October 2022 arXiv papers — page 42

José A. Carrillo, Ruiwen Shu

We consider the pressureless Euler-Poisson equations with quadratic confinement. For spatial dimension $d\ge 2,\,d\ne 4$, we give a necessary and sufficient condition for the existence of radial global smooth solutions, which is formulated explicitly in terms of the initial data. This condition appears to be much more restrictive than the critical-threshold

Wei Wang

The $k$-Cauchy-Fueter complex, $k=0,1,\ldots$, in quaternionic analysis are the counterpart of the Dolbeault complex in the theory of several complex variables. In this paper, we construct explicitly boundary complexes of these complexes on boundaries of domains, corresponding to the tangential Cauchy-Riemann complex in complex analysis.They are only known b

Elie Kapengut, Bruce Mizrach

On September 15, 2022, the Ethereum network adopted a proof-of-stake (PoS) consensus mechanism. We study the impact on the network and competing platforms in a two month event window around the Beacon chain merge. We find that the transition to PoS has reduced energy consumption by 99.98%. Miners have not transformed into validators, and total block reward i

Francisco Lopez-Tiro, Juan Pablo Betancur-Rengifo, Arturo Ruiz-Sanchez, Ivan Reyes-Amezcua

Knowing the cause of kidney stone formation is crucial to establish treatments that prevent recurrence. There are currently different approaches for determining the kidney stone type. However, the reference ex-vivo identification procedure can take up to several weeks, while an in-vivo visual recognition requires highly trained specialists. Machine learning

Pan Yan

In this note, we revisit the Rankin-Selberg integral of Shimura type for generic representations of $\mathrm{SL}_2\times \mathrm{GL}_2$, constructed by Ginzburg, Rallis, and Soudry. We give a different and more ``intrinsic'' proof of the unramified computation. In contrast to their proof we avoid local functional equation for the general linear groups but us

Siheon Ryee, Tim O. Wehling

Mott-Hubbard and Hund electron correlations have been realized thus far in separate classes of materials. Here, we show that a single moir\'e homobilayer encompasses both kinds of physics in a controllable manner. We develop a microscopic multiband model that we solve by dynamical mean-field theory to nonperturbatively address the local many-body correlation

Xin Zhang, Rabab Abdelfattah, Yuqi Song, Xiaofeng Wang

Compared with multi-class classification, multi-label classification that contains more than one class is more suitable in real life scenarios. Obtaining fully labeled high-quality datasets for multi-label classification problems, however, is extremely expensive, and sometimes even infeasible, with respect to annotation efforts, especially when the label spa

Costas Mavromatis, George Karypis

Knowledge Graph Question Answering (KGQA) involves retrieving entities as answers from a Knowledge Graph (KG) using natural language queries. The challenge is to learn to reason over question-relevant KG facts that traverse KG entities and lead to the question answers. To facilitate reasoning, the question is decoded into instructions, which are dense questi

Prerona Dutta

The present paper studies a method of finding Lagrangian transformations, in the form of particle paths, for all scalar conservation laws having a smooth flux. These are found using the notion of weak diffeomorphisms. More precisely, from any given scalar conservation law, we derive a Temple system having one linearly degenerate and one genuinely nonlinear f

Felix Lev

Although the phenomenon of neutrino oscillations is confirmed in many experiments, the theoretical explanation of this phenomenon in the literature is essentially model dependent and is not based on rigorous physical principles. We propose an approach where the neutrino is treated as a massless elementary particle in anti-de Sitter (AdS) invariant quantum th

Claire Robin, Christian Requena-Mesa, Vitus Benson, Lazaro Alonso

Forecasting the state of vegetation in response to climate and weather events is a major challenge. Its implementation will prove crucial in predicting crop yield, forest damage, or more generally the impact on ecosystems services relevant for socio-economic functioning, which if absent can lead to humanitarian disasters. Vegetation status depends on weather

Weiran Yao, Guangyi Chen, Kun Zhang

Recently in the field of unsupervised representation learning, strong identifiability results for disentanglement of causally-related latent variables have been established by exploiting certain side information, such as class labels, in addition to independence. However, most existing work is constrained by functional form assumptions such as independent so

Xin Zhang, Rabab Abdelfattah, Yuqi Song, Samuel A. Dauchert

Depth information is the foundation of perception, essential for autonomous driving, robotics, and other source-constrained applications. Promptly obtaining accurate and efficient depth information allows for a rapid response in dynamic environments. Sensor-based methods using LIDAR and RADAR obtain high precision at the cost of high power consumption, price

A. J. Howard, M. Britton, Z. L. Streeter, C. Cheng

Filming atomic motion within molecules is an active pursuit of molecular physics and quantum chemistry. A promising method is laser-induced Coulomb Explosion Imaging (CEI) where a laser pulse rapidly ionizes many electrons from a molecule, causing the remaining ions to undergo Coulomb repulsion. The ion momenta are used to reconstruct the molecular geometry

Alessandro Arsie, Nataliya A. Balabanova

In this paper, we investigate collision orbits of two identical bodies placed on the surface of a two-dimensional sphere and interacting via an attracting potential of the form $V(q)=-\cot(q)$, where $q$ is the angle formed by the position vectors of the two bodies. We describe the $\omega$-limit set of the variables in the symplectically reduced system corr

Madison Sutula, Ian Christen, Eric Bersin, Michael P. Walsh

Solid-state quantum emitters have emerged as a leading quantum memory for quantum networking applications. However, standard optical characterization techniques are neither efficient nor repeatable at scale. In this work, we introduce and demonstrate spectroscopic techniques that enable large-scale, automated characterization of color centers. We first demon

Bailey Tallman, Alexandra Boone, Adhithya Vijayakumar, Fiona Lopez

As astronomical observations and their interpretation improve, the case for cold dark matter (CDM) becomes increasingly persuasive. A particularly appealing version of CDM is a weakly interacting massive particle (WIMP) with a mass near the electroweak scale, which can naturally have the observed relic abundance after annihilation in the early universe. But

Dennis Hartmann, Verena Schmid, Philip Meyer, Iñaki Soto-Rey

Performance measures are an important tool for assessing and comparing different medical image segmentation algorithms. Unfortunately, the current measures have their weaknesses when it comes to assessing certain edge cases. These limitations arouse when images with a very small region of interest or without a region of interest at all are assessed. As a sol

Antoni Rosinol, John J. Leonard, Luca Carlone

We propose a novel geometric and photometric 3D mapping pipeline for accurate and real-time scene reconstruction from monocular images. To achieve this, we leverage recent advances in dense monocular SLAM and real-time hierarchical volumetric neural radiance fields. Our insight is that dense monocular SLAM provides the right information to fit a neural radia

Olivia Borghi, Marcy Robertson

These notes represent the transcript of three, 90 minute lectures given by the second author at the CRM in Barcelona in 2021 as part of the "Higher Structures and Operadic Calculus" workshop. The goal of the series was to introduce and motivate modular infinity operads via their application to what is often called "Grothendieck-Teichmueller" theory.

Jeffrey R. Smith, Yao Xie, Christopher S. Josef, Rishikesan Kamaleswaran

Sepsis is a severe medical condition caused by a dysregulated host response to infection that has a high incidence and mortality rate. Even with such a high-level occurrence rate, the detection and diagnosis of sepsis continues to pose a challenge. There is a crucial need to accurately forecast the onset of sepsis promptly while also identifying the specific

Zoey Qiuyu Chen, Karl Van Wyk, Yu-Wei Chao, Wei Yang

Dexterous robotic hands have the capability to interact with a wide variety of household objects to perform tasks like grasping. However, learning robust real world grasping policies for arbitrary objects has proven challenging due to the difficulty of generating high quality training data. In this work, we propose a learning system (ISAGrasp) for leveraging

Yosuke Nakata, Toshihiro Nakanishi, Ryo Takahashi, Fumiaki Miyamaru

Since the latter half of the 20th century, the use of metal in optics has become a promising plasmonics field for controlling light at a deep subwavelength scale. Surface plasmon polaritons localized on metal surfaces are crucial in plasmonics. However, despite the long history of plasmonics, the underlying mechanism producing the surface waves is not fully

Linbo Wang, Eric Tchetgen Tchetgen, Torben Martinussen, Stijn Vansteelandt

We respond to comments on our paper, titled "Instrumental variable estimation of the causal hazard ratio."

Hannes Westermann, Jaromir Savelka, Vern R. Walker, Kevin D. Ashley

We propose an adaptive environment (CABINET) to support caselaw analysis (identifying key argument elements) based on a novel cognitive computing framework that carefully matches various machine learning (ML) capabilities to the proficiency of a user. CABINET supports law students in their learning as well as professionals in their work. The results of our e

Alberto Tono, Heyaojing Huang, Ashwin Agrawal, Martin Fischer

Today's architectural engineering and construction (AEC) software require a learning curve to generate a three-dimension building representation. This limits the ability to quickly validate the volumetric implications of an initial design idea communicated via a single sketch. Allowing designers to translate a single sketch to a 3D building will enable owner

Polly Y. Yu

A class of polynomial dynamical systems called complex-balanced are locally stable and conjectured to be globally stable. In general, complex-balancing is not a robust property, i.e., small changes in parameter values may result in the loss of the complex-balanced property. We show that robustly permanent complex-balanced systems are globally stable even aft

George Contopoulos

The most important theory of the spiral arms of galaxies is the density wave theory based on the Lin-Shu dispersion relation. However, the density waves move with the group velocity towards the inner Lindblad resonance and tend to disappear. Various mechanisms to replenish the spiral waves have been proposed. Nonlinear effects play an important role near the

Sebastian Szyller, Rui Zhang, Jian Liu, N. Asokan

Machine learning (ML) models are costly to train as they can require a significant amount of data, computational resources and technical expertise. Thus, they constitute valuable intellectual property that needs protection from adversaries wanting to steal them. Ownership verification techniques allow the victims of model stealing attacks to demonstrate that

David Alvarez-Melis, Nicolò Fusi, Lester Mackey, Tal Wagner

Optimal Transport (OT) is a fundamental tool for comparing probability distributions, but its exact computation remains prohibitive for large datasets. In this work, we introduce novel families of upper and lower bounds for the OT problem constructed by aggregating solutions of mini-batch OT problems. The upper bound family contains traditional mini-batch av

Martin Scharlemann

In 1980 J. Powell proposed that, for every genus $g$, five specific elements suffice to generate the Goeritz group $\mathcal {G}_g$ of genus $g$ Heegaard splittings of $S^3$. Powell's Conjecture remains undecided for $g \geq 4$. Let $\mathcal{P}_g \subset \mathcal {G}_g$ denote the subgroup generated by Powell's elements. Here we show that, for each genus $g

Sandeep Soni, David Bamman, Jacob Eisenstein

A standard measure of the influence of a research paper is the number of times it is cited. However, papers may be cited for many reasons, and citation count offers limited information about the extent to which a paper affected the content of subsequent publications. We therefore propose a novel method to quantify linguistic influence in timestamped document

Chiara Franceschini, Rouven Frassek, Cristian Giardinà

We consider a stochastic process of heat conduction where energy is redistributed along a chain between nearest neighbor sites via an improper beta distribution. Similar to the well-known Kipnis-Marchioro-Presutti (KMP) model, the finite chain is coupled at its ends with two reservoirs that break the conservation of energy when working at different temperatu

Sahithya Ravi, Aditya Chinchure, Leonid Sigal, Renjie Liao

There has been a growing interest in solving Visual Question Answering (VQA) tasks that require the model to reason beyond the content present in the image. In this work, we focus on questions that require commonsense reasoning. In contrast to previous methods which inject knowledge from static knowledge bases, we investigate the incorporation of contextuali

Wangda Zhang, Matteo Interlandi, Paul Mineiro, Shi Qiao

Modern analytical workloads are highly heterogeneous and massively complex, making generic query optimizers untenable for many customers and scenarios. As a result, it is important to specialize these optimizers to instances of the workloads. In this paper, we continue a recent line of work in steering a query optimizer towards better plans for a given workl

Viorel Barbu, Michael Röckner

One proves in this work that the nonlinear semigroup $S(t)$ in $L^1(\mathbb R^d)$, $d\geq 3$, associated with the nonlinear Fokker-Planck equation $u_t-\Delta\beta(u)+\text{div}(Db(u)u){=}0$, $u(0)=u_0$ in $(0,\infty)\times\mathbb R^d$, under suitable conditions on the coefficients $\beta:\mathbb R\to\mathbb R$, $D:\mathbb R^d\to\mathbb R^d$ and $b:\mathbb R

Baihan Lin

In recent years, reinforcement learning and bandits have transformed a wide range of real-world applications including healthcare, finance, recommendation systems, robotics, and last but not least, the speech and natural language processing. While most speech and language applications of reinforcement learning algorithms are centered around improving the tra

Javier A. Almonacid, Nilima Nigam

The propagation of internal gravity waves in stratified media, such as those found in ocean basins and lakes, leads to the development of geometrical patterns called "attractors". These structures accumulate much of the wave energy and make the fluid flow highly singular. In more analytical terms, the cause of this phenomenon has been attributed to the prese

Joonghyun Lee, John Spencer, Siyuan Shao, Juan Augusto Paredes

This paper presents an adaptive autopilot for fixed-wing aircraft and compares its performance with a fixed-gain autopilot. The adaptive autopilot is constructed by augmenting the autopilot architecture with adaptive control laws that are updated using retrospective cost adaptive control. In order to investigate the performance of the adaptive autopilot, the

Ali Barzegar, Daniele Oriti

In this paper, we investigate similarities and differences between the main neo-Copenhagen (or "epistemic-pragmatist") interpretations of quantum mechanics, here identified as those defined by the rejection of an ontological nature of the quantum states and the simultaneous avoidance of hidden variables, while maintaining the quantum formalism unchanged. We

Geoffrey G. Messier

The Simplified Access Metric (SAM) is a new approach for characterizing emergency shelter access patterns as a measure of shelter client vulnerability. The goal of SAM is to provide shelter operators with an intuitive way to understand access patterns that can be implemented by non-technical staff using spreadsheet operations. Client data from a large North

Eric Culver, Stephen G. Hartke

We show that the choosability of the square of planar graphs of max degree 4 without five cycles is at most 12. Keywords: planar graph, choosability AMS Mathematics Subject Classification: 05C15

Yifan Hou, Wenxiang Jiao, Meizhen Liu, Carl Allen

Large language models appear to learn facts from the large text corpora they are trained on. Such facts are encoded implicitly within their many parameters, making it difficult to verify or manipulate what knowledge has been learned. Language models have recently been extended to multilingual language models (MLLMs), enabling knowledge to be learned across h

Jana Pásztorová, Aria Mansouri Tehrani, Ivica Živković, Nicola A. Spaldin

We address the degeneracy of the ground state multiplet on the 5$d^1$ Re$^{6+}$ ion in double perovskite Ba$_{2}$MgReO$_{6}$ using a combination of specific heat measurements and density functional calculations. For Ba$_{2}$MgReO$_{6}$, two different ground state multiplets have previously been proposed - a quartet (with degeneracy $N$=4) [1] and a doublet (

Ru-Yue Wang, Jian-Fu Zhang, Alex Lazarian, Hua-Ping Xiao

We study the observable properties of compressible MHD turbulence covering different turbulence regimes, based on synthetic synchrotron observations arising from 3D MHD numerical simulations. Using the synchrotron emissivity and intensity, we first explore how the cosmic ray spectral indices affect the measurements of turbulence properties by employing norma

Emily Calamari, Jacqueline K. Faherty, Ben Burningham, Eileen Gonzales

We present results from an atmospheric retrieval analysis of Gl 229B using the BREWSTER retrieval code. We find the best fit model to be cloud-free, consistent with the T dwarf retrieval work of Line et al. 2017, Zalesky et al. 2022 and Gonzales et al. 2020. Fundamental parameters (mass, radius, log(L_{Bol}/L_{Sun}), log(g)) determined from our model agree w

Supriyo Bandyopadhyay

The notion of a spin field effect transistor, where transistor action is realized by manipulating the spin degree of freedom of charge carriers instead of the charge degree of freedom, has captivated researchers for at least three decades. These transistors are usually implemented by modulating the spin orbit interaction in a two- or one-dimensional semicond

Setareh Cohan, Nam Hee Kim, David Rolnick, Michiel van de Panne

Policies produced by deep reinforcement learning are typically characterised by their learning curves, but they remain poorly understood in many other respects. ReLU-based policies result in a partitioning of the input space into piecewise linear regions. We seek to understand how observed region counts and their densities evolve during deep reinforcement le

Fei Gao, Rodrigo E. Menchón, Aran Garcia-Lekue, Mads Brandbyge

Recently, porphyrin units have been attached to graphene nanoribbons (Por-GNR) enabling a multitude of possible structures. Here we report first principles calculations of two prototypical, experimentally feasible, Por-GNR hybrids, one of which displays a small band gap relevant for its use as electrode in a device. Embedding a Fe atom in the porphyrin cause

Kenechukwu C. Mbanisi, Hideyuki Kimpara, Zhi Li, Danil Prokhorov

This study presents a novel approach for modeling and simulating human-vehicle interactions in order to examine the effects of automated driving systems (ADS) on driving performance and driver control workload. Existing driver-ADS interaction studies have relied on simulated or real-world human driver experiments that are limited in providing objective evalu

Jeremy Quastel, Alejandro Ramirez, Balint Virag

We use a version of the Skorokhod integral to give a simple and rigorous formulation of the Wick-ordered (stochastic) heat equation with planar white noise, representing the free energy of an undirected random polymer. The solution for all times is expressed as the L1 limit of a martingale given by the Feyman-Kac formula and defines a randomized shift, or Ga

Piotr Surówka, Anton Souslov, Frank Jülicher, Debarghya Banerjee

Stress-strain constitutive relations in solids with an internal angular degree of freedom can be modelled using Cosserat (also called micropolar) elasticity. In this paper, we explore the phenomenology for a natural extension of Cosserat materials that includes chiral active components and odd elasticity. We calculate static elastic properties of such a soli

Samrudhdhi B Rangrej, Kevin J Liang, Tal Hassner, James J Clark

Many online action prediction models observe complete frames to locate and attend to informative subregions in the frames called glimpses and recognize an ongoing action based on global and local information. However, in applications with constrained resources, an agent may not be able to observe the complete frame, yet must still locate useful glimpses to p

Mark Ibrahim, Quentin Garrido, Ari Morcos, Diane Bouchacourt

Recent state-of-the-art vision models introduced new architectures, learning paradigms, and larger pretraining data, leading to impressive performance on tasks such as classification. While previous generations of vision models were shown to lack robustness to factors such as pose, it's unclear the extent to which this next generation of models are more robu

Hyoung-In Lee, Christopher Gaul

We have derived a dispersion relation governing the surface plasmon resonance established along a planar interface between a metal and a chiral medium (chiral case). Resulting numerical solutions are compared with the well-known results obtained for a metal-dielectric interface (achiral case). Comparisons show that the chiral case exhibits smaller phase spee

Jerry Ng, H. Harry Asada

This paper presents a Koopman lifting linearization method that is applicable to nonlinear dynamical systems having both stable and unstable regions. It is known that DMD and other standard data-driven methods face a fundamental difficulty in constructing a Koopman model when applied to unstable systems. Here we solve the problem by incorporating knowledge a

Aarshvi Gajjar, Chinmay Hegde, Christopher Musco

We consider the problem of active learning for single neuron models, also sometimes called ``ridge functions'', in the agnostic setting (under adversarial label noise). Such models have been shown to be broadly effective in modeling physical phenomena, and for constructing surrogate data-driven models for partial differential equations. Surprisingly, we show

Abdulaziz Alhamadani, Xuchao Zhang, Jianfeng He, Chang-Tien Lu

Text summarization has been intensively studied in many languages, and some languages have reached advanced stages. Yet, Arabic Text Summarization (ATS) is still in its developing stages. Existing ATS datasets are either small or lack diversity. We build, LANS, a large-scale and diverse dataset for Arabic Text Summarization task. LANS offers 8.4 million arti

Noam Levi, Itay Bloch, Marat Freytsis, Tomer Volansky

We propose a new method to probe the learning mechanism of Deep Neural Networks (DNN) by perturbing the system using Noise Injection Nodes (NINs). These nodes inject uncorrelated noise via additional optimizable weights to existing feed-forward network architectures, without changing the optimization algorithm. We find that the system displays distinct phase

Zejian Cui, Joao Cartucho, Stamatia Giannarou, Ferdinando Rodriguez y Baena

Telesurgical robotic systems provide a well established form of assistance in the operating theater, with evidence of growing uptake in recent years. Until now, the da Vinci surgical system (Intuitive Surgical Inc, Sunnyvale, California) has been the most widely adopted robot of this kind, with more than 6,700 systems in current clinical use worldwide [1]. T

Kangming Li, Brian DeCost, Kamal Choudhary, Michael Greenwood

Recent advances in machine learning (ML) methods have led to substantial improvement in materials property prediction against community benchmarks, but an excellent benchmark score may not imply good generalization of performance. Here we show that ML models trained on the Materials Project 2018 (MP18) dataset can have severely degraded prediction performanc

Maoyu Zhang, Jingfei Zhang, Wenlin Dai

Dynamic heterogeneous networks describe the temporal evolution of interactions among nodes and edges of different types. While there is a rich literature on finding communities in dynamic networks, the application of these methods to dynamic heterogeneous networks can be inappropriate, due to the involvement of different types of nodes and edges and the need

Nikhil Kumar Tomar, Debesh Jha, Ulas Bagci

Colorectal cancer (CRC) is the second leading cause of cancer-related death worldwide. Excision of polyps during colonoscopy helps reduce mortality and morbidity for CRC. Powered by deep learning, computer-aided diagnosis (CAD) systems can detect regions in the colon overlooked by physicians during colonoscopy. Lacking high accuracy and real-time speed are t

Claudia Flores-Saviaga, Shangbin Feng, Saiph Savage

The limited information (data voids) on political topics relevant to underrepresented communities has facilitated the spread of disinformation. Independent journalists who combat disinformation in underrepresented communities have reported feeling overwhelmed because they lack the tools necessary to make sense of the information they monitor and address the

Nazlı Doğan

The diametral dimension, $\Delta(E)$, and the approximate diametral dimension, $\delta(E)$ of an element $E$ of a large class of nuclear Fr\'echet spaces are set theoretically between the corresponding invariant of power series spaces $\Lambda_{1}(\varepsilon)$ and $\Lambda_{\infty}(\varepsilon)$ for some exponent sequence $\varepsilon$. Aytuna et al., \cite

Ivan Demidov, Yuri Lyubarsky

The luminosity of the Soft Gamma Repeater (SGR) flares significantly exceeds the Eddington luminosity. This is because they emit mainly in the E-mode, for which the radiative cross-sections are strongly suppressed. The energy is released in the magnetosphere forming a magnetically trapped pair fireball, and the surface of the star is illuminated by the power

Tzu-Jui Julius Wang, Jorma Laaksonen, Tomas Langer, Heikki Arponen

Weakly-supervised vision-language (V-L) pre-training (W-VLP) aims at learning cross-modal alignment with little or no paired data, such as aligned images and captions. Recent W-VLP methods, which pair visual features with object tags, help achieve performances comparable with some VLP models trained with aligned pairs in various V-L downstream tasks. This, h

Yacine Chitour, Sébastien Fueyo, Guilherme Mazanti, Mario Sigalotti

The paper deals with the controllability of finite-dimensional linear difference delay equations, i.e., dynamics for which the state at a given time $t$ is obtained as a linear combination of the control evaluated at time $t$ and of the state evaluated at finitely many previous instants of time $t-\Lambda_1,\dots,t-\Lambda_N$. Based on the realization theory

Tyler Millhouse, Melanie Moses, Melanie Mitchell

In April of 2022, the Santa Fe Institute hosted a workshop on embodied, situated, and grounded intelligence as part of the Institute's Foundations of Intelligence project. The workshop brought together computer scientists, psychologists, philosophers, social scientists, and others to discuss the science of embodiment and related issues in human intelligence,

Rachelle Austin, Yusef Farah, Thomas Sayer, Brad M. Luther

Hot carrier-based energy conversion systems could double the efficiency of conventional solar energy technology or drive photochemical reactions that would not be possible using fully thermalized, ``cool'' carriers, but current strategies require expensive multi-junction architectures. Using an unprecedented combination of photoelectrochemical and in situ tr

Christopher K. H. Borg, Eric S. Muckley, Clara Nyby, James E. Saal

The predictive capabilities of machine learning (ML) models used in materials discovery are typically measured using simple statistics such as the root-mean-square error (RMSE) or the coefficient of determination ($r^2$) between ML-predicted materials property values and their known values. A tempting assumption is that models with low error should be effect

Felipe Reyes-Osorio, Karen Rodriguez-Ramirez

We study the low-energy collective properties of a 1D spin-1 Bose gas using bosonization. After giving an overview of the technique, emphasizing the physical aspects, we apply it to the $S=1$ Bose-Hubbard Hamiltonian and find a novel separation of the quadrupole-spin-charge sectors, confirmed by time-MPS numerical simulations. Additionally, through the singl

Tianren Gu, Xiao Yuan, Bujiao Wu

Boson is one of the most basic types of particles and preserves the commutation relation. An efficient way to measure a bosonic system is important not only for simulating complex physics phenomena of bosons (such as nuclei) on a qubit based quantum computer, but for extracting classical information from a quantum simulator/computer that itself is built with

C. L. Carilli, M. Neeleman

We present simulations of the capabilities of the ngVLA to image at $\sim 0.75$ kpc resolution ($0.085"$), molecular line emission from star forming disk galaxies at high redshift. The results are compared to the current capabilities of ALMA. ALMA can detect the integrated emission, and determine the velocity gradient and size across the brighter emission re

Jithendaraa Subramanian, Yashas Annadani, Ivaxi Sheth, Nan Rosemary Ke

Causal learning has long concerned itself with the accurate recovery of underlying causal mechanisms. Such causal modelling enables better explanations of out-of-distribution data. Prior works on causal learning assume that the high-level causal variables are given. However, in machine learning tasks, one often operates on low-level data like image pixels or

Anish K. Vallapuram, Young D. Kwon, Lik-Hang Lee, Fengli Xu

A particular phenomenon of interest in Retail Economics is the spillover effect of anchor stores (specific stores with a reputable brand) to non-anchor stores in terms of customer traffic. Prior works in this area rely on small and survey-based datasets that are often confidential or expensive to collect on a large scale. Also, very few works study the under

Oliver Tough

For general absorbed Markov processes $(X_t)_{0\leq t<\tau_{\partial}}$ having a quasi-stationary distribution (QSD) $\pi$ and absorption time $\tau_{\partial}$, we introduce a Dobrushin-type criterion providing for exponential convergence in $L^{\infty}(\pi)$ as $t\rightarrow\infty$ of the density $\frac{d\mathcal{L}_{\mu}(X_t\lvert \tau_{\partial}>t)}{d\pi

Amira A. Tawfeek, Bernardo Cervantes Sodi, Jacopo Fritz, Alessia Moretti

We present a study of barred galaxies in the cluster environment, exploiting a sample of galaxies drawn from the extended WIde-field Nearby Galaxy-cluster Survey (OmegaWINGS) that covers up to the outer regions of 32 local X-ray selected clusters. Barred galaxies are identified through a semi-automatic analysis of ellipticity and position angle profiles. We

Joachim Jelisiejew, Tomasz Mańdziuk

We investigate the question whether a given homogeneous ideal is a limit of saturated ones. We provide cohomological necessary criteria for this to hold and apply them to a range of examples. Our motivation comes from the theory of border apolarity and varieties of sums of powers, where the question above is tightly connected to proving new lower bounds for

Xiang Ji, Yesim Sungu-Eryilmaz, Elaheh Momeni, Reza Rawassizadeh

Natural language processing applications, such as conversational agents and their question-answering capabilities, are widely used in the real world. Despite the wide popularity of large language models (LLMs), few real-world conversational agents take advantage of LLMs. Extensive resources consumed by LLMs disable developers from integrating them into end-u

Siyang Wang, Gunilla Kreiss

We develop a hybrid spatial discretization for the wave equation in second order form, based on high-order accurate finite difference methods and discontinuous Galerkin methods. The hybridization combines computational efficiency of finite difference methods on Cartesian grids and geometrical flexibility of discontinuous Galerkin methods on unstructured mesh

Azton I. Wells, Michael L. Norman

We incorporate new scale-intelligent models of metal-enriched star formation (\starss) with surrogate models of primordial stellar feedback (\starnet) into the astrophysics simulation code \enzo to analyze the impact of heterogeneous metal enrichment on the first galaxies. Our study includes the earliest generations of stars and the protogalaxies ($10^6 \les

Evonne P. C. Lee, Guangzhi Sun, Chao Zhang, Philip C. Woodland

In speaker diarisation, speaker embedding extraction models often suffer from the mismatch between their training loss functions and the speaker clustering method. In this paper, we propose the method of spectral clustering-aware learning of embeddings (SCALE) to address the mismatch. Specifically, besides an angular prototype cal (AP) loss, SCALE uses a nov

Alexis Ross, Matthew E. Peters, Ana Marasović

Rationalization is fundamental to human reasoning and learning. NLP models trained to produce rationales along with predictions, called self-rationalization models, have been investigated for their interpretability and utility to end-users. However, the extent to which training with human-written rationales facilitates learning remains an under-explored ques

Dootika Vats, Felipe Acosta, Mark L. Huber, Galin L. Jones

An introduction to the use of linchpin variables in Markov chain Monte Carlo (MCMC) is provided. Before the widespread adoption of MCMC methods, conditional sampling using linchpin variables was essentially the only practical approach for simulating from multivariate distributions. With the advent of MCMC, linchpin variables were largely ignored. However, th

Koustubh Phalak, Junde Li, Swaroop Ghosh

Quantum supremacy in many applications using well-known quantum algorithms rely on availability of data in quantum format. Quantum Random Access Memory (QRAM), an equivalent of classical Random Access Memory (RAM), fulfills this requirement. However, the existing QRAM proposals either require qutrit technology and/or incur access challenges. We propose an ap

Mónika Farsang, Paul Mineiro, Wangda Zhang

Contextual bandits with average-case statistical guarantees are inadequate in risk-averse situations because they might trade off degraded worst-case behaviour for better average performance. Designing a risk-averse contextual bandit is challenging because exploration is necessary but risk-aversion is sensitive to the entire distribution of rewards; nonethel

Ziwei Fan, Zhiwei Liu, Chen Wang, Peijie Huang

Sequential Recommendation (SR) models user dynamics and predicts the next preferred items based on the user history. Existing SR methods model the 'was interacted before' item-item transitions observed in sequences, which can be viewed as an item relationship. However, there are multiple auxiliary item relationships, e.g., items from similar brands and with

E. Redaelli, A. Chacón-Tanarro, P. Caselli, M. Tafalla

Prestellar cores, the birthplace of Sun-like stars, form from the fragmentation of the filamentary structure that composes molecular clouds, from which they must inherit at least partially the kinematics. Furthermore, when they are on the verge of gravitational collapse, they show signs of subsonic infall motions. How extended these motions are, which depend

Amit Galor, Roy Orfaig, Ben-Zion Bobrovsky

Transformer networks have been a focus of research in many fields in recent years, being able to surpass the state-of-the-art performance in different computer vision tasks. However, in the task of Multiple Object Tracking (MOT), leveraging the power of Transformers remains relatively unexplored. Among the pioneering efforts in this domain, TransCenter, a Tr

Kristijan Armeni, Christopher Honey, Tal Linzen

When a language model is trained to predict natural language sequences, its prediction at each moment depends on a representation of prior context. What kind of information about the prior context can language models retrieve? We tested whether language models could retrieve the exact words that occurred previously in a text. In our paradigm, language models

Mohamed M. Anber, Erich Poppitz

We calculate the gaugino condensate in $SU(2)$ super Yang-Mills theory on an asymmetric four-torus $\mathbb T^4$ with 't Hooft's twisted boundary conditions. The $\mathbb T^4$ asymmetry is controlled by a dimensionless detuning parameter $\Delta$, proportional to $L_3 L_4 - L_1 L_2$, with $L_i$ denoting the $\mathbb T^4$ periods. We perform our calculations

Mohammad Samragh, Arnav Kundu, Ting-Yao Hu, Minsik Cho

This paper explores the possibility of using visual object detection techniques for word localization in speech data. Object detection has been thoroughly studied in the contemporary literature for visual data. Noting that an audio can be interpreted as a 1-dimensional image, object localization techniques can be fundamentally useful for word localization. B

Jochen Glück, Andrii Mironchenko

We consider generators of positive $C_0$-semigroups and, more generally, resolvent positive operators $A$ on ordered Banach spaces and seek for conditions ensuring the negativity of their spectral bound $s(A)$. Our main result characterizes $s(A) < 0$ in terms of so-called \emph{small-gain conditions} that describe the behaviour of $Ax$ for positive vectors

Dmitri Denisov, Costas Vellidis

The Tevatron collider led the World energy frontier program in particle physics during the late 20th and early 21st centuries. During this exciting period the standard model of particle physics was in its final stages of development and the search for physics beyond the standard model became one of the main research topics. In this review article we summariz

Alfonso Nieto-Castanon

This chapter describes several procedures used to prepare fMRI data for statistical analyses. It includes the description of common preprocessing steps, such as spatial realignment, coregistration, and spatial normalization, aimed at the spatial alignment of all fMRI data within- and between- subjects, as well as several denoising procedures aimed at minimiz

Yeuk Hay Joshua Lam

Let $p$ be a fixed prime number, and $q$ a power of $p$. For any curve over $\mathbb{F}_q$ and any local system on it, we have a number field generated by the traces of Frobenii at closed points, known as the trace field. We show that as we range over all pointed curves of type $(g,n)$ in characteristic $p$ and rank two local systems satisfying a condition a

Fabian Krüger, Hendrik Plett

The fixed-event forecasting setup is common in economic policy. It involves a sequence of forecasts of the same (`fixed') predictand, so that the difficulty of the forecasting problem decreases over time. Fixed-event point forecasts are typically published without a quantitative measure of uncertainty. To construct such a measure, we consider forecast postpr

Thomas Fink, Yang-Hui He

There has been a recent surge of interest in what causes aging. This has been matched by unprecedented research investment in the field from tech companies. But, despite considerable effort from a broad range of researchers, we do not have a rigorous mathematical theory of programmed aging. To address this, we recently derived a mortality equation that gover

Simon Chiche, Valentin Decoene

Radio-detection is a mature technique that has gained large momentum over the past decades. Its physical detection principle is mainly driven by the electromagnetic part of the shower, and is therefore not too sensitive to uncertainties on hadronic interactions. Furthermore its technical detection principle allows for a 100% duty cycle, and large surface cov