April 2023 arXiv papers — page 49

Emmy Blumenthal, Pankaj Mehta

A fundamental problem in ecology is to understand how competition shapes biodiversity and species coexistence. Historically, one important approach for addressing this question has been to analyze consumer resource models using geometric arguments. This has led to broadly applicable principles such as Tilman's $R^*$ and species coexistence cones. Here, we ex

Ping Jiang, Junji Oaki, Yoshiyuki Ishihara, Junichiro Ooga

Multiple-suction-cup grasping can improve the efficiency of bin picking in cluttered scenes. In this paper, we propose a grasp planner for a vacuum gripper to use multiple suction cups to simultaneously grasp multiple objects or an object with a large surface. To take on the challenge of determining where to grasp and which cups to activate when grasping, we

Rudeep Gaur, Matt Visser

Black and white holes play remarkably contrasting roles in general relativity versus observational astrophysics. While there is overwhelming observational evidence for the existence of compact objects that are "cold, dark, and heavy", which thereby are natural candidates for black holes, the theoretically viable time-reversed variants -- the "white holes" --

Juexiao Zhou, Xiaonan He, Liyuan Sun, Jiannan Xu

Skin and subcutaneous diseases rank high among the leading contributors to the global burden of nonfatal diseases, impacting a considerable portion of the population. Nonetheless, the field of dermatology diagnosis faces three significant hurdles. Firstly, there is a shortage of dermatologists accessible to diagnose patients, particularly in rural regions. S

Y. Xu, C. J. Hao, D. J. Liu, Z. H. Lin

In spite of much work, the overall spiral structure morphology of the Milky Way remains somewhat uncertain. In the last two decades, accurate distance measurements have provided us with an opportunity to solve this issue. Using the precise locations of very young objects, for the first time, we propose that our galaxy has a multiple-arm morphology that consi

Haoyang Ji, Wenxiu Ma

In this paper we study a class of bimodal cubic polynomials for which its critical points have the same $\omega$-limit set which is an invariant Cantor set. These maps have generalized Fibonacci combinatorics in terms of generalized renormalization on the twin principal nest. It is proved that such maps possess `decay of geometry' in the sense that the scali

Ellis R. Owen, Qin Han, Kinwah Wu

Ultra high-energy (UHE) cosmic rays (CRs) from distant sources interact with intergalactic radiation fields, leading to their spallation and attenuation. They are also deflected in intergalactic magnetic fields (IGMFs), particularly those associated with Mpc-scale structures. These deflections extend the propagation times of CR particles, forming a magnetic

Huiyu Gao, Wei Mao, Miaomiao Liu

We propose VisFusion, a visibility-aware online 3D scene reconstruction approach from posed monocular videos. In particular, we aim to reconstruct the scene from volumetric features. Unlike previous reconstruction methods which aggregate features for each voxel from input views without considering its visibility, we aim to improve the feature fusion by expli

Weijia Yang, Sarah N. Sparrow, David C. H. Wallom

This paper proposes a generalised and robust multi-factor Gated Recurrent Unit (GRU) based Deep Learning (DL) model to forecast electricity load in distribution networks during wildfire seasons. The flexible modelling methods consider data input structure, calendar effects and correlation-based leading temperature conditions. Compared to the regular use of i

Amir Sagiv, Michael I. Weinstein

The spectral analysis of the unitary monodromy operator, associated with a time-periodically (paramatrically) forced Schrodinger equation, is a question of longstanding interest. Here, we consider this question for Hamiltonians of the form $$H^{\varepsilon}(t)=H^0 + \varepsilon^a W(\varepsilon^a t, -i\nabla)\, ,$$ where $H^0$ is an unperturbed autonomous Ham

Christopher Li

As a co-author of the paper Theoretical understanding of evolutionary dynamics on inhomogeneous networks, I would like to express my disagreement with the conclusion of the paper. In this response, I present a thorough examination of the assertions and methods in the paper. Although I may disagree with several practices in the research group, I will confine

Kaushik Satapathy, Dimitrios Psaltis, Feryal Ozel

In the collisionless plasmas of radiatively inefficient accretion flows, heating and acceleration of ions and electrons is not well understood. Recent studies in the gyrokinetic limit revealed the importance of incorporating both the compressive and Alfvenic cascades when calculating the partition of dissipated energy between the plasma species. In this pape

Jorge Cuadra

Reports from the 2nd and 3rd Century AD attribute the first measurement of the angular size of the Sun to Thales of Miletus, in the 6th Century BC. Cleomedes, also in the 2nd Century AD, described a method to perform the measurement, based on timing the duration of the sunrise. Several modern authors have suggested Thales used Cleomedes' method, but others a

Miles H. Currie, Victoria S. Meadows, Kaitlin C. Rasmussen

Within the next decade, atmospheric O$_2$ on Earth-like M dwarf planets may be accessible with visible--near-infrared, high spectral resolution extremely large ground-based telescope (ELT) instruments. However, the prospects for using ELTs to detect environmental properties that provide context for O$_2$ have not been thoroughly explored. Additional molecule

Alexander Nahmad-Rohen, Augusto García-Valenzuela

We present a multiple-scattering model for the effective refractive index of an arbitrarily dense suspension of forward-scattering particles. The model provides a very simple formula for the effective refractive index of such a suspension and reproduces with high accuracy available experimental results. Furthermore, the derivation we present herein is mathem

Felipe S. Abrahão, Ricardo P. Cavassane, Michael Winter, Mariana Vitti Rodrigues

In this article, we discuss the ubiquity of Big Data and machine learning in society and propose that it evinces the need of further investigation of their fundamental limitations. We extend the ``too much information tends to behave like very little information'' phenomenon to formal knowledge about lawlike universes and arbitrary collections of computably

Micah Loverro, Adrian Vasiu

Let $p$ be a prime. Given a split semisimple group scheme $G$ over a normal integral domain $R$ which is a faithfully flat $\mathbb Z_{(p)}$-algebra, we classify all finite dimensional representations $V$ of the fiber $G_K$ of $G$ over $K:=\text{Frac}(R)$ with the property that the set of lattices of $V$ with respect to $R$ which are $G$-modules is as well t

Patrick J. Roddy

Wavelets are widely used in various disciplines to analyse signals both in space and scale. Whilst many fields measure data on manifolds (i.e., the sphere), often data are only observed on a partial region of the manifold. Wavelets are a typical approach to data of this form, but the wavelet coefficients that overlap with the boundary become contaminated and

Donghua Wang, Wen Yao, Tingsong Jiang, Weien Zhou

Wide deployment of deep neural networks (DNNs) based applications (e.g., style transfer, cartoonish), stimulating the requirement of copyright protection of such application's production. Although some traditional visible copyright techniques are available, they would introduce undesired traces and result in a poor user experience. In this paper, we propose

Amin Rahimi Dalkhani, Mus'ab Al Hasani, Guy Drijkoningen, Cornelis Weemstra

Distributed Acoustic Sensing (DAS) is a novel technology that allows sampling of the seismic wavefield densely over a broad frequency band. This makes it an ideal tool for surface wave studies. In this study, we evaluate the potential of DAS to image the near-surface using synthetic data and active-source field DAS data recorded with straight fibers in Groni

Ceyhun Efe Kayan, Talha Enes Koksal, Arda Sevinc, Abdurrahman Gumus

The rapid and accurate detection of COVID-19 cases is critical for timely treatment and preventing the spread of the disease. In this study, a two-stage feature extraction framework using eight state-of-the-art pre-trained deep Convolutional Neural Networks (CNNs) and an autoencoder is proposed to determine the health conditions of patients (COVID-19, Normal

Johannes C. B. Dietschreit, Dennis J. Diestler, Rafael Gómez-Bombarelli

The description of chemical processes at the molecular level is often facilitated by use of reaction coordinates, or collective variables (CVs). The CV measures the progress of the reaction and allows the construction of profiles that track the evolution of a specific property as the reaction progresses. Whereas CVs are routinely used, especially alongside e

Richard Mayne, Nic Roberts, Neil Phillips, Roshan Weerasekera

Living fungal mycelium networks are proven to have properties of memristors, capacitors and various sensors. To further progress our designs in fungal electronics we need to evaluate how electrical signals can be propagated through mycelium networks. We investigate the ability of mycelium-bound composites to convey electrical signals, thereby enabling the tr

Abdennour Kitouni, Sergei Silvestrov

The aim of this work is to investigate the properties and classification of an interesting class of $4$-dimensional $3$-Hom-Lie algebras with a nilpotent twisting map $\alpha$ and eight structure constants as parameters. Derived series and central descending series are studied for all algebras in this class and are used to divide it into five non-isomorphic

Valentin Konakov, Enno Mammen, Lorick Huang

The Robbins-Monro algorithm is a recursive, simulation-based stochastic procedure to approximate the zeros of a function that can be written as an expectation. It is known that under some technical assumptions, Gaussian limit distributions approximate the stochastic performance of the algorithm. Here, we are interested in strong approximations for Robbins-Mo

Musang Gong, Min Yu, Ralf Betzholz, Yaoming Chu

In a solid-state spin system, we experimentally demonstrate a protocol for quantum-state population transfer with an improved efficiency compared to traditional stimulated Raman adiabatic passage (STIRAP). Using the ground-state triplet of the nitrogen-vacancy center in diamond, we show that the required evolution time for high-fidelity state transfer can be

Ji Yu

Despite their superior performance, deep-learning methods often suffer from the disadvantage of needing large-scale well-annotated training data. In response, recent literature has seen a proliferation of efforts aimed at reducing the annotation burden. This paper focuses on a weakly-supervised training setting for single-cell segmentation models, where the

Harim Jin, Sung-Chul Yoon, Sergei Blinnikov

Type Ib and Ic supernovae (SNe Ib/Ic) originate from hydrogen-deficient massive star progenitors, of which the exact properties are still much debated. Using the SN data in the literature, we investigate the optical $B-V$ color of SNe Ib/Ic at the $V-$band peak and show that SNe Ib are systematically bluer than SNe Ic. We construct SN models from helium-rich

Abhinau K. Venkataramanan

The perception of color is one of the most important aspects of human vision. From an evolutionary perspective, the accurate perception of color is crucial to distinguishing friend from foe, and food from fatal poison. As a result, humans have developed a keen sense of color and are able to detect subtle differences in appearance, while also robustly identif

Costas Mavromatis, Vassilis N. Ioannidis, Shen Wang, Da Zheng

How can we learn effective node representations on textual graphs? Graph Neural Networks (GNNs) that use Language Models (LMs) to encode textual information of graphs achieve state-of-the-art performance in many node classification tasks. Yet, combining GNNs with LMs has not been widely explored for practical deployments due to its scalability issues. In thi

Hikaru Tamura, Sergei Khlebnikov, Cheng-An Chen, Chen-Lung Hung

Understanding the dynamics and stability of transonic flows in quantum fluids, especially for those beyond one spatial dimension, is an outstanding challenge, with applications ranging from nonlinear optics and condensed matter to analogue gravity. One intriguing possibility is that a system with a spatially bounded supersonic flow may evolve into a self-osc

Artur Santos Nascimento, Welerson Augusto Lino de Jesus Melo, Daniel Oliveira Dantas, Beatriz Trinchão Andrade

Feature point (FP) detection is a fundamental step of many computer vision tasks. However, FP detectors are usually designed for low dynamic range (LDR) images. In scenes with extreme light conditions, LDR images present saturated pixels, which degrade FP detection. On the other hand, high dynamic range (HDR) images usually present no saturated pixels but FP

Parth Pavaskar, Huirong Yan, Jungyeon Cho

The Davis-Chandrasekhar-Fermi (DCF) method is widely employed to estimate the mean magnetic field strength in astrophysical plasmas. In this study, we present a numerical investigation using the DCF method in conjunction with a promising new diagnostic tool for studying magnetic fields: the polarization of spectral lines resulting from the atomic alignment e

Miriam Jäger, Patrick Hübner, Dennis Haitz, Boris Jutzi

Neural Radiance Fields (NeRFs) are trained using a set of camera poses and associated images as input to estimate density and color values for each position. The position-dependent density learning is of particular interest for photogrammetry, enabling 3D reconstruction by querying and filtering the NeRF coordinate system based on the object density. While t

Xiaolin Hu

Natural language understanding is one of the most challenging topics in artificial intelligence. Deep neural network methods, particularly large language module (LLM) methods such as ChatGPT and GPT-3, have powerful flexibility to adopt informal text but are weak on logical deduction and suffer from the out-of-vocabulary (OOV) problem. On the other hand, rul

Francesco Capuano, Davorin Peceli, Gabriele Tiboni, Raffaello Camoriano

High Power Laser's (HPL) optimal performance is essential for the success of a wide variety of experimental tasks related to light-matter interactions. Traditionally, HPL parameters are optimised in an automated fashion relying on black-box numerical methods. However, these can be demanding in terms of computational resources and usually disregard transient

Ali Al-Ameri, Jaeyoung Park, Juan Sanchez, Xuesong Cai

Channel sounding is essential for the development of radio systems. One flexible strategy is the switched-array-based channel sounding, where antenna elements are activated at different time instants to measure the channel spatial characteristics. Although its hardware complexity is decreased due to fewer radio-frequency (RF) chains, sequentially switching t

Jian Yang

We show that certain ways of solving some combinatorial optimization problems can be understood as using query planes to divide the space of problem instances into polyhedra that could fit into those that characterize the problem's various solutions. This viewpoint naturally leads to a splinter-proneness property that is then shown to be responsible for the

Atul Dhingra, Gaurav Sood

How do you scale a machine learning product at a startup? In particular, how do you serve a greater volume, velocity, and variety of queries cost-effectively? We break down costs into variable costs-the cost of serving the model and performant-and fixed costs-the cost of developing and training new models. We propose a framework for conceptualizing these cos

Peter Borg

Given a set $\mathcal{F}$ of graphs, we call a copy of a graph in $\mathcal{F}$ an $\mathcal{F}$-graph. The $\mathcal{F}$-isolation number of a graph $G$, denoted by $\iota(G,\mathcal{F})$, is the size of a smallest set $D$ of vertices of $G$ such that the closed neighbourhood of $D$ intersects the vertex sets of the $\mathcal{F}$-graphs contained by $G$ (eq

Divyanshu Pandey, Adithya Venugopal, Harry Leib

In past few decades, tensor algebra also known as multi-linear algebra has been developed and customized as a tool to be used for various engineering applications. In particular, with the help of a special form of tensor contracted product, known as the Einstein Product and its properties, many of the known concepts from Linear Algebra could be extended to a

Vinicius M. Placco, Letizia Stanghellini

This article presents an overview of the US National Gemini Office (US NGO) and its role within the International Gemini Observatory user community. Throughout the years, the US NGO charter changed considerably to accommodate the evolving needs of astronomers and the observatory. The current landscape of observational astronomy requires effective communicati

J. Cuevas-Maraver, P. G. Kevrekidis, Q. Y. Chen, G. A. Kevrekidis

We explore the inclusion of vaccination in compartmental epidemiological models concerning the delta and omicron variants of the SARS-CoV-2 virus that caused the COVID-19 pandemic. We expand on our earlier compartmental-model work by incorporating vaccinated populations. We present two classes of models that differ depending on the immunological properties o

Anna P. Meyer, Aws Albarghouthi, Loris D'Antoni

We introduce dataset multiplicity, a way to study how inaccuracies, uncertainty, and social bias in training datasets impact test-time predictions. The dataset multiplicity framework asks a counterfactual question of what the set of resultant models (and associated test-time predictions) would be if we could somehow access all hypothetical, unbiased versions

Jessica Schonhut-Stasik, Joel C. Zinn, Keivan G. Stassun, Marc Pinsonneault

We present a catalog of fundamental stellar properties for 7,673 evolved stars, including stellar radii and masses, determined from the combination of spectroscopic observations from the Apache Point Observatory Galactic Evolution Experiment (APOGEE), part of the Sloan Digital Sky Survey IV (SDSS), and asteroseismology from K2. The resulting APO-K2 catalog p

Jian Yang

We deal with coalitional games possessing strictly positive values. Individually rational allocations of such a game has clear fractional interpretations. Many concepts, including the long-existing core and other stability notions more recently proposed by Yang \cite{Y22}, can all be re-cast in this fractional mode. The latter allows a certain ranking betwee

Jian Yang

We are concerned with the stability of a coalitional game, i.e., a transferable-utility (TU) cooperative game. First, the concept of core can be weakened so that the blocking of changes is limited to only those with multilateral backings. This principle of consensual blocking, as well as the traditional core-defining principle of unilateral blocking and one

Daniel Hothem, Kevin Young, Tommie Catanach, Timothy Proctor

Accurately predicting a quantum computer's capability -- which circuits it can run and how well it can run them -- is a foundational goal of quantum characterization and benchmarking. As modern quantum computers become increasingly hard to simulate, we must develop accurate and scalable predictive capability models to help researchers and stakeholders decide

R. E. Kastner

The traditional, standard approach to quantum theory is to assume that the theory ``really'' contains only unitary physical dynamics--i.e., that the only physically quantifiable evolution is that given by the time-dependent Schrodinger equation. This leads to two distinct classes of interpretations for the standard theory in its orthodox form: (i) an Everett

Ataollah Kalantari Osgouei, Hasan Kocer, Halil Isik, Yilmaz Durna

Thermal infrared (IR) radiation has attracted considerable attention due to its applications ranging from radiative cooling to thermal management. In this paper, we design a multi-band graphene-based metamaterial absorber compatible with infrared applications and radiative cooling performance. The proposed structure consists of the single-sized metal-insulat

Adrienne Kline, Vinesh Appadurai, Yuan Luo, Sanjiv Shah

Leveraging medical record information in the era of big data and machine learning comes with the caveat that data must be cleaned and de-identified. Facilitating data sharing and harmonization for multi-center collaborations are particularly difficult when protected health information (PHI) is contained or embedded in image meta-data. We propose a novel libr

Xi Chen, Shyamal Patel

We prove a $k^{-\Omega(\log(\varepsilon_2 - \varepsilon_1))}$ lower bound for adaptively testing whether a Boolean function is $\varepsilon_1$-close to or $\varepsilon_2$-far from $k$-juntas. Our results provide the first superpolynomial separation between tolerant and non-tolerant testing for a natural property of boolean functions under the adaptive settin

Rachit Nigam, Pedro Henrique Azevedo De Amorim, Adrian Sampson

Modular design is a key challenge for enabling large-scale reuse of hardware modules. Unlike software, however, hardware designs correspond to physical circuits and inherit constraints from them. Timing constraints -- which cycle a signal arrives, when an input is read -- and structural constraints -- how often a multiplier accepts new inputs -- are fundamen

P. N. Bibikov

The ferromagnetically polarized gapped XXZ spin chain is studied at low temperatures. Utilizing only the one- and two-magnon spectrums and focusing on the magnon-creation contribution to the transverse dynamical susceptibility, we represent the latter in the form of the Dyson equation. Then, following the well known correspondence between the imaginary part

Alex Abreu, Antonio Nigro

For each indifference graph, there is an associated regular semisimple Hessenberg variety, whose cohomology recovers the chromatic symmetric function of the graph. The decomposition theorem applied to the forgetful map from the regular semisimple Hessenberg variety to the projective space describes the cohomology of the Hessenberg variety as a sum of smaller

Tao Li, Zhangke Liang, Andreas Dreizler, Benjamin Böhm

In the present work, accurate determination of single-particle ignition is focused on using high-speed optical diagnostics combined with machine learning approaches. Ignition of individual particles in a laminar flow reactor are visualized by simultaneous 10 kHz OH-LIF and DBI measurements. Two coal particle sizes of 90-125{\mu}m and 160-200{\mu}m are invest

Chaitra Hedge, Gezheng Wen, Layne C. Price

Activity classification has become a vital feature of wearable health tracking devices. As innovation in this field grows, wearable devices worn on different parts of the body are emerging. To perform activity classification on a new body location, labeled data corresponding to the new locations are generally required, but this is expensive to acquire. In th

Anik Saha, Alex Gittens, Bulent Yener

Pre-trained contextual language models are ubiquitously employed for language understanding tasks, but are unsuitable for resource-constrained systems. Noncontextual word embeddings are an efficient alternative in these settings. Such methods typically use one vector to encode multiple different meanings of a word, and incur errors due to polysemy. This pape

Lars T. Kreutzer, Edward Gillen, Joshua T. Briegal, Didier Queloz

We present a generalised estimator for the autocorrelation function, S-ACF, which is an extended version of the standard estimator of the autocorrelation function (ACF). S-ACF is a versatile definition that can robustly and efficiently extract periodicity and signal shape information from a time series, independent of the time sampling and with minimal assum

Boris Velasevic, Rohit Parasnis, Christopher G. Brinton, Navid Azizan

We consider the problem of solving a large-scale system of linear equations in a distributed or federated manner by a taskmaster and a set of machines, each possessing a subset of the equations. We provide a comprehensive comparison of two well-known classes of algorithms used to solve this problem: projection-based methods and optimization-based methods. Fi

Yasir Alanazi, Malachi Schram, Kishansingh Rajput, Steven Goldenberg

We present a multi-module framework based on Conditional Variational Autoencoder (CVAE) to detect anomalies in the power signals coming from multiple High Voltage Converter Modulators (HVCMs). We condition the model with the specific modulator type to capture different representations of the normal waveforms and to improve the sensitivity of the model to ide

Manaar Alam, Hithem Lamri, Michail Maniatakos

Federated Learning (FL) enables collaborative deep learning training across multiple participants without exposing sensitive personal data. However, the distributed nature of FL and the unvetted participants' data makes it vulnerable to backdoor attacks. In these attacks, adversaries inject malicious functionality into the centralized model during training,

Iker García-Ferrero, Jon Ander Campos, Oscar Sainz, Ander Salaberria

Named Entity Recognition (NER) is a core natural language processing task in which pre-trained language models have shown remarkable performance. However, standard benchmarks like CoNLL 2003 do not address many of the challenges that deployed NER systems face, such as having to classify emerging or complex entities in a fine-grained way. In this paper we pre

Joppe de Ree, Matthijs Oosterveen, Dinand Webbink

This paper analyzes the effects of educational tracking and the quality of track assignment decisions. We motivate our analysis using a model of optimal track assignment under uncertainty. This model generates predictions about the average effects of tracking at the margin of the assignment process. In addition, we recognize that the average effects do not m

Srijata Lahiri, Saurabh Basu

Haldane model is a celebrated tight binding toy model in a 2D honeycomb lattice that exhibits quantized Hall conductance in the absence of an external magnetic field. In our work, we deform the bands of the Haldane model smoothly by varying one of its three nearest neighbour hopping amplitudes ($t_1$), while keeping the other two ($t$) fixed. This breaks the

Yin Yong Chee, Parham Oveissi, Siyuan Shao, Joonghyun Lee

This paper modifies an adaptive multicopter autopilot to mitigate instabilities caused by adaptive parameter drift and presents simulation and experimental results to validate the modified autopilot. The modified adaptive controller is obtained by including a static nonlinearity in the adaptive loop, updated by the retrospective cost adaptive control algorit

Daniel R. Hawtin, Cheryl E. Praeger, Jin-Xin Zhou

A \emph{mixed dihedral group} is a group $H$ with two disjoint subgroups $X$ and $Y$, each elementary abelian of order $2^n$, such that $H$ is generated by $X\cup Y$, and $H/H'\cong X\times Y$. In this paper we give a sufficient condition such that the automorphism group of the Cayley graph $\Cay(H,(X\cup Y)\setminus\{1\})$ is equal to $H: A(H,X,Y)$, where $

Piyush Batra, Gagan Raj Singh, Ritik Gandhi

In an increasingly digitized world, the secure management and trade of digital assets have become a pressing issue. This project aims to address this challenge by developing a decentralized application (dApp) that leverages blockchain technology and deep learning models to provide secure and efficient digital asset management, with a focus on NFTs. The dApp

Omar Melikechi, David B. Dunson

We introduce Cayley transform ellipsoid fitting (CTEF), an algorithm that uses the Cayley transform to fit ellipsoids to noisy data in any dimension. Unlike many ellipsoid fitting methods, CTEF is ellipsoid specific, meaning it always returns elliptic solutions, and can fit arbitrary ellipsoids. It also significantly outperforms other fitting methods when da

Mike Prest, Rose Wagstaffe

We give an account of model theory in the context of compactly generated triangulated and tensor-triangulated categories ${\cal T}$. We describe pp formulas, pp-types and free realisations in such categories and we prove elimination of quantifiers and elimination of imaginaries. We compare the ways in which definable subcategories of ${\cal T}$ may be specif

Hao Xiang, Runsheng Xu, Jiaqi Ma

Vehicle-to-Vehicle technologies have enabled autonomous vehicles to share information to see through occlusions, greatly enhancing perception performance. Nevertheless, existing works all focused on homogeneous traffic where vehicles are equipped with the same type of sensors, which significantly hampers the scale of collaboration and benefit of cross-modali

Ben Kain

We study the Dirac equation minimally coupled to general relativity using quantum field theory and the semiclassical gravity approximation. Previous studies of the Einstein-Dirac system did not quantize the Dirac field and required multiple independent Dirac fields to preserve spherical symmetry. We canonically quantize a single Dirac field in a static spher

Alexey V. Bolsinov, Andrey Yu. Konyaev, Vladimir S. Matveev

The paper contains two lines of results: the first one is a study of symmetries and conservation laws of gl-regular Nijenhuis operators. We prove the splitting Theorem for symmetries and conservation laws of Nijenhuis operators, show that the space of symmetries of a gl-regular Nijenhuis operator forms a commutative algebra with respect to (pointwise) matrix

Sukjoo Lee

Mirror symmetry for a semi-stable degeneration of a Calabi-Yau manifold was first investigated by Doran-Harder-Thompson when the degeneration fiber is a union of two (quasi)-Fano manifolds. They propose a topological construction of a mirror Calabi-Yau that is a gluing of two Landau-Ginzburg models mirror to those Fano manifolds. We extend this construction

Wenqiao Zhang, Changshuo Liu, Lingze Zeng, Beng Chin Ooi

Conventional multi-label classification (MLC) methods assume that all samples are fully labeled and identically distributed. Unfortunately, this assumption is unrealistic in large-scale MLC data that has long-tailed (LT) distribution and partial labels (PL). To address the problem, we introduce a novel task, Partial labeling and Long-Tailed Multi-Label Class

Ricardo Caroca, Patrick Concha, Javier Matulich, Evelyn Rodríguez

In this work we present novel and known three-dimensional hypergravity theories which are obtained by applying the powerful semigroup expansion method. We show that the expansion procedure considered here yields a consistent way of coupling different three-dimensional Chern-Simons gravity theories with massless spin-$\frac{5}{2}$ gauge fields. First, by expa

Mickaël D. Chekroun, Tom Dror, Ilan Koren, Honghu Liu

Modeling emergent, multiscale patterns in complex systems remains a persistent interdisciplinary challenge, particularly when deciphering transient, highly coupled dynamics from severely limited data. To overcome the inherent spectral sparsity of standard finite-dimensional stochastic models, we introduce Multilayered Stochastic Hierarchical Delay Models (MS

Nilantha Premakumara, Brian Jalaian, Niranjan Suri, Hooman Samani

Robustness against real-world distribution shifts is crucial for the successful deployment of object detection models in practical applications. In this paper, we address the problem of assessing and enhancing the robustness of object detection models against natural perturbations, such as varying lighting conditions, blur, and brightness. We analyze four st

Patrick John Chia, Giuseppe Attanasio, Jacopo Tagliabue, Federico Bianchi

Recommender Systems today are still mostly evaluated in terms of accuracy, with other aspects beyond the immediate relevance of recommendations, such as diversity, long-term user retention and fairness, often taking a back seat. Moreover, reconciling multiple performance perspectives is by definition indeterminate, presenting a stumbling block to those in th

Lingyao Li, Lizhou Fan, Shubham Atreja, Libby Hemphill

Harmful content is pervasive on social media, poisoning online communities and negatively impacting participation. A common approach to address this issue is to develop detection models that rely on human annotations. However, the tasks required to build such models expose annotators to harmful and offensive content and may require significant time and cost

Zachary Susskind, Aman Arora, Igor D. S. Miranda, Alan T. L. Bacellar

The deployment of AI models on low-power, real-time edge devices requires accelerators for which energy, latency, and area are all first-order concerns. There are many approaches to enabling deep neural networks (DNNs) in this domain, including pruning, quantization, compression, and binary neural networks (BNNs), but with the emergence of the "extreme edge"

Zhandos A. Moldabekov, Xuecheng Shao, Michele Pavanello, Jan Vorberger

Orbital-free density functional theory (OF-DFT) constitutes a computationally highly effective tool for modeling electronic structures of systems ranging from room-temperature materials to warm dense matter. Its accuracy critically depends on the employed kinetic energy (KE) density functional, which has to be supplied as an external input. In this work we c

Damien Tageddine, Jean-Christophe Nave

This paper is a follow-up on the \emph{noncommutative differential geometry on infinitesimal spaces} [15]. In the present work, we extend the algebraic convergence from [15] to the geometric setting. On the one hand, we reformulate the definition of finite dimensional compatible Dirac operators using Clifford algebras. This definition also leads to a new con

Duc Hoa Tran, Michel Meunier, Farida Cheriet

For any type of microscopy image, getting a deep learning model to work well requires considerable effort to select a suitable architecture and time to train it. As there is a wide range of microscopes and experimental setups, designing a single model that can apply to multiple imaging domains, instead of having multiple per-domain models, becomes more essen

Wontae Kim

This paper provides a local and global Calder\'on-Zygmund type estimate of a weak solution to the parabolic double-phase system. The proof of local estimate is based on comparison estimates and the scaling invariant property of the parabolic double-phase system in the intrinsic cylinders of the stopping time argument setting. For the proof of the global esti

Hugo Inzirillo, Ludovic De Villelongue

It is difficult to identify anomalies in time series, especially when there is a lot of noise. Denoising techniques can remove the noise but this technique can cause a significant loss of information. To detect anomalies in the time series we have proposed an attention free conditional autoencoder (AF-CA). We started from the autoencoder conditional model on

Lie He, Shiva Prasad Kasiviswanathan

In this paper, we study the conditional stochastic optimization (CSO) problem which covers a variety of applications including portfolio selection, reinforcement learning, robust learning, causal inference, etc. The sample-averaged gradient of the CSO objective is biased due to its nested structure, and therefore requires a high sample complexity for converg

Minghui Zhang, Alex Sokolov, Weixin Cai, Si-Qing Chen

Natural language generation (NLG) is one of the most impactful fields in NLP, and recent years have witnessed its evolution brought about by large language models (LLMs). As the key instrument for writing assistance applications, they are generally prone to replicating or extending offensive content provided in the input. In low-resource data regime, they ca

Alessandro Pierro, Kristine Heiney, Shamit Shrivastava, Giulia Marcucci

As demand for computational resources reaches unprecedented levels, research is expanding into the use of complex material substrates for computing. In this study, we interface with a model of a hydrodynamic system, under development by a startup, as a computational reservoir and optimize its properties using an evolution in materio approach. Input data are

Penha Maria Cardozo Dias

The association of information with entropy has been argued on plausibility arguments involving the operation of imaginary engines and beings, and it is not a universal theorem. In this paper, a theorem by Charles Bennett on reversible computation is recognized as the much needed theorem. It is proposed a real, non thermal engine, operated by humans. Its ope

G. W. Misch, M. R. Mumpower, F. G. Kondev, S. T. Marley

We report on the Facility for Rare Isotope Beams (FRIB) Theory Alliance topical program "Nuclear Isomers in the Era of FRIB". We outline the many ways isomers influence and contribute to nuclear science and technology, especially in the four FRIB pillars: properties of rare isotopes, nuclear astrophysics, fundamental symmetries, and applications for the nati

Paul Schwahn

We give a new formula for the Lichnerowicz Laplacian on normal homogeneous spaces in terms of Casimir operators. We derive some practical estimates and apply them to the known list of non-symmetric, compact, simply connected homogeneous spaces $G/H$ with $G$ simple whose standard metric is Einstein. This yields many new examples of Einstein metrics which are

Alexander Cantoral, Sergio Romaña

Let $(M, g)$ be a complete Riemannian manifold without focal points and curvature bounded below. We prove that when the average of the sectional curvature in tangent planes along geodesics is negative and uniformly away from zero, then the geodesic flow is of Anosov type. We use this result to construct a non-compact manifold of non-positive curvature with t

Arup Biswas, Arnab Pal, Debasish Mondal, Somrita Ray

`Gating' is a widely observed phenomenon in biochemistry that describes the transition between the activated (or open) and deactivated (or closed) states of an ion-channel, which makes transport through that channel highly selective. In general, gating is a mechanism that imposes an additional restriction on a transport, as the process ends only when the `ga

David Hason Rudd, Huan Huo, Guandong Xu

Customer churn describes terminating a relationship with a business or reducing customer engagement over a specific period. Two main business marketing strategies play vital roles to increase market share dollar-value: gaining new and preserving existing customers. Customer acquisition cost can be five to six times that for customer retention, hence investin

Panagiotis Promponas, Víctor Valls, Leandros Tassiulas

We study the problem of operating a quantum switch with memory constraints. In particular, the switch has to allocate quantum memories to clients to generate link-level entanglements (LLEs), and then use these to serve end-to-end entanglements requests. The paper's main contributions are (i) to characterize the switch's capacity region, and (ii) to propose a

Anya E. Nugent, Abigail E. Polin, Peter E. Nugent

In recent years, there has been ample evidence that Type Ia supernova (SNe Ia) with high Si 2 velocities near peak brightness are distinguished from SNe Ia of lower velocities and may indeed represent a separate progenitor system. These SNe Ia can contaminate the population of normal events used for cosmological analyses, creating unwanted biases in the fina

Li Zhong

Broken access control is one of the most common security vulnerabilities in web applications. These vulnerabilities are the major cause of many data breach incidents, which result in privacy concern and revenue loss. However, preventing and detecting access control vulnerabilities proactively in web applications could be difficult. Currently, these vulnerabi

Hooman Barati Sedeh, Natalia M. Litchinitser

Electric anapole states, arising due to the destructive interferences of primitive and toroidal electric dipole moments, have been recently introduced as the fundamental class of non-scattering sources with several potential applications ranging from nonlinear optics to thermodynamics thanks to their field confinement and minimal scattering fingerprints. Nev

Qisheng Liao, Meiting Lai, Preslav Nakov

This paper describes our system for SemEval-2023 Task 3 Subtask 2 on Framing Detection. We used a multi-label contrastive loss for fine-tuning large pre-trained language models in a multi-lingual setting, achieving very competitive results: our system was ranked first on the official test set and on the official shared task leaderboard for five of the six la