October 2022 arXiv papers — page 30

Frédéric Déglise, Niels Feld, Fangzhou Jin

We compute the perverse delta-homotopy heart of the motivic stable homotopy category over a base scheme with a dimension function delta, rationally or after inverting the exponential characteristic in the equicharacteristic case. In order to do that, we define the notion of homological Milnor-Witt cycle modules and construct a homotopy-invariant Rost-Schmid

Lingzhi Li, Zhen Shen, Zhongshu Wang, Li Shen

We present an explicit-grid based method for efficiently reconstructing streaming radiance fields for novel view synthesis of real world dynamic scenes. Instead of training a single model that combines all the frames, we formulate the dynamic modeling problem with an incremental learning paradigm in which per-frame model difference is trained to complement t

Tianchun Wang, Wei Cheng, Dongsheng Luo, Wenchao Yu

Personalized Federated Learning (PFL) which collaboratively trains a federated model while considering local clients under privacy constraints has attracted much attention. Despite its popularity, it has been observed that existing PFL approaches result in sub-optimal solutions when the joint distribution among local clients diverges. To address this issue,

Matthias Ruf, Caterina Ida Zeppieri

We study the limit behaviour of a sequence of non-convex, vectorial, random integral functionals, defined on $W^{1,1}$, whose integrands satisfy degenerate linear growth conditions. These involve suitable random, scale-dependent weight-functions. Under minimal assumptions on the integrand and on the weight-functions, we show that the sequence of functionals

Lakshya Agarwal, Subhayan Sahu, Shenglong Xu

In this work, we derive an exact hydrodynamical description for the coupled, charge and operator dynamics, in a quantum many-body system with U(1) symmetry. Using an emergent symmetry in the complex Brownian SYK model with charge conservation, we map the operator dynamics in the model to the imaginary-time dynamics of an SU(4) spin-chain. We utilize the emer

Mark Magsino, Yixin Xu

We report on a search for CAZAC sequences by using nonlinear sum of squares optimization. Up to equivalence, we found all length 7 CAZAC sequences. We obtained evidence suggesting there are finitely many length 10 CAZAC sequences with a total of 3040 sequences. Last, we compute longer sequences and compare their aperiodic autocorrelation properties to known

Andrew Audibert, Yang Chen, Dan Graur, Ana Klimovic

Machine learning (ML) computations commonly execute on expensive specialized hardware, such as GPUs and TPUs, which provide high FLOPs and performance-per-watt. For cost efficiency, it is essential to keep these accelerators highly utilized. This requires preprocessing input data at the rate at which the accelerators can ingest and perform ML computations on

Keigo Takahashi, Hiroyasu Matsuura, Hideaki Maebashi, Masao Ogata

We present systematic theoretical results on thermoelectric effects in semimetals based on the variational method of the linearized Boltzmann equation. Inelastic electron-hole scattering is known to play an important role in the unusual transport of semimetals, including the broad $T^2$ temperature dependence of the electrical resistivity and the strong viol

Bhaskar Mitra, Ankit Singhal, Soumya Kundu, James P. Ogle

The influx of non-linear power electronic loads into the distribution network has the potential to disrupt the existing distribution transformer operations. They were not designed to mediate the excessive heating losses generated from the harmonics. To have a good understanding of current standing challenges, a knowledge of the generation and load mix as wel

Yixuan Weng, Bin Li

The goal of visual answering localization (VAL) in the video is to obtain a relevant and concise time clip from a video as the answer to the given natural language question. Early methods are based on the interaction modelling between video and text to predict the visual answer by the visual predictor. Later, using the textual predictor with subtitles for th

Momonari Kudo, Tasuku Nakagawa, Tsuyoshi Takagi

In arithmetic and algebraic geometry, superspecial (s.sp.\ for short) curves are one of the most important objects to be studied, with applications to cryptography and coding theory. If $g \geq 4$, it is not even known whether there exists such a curve of genus $g$ in general characteristic $p > 0$, and in the case of $g=4$, several computational approaches

Heiko Gimperlein, Runan He, Andrew A. Lacey

We consider the formation of finite-time quenching singularities for solutions of semi-linear wave equations with negative power nonlinearities, as can model micro-electro-mechanical systems (MEMS). For radial initial data we obtain, formally, the existence of a sequence of quenching self-similar solutions. Also from formal asymptotic analysis, a solution to

Alice S. Booth, John D. Ilee, Catherine Walsh, Mihkel Kama

Molecular line observations are powerful tracers of the physical and chemical conditions across the different evolutionary stages of star, disk and planet formation. Using the high angular resolution and unprecedented sensitivity of the Atacama Large Millimeter Array (ALMA) there is now a drive to detect small-scale gas structures in protoplanetary disks tha

Sifat Rezwan, Juan A. Cabrera, Frank H. P. Fitzek

The trend of future communication systems is to aim for the steering and control of cyber physical systems. These systems can quickly become congested in environments like those presented in Industry 4.0. In these scenarios, a plethora of sensor data is transmitted wirelessly to multiple in network controllers that compute the control functions of the cyber

Arman Goshtasbi, Ali Sadeghi

The ability to adapt and conform to angular and uneven surfaces improves the suction cup's performance in grasping and manipulation. However, in most cases, the adaptation costs lack of required stiffness for manipulation after surface attachment; thus, the ideal scenario is to have compliance during adaptation and stiffness after attachment to the surface.

Ping He, Bo-Qiang Ma

The Large High Altitude Air Shower Observatory~(LHAASO) is one of the most sensitive gamma-ray detector arrays, whose ultrahigh-energy~(UHE) work bands not only help to study the origin and acceleration mechanism of UHE cosmic rays, but also provide the opportunity to test fundamental physics concepts such as Lorentz symmetry. LHAASO directly observes the $1

Gerben I. Beintema, Maarten Schoukens, Roland Tóth

Using Artificial Neural Networks (ANN) for nonlinear system identification has proven to be a promising approach, but despite of all recent research efforts, many practical and theoretical problems still remain open. Specifically, noise handling and models, issues of consistency and reliable estimation under minimisation of the prediction error are the most

Yi Zhou, Danushka Bollegala

We show that the $\ell_2$ norm of a static sense embedding encodes information related to the frequency of that sense in the training corpus used to learn the sense embeddings. This finding can be seen as an extension of a previously known relationship for word embeddings to sense embeddings. Our experimental results show that, in spite of its simplicity, th

Mohaddeseh Bastan, Mihai Surdeanu, Niranjan Balasubramanian

Natural language inference (NLI) is critical for complex decision-making in biomedical domain. One key question, for example, is whether a given biomedical mechanism is supported by experimental evidence. This can be seen as an NLI problem but there are no directly usable datasets to address this. The main challenge is that manually creating informative nega

Heng Dong, Tonghan Wang, Jiayuan Liu, Chongjie Zhang

Modular Reinforcement Learning (RL) decentralizes the control of multi-joint robots by learning policies for each actuator. Previous work on modular RL has proven its ability to control morphologically different agents with a shared actuator policy. However, with the increase in the Degree of Freedom (DoF) of robots, training a morphology-generalizable modul

Ali Aghdaei, Zhuo Feng

This paper introduces a scalable algorithmic framework (HyperEF) for spectral coarsening (decomposition) of large-scale hypergraphs by exploiting hyperedge effective resistances. Motivated by the latest theoretical framework for low-resistance-diameter decomposition of simple graphs, HyperEF aims at decomposing large hypergraphs into multiple node clusters w

Shivang Agarwal, Daniel R. Kattnig, Clarice D. Aiello, Amartya S. Banerjee

The Posner molecule (calcium phosphate trimer), has been hypothesized to function as a biological quantum information processor due to its supposedly long-lived entangled $^{31}$P nuclear spin states. This hypothesis was challenged by our recent finding that the molecule lacks a well-defined rotational axis of symmetry -- an essential assumption in the propo

Caroline L. Jones, Stefan L. Ludescher, Albert Aloy, Markus P. Mueller

We demonstrate a fundamental relation between the structures of physical space and of quantum theory: the set of quantum correlations in a rotational prepare-and-measure scenario can be derived from covariance alone, without assuming quantum physics. To show this, we consider a semi-device-independent randomness generation scheme where one of two spatial rot

Anar Rustamov

A review is given on recent experimental and theoretical/phenomenological developments regarding the phase structure of the strongly interacting matter. Specifically, evolution with the collision energy of net-proton number fluctuations as measured by several experiments are presented and their implications for the QCD phase diagram are outlined. In addition

Thomas Jannaud, Claudio Zanni, Jonathan Ferreira

Aims. We wish to establish a firm link between jet simulations and analytical studies of magnetically-driven steady-state jets from Keplerian accretion disks. In particular, the latter have predicted the existence of recollimation shocks due to the dominant hoop-stress, so far never observed in platform simulations. Methods. We perform a set of axisymmetric

Biviana Marcela Suárez-Sierra, Arrigo Coen, Carlos Alberto Taimal

Although the applications of Non-Homogeneous Poisson Processes to model and study the threshold overshoots of interest in different time series of measurements have proven to provide good results, they needed to be complemented with an efficient and automatic diagnostic technique to establish the location of the change-points, which, when taken into account,

Emanuel Indrei

In 1947, P\'olya proved that if $n=3,4$ the regular polygon $P_n$ minimizes the principal frequency of an n-gon with given area $\alpha>0$ and suggested that the same holds when $n \ge 5$. In $1951,$ P\'olya & Szeg\"o discussed the possibility of counterexamples in the book "Isoperimetric Inequalities In Mathematical Physics." This paper constructs explicit

Mozes van de Kar, Mengzhou Xia, Danqi Chen, Mikel Artetxe

Masked language models like BERT can perform text classification in a zero-shot fashion by reformulating downstream tasks as text infilling. However, this approach is highly sensitive to the template used to prompt the model, yet practitioners are blind when designing them in strict zero-shot settings. In this paper, we propose an alternative mining-based ap

Richard A. Davison, Blaise Goutéraux, Eric Mefford

Certain holographic states of matter with a global U(1) symmetry support a sound mode at zero temperature, caused neither by spontaneous symmetry breaking of the global U(1) nor by the emergence of a Fermi surface in the infrared. In this work, we show that such a mode is also found in zero density holographic quantum critical states. We demonstrate that in

Kevin Nguyen, Peter West

We present a unified treatment of the conserved asymptotic charges associated with any bosonic massless particle in any spacetime dimension. In particular we provide master formulae for the asymptotic charges and the central extensions in the corresponding charge algebras. These formulae can be explicitly evaluated for any given theory. For illustration we a

Trisha Mittal, Zakaria Aldeneh, Masha Fedzechkina, Anurag Ranjan

Synthesizing natural head motion to accompany speech for an embodied conversational agent is necessary for providing a rich interactive experience. Most prior works assess the quality of generated head motion by comparing them against a single ground-truth using an objective metric. Yet there are many plausible head motion sequences to accompany a speech utt

Botond Fazekas, Dmitrii Lachinov, Guilherme Aresta, Julia Mai

Bruch's membrane (BM) segmentation on optical coherence tomography (OCT) is a pivotal step for the diagnosis and follow-up of age-related macular degeneration (AMD), one of the leading causes of blindness in the developed world. Automated BM segmentation methods exist, but they usually do not account for the anatomical coherence of the results, neither provi

Rupert L. Frank

We are interested in sharp functional inequalities for the coherent state transform related to the Wehrl conjecture and its generalizations. This conjecture was settled by Lieb in the case of the Heisenberg group and then by Lieb and Solovej for SU(2) and by Kulikov for SU(1,1) and the affine group. In this paper, we give alternative proofs and characterize,

Zihao Wu, Huy Tran, Hamed Pirsiavash, Soheil Kolouri

Continual and multi-task learning are common machine learning approaches to learning from multiple tasks. The existing works in the literature often assume multi-task learning as a sensible performance upper bound for various continual learning algorithms. While this assumption is empirically verified for different continual learning benchmarks, it is not ri

Oscar Bustos-Brinez, Joseph Gallego-Mejia, Fabio A. González

This paper presents a novel density estimation method for anomaly detection using density matrices (a powerful mathematical formalism from quantum mechanics) and Fourier features. The method can be seen as an efficient approximation of Kernel Density Estimation (KDE). A systematic comparison of the proposed method with eleven state-of-the-art anomaly detecti

S. Berrone, C. Canuto, M. Pintore, N. Sukumar

In this paper, we present and compare four methods to enforce Dirichlet boundary conditions in Physics-Informed Neural Networks (PINNs) and Variational Physics-Informed Neural Networks (VPINNs). Such conditions are usually imposed by adding penalization terms in the loss function and properly choosing the corresponding scaling coefficients; however, in pract

Sizhen Bian, Vitor Fortes Rey, Siyu Yuan, Paul Lukowicz

The current dominated wearable body motion sensor is IMU. This work presented an alternative wearable motion-sensing approach: human body capacitance (HBC, also commonly defined as body-area electric field). While being less robust in tracking the posture and trajectory, HBC has two properties that make it an attractive. First, the deployment of the sensing

Hanxue Liang, Zhiwen Fan, Rishov Sarkar, Ziyu Jiang

Multi-task learning (MTL) encapsulates multiple learned tasks in a single model and often lets those tasks learn better jointly. However, when deploying MTL onto those real-world systems that are often resource-constrained or latency-sensitive, two prominent challenges arise: (i) during training, simultaneously optimizing all tasks is often difficult due to

Chiang-Mei Chen, Toshimasa Ishige, Sang Pyo Kim, Akitoshi Takayasu

To find the pair production, absorption cross section and quasi-normal modes in background fields, we advance the monodromy method that makes use of the regular singular points of wave equations. We find the mean number of pairs produced in background fields whose mode equations belong to the Riemann differential equation and apply the method to the three pa

Simar Kareer, Naoki Yokoyama, Dhruv Batra, Sehoon Ha

We present Visual Navigation and Locomotion over obstacles (ViNL), which enables a quadrupedal robot to navigate unseen apartments while stepping over small obstacles that lie in its path (e.g., shoes, toys, cables), similar to how humans and pets lift their feet over objects as they walk. ViNL consists of: (1) a visual navigation policy that outputs linear

Saleh Rezaeiravesh, Timofey Mukha, Philipp Schlatter

High-fidelity scale-resolving simulations of turbulent flows quickly become prohibitively expensive, especially at high Reynolds numbers. As a remedy, we may use multifidelity models (MFM) to construct predictive models for flow quantities of interest (QoIs), with the purpose of uncertainty quantification, data fusion and optimization. For numerical simulati

Keerthana Gurushankar, Praveen Venkatesh, Pulkit Grover

We propose two new measures for extracting the unique information in $X$ and not $Y$ about a message $M$, when $X, Y$ and $M$ are joint random variables with a given joint distribution. We take a Markov based approach, motivated by questions in fair machine learning, and inspired by similar Markov-based optimization problems that have been used in the Inform

Alexander Kulyabin, Edvard T. Musaev

We analyze conditions for a tri-vector deformation of a supergravity background to preserve some supersymmetry. Working in the formalism of the SL(5) exceptional field theory, we present its supersymmetry transformations and introduce an additional USp(4) transformation to stay in the supergravity frame. This transformation acts on local indices and deforms

Ievgen Makedonskyi, Andriy Regeta

We prove that the bracket width of the simple Lie algebra of vector fields $\rm{Vec}(C)$ of a smooth irreducible affine curve $C$ with a trivial tangent sheaf is at most three. In addition, if $C$ is a plane curve, the bracket width of $\rm{Vec}(C)$ is at most two and if moreover $C$ has a unique place at infinity, the bracket width of $\rm{Vec}(C)$ is exact

Kaiyuan Gao, Lijun Wu, Jinhua Zhu, Tianbo Peng

Antibodies are versatile proteins that can bind to pathogens and provide effective protection for human body. Recently, deep learning-based computational antibody design has attracted popular attention since it automatically mines the antibody patterns from data that could be complementary to human experiences. However, the computational methods heavily rely

Raphaël Salazar, Sara Varotto, Céline Vergnaud, Vincent Garcia

Multilayers based on quantum materials (complex oxides, topological insulators, transition-metal dichalcogenides, etc) have enabled the design of devices that could revolutionize microelectronics and optoelectronics. However, heterostructures incorporating quantum materials from different families remain scarce, while they would immensely broaden the range o

Forrest Valdez, Viphretuo Mere, Xiaoxi Wang, Nicholas Boynton

High bandwidth, low voltage electro-optic modulators with high optical power handling capability are important for improving the performance of analog optical communications and RF photonic links. Here we designed and fabricated a thin-film lithium niobate (LN) Mach-Zehnder modulator (MZM) which can handle high optical power of 110 mW, while having 3-dB band

Pierre H. Richemond, Sander Dieleman, Arnaud Doucet

Diffusion models typically operate in the standard framework of generative modelling by producing continuously-valued datapoints. To this end, they rely on a progressive Gaussian smoothing of the original data distribution, which admits an SDE interpretation involving increments of a standard Brownian motion. However, some applications such as text generatio

Haozhe Liu, Wentian Zhang, Jinheng Xie, Haoqian Wu

Convolutional neural networks (CNN) have demonstrated remarkable performance when the training and testing data are from the same distribution. However, such trained CNN models often largely degrade on testing data which is unseen and Out-Of-the-Distribution (OOD). To address this issue, we propose a novel "Decoupled-Mixup" method to train CNN models for OOD

Dmitry A. Skorik

The work is devoted to the construction of a new type of intervals -- functional intervals. These intervals are built on the idea of expanding boundaries from numbers to functions. Functional intervals have shown themselves to be promising for further study and use, since they have more rich algebraic properties compared to classical intervals lamy. In the w

Hamid Abban, Ivan Cheltsov, Alexander Kasprzyk, Yuchen Liu

The family of smooth Fano 3-folds with Picard rank 1 and anticanonical volume 4 consists of quartic 3-folds and of double covers of the 3-dimensional quadric branched along an octic surface. They can all be parametrised as complete intersections of a quadric and a quartic in the weighted projective space $\mathbb{P}(1,1,1,1,1,2)$, denoted by $X_{2,4} \subset

Johan Larsson, Quentin Klopfenstein, Mathurin Massias, Jonas Wallin

The lasso is the most famous sparse regression and feature selection method. One reason for its popularity is the speed at which the underlying optimization problem can be solved. Sorted L-One Penalized Estimation (SLOPE) is a generalization of the lasso with appealing statistical properties. In spite of this, the method has not yet reached widespread intere

Dominik Vilsmeier, Rahul Singh, Mei Bai

The number and relative placement of BPMs and steerers with respect to the quadrupoles in a circular lattice can lead to degeneracy in the context of inverse modeling of accelerator optics. Further, the measurement uncertainties introduced by beam position monitors can propagate by the inverse modeling process in ways that prohibit the successful estimation

Maximilian Stritzinger, Francesco Taddia, Stephen S. Lawrence, Ferdinando Patat

We present a multi-band sequence of $Hubble~Space~Telescope$ images documenting the emergence and evolution of multiple light echoes (LEs) linked to the stripped-envelope supernova (SN) 2016adj located in the central dust-lane of Centaurus A. Following point-spread function subtraction, we identify the earliest LE emission associated with a SN at only $+$34

Ivan Cheltsov, Jihun Park

We study degree of irrationality of quasismooth anticanonically embedded weighted Fano 3-fold hypersurfaces that have terminal singularities.

Dario Benedetti, Razvan Gurau, Hannes Keppler, Davide Lettera

We consider the 0-dimensional quartic $O(N)$ vector model and present a complete study of the partition function $Z(g,N)$ and its logarithm, the free energy $W(g,N)$, seen as functions of the coupling $g$ on a Riemann surface. Using constructive field theory techniques we prove that both $Z(g,N)$ and $W(g,N)$ are Borel summable functions along all the rays i

Han Liu, Michelle K. Sigona, Thomas J. Manuel, Li Min Chen

Transcranial focused ultrasound (tFUS) is a therapeutic ultrasound method that focuses sound through the skull to a small region noninvasively and often under MRI guidance. CT imaging is used to estimate the acoustic properties that vary between individual skulls to enable effective focusing during tFUS procedures, exposing patients to potentially harmful ra

Amine Bendahmane, Redouane Tlemsani

Butterfly Optimization Algorithm (BOA) is a recent metaheuristic that has been used in several optimization problems. In this paper, we propose a new version of the algorithm (xBOA) based on the crossover operator and compare its results to the original BOA and 3 other variants recently introduced in the literature. We also proposed a framework for solving t

Khatereh Azizi, Matteo Gori, Uriel Morzan, Ali Hassanali

The microscopic origins of terahertz (THz) vibrational modes in biological systems are an active and open area of current research. Recent experiments [Physical Review X 8, 031061 (2018)] have revealed the presence of a pronounced mode at $\sim$0.3 THz in fluorophore-decorated bovine serum albumin (BSA) protein in aqueous solution under nonequilibrium condit

Yi C. Huang, Hatem Zaag

In a recent work, Duong, Ghoul and Zaag determined the gradient profile for blowup solutions of standard semilinear heat equation with power nonlinearities in the (supposed to be) generic case. Their method refines the constructive techniques introduced by Bricmont and Kupiainen and further developed by Merle and Zaag. In this paper, we extend their refineme

Wei Ding, Sansit Patnaik, Fabio Semperlotti

This study presents a generalized multiscale multimesh finite element method ($\text{M}^2$-FEM) that addresses several long-standing challenges in the numerical simulation of integral structural theories, often used to model multiscale and nonlocal effects. The major challenges in the numerical simulation of integral boundary value problems are primarily roo

Charlie Budd, Luis C. Garcia-Peraza-Herrera, Martin Huber, Sebastien Ourselin

Endoscopic content area refers to the informative area enclosed by the dark, non-informative, border regions present in most endoscopic footage. The estimation of the content area is a common task in endoscopic image processing and computer vision pipelines. Despite the apparent simplicity of the problem, several factors make reliable real-time estimation su

Ivan Cheltsov, Elena Denisova, Kento Fujita

We prove that all smooth Fano threefolds in the families 2.1, 2.2, 2.3, 2.4, 2.6 and 2.7 are K-stable, and we also prove that smooth Fano threefolds in the family 2.5 that satisfy one very explicit generality condition are K-stable.

R. C. Batista, H. P. de Oliveira, L. R. W. Abramo

We study the spherical collapse of non-top-hat matter fluctuations in the presence of dark energy with arbitrary sound speed. The model is described by a system of partial differential equations solved using a pseudo-spectral method with collocation points. This method can reproduce the known analytical solutions in the linear regime with an accuracy better

Léo Vacher, Jonathan Aumont, François Boulanger, Ludovic Montier

The change of physical conditions across the turbulent and magnetized interstellar medium (ISM) induces a 3D spatial variation of the properties of Galactic polarized emission. The observed signal results from the averaging of different spectral energy distributions (SED) and polarization angles, along and between lines of sight. As a consequence, the total

Aakash Khandelwal, Nilay Kant, Ranjan Mukherjee

The problem of designing and stabilizing impact-free, energy-conserving gaits is considered for underactuated, point-foot planar bipeds. Virtual holonomic constraints are used to design energy-conserving gaits. A desired gait corresponds to a periodic hybrid orbit and is stabilized using the Impulse Controlled Poincar\'e Map approach. Numerical simulations f

Alexander J. MacLeod, Prokopis Hadjisolomou, Tae Moon Jeong, Sergei V. Bulanov

High-power laser facilities give experimental access to fundamental strong-field quantum electrodynamics processes. A key effect to be explored is the nonlinear Breit-Wheeler process: the conversion of high-energy photons into electron-positron pairs through the interaction with a strong electromagnetic field. A major challenge to observing nonlinear Breit-W

Ken'ichi Yoshida

We introduce holed cone structures on 3-manifolds to generalize cone structures. In the same way as a cone structure, a holed cone structure induces the holonomy representation. We consider the deformation space consisting of the holed cone structures on a 3-manifold whose holonomy representations are irreducible. This deformation space for positive cone ang

Anna Ivagnes, Nicola Demo, Gianluigi Rozza

In this work, we propose a model order reduction framework to deal with inverse problems in a non-intrusive setting. Inverse problems, especially in a partial differential equation context, require a huge computational load due to the iterative optimization process. To accelerate such a procedure, we apply a numerical pipeline that involves artificial neural

Tommaso Fornaciari, Dirk Hovy, Federico Bianchi

The most common ways to explore latent document dimensions are topic models and clustering methods. However, topic models have several drawbacks: e.g., they require us to choose the number of latent dimensions a priori, and the results are stochastic. Most clustering methods have the same issues and lack flexibility in various ways, such as not accounting fo

Upalaparna Banerjee, Joydeep Chakrabortty, Christoph Englert, Shakeel Ur Rahaman

The shift in focus towards searches for physics beyond the Standard Model (SM) employing model-independent Effective Field Theory (EFT) methods necessitates a rigorous approach to matching to guarantee the validity of the obtained results and constraints. The limits on the leading dimension-six EFT effects can be rather inaccurate for LHC searches that suffe

Micah Bowles, Hongming Tang, Eleni Vardoulaki, Emma L. Alexander

We define deriving semantic class targets as a novel multi-modal task. By doing so, we aim to improve classification schemes in the physical sciences which can be severely abstracted and obfuscating. We address this task for upcoming radio astronomy surveys and present the derived semantic radio galaxy morphology class targets.

Priyanka deSouza, Karoline Barkjohn, Andrea Clements, Jenny Lee

Low-cost sensors (LCS) are increasingly being used to measure fine particulate matter (PM2.5) concentrations in cities around the world. One of the most commonly deployed LCS is the PurpleAir with about 15,000 sensors deployed in the United States. However, the change in sensor performance over time has not been well studied. It is important to understand th

Claudio Battiloro, Paolo Di Lorenzo, Sergio Barbarossa

This paper introduces topological Slepians, i.e., a novel class of signals defined over topological spaces (e.g., simplicial complexes) that are maximally concentrated on the topological domain (e.g., over a set of nodes, edges, triangles, etc.) and perfectly localized on the dual domain (e.g., a set of frequencies). These signals are obtained as the princip

Paul Alsing, Phil Battle, Joshua C. Bienfang, Tammie Borders

Quantum information science and technology (QIST) is a critical and emerging technology with the potential for enormous world impact and is currently invested in by over 40 nations. To bring these large-scale investments to fruition and bridge the lower technology readiness levels (TRLs) of fundamental research at universities to the high TRLs necessary to r

Pierre Glaser, Michael Arbel, Samo Hromadka, Arnaud Doucet

We introduce two synthetic likelihood methods for Simulation-Based Inference (SBI), to conduct either amortized or targeted inference from experimental observations when a high-fidelity simulator is available. Both methods learn a conditional energy-based model (EBM) of the likelihood using synthetic data generated by the simulator, conditioned on parameters

Marie Kunešová, Zbyněk Zajíc

Self-supervised learning approaches have lately achieved great success on a broad spectrum of machine learning problems. In the field of speech processing, one of the most successful recent self-supervised models is wav2vec 2.0. In this paper, we explore the effectiveness of this model on three basic speech classification tasks: speaker change detection, ove

Denjoe O'Connor, Brian P. Dolan

We construct the fuzzy spaces based on the three non-trivial co-adjoint orbits of the exceptional simple Lie group, $G_2$.

Yong Siah Teo, Seongwook Shin, Hyukgun Kwon, Seok-Hyung Lee

Virtual distillation is an error-mitigation technique that reduces quantum-computation errors without assuming the noise type. In scenarios where the user of a quantum circuit is required to additionally employ peripherals, such as delay lines, that introduce excess noise, we find that the error-mitigation performance can be improved if the peripheral, whene

Vivek Reddy Pininti, Gopal Bhatta, Sagarika Paul, Aman Kumar

We present a first systematic time series study of a sample of blazars observed by the Transiting Exoplanet Survey Satellite $\textit{TESS}$ spacecraft. By cross matching the positions of the sources in the TESS observations with those from Roma-BZCAT, 29 blazars including both BL Lacerate objects and flat-spectrum radio quasars were identified. The observat

Siliang Zhang, Jouni Kuha, Fiona Steele

We define a model for the joint distribution of multiple continuous latent variables which includes a model for how their correlations depend on explanatory variables. This is motivated by and applied to social scientific research questions in the analysis of intergenerational help and support within families, where the correlations describe reciprocity of h

Cheng-Yuan Lu, Ming-Hui Yu, Xian-Hui Ge, Li-Jun Tian

We consider the entanglement island in a deformed Jackiw-Teitelboim black hole in the presence of the phase transition. This black hole has the van der Waals-Maxwell-like phase structure as it is coupled with a Maxwell field. We study the behavior of the Page curve of this black hole by using the island paradigm. In the fixed charge ensemble, we discuss diff

Stefan Kojchev, Robert Hult, Jonas Fredriksson

We investigate the problem of coordinating multiple automated vehicles (AVs) in confined areas. This problem can be formulated as an optimal control problem (OCP) where the motion of the AVs is optimized such that collisions are avoided in cross-intersections, merge crossings, and narrow roads. The problem is combinatorial and solving it to optimality is pro

Haoyu Li, Xuan Wang, Tong Liu, Dingyi Fang

The anti-interference capability of wireless links is a physical layer problem for edge computing. Although convolutional codes have inherent error correction potential due to the redundancy introduced in the data, the performance of the convolutional code is drastically degraded due to multipath effects on the channel. In this paper, we propose the use of a

Jiangpeng He, Luotao Lin, Heather Eicher-Miller, Fengqing Zhu

Food classification serves as the basic step of image-based dietary assessment to predict the types of foods in each input image. However, food image predictions in a real world scenario are usually long-tail distributed among different food classes, which cause heavy class-imbalance problems and a restricted performance. In addition, none of the existing lo

Andrei Kiselev, Jeonghyeon Kim, Olivier J. F. Martin

We discuss some examples where numerical simulations based on effectively fabricated nanostructures can provide additional insights into an experiment. Focusing on plasmonics, we study Fano resonant systems for optical trapping, realistic dipole antennas for near-field enhancement, and hybrid nanostructures that combine plasmonic metals with dielectrics refr

Barnali Das, Poonam Chandra, Matt E. Shultz, Paolo Leto

Coherent radio emission via electron cyclotron maser emission (ECME) from hot magnetic stars was discovered more than two decades ago, but the physical conditions that make the generation of ECME favourable remain uncertain. Only recently was an empirical relation, connecting ECME luminosity with the stellar magnetic field and temperature, proposed to explai

Santtu Tikka

In the framework of structural causal models, counterfactual queries describe events that concern multiple alternative states of the system under study. Counterfactual queries often take the form of "what if" type questions such as "would an applicant have been hired if they had over 10 years of experience, when in reality they only had 5 years of experience

Stefan Schmollinger, Si Chen, Sabeeha S. Merchant

All organisms, fundamentally, are made from the same raw material, namely the elements of the periodic table. Biochemical diversity is achieved with how these elements are utilized, for what purpose and in which physical location. Determining elemental distributions, especially those of trace elements that facilitate metabolism as cofactors in the active cen

Omkar Bhilare, Rahul Singh, Vedant Paranjape, Sravan Chittupalli

Because of the availability of larger datasets and recent improvements in the generative model, more realistic Deepfake videos are being produced each day. People consume around one billion hours of video on social media platforms every day, and thats why it is very important to stop the spread of fake videos as they can be damaging, dangerous, and malicious

Albert Zeyer, Robin Schmitt, Wei Zhou, Ralf Schlüter

We introduce a novel segmental-attention model for automatic speech recognition. We restrict the decoder attention to segments to avoid quadratic runtime of global attention, better generalize to long sequences, and eventually enable streaming. We directly compare global-attention and different segmental-attention modeling variants. We develop and compare tw

Xin-Qi Luo, Zhi-Wei Sun

Let $p$ be an odd prime. For $b,c\in\mathbb Z$, Sun introduced the determinant $$D_p(b,c)=\left|(i^2+bij+cj^2)^{p-2}\right|_{1\leqslant i,j \leqslant p-1},$$ and investigated the Legendre symbol $(\frac{D_p(b,c)}p)$. Recently Wu, She and Ni proved that $(\frac{D_p(1,1)}p)=(\frac {-2}p)$ if $p\equiv2\pmod 3$, which confirms a previous conjecture of Sun. In th

Zunian Luo

This paper examines the effect of the federal EV income tax subsidy on EV sales. I find that reduction of the federal subsidy caused sales to decline by $43.2 \%$. To arrive at this result, I employ historical time series data from the Department of Energy Alternative Fuels Data Center. Using the fact that the subsidy is available only for firms with fewer t

K. Mal, S. Kumar, G. Rodrigues, R. Singh

A radially-coupled coaxial fast Faraday cup design was presented in [1] for high-intensity non-relativistic proton beams. In this work, we discuss a modification of that design in the context of a relatively lower intensity ion beam for longitudinal charge profile measurements. Particle-in-cell and time domain electromagnetic simulations of the new design wi

Yara Rizk, Praveen Venkateswaran, Vatche Isahagian, Vinod Muthusamy

The inception of large language models has helped advance state-of-the-art performance on numerous natural language tasks. This has also opened the door for the development of foundation models for other domains and data modalities such as images, code, and music. In this paper, we argue that business process data representations have unique characteristics

Alexandre Duval, Anita Paas, Abdalwhab Abdalwhab, David St-Onge

The drone industry is diversifying and the number of pilots increases rapidly. In this context, flight schools need adapted tools to train pilots, most importantly with regard to their own awareness of their physiological and cognitive limits. In civil and military aviation, pilots can train themselves on realistic simulators to tune their reaction and refle

Stefan Kojchev, Robert Hult, Jonas Fredriksson

Confined areas present an opportunity for early deployment of autonomous vehicles (AV) due to the absence of non-controlled traffic participants. In this paper, we present an approach for coordination of multiple AVs in confined sites. The method computes speed-profiles for the AVs such that collisions are avoided in cross-intersection and merge crossings. S

Piotr Machura, Robert Paluch

Well-established methods of locating the source of information in a complex network are usually derived with the assumption of complete and exact knowledge of network topology. We study the performance of three such algorithms (LPTVA, GMLA and Pearson correlation algorithm) in scenarios that do not fulfill this assumption by modifying the network prior to lo

Peyman Afshani, Pingan Cheng

We give a simplified and improved lower bound for the simplex range reporting problem. We show that given a set $P$ of $n$ points in $\mathbb{R}^d$, any data structure that uses $S(n)$ space to answer such queries must have $Q(n)=\Omega((n^2/S(n))^{(d-1)/d}+k)$ query time, where $k$ is the output size. For near-linear space data structures, i.e., $S(n)=O(n\l

Roel Hulsman

Modern black-box predictive models are often accompanied by weak performance guarantees that only hold asymptotically in the size of the dataset or require strong parametric assumptions. In response to this, split conformal prediction represents a promising avenue to obtain finite-sample guarantees under minimal distribution-free assumptions. Although predic