October 2022 arXiv papers — page 44

Sachin Kasture, Oleksandr Kyriienko, Vincent E. Elfving

Quantum Generative Modelling (QGM) relies on preparing quantum states and generating samples from these states as hidden - or known - probability distributions. As distributions from some classes of quantum states (circuits) are inherently hard to sample classically, QGM represents an excellent testbed for quantum supremacy experiments. Furthermore, generati

Taoli Cheng

The application of machine learning in sciences has seen exciting advances in recent years. As a widely applicable technique, anomaly detection has been long studied in the machine learning community. Especially, deep neural nets-based out-of-distribution detection has made great progress for high-dimensional data. Recently, these techniques have been showin

Zhaopeng Dou, Zhongdao Wang, Weihua Chen, Yali Li

Current person image retrieval methods have achieved great improvements in accuracy metrics. However, they rarely describe the reliability of the prediction. In this paper, we propose an Uncertainty-Aware Learning (UAL) method to remedy this issue. UAL aims at providing reliability-aware predictions by considering data uncertainty and model uncertainty simul

Chaitanya Malaviya, Sudeep Bhatia, Mark Yatskar

Cognitive psychologists have documented that humans use cognitive heuristics, or mental shortcuts, to make quick decisions while expending less effort. While performing annotation work on crowdsourcing platforms, we hypothesize that such heuristic use among annotators cascades on to data quality and model robustness. In this work, we study cognitive heuristi

Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi

We introduce a state-of-the-art real-time, high-fidelity, audio codec leveraging neural networks. It consists in a streaming encoder-decoder architecture with quantized latent space trained in an end-to-end fashion. We simplify and speed-up the training by using a single multiscale spectrogram adversary that efficiently reduces artifacts and produce high-qua

Yury Malyshkin, Maksim Zhukovskii

Since the paper of Kleinberg and Kleinberg, SODA'05, where it was proven that the preferential attachment random graph with degeneracy at least 3 does not obey the first order 0-1 law, no general methods were developed to study logical limit laws for recursive random graph models with arbitrary degeneracy. Even in the (possibly) simplest case of the uniform

Malcolm Fairbairn, Juan Urrutia, Ville Vaskonen

Gravitational lensing of gravitational waves provides a potential new probe of dark matter structures. In this work, we consider the microlensing effect on gravitational wave signals from black hole binaries induced by low-mass dark matter halos that do not retain enough baryonic matter to hold stars. We clarify systematically when this microlensing effect i

Mengjiao Yang, Dale Schuurmans, Pieter Abbeel, Ofir Nachum

Future- or return-conditioned supervised learning is an emerging paradigm for offline reinforcement learning (RL), where the future outcome (i.e., return) associated with an observed action sequence is used as input to a policy trained to imitate those same actions. While return-conditioning is at the heart of popular algorithms such as decision transformer

Carlos García-García, David Alonso, Pedro G. Ferreira, Boryana Hadzhiyska

An accurate calibration of the source redshift distribution $p(z)$ is a key aspect in the analysis of cosmic shear data. This, one way or another, requires the use of spectroscopic or high-quality photometric samples. However, the difficulty to obtain colour-complete spectroscopic samples matching the depth of weak lensing catalogs means that the analyses of

Zinan Zhao, Yingjie Chen, Xueyan Hu, Ronghao Bao

Dielectric elastomers (DEs) are a type of multifunctional materials with salient features that are very attractive in developing soft, lightweight, and small-scale transducers and robotics. This paper reviews the mechanics of soft DE structures, focusing on their dynamic responses, including vibrations and waves in typical structures. The basic formulations

Hao Liu, Xinyang Geng, Lisa Lee, Igor Mordatch

Large language models (LLM) trained using the next-token-prediction objective, such as GPT3 and PaLM, have revolutionized natural language processing in recent years by showing impressive zero-shot and few-shot capabilities across a wide range of tasks. In this work, we propose a simple technique that significantly boosts the performance of LLMs without addi

Hao Liu, Lisa Lee, Kimin Lee, Pieter Abbeel

Humans are excellent at understanding language and vision to accomplish a wide range of tasks. In contrast, creating general instruction-following embodied agents remains a difficult challenge. Prior work that uses pure language-only models lack visual grounding, making it difficult to connect language instructions with visual observations. On the other hand

Jared Miller, Tianyu Dai, Mario Sznaier

This work presents a sum-of-squares (SOS) based framework to perform data-driven stabilization and robust control tasks on discrete-time linear systems where the full-state observations are corrupted by L-infinity bounded input, measurement, and process noise (error in variable setting). Certificates of state-feedback superstability, quadratic stability or p

Cheryne Jonay, Joaquin F. Rodriguez-Nieva, Vedika Khemani

Random quantum circuits are paradigmatic models of minimally structured and analytically tractable chaotic dynamics. We study a family of Floquet unitary circuits with Haar random $U(1)$ charge conserving dynamics; the minimal such model has nearest-neighbor gates acting on spin 1/2 qubits, and a single layer of even/odd gates repeated periodically in time.

Amira Ibrahim El-Desokey

In this paper, I outline several conceptual and methodological issues related to modeling individual and group processes embedded in clustered/hierarchical data structures. We position multilevel modeling techniques within a broader set of univariate and multivariate methods commonly used to study different types of data structures. We then illustrate how th

Zhaoqi Leng, Shuyang Cheng, Benjamin Caine, Weiyue Wang

Data augmentation is an important technique to improve data efficiency and save labeling cost for 3D detection in point clouds. Yet, existing augmentation policies have so far been designed to only utilize labeled data, which limits the data diversity. In this paper, we recognize that pseudo labeling and data augmentation are complementary, thus propose to l

Alfonso M. Ganan-Calvo

The average Rayleigh capillary breakup length of a cylindrical Newtonian viscous liquid jet moving with homogeneous velocity $\hat{U}$ (negligible external forces) must be determined by the selection of normal modes with time-independent amplitude and wavelength (invariant modes, IMs). Both positive and negative group velocity IMs exist in ample ranges of th

Yimin Luo, Mengyang Gu, Minwook Park, Xinyi Fang

The ability of cells to reorganize in response to external stimuli is important in areas ranging from morphogenesis to tissue engineering. Elongated cells can co-align due to steric effects, forming states with local order. We show that molecular-scale substrate anisotropy can direct cell organization, resulting in the emergence of nematic order on tissue sc

Christian Schroeder de Witt, Samuel Sokota, J. Zico Kolter, Jakob Foerster

Steganography is the practice of encoding secret information into innocuous content in such a manner that an adversarial third party would not realize that there is hidden meaning. While this problem has classically been studied in security literature, recent advances in generative models have led to a shared interest among security and machine learning rese

Yingjie Chen, Bin Wu, Michel Destrade, Weiqiu Chen

The operating frequency range of passive topological phononic crystals is generally fixed and narrow, limiting their practical applications. To overcome this difficulty, here we design and investigate a one-dimensional soft dielectric phononic crystal (PC) plate system with actively tunable topological interface states via the mechanical and electric loads.

Sara Mohammad-Taheri, Vartika Tewari, Rohan Kapre, Ehsan Rahiminasab

Estimating a causal query from observational data is an essential task in the analysis of biomolecular networks. Estimation takes as input a network topology, a query estimation method, and observational measurements on the network variables. However, estimations involving many variables can be experimentally expensive, and computationally intractable. Moreo

Jason Aebischer, Gino Isidori, Marko Pesut, Ben A. Stefanek

In light of new data we present an updated phenomenological analysis of the simplified $U_1$-leptoquark model addressing charged-current $B$-meson anomalies. The analysis shows a good compatibility of low-energy data (dominated by the lepton flavor universality ratios $R_D$ and $R_{D^*}$) with the high-energy constraints posed by $pp\to \tau\bar\tau$ Drell-Y

Mohatashem Reyaz Makhdoomi, Vivek Muralidharan, Juan Sandoval, Miguel Olivares-Mendez

In robotic manipulation, end-effector compliance is an essential precondition for performing contact-rich tasks, such as machining, assembly, and human-robot interaction. Most robotic arms are position-controlled stiff systems at a hardware level. Thus, adding compliance becomes essential. Compliance in those systems has been recently achieved using Forward

Ana Aleksandric, Sayak Saha Roy, Shirin Nilizadeh

Online toxic attacks, such as harassment, trolling, and hate speech have been linked to an increase in offline violence and negative psychological effects on victims. In this paper, we studied the impact of toxicity on users' online behavior. We collected a sample of 79.8k Twitter conversations. Then, through a longitudinal study, for nine weeks, we tracked

Shohini Bhattacharya, Yoshitaka Hatta, Werner Vogelsang

Inspired by recent work by Tarasov and Venugopalan, we calculate the one-loop quark box diagrams relevant to polarized and unpolarized Deep Inelastic Scattering (DIS) by introducing off-forward momentum $l^\mu$ as an infrared regulator. In the polarized case, we rederive the pole $1/l^2$ related to the axial (chiral) anomaly. In addition, we obtain the usual

Eriko Hironaka, Chi Cheuk Tsang

We show that given a fully-punctured pseudo-Anosov map $f:S \to S$ whose punctures lie in at least two orbits under the action of $f$, the expansion factor $\lambda(f)$ satisfies the inequality $\lambda(f)^{|\chi(S)|} \ge \mu^4 \approx 6.85408$, where $\mu = \frac{1 + \sqrt{5}}{2} \approx 1.61803$ is the golden ratio. The proof involves a study of standardly

Joshua Albrecht, Abraham J. Fetterman, Bryden Fogelman, Ellie Kitanidis

Despite impressive successes, deep reinforcement learning (RL) systems still fall short of human performance on generalization to new tasks and environments that differ from their training. As a benchmark tailored for studying RL generalization, we introduce Avalon, a set of tasks in which embodied agents in highly diverse procedural 3D worlds must survive b

Dario Coscia, Laura Meneghetti, Nicola Demo, Giovanni Stabile

Convolutional Neural Network (CNN) is one of the most important architectures in deep learning. The fundamental building block of a CNN is a trainable filter, represented as a discrete grid, used to perform convolution on discrete input data. In this work, we propose a continuous version of a trainable convolutional filter able to work also with unstructured

David Helminiak, Hang Hu, Julia Laskin, Dong Hye Ye

Mass Spectrometry Imaging (MSI), using traditional rectilinear scanning, takes hours to days for high spatial resolution acquisitions. Given that most pixels within a sample's field of view are often neither relevant to underlying biological structures nor chemically informative, MSI presents as a prime candidate for integration with sparse and dynamic sampl

Quercus Hernández, Alberto Badías, Francisco Chinesta, Elías Cueto

The imminent impact of immersive technologies in society urges for active research in real-time and interactive physics simulation for virtual worlds to be realistic. In this context, realistic means to be compliant to the laws of physics. In this paper we present a method for computing the dynamic response of (possibly non-linear and dissipative) deformable

C. P. Dullemond, A. Ziampras, D. Ostertag, C. Dominik

Context: Recent observations with the Atacama Large Millimeter Array (ALMA) have shown that the large dust aggregates observed at millimeter wavelengths settle to the midplane into a remarkably thin layer. Aims: We intend to find out if the geometric thinness of these layers is evidence against the vertical shear instability (VSI) operating in these disks. M

Gergely Harcos, Kannan Soundararajan

Let $L/K$ be a Galois extension of number fields with Galois group $G$. We show that if the density of prime ideals in $K$ that split totally in $L$ tends to $1/|G|$ with a power saving error term, then the density of prime ideals in $K$ whose Frobenius is a given conjugacy class $C\subset G$ tends to $|C|/|G|$ with the same power saving error term. We deduc

Zhiyu Liu, Yongbin Ruan

We prove a conjectural vanishing result for Gopakumar--Vafa invariants of quintic 3-folds, referred to as Castelnuovo bound in the literature. Furthermore, we calculate Gopakumar--Vafa invariants at Castelnuovo bound $g=\frac{d^2+5d+10}{10}$. As physicists showed, these two properties allow us to compute all Gromov--Witten invariants of quintic 3-folds up to

Samuel Anyaso-Samuel, Somnath Datta

Informative cluster size (ICS) arises in situations with clustered data where a latent relationship exists between the number of participants in a cluster and the outcome measures. Although this phenomenon has been sporadically reported in statistical literature for nearly two decades now, further exploration is needed in certain statistical methodologies to

Diego Taglienti, Fabio Guglietta, Mauro Sbragaglia

We propose an extension of the Maffettone-Minale (MM) model to predict the dynamics of an ellipsoidal droplet in a shear flow. The parameters of the MM model are traditionally retrieved in the framework of the perturbation theory for small deformations, i.e., small capillary numbers ($\mbox{Ca} \ll 1$) applied to Stokes equations. In this work, we take a nov

Tharathep Plienbumrung, Maria Daghofer, Michael T. Schmid, Andrzej M. Oleś

An effective two-dimensional two-band model for infinite-layer nickelates consists of bands obtained from $d_{x^2-y^2}$ and $s$--like orbitals. We investigate whether it could be mapped onto a single-band Hubbard model and the filling of Hubbard bands. We find that both one-band physics and a Kondo-lattice regime emerge from the same two-orbital model, depen

Lorenzo Cazon, Rúben Conceição, Felix Riehn

In extensive air shower experiments, the number of muons crossing a detector at a given position, as well as their arrival time, arrival direction, and energy, are determined by a more fundamental 3-dimensional distribution linked to the hadronic core of the shower. Muons are produced high up in the atmosphere after the decay of mesons in the hadronic cascad

Qian Xu, Guo Zheng, Yu-Xin Wang, Peter Zoller

We propose an autonomous quantum error correction scheme using squeezed cat (SC) code against the dominant error source, excitation loss, in continuous-variable systems. Through reservoir engineering, we show that a structured dissipation can stabilize a two-component SC while autonomously correcting the errors. The implementation of such dissipation only re

Yongki Lee

We extend the wave breaking condition in Seliger's work [Proc. R. Soc. Lond. Ser. A., 303 (1968)], which has been used widely to prove wave breaking phenomena for nonlinear nonlocal shallow water equations.

Swati Jindal, Roberto Manduchi

Self-supervised learning (SSL) has become prevalent for learning representations in computer vision. Notably, SSL exploits contrastive learning to encourage visual representations to be invariant under various image transformations. The task of gaze estimation, on the other hand, demands not just invariance to various appearances but also equivariance to the

Chaoyi Pan, Marion Lepert, Shenli Yuan, Rika Antonova

In this paper, we present a method to manipulate unknown objects in-hand using tactile sensing without relying on a known object model. In many cases, vision-only approaches may not be feasible; for example, due to occlusion in cluttered spaces. We address this limitation by introducing a method to reorient unknown objects using tactile sensing. It increment

Yi Chung

A new heavy $Z'$ vector boson provides a possible explanation for the neutral current B anomalies and points to the scale where we expect the solution to the hierarchy problem to appear. In this paper, we explore a modified Top Seesaw model based on the global symmetry breaking of $U(3)_L$ to $U(2)_L$. The symmetry is partially gauged such that the breaking

Xue-Yong Fu, Cheng Chen, Md Tahmid Rahman Laskar, Shayna Gardiner

Entity-level sentiment analysis predicts the sentiment about entities mentioned in a given text. It is very useful in a business context to understand user emotions towards certain entities, such as products or companies. In this paper, we demonstrate how we developed an entity-level sentiment analysis system that analyzes English telephone conversation tran

Lingxiao Li, Qiang Liu, Anna Korba, Mikhail Yurochkin

Sampling from a target measure whose density is only known up to a normalization constant is a fundamental problem in computational statistics and machine learning. In this paper, we present a new optimization-based method for sampling called mollified interaction energy descent (MIED). MIED minimizes a new class of energies on probability measures called mo

Camellia Zakaria, Pin Sym Foong, Chang Siang Lim, Pavithren V. S. Pakianathan

Digital contact tracing can limit the spread of infectious diseases. Nevertheless, there remain barriers to attaining sufficient adoption. In this study, we investigate how willingness to participate in contact tracing is affected by two critical factors: the modes of data collection and the type of data collected. We conducted a scenario-based survey study

Antoine Bannier, Benoit Laslier

Critical models are, almost by definition, supposed to feature both slow decay of correlations for local observables while retaining some mixing even for macroscopic observables. A strong version of the latter property is that changing boundary conditions cannot have a singular (in the measure theoretic sense) effect on the model away from the boundary, even

Christoph Lüscher, Mohammad Zeineldeen, Zijian Yang, Tina Raissi

Language barriers present a great challenge in our increasingly connected and global world. Especially within the medical domain, e.g. hospital or emergency room, communication difficulties and delays may lead to malpractice and non-optimal patient care. In the HYKIST project, we consider patient-physician communication, more specifically between a German-sp

Haozhe Jiang, Qiwen Cui, Zhihan Xiong, Maryam Fazel

This paper investigates when one can efficiently recover an approximate Nash Equilibrium (NE) in offline congestion games. The existing dataset coverage assumption in offline general-sum games inevitably incurs a dependency on the number of actions, which can be exponentially large in congestion games. We consider three different types of feedback with decre

Kishen N. Gowda, Thomas Pensyl, Aravind Srinivasan, Khoa Trinh

The current best approximation algorithms for $k$-median rely on first obtaining a structured fractional solution known as a bi-point solution, and then rounding it to an integer solution. We improve this second step by unifying and refining previous approaches. We describe a hierarchy of increasingly-complex partitioning schemes for the facilities, along wi

Trishen S. Gunaratnam, Dmitrii Krachun, Christoforos Panagiotis

We prove the existence of a tricritical point for the Blume-Capel model on $\mathbb{Z}^d$ for every $d\geq 2$. The proof in $d\geq 3$ relies on a novel combinatorial mapping to an Ising model on a larger graph, the techniques of Aizenman, Duminil-Copin, and Sidoravicious (Comm. Math. Phys, 2015), and the celebrated infrared bound. In $d=2$, the proof relies

Steven Bethard

Modern neural network libraries all take as a hyperparameter a random seed, typically used to determine the initial state of the model parameters. This opinion piece argues that there are some safe uses for random seeds: as part of the hyperparameter search to select a good model, creating an ensemble of several models, or measuring the sensitivity of the tr

Mehrdad T Kiani, Quynh P Sam, Gangtae Jin, Betül Pamuk

Reduced dimensionality leads to emergent phenomena in quantum materials and there is a need for accelerated materials discovery of nanoscale quantum materials in reduced dimensions. Thermomechanical nanomolding is a rapid synthesis method that produces high quality single-crystalline quantum nanowires with controlled dimensions over wafer-scale sizes. Herein

Kwabena Amponsah-Kaakyire, Daria Pylypenko, Josef van Genabith, Cristina España-Bonet

Recent work has shown that neural feature- and representation-learning, e.g. BERT, achieves superior performance over traditional manual feature engineering based approaches, with e.g. SVMs, in translationese classification tasks. Previous research did not show $(i)$ whether the difference is because of the features, the classifiers or both, and $(ii)$ what

Li Kevin Wenliang

Score matching (SM) is a convenient method for training flexible probabilistic models, which is often preferred over the traditional maximum-likelihood (ML) approach. However, these models are less interpretable than normalized models; as such, training robustness is in general difficult to assess. We present a critical study of existing variational SM objec

Matthew Bendel, Rizwan Ahmad, Philip Schniter

In image recovery problems, one seeks to infer an image from distorted, incomplete, and/or noise-corrupted measurements. Such problems arise in magnetic resonance imaging (MRI), computed tomography, deblurring, super-resolution, inpainting, phase retrieval, image-to-image translation, and other applications. Given a training set of signal/measurement pairs,

Camellia Zakaria, Gizem Yilmaz, Priyanka Mammen, Michael Chee

The availability of commercial wearable trackers equipped with features to monitor sleep duration and quality has enabled more useful sleep health monitoring applications and analyses. However, much research has reported the challenge of long-term user retention in sleep monitoring through these modalities. Since modern Internet users own multiple mobile dev

Lorenzo Lupo, Marco Dinarelli, Laurent Besacier

A straightforward approach to context-aware neural machine translation consists in feeding the standard encoder-decoder architecture with a window of consecutive sentences, formed by the current sentence and a number of sentences from its context concatenated to it. In this work, we propose an improved concatenation approach that encourages the model to focu

Sergey Goncharov, Stefan Milius, Lutz Schröder, Stelios Tsampas

Compositionality proofs in higher-order languages are notoriously involved, and general semantic frameworks guaranteeing compositionality are hard to come by. In particular, Turi and Plotkin's bialgebraic abstract GSOS framework, which has been successfully applied to obtain off-the-shelf compositionality results for first-order languages, so far does not ap

Shahab Asoodeh, Huanyu Zhang

We investigate the contraction properties of locally differentially private mechanisms. More specifically, we derive tight upper bounds on the divergence between $PK$ and $QK$ output distributions of an $\epsilon$-LDP mechanism $K$ in terms of a divergence between the corresponding input distributions $P$ and $Q$, respectively. Our first main technical resul

Maad Ebrahim, Abdelhakim Hafid

The advent of Cloud Computing enabled the proliferation of IoT applications for smart environments. However, the distance of these resources makes them unsuitable for delay-sensitive applications. Hence, Fog Computing has emerged to provide such capabilities in proximity to end devices through distributed resources. These limited resources can collaborate to

Yiying Yang, Xinhang Li, Zheng Yuan, Qinwen Wang

The multi-vehicle pursuit (MVP), as a problem abstracted from various real-world scenarios, is becoming a hot research topic in Intelligent Transportation System (ITS). The combination of Artificial Intelligence (AI) and connected vehicles has greatly promoted the research development of MVP. However, existing works on MVP pay little attention to the importa

Jurgis Pasukonis, Timothy Lillicrap, Danijar Hafner

Intelligent agents need to remember salient information to reason in partially-observed environments. For example, agents with a first-person view should remember the positions of relevant objects even if they go out of view. Similarly, to effectively navigate through rooms agents need to remember the floor plan of how rooms are connected. However, most benc

Kenneth Li, Aspen K. Hopkins, David Bau, Fernanda Viégas

Language models show a surprising range of capabilities, but the source of their apparent competence is unclear. Do these networks just memorize a collection of surface statistics, or do they rely on internal representations of the process that generates the sequences they see? We investigate this question by applying a variant of the GPT model to the task o

Richard Dodson, Maria J. Rioja

We report on the development of new code to support the beam waveguide antenna mount types in AIPS, which will allow polarisation analysis of observations made using these antennas. Beam Wave-guide antennas in VLBI are common in communication antennas that have been repurposed (e.g. Warkworth, Yamaguchi). The mount type affects the differential phase between

Conor J. Benson, Daniel J. Scheeres

Spin state predictions for defunct satellites in geosynchronous earth orbit (GEO) are valuable for active debris removal and servicing missions as well as material shedding studies and attitude-dependent solar radiation pressure (SRP) modeling. Previous studies have shown that solar radiation torques can explain the observed spin state evolution of some GEO

Paola Boito, Roberto Grena

Four new centrality measures for directed networks based on unitary, continuous-time quantum walks (CTQW) in $n$ dimensions -- where $n$ is the number of nodes -- are presented, tested and discussed. The main idea behind these methods consists in re-casting the classical HITS and PageRank algorithms as eigenvector problems for symmetric matrices, and using t

Maonan Wang, Yutong Xu, Xi Xiong, Yuheng Kan

Traffic signal control has the potential to reduce congestion in dynamic networks. Recent studies show that traffic signal control with reinforcement learning (RL) methods can significantly reduce the average waiting time. However, a shortcoming of existing methods is that they require model retraining for new intersections with different structures. In this

Juyoung Lee, Sanghyuk Lee

We investigate $L^p$ boundedness of the maximal function defined by the averaging operator $f\to \mathcal{A}_t^s f$ over the two-parameter family of tori $\mathbb{T}_t^{s}:=\{ ( (t+s\cos\theta)\cos\phi,\,(t+s\cos\theta)\sin\phi,\, s\sin\theta ): \theta, \phi \in [0,2\pi) \}$ with $c_0t>s>0$ for some $c_0\in (0,1)$. We prove that the associated (two-parameter

Simon R Davies, Richard Macfarlane, William J. Buchanan

Ransomware is a malicious class of software that utilises encryption to implement an attack on system availability. The target's data remains encrypted and is held captive by the attacker until a ransom demand is met. A common approach used by many crypto-ransomware detection techniques is to monitor file system activity and attempt to identify encrypted fil

Antoine Abram, Christophe Reutenauer, Franco Saliola

We construct a complete system of primitive orthogonal idempotents and give an explicit quiver presentation of the monoid algebra of the stylic monoid introduced by Abram and Reutenauer [arXiv:2106.06556].

Joachim Wabnig, Josef Resch, Dominik Theuerkauf, Fabian Anmasser

Within the model of topological particles (MTP) we determine the interaction energy of monopole pairs, sources and sinks of a Coulombic field. The monopoles are represented by topological solitons of finite size and mass, described by a field without any divergences. We fix the soliton centres in numerical calculations at varying distance. Due to the finite

Haanvid Lee, Jongmin Lee, Yunseon Choi, Wonseok Jeon

We consider local kernel metric learning for off-policy evaluation (OPE) of deterministic policies in contextual bandits with continuous action spaces. Our work is motivated by practical scenarios where the target policy needs to be deterministic due to domain requirements, such as prescription of treatment dosage and duration in medicine. Although importanc

Fábio S. Bemfica, Marcelo M. Disconzi, Jorge Noronha, Robert J. Scherrer

We investigate the out-of-equilibrium dynamics of viscous fluids in a spatially flat Friedmann-Lema\^itre-Robertson-Walker cosmology using the most general causal and stable viscous energy-momentum tensor defined at first order in spacetime derivatives. In this new framework a pressureless viscous fluid having density $\rho$ can evolve to an asymptotic futur

Yuan Gao, Yukai Gong, Victor Paredes, Ayonga Hereid

Controller design for bipedal walking on dynamic rigid surfaces (DRSes), which are rigid surfaces moving in the inertial frame (e.g., ships and airplanes), remains largely uninvestigated. This paper introduces a hierarchical control approach that achieves stable underactuated bipedal robot walking on a horizontally oscillating DRS. The highest layer of our a

Giovanni Vladilo

The abundance and biological role of potassium suggest that its unstable nuclide was present in all stages of terrestrial biogenesis. With its enhanced isotopic ratio in the Archean eon, $^{40}$K may have contributed to the special, perhaps unique, biogenetic conditions that were present in the primitive Earth. Compared to the U and Th radionuclides, $^{40}$

G. C. Jones, R. Maiolino, C. Circosta, J. Scholtz

Gaseous outflows are key phenomena in the evolution of galaxies, as they affect star formation (either positively or negatively), eject gas from the core or disk, and directly cause mixing of pristine and processed material. Active outflows may be detected through searches for broad spectral line emission or high-velocity gas, but it is also possible to dete

C. Lardo, M. Salaris, S. Cassisi, N. Bastian

Photometric investigations have revealed that Galactic globular clusters exhibit internal metallicity variations amongst the so-called first-population stars, until now considered to have a homogeneous initial chemical composition. This is not fully supported by the sparse spectroscopic evidence, which so far gives conflicting results. Here, we present a hig

Hochul Hwang, Tim Xia, Ibrahima Keita, Ken Suzuki

A robot guide dog has compelling advantages over animal guide dogs for its cost-effectiveness, potential for mass production, and low maintenance burden. However, despite the long history of guide dog robot research, previous studies were conducted with little or no consideration of how the guide dog handler and the guide dog work as a team for navigation. T

Melanie Rupflin

We prove a sharp criterion on the decay of the tension of almost harmonic maps from degenerating surfaces that ensures that such maps subconverge to a limiting object that is made up entirely of harmonic maps.

Zhedong Zhang, Xiaoyu Nie, Dangyuan Lei, Shaul Mukame

We present a microscopic theory for nonlinear optical spectroscopy of N molecules in an optical cavity. A quantum Langevin analytical expression is derived for the time- and frequency-resolved signals accounting for arbitrary numbers of vibrational excitations. We identify clear signatures of the polariton-polaron interaction from multidimensional projection

C. J. Benson, D. J. Scheeres, M. Brozovic, S. Chesley

We explore the effects of the 2029 Earth encounter on asteroid (99942) Apophis' non-principal axis spin state, leveraging refined orbit, spin state, and inertia information provided by more recent optical and radar observations. Propagating the asteroids' coupled orbit and rigid body attitude dynamics through the flyby, we present the range of possible post-

Victor Grand, Baptiste Flipon, Alexis Gaillac, Marc Bernacki

Dynamic recrystallization is one of the main phenomena responsible for microstructure evolutions during hot forming. Consequently, getting a better understanding of DRX mechanisms and being able to predict them is crucial. This paper proposes a full-field numerical framework to predict the evolution of subgrain structures upon grain growth, continuous dynami

Junya Sato, Shoji Kido

Multi-organ segmentation enables organ evaluation, accounts the relationship between multiple organs, and facilitates accurate diagnosis and treatment decisions. However, only few models can perform segmentation accurately because of the lack of datasets and computational resources. On AMOS2022 challenge, which is a large-scale, clinical, and diverse abdomin

Marie Cassing, Alexander Brisebois, Muhammad Azeem, Jürgen Schaffner-Bielich

The generic properties of compact objects made of two different fluids of dark matter are studied in a scale invariant approach. We investigate compact objects with a core-shell structure, where the two fluids are separated, and with mixed dark matter components, where both dark matter fluids are immersed within each other. The constellations considered are

Chantal Amrhein, Barry Haddow

For real-life applications, it is crucial that end-to-end spoken language translation models perform well on continuous audio, without relying on human-supplied segmentation. For online spoken language translation, where models need to start translating before the full utterance is spoken, most previous work has ignored the segmentation problem. In this pape

Adam Mair, Kabe Moen, Yongming Wen

We prove new sufficient bump conditions for general iterated commutators of Calder\'on-Zygmund operators and fractional integral operators. As an application of our results we derive two weight compactness theorems for higher order iterated commutators with a $CMO$ function.

Huihong Shi, Haoran You, Yang Zhao, Zhongfeng Wang

Multiplication is arguably the most cost-dominant operation in modern deep neural networks (DNNs), limiting their achievable efficiency and thus more extensive deployment in resource-constrained applications. To tackle this limitation, pioneering works have developed handcrafted multiplication-free DNNs, which require expert knowledge and time-consuming manu

Richard Plesh, Peter Peer, Vitomir Štruc

We present GlassesGAN, a novel image editing framework for custom design of glasses, that sets a new standard in terms of image quality, edit realism, and continuous multi-style edit capability. To facilitate the editing process with GlassesGAN, we propose a Targeted Subspace Modelling (TSM) procedure that, based on a novel mechanism for (synthetic) appearan

Elisabeta Lusso, Lapo Casetti, Maurizio Pancrazzi, Marco Romoli

For the first time, an Italian University has the possibility to perform a multi-year observing campaign at a world-class telescope. This hands-on experience had a significant impact on the students' university path: from learning specific observing techniques on-site to teamwork and collaboration. In this paper we present the results of an observing campaig

Timo Hillmann, Fernando Quijandría

Noise-biased qubits are a promising route toward significantly reducing the hardware overhead associated with quantum error correction. The squeezed cat code, a non-local encoding in phase space based on squeezed coherent states, is an example of a noise-biased (bosonic) qubit with exponential error bias. Here we propose and analyze the error correction perf

Xinyi Wang, Mei-jen Lee, Qing Zhao, Lang Tong

We consider novelty detection in time series with unknown and nonparametric probability structures. A deep learning approach is proposed to causally extract an innovations sequence consisting of novelty samples statistically independent of all past samples of the time series. A novelty detection algorithm is developed for the online detection of novel change

Stephan Bröker, Jens Bickmann, Michael te Vrugt, Michael E. Cates

Applications of active particles require a method for controlling their dynamics. While this is typically achieved via direct interventions, indirect interventions based, e.g., on an orientation-dependent self-propulsion speed of the particles, become increasingly popular. In this work, we investigate systems of interacting active Brownian spheres in two spa

Mark Ibrahim, Diane Bouchacourt, Ari Morcos

Deep learning has led to remarkable advances in computer vision. Even so, today's best models are brittle when presented with variations that differ even slightly from those seen during training. Minor shifts in the pose, color, or illumination of an object can lead to catastrophic misclassifications. State-of-the art models struggle to understand how a set

Arshak Minasyan, Tigran Galstyan, Sona Hunanyan, Arnak Dalalyan

We consider the problem of finding the matching map between two sets of $d$-dimensional noisy feature-vectors. The distinctive feature of our setting is that we do not assume that all the vectors of the first set have their corresponding vector in the second set. If $n$ and $m$ are the sizes of these two sets, we assume that the matching map that should be r

Francisco J. Fernández-Polo, Jorge J. Garcés, Lei Li, Antonio M. Peralta

We introduce the Jordan-strict topology on the multipliers algebra of a JB$^*$-algebra, a notion which was missing despite the fourty years passed after the first studies on Jordan multipliers. In case that a C$^*$-algebra $A$ is regarded as a JB$^*$-algebra, the J-strict topology of $M(A)$ is precisely the well-studied C$^*$-strict topology. We prove that e

Sanchit Gandhi, Patrick von Platen, Alexander M. Rush

Speech recognition applications cover a range of different audio and text distributions, with different speaking styles, background noise, transcription punctuation and character casing. However, many speech recognition systems require dataset-specific tuning (audio filtering, punctuation removal and normalisation of casing), therefore assuming a-priori know

Christophe Alard

This paper analyse the properties of minimal solutions for the reconstruction of the lens potential in the singular perturbative approach. These minimal solutions corresponds to an expansion with a minimal degree in Fourier expansion of the perturbative fields. Using these minimal solutions prevent spurious physically meaningless terms in the reconstruction

Vasiliki Evdoridou, Adi Glücksam, Leticia Pardo-Simón

We introduce a new approximation technique into the context of complex dynamics that allows us to construct examples of transcendental entire functions with unbounded wandering domains. We provide examples of entire functions with an orbit of unbounded fast escaping wandering domains, answering a long-standing question of Rippon and Stallard. Moreover, these

Saikat Das, Soebur Razzaque

The brightest long gamma-ray burst detected so far by the Swift-BAT and Fermi-GBM telescopes, GRB~221009A, provides an unprecedented opportunity for understanding the high-energy processes in extreme transient phenomena. We find that the conventional leptonic models, synchrotron and synchrotron-self-Compton, for the afterglow emission from this source have d

Yuegao Liu, Chao Cai, Shengcai Zhu, Zhi Zheng

The search for new phases is an important direction in materials science. The phase transition of sulfides results in significant changes in catalytic performance, such as MoS2 and WS2. Cubic pentlandite [cPn, (Fe, Ni)9S8] can be a functional material in batteries, solar cells, and catalytic fields. However, no report about the material properties of other p