September 2022 arXiv papers — page 2

Michael S. Albergo, Eric Vanden-Eijnden

A generative model based on a continuous-time normalizing flow between any pair of base and target probability densities is proposed. The velocity field of this flow is inferred from the probability current of a time-dependent density that interpolates between the base and the target in finite time. Unlike conventional normalizing flow inference methods base

Matheus V. X. Ferreira, David C. Parkes

Trading on decentralized exchanges has been one of the primary use cases for permissionless blockchains with daily trading volume exceeding billions of U.S.~dollars. In the status quo, users broadcast transactions and miners are responsible for composing a block of transactions and picking an execution ordering -- the order in which transactions execute in t

Will Hide, Joe Thomas

We study the number of short geodesics and small eigenvalues on Weil-Petersson random genus zero hyperbolic surfaces with $n$ cusps in the regime $n\to\infty$. Inspired by work of Mirzakhani and Petri \cite{Mi.Pe19}, we show that the random multi-set of lengths of closed geodesics converges, after a suitable rescaling, to a Poisson point process with explici

Gian Marco Visani, Michael N. Pun, Arman Angaji, Armita Nourmohammad

Group-equivariant neural networks have emerged as a data-efficient approach to solve classification and regression tasks, while respecting the relevant symmetries of the data. However, little work has been done to extend this paradigm to the unsupervised and generative domains. Here, we present Holographic-(Variational) Auto Encoder (H-(V)AE), a fully end-to

Angelo Bratta, Avadesh Meduri, Michele Focchi, Ludovic Righetti

In legged logomotion, online trajectory optimization techniques generally depend on heuristic-based contact planners in order to have low computation times and achieve high replanning frequencies. In this work, we propose ContactNet, a fast acyclic contact planner based on a multi-output regression neural network. ContactNet ranks discretized stepping region

Rindranirina Ramamonjison, Haley Li, Timothy T. Yu, Shiqi He

We describe an augmented intelligence system for simplifying and enhancing the modeling experience for operations research. Using this system, the user receives a suggested formulation of an optimization problem based on its description. To facilitate this process, we build an intuitive user interface system that enables the users to validate and edit the su

William Dorrell, Peter E. Latham, Timothy E. J. Behrens, James C. R. Whittington

To afford flexible behaviour, the brain must build internal representations that mirror the structure of variables in the external world. For example, 2D space obeys rules: the same set of actions combine in the same way everywhere (step north, then south, and you won't have moved, wherever you start). We suggest the brain must represent this consistent

Jie Ren, Jiaming Luo, Yao Zhao, Kundan Krishna

Machine learning algorithms typically assume independent and identically distributed samples in training and at test time. Much work has shown that high-performing ML classifiers can degrade significantly and provide overly-confident, wrong classification predictions, particularly for out-of-distribution (OOD) inputs. Conditional language models (CLMs) are p

Rajenki Das, Mark Muldoon, Mark Lunt, John McBeth

It is well-known that mood and pain interact with each other, however individual-level variability in this relationship has been less well quantified than overall associations between low mood and pain. Here, we leverage the possibilities presented by mobile health data, in particular the "Cloudy with a Chance of Pain" study, which collected longitud

Connor Mooney, Yang Yang

We construct nonlinear entire anisotropic minimal graphs over $\mathbb{R}^4$, completing the solution to the anisotropic Bernstein problem. The examples we construct have a variety of growth rates, and our approach both generalizes to higher dimensions and recovers and elucidates known examples of entire minimal graphs over $\mathbb{R}^{n},\, n \geq 8$.

Gregory Ashton

Interferometric gravitational-wave observatories have opened a new era in astronomy. The rich data produced by an international network enables detailed analysis of the curved space-time around black holes. With nearly one hundred signals observed so far and thousands expected in the next decade, their population properties enable insights into stellar evolu

Stephen Brown, William L. Rodi, Marco Seracini, Chen Gu

We consider the application of machine learning to the evaluation of geothermal resource potential. A supervised learning problem is defined where maps of 10 geological and geophysical features within the state of Nevada, USA are used to define geothermal potential across a broad region. We have available a relatively small set of positive training sites (kn

Boris Springborn

We classify and enumerate all rational numbers with approximation constant at least $\frac{1}{3}$ using hyperbolic geometry. Rational numbers correspond to geodesics in the modular torus with both ends in the cusp, and the approximation constant measures how far they stay out of the cusp neighborhood in between. Compared to the original approach, the geometr

Christopher Couzens, Niall T. Macpherson, Achilleas Passias

We construct multiple embeddings of all solutions of $d=5$ minimal (un)gauged supergravity into massive Type IIA supergravity. The internal spaces and warpings of such embeddings are the same as those of the $\mathcal{N}=1$ supersymmetric (Mink$_5$) AdS$_5$ vacua, with the slight modification that the U(1) R-symmetry direction becomes fibered over the extern

Saray Arteaga, Ling-Yun Dai, Adolfo Guevara, Pablo Roig

We show the discrepancy between the isospin-rotated $e^+e^-\toηπ^-π^+$ cross-section -- measured by various collaborations -- and the Belle $τ^-\toηπ^-π^0ν_τ$ spectrum, which cannot be explained by heavy new physics non-standard interactions. We give for the first time the framework needed to study these beyond the standard model contributions in three-meson

Lyes Khacef, Philipp Klein, Matteo Cartiglia, Arianna Rubino

Understanding how biological neural networks carry out learning using spike-based local plasticity mechanisms can lead to the development of powerful, energy-efficient, and adaptive neuromorphic processing systems. A large number of spike-based learning models have recently been proposed following different approaches. However, it is difficult to assess if a

Luke McConnell

The LHCb detector at the LHC offers unique coverage of forward rapidities for studies of Central Exclusive Production (CEP) and soft QCD. CEP measurements allow the investigation of the nature of pomerons, and provide constraints on low-x gluon phenomenology, probing potential saturation effects. Moreover LHCb can test phenomenological models of soft QCD pro

Spyridon Dendrinos, Andrei Mustata, Marco Vitturi

We draw a connection between the affine invariant surface measures constructed by P. Gressman and the boundedness of a certain geometric averaging operator associated to surfaces of codimension $2$ and related to the Fourier Restriction Problem for such surfaces. For a surface given by $(ξ, Q_1(ξ), Q_2(ξ))$, with $Q_1,Q_2$ quadratic forms on $\mathbb{R}^d$,

Thorren Kirschbaum, Börries von Seggern, Joachim Dzubiella, Annika Bande

Nanodiamonds have a wide range of applications including catalysis, sensing, tribology and biomedicine. To leverage nanodiamond design via machine learning, we introduce the new dataset ND5k, consisting of 5,089 diamondoid and nanodiamond structures and their frontier orbital energies. ND5k structures are optimized via tight-binding density functional theory

Shuai Zhao, Linchao Zhu, Xiaohan Wang, Yi Yang

Self-supervised learning makes significant progress in pre-training large models, but struggles with small models. Mainstream solutions to this problem rely mainly on knowledge distillation, which involves a two-stage procedure: first training a large teacher model and then distilling it to improve the generalization ability of smaller ones. In this work, we

Yusuke Kimura, Hidetoshi Nishimori

Simulated quantum annealing is a generic classical protocol to simulate some aspects of quantum annealing and is sometimes regarded as a classical alternative to quantum annealing in finding the ground state of a classical Ising model. We derive a generic condition for simulated quantum annealing to converge to thermal equilibrium at a given, typically low,

Wei Tao, Ruilin Zhu, Zhen-Jun Xiao

We present the next-to-next-to-leading order (NNLO) QCD corrections to the decay constants for both the pseudoscalar and vector beauty-charmed mesons $B_{c}$ and $B^*_{c}$ in nonrelativistic QCD effective theory. Explicit NNLO calculation verified that the $B_c$ decay constant from pseudoscalar current is identical with the $B_c$ decay constant from axial-ve

Federico Vismara, Tommaso Benacchio

We introduce an extended discontinuous Galerkin discretization of hyperbolic-parabolic problems on multidimensional semi-infinite domains. Building on previous work on the one-dimensional case, we split the strip-shaped computational domain into a bounded region, discretized by means of discontinuous finite elements using Legendre basis functions, and an unb

Weishi Yuan, Jiaming Wang, Philip M. Singer, Rebecca W. Smaha

Kagome lattice Heisenberg antiferromagnets are known to be highly sensitive to perturbations caused by structural disorder. NMR is a local probe ideally suited for investigating such disorder-induced effects, but in practice large distributions in the conventional one-dimensional NMR data make it difficult to distinguish the intrinsic behavior expected for p

Ziyuan Qin, Huahui Yi, Qicheng Lao, Kang Li

The large-scale pre-trained vision language models (VLM) have shown remarkable domain transfer capability on natural images. However, it remains unknown whether this capability can also apply to the medical image domain. This paper thoroughly studies the knowledge transferability of pre-trained VLMs to the medical domain, where we show that well-designed med

D. Yu. Akimov, I. S. Alexandrov, R. R. Alyev, V. A. Belov

The RED-100 two-phase xenon emission detector has been deployed at 19-m distance from the reactor core of the Kalinin Nuclear Power Plant (KNPP) in 2021 - 2022 for investigation of the possibility to observe reactor antineutrinos using the effect of coherent elastic neutrino-nucleus scattering (CEνNS). The performance of the main systems of the RED-100 setup

R. T. K. Schock, J. Neuwald, W. Möckel, M. Kronseder

Molybdenum disulfide nanoribbons and nanotubes are quasi-1D semiconductors with strong spin-orbit interaction, a nanomaterial highly promising for quantum electronic applications. Here, it is demonstrated that a bismuth semimetal layer between the contact metal and this nanomaterial strongly improves the properties of the contacts. Two-point resistances on t

Oskar Kviman, Ricky Molén, Alexandra Hotti, Semih Kurt

In this paper, we show how the mixture components cooperate when they jointly adapt to maximize the ELBO. We build upon recent advances in the multiple and adaptive importance sampling literature. We then model the mixture components using separate encoder networks and show empirically that the ELBO is monotonically non-decreasing as a function of the number

Paul D. Nation, Matthew Treinish

We present a quantum circuit optimization technique that takes into account the variability in error rates that is inherent across present day noisy quantum computing platforms. This method can be run post qubit routing or post-compilation, and consists of computing isomorphic subgraphs to input circuits and scoring each using heuristic cost functions derive

Andreea Dogaru, Andrei Timotei Ardelean, Savva Ignatyev, Egor Zakharov

In recent years, neural distance functions trained via volumetric ray marching have been widely adopted for multi-view 3D reconstruction. These methods, however, apply the ray marching procedure for the entire scene volume, leading to reduced sampling efficiency and, as a result, lower reconstruction quality in the areas of high-frequency details. In this wo

Xiao Wang, Rohit Sharma, Petra Becker, Ladislav Bohatý

We present a study of high-quality BaCo$_2$(PO$_4$)$_2$ single crystals via magnetization, heat-capacity, thermal-expansion and magnetostriction measurements. Sharp anomalies in the thermodynamic properties at $T_N=3.4\,$K reveal a long-range antiferromagnetic order in these single-crystalline samples, which is absent in polycrystalline BaCo$_2$(PO$_4$)$_2$.

Sara Leardini, Yi Zhou, Andrea Tesi, Miguel Morales

Characterization of diamond-like carbon (DLC) coatings at cryogenic temperatures (down to 77 K) is presented, covering the electrical resistivity range of practical interest to gaseous and liquid particle instrumentation: 10^-1-10^5 Mohm/sq. The good behaviour observed in terms of linearity, surface uniformity and stability with time and transported charge a

Irina Vorontsova, Roman Goncharov, Angelina Tarabrina, Fedor Kiselev

A theoretical research and numerical simulation of the noise influence caused by spontaneous Raman scattering, four-wave mixing, and linear channel crosstalk on the performance of QKD systems was conducted. Three types of QKD systems were considered: coherent one-way (COW) QKD protocol, subcarrier-wave (SCW) QKD system, and continuous-variable (CV) QKD integ

Yuki Takezawa, Han Bao, Kenta Niwa, Ryoma Sato

SGD with momentum is one of the key components for improving the performance of neural networks. For decentralized learning, a straightforward approach using momentum is Distributed SGD (DSGD) with momentum (DSGDm). However, DSGDm performs worse than DSGD when the data distributions are statistically heterogeneous. Recently, several studies have addressed th

Kaoutar Daoud Hiri, Matjaž Hren, Tomaž Curk

Motivation: The rapid growth of metagenomics sequencing data makes metagenomics increasingly dependent on computational and statistical methods for fast and efficient analysis. Consequently, novel analysis tools for big-data metagenomics are constantly emerging. One of the biggest challenges for researchers occurs in the analysis planning stage: selecting th

Anil Batra, Shreyank N Gowda, Frank Keller, Laura Sevilla-Lara

Understanding the steps required to perform a task is an important skill for AI systems. Learning these steps from instructional videos involves two subproblems: (i) identifying the temporal boundary of sequentially occurring segments and (ii) summarizing these steps in natural language. We refer to this task as Procedure Segmentation and Summarization (PSS)

Stéphane Michoulier, Jean-François Gonzalez

In protoplanetary discs, the coagulation of dust grains into large aggregates still remains poorly understood. Grain porosity appears to be a promising solution to allow the grains to survive and form planetesimals. Furthermore, dust shattering has generally been considered to come only from collisional fragmentation; however, a new process was recently intr

Dingyi Shi, Fan Shang, Bingsheng Chen, Paul Expert

Clusters or communities can provide a coarse-grained description of complex systems at multiple scales, but their detection remains challenging in practice. Community detection methods often define communities as dense subgraphs, or subgraphs with few connections in-between, via concepts such as the cut, conductance, or modularity. Here we consider another p

Guoce Xin, Chen Zhang, Yue Zhou, Yueming Zhong

In this paper, we discover a new noncommutative algebra. We refer this algebra as the constant term algebra of type $A$, which is generated by certain constant term operators. We characterize a structural result of this algebra by establishing an explicit basis in terms of certain forests. This algebra arises when we apply the method of the iterated Laurent

Ido Goorevitch, Ron Holzman

We prove that a family $\mathcal{T}$ of distinct triangles on $n$ given vertices that does not have a rainbow triangle (that is, three edges, each taken from a different triangle in $\mathcal{T}$, that form together a triangle) must be of size at most $\frac{n^2}{8}$. We also show that this result is sharp and characterize the extremal case. In addition, we

Anne Baanen, Alex J. Best, Nirvana Coppola, Sander R. Dahmen

Diophantine equations are a popular and active area of research in number theory. In this paper we consider Mordell equations, which are of the form $y^2=x^3+d$, where $d$ is a (given) nonzero integer number and all solutions in integers $x$ and $y$ have to be determined. One non-elementary approach for this problem is the resolution via descent and class gr

James C. R. Whittington, Will Dorrell, Surya Ganguli, Timothy E. J. Behrens

Neurons in the brain are often finely tuned for specific task variables. Moreover, such disentangled representations are highly sought after in machine learning. Here we mathematically prove that simple biological constraints on neurons, namely nonnegativity and energy efficiency in both activity and weights, promote such sought after disentangled representa

Benjamin Paul Chamberlain, Sergey Shirobokov, Emanuele Rossi, Fabrizio Frasca

Many Graph Neural Networks (GNNs) perform poorly compared to simple heuristics on Link Prediction (LP) tasks. This is due to limitations in expressive power such as the inability to count triangles (the backbone of most LP heuristics) and because they can not distinguish automorphic nodes (those having identical structural roles). Both expressiveness issues

Ji Wang, Jason J. Wang, Jean-Baptiste Ruffio, Geoffrey A. Blake

The formation and evolution pathway for the directly-imaged multi-planetary system HR 8799 remains mysterious. Accurate constraints on the chemical composition of the planetary atmosphere(s) are key to solving the mystery. We perform a detailed atmospheric retrieval on HR 8799~c to infer the chemical abundances and abundance ratios using a combination of pho

Itai Gat, Felix Kreuk, Tu Anh Nguyen, Ann Lee

Generative Spoken Language Modeling research focuses on optimizing speech Language Models (LMs) using raw audio recordings without accessing any textual supervision. Such speech LMs usually operate over discrete units obtained from quantizing internal representations of self-supervised models. Although such units show impressive modeling results, their robus

Alexey V. Verkhovtsev, Yury Erofeev, Andrey V. Solov'yov

Physical mechanisms of structural transformations in deposited metallic clusters exposed to an electron beam of a transmission electron microscope (TEM) are studied theoretically and computationally. Recent TEM experiments with size-selected Au$_{923}$ clusters softly deposited on a carbon substrate showed that the clusters undergo structural transformations

Gohar Hovhannesyan, Maxence Lepers

Laser cooling and trapping of lanthanides has opened the possibility to carry out new experiments with ultracold dipolar gases, for example for quantum simulation of solid state physics. To identify new suitable candidates for laser-cooling, it is important to have a precise spectroscopic knowledge of the atom under consideration. Along this direction, we pr

Ben Craps, Juan Hernandez, Mikhail Khramtsov, Maria Knysh

We revisit the model of an AdS$_2$ black hole in JT gravity evaporating into an external bath. We study when, and how much, information about the black hole interior can be accessed through different portions of the Hawking radiation collected in the bath, and we obtain the corresponding full quantitative Page curves. As a refinement of previous results, we

Marco Cattaneo, Gian Luca Giorgi, Roberta Zambrini, Sabrina Maniscalco

The quantum collision models are a useful method to describe the dynamics of an open quantum system by means of repeated interactions between the system and some particles of the environment, which are usually termed "ancillas". In this paper, we review the main collision models for the dynamics of multipartite open quantum systems, which are compose

Alexander C Murph, Jan Hannig, Jonathan P Williams

We introduce a novel approach to inference on parameters that take values in a Riemannian manifold embedded in a Euclidean space. Parameter spaces of this form are ubiquitous across many fields, including chemistry, physics, computer graphics, and geology. This new approach uses generalized fiducial inference to obtain a posterior-like distribution on the ma

Michelle Chen Huebscher, Christian Buck, Massimiliano Ciaramita, Sascha Rothe

Learning to search is the task of building artificial agents that learn to autonomously use a search box to find information. So far, it has been shown that current language models can learn symbolic query reformulation policies, in combination with traditional term-based retrieval, but fall short of outperforming neural retrievers. We extend the previous le

V. Pomjakushin, I. Plokhikh, J. S. White, Y. Fujishiro

From new neutron powder diffraction experiments on the chiral cubic ($P2{_1}3$) magnet manganese germanide MnGe, we analyse all of the possible crystal symmetry-allowed magnetic superstructures that are determined successfully from the data. The incommensurate propagation vectors $k$ of the magnetic structure are found to be aligned with the [100] cubic axes

Tianlin Liu, Joan Puigcerver, Mathieu Blondel

Regularized optimal transport (OT) is now increasingly used as a loss or as a matching layer in neural networks. Entropy-regularized OT can be computed using the Sinkhorn algorithm but it leads to fully-dense transportation plans, meaning that all sources are (fractionally) matched with all targets. To address this issue, several works have investigated quad

Mark Edelman, Avigayil Helman

In this paper, we derive analytic expressions for coefficients of the equations that allow calculations of asymptotically periodic points in fractional difference maps. Numerical solution of these equations allows us to draw the bifurcation diagram for the fractional difference logistic map. Based on the numerically calculated bifurcation points, we make a c

June-Young M. Lee, Changki Hong, Tomer Alkalay, Noam Schiller

Correlations of partitioned particles carry essential information about their quantumness. Partitioning full beams of charged particles leads to current fluctuations, with their autocorrelation (namely, shot noise) revealing the particle' charge. This is not the case when the partitioned particle beams are diluted. Bosons or fermions will exhibit particl

Felix Erben, Vera Gülpers, Maxwell T. Hansen, Raoul Hodgson

We present a strategy for calculating the rare decay of a $Σ^+ (uus)$ baryon to a proton $(uud)$ and di-lepton pair using lattice QCD. To determine this observable one needs to numerically evaluate baryonic two-, three-, and four-point correlation functions related to the target process. In particular, the four-point function arises from the insertion of inc

Ruben Campos Delgado, Stefan Forste

We study $\mathcal{N}=2$ supersymmetric Jackiw-Teitelboim (JT) gravity at finite temperature coupled to matter. The matter fields are related to superconformal primaries by AdS/CFT duality. Due to broken super reparametrisation invariance in the SCFT dual, there are corrections to superconformal correlators. These are generated by the exchange of super-Schwa

Kolja Knauer, Gil Puig i Surroca

We show that every commutative idempotent monoid (a.k.a lattice) is the endomorphism monoid of a subcubic graph. This solves a problem of Babai and Pultr [J. Comb.~Theory, Ser.~B, 1980] and the degree bound is best-possible. On the other hand, we show that no class excluding a minor can have all commutative idempotent monoids among its endomorphism monoids.

Yoshihiro Okawa, Tomotake Sasaki, Hitoshi Yanami, Toru Namerikawa

Recent rapid developments in reinforcement learning algorithms have been giving us novel possibilities in many fields. However, due to their exploring property, we have to take the risk into consideration when we apply those algorithms to safety-critical problems especially in real environments. In this study, we deal with a safe exploration problem in reinf

Wanqin Ma, Huifeng Yao, Yiqun Lin, Jiarong Guo

Developing a deep learning method for medical segmentation tasks heavily relies on a large amount of labeled data. However, the annotations require professional knowledge and are limited in number. Recently, semi-supervised learning has demonstrated great potential in medical segmentation tasks. Most existing methods related to cardiac magnetic resonance ima

Navin Raj Prabhu, Nale Lehmann-Willenbrock, Timo Gerkman

To train machine learning algorithms to predict emotional expressions in terms of arousal and valence, annotated datasets are needed. However, as different people perceive others' emotional expressions differently, their annotations are subjective. To account for this, annotations are typically collected from multiple annotators and averaged to obtain gr

Inés García-Redondo, Anthea Monod, Anna Song

Within the context of topological data analysis, the problems of identifying topological significance and matching signals across datasets are important and useful inferential tasks in many applications. The limitation of existing solutions to these problems, however, is computational speed. In this paper, we harness the state-of-the-art for persistent homol

Longjun Xiang, Chao Zhang, Luyang Wang, Jian Wang

The linear intrinsic anomalous Hall effect (IAHE) and second-order IAHE have been intensively investigated in time-reversal broken systems. However, as one of the important members of the nonlinear Hall family, the investigation of third-order IAHE remains absent due to the lack of an appropriate theoretical approach, although the third-order extrinsic AHE h

Fiona Mukherjee, Anye Shi, Xin Wang, Fengqi You

Identifying and removing microplastics (MPs) from the environment is a global challenge. This study explores how the colloidal fraction of common MPs behave at aqueous interfaces of liquid crystal (LC) films. We observed polyethylene (PE) and polystyrene (PS) microparticles to be captured at the LC interface and exhibit distinct two-dimensional aggregation p

R. Dupuy, J. Filser, C. Richter, T. Buttersack

The determination of depth profiles across interfaces is of primary importance in many scientific and technological areas. Photoemission spectroscopy is in principle well suited for this purpose, yet a quantitative implementation for investigations of liquid-vapor interfaces is hindered by the lack of understanding of electron-scattering processes in liquids

Luca Moschella, Valentino Maiorca, Marco Fumero, Antonio Norelli

Neural networks embed the geometric structure of a data manifold lying in a high-dimensional space into latent representations. Ideally, the distribution of the data points in the latent space should depend only on the task, the data, the loss, and other architecture-specific constraints. However, factors such as the random weights initialization, training h

Akim Kotelnikov, Dmitry Baranchuk, Ivan Rubachev, Artem Babenko

Denoising diffusion probabilistic models are currently becoming the leading paradigm of generative modeling for many important data modalities. Being the most prevalent in the computer vision community, diffusion models have also recently gained some attention in other domains, including speech, NLP, and graph-like data. In this work, we investigate if the f

Mashkoor Ali, Ankik Kumar Giri, Philippe Laurencot

A discrete version of the nonlinear collision-induced breakage equation is studied. Existence of solutions is investigated for a broad class of unbounded collision kernels and daughter distribution functions, the collision kernel $a_{i,j}$ satisfiying $a_{i,j} \leq A i j$ for some $A>0$. More precisely, it is proved that given suitable conditions, there exis

Jian-Gang Tang

In this article, we discuss the question of whether P equals NP, we do not follow the line of research of many researchers, which is to try to find such a problem Q, and the problem Q belongs to the class of NP-complete, if the problem Q is proved to belong to P, then P and NP are the same, if the problem Q is proved not to belong to P, then P and NP are sep

Minkyu Kim, Hyun-Soo Choi, Jinho Kim

Neural Additive Models (NAMs) have recently demonstrated promising predictive performance while maintaining interpretability. However, their capacity is limited to capturing only first-order feature interactions, which restricts their effectiveness on real-world datasets. To address this limitation, we propose Higher-order Neural Additive Models (HONAMs), an

Fan Bao, Min Zhao, Zhongkai Hao, Peiyao Li

Inverse molecular design is critical in material science and drug discovery, where the generated molecules should satisfy certain desirable properties. In this paper, we propose equivariant energy-guided stochastic differential equations (EEGSDE), a flexible framework for controllable 3D molecule generation under the guidance of an energy function in diffusi

Mohatashem Reyaz Makhdoomi, Vivek Muralidharan, Kuldeep R. Barad, Juan Sandoval

On-orbit operations such as servicing and assembly are considered a priority for the future space industry. Ground-based facilities that emulate on-orbit interactions are key tools for developing and testing space technology. This paper presents a control framework to emulate on-orbit operations using on-ground robotic manipulators. It combines Virtual Forwa

Ali Younes, Simone Schaub-Meyer, Georgia Chalvatzaki

Extracting informative representations from videos is fundamental for effectively learning various downstream tasks. We present a novel approach for unsupervised learning of meaningful representations from videos, leveraging the concept of image spatial entropy (ISE) that quantifies the per-pixel information in an image. We argue that \textit{local entropy}

Weijie Wang, Bo Li, Nicu Sebe, Bruno Lepri

Due to the subjective crowdsourcing annotations and the inherent inter-class similarity of facial expressions, the real-world Facial Expression Recognition (FER) datasets usually exhibit ambiguous annotation. To simplify the learning paradigm, most previous methods convert ambiguous annotation results into precise one-hot annotations and train FER models in

Ignacio Vizzo, Tiziano Guadagnino, Benedikt Mersch, Louis Wiesmann

Robust and accurate pose estimation of a robotic platform, so-called sensor-based odometry, is an essential part of many robotic applications. While many sensor odometry systems made progress by adding more complexity to the ego-motion estimation process, we move in the opposite direction. By removing a majority of parts and focusing on the core elements, we

Yan-Jin Wang, Min Zha, Shi-Cong Hu, Chuan-Dong Gao

Background estimation is essential when studying TeV gamma-ray astronomy for extensive air shower arrays. In this work, by applying four applying four different methods including equi-zenith angle method, surrounding window method, direct integration method, and time-swapping method, the number of the background events is calculated. Based on simulation samp

Klára Karasová, Benjamin Vejnar

We prove that every Peano continuum with uncountably many local cut points is a topological fractal. This extends some recent results and it partially answers a conjecture by Hata. We also discuss the number of mappings which are necessary for witnessing the structure of a topological fractal.

Evis Ieronymou

We use recent advances in the local evaluation of Brauer elements to study the role played by {\it odd} torsion elements of the Brauer group in the arithmetic of diagonal quartic surfaces over {\it arbitrary} number fields. We show that over a local field if the order of the Brauer element is odd and coprime to the residue characteristic then the evaluation

Tomas Reis

In this thesis we explore the physics of renormalons in integrable models under the framework of resurgence. In the first part, we review some background on resurgence, integrability and renormalons, including a discussion of large N renormalons and ring diagrams. In the second part, we start from the Bethe ansatz integral equations and obtain exact trans-se

YanNan Liu, Jian Lu

The dual Minkowski problem in the two-dimensional plane is studied in this paper. By combining the theoretical analysis and numerical estimation of an integral with parameters, we find the number of solutions to this problem for the constant dual curvature case when $0<q\leq4$. An improved nonuniqueness result when $q>4$ is also obtained. As an application,

Abicumaran Uthamacumaran, Felipe S. Abrahão, Narsis A. Kiani, Hector Zenil

We demonstrate that the assembly pathway method underlying assembly theory (AT) is an encoding scheme widely used by popular statistical compression algorithms. We show that in all cases (synthetic or natural) AT performs similarly to other simple coding schemes and underperforms compared to system-related indexes based upon algorithmic probability that take

Carlo Alfano, Patrick Rebeschini

We analyze the convergence rate of the unregularized natural policy gradient algorithm with log-linear policy parametrizations in infinite-horizon discounted Markov decision processes. In the deterministic case, when the Q-value is known and can be approximated by a linear combination of a known feature function up to a bias error, we show that a geometrical

Karyn Le Hur

Here, we reveal our recent progress on a geometrical approach of quantum physics and topological crystals linking with Dirac magnetic monopoles and gauge fields through classical electrodynamics. The Bloch sphere of a quantum spin-1/2 particle acquires an integer topological charge in the presence of a radial magnetic field. We show that global topological p

Tanguy Lefort, Benjamin Charlier, Alexis Joly, Joseph Salmon

In supervised learning - for instance in image classification - modern massive datasets are commonly labeled by a crowd of workers. The obtained labels in this crowdsourcing setting are then aggregated for training, generally leveraging a per-worker trust score. Yet, such workers oriented approaches discard the tasks&#39; ambiguity. Ambiguous tasks might foo

Rohit Menon, Tobias Zaenker, Nils Dengler, Maren Bennewitz

Active perception for fruit mapping and harvesting is a difficult task since occlusions occur frequently and the location as well as size of fruits change over time. State-of-the-art viewpoint planning approaches utilize computationally expensive ray casting operations to find good viewpoints aiming at maximizing information gain and covering the fruits in t

Bob Oliver

For a finite abelian $p$-group $A$ and a subgroup $Γ\le\text{Aut}(A)$, we say that the pair $(Γ,A)$ is fusion realizable if there is a saturated fusion system $\mathcal{F}$ over a finite $p$-group $S\ge A$ such that $C_S(A)=A$, $\textrm{Aut}_{\mathcal{F}}(A)=Γ$ as subgroups of $\text{Aut}(A)$, and $A$ is not normal in $\mathcal{F}$. In this paper, we develop

Javier Huertas-Tato, Alejandro Martin, David Camacho

Authors writing documents imprint identifying information within their texts: vocabulary, registry, punctuation, misspellings, or even emoji usage. Previous works use hand-crafted features or classification tasks to train their authorship models, leading to poor performance on out-of-domain authors. Using stylometric representations is more suitable, but thi

Fenglong You

Given a smooth log Calabi--Yau pair $(X,D)$, we use the intrinsic mirror symmetry construction to define the mirror proper Landau--Ginzburg potential and show that it is a generating function of two-point relative Gromov--Witten invariants of $(X,D)$. We compute certain relative invariants with several negative contact orders, and then apply the relative mir

Antoine Barrier, Aurélien Garivier, Gilles Stoltz

We lay the foundations of a non-parametric theory of best-arm identification in multi-armed bandits with a fixed budget T. We consider general, possibly non-parametric, models D for distributions over the arms; an overarching example is the model D = P(0,1) of all probability distributions over [0,1]. We propose upper bounds on the average log-probability of

Luca Traini, Vittorio Cortellessa, Daniele Di Pompeo, Michele Tucci

Microbenchmarking is a widely used form of performance testing in Java software. A microbenchmark repeatedly executes a small chunk of code while collecting measurements related to its performance. Due to Java Virtual Machine optimizations, microbenchmarks are usually subject to severe performance fluctuations in the first phase of their execution (also know

Nathan Satchell, P. Quarterman, J. A. Borchers, Gavin Burnell

Studies of ferromagnet-superconductor hybrid systems have uncovered magnetic interactions between the competing electronic orderings. The electromagnetic (EM) proximity effect predicts the formation of a spontaneous vector potential inside a superconductor placed in proximity to a ferromagnet. In this work, we use a Nb superconducting layer and Ni ferromagne

Li-Shing Lin, Kento Yasuda, Kenta Ishimoto, Yuto Hosaka

Using Onsager&#39;s variational principle, we derive dynamical equations for a nonequilibrium active system with odd elasticity. The elimination of the extra variable that is coupled to the nonequilibrium driving force leads to the nonreciprocal set of equations for the material coordinates. The obtained nonreciprocal equations manifest the physical origin o

Denis Coquenet

Handwritten text recognition has been widely studied in the last decades for its numerous applications. Nowadays, the state-of-the-art approach consists in a three-step process. The document is segmented into text lines, which are then ordered and recognized. However, this three-step approach has many drawbacks. The three steps are treated independently wher

Marina S. Butuzova, Alexander B. Pushkarev

The magnetic field plays a significant role in the phenomenon of highly collimated jets of active galactic nuclei (AGN). Relativistic effects prevent the direct reconstruction of the magnetic field direction as transverse to electric vectors on radio maps. We determined the topology of the B-field by modeling the transverse distributions of the total and lin

Nils Berglund, Rita Nader

We consider slowly time-dependent singular stochastic partial differential equations on the two-dimensional torus, driven by weak space-time white noise, and renormalised in the Wick sense. Our main results are concentration results on sample paths near stable equilibrium branches of the equation without noise, measured in appropriate Besov and Hölder norms.

Parul Jain, Siddhi Swarupa Jena, Subhash Mahapatra

We study various holographic pure and mixed state entanglement measures in the confined/deconfined phases of a bottom-up AdS/QCD model in the presence of a background magnetic field. We analyse the entanglement entropy, entanglement wedge cross-section, mutual information, and entanglement negativity and investigate how a background magnetic field leaves its

Nadir Matringe, Justin Trias

Let $R$ be an algebraically closed field and $\ell$ be its characteristic. Let $G$ be a locally profinite group having a compact open subgroup of invertible pro-order in $R$. Take $N$ a closed subgroup of $G$ exhausted by compact subgroups of invertible pro-orders in $R$ and fix a smooth character $θ$ of $N$. For $π$ an irreducible smooth $R$-representation

Felix Kreuk, Gabriel Synnaeve, Adam Polyak, Uriel Singer

We tackle the problem of generating audio samples conditioned on descriptive text captions. In this work, we propose AaudioGen, an auto-regressive generative model that generates audio samples conditioned on text inputs. AudioGen operates on a learnt discrete audio representation. The task of text-to-audio generation poses multiple challenges. Due to the way

Ariel Neufeld, Julian Sester

We present a novel $Q$-learning algorithm tailored to solve distributionally robust Markov decision problems where the corresponding ambiguity set of transition probabilities for the underlying Markov decision process is a Wasserstein ball around a (possibly estimated) reference measure. We prove convergence of the presented algorithm and provide several exa

ATLAS Collaboration

A generic search for resonances is performed with events containing a $Z$ boson with transverse momentum greater than 100 GeV, decaying into $e^+e^-$ or $μ^+μ^-$. The analysed data collected with the ATLAS detector in proton-proton collisions at a centre-of-mass energy of 13 TeV at the Large Hadron Collider correspond to an integrated luminosity of 139 fb$^{