May 2022 arXiv papers — page 59

Horatiu Nastase, Jacob Sonnenschein

In this paper we consider an Euler fluid coupled to external electromagnetism. We prove that the Hopfion fluid-electromagnetic knot, carrying fluid and electromagnetic (EM) helicities, solves the fluid dynamical equations as well as the Abanov Wiegmann (AW) equations for helicities, which are inspired by the axial-current anomaly of a Dirac fermion. We also

Kaveh Rahbardar Mojaver, Odile Liboiron-Ladouceur

We design a Multi-Transverse-Mode Optical Processor (MTMOP) on 220 nm thick Silicon Photonics exploiting the first two quasi-transverse electric modes (TE0 and TE1). The objective is to measure the optical phase, required for programming the optical processor, without use of conventional optical phase detection techniques (e.g., coherent detection). In the p

Dávid Guszejnov, Michael Y. Grudić, Stella S. R. Offner, Claude-André Faucher-Giguère

One of the key mysteries of star formation is the origin of the stellar initial mass function (IMF). The IMF is observed to be nearly universal in the Milky Way and its satellites, and significant variations are only inferred in extreme environments, such as the cores of massive elliptical galaxies. In this work we present simulations from the STARFORGE proj

Eman Naser-Karajah, Nabil Arman, Mustafa Jarrar

Extracting synonyms from dictionaries or corpora is gaining special attention as synonyms play an important role in improving NLP application performance. This paper presents a survey of the different approaches and trends used in automatically extracting the synonyms. These approaches can be divided into four main categories. The first approach is to find t

Junwei Zhao, S. P. Rajaguru, Ruizhu Chen

A set of 464-min high-resolution high-cadence observations were acquired for a region near the Sun's disk center using the Interferometric BI-dimensional Spectrometer (IBIS) installed at the Dunn Solar Telescope. Ten sets of Dopplergrams are derived from the bisector of the spectral line corresponding approximately to different atmospheric heights, and two s

Felix Drinkall, Stefan Zohren, Janet B. Pierrehumbert

We present a novel approach incorporating transformer-based language models into infectious disease modelling. Text-derived features are quantified by tracking high-density clusters of sentence-level representations of Reddit posts within specific US states' COVID-19 subreddits. We benchmark these clustered embedding features against features extracted from

Nadia M. Ady, Roshan Shariff, Johannes Günther, Patrick M. Pilarski

Curiosity for machine agents has been a focus of intense research. The study of human and animal curiosity, particularly specific curiosity, has unearthed several properties that would offer important benefits for machine learners, but that have not yet been well-explored in machine intelligence. In this work, we introduce three of the most immediate of thes

Viacheslav Meshchaninov, Ivan Molodetskikh, Dmitriy Vatolin

Upscaled video detection is a helpful tool in multimedia forensics, but it is a challenging task that involves various upscaling and compression algorithms. There are many resolution-enhancement methods, including interpolation and deep-learning-based super-resolution, and they leave unique traces. In this work, we propose a new upscaled-resolution-detection

Kevin Black, Tulika Bose, Yuze Chen, Sridhara Dasu

Plots summarizing the constraints on Dark Matter models can help visualize synergies between different searches for the same kind of experiment, as well as between different experiments. In this whitepaper, we present an update to the European Strategy Briefing Book plots, from the perspective of collider searches within the Dark Matter at the Energy Frontie

Mehran Kazemi, Anton Tsitsulin, Hossein Esfandiari, MohammadHossein Bateni

Representative Selection (RS) is the problem of finding a small subset of exemplars from a dataset that is representative of the dataset. In this paper, we study RS for attributed graphs, and focus on finding representative nodes that optimize the accuracy of a model trained on the selected representatives. Theoretically, we establish a new hardness result f

Nic Weber, Brandon Locke

This chapter addresses emergent ethical issues in producing, using, curating, and providing services for open data. Our goal is to provide an introduction to how ethical topics in open data manifest in practical dilemmas for scholarly communications and some approaches to understanding and working through them. We begin with a brief overview of what can be t

Meng Yu, Yong Xu, Chunlei Zhang, Shi-Xiong Zhang

Acoustic echo cancellation (AEC) plays an important role in the full-duplex speech communication as well as the front-end speech enhancement for recognition in the conditions when the loudspeaker plays back. In this paper, we present an all-deep-learning framework that implicitly estimates the second order statistics of echo/noise and target speech, and join

Chia-Chien Hung, Anne Lauscher, Ivan Vulić, Simone Paolo Ponzetto

Research on (multi-domain) task-oriented dialog (TOD) has predominantly focused on the English language, primarily due to the shortage of robust TOD datasets in other languages, preventing the systematic investigation of cross-lingual transfer for this crucial NLP application area. In this work, we introduce Multi2WOZ, a new multilingual multi-domain TOD dat

Lukas Lange, Jannik Strötgen, Heike Adel, Dietrich Klakow

The detection and normalization of temporal expressions is an important task and preprocessing step for many applications. However, prior work on normalization is rule-based, which severely limits the applicability in real-world multilingual settings, due to the costly creation of new rules. We propose a novel neural method for normalizing temporal expressio

Michael Gutperle, Charlie Hultgreen-Mena

In this paper, we continue the study of Janus and RG-flow interfaces in three dimensional supergravity continuing the work presented in [1]. We consider ${\cal N}=8$ gauged supergravity theories which have a ${\cal N}=(4,4)$ $AdS_3$ vacuum with $D^1(2,1;\alpha) \times D^1(2,1;\alpha)$ symmetry for general $\alpha$. We derive the BPS flow equations and find n

Mustafa Eyceoz, Justin Lee, Homayoon Beigi

While most modern speech Language Identification methods are closed-set, we want to see if they can be modified and adapted for the open-set problem. When switching to the open-set problem, the solution gains the ability to reject an audio input when it fails to match any of our known language options. We tackle the open-set task by adapting two modern-day s

Melani Sanchez-Garcia, Roberto Morollon-Ruiz, Ruben Martinez-Cantin, Jose J. Guerrero

Current visual implants still provide very low resolution and limited field of view, thus limiting visual acuity in implanted patients. Developments of new strategies of artificial vision simulation systems by harnessing new advancements in technologies are of upmost priorities for the development of new visual devices. In this work, we take advantage of vir

Junbo Zhu, Changmin Lee, Fahad Mahmood, Takehito Suzuki

We report a transport, thermodynamic, and spectroscopic study of the recently identified topological semiconductor ZrTe$_5$ with a focus on elucidating the connections between its band structure and unusual thermoelectric properties. Using time and angle resolved photoemission spectroscopy (tr-ARPES) we observe a small electronic band gap and temperature dep

Daniel Timmer, Daniel C. Lünemann, Anitta R. Thomas, Anders Frederiksen

The primary step in the elusive ability of migratory birds to sense weak Earth-strength magnetic fields is supposedly the light-induced formation of a long-lived, magnetically sensitive radical pair inside a cryptochrome flavoprotein located in the retina of these birds. Blue light absorption by a flavin chromophore triggers a series of sequential electron t

Christopher Salahub

A structural genetic model incorporating a modern understanding of the genome and common practice in genome-wide association studies is derived mathematically. The model shows the Haldane map distance as a direct consequence of the structure of the genome. An expression for genetic correlation is derived under the model and compared to data resulting from th

Alex Morehead, Xiao Chen, Tianqi Wu, Jian Liu

Protein complexes are macromolecules essential to the functioning and well-being of all living organisms. As the structure of a protein complex, in particular its region of interaction between multiple protein subunits (i.e., chains), has a notable influence on the biological function of the complex, computational methods that can quickly and effectively be

Jonathan Trisnadi, Mingjiamei Zhang, Lauren Weiss, Cheng Chin

The quantum matter synthesizer (QMS) is a new quantum simulation platform in which individual particles in a lattice can be resolved and re-arranged into arbitrary patterns. The ability to spatially manipulate ultracold atoms and control their tunneling and interactions at the single-particle level allows full control of a many-body quantum system. We presen

Gianluca Stellin, Karl-Heinz Speidel, Ulf-G. Meißner

We investigate the magnetic dipole moments in even-even self-conjugate nuclei from ${}^{12}\mathrm{C}$ to ${}^{44}\mathrm{Ti}$. For the latter, the measured gyromagnetic factors of excited states turn out to assume the same value of $g \approx + 0.5$ within statistical errors. This peculiar feature can be interpreted on the basis of collective excitations of

Giacomo Giudice, Federica Maria Surace, Hannes Pichler, Giuliano Giudici

Trimers are defined as two adjacent edges on a graph. We study the quantum states obtained as equal-weight superpositions of all trimer coverings of a lattice, with the constraint of having a trimer on each vertex: the so-called trimer resonating-valence-bond (tRVB) states. Exploiting their tensor network representation, we show that these states can host $\

Md Ifraham Iqbal, Md. Saddam Hossain Mukta, Ahmed Rafi Hasan

Traditional Machine Learning (ML) models like Support Vector Machine, Random Forest, and Logistic Regression are generally preferred for classification tasks on tabular datasets. Tabular data consists of rows and columns corresponding to instances and features, respectively. Past studies indicate that traditional classifiers often produce unsatisfactory resu

Sam Hadden, Daniel Tamayo

We present celmech, an open-source Python package designed to facilitate a wide variety of celestial mechanics calculations. The package allows users to formulate and integrate equations of motion incorporating user-specified terms from the classical disturbing function expansion of the interaction potential between pairs of planets. The code can be applied,

Sajad Abbar, Jose Alonso Carpio, Kohta Murase

The core-collapse of massive stars and merger of neutron star binaries are among the most promising candidate sites for the production of high-energy cosmic neutrinos. We demonstrate that the high-energy neutrinos produced in such extreme environments can experience efficient flavor conversions on scales much shorter than those expected in vacuum, due to the

Gopal Chandra Santra, Fred Jendrzejewski, Philipp Hauke, Daniel J. Egger

Variational quantum algorithms offer fascinating prospects for the solution of combinatorial optimization problems using digital quantum computers. However, the achievable performance in such algorithms and the role of quantum correlations therein remain unclear. Here, we shed light on this open issue by establishing a tight connection to the seemingly unrel

Andrea Derdzinski, Lucio Mayer

We investigate the properties of stars born via gravitational instability in accretion discs around supermassive black holes (SMBHs) in active galactic nuclei (AGN), and how this varies with the SMBH mass, accretion rate, or viscosity. We show with geometrically thin, steady-state disc solutions that fragmentation results in different populations of stars wh

Claudia Cornella, Ferruccio Feruglio, Luca Vecchi

We derive the finite one-loop counterterm required to restore the Ward Identities broken by the regularization scheme in chiral gauge theories. Our result is an analytic expression applicable to a wide class of regularizations satisfying a few general properties. We adopt the background field method, which ensures background gauge invariance in the quantized

Barry M. Dillon, Radha Mastandrea, Benjamin Nachman

We investigate a method of model-agnostic anomaly detection through studying jets, collimated sprays of particles produced in high-energy collisions. We train a transformer neural network to encode simulated QCD "event space" dijets into a low-dimensional "latent space" representation. We optimize the network using the self-supervised contrastive loss, which

Nobuyuki Okuma, Masatoshi Sato

The past decades have witnessed an explosion of interest in topological materials, and a lot of mathematical concepts have been introduced in condensed matter physics. Among them, the bulk-boundary correspondence is the central topic in topological physics, which has inspired researchers to focus on boundary physics. Recently, the concepts of topological pha

Jason L. Sanders, Leigh C. Smith, Carlos González-Fernández, Philip Lucas

The extinction law from $0.9$ to $8$ microns in the inner $3\times3$ deg$^2$ of the Milky Way is measured using data from VISTA Variables in the Via Lactea, GLIMPSE and WISE. Absolute extinction ratios are found by requiring that the observed red clump density peaks at the GRAVITY collaboration distance to the Galactic centre. When combined with selective ex

Charles Wang, Xi Yin

We illustrate the relation between supermoduli integration and picture changing operators (PCOs) particularly concerning the role of vertical integration, in the context of superstring vacuum amplitudes, by an explicit comparison of different parameterizations of the supermoduli space of genus two super Riemann surfaces.

Josh Borrow, Mark Vogelsberger, Stephanie O'Neil, Michael A. McDonald

Backsplash galaxies are galaxies that once resided inside a cluster, and have migrated back oustide as they move towards the apocentre of their orbit. The kinematic properties of these galaxies are well understood, thanks to the significant study of backsplashers in dark matter-only simulations, but their intrinsic properties are not well constrained due to

Eric R. Anschuetz, Lena Funcke, Patrick T. Komiske, Serhii Kryhin

Classical and quantum annealing are computing paradigms that have been proposed to solve a wide range of optimization problems. In this paper, we aim to enhance the performance of annealing algorithms by introducing the technique of degeneracy engineering, through which the relative degeneracy of the ground state is increased by modifying a subset of terms i

Anand Bhattad, D. A. Forsyth

We propose a novel method, StyLitGAN, for relighting and resurfacing generated images in the absence of labeled data. Our approach generates images with realistic lighting effects, including cast shadows, soft shadows, inter-reflections, and glossy effects, without the need for paired or CGI data. StyLitGAN uses an intrinsic image method to decompose an imag

Tao Ge, Heming Xia, Xin Sun, Si-Qing Chen

We study lossless acceleration for seq2seq generation with a novel decoding algorithm -- Aggressive Decoding. Unlike the previous efforts (e.g., non-autoregressive decoding) speeding up seq2seq generation at the cost of quality loss, our approach aims to yield the identical (or better) generation compared with autoregressive decoding but in a significant spe

Lihua Dong, Yun Guo, Ajaharul Islam, Alexander Rothkopf

We generalize a complex heavy-quark potential model from an isotropic QCD plasma to an anisotropic one by replacing the Debye mass $m_D$ with an anisotropic screening mass depending on the quark pair alignment with respect to the direction of anisotropy. Such an angle-dependent mass is determined by matching the perturbative contributions in the potential mo

Norman Danner, James S. Royer

We exhibit a sound and complete implicit-complexity formalism for functions feasibly computable by structural recursions over inductively defined data structures. Feasibly computable here means that the structural-recursive definition runs in time polynomial in the size of the representation of the inputs where these representations may make use of data shar

Jean Prost, Antoine Houdard, Andrés Almansa, Nicolas Papadakis

We investigate the problem of producing diverse solutions to an image super-resolution problem. From a probabilistic perspective, this can be done by sampling from the posterior distribution of an inverse problem, which requires the definition of a prior distribution on the high-resolution images. In this work, we propose to use a pretrained hierarchical var

Jian-Feng Ge, Koen M. Bastiaans, Damianos Chatzopoulos, Doohee Cho

Majorana bound states are putative collective excitations in solids that exhibit the self-conjugate property of Majorana fermions - they are their own antiparticles. In iron-based superconductors, zero-energy states in vortices have been reported as potential Majorana bound states, but the evidence remains controversial. Here, we use scanning tunneling noise

Mari Carmen Bañuls

Tensor networks provide extremely powerful tools for the study of complex classical and quantum many-body problems. Over the last two decades, the increment in the number of techniques and applications has been relentless, and especially the last ten years have seen an explosion of new ideas and results that may be overwhelming for the newcomer. This short r

Marco D'Addezio, Pol van Hoften

We prove the Hecke orbit conjecture of Chai--Oort for Shimura varieties of Hodge type at odd primes of good reduction. We use a novel result for the local monodromy groups of $F$-isocrystals "coming from geometry", which refines Crew's parabolicity conjecture. In the course of the proof, we also introduce a noncommutative generalisation of Serre--Tate coordi

Ziming Liu, Ouail Kitouni, Niklas Nolte, Eric J. Michaud

We aim to understand grokking, a phenomenon where models generalize long after overfitting their training set. We present both a microscopic analysis anchored by an effective theory and a macroscopic analysis of phase diagrams describing learning performance across hyperparameters. We find that generalization originates from structured representations whose

Jue Jiang, Neelam Tyagi, Kathryn Tringale, Christopher Crane

Vision transformers, with their ability to more efficiently model long-range context, have demonstrated impressive accuracy gains in several computer vision and medical image analysis tasks including segmentation. However, such methods need large labeled datasets for training, which is hard to obtain for medical image analysis. Self-supervised learning (SSL)

M. E. Shirokov

A criterion and necessary conditions for convergence (local continuity) of the quantum relative entropy are obtained. Some applications of these results are considered. In particular, the preservation of local continuity of the quantum relative entropy under completely positive linear maps is established.

Wei Zhang, Yu Bao

The Matrix Decomposition techniques have been a vital computational approach to analyzing the hierarchy of functional connectivity in the human brain. However, there are still four shortcomings of these methodologies: 1). Large training samples; 2). Manually tuning hyperparameters; 3). Time-consuming and require extensive computational source; 4). It cannot

Sadra Jazayeri, Sébastien Renaux-Petel

Speed matters. How the masses and spins of new particles active during inflation can be read off from the statistical properties of primordial density fluctuations is well understood. However, not when the propagation speeds of the new degrees of freedom and of the curvature perturbation differ, which is the generic situation in the effective field theory of

Ryo Terasawa, Ryuichi Takahashi, Takahiro Nishimichi, Masahiro Takada

The spatial curvature ($\Omega_K$) of the Universe is one of the most fundamental quantities that could give a link to the early universe physics. In this paper we develop an approximate method to compute the nonlinear matter power spectrum, $P(k)$, for "non-flat" $\Lambda$CDM models using the separate universe (SU) ansatz which states that the effect of the

Melani Sanchez-Garcia, Tushar Chauhan, Benoit R. Cottereau, Michael Beyeler

Deep neural networks have surpassed human performance in key visual challenges such as object recognition, but require a large amount of energy, computation, and memory. In contrast, spiking neural networks (SNNs) have the potential to improve both the efficiency and biological plausibility of object recognition systems. Here we present a SNN model that uses

Alexander Kolesnikov, André Susano Pinto, Lucas Beyer, Xiaohua Zhai

We introduce UViM, a unified approach capable of modeling a wide range of computer vision tasks. In contrast to previous models, UViM has the same functional form for all tasks; it requires no task-specific modifications which require extensive human expertise. The approach involves two components: (I) a base model (feed-forward) which is trained to directly

Noah Glennon, Ethan O. Nadler, Nathan Musoke, Arka Banerjee

Ultralight axions (ULAs) are promising dark matter candidates that can have a distinct impact on the formation and evolution of structure on nonlinear scales relative to the cold, collisionless dark matter (CDM) paradigm. However, most studies of structure formation in ULA models do not include the effects of self-interactions, which are expected to arise ge

Shikha Awasthi, Aditi Sharma, Swapna Gora, O. S. K. S. Sastri

Single particle energy states as described by nuclear shell model are obtained for doubly magic nuclei using Gnumeric worksheet environment. Numerov method rephrased in matrix form is utilised to solve time-independent Schrodinger equation (TISE) within mean-field approximation, described by Woods-Saxon (WS) potential along with spin-orbit term, to obtain th

Jiashuo Fan, Bin Gao, Huan Jin, Lihui Jiang

Deep neural networks (DNNs) have witnessed great successes in semantic segmentation, which requires a large number of labeled data for training. We present a novel learning framework called Uncertainty guided Cross-head Co-training (UCC) for semi-supervised semantic segmentation. Our framework introduces weak and strong augmentations within a shared encoder

Daniela Pérez, Gustavo E. Romero

We analyze whether a black hole can exist and survive in a universe that goes through a cosmological bounce. To this end, we investigate a central inhomogeneity embedded in a bouncing cosmological background modeled by the comoving generalized McVittie metric. Contrary to other dynamical metrics available in the literature, this solution allows for the inter

Simone Ferraro, Emmanuel Schaan, Elena Pierpaoli

Non-linear growth of structure causes the gravitational potentials to grow with time, and this leaves an imprint on the small-scale temperature fluctuations of the Cosmic Microwave Background (CMB), a signal known as the Rees-Sciama (RS) effect. Building on previous studies, here we investigate the detectability of the RS effect by cross-correlating upcoming

Omar Lazar, Yatao Li, Liutang Xue

We study the temperature front problem for the 3D viscous Boussinesq equation. We prove that the $C^{k,\gamma}$ ($k\geq 1$, $0<\gamma< 1$) and $W^{2,\infty}$ regularity of a temperature front is locally preserved along the evolution as well as globally preserved under a smallness condition in a critical space. In particular, beside giving another proof of th

Shangding Gu, Long Yang, Yali Du, Guang Chen

Reinforcement Learning (RL) has achieved tremendous success in many complex decision-making tasks. However, safety concerns are raised during deploying RL in real-world applications, leading to a growing demand for safe RL algorithms, such as in autonomous driving and robotics scenarios. While safe control has a long history, the study of safe RL algorithms

Emin Ucer, Mithat Kisacikoglu

Electric vehicles (EVs) are finally making their way onto the roads, but the challenges concerning long charging times and impact on congestion of the power distribution grid are still not resolved. Proposed solutions depend on heavy communication and rigorous computation and mostly need real-time connectivity for optimal operation; thereby, they are not sca

Nathan Kallus

The fundamental problem of causal inference -- that we never observe counterfactuals -- prevents us from identifying how many might be negatively affected by a proposed intervention. If, in an A/B test, half of users click (or buy, or watch, or renew, etc.), whether exposed to the standard experience A or a new one B, hypothetically it could be because the c

Artem Kaznatcheev, Konrad Paul Kording

Deep Learning (DL) is a surprisingly successful branch of machine learning. The success of DL is usually explained by focusing analysis on a particular recent algorithm and its traits. Instead, we propose that an explanation of the success of DL must look at the population of all algorithms in the field and how they have evolved over time. We argue that cult

Shoki Mori, Ute Boettger, Maximilian Buder, Yuichiro Cho

Accurate wavelength calibration is critical for qualitative and quantitative spectroscopic measurements. Many spectrometers for planetary exploration are equipped with onboard calibration sources. However, such calibration sources are not always available because planetary lander missions often have strong limitations in size and mass. In this study, we prop

Jorge Ramírez-Ruiz, Dmytro Grytskyy, Chiara Mastrogiuseppe, Yamen Habib

Most theories of behavior posit that agents tend to maximize some form of reward or utility. However, animals very often move with curiosity and seem to be motivated in a reward-free manner. Here we abandon the idea of reward maximization, and propose that the goal of behavior is maximizing occupancy of future paths of actions and states. According to this m

Yunjun Gao, Xiaoze Liu, Junyang Wu, Tianyi Li

Entity alignment (EA) aims at finding equivalent entities in different knowledge graphs (KGs). Embedding-based approaches have dominated the EA task in recent years. Those methods face problems that come from the geometric properties of embedding vectors, including hubness and isolation. To solve these geometric problems, many normalization approaches have b

Jan Meibohm, Massimiliano Esposito

We study the time evolution of thermodynamic observables that characterise the dissipative nature of thermal relaxation after an instantaneous temperature quench. Combining tools from stochastic thermodynamics and large-deviation theory, we develop a powerful theory for computing the large-deviation statistics of such observables. Our method naturally leads

Leonard Goff

This paper studies the identifying power of bunching at kinks when the researcher does not assume a parametric choice model. I find that in a general choice model, identifying the average causal response to the policy switch at a kink amounts to confronting two extrapolation problems, each about the distribution of a counterfactual choice that is observed on

Dezhong Tong, Andrew Choi, Jungseock Joo, M. Khalid Jawed

Accurate frictional contact is critical in simulating the assembly of rod-like structures in the practical world, such as knots, hairs, flagella, and more. Due to their high geometric nonlinearity and elasticity, rod-on-rod contact remains a challenging problem tackled by researchers in both computational mechanics and computer graphics. Typically, frictiona

Annika Hagemann, Marcel Stephan Kehl, Jonas Dehning, F. Paul Spitzner

Information processing in the brain requires integration of information over time. Such an integration can be achieved if signals are maintained in the network activity for the required period, as quantified by the intrinsic timescale. While short timescales are considered beneficial for fast responses to stimuli, long timescales facilitate information stora

Karol Horodecki, Jingfang Zhou, Maciej Stankiewicz, Roberto Salazar

Quantum contextuality is one of the most recognized resources in quantum communication and computing scenarios. We provide a new quantifier of this resource, the rank of contextuality (RC). We define RC as the minimum number of non-contextual behaviors that are needed to simulate a contextual behavior. We show that the logarithm of RC is a natural contextual

Saeed Mehri, Jeppe C. Dyre, Trond S. Ingebrigtsen

This paper presents a numerical study of the Gay-Berne liquid crystal model with parameters corresponding to calamitic (rod-shaped) molecules. The focus is on the isotropic and nematic phases at temperatures above unity. There we find strong correlations between the virial and potential-energy thermal fluctuations, reflecting the hidden-scale invariance symm

Christina Peters, Aaron Higuera, Shixiao Liang, Venkat Roy

Robust position reconstruction is paramount for enabling discoveries in astroparticle physics as backgrounds are significantly reduced by only considering interactions within the fiducial volume. In this work, we present for the first time a method for position reconstruction using a Bayesian network which provides per interaction uncertainties. We demonstra

Yik Chuen San, Maxim Perelstein, Philip Tanedo

A dark Z is a massive Abelian gauge boson which is coupled to the Standard Model through both kinetic and mass mixing with the electroweak sector. We study the phenomenology of the dark Z at an energy-frontier electron-positron collider, such as the proposed International Linear Collider (ILC). We show that precision electroweak constraints and the current b

Sarthak Das, Garima Gupta, Suman Chatterjee, Kenji Watanabe

Strong Coulomb interaction in monolayer semiconductors allows them to host optically active large many-body states, such as the five-particle state, charged biexciton. Strong nonlinear light absorption by the charged biexciton under spectral resonance, coupled with its charged nature, makes it intriguing for nonlinear photodetection - an area that is hithert

Makis Arsenis, Robert Kleinberg

Prophet inequalities are performance guarantees for online algorithms (a.k.a. stopping rules) solving the following "hiring problem": a decision maker sequentially inspects candidates whose values are independent random numbers and is asked to hire at most one candidate by selecting it before inspecting the values of future candidates in the sequence. A clas

Daniel Agassy, Dani Dorfman, Haim Kaplan

A $(\phi,\epsilon)$-expander-decomposition of a graph $G$ (with $n$ vertices and $m$ edges) is a partition of $V$ into clusters $V_1,\ldots,V_k$ with conductance $\Phi(G[V_i]) \ge \phi$, such that there are at most $\epsilon m$ inter-cluster edges. Such a decomposition plays a crucial role in many graph algorithms. We give a randomized $\tilde{O}(m/\phi)$ ti

Sohei Ashida

The Hartree-Fock equation is a fundamental equation in many-electron problems. It is of practical importance in quantum chemistry to find solutions to the Hartree-Fock equation. The self-consistent field (SCF) method is a standard numerical calculation method to solve the Hartree-Fock equation. In this paper we prove that the sequence of the functions obtain

Abhiram Kidambi, Masaki Okada, Taizan Watari

The study of rational conformal field theories in the moduli space is of particular interest since these theories correspond to points in moduli space where the algebraic and arithmetic structure are usually richer, while also being points where non--trivial physics occurs (such as in the study of attractor black holes and BPS states at rational points). Thi

Venkat Varada, Mina Ghashami, Jitesh Mehta, Haotian Jiang

Entity Resolution (ER) in voice assistants is a prime component during run time that resolves entities in users request to real world entities. ER involves two major functionalities 1. Relevance generation and 2. Ranking. In this paper we propose a low cost relevance generation framework by generating features using customer implicit and explicit feedback si

Mikhail A. Skvortsov, Mohsen Amini, Vladimir E. Kravtsov

We study the response of an isolated quantum system governed by the Hamiltonian drawn from the Gaussian Rosenzweig-Porter random matrix ensemble to a perturbation controlled by a small parameter. We focus on the density of states, local density of states and the eigenfunction amplitude overlap correlation functions which are calculated exactly using the mapp

Alejandro Morano, Osvaldo Santillán

In the present letter, the dynamics of a spin one-half particle with non abelian charge, interacting with a non abelian monopole like configuration, is studied. In the non spinning case, these equations correspond to the Wong ones [1], and the associated dynamics has been extensively studied in [2]-[3]. The classical limit of a spinning particle in an abelia

René Mellema, Frank Dignum

Within social simulation, we often want agents to interact both with larger systems of norms, as well as respond to their own and other agents norm violations. However, there are currently no norm specifications that allow us to interact with all of these components. To address this issue, this paper introduces the concept of violation modalities in CTL. The

G. S. Bisnovatyi-Kogan

Soft gamma repeaters (SGR) are identified as single neutron stars (NS) inside the Galaxy, or nearby galaxies, with sporadic transient gamma radiation. A total number of discovered SGR, including relative Anomalous X-ray pulsars (AXP), is few tens of objects. Many of them show periodic radiation, connected with NS rotation, with periods 2-12 s. The slow rotat

Henrique S. Assumpção, Fabrício Souza, Leandro Lacerda Campos, Vinícius T. de Castro Pires

Money laundering has become one of the most relevant criminal activities in modern societies, as it causes massive financial losses for governments, banks and other institutions. Detecting such activities is among the top priorities when it comes to financial analysis, but current approaches are often costly and labor intensive partly due to the sheer amount

Tommaso Bianchi, Surudhi Asokraj, Alessandro Brighente, Mauro Conti

Dynamic Wireless Power Transfer (DWPT) can be used for on-demand recharging of Electric Vehicles (EV) while driving. However, DWPT raises numerous security and privacy concerns. Recently, researchers demonstrated that DWPT systems are vulnerable to adversarial attacks. In an EV charging scenario, an attacker can prevent the authorized customer from charging,

Reza Ghorbanchian, Vito Latora, Ginestra Bianconi

Multiplex networks describe systems whose interactions can be of different nature, and are fundamental to understand complexity of networks beyond the framework of simple graphs. Recently it has been pointed out that restricting the attention to pairwise interactions is also a limitation, as the vast majority of complex systems include higher-order interacti

Gilderlan Tavares de Araújo, André Lima Férrer de Almeida, Rémy Boyer, Gábor Fodor

Intelligent reflecting surface (IRS) is a promising technology for the 6th generation of wireless systems, realizing the smart radio environment concept. In this paper, we present a novel tensor-based receiver for IRS-assisted multiple-input multiple-output communications capable of jointly estimating the channels and the transmitted data streams in a semi-b

Nora Brambilla, Miguel Ángel Escobedo, Ajaharul Islam, Michael Strickland

Using the potential non-relativistic quantum chromodynamics (pNRQCD) effective field theory, we derive a Lindblad equation for the evolution of the heavy-quarkonium reduced density matrix that is accurate to next-to-leading order (NLO) in the ratio of the binding energy of the state to the temperature of the medium. The resulting NLO Lindblad equation can be

Yunxing Su, Alfonso Castillo, On Shun Pak, Lailai Zhu

The effects of viscoelasticity have been shown to manifest themselves via symmetry breaking. In this investigation, we show a novel phenomenon that arises from this idea. We observe that when a dense sphere is rotated near a wall (the rotation being aligned with the wall-normal direction and gravity), it levitates to a fixed distance away from the wall. Sinc

Sadhika Malladi, Kaifeng Lyu, Abhishek Panigrahi, Sanjeev Arora

Approximating Stochastic Gradient Descent (SGD) as a Stochastic Differential Equation (SDE) has allowed researchers to enjoy the benefits of studying a continuous optimization trajectory while carefully preserving the stochasticity of SGD. Analogous study of adaptive gradient methods, such as RMSprop and Adam, has been challenging because there were no rigor

Alexander Hötger, Tomer Amit, Julian Klein, Katja Barthelmi

Single spin defects in 2D transition-metal dichalcogenides are natural spin-photon interfaces for quantum applications. Here we report high-field magneto-photoluminescence spectroscopy from three emission lines (Q1, Q2 and Q*) of He-ion induced sulfur vacancies in monolayer $\text{MoS}_2$. Analysis of the asymmetric PL lineshapes in combination with the diam

Alice Contat

We give new equations which characterize the generating functions of planar quadrangulations and planar triangulations, with zero, one or two boundaries. The proof is inspired by the Lackner--Panholzer last car decomposition of parking trees (arXiv:1504.04972) and consists in applying a similar decomposition to the peeling trees of planar maps.

S. Brendle, M. Eichmair

We give an alternative proof of the Michael-Simon-Sobolev inequality using techniques from optimal transport. The inequality is sharp for submanifolds of codimension $2$.

Mark G. Alford, Liam Brodie, Alexander Haber, Ingo Tews

We describe and implement a procedure for determining the couplings of a Relativistic Mean-Field Theory (RMFT) that is optimized for application to neutron star phenomenology. In the standard RMFT approach, the couplings are constrained by comparing the theory's predictions for symmetric matter at saturation density with measured nuclear properties. The theo

Bowen Jin, Yu Zhang, Qi Zhu, Jiawei Han

Representation learning on networks aims to derive a meaningful vector representation for each node, thereby facilitating downstream tasks such as link prediction, node classification, and node clustering. In heterogeneous text-rich networks, this task is more challenging due to (1) presence or absence of text: Some nodes are associated with rich textual inf

G. F. Paraschos, T. P. Krichbaum, J. -Y. Kim, J. A. Hodgson

3C84 (NGC1275) is one of the brightest radio sources in the mm radio-bands, which led to a plethora of VLBI observations at numerous frequencies over the years. They reveal a two-sided jet structure, with an expanding but not well-collimated parsec-scale jet, pointing southward. High resolution mm-VLBI observations allow the study and imaging of the jet base

Vladimir Koltchinskii

Let $E$ be a separable Banach space and let $X, X_1,\dots, X_n, \dots$ be i.i.d. Gaussian random variables taking values in $E$ with mean zero and unknown covariance operator $\Sigma: E^{\ast}\mapsto E.$ The complexity of estimation of $\Sigma$ based on observations $X_1,\dots, X_n$ is naturally characterized by the so called effective rank of $\Sigma:$ ${\b

Ravid Shwartz-Ziv, Micah Goldblum, Hossein Souri, Sanyam Kapoor

Deep learning is increasingly moving towards a transfer learning paradigm whereby large foundation models are fine-tuned on downstream tasks, starting from an initialization learned on the source task. But an initialization contains relatively little information about the source task. Instead, we show that we can learn highly informative posteriors from the

Charles Millard, Mark Chiew

In recent years, there has been attention on leveraging the statistical modeling capabilities of neural networks for reconstructing sub-sampled Magnetic Resonance Imaging (MRI) data. Most proposed methods assume the existence of a representative fully-sampled dataset and use fully-supervised training. However, for many applications, fully sampled training da

Luca Rossini, Paolo Ferrari, Francesco Ruscelli, Arturo Laurenzi

The deployment of robots within realistic environments requires the capability to plan and refine the loco-manipulation trajectories on the fly to avoid unexpected interactions with a dynamic environment. This extended abstract provides a pipeline to offline plan a configuration space global trajectory based on a randomized strategy, and to online locally re