April 2023 arXiv papers — page 6

Rajshekhar Das, Jonathan Francis, Sanket Vaibhav Mehta, Jean Oh

Self-training based on pseudo-labels has emerged as a dominant approach for addressing conditional distribution shifts in unsupervised domain adaptation (UDA) for semantic segmentation problems. A notable drawback, however, is that this family of approaches is susceptible to erroneous pseudo labels that arise from confirmation biases in the source domain and

Jesse Kim, Jeffrey M. Rabin, Brendon Rhoades

Affine superspace $\mathbb{C}^{1 \mid n}$ has a single bosonic coordinate $z$ and $n$ fermionic coordinates $\theta_1, \dots, \theta_n$. Let $M$ be the supertorus obtained by quotienting $\mathbb{C}^{1 \mid n}$ by the abelian group generated by the maps $S: (z,\theta_1, \dots, \theta_n) \mapsto (z + 1, \theta_1, \dots, \theta_n)$ and $T: (z, \theta_1, \dots,

Marcelo Coniglio, Martín Figallo

Tetravalent modal logic (T ML) was introduced by Font and Rius in 2000; and it is an expansion of the Belnap-Dunn four{valued logic FOUR, a logical system that is well{known for the many applications it has been found in several fields. Besides, T ML is the logic that preserve degrees of truth with respect to Monteiro's tetravalent modal algebras. Among othe

Huazhong Lü, Xianyue Li, Heping Zhang

The anti-Kekulé number of a connected graph $G$ is the smallest number of edges whose deletion results in a connected subgraph having no Kekulé structures (perfect matchings). As a common generalization of (conditional) matching preclusion number and anti-Kekulé number of a graph $G$, we introduce $s$-restricted matching preclusion number of $G$ as the small

Panpan Ren, Martin Grothaus, Feng-Yu Wang

The well-posedness and exponential ergodicity are proved for stochastic Hamiltonian systems containing a singular drift term which is locally integrable in the component with noise. As an application, the well-posedness and uniform exponential ergodicity are derived for a class of singular degenerated McKean-Vlasov SDEs.

Jaime Fabián Nieto Castellanos, Ivan Hip, Wolfgang Bietenholz

We study the Schwinger model with $N_{\rm f} \geq 2$ degenerate fermion flavors, by means of lattice simulations. We use dynamical Wilson fermions for $N_{\rm f} = 2$, and re-weighted quenched configurations for overlap-hypercube fermions with $N_{\rm f} \leq 6$. In this framework, we explore an analogue of the QCD pion decay constant $F_{\pi}$, which is dim

Jiaju Qi, Lei Lei, Kan Zheng, Simon X. Yang

In this paper, we investigate the scheduling issue of diesel generators (DGs) in an Internet of Things (IoT)-Driven isolated microgrid (MG) by deep reinforcement learning (DRL). The renewable energy is fully exploited under the uncertainty of renewable generation and load demand. The DRL agent learns an optimal policy from history renewable and load data of

Zhuyun Zhou, Zongwei Wu, Danda Pani Paudel, Rémi Boutteau

Moving object segmentation (MOS) in dynamic scenes is an important, challenging, but under-explored research topic for autonomous driving, especially for sequences obtained from moving ego vehicles. Most segmentation methods leverage motion cues obtained from optical flow maps. However, since these methods are often based on optical flows that are pre-comput

Ben Johnsrude

We note that the subpolynomial factor for the $\ell^qL^p$ small cap decoupling constants for the truncated parabola $\mathbb{P}^1=\{(t,t^2):|t|\leq 1\}$ may be controlled by a suitable power of $\log R$. This is achieved by considering a suitable amplitude-dependent wave envelope estimate, as was introduced in a recent paper of Guth and Maldague to demonstra

Yue Ling, Taofiqhasan Mahmood

Aerobreakup of drops is a fundamental two-phase flow problem that is essential to many spray applications. A parametric numerical study was performed by varying the gas stream velocity, focusing on the regime of moderate Weber numbers, in which the drop deforms to a forward bag. When the bag is unstable, it inflates and disintegrates into small droplets. Det

Eduardo Cerpa, Matías Courdurier, Esteban Hernández, Leonel E. Medina

The use of high-frequency currents in neurostimulation has received increased attention in recent years due to its varied effects on tissues and cells. Nonlinear differential equations are commonly used as models for Neurons, and averaging methods are suitable for addressing questions like stability when considering single-frequency sources. A recent strateg

Monika Henzinger, Paul Liu, Jan Vondrak, Da Wei Zheng

The maximization of submodular functions have found widespread application in areas such as machine learning, combinatorial optimization, and economics, where practitioners often wish to enforce various constraints; the matroid constraint has been investigated extensively due to its algorithmic properties and expressive power. Recent progress has focused on

Hsuan-I Ho, Lixin Xue, Jie Song, Otmar Hilliges

In this paper, we propose a novel hybrid representation and end-to-end trainable network architecture to model fully editable and customizable neural avatars. At the core of our work lies a representation that combines the modeling power of neural fields with the ease of use and inherent 3D consistency of skinned meshes. To this end, we construct a trainable

W. A. Zúñiga-Galindo, B. A. Zambrano-Luna

This work aims to study the interplay between the Wilson-Cowan model and the connection matrices. These matrices describe the cortical neural wiring, while the Wilson-Cowan equations provide a dynamical description of neural interaction. We formulate the Wilson-Cowan equations on locally compact Abelian groups. We show that the Cauchy problem is well-posed.

Edgar Pacheco

To explore social media use in New Zealand, a sample of 1001 adults aged 18 and over were surveyed in November 2021. Participants were asked about the frequency of their use of different social media platforms (text message included). This report describes how often each of the nine social media sites and apps covered in the survey are used individually on a

Kent K. Chang, Mackenzie Cramer, Sandeep Soni, David Bamman

In this work, we carry out a data archaeology to infer books that are known to ChatGPT and GPT-4 using a name cloze membership inference query. We find that OpenAI models have memorized a wide collection of copyrighted materials, and that the degree of memorization is tied to the frequency with which passages of those books appear on the web. The ability of

Mihailo Djordjevic, Tijana Radenkovic, Pavle Stipsic, Marko Vojinovic

When discussing the gauge symmetries of any theory, the Henneaux-Teitelboim transformations are often underappreciated or even completely ignored, due to their on-shell triviality. Nevertheless, these gauge transformations play an important role in understanding the structure of the full gauge symmetry group of any theory, especially regarding the subgroup o

Iva Vasic, Roberto Pierdicca, Emanuele Frontoni, Bata Vasic

This paper presents a new technique for the virtual reality (VR) visu-alization of complex volume images obtained from computer tomography (CT) and Magnetic Resonance Imaging (MRI) by combining three-dimensional (3D) mesh processing and software coding within the gaming engine. The method operates on real representations of human organs avoiding any structur

Ruchao Fan, Yunzheng Zhu, Jinhan Wang, Abeer Alwan

Recently, self-supervised learning (SSL) from unlabelled speech data has gained increased attention in the automatic speech recognition (ASR) community. Typical SSL methods include autoregressive predictive coding (APC), Wav2vec2.0, and hidden unit BERT (HuBERT). However, SSL models are biased to the pretraining data. When SSL models are finetuned with data

Christian G. Parigger

This work communicates analysis of aluminum monoxide, AlO, laser-plasma emission records using line strength data and the ExoMol astrophysical database. A nonlinear fitting program computes comparisons of measured and simulated diatomic molecular spectra. Predicted cyanide spectra of the AlO, ${\rm B}\ ^2\,\Sigma^+ \longrightarrow {\rm X} \ ^2\,\Sigma^+$, $\

Peetak Mitra, Majid Haghshenas, Niccolo Dal Santo, Conor Daly

High-fidelity computational fluid dynamics (CFD) simulations for design space explorations can be exceedingly expensive due to the cost associated with resolving the finer scales. This computational cost/accuracy trade-off is a major challenge for modern CFD simulations. In the present study, we propose a method that uses a trained machine learning model tha

Yue-Wen Fang, Craig A. J. Fisher, Akihide Kuwabara, Xin-Wei Shen

Using first-principles calculations we examine the crystal structures and phase transitions of nitride perovskite LaWN$_3$. Lattice dynamics calculations indicate that the ground-state structure belongs to space group $R3c$. Two competitive phase transition pathways are identified which are characterized by symmetry-adapted distortion modes. The results sugg

N. Y. Agafonova, A. Alexandrov, A. M. Anokhina, T. Asada

We present a study of a directional search for Dark Matter boosted forward when scattered by cosmic-ray nuclei, using a module of the NEWSdm experiment. The boosted Dark Matter flux at the edge of the Earth's atmosphere is expected to be pointing to the Galactic Center, with a flux 15 to 20 times larger than in the transverse direction. The module of the NEW

Ali Tazarv, Sina Labbaf, Amir Rahmani, Nikil Dutt

Most existing sensor-based monitoring frameworks presume that a large available labeled dataset is processed to train accurate detection models. However, in settings where personalization is necessary at deployment time to fine-tune the model, a person-specific dataset needs to be collected online by interacting with the users. Optimizing the collection of l

Naushad Ahmad Kamar, Daniel A. Paz, Mohammad F. Maghrebi

The spin-boson model, describing a two-level system strongly coupled to a bosonic bath, is extensively studied as a paradigmatic dissipative quantum system, exhibiting rich dynamical behavior and even a localization transition in the strong coupling regime. Here, we additionally consider dephasing as a source of Markovian dissipation on top of the non-Markov

Christian Mattjie, Luis Vinicius de Moura, Rafaela Cappelari Ravazio, Lucas Silveira Kupssinskü

Segmentation in medical imaging is a critical component for the diagnosis, monitoring, and treatment of various diseases and medical conditions. Presently, the medical segmentation landscape is dominated by numerous specialized deep learning models, each fine-tuned for specific segmentation tasks and image modalities. The recently-introduced Segment Anything

Michael W. Coughlin, Joshua S. Bloom, Guy Nir, Sarah Antier

SkyPortal is an open-source software package designed to efficiently discover interesting transients, manage follow-up, perform characterization, and visualize the results. By enabling fast access to archival and catalog data, cross-matching heterogeneous data streams, and the triggering and monitoring of on-demand observations for further characterization,

Dake Chen, Xuan Zhou, Yinghua Hu, Yuke Zhang

Logic locking has become a promising approach to provide hardware security in the face of a possibly insecure fabrication supply chain. While many techniques have focused on locking combinational logic (CL), an alternative latch-locking approach in which the sequential elements are locked has also gained significant attention. Latch (LAT) locking duplicates

Mauricio Valenzuela, Jorge Zanelli

Counting the degrees of freedom of the massless Rarita-Schwinger theory is revisited using Behrends-Fronsdal projectors. The identification of the gauge invariant part of the vector-spinor is thus straightforward, consisting of spins 1/2 and 3/2. The validity of this statement is supported by the explicit solution found in the standard gamma-traceless gauge.

Hau Trung Dang, Eirik Keilegavlen, Inga Berre

Hydraulic stimulation is a critical process for increasing the permeability of fractured geothermal reservoirs. This technique relies on coupled hydromechanical processes induced by reservoir stimulation through pressurized fluid injection into the rock formation. The injection of fluids causes poromechanical stress changes that can lead to the dilation of f

Yuchen Liu, Natasha Ong, Kaiyan Peng, Bo Xiong

We present Multiscale Multiview Vision Transformers (MMViT), which introduces multiscale feature maps and multiview encodings to transformer models. Our model encodes different views of the input signal and builds several channel-resolution feature stages to process the multiple views of the input at different resolutions in parallel. At each scale stage, we

Alexandre Duval, Victor Schmidt, Alex Hernandez Garcia, Santiago Miret

Applications of machine learning techniques for materials modeling typically involve functions known to be equivariant or invariant to specific symmetries. While graph neural networks (GNNs) have proven successful in such tasks, they enforce symmetries via the model architecture, which often reduces their expressivity, scalability and comprehensibility. In t

Pablo Amster, Andrés Rivera, John A. Arredondo

In this paper, we prove the existence of two positive $T$-periodic solutions of an electrostatic actuator modeled by the time-delayed Duffing equation $$\ddot{x}(t)+f_{D}(x(t),\dot{x}(t))+ x(t)=1- \dfrac{e \mathcal{V}^{2}(t,x(t),x_{d}(t),\dot{x}(t),\dot{x}_{d}(t))}{x^2(t)}, \qquad x(t)\in\,]0,\infty[ $$ where $x_{d}(t)=x(t-d)$ and $\dot{x}_{d}(t)=\dot{x}(t-d

Erika Pirnes

Motivated by a problem in graph theory, this article introduces an algebra called the balanced algebra. This algebra is defined by generators and relations, and the main goal is to find a minimal set of relations for it.

Irving I. Gaspar, Luis A. Hernández, Renato Zamora

We study the nature of the chiral symmetry restoration within the Yukawa model with spontaneous symmetry breaking. We work with scalar and fermion fields which are subject to the effects of a rotating system. In this work, we show the derivation of the scalar field propagator in a rotating medium using the Fock-Schwinger proper-time method. We compute analyt

Timothy A. Smith, Stephen G. Penny, Jason A. Platt, Tse-Chun Chen

The immense computational cost of traditional numerical weather and climate models has sparked the development of machine learning (ML) based emulators. Because ML methods benefit from long records of training data, it is common to use datasets that are temporally subsampled relative to the time steps required for the numerical integration of differential eq

Daniel H. T. Franco

The purpose of this short review, based in part on ideas developed in an article by the author and Fagundes [3], is to emphasize that the gauge-fixing condition, necessary to eliminate the spurious degrees of freedom of the electromagnetic field, is elegantly handled by Dencker's work on the propagation of polarization sets for systems of real principal type

Rebecca W. Smaha, John S. Mangum, Ian A. Leahy, Julian Calder

Nitride perovskites are an emerging class of materials that have been predicted to display a range of interesting physics and functional properties, but they are under-explored due to the difficulty of synthesizing oxygen-free nitrides. LaWN3, recently reported as the first oxygen-free nitride perovskite, exhibited polar symmetry and a large piezoelectric co

Tong Zhou, Yukui Luo, Shaolei Ren, Xiaolin Xu

As a type of valuable intellectual property (IP), deep neural network (DNN) models have been protected by techniques like watermarking. However, such passive model protection cannot fully prevent model abuse. In this work, we propose an active model IP protection scheme, namely NNSplitter, which actively protects the model by splitting it into two parts: the

Lucas Amoudruz, Athena Economides, Petros Koumoutsakos

Red blood cells (RBCs) are vital for transporting oxygen from the lungs to the body's tissues through the intricate circulatory system. They achieve this by binding and releasing oxygen molecules to the abundant hemoglobin within their cytosol. The volume of RBCs affects the amount of oxygen they can carry, yet whether this volume is optimal for transporting

Richard N. Ball

(Completely regular) locales generalize (Tychonoff) spaces; indeed, the passage from a locale to its spatial sublocale is a well understood coreflection. But a locale also possesses an equally important pointless sublocale, and with morphisms suitably restricted, the passage from a locale to its pointless sublocale is also a coreflection. Our main theorem is

Yuting Huang, Simon S. Toedtli, Gregory P. Chini, Beverley J. McKeon

The quadratic convection term in the incompressible Navier-Stokes equations is considered as a non-linear forcing to the linear operator, and it is studied in the Fourier domain through the analysis of interactions between triadically compatible wavenumber-frequency triplets. Interaction coefficients are proposed to quantify the contribution to the forcing b

Francesco Regazzoni, Stefano Pagani, Matteo Salvador, Luca Dede'

Predicting the evolution of systems that exhibit spatio-temporal dynamics in response to external stimuli is a key enabling technology fostering scientific innovation. Traditional equations-based approaches leverage first principles to yield predictions through the numerical approximation of high-dimensional systems of differential equations, thus calling fo

Jiasen Guo, Pousali Ghosh, Daniel Hill, Yiyao Chen

Topological magnetic charges, arising due to the non-vanishing magnetic flux on spin ice vertices, serve as the origin of magnetic monopoles that traverse the underlying lattice effortlessly. Unlike spin ice materials of atomic origin, the dynamic state in artificial honeycomb spin ice is conventionally described in terms of finite size domain wall kinetics

Yaofeng Desmond Zhong, Jiequn Han, Biswadip Dey, Georgia Olympia Brikis

Differentiable simulation enables gradients to be back-propagated through physics simulations. In this way, one can learn the dynamics and properties of a physics system by gradient-based optimization or embed the whole differentiable simulation as a layer in a deep learning model for downstream tasks, such as planning and control. However, differentiable si

Ren-Cang Li

The NEPv approach has been increasingly used lately for optimization on the Stiefel manifold arising from machine learning. General speaking, the approach first turns the first order optimality condition, also known as the KKT condition, into a nonlinear eigenvalue problem with eigenvector dependency (NEPv) or a nonlinear polar decomposition with orthogonal

Mingyang Wang, Heike Adel, Lukas Lange, Jannik Strötgen

This paper describes our system developed for the SemEval-2023 Task 12 "Sentiment Analysis for Low-resource African Languages using Twitter Dataset". Sentiment analysis is one of the most widely studied applications in natural language processing. However, most prior work still focuses on a small number of high-resource languages. Building reliable sentiment

Fatemeh Ghaffari, Mark C. Wilson

We introduce and analyse a simple probabilistic model of article production and citation behavior that explicitly assumes that there is no decline in citability of a given article over time. It makes predictions about the number and age of items appearing in the reference list of an article. The latter topics have been studied before, but only in the context

Xiongchao Chen, Zhigang Peng, Gerardo Hermosillo Valadez

Magnetic resonance imaging (MRI) is widely employed for diagnostic tests in neurology. However, the utility of MRI is largely limited by its long acquisition time. Acquiring fewer k-space data in a sparse manner is a potential solution to reducing the acquisition time, but it can lead to severe aliasing reconstruction artifacts. In this paper, we present a n

Hastings Greer, Lin Tian, Francois-Xavier Vialard, Roland Kwitt

Inverse consistency is a desirable property for image registration. We propose a simple technique to make a neural registration network inverse consistent by construction, as a consequence of its structure, as long as it parameterizes its output transform by a Lie group. We extend this technique to multi-step neural registration by composing many such networ

Mustafa Can Camur, Chin-Yuan Tseng, Aristotelis E. Thanos, Chelsea C. White

COVID-19 resulted in some of the largest supply chain disruptions in recent history. To mitigate the impact of future disruptions, we propose an integrated hybrid simulation framework to couple nonstationary demand signals from an event like COVID-19 with a model of an end-to-end supply chain. We first create a system dynamics susceptible-infected-recovered

Krystian Gajdzica

Let $\mathcal{A}=\left(a_i\right)_{i=1}^\infty$ be a weakly increasing sequence of positive integers and let $k$ be a fixed positive integer. For an arbitrary integer $n$, the restricted partition $p_\mathcal{A}(n,k)$ enumerates all the partitions of $n$ whose parts belong to the multiset $\{a_1,a_2,\ldots,a_k\}$. In this paper we investigate some generaliza

Dang Bui, Wanwan Li, Hong Huang

In this paper, we present CarGameAR: An Integrated AR Car Game Authoring Interface for Custom-Built Car Programed on Arduino Board. The car consists of an Arduino board, an H-bridge, and motors. The objective of the project is to create a system that can move a car in different directions using a computer application. The system uses Unity software to create

Shiva Nejati, Lev Sorokin, Damir Safin, Federico Formica

Surrogate-assisted search-based testing (SA-SBT) aims to reduce the computational time for testing compute-intensive systems. Surrogates enhance testing techniques by improving test case generation focusing the testing budget on the most critical portions of the input domain. In addition, they can serve as approximations of the system under test (SUT) to pre

Jun Kataoka, Hyunsoo Yoon

This paper presents an unsupervised domain adaptation (UDA) method for predicting unlabeled target domain data, specific to complex UDA tasks where the domain gap is significant. Mainstream UDA models aim to learn from both domains and improve target discrimination by utilizing labeled source domain data. However, the performance boost may be limited when th

Cheng Peng, Yizhou Li, Stan Uryasev

We study the problem of modeling univariate distributions via their quantile functions. We introduce a flexible family of distributions whose quantile function is a linear combination of basis quantiles. Because the model is linear in its parameters, estimation reduces to constrained linear regression, yielding a convex optimization problem that readily acco

Yang Yu, Philip Goyal

A measurement performed on a quantum system is an act of gaining information about its state, a view that is widespread in practical and foundational work in quantum theory. However, the concept of information in quantum theory reconstructions is multiply-defined, and its conceptual foundations remain surprisingly under-explored. In this paper, we investigat

Kiran Kokilepersaud, Mohit Prabhushankar, Yavuz Yarici, Ghassan AlRegib

In this work, we present a methodology to shape a fisheye-specific representation space that reflects the interaction between distortion and semantic context present in this data modality. Fisheye data has the wider field of view advantage over other types of cameras, but this comes at the expense of high radial distortion. As a result, objects further from

Ibai Aedo, Uwe Grimm, Ian Short

We introduce the forward limit set $\Lambda$ of a semigroup $S$ generated by a family of substitutions of a finite alphabet, which typically coincides with the set of all possible s-adic limits of that family. We provide several alternative characterisations of the forward limit set. For instance, we prove that $\Lambda$ is the unique maximal closed and stro

Binnur Görer, Fatma Başak Aydemir

Requirements elicitation interviews are a widely adopted technique, where the interview success heavily depends on the interviewer's preparedness and communication skills. Students can enhance these skills through practice interviews. However, organizing practice interviews for many students presents scalability challenges, given the time and effort required

Saminu Mohammad Aliyu, Idris Abdulmumin, Shamsuddeen Hassan Muhammad, Ibrahim Said Ahmad

We present the findings of our participation in the SemEval-2023 Task 10: Explainable Detection of Online Sexism (EDOS) task, a shared task on offensive language (sexism) detection on English Gab and Reddit dataset. We investigated the effects of transferring two language models: XLM-T (sentiment classification) and HateBERT (same domain -- Reddit) for multi

Nicolas Garcia Trillos, Matt Jacobs, Jakwang Kim

We study three models of the problem of adversarial training in multiclass classification designed to construct robust classifiers against adversarial perturbations of data in the agnostic-classifier setting. We prove the existence of Borel measurable robust classifiers in each model and provide a unified perspective of the adversarial training problem, expa

Rong-Gen Cai, Shao-Jiang Wang, Zi-Yan Yuwen

For a cosmological first-order phase transition in the early Universe, the associated stochastic gravitational wave background is usually dominated by sound waves from plasma fluid motions, which have been analytically modeled as a random superposition of freely propagating sound shells but with the force by the scalar field that produces the self-similar pr

Jorge Gamboa, Justo López-Sarrión, Fernando Méndez, Natalia Tapia Arellano

We go through several previous corrections and contributions to the muon $g-2$, starting from the dark photon hypothesis to the dark Z. We explore the inputs from a dark Z boson virtual mediator in a first order loop. We consider not only the QED like contributions in the theory but also weak interactions. We obtain a new factor that adds corrections to the

Qiang Du, Huaiyu Li, Michael Weinstein, Lu Zhang

We present discontinuous Galerkin (DG) methods for solving a first-order semi-linear hyperbolic system, which was originally proposed as a continuum model for a one-dimensional dimer lattice of topological resonators. We examine the energy-conserving or energy-dissipating property in relation to the choices of simple, mesh-independent numerical fluxes. We de

Andreas Maurer

A bound uniform over various loss-classes is given for data generated by stationary and phi-mixing processes, where the mixing time (the time needed to obtain approximate independence) enters the sample complexity only in an additive way. For slowly mixing processes this can be a considerable advantage over results with multiplicative dependence on the mixin

Sharan Banagiri, Christopher P. L. Berry, Gareth S. Cabourn Davies, Leo Tsukada

Recent gravitational-wave transient catalogs have used \pastro{}, the probability that a gravitational-wave candidate is astrophysical, to select interesting candidates for further analysis. Unlike false alarm rates, which exclusively capture the statistics of the instrumental noise triggers, \pastro{} incorporates the rate at which triggers are generated by

Chirag Gupta, Aaditya Ramdas

We present an online post-hoc calibration method, called Online Platt Scaling (OPS), which combines the Platt scaling technique with online logistic regression. We demonstrate that OPS smoothly adapts between i.i.d. and non-i.i.d. settings with distribution drift. Further, in scenarios where the best Platt scaling model is itself miscalibrated, we enhance OP

Benedetta Belfatto, Zurab Berezhiani

The Standard Model does not constrain the form of the Yukawa matrices and thus the origin of fermion mass hierarchies and mixing pattern remains puzzling. On the other hand, there are intriguing relations between fermion masses and mixing angles which may point towards specific textures of Yukawa matrices. One of the classic hypothesis is the zero texture pr

Javad Khoramdel, Soheila Hatami, Majid Sadedel

During the COVID-19 pandemic, wearing a face mask has been known to be an effective way to prevent the spread of COVID-19. In lots of monitoring tasks, humans have been replaced with computers thanks to the outstanding performance of the deep learning models. Monitoring the wearing of a face mask is another task that can be done by deep learning models with

Nurislam Tursynbek, Marc Niethammer

Inspired by recent findings that generative diffusion models learn semantically meaningful representations, we use them to discover the intrinsic hierarchical structure in biomedical 3D images using unsupervised segmentation. We show that features of diffusion models from different stages of a U-Net-based ladder-like architecture capture different hierarchy

Florian Arbes, Constantin Greif, Karsten Urban

The Kolmogorov $N$-width describes the best possible error one can achieve by elements of an $N$-dimensional linear space. Its decay has extensively been studied in Approximation Theory and for the solution of Partial Differential Equations (PDEs). Particular interest has occurred within Model Order Reduction (MOR) of parameterized PDEs e.g.\ by the Reduced

James K. Freericks, Leanne Doughty

Undergraduate quantum mechanics focuses on teaching through a wavefunction approach in the position-space representation. This leads to a differential equation perspective for teaching the material. However, we know that abstract representation-independent approaches often work better with students, by comparing student reactions to learning the series solut

Zhengzhong Liang, Zeyu Zhang, Steven Bethard, Mihai Surdeanu

Languages models have been successfully applied to a variety of reasoning tasks in NLP, yet the language models still suffer from compositional generalization. In this paper we present Explainable Verbal Reasoner Plus (EVR+), a reasoning framework that enhances language models' compositional reasoning ability by (1) allowing the model to explicitly generate

Carlos Mejuto-Zaera, Alexander F. Kemper

A typical task for classical and quantum computing in chemistry is finding a potential energy surface (PES) along a reaction coordinate, which involves solving the quantum chemistry problem for many points along the reaction path. Developing algorithms to accomplish this task on quantum computers has been an active area of development, yet finding all the re

Alessandro M. Orjuela, J. K. Freericks

The free expansion of a Gaussian wavepacket is a problem commonly discussed in undergraduate quantum classes by directly solving the time-dependent Schrodinger equation as a differential equation. In this work, we provide an alternative way to calculate the free expansion by recognizing that the Gaussian wavepacket can be thought of as the ground state of a

Thanasis Karakasis, Nick E. Mavromatos, Eleftherios Papantonopoulos

We discuss exact regular compact object solutions in higher dimensional extensions of General Relativity sourced by a phantom scalar field in arbitrary $D$ spacetime dimensions ($D>2$), for which a central singularity is absent. We follow a bottom-up approach, by means of which, by imposing the desired form of the solution to the metric function, we derive t

Artem Chebotarenko

We generalize the Khinchin singularity phenomenon for the problem when, for a given irrational linear subspace, we are looking for rational subspaces that form the smallest angle with the given

Jennifer Barnes, Paul C. Duffell

The astrophysical environments capable of triggering heavy-element synthesis via rapid neutron capture (the r-process) remain uncertain. While binary neutron star mergers (NSMs) are known to forge r-process elements, certain rare supernovae (SNe) have been theorized to supplement, or even dominate, r-production by NSMs. However, the most direct evidence for

Dorothee D. Haroske, Susana D. Moura, Leszek Skrzypczak

We study unboundedness properties of functions belonging to generalised Morrey spaces ${\mathcal M}_{\varphi,p}({\mathbb R}^d)$ and generalised Besov-Morrey spaces ${\mathcal N}^{s}_{\varphi,p,q}({\mathbb R}^d)$ by means of growth envelopes. For the generalised Morrey spaces we arrive at the same three possible cases as for classical Morrey spaces $\mathcal{

Hoang Anh Just, Feiyang Kang, Jiachen T. Wang, Yi Zeng

Traditionally, data valuation (DV) is posed as a problem of equitably splitting the validation performance of a learning algorithm among the training data. As a result, the calculated data values depend on many design choices of the underlying learning algorithm. However, this dependence is undesirable for many DV use cases, such as setting priorities over d

P. Rezende-Gonçalves, M. Thees, J. Rojas Castillo, D. Silvera-Vega

TaTe$_4$ is a quasi-1D tetrachalcogenide that exhibits a CDW instability caused by a periodic lattice distortion. Recently, pressure-induced superconductivity has been achieved in this compound, revealing a competition between these different ground states and making TaTe$_4$ very interesting for fundamental studies. Although TaTe$_4$ exhibits CDW ordering b

Zeyu Wang, Yu Wu

Retrieving target information based on input query is of fundamental importance in many real-world applications. In practice, it is not uncommon for the initial search to fail, where additional feedback information is needed to guide the searching process. In this work, we study a setting where the feedback is provided through users clicking liked and dislik

Taishan Yi, Xiao-Qiang Zhao

This paper is devoted to the study of propagation dynamics for a large class of non-monotone evolution systems. In two directions of the spatial variable, such a system has two limiting systems admitting the spatial translation invariance. Under the assumption that each of these two limiting systems has both leftward and rightward spreading speeds, we establ

Emre Kıcıman, Robert Ness, Amit Sharma, Chenhao Tan

The causal capabilities of large language models (LLMs) are a matter of significant debate, with critical implications for the use of LLMs in societally impactful domains such as medicine, science, law, and policy. We conduct a "behavorial" study of LLMs to benchmark their capability in generating causal arguments. Across a wide range of tasks, we find that

Niklas Garner, Natalie M. Paquette

We initiate the study of how the insertion of magnetically charged states in 4d self-dual gauge theories impacts the 2d chiral algebras supported on the celestial sphere at asymptotic null infinity, from the point of view of the 4d/2d twistorial correspondence introduced by Costello and the second author. By reducing the 6d twistorial theory to a 3d holomorp

Vivek Ramavajjala, Peetak P. Mitra

Data-driven weather prediction models (DDWPs) have made rapid strides in recent years, demonstrating an ability to approximate Numerical Weather Prediction (NWP) models to a high degree of accuracy. The fast, accurate, and low-cost DDWP forecasts make their use in operational forecasting an attractive proposition, however, there remains work to be done in ri

A. -M. Lagrange, F. Philipot, P. Rubini, N. Meunier

Context. Giant planets play a major role in multiple planetary systems. Knowing their demographics is important to test their overall impact on planetary systems formation. It is also important to test their formation processes. Recently, three radial velocity surveys have established radial distributions of giant planets. All show a steep increase up to 1-3

Samiul Based Shuvo, Tasnia Binte Mamun

Lung cancer is a leading cause of cancer-related deaths worldwide, and early detection is crucial for improving patient outcomes. Nevertheless, early diagnosis of cancer is a major challenge, particularly in low-resource settings where access to medical resources and trained radiologists is limited. The objective of this study is to propose an automated end-

Qiurui Li

Let $(R,m)$ be a Noetherian local ring and $I$ an ideal with finite projective dimension. If $R/I$ satisfies some property $\mathcal{P}$, it is natural to ask whether $R$ would also satisfy this property $\mathcal{P}$. This is called the generalized deformation problem. In this paper we discuss some properties that would satisfy this problem. There are two m

Patrick Bajari, Zhihao Cen, Victor Chernozhukov, Manoj Manukonda

We develop empirical models that efficiently process large amounts of unstructured product data (text, images, prices, quantities) to produce accurate hedonic price estimates and derived indices. To achieve this, we generate abstract product attributes (or ``features'') from descriptions and images using deep neural networks. These attributes are then used t

Chen Chen, Junqing Zhang, Tianyu Lu, Magnus Sandell

Physical-layer key generation (PKG) based on wireless channels is a lightweight technique to establish secure keys between legitimate communication nodes. Recently, intelligent reflecting surfaces (IRSs) have been leveraged to enhance the performance of PKG in terms of secret key rate (SKR), as it can reconfigure the wireless propagation environment and intr

Shaoyan Pan, Chih-Wei Chang, Junbo Peng, Jiahan Zhang

This study aims to develop a novel Cycle-guided Denoising Diffusion Probability Model (CG-DDPM) for cross-modality MRI synthesis. The CG-DDPM deploys two DDPMs that condition each other to generate synthetic images from two different MRI pulse sequences. The two DDPMs exchange random latent noise in the reverse processes, which helps to regularize both DDPMs

Nagabhushan Somraj, Rajiv Soundararajan

Neural radiance fields (NeRF) have achieved impressive performances in view synthesis by encoding neural representations of a scene. However, NeRFs require hundreds of images per scene to synthesize photo-realistic novel views. Training them on sparse input views leads to overfitting and incorrect scene depth estimation resulting in artifacts in the rendered

Hadi Hosseini, Shivika Narang, Tomasz Wąs

We initiate the study of fair distribution of delivery tasks among a set of agents wherein delivery jobs are placed along the vertices of a graph. Our goal is to fairly distribute delivery costs (modeled as a submodular function) among a fixed set of agents while satisfying some desirable notions of economic efficiency. We adopt well-established fairness con

Elizabeth M. Jacobs, Fani Deligianni., Frank Pollick

The fight or flight phenomena is of evolutionary origin and responsible for the type of defensive behaviours enacted, when in the face of threat. This review attempts to draw the link between fear and aggression as behavioural motivations for fight or flight defensive behaviours. Hence, this review intends to examine whether fight or flight behavioural respo

Ben Craps, Marine De Clerck, Oleg Evnin, Philip Hacker

There is a widespread perception that dynamical evolution of integrable systems should be simpler in a quantifiable sense than the evolution of generic systems, though demonstrating this relation between integrability and reduced complexity in practice has remained elusive. We provide a connection of this sort by constructing a specific matrix in terms of th

Anthony Chow, Michael E. Rowan, Lorenzo Sironi, Jordy Davelaar

We study the linear stability of a planar interface separating two fluids in relative motion, focusing on the symmetric configuration where the two fluids have the same properties (density, temperature, magnetic field strength, and direction). We consider the most general case with arbitrary sound speed $c_{\rm s}$, Alfv\'en speed $v_{\rm A}$, and magnetic f

Dongjie Cheng, Ziyuan Qin, Zekun Jiang, Shaoting Zhang

The Segment Anything Model (SAM) made an eye-catching debut recently and inspired many researchers to explore its potential and limitation in terms of zero-shot generalization capability. As the first promptable foundation model for segmentation tasks, it was trained on a large dataset with an unprecedented number of images and annotations. This large-scale

Fantine Huot, Joshua Maynez, Shashi Narayan, Reinald Kim Amplayo

While conditional generation models can now generate natural language well enough to create fluent text, it is still difficult to control the generation process, leading to irrelevant, repetitive, and hallucinated content. Recent work shows that planning can be a useful intermediate step to render conditional generation less opaque and more grounded. We pres