April 2023 arXiv papers — page 69

Shubham Vatsal, Adam Meyers, John E. Ortega

Models based on bidirectional encoder representations from transformers (BERT) produce state of the art (SOTA) results on many natural language processing (NLP) tasks such as named entity recognition (NER), part-of-speech (POS) tagging etc. An interesting phenomenon occurs when classifying long documents such as those from the US supreme court where BERT-bas

Aniket Murhekar, David Arbour, Tung Mai, Anup Rao

Several cloud-based applications, such as cloud gaming, rent servers to execute jobs which arrive in an online fashion. Each job has a resource demand and must be dispatched to a cloud server which has enough resources to execute the job, which departs after its completion. Under the `pay-as-you-go' billing model, the server rental cost is proportional to th

Guillaume Conchon--Kerjan

In this paper, we study the random walk on a supercritical branching process with an uncountable and unbounded set of types supported on the $d$-regular tree $\mathbb{T}_d$ ($d\geq 3$), namely the cluster $\mathcal{C}_\circ^h$ of the root in the level set of the Gaussian Free Field (GFF) above an arbitrary value $h\in (-\infty, h_{\star})$. The value $h_{\st

Jorge Bellorin, Claudio Borquez, Byron Droguett

We perform the Batalin-Fradkin-Vilkovisky quantization of the anisotropic conformal Horava theory in d spatial dimensions. We introduce a model with a conformal potential suitable for any dimension. We define an anisotropic and local gauge-fixing condition that accounts for the spatial diffeomorphisms and the anisotropic Weyl transformations. We show that th

Jacob Deery, Chang Won Lee, Steven Waslander

We introduce ProPanDL, a family of networks capable of uncertainty-aware panoptic segmentation. Unlike existing segmentation methods, ProPanDL is capable of estimating full probability distributions for both the semantic and spatial aspects of panoptic segmentation. We implement and evaluate ProPanDL variants capable of estimating both parametric (Variance N

Xiaoshu Zeng, Gianluca Geraci, Michael S. Eldred, John D. Jakeman

Multifidelity uncertainty quantification (MF UQ) sampling approaches have been shown to significantly reduce the variance of statistical estimators while preserving the bias of the highest-fidelity model, provided that the low-fidelity models are well correlated. However, maintaining a high level of correlation can be challenging, especially when models depe

Austin Han, Fabien Evrard, Olivier Desjardins

This paper evaluates and compares the accuracy and robustness of curvature estimation methods for three-dimensional interfaces represented implicitly by discrete volume fractions on a Cartesian mesh. The height function (HF) method is compared to three paraboloid fitting methods: fitting to the piecewise linear interface reconstruction centroids (PC), fittin

A. Mazel, I. Stuhl, Y. Suhov

We study the hard-core model of statistical mechanics on a unit cubic lattice $\mathbb{Z}^3$, which is intrinsically related to the sphere-packing problem for spheres with centers in $\mathbb{Z}^3$. The model is defined by the sphere diameter $D>0$ which is interpreted as a Euclidean exclusion distance between point particles located at spheres centers. The

Carsten Peterson

We study joint eigenfunctions of the spherical Hecke algebra acting on $L^2(\Gamma_n \backslash G / K)$ where $G = \text{PGL}(3, F)$ with $F$ a non-archimedean local field of arbitrary characteristic, $K = \text{PGL}(3, O)$ with $O$ the ring of integers of $F$, and $(\Gamma_n)$ is a sequence of torsion-free lattices. We prove a form of equidistribution on av

Vithya Yogarajan, Gillian Dobbie, Henry Gouk

An indigenous perspective on the effectiveness of debiasing techniques for pre-trained language models (PLMs) is presented in this paper. The current techniques used to measure and debias PLMs are skewed towards the US racial biases and rely on pre-defined bias attributes (e.g. "black" vs "white"). Some require large datasets and further pre-training. Such t

Baixiang Huang, Bryan Hooi, Kai Shu

Road safety is a major global public health concern. Effective traffic crash prediction can play a critical role in reducing road traffic accidents. However, Existing machine learning approaches tend to focus on predicting traffic accidents in isolation, without considering the potential relationships between different accident locations within road networks

Ankur Ankan, Johannes Textor

Bayesian Networks (BNs) are used in various fields for modeling, prediction, and decision making. pgmpy is a python package that provides a collection of algorithms and tools to work with BNs and related models. It implements algorithms for structure learning, parameter estimation, approximate and exact inference, causal inference, and simulations. These imp

Harshvardhan Uppaluru, Hossein Rastgoftar

In this paper, we develop and present a novel strategy for safe coordination of a large-scale multi-agent team with ``\textit{local deformation}" capabilities. Multi-agent coordination is defined by our proposed method as a multi-layer deformation problem specified as a Deep Neural Network (DNN) optimization problem. The proposed DNN consists of $p$ hidden l

Adrian de Wynter, Xun Wang, Alex Sokolov, Qilong Gu

We present an empirical evaluation of various outputs generated by nine of the most widely-available large language models (LLMs). Our analysis is done with off-the-shelf, readily-available tools. We find a correlation between percentage of memorized text, percentage of unique text, and overall output quality, when measured with respect to output pathologies

Quinn T. Campbell, Shashank Misra, Andrew D. Baczewski

Recent work on atomic-precision dopant incorporation technologies has led to the creation of both boron and aluminum $\delta$-doped layers in silicon with densities above the solid solubility limit. We use density functional theory to predict the band structure and effective mass values of such $\delta$ layers, first modeling them as ordered supercells. Stru

Saikat Banerjee, Wei Zhu, Shi-Zeng Lin

Quantum spin liquid (QSL) has become an exciting topic in interacting spin systems that do not order magnetically down to the lowest experimentally accessible temperature; however, conclusive experimental evidence remains lacking. Motivated by the recent surge of theoretical and experimental interest in a half-filled Hubbard model on the triangular lattice,

Haiyu Wu, Kevin W. Bowyer

The issue of demographic disparities in face recognition accuracy has attracted increasing attention in recent years. Various face image datasets have been proposed as 'fair' or 'balanced' to assess the accuracy of face recognition algorithms across demographics. These datasets typically balance the number of identities and images across demographics. It is

Vibhoothi, Daniel Joseph Ringis, Xin Shu, François Pitié

Cloud-based deployment of content production and broadcast workflows has continued to disrupt the industry after the pandemic. The key tools required for unlocking cloud workflows, e.g., transcoding, metadata parsing, and streaming playback, are increasingly commoditized. However, as video traffic continues to increase there is a need to consider tools which

Sean F Peterson, Sourav Sen Choudhury, Yves Idzerda

Thermal conductivity measurements can provide key and experimentally verifiable insight into the electronic transport of unconventional superconductors. In this work, electronic thermal transport of two-dimensional tight-binding metallic systems with coexisting $d$-wave superconducting (SC) and antiferromagnetic spin density wave (SDW) orders with nesting ve

Krzysztof Pióro

The tree edit distance problem is a natural generalization of the classic string edit distance problem. Given two ordered, edge-labeled trees $T_1$ and $T_2$, the edit distance between $T_1$ and $T_2$ is defined as the minimum total cost of operations that transform $T_1$ into $T_2$. In one operation, we can contract an edge, split a vertex into two or chang

Onno Huijgen, Luuk Coopmans, Peyman Najafi, Marcello Benedetti

The quantum Boltzmann machine (QBM) is a generative machine learning model for both classical data and quantum states. Training the QBM consists of minimizing the relative entropy from the model to the target state. This requires QBM expectation values which are computationally intractable for large models in general. It is therefore important to develop heu

Xin Guo, Anran Hu, Matteo Santamaria, Mahan Tajrobehkar

Mean-field games (MFGs) are limiting models to approximate $N$-player games, with a number of applications. Despite the ever-growing numerical literature on computation of MFGs, there is no library that allows researchers and practitioners to easily create and solve their own MFG problems. The purpose of this document is to introduce MFGLib, an open-source P

Yizhou Huang, Yi-Hsiang Huang, Haozhi Wang, Zach Steffen

Low-loss superconducting rf devices are required when used for quantum computation. Here, we present a series of measurements and simulations showing that conducting losses in the packaging of our superconducting resonator devices affect the maximum achievable internal quality factors (Qi) for a series of thin-film Al quarter-wave resonators with fundamental

Kristian Bredies, Marcello Carioni, Martin Holler, Yury Korolev

In this paper we introduce the class of infinite infimal convolution functionals and apply these functionals to the regularization of ill-posed inverse problems. The proposed regularization involves an infimal convolution of a continuously parametrized family of convex, positively one-homogeneous functionals defined on a common Banach space $X$. We show that

A. Yu. Karasev, A. E. Teretenkov

In this work we consider the master equations for composite open quantum systems. We provide purely algebraic formulae for terms of perturbation series defining such equations. We also give conditions under which the Bogolubov-van Hove limit exists and discuss some corrections to this limit. We present an example to illustrate our results. In particular, thi

Csaba Vincze, Ábris Nagy

A distance mean function measures the average distance of points from the elements of a given set of points (focal set) in the space. The level sets of a distance mean function are called generalized conics. In case of infinite focal points the average distance is typically given by integration over the focal set. The paper contains a survey on the applicati

Santiago Perez, Dario Rodrigues, Juan Estrada, Roni Harnik

Oscura is a planned light-dark matter search experiment using Skipper-CCDs with a total active mass of 10 kg. As part of the detector development, the collaboration plans to build the Oscura Integration Test (OIT), an engineering test with 10% of the total mass. Here we discuss the early science opportunities with the OIT to search for millicharged particles

Kazufumi Takahashi, Masato Minamitsuji, Hayato Motohashi

Generalized disformal transformations enable us to construct the generalized disformal Horndeski theories, which form the most general class of ghost-free scalar-tensor theories to this date. We extend the effective field theory (EFT) of cosmological perturbations to incorporate these generalized disformal Horndeski theories. The main difference from the con

Mengxing Ye, Zhentao Wang, Rafael M Fernandes, Andrey V Chubukov

We study pseudogap behavior in a metal near a spin density wave (SDW) instability due to thermal magnetic fluctuations. We consider the $t-t'$ Hubbard model on a square lattice at a finite doping, at intermediate coupling strength, and analyze the thermal evolution of the electron spectral function between a SDW ordered state at low temperatures and a normal

Tao Zhang, Sarthak Gupta, Madeline A. Lancaster, J. M. Schwarz

How genes affect tissue scale organization remains a longstanding biological puzzle. As experimental efforts aim to quantify gene expression, chromatin organization, cellular structure, and tissue structure, computational modeling lags behind. To address this gap, we merge a cellular-based tissue model with a nuclear model that includes a deformable lamina s

Rastislav Turányi, Sanghamitra Mukhopadhyay

Adoption of renewable energy is essential to address the challenge of climate change, but that necessitates energy storage technologies. Lithium-ion batteries, the most ubiquitous solution, are insufficient for large-scale applications, so sodium-ion batteries (SIBs), an alternative, are of great current interest. To design and synthesise a commercially viab

Görkem Berkay Koç, Berk Çiloğlu, Metin Öztürk, Halim Yanikomeroglu

There is a consensus in the literature that cell-switching is a viable solution to tackle the draconian increase in the energy consumption of cellular networks. Although the literature is full of works addressing the energy consumption problem via cell-switching, where small cells with low or no load are turned off and the traffic is offloaded to either adja

E. Black, P. Kratzer, J. M. Morbec

Using first-principles calculations based on density-functional theory, we investigated the adsorption of pentacene molecules on monolayer two-dimensional transition metal dichalcogenides (TMD). We considered the four most popular TMDs, namely, MoS$_2$, MoSe$_2$, WS$_2$ and WSe$_2$, and we examined the structural and electronic properties of pentacene/TMD sy

J. Olivares, N. Lodieu, V. J. S. Béjar, E. L. Martín

Context. Open clusters (OCs) are fundamental benchmarks where theories of star formation and stellar evolution can be tested and validated. Coma Ber and Latyshev 2 (Group X) are the second and third OCs closest to the Sun, making them excellent targets to search for low-mass stars and ultra-cool dwarfs. In addition, this pair will experience a flyby in 10-16

Christian Táfula

In this note, we classify the conjugacy classes of $\widetilde{\mathrm{SL}}_2(\mathbb{R})$, the universal covering group of $\mathrm{PSL}_2(\mathbb{R})$. For any non-central element $\alpha \in \widetilde{\mathrm{SL}}_2(\mathbb{R})$, we show that its conjugacy class may be determined by three invariants: (i) Trace: the trace (valued in the set of positive re

Kumar J. B. Ghosh, Sumit Ghosh

In this article we present the application of classical and quantum-classical hybrid anomaly detection schemes to explore exotic configuration with anomalous features. We consider the Anderson model as a prototype where we define two types of anomalies - a high conductance in presence of strong impurity and low conductance in presence of weak impurity - as a

Vladimir A. Kobzar, Krishnan Mody

This work addresses the block-diagonal semidefinite program (SDP) relaxations for the clique number of the Paley graphs. The size of the maximal clique (clique number) of a graph is a classic NP-complete problem; a Paley graph is a deterministic graph where two vertices are connected if their difference is a quadratic residue (square) in a finite field with

Kleanthis Avramidis, Kranti Adsul, Digbalay Bose, Shrikanth Narayanan

This paper presents the approach and results of USC SAIL's submission to the Signal Processing Grand Challenge 2023 - e-Prevention (Task 2), on detecting relapses in psychotic patients. Relapse prediction has proven to be challenging, primarily due to the heterogeneity of symptoms and responses to treatment between individuals. We address these challenges by

Julianne J. Dalcanton, Eric F. Bell, Yumi Choi, Andrew E. Dolphin

We present a new approach to measuring the thickness of a partially face-on stellar disk, using dust geometry. In a moderately-inclined disk galaxy, the fraction of reddened stars is expected to be 50% everywhere, assuming that dust lies in a thin midplane. In a thickened disk, however, a wide range of radii project onto the line of sight. Assuming stellar d

Liyuan Liu, Chengyu Dong, Xiaodong Liu, Bin Yu

Backpropagation, the cornerstone of deep learning, is limited to computing gradients for continuous variables. This limitation poses challenges for problems involving discrete latent variables. To address this issue, we propose a novel approach to approximate the gradient of parameters involved in generating discrete latent variables. First, we examine the w

Sivaprasad Omanakuttan, Jonathan A. Gross

The physical symmetries of a system play a central role in quantum error correction. In this work we encode a qubit in a collection of systems with angular-momentum symmetry (spins), extending the tools developed in Phys. Rev. Lett. 127, 010504 for single large spins. By considering large spins present in atomic systems and focusing on their collective symme

Julián García Pardiñas, Marta Calvi, Jonas Eschle, Andrea Mauri

The LHCb experiment at the Large Hadron Collider (LHC) is designed to perform high-precision measurements of heavy-hadron decays, which requires the collection of large data samples and a good understanding and suppression of multiple background sources. Both factors are challenged by a five-fold increase in the average number of proton-proton collisions per

Nafis Tanveer Islam, Gonzalo De La Torre Parra, Dylan Manuel, Elias Bou-Harb

Over the years, open-source software systems have become prey to threat actors. Even as open-source communities act quickly to patch the breach, code vulnerability screening should be an integral part of agile software development from the beginning. Unfortunately, current vulnerability screening techniques are ineffective at identifying novel vulnerabilitie

I. Arzhantsev, S. Kaliman, M. Zaidenberg

It was shown in [S. Kaliman, M. Zaidenberg, Gromov ellipticity of cones over projective manifolds, Math. Res. Lett. (to appear), arXiv:2303.02036 (2023)] that the affine cones over flag manifolds and rational smooth projective surfaces are elliptic in the sense of Gromov. The latter remains true after successive blowups of points on these varieties. In the p

Kristin Kliemt, Michelle Ocker, Sarah Krebber, Susanne Schulz

A combined experimental and theoretical study of the layered antiferromagnetic compound DyRh$_2$Si$_2$ in the ThCr$_2$Si$_2$-type structure is presented. The heat capacity shows two transitions upon cooling, the first one at the N{\'e}el temperature $T_{\rm N}=55\,\rm K$ and a second one at $T_{\rm N2}=12\,\rm K$. Using magnetization measurements, we study t

Peng Chen, Xuan Thinh Duong, Ji Li, Liang Song

Let $X$ be a metric space with doubling measure, and $L$ be a nonnegative self-adjoint operator on $L^2(X)$ whose heat kernel satisfies the Gaussian upper bound. Let $f$ be in the space $ {\rm BMO}_L(X)$ associated with the operator $L$ and we define its distance from the subspace $L^{\infty}(X)$ under the $ {\rm BMO}_L(X)$ norm as follows: $$ {\rm dist} (f,

Yongli Sang, Xin Dang

Gini distance correlation (GDC) was recently proposed to measure the dependence between a categorical variable, Y, and a numerical random vector, X. It mutually characterizes independence between X and Y. In this article, we utilize the GDC to establish a feature screening for ultrahigh-dimensional discriminant analysis where the response variable is categor

Sina Masnadi, Yahya Hmaiti, Eugene Taranta, Joseph J. LaViola

To assess the impact of clutter on egocentric distance perception, we performed a mixed-design study with 60 participants in four different virtual environments (VEs) with three levels of clutter. Additionally, we compared the indoor/outdoor VE characteristics and the HMD's FOV. The participants wore a backpack computer and a wide FOV head-mounted display (H

Nicolás Grandi, Vladimir Juričić, Ignacio Salazar Landea, Rodrigo Soto-Garrido

Flat band electronic systems exhibit a rich landscape of correlation-driven phases. Motivated by these developments, in this paper, we explicitly include the effects of the chemical potential in a holographic model featuring approximately flat bands. In particular, we explore the phase diagram of this holographic flat band system as a function of the chemica

Hafsa Bousbiat, Roumaysa Bousselidj, Yassine Himeur, Abbes Amira

Consumer's privacy is a main concern in Smart Grids (SGs) due to the sensitivity of energy data, particularly when used to train machine learning models for different services. These data-driven models often require huge amounts of data to achieve acceptable performance leading in most cases to risks of privacy leakage. By pushing the training to the edge, F

Nikolay Moshchevitin

We give an easy optimal bound for the dimension of the subspaces generated by the best Diophantine approximations.

Bingchen Zhao, Jiahao Wang, Wufei Ma, Artur Jesslen

Enhancing the robustness of vision algorithms in real-world scenarios is challenging. One reason is that existing robustness benchmarks are limited, as they either rely on synthetic data or ignore the effects of individual nuisance factors. We introduce OOD-CV-v2, a benchmark dataset that includes out-of-distribution examples of 10 object categories in terms

Junyao Wang, Arnav Vaibhav Malawade, Junhong Zhou, Shih-Yuan Yu

Effectively capturing intricate interactions among road users is of critical importance to achieving safe navigation for autonomous vehicles. While graph learning (GL) has emerged as a promising approach to tackle this challenge, existing GL models rely on predefined domain-specific graph extraction rules that often fail in real-world drastically changing sc

Andrei Khrennikov

The aim of this review is to highlight the possibility to apply the mathematical formalism and methodology of quantum theory to model behaviour of complex biosystems, from genomes and proteins to animals, humans, ecological and social systems. Such models are known as quantum-like and they should be distinguished from genuine quantum physical modeling of bio

Tianran Chen

Numerical algebraic geometry revolves around the study of solutions to polynomial systems via numerical methods. The polyhedral homotopy of Huber and Sturmfels for computing isolated solutions and the concept of witness sets as numerical representations of non-isolated solution components, put forth by Sommese and Wampler, are two pillars of this field. In t

Haoxiang Zhang, Juliana Freire, Yash Garg

Recent advancements in software and hardware technologies have enabled the use of AI/ML models in everyday applications has significantly improved the quality of service rendered. However, for a given application, finding the right AI/ML model is a complex and costly process, that involves the generation, training, and evaluation of multiple interlinked step

Akshay Ramachandran, Kevin Shu, Alex L. Wang

This paper studies hidden convexity properties associated with constrained optimization problems over the set of rotation matrices $\text{SO}(n)$. Such problems are nonconvex due to the constraint $X \in \text{SO}(n)$. Nonetheless, we show that certain linear images of $\text{SO}(n)$ are convex, opening up the possibility for convex optimization algorithms w

David Montgomery, Federico Municchi, Karin Leiderman

Blood clotting involves the coupled processes of platelet aggregation and coagulation. Simulating clotting under flow in complex geometries is challenging due to multiple temporal and spatial scales and high computational cost. clotFoam is an open-source software developed in OpenFOAM that employs a continuum model of platelet advection, diffusion, and aggre

Zicong Hong, Song Guo, Enyuan Zhou, Jianting Zhang

Sharding scales throughput by splitting blockchain nodes into parallel groups. However, different shards' independent and random scheduling for cross-shard transactions results in numerous conflicts and aborts, since cross-shard transactions from different shards may access the same account. A deterministic ordering can eliminate conflicts by determining a g

Marina Neseem, Ahmed Agiza, Sherief Reda

Modern Augmented reality applications require performing multiple tasks on each input frame simultaneously. Multi-task learning (MTL) represents an effective approach where multiple tasks share an encoder to extract representative features from the input frame, followed by task-specific decoders to generate predictions for each task. Generally, the shared en

Harry Rubin-Falcone, Joyce Lee, Jenna Wiens

In time-series forecasting, future target values may be affected by both intrinsic and extrinsic effects. When forecasting blood glucose, for example, intrinsic effects can be inferred from the history of the target signal alone (\textit{i.e.} blood glucose), but accurately modeling the impact of extrinsic effects requires auxiliary signals, like the amount

Vittorio Astarita

This study provides a practical introduction to high-frequency trading in blockchain-based currency markets. These types of markets have some specific characteristics that differentiate them from the stock markets, such as a large number of trading exchanges (centralized and decentralized), relative simplicity in moving funds from one exchange to another, an

Maximilian Egger, Serge Kas Hanna, Rawad Bitar

In distributed machine learning, a central node outsources computationally expensive calculations to external worker nodes. The properties of optimization procedures like stochastic gradient descent (SGD) can be leveraged to mitigate the effect of unresponsive or slow workers called stragglers, that otherwise degrade the benefit of outsourcing the computatio

Ya-Ting Yang, Tao Li, Quanyan Zhu

Misinformation has become a growing issue on online social platforms (OSPs), especially during elections or pandemics. To combat this, OSPs have implemented various policies, such as tagging, to notify users about potentially misleading information. However, these policies are often transparent and therefore susceptible to being exploited by content creators

Xiaohan Zhang, Yan Ding, Saeid Amiri, Hao Yang

Classical planning systems have shown great advances in utilizing rule-based human knowledge to compute accurate plans for service robots, but they face challenges due to the strong assumptions of perfect perception and action executions. To tackle these challenges, one solution is to connect the symbolic states and actions generated by classical planners to

Bolun Dai, Rooholla Khorrambakht, Prashanth Krishnamurthy, Vinícius Gonçalves

Control barrier functions (CBFs) have been widely applied to safety-critical robotic applications. However, the construction of control barrier functions for robotic systems remains a challenging task. Recently, collision detection using differentiable optimization has provided a way to compute the minimum uniform scaling factor that results in an intersecti

Brian M. Sutin, Anthony B. Davis, Kevin H. Baines, James A. Cutts

Does radiometry (e.g., signal-to-noise ratio) limit the performance of near-IR subcloud imaging of our sister planet's surface at night? It does not. We compute subcloud radiometry using above-cloud observations, an assumed ground temperature, sub-cloud absorption and emission modeling, and Rayleigh scattering simulations. We thus confirm both archival and r

Pablo Montes, Marcelo E. Rubio, Oscar A. Reula

We present the first numerical simulations of the symmetric--hyperbolic theory for conformal dissipative relativistic fluids developed in [1]. In this theory, the information of the fluid dynamics is encoded in a scalar generating function which depends on three free parameters. By adapting the WENO-Z high-resolution shock-capturing central scheme, we show n

Cheng-Yu Pai, Zilong Liu, Chunlei Li, Chao-Yu Chen

This paper presents a novel class of complex-valued sparse complementary pairs (SCPs), each consisting of a number of zero values and with additional zero-correlation zone (ZCZ) property for the aperiodic autocorrelations and crosscorrelations of the two constituent sequences. Direct constructions of SCPs and their mutually-orthogonal mates based on restrict

Swapnil Mane, Vaibhav Khatavkar

The enormous use of sarcastic text in all forms of communication in social media will have a physiological effect on target users. Each user has a different approach to misusing and recognising sarcasm. Sarcasm detection is difficult even for users, and this will depend on many things such as perspective, context, special symbols. So, that will be a challeng

Neophytos Charalambides, Alfred O. Hero

Graphs arising in statistical problems, signal processing, large networks, combinatorial optimization, and data analysis are often dense, which causes both computational and storage bottlenecks. One way of \textit{sparsifying} a \textit{weighted} graph, while sharing the same vertices as the original graph but reducing the number of edges, is through \textit

Pooya Fayyazsanavi, Zhiqiang Wan, Will Hutchcroft, Ivaylo Boyadzhiev

While the existing deep learning-based room layout estimation techniques demonstrate good overall accuracy, they are less effective for distant floor-wall boundary. To tackle this problem, we propose a novel uncertainty-guided approach for layout boundary estimation introducing new two-stage CNN architecture termed U2RLE. The initial stage predicts both floo

William M. McGovern

We compute the fake degrees of representations of classical Weyl groups in terms of domino tableaux.

David Boyd, Robert Buchheim, Sean Curry, Frank Parks

We report results of an observing campaign to study the dwarf M flare star EV Lacertae. Between October 2021 and January 2022 we obtained concurrent B band photometry and low resolution spectroscopy of EV Lac on 39 occasions during 10 of which we observed flares with amplitude greater than 0.1 magnitude. Spectra were calibrated in absolute flux using concurr

Yuming Du, Robin Kips, Albert Pumarola, Sebastian Starke

With the recent surge in popularity of AR/VR applications, realistic and accurate control of 3D full-body avatars has become a highly demanded feature. A particular challenge is that only a sparse tracking signal is available from standalone HMDs (Head Mounted Devices), often limited to tracking the user's head and wrists. While this signal is resourceful fo

Eunhyek Joa, Hotae Lee, Eric Yongkeun Choi, Francesco Borrelli

This paper presents a novel energy-efficient motion planning algorithm for Connected Autonomous Vehicles (CAVs) on urban roads. The approach consists of two components: a decision-making algorithm and an optimization-based trajectory planner. The decision-making algorithm leverages Signal Phase and Timing (SPaT) information from connected traffic lights to s

June-Young Kim, Ho-Yeon Won, Jose L. Goity, Christian Weiss

$N \rightarrow \Delta$ transitions offer new possibilities for exploring the isovector component of the QCD quark angular momentum (AM) operator causing the $J^{u - d}$ flavor asymmetry in the nucleon. We extend the concept of QCD AM to transitions between baryon states, using light-front densities of the energy-momentum tensor in transversely localized stat

Maciej Dunajski

I discuss the impact of the positive cosmological constant on the interplay between the equivalence principle in general relativity, and the rules of quantum mechanics. At the non--relativistic level there is an ambiguity in the definition of a phase of a wave function measured by inertial and accelerating observes. This is the cosmological analogue of the P

Markus Johannes Stroppel

An isomorphism between two hermitian unitals is proved, and used to treat isomorphisms of classical groups that are related to the isomorphism between certain simple real Lie algebras of types A and D (and rank 3).

David A. Huse

This is a very brief review article, written for a book (in preparation) in memory of Michael E. Fisher and to celebrate 50+ years since the Wilson-Fisher renormalization group. Strong-randomness renormalization groups were first developed to treat various quantum critical ground states, especially in one-dimensional systems. After briefly reviewing some of

Neelkamal Somisetty, Harsha Nagarajan, Swaroop Darbha

This paper focuses on designing edge-weighted networks, whose robustness is characterized by maximizing algebraic connectivity, or the second smallest eigenvalue of the Laplacian matrix. This problem is motivated by cooperative vehicle localization, where accurately estimating relative position measurements and establishing communication links are essential.

David A. Mazziotti

Here we present a many-body theory based on a solution of the $N$-representability problem in which the ground-state two-particle reduced density matrix (2-RDM) is determined directly without the many-particle wave function. We derive an equation that re-expresses physical constraints on higher-order RDMs to generate direct constraints on the 2-RDM, which ar

Tânia Esteves, Ricardo Macedo, Rui Oliveira, João Paulo

We present DIO, a generic tool for observing inefficient and erroneous I/O interactions between applications and in-kernel storage systems that lead to performance, dependability, and correctness issues. DIO facilitates the analysis and enables near real-time visualization of complex I/O patterns for data-intensive applications generating millions of storage

Daebeom Choi

Finding the maximal dimension of complete subvarieties of the moduli space of smooth $n$-pointed curves of genus $g$ is a long-standing open problem. Here we show that for $g\ge 3\cdot 2^{d-1}$, if the characteristic of the base field is greater than $2$, then $\rm{M}_g$ contains a complete subvariety of dimension $d$. Furthermore, in positive characteristic

Arturo Merino, Torsten Mütze

In this paper, we present a new framework that exploits combinatorial optimization for efficiently generating a large variety of combinatorial objects based on graphs, matroids, posets and polytopes. Our method relies on a simple and versatile algorithm for computing a Hamilton path on the skeleton of any 0/1-polytope ${\rm conv}(X)$, where $X\subseteq \{0,1

Asim Waheed, Vasisht Duddu, N. Asokan

Graph neural networks (GNNs) have emerged as a state-of-the-art approach to model and draw inferences from large scale graph-structured data in various application settings such as social networking. The primary goal of a GNN is to learn an embedding for each graph node in a dataset that encodes both the node features and the local graph structure around the

Nelson Antunes, Sayan Banerjee, Shankar Bhamidi, Vladas Pipiras

We analyze dynamic random network models where younger vertices connect to older ones with probabilities proportional to their degrees as well as a propensity kernel governed by their attribute types. Using stochastic approximation techniques we show that, in the large network limit, such networks converge in the local weak sense to limiting infinite random

Adrian Dumitru, Alex Kovner, Vladimir V. Skokov

We calculate the entanglement entropy of a model proton wave function in coordinate space by integrating out degrees of freedom outside a small circular region $\bar A$ of radius $L$, where $L$ is much smaller than the size of the proton. The wave function provides a nonperturbative distribution of three valence quarks. In addition, we include the perturbati

K. J. Amsberry, J. A. Bergquist, T. A. Horstkamp, M. H. Lee

Saki and Kiani proved that the subrack lattice of a rack $R$ is necessarily complemented if $R$ is finite but not necessarily complemented if $R$ is infinite. In this paper, we investigate further avenues related to the complementation of subquandles. Saki and Kiani's example of an infinite rack without complements is a quandle, which is neither ind-finite n

F. Perrotta, M. Giulietti, M. Massardi, G. Gandolfi

We report ALMA high-resolution observations of water emission lines $p-{\rm{H_2O}} (2_{02}-1_{11}$), $o-{\rm{H_2O}} (3_{21}-3_{12})$, $p-{\rm{H_2O}} (4_{22}-4_{13})$, in the strongly lensed galaxy HATLASJ113526.2-01460 at redshift z $\sim$ 3.1. From the lensing-reconstructed maps of water emission and line profiles, we infer the general physical properties o

Ameya Raul, Amey Porobo Dharwadker, Brad Schumitsch

Learning large-scale industrial recommender system models by fitting them to historical user interaction data makes them vulnerable to conformity bias. This may be due to a number of factors, including the fact that user interests may be difficult to determine and that many items are often interacted with based on ecosystem factors other than their relevance

A. V. Glushkov, K. G. Lebedev, A. V. Sabourov

Particle lateral distributions were investigated in cosmic ray air showers with energy $E_0 \simeq 10^{18}$ eV registered at the Yakutsk array with surface and underground scintillation detectors with $\simeq 1 \times \sec\theta$~GeV threshold during the period of continuous observations from 1986 to 2016. The analysis covers events with arrival direction ze

Federico Scavia, Fumiaki Suzuki

We extend the basic theory of the coniveau and strong coniveau filtrations to the $\ell$-adic setting. By adapting the examples of Benoist--Ottem to the $\ell$-adic context, we show that the two filtrations differ over any algebraically closed field of characteristic not $2$. When the base field $\mathbb{F}$ is finite, we show that the equality of the two fi

Patrick M. Schnell, Matthew Wascher, Grzegorz A. Rempala

During the COVID-19 pandemic, many institutions such as universities and workplaces implemented testing regimens with every member of some population tested longitudinally, and those testing positive isolated for some time. Although the primary purpose of such regimens was to suppress disease spread by identifying and isolating infectious individuals, testin

Richard Cardone, Smruti Padhy, Steven Black, Sean Cleveland

Research challenges such as climate change and the search for habitable planets increasingly use academic and commercial computing resources distributed across different institutions and physical sites. Furthermore, such analyses often require a level of automation that precludes direct human interaction, and securing these workflows involves adherence to se

Shweta Ann Jacob, Paul Louis, Amirali Salehi-Abari

Subgraph classification is an emerging field in graph representation learning where the task is to classify a group of nodes (i.e., a subgraph) within a graph. Subgraph classification has applications such as predicting the cellular function of a group of proteins or identifying rare diseases given a collection of phenotypes. Graph neural networks (GNNs) are

André Costa, Vincent Grandjean, Maria Michalska

A closed subset of $\mathbb{R}^q$, definable in some given o-minimal structure, is Lipschitz normally embedded in $\mathbb{R}^q$ if and only if its one-point compactification is Lipschitz normally embedded in the unit sphere ${\bf S}^q$($ = \mathbb{R}^q \cup \{\infty \}$), i.e. the closure of its image by the inverse of the stereographic projection is Lipsch

Valentina Casarino, Paolo Ciatti, Peter Sjögren

Consider the variation seminorm of the Ornstein-Uhlenbeck semigroup $H_t$ in dimension one, taken with respect to $t$. We show that this seminorm defines an operator of weak type $(1,1)$ for the relevant Gaussian measure. The analogous $L^p$ estimates for $1<p<\infty$ were already known.

Yifan Yang, Shuo Chen, Ming Wang

A block covariance structure is widely observed across large-scale and high-dimensional datasets in diverse fields such as biology, medicine, engineering, economics, and finance. This pattern entails partitioning a covariance matrix into uniform blocks, where each block exhibits equal variances and covariances. The importance of uniform-block structures lies

Laurin M. Gray, Katherine L. Rhode, Lukas Leisman, Pavel E. Mancera Piña

We present deep optical imaging and photometry of four objects classified as "Almost-Dark" galaxies in the ALFALFA survey because of their gas-rich nature and extremely faint or missing optical emission in existing catalogs. They have HI masses of $10^7$-$10^9$ $M_{\odot}$ and distances of $\sim$9-100 Mpc. Observations with the WIYN 3.5m telescope and One De