on the ensemble of cluster stars make these stars suitable as benchmarks. Aims. We aimed 1) to identify previously unknown red giants in the open clusters NGC 6791 and N

Yiting Chen, Zhanpeng Zhou, Junchi Yan

The behavior of neural networks still remains opaque, and a recently widely noted phenomenon is that networks often achieve similar performance when initialized with different random parameters. This phenomenon has attracted significant attention in measuring the similarity between features learned by distinct networks. However, feature similarity could be v

Philipp Strasberg, Joseph Schindler

Within the many worlds interpretation (MWI) it is believed that, as time passes on, the linearity of the Schr\"odinger equation together with decoherence generate an exponentially growing tree of branches where "everything happens", provided the branches are defined for a decohering basis. By studying an example, using exact numerical diagonalization of the

Guodong Sun, Francois Baccelli, Ke Feng, Luis Uzeda Garcia

The reconfigurable intelligent surface (RIS) technology allows one to engineer spatial diversity in complex cellular networks. This paper provides a framework for the system-level performance assessment of RIS-assisted networks and in particular downlink coverage probability and ergodic rate. To account for the inherent randomness in the spatial deployments

Dongming Wu, Jiahao Chang, Fan Jia, Yingfei Liu

Topology reasoning aims to comprehensively understand road scenes and present drivable routes in autonomous driving. It requires detecting road centerlines (lane) and traffic elements, further reasoning their topology relationship, i.e., lane-lane topology, and lane-traffic topology. In this work, we first present that the topology score relies heavily on de

Felipe Tellez, Jorge Ortiz

This paper presents a comparative analysis between the Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), two vital artificial intelligence algorithms, focusing on optimizing Elliptic Curve Cryptography (ECC) parameters. These encompass the elliptic curve coefficients, prime number, generator point, group order, and cofactor. The study provides in

Baichuan Huang, Jingjin Yu, Siddarth Jain

In this paper, we explore the dynamic grasping of moving objects through active pose tracking and reinforcement learning for hand-eye coordination systems. Most existing vision-based robotic grasping methods implicitly assume target objects are stationary or moving predictably. Performing grasping of unpredictably moving objects presents a unique set of chal

Soumyaranjan Khuntia, Wageesh Mishra, Sudheer K Mishra, Yuming Wang

Coronal Mass Ejections (CMEs) are the most energetic expulsions of magnetized plasma from the Sun that play a crucial role in space weather dynamics. This study investigates the diverse kinematics and thermodynamic evolution of two CMEs (CME1: 2011 September 24 and CME2: 2018 August 20) at coronal heights where thermodynamic measurements are limited. The pea

Bang C. Huynh, Meilani Wibowo-Teale, Andrew M. Wibowo-Teale

Symmetry provides a powerful machinery to classify, interpret, and understand quantum-mechanical theories and results. However, most contemporary quantum chemistry packages lack the ability to handle degeneracy and symmetry breaking effects, especially in non-Abelian groups, nor are they able to characterize symmetry in the presence of external magnetic or e

G. Shankar, Joseph Maciejko

The Dirac spin liquid (DSL) is a two-dimensional (2D) fractionalized Mott insulator featuring massless Dirac spinon excitations coupled to a compact $U(1)$ gauge field, which allows for flux-tunneling instanton events described by magnetic monopoles in (2+1)D Euclidean spacetime. The state-operator correspondence of conformal field theory has been used recen

S. Paradiso, M. DiMarco, M. Chen, G. McGee

Despite increasingly precise observations and sophisticated theoretical models, the discrepancy between measurements of H0 from the cosmic microwave background or from Baryon Acoustic Oscillations combined with Big-Bang Nucleosynthesis versus those from local distance ladder probes -- commonly known as the $H_0$ tension -- continues to perplex the scientific

Ying Wu, Hanzhong Liu, Kai Ren, Shujie Ma

Interpretability plays a crucial role in the application of statistical learning to estimate heterogeneous treatment effects (HTE) in complex diseases. In this study, we leverage a rule-based workflow, namely causal rule learning (CRL), to estimate and improve our understanding of HTE for atrial septal defect, addressing an overlooked question in the previou

Erik Bates, Youngtak Sohn

The Potts spin glass is a generalization of the Sherrington--Kirkpatrick (SK) model that allows for spins to take more than two values. Based on a novel synchronization mechanism, Panchenko (2018) showed that the limiting free energy is given by a Parisi-type variational formula. The functional order parameter in this formula is a probability measure on a mo

Wangbo Yu, Li Yuan, Yan-Pei Cao, Xiangjun Gao

Recent advances in diffusion models have enabled 3D generation from a single image. However, current methods often produce suboptimal results for novel views, with blurred textures and deviations from the reference image, limiting their practical applications. In this paper, we introduce HiFi-123, a method designed for high-fidelity and multi-view consistent

Marc Rußwurm, Konstantin Klemmer, Esther Rolf, Robin Zbinden

Learning representations of geographical space is vital for any machine learning model that integrates geolocated data, spanning application domains such as remote sensing, ecology, or epidemiology. Recent work embeds coordinates using sine and cosine projections based on Double Fourier Sphere (DFS) features. These embeddings assume a rectangular data domain

Liam Cregg, Fady Alajaji, Serdar Yuksel

We study the problem of zero-delay coding for the transmission of a Markov source over a noisy channel with feedback and present a reinforcement learning solution which is guaranteed to achieve near-optimality. To this end, we formulate the problem as a Markov decision process (MDP) where the state is a probability-measure valued predictor/belief and the act

Anirudh Sivakumar, Pankaj Kumar Mishra, Ahmad A. Hujeirat, Paulsamy Muruganandam

We investigate the scaling of the energy cascade in a harmonically trapped, turbulent, rotating Bose-Einstein condensate (BEC) in two dimensions. We achieve turbulence by injecting a localized perturbation into the condensate and gradually increasing its rotation frequency from an initial value to a maximum. The main characteristics of the resulting turbulen

J. K. Cannon, S. G. Bishop, J. P. Hadden, H. B. Yagci

Color centers in wide-bandgap semiconductors are a promising class of solid-state quantum light source, many of which operate at room temperature. We examine a family of color centers in aluminum nitride, which emits close to 620 nm. We present a technique to rapidly map an ensemble of these single photon emitters, identifying all emitters, not just those wi

Ece Ozkan, Xavier Boix

Current machine learning methods for medical image analysis primarily focus on developing models tailored for their specific tasks, utilizing data within their target domain. These specialized models tend to be data-hungry and often exhibit limitations in generalizing to out-of-distribution samples. In this work, we show that employing models that incorporat

Evan Craft, Mark Gonzalez, Kyle Lee, Bianka Meçaj

Splitting functions are universal functions describing the collinear dynamics of gauge theories, and as such are crucial ingredients for a wide variety of calculations in perturbative QCD. We present analytic results for the triple collinear splitting functions in QCD with a single massive parton. We derive the splitting functions using two distinct methods;

Lei Yi, Ivan Girotto, Federico Toschi, Chao Sun

Catastrophic phase inversion, the sudden breakdown of a dense emulsion, occurs when the dispersed majority phase irreversibly exchanges role with the continuous minority phase. This common process has been extensively studied over the past decades and yet its fundamental physical mechanism has remained largely unexplored. Here we experimentally and numerical

Alexander Mclean Marshall, Michael Andrew McCann, Mitesh Patel, Konstantinos A. Petridis

We present a method that maximises the experimental sensitivity to new physics contributions in $B^\pm\to\pi^\pm\mu^+\mu^-$ decays. This method relies on performing an unbinned maximum likelihood fit to both the measured dimuon $q^2$ distribution of $B^\pm\to\pi^\pm\mu^+\mu^-$ decays, and theory calculations at spacelike $q^2$, where QCD predictions are most

Hailiang Liu, Levon Nurbekyan, Xuping Tian, Yunan Yang

We propose an adaptive step size with an energy approach for a suitable class of preconditioned gradient descent methods. We focus on settings where the preconditioning is applied to address the constraints in optimization problems, such as the Hessian-Riemannian and natural gradient descent methods. More specifically, we incorporate these preconditioned gra

Rafael Martínez Márquez, Giuseppe Patanè

Urban mobility forecast and analysis can be addressed through grid-based and graph-based models. However, graph-based representations have the advantage of more realistically depicting the mobility networks and being more robust since they allow the implementation of Graph Theory machinery, enhancing the analysis and visualisation of mobility flows. We defin

E. Huesca Santiago, K. D. de Vries, P. Allison, J. Beatty

To probe the cosmic particle flux at the highest energies, large volumes of dense material like ice have to be monitored. This can be achieved by exploiting the radio signal. In this work, we provide a macroscopic model to predict the radar echo signatures found when a radio signal is reflected from a cosmic-ray or neutrino-induced particle cascade propagati

Sebastian Belkner, Julien Carron, Louis Legrand, Caterina Umiltà

The tightest constraints on the tensor-to-scalar ratio $r$ can only be obtained after removing a substantial fraction of the lensing $B$-mode sample variance. The planned CMB-S4 experiment will remove the lensing $B$-mode signal internally by reconstructing the gravitational lenses from high-resolution observations. We document here a first lensing reconstru

Anjeza Krakulli, Elton Pasku

The theory of fuzzy semigroups is a branch of mathematics that arose in early 90's as an effort to characterize properties of semigroups by the properties of their fuzzy subsystems which include, fuzzy subsemigroups and their alike, fuzzy one (resp. two) sided ideals, fuzzy quasi-ideals, fuzzy bi-ideals etc. To be more precise, a fuzzy subsemigroup of a give

Alberto Cobos Rabano, Etienne Mann, Cristina Manolache, Renata Picciotto

Given $\mathfrak{F}$ a coherent sheaf on a Noetherian integral algebraic stack $\mathfrak{P}$, we give two constructions of stacks $\widetilde{\mathfrak{P}}$, equipped with birational morphisms $p:\widetilde{\mathfrak{P}}\to \mathfrak{P}$ such that $p^*\mathfrak{F}$ is simpler: in the Rossi construction, the torsion free part of $p^*\mathfrak{F}$ is locally

Antonio Pedro Santos Alves, Marcos Kalinowski, Görkem Giray, Daniel Mendez

Systems that use Machine Learning (ML) have become commonplace for companies that want to improve their products and processes. Literature suggests that Requirements Engineering (RE) can help address many problems when engineering ML-enabled systems. However, the state of empirical evidence on how RE is applied in practice in the context of ML-enabled system

Julia R. Rogers, Gergő Nikolényi, Mohammed AlQuraishi

Numerous cellular functions rely on protein$\unicode{x2013}$protein interactions. Efforts to comprehensively characterize them remain challenged however by the diversity of molecular recognition mechanisms employed within the proteome. Deep learning has emerged as a promising approach for tackling this problem by exploiting both experimental data and basic b

Arash Khalvan, Amirhossein Zali, Mahmoud Ahmadian Attari

Due to the weakness of public key cryptosystems encounter of quantum computers, the need to provide a solution was emerged. The McEliece cryptosystem and its security equivalent, the Niederreiter cryptosystem, which are based on Goppa codes, are one of the solutions, but they are not practical due to their long key length. Several prior attempts to decrease

Andrés Chirre

We provide explicit bounds in the theory of the Riemann zeta-function at the line $\Re{s}=1$, assuming that the Riemann hypothesis holds until the height $T$. In particular, we improve some bounds, in finite regions, for the logarithmic derivative and the reciprocal of the Riemann zeta-function.

L. A. Riley, D. T. Simms, L. T. Baby, A. L. Conley

We performed a measurement of the $^{52}$Cr$(d,p)^{53}$Cr reaction at 16 MeV using the Florida State University Super-Enge Split-Pole Spectrograph (SE-SPS) and observed 26 states. While all of the states observed here had been seen in previous $(d,p)$ experiments, we changed five $L$ assignments from those reported previously and determined $L$ values for ni

Benjamin Boys, Mark Girolami, Jakiw Pidstrigach, Sebastian Reich

Diffusion generative models unlock new possibilities for inverse problems as they allow for the incorporation of strong empirical priors in scientific inference. Recently, diffusion models are repurposed for solving inverse problems using Gaussian approximations to conditional densities of the reverse process via Tweedie's formula to parameterise the mean, c

Clément Dombry, Simone A. Padoan, Stefano Rizzelli

The Peaks Over Threshold (POT) method is the most popular statistical method for the analysis of univariate extremes. Even though there is a rich applied literature on Bayesian inference for the POT, the asymptotic theory for such proposals is missing. Even more importantly, the ambitious and challenging problem of predicting future extreme events according

Renato Huzak, Kristian Uldall Kristiansen, Goran Radunović

In this paper we define the notion of slow divergence integral along sliding segments in regularized planar piecewise smooth systems. The boundary of such segments may contain diverse tangency points. We show that the slow divergence integral is invariant under smooth equivalences. This is a natural generalization of the notion of slow divergence integral al

Andrei Lerner, Kangwei Li, Sheldy Ombrosi, Israel P. Rivera-Ríos

In a recent work by Cruz-Uribe et al. was obtained that \[|\{x\in{\mathbb{R}^d}:w(x)|G(fw^{-1})(x)|>\alpha\}|\lesssim\frac{[w]_{A_1}^2}{\alpha}\int_{{\mathbb{R}^d}}|f|dx\] both in the matrix and scalar settings, where $G$ is either the Hardy-Littlewood maximal function or any Calder\'on-Zygmund operator. In this note we show that the quadratic dependence on

Anouk Zandbergen, Tycho van Noorden, Alexander Heinlein

Computational fluid dynamics (CFD) simulations of viscous fluids described by the Navier-Stokes equations are considered. Depending on the Reynolds number of the flow, the Navier-Stokes equations may exhibit a highly nonlinear behavior. The system of nonlinear equations resulting from the discretization of the Navier-Stokes equations can be solved using nonl

Daniele Colosi, Robert Oeckl, Adamantia Zampeli

We demonstrate that the recently introduced evanescent particles of a massive scalar field can be emitted and absorbed by an Unruh-DeWitt detector. In doing so the particles carry away from or deposit on the detector a quantized amount of energy, in a manner quite analogous to ordinary propagating particles. In contradistinction to propagating particles the

Tiezhi Wang, Nils Strodthoff

Scoring sleep stages in polysomnography recordings is a time-consuming task plagued by significant inter-rater variability. Therefore, it stands to benefit from the application of machine learning algorithms. While many algorithms have been proposed for this purpose, certain critical architectural decisions have not received systematic exploration. In this s

Shenglai Zeng, Yaxin Li, Jie Ren, Yiding Liu

Large language models (LLMs) have shown great capabilities in various tasks but also exhibited memorization of training data, raising tremendous privacy and copyright concerns. While prior works have studied memorization during pre-training, the exploration of memorization during fine-tuning is rather limited. Compared to pre-training, fine-tuning typically

Tong Yuan, Jian Yang, Zeyi Wen

Although existing machine learning-based methods for traffic accident analysis can provide good quality results to downstream tasks, they lack interpretability which is crucial for this critical problem. This paper proposes an interpretable framework based on Bayesian Networks for traffic accident prediction. To enable the ease of interpretability, we design

Dorothea Strecker, Heinz Pampel, Rouven Schabinger, Nina Leonie Weisweiler

Currently, there is limited research investigating the phenomenon of research data repositories being shut down, and the impact this has on the long-term availability of data. This paper takes an infrastructure perspective on the preservation of research data by using a registry to identify 191 research data repositories that have been closed and presenting

Chen Xu, Yun-Bin Zhao, Zhipeng Lu, Ye Zhang

We design a new iterative algorithm, called REINFORCE-OPT, for solving a general type of optimization problems. This algorithm parameterizes the solution search rule and iteratively updates the parameter using a reinforcement learning (RL) algorithm resembling REINFORCE. To gain a deeper understanding of the RL-based methods, we show that REINFORCE-OPT essen

Alvaro Cauderan, Gauthier Boeshertz, Florian Schwarb, Calvin Zhang

We present an algorithm that learns to imitate expert behavior and can transfer to previously unseen domains without retraining. Such an algorithm is extremely relevant in real-world applications such as robotic learning because 1) reward functions are difficult to design, 2) learned policies from one domain are difficult to deploy in another domain and 3) l

Brian Knaeble

Spurious association between X and Y may be due to a confounding variable W. Statisticians may adjust for W using a variety of techniques. This paper presents the results of simulations conducted to assess the performance of those techniques under various, elementary, data-generating processes. The results indicate that no technique is best overall and that

Christian Tomani, David Vilar, Markus Freitag, Colin Cherry

Maximum-a-posteriori (MAP) decoding is the most widely used decoding strategy for neural machine translation (NMT) models. The underlying assumption is that model probability correlates well with human judgment, with better translations getting assigned a higher score by the model. However, research has shown that this assumption does not always hold, and ge

Toshiki Yasuda, Yudai Suzuki, Tomoyuki Kubota, Kohei Nakajima

Reservoir computing is a machine learning framework that uses artificial or physical dissipative dynamics to predict time-series data using nonlinearity and memory properties of dynamical systems. Quantum systems are considered as promising reservoirs, but the conventional quantum reservoir computing (QRC) models have problems in the execution time. In this

José M. Espinar, Diego A. Marín

We consider the eigenvalue problem $\Delta^{\mathbb{S}^2} \xi + 2 \xi=0 $ in $ \Omega $ and $\xi = 0 $ along $ \partial \Omega $, being $\Omega$ the complement of a disjoint and finite union of smooth and bounded simply connected regions in the two-sphere $\mathbb{S}^2$. Imposing that $|\nabla \xi|$ is locally constant along $\partial \Omega$ and that $\xi$

Juan P. Mendez, Denis Mamaluy

The precise positioning of dopants in semiconductors using scanning tunneling microscopes has led to the development of planar dopant-based devices, also known as $\delta$-layers, facilitating the exploration of new concepts in classical and quantum computing. Recently it have been shown that two distinct conductivity regimes (low- and high- bias regimes) ex

Youcef Remil, Anes Bendimerad, Romain Mathonat, Chedy Raissi

Automatic crash bucketing is a crucial phase in the software development process for efficiently triaging bug reports. It generally consists in grouping similar reports through clustering techniques. However, with real-time streaming bug collection, systems are needed to quickly answer the question: What are the most similar bugs to a new one?, that is, effi

Piyush Singh Pasi, Karthikeya Battepati, Preethi Jyothi, Ganesh Ramakrishnan

The problem of audio-to-text alignment has seen significant amount of research using complete supervision during training. However, this is typically not in the context of long audio recordings wherein the text being queried does not appear verbatim within the audio file. This work is a collaboration with a non-governmental organization called CARE India tha

Hiromi Ebisu, Masazumi Honda, Taiichi Nakanishi

Due to the recent studies of the fracton topological phases, which host deconfined quasi-particle excitations with mobility restrictions, the concept of symmetries have been updated. Focusing on one of such new symmetries, multipole symmetries, including global, dipole, and quadruple symmetries, and gauge fields associated with them, we construct a new sets