May 2023 arXiv papers — page 127

Azadeh Dinparastdjadid, Isaac Supeene, Johan Engstrom

The quantitative measurement of how and when we experience surprise has mostly remained limited to laboratory studies, and its extension to naturalistic settings has been challenging. Here we demonstrate, for the first time, how computational models of surprise rooted in cognitive science and neuroscience combined with state-of-the-art machine learned genera

D. S. Barker, P. K. Elgee, A. Sitaram, E. B. Norrgard

We study the forces and optical pumping within grating magneto-optical traps (MOTs) operating on transitions with non-trivial level structure. In contrast to the standard six-beam MOT configuration, rate equation modelling predicts that the asymmetric laser geometry of a grating MOT will produce spin-polarized atomic samples. Furthermore, the Land\'e $g$-fac

Viet Bach Nguyen, Truong Son Hy, Long Tran-Thanh, Nhung Nghiem

Modeling and simulations of pandemic dynamics play an essential role in understanding and addressing the spreading of highly infectious diseases such as COVID-19. In this work, we propose a novel deep learning architecture named Attention-based Multiresolution Graph Neural Networks (ATMGNN) that learns to combine the spatial graph information, i.e. geographi

Pedro Zattoni Scroccaro, Bilge Atasoy, Peyman Mohajerin Esfahani

In Inverse Optimization (IO), an expert agent solves an optimization problem parametric in an exogenous signal. From a learning perspective, the goal is to learn the expert's cost function given a dataset of signals and corresponding optimal actions. Motivated by the geometry of the IO set of consistent cost vectors, we introduce the "incenter" concept, a ne

Suat Evren

We show that computing the optimal social surplus requires $\Omega(mn)$ bits of communication between the website and the bidders in a sponsored search auction with $n$ slots on the website and with tick size of $2^{-m}$ in the discrete model, even when bidders are allowed to freely communicate with each other.

Sergey Fomin, Pavlo Pylyavskyy

We show that various classical theorems of real/complex linear incidence geometry, such as the theorems of Pappus, Desargues, M\"obius, and so on, can be interpreted as special cases of a single "master theorem" that involves an arbitrary tiling of a closed oriented surface by quadrilateral tiles. This yields a general mechanism for producing new incidence t

Nicolas Bouchot

The purpose of this paper is to study a one-dimensional polymer penalized by its range and placed in a random environment $\omega$. The law of the simple symmetric random walk up to time $n$ is modified by the exponential of the sum of $\beta \omega_z - h$ sitting on its range, with~$h$ and $\beta$ positive parameters. It is known that, at first order, the p

A. Zelenskiy, M. L. Plumer, B. W. Southern, M. E. Zhitomirsky

We study a Heisenberg-Dzyaloshinskii-Moriya Hamiltonian on AB-stacked kagome bilayers at finite temperature. In a large portion of the parameter space, we observe three transitions upon cooling the system: a crossover from Heisenberg to the XY chiral paramagnet, Kosterlitz-Thouless transition to a chiral nematic phase, and a fluctuation-induced first-order t

Mateja Bošković, Enrico Barausse

New light scalar degrees of freedom may alleviate the dark matter and dark energy problems, but if coupled to matter, they generally mediate a fifth force. In order for this fifth force to be consistent with existing constraints, it must be suppressed close to matter sources, e.g. through a non-linear screening mechanism. In this work, we investigate the non

M. H. Griessmann, A. C. Martinez-Becerril, J. S. Lundeen

Laser beam quality is a key parameter for both industry and science. However, the most common measure, the M2 parameter, requires numerous intensity spatial-profiles for its determination. This is particularly inconvenient for modelling the impact of photonic devices on M2, such as metalenses and thin-film stacks, since models typically output a single elect

Luísa Borsato, Eduardo Horta, Rafael Rigão Souza

We provide a collection of examples involving the concept of a product disintegration, which generalizes exchangeability.

Raymond Fok, Daniel S. Weld

The current literature on AI-advised decision making -- involving explainable AI systems advising human decision makers -- presents a series of inconclusive and confounding results. To synthesize these findings, we propose a simple theory that elucidates the frequent failure of AI explanations to engender appropriate reliance and complementary decision makin

Simon Valentin, Steven Kleinegesse, Neil R. Bramley, Peggy Seriès

Computational models are powerful tools for understanding human cognition and behavior. They let us express our theories clearly and precisely, and offer predictions that can be subtle and often counter-intuitive. However, this same richness and ability to surprise means our scientific intuitions and traditional tools are ill-suited to designing experiments

M. Amy, M. Crawford, A. N. Glaudell, M. L. Macasieb

If a set $\mathbb{G}$ of quantum gates is countable, then the operators that can be exactly represented by a circuit over $\mathbb{G}$ form a strict subset of the collection of all unitary operators. When $\mathbb{G}$ is universal, one circumvents this limitation by resorting to repeated gate approximations: every occurrence of a gate which cannot be exactly

Anna Lueber, Daniel Kitzmann, Chloe E. Fisher, Brendan P. Bowler

Understanding differences between sub-stellar spectral data and models has proven to be a major challenge, especially for self-consistent model grids that are necessary for a thorough investigation of brown dwarf atmospheres. Using the supervised machine learning method of the random forest, we study the information content of 14 previously published model g

William Witczak-Krempa

We grow the web of dualities for conformal field theories (CFTs) in 2+1 spacetime dimensions to include quantum critical transitions of Dirac fermions. Our construction uses the seed duality, equating a free Dirac fermion with a complex boson coupled to a Chern-Simons gauge field, to express various Gross-Neveu-Yukawa (GNY) critical points of N fermions in t

Souad Taouti, Hadda Cherroun, Djelloul Ziadi

Tree kernels are fundamental tools that have been leveraged in many applications, particularly those based on machine learning for Natural Language Processing tasks. In this paper, we devise a parallel implementation of the sequential algorithm for the computation of some tree kernels of two finite sets of trees (Ouali-Sebti, 2015). Our comparison is narrowe

Georgia Chalvatzaki, Ali Younes, Daljeet Nandha, An Le

Long-horizon task planning is essential for the development of intelligent assistive and service robots. In this work, we investigate the applicability of a smaller class of large language models (LLMs), specifically GPT-2, in robotic task planning by learning to decompose tasks into subgoal specifications for a planner to execute sequentially. Our method gr

Kirsten Fischer, David Dahmen, Moritz Helias

Residual networks have significantly better trainability and thus performance than feed-forward networks at large depth. Introducing skip connections facilitates signal propagation to deeper layers. In addition, previous works found that adding a scaling parameter for the residual branch further improves generalization performance. While they empirically ide

W. Arendt, A. F. M. ter Elst, M. Sauter

In this article we consider the Dirichlet problem on a bounded domain $\Omega \subset {\bf R}^d$ with respect to a second-order elliptic differential operator in divergence form. We do not assume a divergence condition as in the pioneering work by Stampacchia, but merely assume that $0$ is not a Dirichlet eigenvalue. The purpose of this article is to define

Zhe Liu, Xiaoqing Ye, Zhikang Zou, Xinwei He

Multi-modal 3D object detection has received growing attention as the information from different sensors like LiDAR and cameras are complementary. Most fusion methods for 3D detection rely on an accurate alignment and calibration between 3D point clouds and RGB images. However, such an assumption is not reliable in a real-world self-driving system, as the al

Zongyu Li, Jason Hu, Xiaojian Xu, Liyue Shen

Phase retrieval (PR) is a crucial problem in many imaging applications. This study focuses on resolving the holographic phase retrieval problem in situations where the measurements are affected by a combination of Poisson and Gaussian noise, which commonly occurs in optical imaging systems. To address this problem, we propose a new algorithm called "AWFS" th

Rahul Karmakar, Jaydeb Chakrabarti

Reverse Osmosis(RO) membranes are widespread nowadays for separating the solvent from a solution. RO membranes are made of polymer matrix. Experiments show changes in relative interaction of solvent and solute with RO membrane matrix lead to changes in solvent permeation, solute rejection and fouling. Here we study microscopically separation of binary mixtur

Anubhav Jain, Nasir Memon, Julian Togelius

Facial recognition systems have made significant strides thanks to data-heavy deep learning models, but these models rely on large privacy-sensitive datasets. Further, many of these datasets lack diversity in terms of ethnicity and demographics, which can lead to biased models that can have serious societal and security implications. To address these issues,

Christopher M. Ormerod, Milan Patel, Harry Wang

This article details the advances made to a system that uses artificial intelligence to identify alarming student responses. This system is built into our assessment platform to assess whether a student's response indicates they are a threat to themselves or others. Such responses may include details concerning threats of violence, severe depression, suicide

E. Padilla Gonzalez, D. Andrew Howell, J. Burke, Yize Dong

We present photometric and spectroscopic data for the nearby Type I supernova (SN Ia) 2019eix (originally classified as a SN Ic), from its discovery day up to 100 days after maximum brightness. Before maximum light SN 2019eix resembles a typical SN Ic, albeit lacking the usual \ion{O}{1} feature. Its lightcurve is similar to the typical SN Ic with decline ra

David Loeffler, Óscar Rivero

We use higher Coleman theory to construct a new $p$-adic $L$-function for $\text{GSp}_4 \times \text{GL}_2$. While previous works by the first author, Pilloni, Skinner and Zerbes had considered the $p$-adic variation of classes in the $H^2$ of Shimura varieties for $\text{GSp}_4$, in this note we explore the interpolation of classes in the $H^1$, which allow

Stefania Salvadori, Valentina D'Odorico, Andrea Saccardi, Asa Skuladottir

The first stars were likely more massive than those forming today and thus rapidly evolved, exploding as supernovae and enriching the surrounding gas with their chemical products. In the Local Group, the chemical signature of the first stars has been identified in the so-called Carbon-Enhanced Metal-Poor stars (CEMP-no). On the contrary, a similar C-excess w

Dhrubojyoti Sengupta, Stefano Marchesi, Cristian Vignali, Núria Torres-Albà

We present the broad-band X-ray spectral analysis (0.6-50 keV) of seven Compton-Thick active galactic nuclei (CT-AGN; line-of-sight, l.o.s., column density $>10^{24}$ cm$^{-2}$) candidates selected from the Swift-BAT 100-month catalog, using archival NuSTAR data. This work is in continuation of the on-going research of the Clemson-INAF group to classify CT-A

Chloe Richards, Alexandru Dima, Helvi Witek

Black holes have a unique sensitivity to the presence of ultralight matter fields or modifications of the underlying theory of gravity. In the present paper we combine both features by studying an ultralight, dynamical scalar field that is nonminimally coupled to the gravitational Chern-Simons term. In particular, we numerically simulate the evolution of suc

Michal P. Heller, Alexandre Serantes, Michał Spaliński, Benjamin Withers

As an effective theory, relativistic hydrodynamics is fixed by symmetries up to a set of transport coefficients. A lot of effort has been devoted to explicit calculations of these coefficients. Here we propose a shift in perspective: we deploy bootstrap techniques to rule out theories that are inconsistent with microscopic causality. What remains is a univer

Marek Lewicki, Kristjan Müürsepp, Joosep Pata, Martin Vasar

We study the impact of the ambient fluid on the evolution of collapsing false vacuum bubbles by simulating the dynamics of a coupled bubble-particle system. A significant increase in the mass of the particles across the bubble wall leads to a buildup of those particles inside the false vacuum bubble. We show that the backreaction of the particles on the bubb

Damian van de Heisteeg, Cumrun Vafa, Max Wiesner, David H. Wu

In the context of quantum gravitational systems, we place bounds on regions in field space with slowly varying positive potentials. Using the fact that $V<\Lambda_s^2$, where $\Lambda_s(\phi)$ is the species scale, and the emergent string conjecture, we show this places a bound on the maximum diameter of such regions in field space: $\Delta \phi \leq a \log(

Marvin Qi, David T. Stephen, Xueda Wen, Daniel Spiegel

We employ matrix product states (MPS) and tensor networks to study topological properties of the space of ground states of gapped many-body systems. We focus on families of states in one spatial dimension, where each state can be represented as an injective MPS of finite bond dimension. Such states are short-range entangled ground states of gapped local Hami

Bartolomeo Trefoloni, Elisabeta Lusso, Emanuele Nardini, Guido Risaliti

We present the analysis of the rest frame ultraviolet and optical spectra of 30 bright blue quasars at $z\sim3$, selected to examine the suitability of AGN as cosmological probes. In our previous works, we found an unexpectedly high fraction ($\approx 25 \%$) of X-ray weak quasars in the sample. The latter sources also display a flatter UV continuum and a br

Pilar Coloma, M. C. Gonzalez-Garcia, Michele Maltoni, João Paulo Pinheiro

We derive new constraints on effective four-fermion neutrino non-standard interactions with both quarks and electrons. This is done through the global analysis of neutrino oscillation data and measurements of coherent elastic neutrino-nucleus scattering (CEvNS) obtained with different nuclei. In doing so, we include not only the effects of new physics on neu

Rebecca Diesing

Identifying the accelerators of Galactic cosmic ray protons (CRs) with energies up to a few PeV ($10^{15}$ eV) remains a theoretical and observational challenge. Supernova remnants (SNRs) represent strong candidates, as they provide sufficient energetics to reproduce the CR flux observed at Earth. However, it remains unclear whether they can accelerate parti

Benjamin Nachman, Ramon Winterhalder

Simulations play a key role for inference in collider physics. We explore various approaches for enhancing the precision of simulations using machine learning, including interventions at the end of the simulation chain (reweighting), at the beginning of the simulation chain (pre-processing), and connections between the end and beginning (latent space refinem

Paolo Creminelli, Soubhik Kumar, Borna Salehian, Luca Santoni

We describe a new mechanism that gives rise to dissipation during cosmic inflation. In the simplest implementation, the mechanism requires the presence of a massive scalar field with a softly-broken global $U(1)$ symmetry, along with the inflaton field. Particle production in this scenario takes place on parametrically sub-horizon scales, at variance with th

David Jonas Moser, Lukas Janssen

We identify an exotic quasiuniversal behavior near the all-in-all-out Weyl quantum critical point in three-dimensional Luttinger semimetals, such as the pyrochlore iridates $R_2$Ir$_2$O$_7$, with $R$ a rare-earth element. The quasiuniversal behavior is characterized by power laws with exponents that vary slowly over several orders of magnitude in energy or l

Ioannis D. Gialamas, Hardi Veermäe

We investigate the stability of the electroweak vacuum in metric-affine gravity in which the Standard Model Higgs boson can be non-minimally coupled to both the Ricci scalar and the Holst invariant. We find that vacuum stability is improved in this framework across a wide range of model parameters.

Matteo Turco, Gonçalo M. Quinta, João Seixas, Yasser Omar

The last few years have seen rapid development of applications of quantum computation to quantum field theory. The first algorithms for quantum simulation of scattering have been proposed in the context of scalar and fermionic theories, requiring thousands of logical qubits. These algorithms are not suitable to simulate scattering of incoming bound states, a

M. Armas Padilla, J. M. Corral-Santana, A. Borghese, V. A. Cúneo

Ultracompact X-ray binaries (UCXBs) are a distinctive but elusive family of low-mass X-ray binaries (LMXBs) characterised by their tight orbits and degenerate donor stars. Here we present UltraCompCAT, the first online and comprehensive catalogue of UCXBs. The initial version of UltraCompCAT comprises 49 sources, including 20 'confirmed' UCXBs (those with a

Mario Fernández Navarro, Stephen F. King

We propose a tri-hypercharge (TH) embedding of the Standard Model (SM) in which a separate gauged weak hypercharge is associated with each fermion family. In this way, every quark and lepton multiplet carries unique gauge quantum numbers under the extended gauge group, providing the starting point for a theory of flavour. If the Higgs doublets only carry thi

Sally Dawson, Duarte Fontes, Carlos Quezada-Calonge, Juan José Sanz-Cillero

We consider the 2 Higgs Doublet Model (2HDM) and compare two effective field theory (EFT) approaches to it, according to whether the heavy degrees of freedom are integrated out before (SMEFT) or after (HEFT) spontaneous symmetry breaking. %We show that, in the HEFT, an inconsistent EFT is obtained if one considers an expansion simply in inverse powers of the

Wolfgang Simeth, Andreas Bauer, Christian Franz, Aisha Aqeel

We report resonant elastic X-ray scattering (REXS) of long-range magnetic order in EuPtSi$_{\text{3}}$, combining different scattering geometries with full linear polarization analysis to unambiguously identify magnetic scattering contributions. At low temperatures, EuPtSi$_{\text{3}}$ stabilizes type A antiferromagnetism featuring various long-wavelength mo

Ekaterina I. Makarenko, Stefanie Walch, Seamus D. Clarke, Daniel Seifried

Supernovae (SNe) inject $\sim 10^{51}$ erg in the interstellar medium, thereby shocking and heating the gas. A substantial fraction of this energy is later lost via radiative cooling. We present a post-processing module for the FLASH code to calculate the cooling radiation from shock-heated gas using collisional excitation data from MAPPINGS V. When applying

Gianluca Turin, Andrea Borgarelli, Simone Donetti, Ferruccio Damiani

Cloud computing has radically changed the way organisations operate their software by allowing them to achieve high availability of services at affordable cost. Containerized microservices is an enabling technology for this change, and advanced container orchestration platforms such as Kubernetes are used for service management. Despite the flourishing ecosy

Alex Krolewski, Will J. Percival, Simone Ferraro, Edmond Chaussidon

We detect the cross-correlation between 2.7 million DESI quasar targets across 14,700 deg$^2$ (180 quasars deg$^{-2}$) and Planck 2018 CMB lensing at $\sim$30$\sigma$. We use the cross-correlation on very large scales to constrain local primordial non-Gaussianity via the scale dependence of quasar bias. The DESI quasar targets lie at an effective redshift of

Jinzhao Sun, Lucia Vilchez-Estevez, Vlatko Vedral, Andrew T. Boothroyd

The efficient probing of spectral features is important for characterising and understanding the structure and dynamics of quantum materials. In this work, we establish a framework for probing the excitation spectrum of quantum many-body systems with quantum simulators. Our approach effectively realises a spectral detector by processing the dynamics of obser

Hanchen Xie, Jiageng Zhu, Mahyar Khayatkhoei, Jiazhi Li

Dynamics prediction, which is the problem of predicting future states of scene objects based on current and prior states, is drawing increasing attention as an instance of learning physics. To solve this problem, Region Proposal Convolutional Interaction Network (RPCIN), a vision-based model, was proposed and achieved state-of-the-art performance in long-ter

Lauren E Dang, Edwin Fong, Jens Magelund Tarp, Kim Katrine Bjerring Clemmensen

Introduction: Increasing interest in real-world evidence has fueled the development of study designs incorporating real-world data (RWD). Using the Causal Roadmap, we specify three designs to evaluate the difference in risk of major adverse cardiovascular events (MACE) with oral semaglutide versus standard-of-care: 1) the actual sequence of non-inferiority a

Ilham Variansyah, Ryan G. McClarren

We propose a technique to effectively sample initial neutron and delayed neutron precursor particles for Monte Carlo (MC) simulations of typical off-critical reactor transients. The technique can be seen as an improvement, or alternative, to the existing ones. Similar to some existing techniques, the proposed sampling technique uses the standard MC criticali

Adam Burchardt, Frederik Hahn

This paper introduces the foliage partition, an easy-to-compute LC-invariant for graph states, of computational complexity $\mathcal{O}(n^3)$ in the number of qubits. Inspired by the foliage of a graph, our invariant has a natural graphical representation in terms of leaves, axils, and twins. It captures both, the connection structure of a graph and the $2$-

Muhammad Usman Akbar, Wuhao Wang, Anders Eklund

Diffusion models were initially developed for text-to-image generation and are now being utilized to generate high quality synthetic images. Preceded by GANs, diffusion models have shown impressive results using various evaluation metrics. However, commonly used metrics such as FID and IS are not suitable for determining whether diffusion models are simply r

Max M. Chumley, Firas A. Khasawneh, Andreas Otto, Tomas Gedeon

We introduce two time-delay models of metabolic oscillations in yeast cells. Our model tests a hypothesis that the oscillations occur as multiple pathways share a limited resource which we equate to the number of available ribosomes. We initially explore a single-protein model with a constraint equation governing the total resource available to the cell. The

Dominic LaBella, Maruf Adewole, Michelle Alonso-Basanta, Talissa Altes

Meningiomas are the most common primary intracranial tumor in adults and can be associated with significant morbidity and mortality. Radiologists, neurosurgeons, neuro-oncologists, and radiation oncologists rely on multiparametric MRI (mpMRI) for diagnosis, treatment planning, and longitudinal treatment monitoring; yet automated, objective, and quantitative

Ilham Variansyah, Ryan G. McClarren

We investigate the use of time-dependent surfaces in Monte Carlo transport simulation to accurately model prescribed, continuous object movements. The performance of the continuous time-dependent surface technique, relative to the typical stepping approximations and the recently proposed at-source geometry adjustment technique, is assessed by running a simpl

Junaid Majeed Bhat, R. Shankar, Abhishek Dhar

The quantization of the two terminal conductance in 2D topological systems is justified by the Landauer-Buttiker (LB) theory that assumes perfect point contacts between single channel leads and the sample. We examine this assumption in a microscopic model of a Chern insulator connected to leads, using the nonequilibrium Green's function formalism. We find th

Aviad Heifetz, Enrico Minelli, Herakles Polemarchakis

Purely affective interaction allows the welfare of an individual to depend on her own actions and on the profile of welfare levels of others. Under an assumption on the structure of mutual affection that we interpret as "non-explosive mutual affection," we show that equilibria of simultaneous-move affective interaction are Pareto optimal independently of whe

Sabyasachi Ghosh, Sanyam Saxena, Ajit Rajwade

Popular social media platforms employ neural network based image moderation engines to classify images uploaded on them as having potentially objectionable content. Such moderation engines must answer a large number of queries with heavy computational cost, even though the actual number of images with objectionable content is usually a tiny fraction. Inspire

Yongchun Li, Weijun Xie

A Low-rank Spectral Optimization Problem (LSOP) minimizes a linear objective subject to multiple two-sided linear matrix inequalities intersected with a low-rank and spectral constrained domain set. Although solving LSOP is, in general, NP-hard, its partial convexification (i.e., replacing the domain set by its convex hull) termed "LSOP-R," is often tractabl

Pranav Kulkarni, Adway Kanhere, Paul H. Yi, Vishwa S. Parekh

The Imaging Data Commons (IDC) is a cloud-based database that provides researchers with open access to cancer imaging data, with the goal of facilitating collaboration. However, cohort discovery within the IDC database has a significant technical learning curve. Recently, large language models (LLM) have demonstrated exceptional utility for natural language

Ilham Variansyah, J. P. Morgan, Jordan Northrop, Kyle E. Niemeyer

We discuss the current development of MC/DC (Monte Carlo Dynamic Code). MC/DC is primarily designed to serve as an exploratory Python-based MC transport code. However, it seeks to offer improved performance, massive scalability, and backend portability by leveraging Python code-generation libraries and implementing an innovative abstraction strategy and comp

Domingos S. P. Salazar

In systems far from equilibrium, the statistics of observables are connected to entropy production, leading to the Thermodynamic Uncertainty Relation (TUR). However, the derivation of TURs often involves constraining the parity of observables, such as considering asymmetric currents, making it unsuitable for the general case. We propose a Thermodynamic Varia

Tivadar Lohner, Attila Nemeth, Zsolt Zolnai, Benjamin Kalas

Ion implantation has been a key technology for the controlled surface modification of materials in microelectronics and generally, for tribology, biocompatibility, corrosion resistance and many more. To form shallow junctions in Ge is a challenging task. In this work the formation and accumulation of shallow damage profiles was studied by in-situ spectroscop

Hongjie Dong, Tuoc Phan, Yannick Sire

We study a conormal boundary value problem for a class of quasilinear elliptic equations in bounded domain $\Omega$ whose coefficients can be degenerate or singular of the type $\text{dist}(x, \partial \Omega)^\alpha$, where $\partial \Omega$ is the boundary of $\Omega$ and $\alpha \in (-1, \infty)$ is a given number. We establish weighted Sobolev type estim

Ziwei Fan, Zhiwei Liu, Shelby Heinecke, Jianguo Zhang

Existing recommender systems face difficulties with zero-shot items, i.e. items that have no historical interactions with users during the training stage. Though recent works extract universal item representation via pre-trained language models (PLMs), they ignore the crucial item relationships. This paper presents a novel paradigm for the Zero-Shot Item-bas

Min Hun Lee, Daniel P. Siewiorek, Asim Smailagic, Alexandre Bernardino

Socially assistive robots are increasingly being explored to improve the engagement of older adults and people with disability in health and well-being-related exercises. However, even if people have various physical conditions, most prior work on social robot exercise coaching systems has utilized generic, predefined feedback. The deployment of these system

Joohwan Seo, Jackson Wagner, Anuj Raicura, Jake Kim

We develop two novel vision methods for planning effective grasps for clear plastic bags, as well as a control method to enable a Sawyer arm with a parallel gripper to execute the grasps. The first vision method is based on classical image processing and heuristics (e.g., Canny edge detection) to select a grasp target and angle. The second uses a deep-learni

Javad Tabatabaei, Abdolali Banihashemi, Shant Baghram, Bahram Mashhoon

Nonlocal gravity (NLG), a classical extension of Einstein's theory of gravitation, has been studied mainly in linearized form. In particular, nonlinearities have thus far prevented the treatment of cosmological models in NLG. In this essay, we discuss the local limit of NLG and apply this limit to the expanding homogenous and isotropic universe. The theory o

Peter M. Felker, Zlatko Bačić

We present the computational methodology which for the first time allows rigorous twelve-dimensional (12D) quantum calculations of the coupled intramolecular and intermolecular vibrational states of hydrogen-bonded trimers of flexible diatomic molecules. Its starting point is the approach that we introduced recently for fully coupled 9D quantum calculations

Francesco M. D. Pellegrino, Giuseppe Falci, Elisabetta Paladino

We investigate the second spectrum of charge carrier density fluctuations in graphene within the McWorther model, where noise is induced by electron traps in the substrate. Within this simple picture, we obtain a closed-form expression including both Gaussian and non-Gaussian fluctuations. We show that a very extended distribution of switching rates of the e

Colin Defant, Michael Joseph, Matthew Macauley, Alex McDonough

Much of dynamical algebraic combinatorics focuses on global dynamical systems defined via maps that are compositions of local toggle operators. The second author and Roby studied such maps that result from toggling independent sets of a path graph. We investigate a "toric" analogue of this work by analyzing the dynamics arising from toggling independent sets

Dominic Wynter

We prove quantitative growth estimates for large data solutions to the 1D Boltzmann equation, for a collision kernel with angular cutoff and relative velocity cutoff. We present proofs for the global well-posedness results presented in the note of Biryuk, Craig, and Panferov, in which global solutions for this equation are shown to exist for large data, with

Ondrej Bohdal, Yinbing Tian, Yongshuo Zong, Ruchika Chavhan

Meta-learning and other approaches to few-shot learning are widely studied for image recognition, and are increasingly applied to other vision tasks such as pose estimation and dense prediction. This naturally raises the question of whether there is any few-shot meta-learning algorithm capable of generalizing across these diverse task types? To support the c

Ying Liu, Liucheng Guo, Valeri A. Makarov, Yuxiang Huang

Automated hand gesture recognition has been a focus of the AI community for decades. Traditionally, work in this domain revolved largely around scenarios assuming the availability of the flow of images of the user hands. This has partly been due to the prevalence of camera-based devices and the wide availability of image data. However, there is growing deman

Baohong Guo, Ute Ebert, Jannis Teunissen

We study the effect of an inhomogeneous gas density on positive streamer discharges in air using a 3D fluid model with stochastic photoionization, generalizing earlier work with a 2D axisymmetric model by Starikovskiy and Aleksandrov (2019 Plasma Sources Sci. Technol. 28 095022). We consider various types of planar and (hemi)spherical gas density gradients.

Fan Yang, Zheng Chen, Ziyan Jiang, Eunah Cho

Large language models (LLMs) have recently received significant attention for their exceptional capabilities. Despite extensive efforts in developing general-purpose LLMs that can be utilized in various natural language processing (NLP) tasks, there has been less research exploring their potential in recommender systems. In this paper, we propose a novel fra

Damaris Meier, Dimitrios Ntalampekos

We prove that every 1-Lipschitz map from a closed metric surface onto a closed Riemannian surface that has the same area is an isometry. If we replace the target space with a non-smooth surface, then the statement is not true and we study the regularity properties of such a map under different geometric assumptions. Our proof relies on a coarea inequality fo

Sara C. Billey, Joshua P. Swanson

It is a remarkable fact that for many statistics on finite sets of combinatorial objects, the roots of the corresponding generating function are each either a complex root of unity or zero. These and related polynomials have been studied for many years by a variety of authors from the fields of combinatorics, representation theory, probability, number theory

Dongsung Choi, Changming Yue, Doron Azoury, Zachary Porter

Sr$_2$IrO$_4$ has attracted a lot of attention due to its structural and electronic similarities to La$_2$CuO$_4$ which is the parent compound of high-T$_c$ superconducting cuprates. It was proposed to be a strong spin-orbit coupled J$_{eff}$ = 1/2 Mott insulator, but the Mott nature of its insulating ground state and the origin of the gap have not been conc

Danyal F. Bhutto, Bo Zhu, Jeremiah Z. Liu, Neha Koonjoo

Accurate image reconstruction is at the heart of diagnostics in medical imaging. Supervised deep learning-based approaches have been investigated for solving inverse problems including image reconstruction. However, these trained models encounter unseen data distributions that are widely shifted from training data during deployment. Therefore, it is essentia

Marco Drago, Haakon Andresen, Irene Di Palma, Irene Tamborra

The gravitational wave (GW) and neutrino signals from core-collapse supernovae (CCSNe) are expected to carry pronounced imprints of the standing accretion shock instability (SASI). We investigate whether the correlation between the SASI signatures in the GW and neutrino signals could be exploited to enhance the detection efficiency of GWs. We rely on a bench

Marianne Defresne, Sophie Barbe, Thomas Schiex

In the ongoing quest for hybridizing discrete reasoning with neural nets, there is an increasing interest in neural architectures that can learn how to solve discrete reasoning or optimization problems from natural inputs. In this paper, we introduce a scalable neural architecture and loss function dedicated to learning the constraints and criteria of NP-har

Jianbiao Wang, Tomio Miwa, Takayuki Morikawa

As an emerging travel mode, the modular vehicle system (MVS) is receiving increasing attention. In particular, the operators could connect multiple modular vehicles as an assembled bus in response to the temporary demand varies. Therefore, in this study, the MVS is adopted in the context of customized bus design to satisfy passengers reserved travel demand.

Griffin Adams, Bichlien H Nguyen, Jake Smith, Yingce Xia

Summarization models often generate text that is poorly calibrated to quality metrics because they are trained to maximize the likelihood of a single reference (MLE). To address this, recent work has added a calibration step, which exposes a model to its own ranked outputs to improve relevance or, in a separate line of work, contrasts positive and negative s

Orion Weller, Dawn Lawrie, Benjamin Van Durme

Negation is a common everyday phenomena and has been a consistent area of weakness for language models (LMs). Although the Information Retrieval (IR) community has adopted LMs as the backbone of modern IR architectures, there has been little to no research in understanding how negation impacts neural IR. We therefore construct a straightforward benchmark on

Junyoung Kim, Kenneth Ross, Eric Sedlar, Lukas Stadler

Many useful tasks in data science and machine learning applications can be written as simple variations of matrix multiplication. However, users have difficulty performing such tasks as existing matrix/vector libraries support only a limited class of computations hand-tuned for each unique hardware platform. Users can alternatively write the task as a simple

Siddarth Asokan, Chandra Sekhar Seelamantula

Training Generative adversarial networks (GANs) stably is a challenging task. The generator in GANs transform noise vectors, typically Gaussian distributed, into realistic data such as images. In this paper, we propose a novel approach for training GANs with images as inputs, but without enforcing any pairwise constraints. The intuition is that images are mo

Yutong He, Xinmeng Huang, Yiming Chen, Wotao Yin

Communication compression is an essential strategy for alleviating communication overhead by reducing the volume of information exchanged between computing nodes in large-scale distributed stochastic optimization. Although numerous algorithms with convergence guarantees have been obtained, the optimal performance limit under communication compression remains

Songning Lai, Xifeng Hu, Haoxuan Xu, Zhaoxia Ren

Multimodal sentiment analysis has become an important research area in the field of artificial intelligence. With the latest advances in deep learning, this technology has reached new heights. It has great potential for both application and research, making it a popular research topic. This review provides an overview of the definition, background, and devel

I. A. Maltsev, D. A. Tumakov, R. V. Popov, V. M. Shabaev

The relativistic energies and widths of hydrogen-like ions exposed to the uniform electric field are calculated. The calculations are performed for the ground and lowest excited states using the complex scaling technique in combination with a finite-basis method. The obtained results are compared with the non-relativistic values. The role of relativistic eff

Anirudha Paul

In recent years decentralized currencies developed through Blockchains are increasingly becoming popular because of their transparent nature and absence of a central controlling authority. Though a lot of computation power, disk space, and energy are being used to run this system, most of these resources are dedicated to just keeping the bad actors away by u

D. Yohannes, M. Renzullo, J. Vivalda, A. C. Jacobs

We implemented, optimized and fully tested over multiple runs a superconducting Josephson junction fabrication process tailored for the integrated digital circuits that are used for control and readout of superconducting qubits operating at millikelvin temperatures. This process was optimized for highly energy efficient single flux quantum (ERSFQ) circuits w

Roberto Conti, Gerardo Morsella

Using Araki-Yamagami's characterization of quasi-equivalence for quasi-free representations of the CCRs, we provide an abstract criterion for the existence of isomorphisms of second quantization local von Neumann algebras induced by Bogolubov transformations in terms of the respective one particle modular operators. We discuss possible applications to the pr

Anastasia Olga, Tzirides, Akash Saini, Gabriela Zapata

The launch of ChatGPT in November 2022 precipitated a panic among some educators while prompting qualified enthusiasm from others. Under the umbrella term Generative AI, ChatGPT is an example of a range of technologies for the delivery of computer-generated text, image, and other digitized media. This paper examines the implications for education of one gene

Colin Cooper, Nan Kang, Tomasz Radzik

We propose a simple model of influence in a network, based on edge density. In the model vertices (people) follow the opinion of the group they belong to. The opinion percolates down from an active vertex, the influencer, at the head of the group. Groups can merge, based on interactions between influencers (i.e., interactions along `active edges' of the netw

Hui Xiao, Zhihong Wei

This research considers the ranking and selection with input uncertainty. The objective is to maximize the posterior probability of correctly selecting the best alternative under a fixed simulation budget, where each alternative is measured by its worst-case performance. We formulate the dynamic simulation budget allocation decision problem as a stochastic c

Steven A. Grosz, Kanishka P. Wijewardena, Anil K. Jain

A secure fingerprint recognition system must contain both a presentation attack (i.e., spoof) detection and recognition module in order to protect users against unwanted access by malicious users. Traditionally, these tasks would be carried out by two independent systems; however, recent studies have demonstrated the potential to have one unified system arch