May 2022 arXiv papers — page 27

Robert Dougherty-Bliss, Doron Zeilberger

Inspired by a recent beautiful construction of Armin Straub and Wadim Zudilin, that 'tweaked' the sum of the $s^{th}$ powers of the $n$-th row of Pascal's triangle, getting instead of sequences of numbers, sequences of rational functions, we do the same for general binomial coefficients sums, getting a practically unlimited supply of Ap\'ery limits. While ge

Vittorio Caggiano, Huawei Wang, Guillaume Durandau, Massimo Sartori

Embodied agents in continuous control domains have had limited exposure to tasks allowing to explore musculoskeletal properties that enable agile and nimble behaviors in biological beings. The sophistication behind neuro-musculoskeletal control can pose new challenges for the motor learning community. At the same time, agents solving complex neural control p

Selena Ling, Nicholas Sharp, Alec Jacobson

The Adam optimization algorithm has proven remarkably effective for optimization problems across machine learning and even traditional tasks in geometry processing. At the same time, the development of equivariant methods, which preserve their output under the action of rotation or some other transformation, has proven to be important for geometry problems a

Chinmay Sonar, Subhash Suri, Jie Xue

We consider multiwinner elections in Euclidean space using the minimax Chamberlin-Courant rule. In this setting, voters and candidates are embedded in a $d$-dimensional Euclidean space, and the goal is to choose a committee of $k$ candidates so that the rank of any voter's most preferred candidate in the committee is minimized. (The problem is also equivalen

Mircea Cimpoeas, Alexandru F. Radu

Given a finite group $G$, we study the monomial algebra $R_G$, generated by the monomial characters of $G$. In particular, we note that the integral closure of $R_G$ is contained in the algebra generated by those characters $\chi$ for which their associated Artin L-function $L(s,\chi)$ is holomorphic at $s_0\in\mathbb C\setminus\{1\}$. Also, we discuss the s

Bruno F. Lourenço, Gábor Pataki

In semidefinite programming the dual may fail to attain its optimal value and there could be a duality gap, i.e., the primal and dual optimal values may differ. In a striking paper, Ramana proposed a polynomial size extended dual that does not have these deficiencies and yields a number of fundamental results in complexity theory. In this work we walk the re

James P. F. LeBlanc, Kun Chen, Kristjan Haule, Nikolay V. Prokof'ev

Precise calculations of dynamics in the homogeneous electron gas (jellium model) are of fundamental importance for design and characterization of new materials. We introduce a diagrammatic Monte Carlo technique based on algorithmic Matsubara integration that allows us to compute frequency and momentum resolved finite temperature response directly in the real

Elham Soltanikazemi, Raj S. Roy, Farhan Quadir, Nabin Giri

Predicted inter-chain residue-residue contacts can be used to build the quaternary structure of protein complexes from scratch. However, only a small number of methods have been developed to reconstruct protein quaternary structures using predicted inter-chain contacts. Here, we present an agent-based self-learning method based on deep reinforcement learning

Muhammad Fahad Khan, Khalid Saleem, Tariq Shah, Mohammad Mazyad Hazzazi

The protection of confidential information is a global issue and block encryption algorithms are the most reliable option. The famous information theorist, Claude Shannon has given two desirable characteristics that should exist in a strong cipher which are substitution and permutation in their fundamental research on Communication Theory of Secrecy Systems.

Nicolas Folinsbee, Joel Friedman

By a {\em Riemann function} we mean a function $f\colon{\mathbb Z}^n\to{\mathbb Z}$ such that $f({\bf d})$ is equals $0$ for $d_1+\cdots+d_n$ sufficiently small, and equals $d_1+\cdots+d_n+C$ for a constant, $C$, for $d_1+\cdots+d_n$ sufficiently large. By adding $1$ to the Baker-Norine rank function of a graph, one gets an equivalent Riemann function, and s

Rahul Sawant

We propose a protocol for generating multi-particle entangled states using coherent manipulation of atoms trapped in an optical cavity. We show how entanglement can be adiabatically produced with two control beams and by exploiting cavity-mediated interactions between the atoms. Our methods will allow for optimal generation of entanglement for the measuremen

Fang-Zheng Peng, Mao-Jun Yan, Mario Sánchez Sánchez, Manuel Pavon Valderrama

The heavy hadron spectrum is constrained by symmetries, of which two of the most important ones are heavy-quark spin and SU(3)-flavor symmetries. Here we argue that in the molecular picture the $Y(4230)$ (or $Y(4260)$), the $Y(4360)$ and the recently discovered $Y(4500)$ and $Y(4620)$ vector-like resonances are linked by these two symmetries. By formulating

Miao Lu, Yifei Min, Zhaoran Wang, Zhuoran Yang

We study offline reinforcement learning (RL) in partially observable Markov decision processes. In particular, we aim to learn an optimal policy from a dataset collected by a behavior policy which possibly depends on the latent state. Such a dataset is confounded in the sense that the latent state simultaneously affects the action and the observation, which

Alvaro Alvarez-Parrilla, Jesús Muciño-Raymundo

On Riemann surfaces $M$, there exists a canonical correspondence between a possibly multivalued function $\Psi_X$ whose differential is single valued ($i.e.$ an additively automorphic singular complex analytic function) and a vector field $X$. From the point of view of vector fields, the singularities that we consider are zeros, poles, isolated essential sin

Pushpi Paranamana, Pei Wang, Patrick Shafto

Evolution of beliefs of a society are a product of interactions between people (horizontal transmission) in the society over generations (vertical transmission). Researchers have studied both horizontal and vertical transmission separately. Extending prior work, we propose a new theoretical framework which allows application of tools from Markov chain theory

Mayowa Ayodele

In more recent years, there has been increasing research interest in exploiting the use of application specific hardware for solving optimisation problems. Examples of solvers that use specialised hardware are IBM's Quantum System One and D-wave's Quantum Annealer (QA) and Fujitsu's Digital Annealer (DA). These solvers have been developed to optimise problem

Ankita Paul, Stefan Wagner, Anup Das

Feedback-driven recurrent spiking neural networks (RSNNs) are powerful computational models that can mimic dynamical systems. However, the presence of a feedback loop from the readout to the recurrent layer de-stabilizes the learning mechanism and prevents it from converging. Here, we propose a supervised training procedure for RSNNs, where a second network

Behnam Pourhassan, Mahdi Atashi, Houcine Aounallah, Salman Sajad Wani

In this paper, we investigate the effects of non-perturbative quantum gravitational corrections on a quantum sized AdS black hole. It will be observed that these non-perturbative quantum gravitational corrections modify the stability of this black hole. We will use the non-equilibrium quantum thermodynamics to investigate the evaporation of this black hole b

Ariel Larey, Eliel Aknin, Nati Daniel, Garrett A. Osswald

Eosinophilic esophagitis (EoE) is a chronic allergic inflammatory condition of the esophagus associated with elevated esophageal eosinophils. Second only to gastroesophageal reflux disease, EoE is one of the leading causes of chronic refractory dysphagia in adults and children. EoE diagnosis requires enumerating the density of esophageal eosinophils in esoph

Gianmarco Cherchi, Fabio Pellacini, Marco Attene, Marco Livesu

Boolean operations are among the most used paradigms to create and edit digital shapes. Despite being conceptually simple, the computation of mesh Booleans is notoriously challenging. Main issues come from numerical approximations that make the detection and processing of intersection points inconsistent and unreliable, exposing implementations based on floa

Cibele Cristina Trinca, J. Carmelo Interlando, Reginaldo Palazzo, Antonio Aparecido de Andrade

A toric quantum error-correcting code construction procedure is presented in this work. A new class of an infinite family of toric quantum codes is provided by constructing a classical cyclic code on the square lattice $\mathbb{Z}_{q}\times \mathbb{Z}_{q}$ for all odd integers $q\geq 5$ and, consequently, new toric quantum codes are constructed on such squar

Kağan Kurşungöz, Halime Ömrüuzun Seyrek

In this paper, cylindric partitions into profiles $c=(1,1)$ and $c=(2,0)$ are considered. The generating functions into unrestricted cylindric partitions and cylindric partitions into distinct parts with these profiles are constructed. The constructions are combinatorial and they connect the cylindric partitions with ordinary partitions.

Sophie Hourihane, Katerina Chatziioannou, Marcella Wijngaarden, Derek Davis

The increasing sensitivity of gravitational-wave detectors has brought about an increase in the rate of astrophysical signal detections as well as the rate of "glitches"; transient and non-Gaussian detector noise. Temporal overlap of signals and glitches in the detector presents a challenge for inference analyses that typically assume the presence of only Ga

Can Zhang, Gim Hee Lee

Recent works on unsupervised domain adaptation (UDA) focus on the selection of good pseudo-labels as surrogates for the missing labels in the target data. However, source domain bias that deteriorates the pseudo-labels can still exist since the shared network of the source and target domains are typically used for the pseudo-label selections. The suboptimal

Christoffel Doorman, Victor-Alexandru Darvariu, Stephen Hailes, Mirco Musolesi

A key problem in network theory is how to reconfigure a graph in order to optimize a quantifiable objective. Given the ubiquity of networked systems, such work has broad practical applications in a variety of situations, ranging from drug and material design to telecommunications. The large decision space of possible reconfigurations, however, makes this pro

Subha Maity, Mikhail Yurochkin, Moulinath Banerjee, Yuekai Sun

Deploying machine learning models to new tasks is a major challenge despite the large size of the modern training datasets. However, it is conceivable that the training data can be reweighted to be more representative of the new (target) task. We consider the problem of reweighing the training samples to gain insights into the distribution of the target task

Miroslav Bures, Pavel Blazek, Jiri Nema, Hynek Schvach

Internet of Things (IoT) systems are recently being employed in various types of critical infrastructure, including integrated rescue systems, healthcare, defence, energy and other fields. Recently, the security and safety of IoT systems, in general, has been questioned by a number of studies. Raised concerns do not relate to the IoT technology in principle

Subha Maity, Debarghya Mukherjee, Moulinath Banerjee, Yuekai Sun

Time-varying stochastic optimization problems frequently arise in machine learning practice (e.g. gradual domain shift, object tracking, strategic classification). Although most problems are solved in discrete time, the underlying process is often continuous in nature. We exploit this underlying continuity by developing predictor-corrector algorithms for tim

Cuong Tran, Ferdinando Fioretto, Jung-Eun Kim, Rakshit Naidu

Network pruning is a widely-used compression technique that is able to significantly scale down overparameterized models with minimal loss of accuracy. This paper shows that pruning may create or exacerbate disparate impacts. The paper sheds light on the factors to cause such disparities, suggesting differences in gradient norms and distance to decision boun

Mengyu Li, Jun Yu, Hongteng Xu, Cheng Meng

As a valid metric of metric-measure spaces, Gromov-Wasserstein (GW) distance has shown the potential for matching problems of structured data like point clouds and graphs. However, its application in practice is limited due to the high computational complexity. To overcome this challenge, we propose a novel importance sparsification method, called \textsc{Sp

Gayani Nanayakkara, Nirmalie Wiratunga, David Corsar, Kyle Martin

Good communication is critical to good healthcare. Clinical dialogue is a conversation between health practitioners and their patients, with the explicit goal of obtaining and sharing medical information. This information contributes to medical decision-making regarding the patient and plays a crucial role in their healthcare journey. The reliance on note ta

Steffen Schotthöfer, Emanuele Zangrando, Jonas Kusch, Gianluca Ceruti

Neural networks have achieved tremendous success in a large variety of applications. However, their memory footprint and computational demand can render them impractical in application settings with limited hardware or energy resources. In this work, we propose a novel algorithm to find efficient low-rank subnetworks. Remarkably, these subnetworks are determ

Claudio D. G. Linhares, Daniel M. Lima, Jean R. Ponciano, Mauro M. Olivatto

Physicians work at a very tight schedule and need decision-making support tools to help on improving and doing their work in a timely and dependable manner. Examining piles of sheets with test results and using systems with little visualization support to provide diagnostics is daunting, but that is still the usual way for the physicians' daily procedure, es

Haroldo C. D. Lima Junior, Mateus M. Corrêa, Caio F. B. Macedo, Luís C. B. Crispino

Black holes can be inserted in very rich astrophysical environments, such as accretion disks. Although isolated black holes are simple objects in general relativity, their accretion disks may significantly enrich the field configurations of their surroundings. Alternative theories of gravity can lead to novel black hole solutions, which can be represented by

Zhihan Zhou, Dejiao Zhang, Wei Xiao, Nicholas Dingwall

Learning high-quality dialogue representations is essential for solving a variety of dialogue-oriented tasks, especially considering that dialogue systems often suffer from data scarcity. In this paper, we introduce Dialogue Sentence Embedding (DSE), a self-supervised contrastive learning method that learns effective dialogue representations suitable for a w

T. -W. Zhou, G. Cappellini, D. Tusi, L. Franchi

The Hall effect, which originates from the motion of charged particles in magnetic fields, has deep consequences for the description of materials, extending far beyond condensed matter. Understanding such an effect in interacting systems represents a fundamental challenge, even for small magnetic fields. In this work, we used an atomic quantum simulator in w

Zixian Yang, Xin Liu, Lei Ying

Multi-armed bandit (MAB) is a classic model for understanding the exploration-exploitation trade-off. The traditional MAB model for recommendation systems assumes the user stays in the system for the entire learning horizon. In new online education platforms such as ALEKS or new video recommendation systems such as TikTok and YouTube Shorts, the amount of ti

Thu Nguyen, Quang M. Le, Son N. T. Tu, Binh T. Nguyen

Fisher Discriminant Analysis (FDA) is one of the essential tools for feature extraction and classification. In addition, it motivates the development of many improved techniques based on the FDA to adapt to different problems or data types. However, none of these approaches make use of the fact that the assumption of equal covariance matrices in FDA is usual

Anshika Bansal

Baryon number is conserved in the Standard Model (SM) of particle physics. Baryon Number Violation (BNV) is one of the criteria to explain the matter anti-matter asymmetry of the universe. Various beyond the SM (BSM) scenarios motivate BNV. In that view, it becomes important to look for various BNV decays. \\ In this work, we consider the BNV decay of heavy

Stephen D. Riggs, Jon Loveday, Peter A. Thomas, Annalisa Pillepich

We explore the radial distribution of satellite galaxies in groups in the Galaxy and Mass Assembly (GAMA) survey and the IllustrisTNG simulations. Considering groups with masses $12.0 \leq \log_{10} (\mathcal{M}_h / h^{-1} \mathrm{M}_{\odot}) < 14.8$ at $z<0.267$, we find a good agreement between GAMA and a sample of TNG300 groups and galaxies designed to ma

Charles K. Chui, Qingtang Jiang, Lin Li, Jian Lu

In many applications, it is necessary to retrieve the sub-signal building blocks of a multi-component signal, which is usually non-stationary in real-world and real-life applications. Empirical mode decomposition (EMD), synchrosqueezing transform (SST), signal separation operation (SSO), and iterative filtering decomposition (IFD) have been proposed and deve

Yunsik Jung, Lingfeng Tao, Michael Bowman, Jiucai Zhang

Learning-based grasping can afford real-time grasp motion planning of multi-fingered robotics hands thanks to its high computational efficiency. However, learning-based methods are required to explore large search spaces during the learning process. The search space causes low learning efficiency, which has been the main barrier to its practical adoption. In

Rebecca Briffa, Celia Escamilla-Rivera, Jackson Levi Said, Jurgen Mifsud

We explore the viability of three models in $f(T,B)$ gravity using data from recent surveys based on cosmic chronometers, the Pantheon data set, and baryonic acoustic oscillation data. We also assess the consistency of these models and data set combinations with two important priors on the Hubble constant coming from the SH0ES Team and measurements using the

Batel Oved, Orian Leitersdorf, Ronny Ronen, Shahar Kvatinsky

Recent research has sought to accelerate cryptographic hash functions as they are at the core of modern cryptography. Traditional designs, however, suffer from the von Neumann bottleneck that originates from the separation of processing and memory units. An emerging solution to overcome this bottleneck is processing-in-memory (PIM): performing logic within t

Benjamin Knorr, Alessia Platania, Marc Schiffer

We discuss some aspects of the metric configuration space in quantum gravity in the background field formalism. We give a necessary and sufficient condition for the parameterization of Euclidean metric fluctuations such that i) the signature of the metric is preserved in all configurations that enter the gravitational path integral, and ii) the parameterizat

Lauren Pearce, Graham White, Alexander Kusenko

Scalar fields which carry charge can generally form non-topoligical solitons (Q-balls), if the energy in the extended configuration is less than the energy of an equivalent number of free quanta. For global Q-balls, such solitons exist whenever the potential grows slower than quadratically. We show that even in the absence of attractive interactions, finite

Raúl Carballo-Rubio, Francesco Di Filippo, Stefano Liberati, Costantino Pacilio

Generic models of regular black holes have separate outer and inner horizons, both with nonzero surface gravity. It has been shown that a nonzero inner horizon surface gravity results in exponential instability at the inner horizon controlled by this parameter. This phenomenon takes the name of "mass inflation instability", and its presence has put in questi

Raúl Carballo-Rubio, Francesco Di Filippo, Stefano Liberati, Matt Visser

The images of Sagittarius A$^*$ recently released by the Event Horizon Telescope collaboration have been accompanied [Ap.J.Lett.\,{\bf 930\,\#2}\,(2022)\,L17] by an analysis of the constraints on the possible absence of a trapping horizon, i.e.~on the possibility that the object at the center of our galaxy is an ultra-compact object with a surface re-emittin

Andy Shih, Dorsa Sadigh, Stefano Ermon

Conditional inference on arbitrary subsets of variables is a core problem in probabilistic inference with important applications such as masked language modeling and image inpainting. In recent years, the family of Any-Order Autoregressive Models (AO-ARMs) -- closely related to popular models such as BERT and XLNet -- has shown breakthrough performance in ar

Andrea Negri, Claudio Dalla Vecchia, J. Alfonso L. Aguerri, Yannick Bahé

We computed the luminosity of simulated galaxies of the C-EAGLE project, a suite of 30 high-resolution zoom-in simulations of galaxy clusters based on the EAGLE simulation. The AB magnitudes are derived for different spectral bands, from ultraviolet to infrared, using the simple stellar population modeling based on the E-MILES stellar spectra library. We tak

Aleksandr Azatov, Francesco Garosi, Admir Greljo, David Marzocca

Rare flavour-changing neutral-current transitions $b \to s \mu^+ \mu^-$ probe higher energy scales than what is directly accessible at the LHC. Therefore, the presence of new physics in such transitions, as suggested by the present-day LHCb anomalies, would have a major impact on the motivation and planning of future high-energy colliders. The two most promi

Rahim Leung, K. S. Stelle

Supergravity brane solutions allow for a generalised type of Kaluza-Klein reduction onto brane worldvolumes. The known replacement of a flat worldvolume metric by a Ricci-flat metric constitutes a consistent Kaluza-Klein truncation of the starting higher-dimensional supergravity theory down to a lower-dimensional pure gravity theory. This paper shows how to

F. Delgado, L. Duarte, J. Jones-Perez, C. Manrique-Chavil

The Dimension-5 Seesaw Portal is a Type-I Seesaw model extended by $d=5$ operators involving the sterile neutrino states, leading to new interactions between all neutrinos and the Standard Model neutral bosons. In this work we focus primarily on the implications of these new operators at the GeV-scale. In particular, we recalculate the heavy neutrino full de

Csaba Balázs, Sanjay Bloor, Tomás E. Gonzalo, Will Handley

Axion-like particles (ALPs) decaying into photons are known to affect a wide range of astrophysical and cosmological observables. In this study we focus on ALPs with masses in the keV-MeV range and lifetimes between $10^4$ and $10^{13}$ seconds, corresponding to decays between the end of Big Bang Nucleosynthesis and the formation of the Cosmic Microwave Back

Marco Astorino, Riccardo Martelli, Adriano Viganò

We present a new solution in Einstein's General Relativity representing a Schwarzschild black hole immersed in a rotating universe. Such a solution is constructed analytically by means of the last unexplored Lie point symmetry of the Ernst equations for stationary and axisymmetric spacetimes. This kind of the Ehlers transformation is able to embed any given

Adam M. Dillamore, Vasily Belokurov, N. Wyn Evans, Adrian M. Price-Whelan

We investigate the effects of a massive ($\gtrsim4\times10^{10}M_\odot$) Sagittarius dwarf spheroidal galaxy (Sgr) on stellar streams using test particle simulations in a realistic Milky Way potential. We find that Sgr can easily disrupt streams formed more than $\sim3$ Gyr ago, while stars stripped more recently are generally unaffected. In certain realizat

Yufeng Du, Clara Murgui, Kris Pardo, Yikun Wang

Direct detection experiments for dark matter are increasingly ruling out large parameter spaces. However, light dark matter models with particle masses $<$ GeV are still largely unconstrained. Here we examine a proposal to use atom interferometers to detect a light dark matter subcomponent at sub-GeV masses. We describe the decoherence and phase shifts cause

Zhenda Xie, Zigang Geng, Jingcheng Hu, Zheng Zhang

Masked image modeling (MIM) as pre-training is shown to be effective for numerous vision downstream tasks, but how and where MIM works remain unclear. In this paper, we compare MIM with the long-dominant supervised pre-trained models from two perspectives, the visualizations and the experiments, to uncover their key representational differences. From the vis

Zhijian Liu, Haotian Tang, Alexander Amini, Xinyu Yang

Multi-sensor fusion is essential for an accurate and reliable autonomous driving system. Recent approaches are based on point-level fusion: augmenting the LiDAR point cloud with camera features. However, the camera-to-LiDAR projection throws away the semantic density of camera features, hindering the effectiveness of such methods, especially for semantic-ori

Andrew G. Sullivan, Lucas M. B. Alves, Georgina O. Spence, Isabella P. Leite

Neutron stars in astrophysical binary systems represent exciting sources for multi-messenger astrophysics. A potential source of electromagnetic transients from compact binary systems is the neutron star ocean, the external fluid layer encasing a neutron star. We present a groundwork study into tidal waves in neutron star oceans and their consequences. Speci

Alexandros Karam, Niko Koivunen, Eemeli Tomberg, Ville Vaskonen

We construct an analytically solvable simplified model that captures the essential features for primordial black hole (PBH) production in most models of single-field inflation. The construction makes use of the Wands duality between the constant-roll (or slow-roll) and the preceding ultra-slow-roll phases and can be realized by a simple inflaton potential of

Xia Liu, Geng Liu, Jiaxin Huang, Hao-Kai Zhang

Variational quantum algorithms (VQAs) are expected to establish valuable applications on near-term quantum computers. However, recent works have pointed out that the performance of VQAs greatly relies on the expressibility of the ansatzes and is seriously limited by optimization issues such as barren plateaus (i.e., vanishing gradients). This work proposes t

Akshay Seshadri, Felix Leditzky, Vikesh Siddhu, Graeme Smith

The capacity of a channel characterizes the maximum rate at which information can be transmitted through the channel asymptotically faithfully. For a channel with multiple senders and a single receiver, computing its sum capacity is possible in theory, but challenging in practice because of the nonconvex optimization involved. To address this challenge, we i

Alessandro Alberto Trani, Steven Rieder, Ataru Tanikawa, Giuliano Iorio

We present a novel way of modeling common envelope evolution in binary and few-body systems. We consider the common envelope inspiral as driven by a drag force with a power-law dependence in relative distance and velocity. The orbital motion is resolved either by direct N-body integration or by solving the set of differential equations for the orbital elemen

Guy Amir, Davide Corsi, Raz Yerushalmi, Luca Marzari

Deep reinforcement learning (DRL) has become a dominant deep-learning paradigm for tasks where complex policies are learned within reactive systems. Unfortunately, these policies are known to be susceptible to bugs. Despite significant progress in DNN verification, there has been little work demonstrating the use of modern verification tools on real-world, D

Shoufa Chen, Chongjian Ge, Zhan Tong, Jiangliu Wang

Pretraining Vision Transformers (ViTs) has achieved great success in visual recognition. A following scenario is to adapt a ViT to various image and video recognition tasks. The adaptation is challenging because of heavy computation and memory storage. Each model needs an independent and complete finetuning process to adapt to different tasks, which limits i

W. E. V. Barker

The dynamics of the torsion-powered teleparallel theory are only viable because thirty-six multiplier fields disable all components of the Riemann--Cartan curvature. We generalise this suggestive approach by considering Poincar\'e gauge theory in which sixty such `geometric multipliers' can be invoked to disable any given irreducible part of the curvature, o

Theo J. O'Neill, Remy Indebetouw, Alberto D. Bolatto, Suzanne C. Madden

Stars form within molecular clouds, so characterizing the physical states of molecular clouds is key in understanding the process of star formation. Cloud structure and stability is frequently assessed using metrics including the virial parameter and Larson (1981) scaling relationships between cloud radius, velocity dispersion, and surface density. Departure

Stephan Rabanser, Anvith Thudi, Kimia Hamidieh, Adam Dziedzic

Selective Prediction is the task of rejecting inputs a model would predict incorrectly on. This involves a trade-off between input space coverage (how many data points are accepted) and model utility (how good is the performance on accepted data points). Current methods for selective prediction typically impose constraints on either the model architecture or

Julius Berner, Philipp Grohs, Felix Voigtlaender

Statistical learning theory provides bounds on the necessary number of training samples needed to reach a prescribed accuracy in a learning problem formulated over a given target class. This accuracy is typically measured in terms of a generalization error, that is, an expected value of a given loss function. However, for several applications -- for example

Mehmet Arif Demirtaş, Berke Oral, Mehmet Yasin Akpınar, Onur Deniz

Page-level analysis of documents has been a topic of interest in digitization efforts, and multimodal approaches have been applied to both classification and page stream segmentation. In this work, we focus on capturing finer semantic relations between pages of a multi-page document. To this end, we formalize the task as semantic parsing of interpage relatio

Tamaghna Hazra, Piers Coleman

Observing that several U and Ce based candidate triplet superconductors share a common structural motif, with pairs of magnetic atoms separated by an inversion center, we hypothesize a triplet pairing mechanism based on an interplay of Hund's and Kondo interactions that is unique to this structure. In the presence of Hund's interactions, valence fluctuations

Marco Bagatella, Sammy Christen, Otmar Hilliges

Efficient exploration is a crucial challenge in deep reinforcement learning. Several methods, such as behavioral priors, are able to leverage offline data in order to efficiently accelerate reinforcement learning on complex tasks. However, if the task at hand deviates excessively from the demonstrated task, the effectiveness of such methods is limited. In ou

Serhii D. Koval, Alexander Bihlo, Roman O. Popovych

We carry out the extended symmetry analysis of an ultraparabolic Fokker-Planck equation with three independent variables, which is also called the Kolmogorov equation and is singled out within the class of such Fokker-Planck equations by its remarkable symmetry properties. In particular, its essential Lie invariance algebra is eight-dimensional, which is the

Daniel Beaglehole, Mikhail Belkin, Parthe Pandit

``Benign overfitting'', the ability of certain algorithms to interpolate noisy training data and yet perform well out-of-sample, has been a topic of considerable recent interest. We show, using a fixed design setup, that an important class of predictors, kernel machines with translation-invariant kernels, does not exhibit benign overfitting in fixed dimensio

Manaar Alam, Esha Sarkar, Michail Maniatakos

Federated Learning (FL) enables numerous participants to train deep learning models collaboratively without exposing their personal, potentially sensitive data, making it a promising solution for data privacy in collaborative training. The distributed nature of FL and unvetted data, however, makes it inherently vulnerable to backdoor attacks: In this scenari

Shiyi Qi, Yaoxian Li, Cuiyun Gao, Xiaohong Su

Adapting Deep Learning (DL) techniques to automate non-trivial coding activities, such as code documentation and defect detection, has been intensively studied recently. Learning to predict code changes is one of the popular and essential investigations. Prior studies have shown that DL techniques such as Neural Machine Translation (NMT) can benefit meaningf

Tom Zahavy, Yannick Schroecker, Feryal Behbahani, Kate Baumli

Finding different solutions to the same problem is a key aspect of intelligence associated with creativity and adaptation to novel situations. In reinforcement learning, a set of diverse policies can be useful for exploration, transfer, hierarchy, and robustness. We propose DOMiNO, a method for Diversity Optimization Maintaining Near Optimality. We formalize

Yanghong Huang, Edoardo Mainini, Juan Luis Vázquez, Bruno Volzone

We consider an aggregation-diffusion model, where the diffusion is nonlinear of porous medium type and the aggregation is governed by the Riesz potential of order s. The addition of a quadratic diffusion term produces a more precise competition with the aggregation term for small s, as they have the same scaling if s=0. We prove existence and uniqueness of s

K. D. Humbird, J. L. Peterson

Transfer learning is a promising approach to creating predictive models that incorporate simulation and experimental data into a common framework. In this technique, a neural network is first trained on a large database of simulations, then partially retrained on sparse sets of experimental data to adjust predictions to be more consistent with reality. Previ

G. L. Klimchitskaya, V. M. Mostepanenko, O. Yu. Tsybin

The nonequilibrium Casimir-Polder force between a nanoparticle and a graphene sheet kept at different temperatures is investigated in the framework of Dirac model using the formalism of the polarization tensor. It is shown that the force magnitude increases with increasing temperature of a graphene sheet. At larger separations an impact of nonequilibrium con

Daniel Gil-Muñoz

For an odd prime number $p$, we consider degree $p$ extensions $L/K$ of $p$-adic fields with normal closure $\widetilde{L}$ such that the Galois group of $\widetilde{L}/K$ is the dihedral group of order $2p$. We shall prove a complete characterization of the freeness of the ring of integers $\mathcal{O}_L$ over its associated order $\mathfrak{A}_{L/K}$ in th

Wojciech Czerwiński, Engel Lefaucheux, Filip Mazowiecki, David Purser

We consider linear cost-register automata (equivalent to weighted automata) over the semiring of nonnegative rationals, which generalise probabilistic automata. The two problems of boundedness and zero isolation ask whether there is a sequence of words that converge to infinity and to zero, respectively. In the general model both problems are undecidable so

Lang Huang, Shan You, Mingkai Zheng, Fei Wang

We present an efficient approach for Masked Image Modeling (MIM) with hierarchical Vision Transformers (ViTs), allowing the hierarchical ViTs to discard masked patches and operate only on the visible ones. Our approach consists of three key designs. First, for window attention, we propose a Group Window Attention scheme following the Divide-and-Conquer strat

Mohit K. Sharma, Shibesh Kumar Jas Pacif, Gulmira Yergaliyeva, Kuralay Yesmakhanova

We investigate the validity of cosmological models with an oscillating scale factor in relation to late-time cosmological observations. We show that these models not only meet the required late time observational constraints but can also alleviate the Hubble tension. As a generic feature of the model, the Hubble parameter increases near the current epoch due

P. Bacon, A. Trovato, M. Bejger

Broadband frequency output of gravitational-wave detectors is a non-stationary and non-Gaussian time series data stream dominated by noise populated by local disturbances and transient artifacts, which evolve on the same timescale as the gravitational-wave signals and may corrupt the astrophysical information. We study a denoising algorithm dedicated to expo

Mu-Che Lin, Ping-Yuan Lo, Franco Nori, Hong-Bin Chen

The ongoing exploration of the ambiguous boundary between the quantum and the classical worlds has spurred substantial developments in quantum science and technology. Recently, the nonclassicality of dynamical processes has been proposed from a quantum-information-theoretic perspective, in terms of witnessing nonclassical correlations with Hamiltonian ensemb

Katsushi Kondo, Satoshi Okuzumi, Shoji Mori

The temperature structure of protoplanetary disks provides an important constraint on where in the disks rocky planets like our own form. Recent nonideal magnetohydrodynamical (MHD) simulations have shown that the internal Joule heating associated with magnetically driven disk accretion is inefficient at heating the disk midplane. A disk temperature model ba

Markus Kühbach, Vitor Vieira Rielli, Sophie Primig, Alaukik Saxena

Characterizing microstructure-material-property relations calls for software tools which extract point-cloud- and continuum-scale-based representations of microstructural objects. Application examples include atom probe, electron, and computational microscopy experiments. Mapping between atomic- and continuum-scale representations of microstructural objects

Danne M. van Roon, Giorgio Volpe, Margarida M. Telo da Gama, Nuno A. M. Araújo

The presence of obstacles is intuitively expected to hinder the diffusive transport of micro-swimmers. However, for chiral micro-swimmers, a low density of obstacles near a surface can enhance their diffusive behavior, due to the rectification of the chiral motion by the obstacles. Here, we study numerically the role that disorder plays in determining the tr

Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis

Domain Adaptation (DA) has received widespread attention from deep learning researchers in recent years because of its potential to improve test accuracy with out-of-distribution labeled data. Most state-of-the-art DA algorithms require an extensive amount of hyperparameter tuning and are computationally intensive due to the large batch sizes required. In th

Dávid Terjék, Diego González-Sánchez

A candidate explanation of the good empirical performance of deep neural networks is the implicit regularization effect of first order optimization methods. Inspired by this, we prove a convergence theorem for nonconvex composite optimization, and apply it to a general learning problem covering many machine learning applications, including supervised learnin

Vijin Venu, Peihang Xu, Mikhail Mamaev, Frank Corapi

Exchange-antisymmetric pair wavefunctions in fermionic systems can give rise to unconventional superconductors and superfluids with non-trivial transport properties. The realisation of these states in controllable quantum systems, such as ultracold gases, could enable new types of quantum simulations, topological quantum gates, and exotic few-body states. Ho

Mikaela Meyer, Aaron Horowitz, Erica Marshall, Kristian Lum

In the criminal justice system, algorithmic risk assessment instruments are used to predict the risk a defendant poses to society; examples include the risk of recidivating or the risk of failing to appear at future court dates. However, defendants are also at risk of harm from the criminal justice system. To date, there exists no risk assessment instrument

Ailing Zeng, Muxi Chen, Lei Zhang, Qiang Xu

Recently, there has been a surge of Transformer-based solutions for the long-term time series forecasting (LTSF) task. Despite the growing performance over the past few years, we question the validity of this line of research in this work. Specifically, Transformers is arguably the most successful solution to extract the semantic correlations among the eleme

Ramin Barati, Reza Safabakhsh, Mohammad Rahmati

The reliability of a learning model is key to the successful deployment of machine learning in various industries. Creating a robust model, particularly one unaffected by adversarial attacks, requires a comprehensive understanding of the adversarial examples phenomenon. However, it is difficult to describe the phenomenon due to the complicated nature of the

Aras Selvi, Mohammad Reza Belbasi, Martin B Haugh, Wolfram Wiesemann

Recent work has leveraged the popular distributionally robust optimization paradigm to combat overfitting in classical logistic regression. While the resulting classification scheme displays a promising performance in numerical experiments, it is inherently limited to numerical features. In this paper, we show that distributionally robust logistic regression

Zichao Huang, Yuanyuan Chen, Lixiang Chen

Generative adversarial learning is currently one of the most prolific fields in artificial intelligence due to its great performance in a variety of challenging tasks such as photorealistic image and video generation. While a quantum version of generative adversarial learning has emerged that promises exponential advantages over its classical counterpart, it

John H. Elton

The payoff in the Chow-Robbins coin-tossing game is the proportion of heads when you stop. Knowing when to stop to maximize expectation was addressed by Chow and Robbins(1965), who proved there exist integers ${k_n}$ such that it is optimal to stop when heads minus tails reaches this. Finding ${k_n}$ exactly was unsolved except for finitely many cases by com

Wiesław Kubiś, Piotr Nowakowski, Tomasz Rzepecki

We discuss homogeneity and universality issues in the theory of abstract linear spaces, namely, structures with points and lines satisfying natural axioms, as in Euclidean or projective geometry. We show that the two smallest projective planes (including the Fano plane) are homogeneous and, assuming the continuum hypothesis, there exists a universal projecti