May 2023 arXiv papers — page 146

Yuyan Ruan, Dawei Yang, Ziqi Tang, An Ran Ran

Optical coherence tomography angiography (OCTA) is a new imaging modality to visualize retinal microvasculature and has been readily adopted in clinics. High-resolution OCT angiograms are important to qualitatively and quantitatively identify potential biomarkers for different retinal diseases accurately. However, one significant problem of OCTA is the inevi

Yiqing Xie, Xiao Liu, Chenyan Xiong

In this work, we present an unsupervised retrieval method with contrastive learning on web anchors. The anchor text describes the content that is referenced from the linked page. This shows similarities to search queries that aim to retrieve pertinent information from relevant documents. Based on their commonalities, we train an unsupervised dense retriever,

Bijan Mazaheri, Atalanti Mastakouri, Dominik Janzing, Michaela Hardt

Statistical prediction models are often trained on data from different probability distributions than their eventual use cases. One approach to proactively prepare for these shifts harnesses the intuition that causal mechanisms should remain invariant between environments. Here we focus on a challenging setting in which the causal and anticausal variables of

Ruijin Liu, Wei Wang, Xiaoling Cui

Quartet superfluid (QSF) is a distinct type of fermion superfluidity that exhibits high-order correlation beyond the conventional BCS pairing paradigm. In this Letter, we report the emergent QSF in 2D mass-imbalanced Fermi mixtures with two-body contact interactions. This is facilitated by the formation of quartet bound state in vacuum that consists of a lig

Ping Wang, Zheng Feng, Yuhe Yang, Delin Zhang

The Orbital Hall effect, which originates from materials with weak spin-orbit coupling, has attracted considerable interest for spin-orbitronic applications. Here, we demonstrate the inverse effect of the orbital Hall effect and observe orbitronic terahertz emission in the Ti and Mn materials. Through spin-orbit transition in the ferromagnetic layer, the gen

Jiashuo Jiang

The Network Revenue Management (NRM) problem is a well-known challenge in dynamic decision-making under uncertainty. In this problem, fixed resources must be allocated to serve customers over a finite horizon, while customers arrive according to a stochastic process. The typical NRM model assumes that customer arrivals are independent over time. However, in

Junwen Qiu, Li Jiang, Andre Milzarek

The proximal stochastic gradient method (PSGD) is one of the state-of-the-art approaches for stochastic composite-type problems. In contrast to its deterministic counterpart, PSGD has been found to have difficulties with the correct identification of underlying substructures (such as supports, low rank patterns, or active constraints) and it does not possess

Xiyang Hu, Yan Huang, Beibei Li, Tian Lu

FinTech lending (e.g., micro-lending) has played a significant role in facilitating financial inclusion. It has reduced processing times and costs, enhanced the user experience, and made it possible for people to obtain loans who may not have qualified for credit from traditional lenders. However, there are concerns about the potentially biased algorithmic d

Rajarshi Bhattacharjee, Gregory Dexter, Cameron Musco, Archan Ray

Let $\mathbf S \in \mathbb R^{n \times n}$ satisfy $\|\mathbf 1-\mathbf S\|_2\le\epsilon n$, where $\mathbf 1$ is the all ones matrix and $\|\cdot\|_2$ is the spectral norm. It is well-known that there exists such an $\mathbf S$ with just $O(n/\epsilon^2)$ non-zero entries: we can let $\mathbf S$ be the scaled adjacency matrix of a Ramanujan expander graph.

Dhruv Gamdha, Kumar Saurabh, Baskar Ganapathysubramanian, Adarsh Krishnamurthy

Accurate simulation of the printing process is essential for improving print quality, reducing waste, and optimizing the printing parameters of extrusion-based additive manufacturing. Traditional additive manufacturing simulations are very compute-intensive and are not scalable to simulate even moderately sized geometries. In this paper, we propose a general

Sadra Sadeghi, Saeedeh Mokarian Zanjani, Sergey Dayneko, Christian J. Imperiale

Quantum dot light-emitting diodes (QLEDs) are promising building blocks for prospective lighting and display applications. Despite the significant advancements achieved towards increasing the efficiency and brightness levels of QLEDs, the unavoidable demand for an inert atmosphere during the fabrication process restrains their potential for large-scale manuf

David Freeborn, Marian Gilton, Chris Mitsch

Haag's theorem is a classic no-go theorem. It rigorously demonstrates there is a logical problem with the interaction picture (IP), one of the most widely used modeling tools in quantum field theory (QFT). The significance of the theorem for the use of the IP in QFT has been the subject of long-running debate, focused around how ``worried'' we should be. In

Timothy A. Collier, Daniel Hauer

In this article, we prove existence and uniqueness of the Barenblatt solution of the evolution equation on the whole Euclidean space where the principle part is the nonlocal fractional p-Laplacian composed with a power function. Our proof generalizes methods developped by J.-L. Vazquez [Nonlinear Anal., 199 (2022), Calc. Var. Partial Differential Equations,

Jose Higueras

I study how to regulate firms' access to consumer data when a regulator faces non-Bayesian uncertainty about how firms will exploit the consumer's information to segment the market and set prices. I fully characterize all worst-case optimal policies when the regulator maximizes consumer surplus: the regulator allows a firm to access data only if the firm can

Krzysztof Główka, Julian Zubek, Joanna Rączaszek-Leonardi

There is significant evidence that real-world communication cannot be reduced to sending signals with context-independent meaning. In this work, based on a variant of the classical Lewis (1969) signaling model, we explore the conditions for the emergence of context-dependent communication in a situated scenario. In particular, we demonstrate that pressure to

Kel Levick, Ilan Shomorony

The problem of reconstructing a sequence from the set of its length-$k$ substrings has received considerable attention due to its various applications in genomics. We study an uncoded version of this problem where multiple random sources are to be simultaneously reconstructed from the union of their $k$-mer sets. We consider an asymptotic regime where $m = n

Doanh Pham

In this paper, we apply the so-called Alexandrov-Bakelman-Pucci (ABP) method to establish some geometric inequalities. We first prove a logarithmic Sobolev inequality for closed $n$-dimensional minimal submanifolds $\Sigma$ of $\mathbb S^{n+m}$. As a consequence, it recovers the classical result that $|\mathbb S^n| \leq |\Sigma|$ for $m = 1,2$. Next, we prov

Lina Fajardo Gómez, Margherita Maria Ferrari, Nataša Jonoska, Masahico Saito

We propose custom made cell complexes, in particular prodsimplicial complexes, in order to analyze data consisting of directed graphs. These are constructed by attaching cells that are products of simplices and are suited to study data of acyclic directed graphs, called here consistently directed graphs. We investigate possible values of the first and second

Miguel Castroviejo-Fernandez, Ilya Kolmanovsky

The paper addresses a problem of constrained spacecraft attitude stabilization with simultaneous reaction wheel (RW) desaturation. The spacecraft has a reaction wheel array (RWA) consisting of four RWs in a pyramidal configuration. The developments exploit a spacecraft dynamics model with gravity gradient torques. The linearized dynamics are shown to be cont

Pranjal Awasthi, Corinna Cortes, Mehryar Mohri

We study a problem of best-effort adaptation motivated by several applications and considerations, which consists of determining an accurate predictor for a target domain, for which a moderate amount of labeled samples are available, while leveraging information from another domain for which substantially more labeled samples are at one's disposal. We presen

Stan Alama, Lia Bronsard, Andrew Colinet

A classical result in the study of Ginzburg-Landau equations is that, for Dirichlet or Neumann boundary conditions, if a sequence of functions has energy uniformly bounded on a logarithmic scale then we can find a subsequence whose Jacobians are convergent in suitable dual spaces and whose renormalized energy is at least the sum of absolute degrees of vortic

Amanda E Hampton, James D Meiss

We previously showed that three-dimensional quadratic diffeomorphisms have anti-integrable (AI) limits that correspond to a quadratic correspondence; a pair of one-dimensional maps. At the AI limit the dynamics is conjugate to a full shift on two symbols. Here we consider a more general AI limit, allowing two parameters of the map to go to infinity. We prove

Wen-Qiang Liu, Zhe Meng, Bo-Wen Song, Jian Li

Deutsch's algorithm is the first quantum algorithm to show the advantage over the classical algorithm. Here we generalize Deutsch's problem to $n$ functions and propose a new quantum algorithm with indefinite causal order to solve this problem. The new algorithm not only reduces the number of queries to the black-box by half over the classical algori

Paul Raux, Felix Ritort

Maxwell's demon is a famous thought experiment and a paradigm of the thermodynamics of information. It is related to Szilard's engine, a two-state information-to-work conversion device in which the demon performs single measurements and extracts work depending on the state measurement outcome. A variant of these models, the Continuous Maxwell Demon (

Chuanhai Liu

This paper takes a look at omnibus tests of goodness of fit in the context of reweighted Anderson-Darling tests and makes threefold contributions. The first contribution is to provide a geometric understanding. It is argued that the test statistic with minimum variance for exchangeable distributional deviations can serve as a good general-purpose test. The s

Sumanjay Dutta, Shashi Jain

In this paper, we perform a comprehensive study of different covariance and precision matrix estimation methods in the context of minimum variance portfolio allocation. The set of models studied by us can be broadly categorized as: Gaussian Graphical Model (GGM) based methods, Shrinkage Methods, Thresholding and Random Matrix Theory (RMT) based methods. Amon

Yi-Da Li, Qing Wang

A new method to work out the Hermitian correspondence of a PT-symmetric quantum mechanical Hamiltonian is proposed. In contrast to the conventional method, the new method ends with a local Hamiltonian of the form p^2/2+m^2x^2/2+v(x) without any higher-derivative terms. This method is demonstrated in the perturbative regime. Possible extensions to multi-varia

Guangliang Cheng, Yunmeng Huang, Xiangtai Li, Shuchang Lyu

Change detection is an essential and widely utilized task in remote sensing that aims to detect and analyze changes occurring in the same geographical area over time, which has broad applications in urban development, agricultural surveys, and land cover monitoring. Detecting changes in remote sensing images is a complex challenge due to various factors, inc

Paul Seurin, Koroush Shirvan

The nuclear fuel loading pattern optimization problem belongs to the class of large-scale combinatorial optimization. It is also characterized by multiple objectives and constraints, which makes it impossible to solve explicitly. Stochastic optimization methodologies including Genetic Algorithms and Simulated Annealing are used by different nuclear utilities

Boris Almonacid

This article presents a framework for generating optimisation models using a pre-trained generative transformer. The framework involves specifying the features that the optimisation model should have and using a language model to generate an initial version of the model. The model is then tested and validated, and if it contains build errors, an automatic ed

Marcos Fajardo, Bartlomiej Wronski, Marco Salvi, Matt Pharr

2D texture maps and 3D voxel arrays are widely used to add rich detail to the surfaces and volumes of rendered scenes, and filtered texture lookups are integral to producing high-quality imagery. We show that filtering textures after evaluating lighting, rather than before BSDF evaluation as is current practice, gives a more accurate solution to the renderin

Yi-Da Li, Qing Wang

Perturbative PT-symmetric quantum field theories with anti-Hermitian and P-odd interaction terms are studied in path integral formalism and the i phi^3 model is calculated in detail. The nonlocal field transformation induced by the C operator and corresponding transformations to Hermitian theories are given systematically, which are manifestly 4-dimensional

Kordag Mehmet Kilic, Jin Sima, Jehoshua Bruck

The $\textit{von Neumann Computer Architecture}$ has a distinction between computation and memory. In contrast, the brain has an integrated architecture where computation and memory are indistinguishable. Motivated by the architecture of the brain, we propose a model of $\textit{associative computation}$ where memory is defined by a set of vectors in $\mathb

Alceu Bissoto, Catarina Barata, Eduardo Valle, Sandra Avila

Distribution shifts are common in real-world datasets and can affect the performance and reliability of deep learning models. In this paper, we study two types of distribution shifts: diversity shifts, which occur when test samples exhibit patterns unseen during training, and correlation shifts, which occur when test data present a different correlation betw

V. A. Dzuba, V. V. Flambaum

A version of the configuration interaction method, which has been recently developed to deal with large number of valence electrons, has been used to calculate magnetic dipole and electric quadrupole hyperfine structure constants for a number of states of erbium and fermium. Calculations for fermium are done for extracting nuclear moments of Fm isotopes from

Mário J. de Oliveira

We show that the quantum Fokker-Planck equation, obtained by a canonical quantization of its classical version, can be transformed into an equation of the Lindblad form. This result allows us to conclude that the quantum Fokker-Planck equation preserves the trace and positivity of the density operator. The Fokker-Planck structure gives explicit expression fo

Silvia Ghinassi, Vikram Giri, Elisa Negrini

We prove that Sobolev spaces on Cartesian and warped products of metric spaces tensorize, only requiring that one of the factors is a doubling space supporting a Poincar\'e inequality.

Tianle Chen, Zheda Mai, Ruiwen Li, Wei-lun Chao

Weakly supervised semantic segmentation (WSSS) aims to bypass the need for laborious pixel-level annotation by using only image-level annotation. Most existing methods rely on Class Activation Maps (CAM) to derive pixel-level pseudo-labels and use them to train a fully supervised semantic segmentation model. Although these pseudo-labels are class-aware, indi

Karthi Srinivasan, Yoram Moses, Rajit Manohar

Mutual exclusion is an important problem in the context of shared resource usage, where only one process can be using the shared resource at any given time. A mutual exclusion protocol that does not use information on the duration for which each process uses the resource can lead to sub-optimal utilization times. We consider a simple two-process mutual exclu

Delin Zhang, Wei Jiang, Hwanhui Yun, Onri Jay Benally

Contrary to topological insulators, topological semimetals possess a nontrivial chiral anomaly that leads to negative magnetoresistance and are hosts to both conductive bulk states and topological surface states with intriguing transport properties for spintronics. Here, we fabricate highly-ordered metallic Pt3Sn and Pt3SnxFe1-x thin films via sputtering tec

Valentin Burkin

This article provides an overview of the importance of requirements gathering in secure software development. It explains the crucial role of Requirements Engineers in defining and understanding the customer's needs and desires, as well as their responsibilities in liaising with the development team. The article also covers various software development life

Andrew Flynn, Vassilios A. Tsachouridis, Andreas Amann

Multifunctional biological neural networks exploit multistability in order to perform multiple tasks without changing any network properties. Enabling artificial neural networks (ANNs) to obtain certain multistabilities in order to perform several tasks, where each task is related to a particular attractor in the network's state space, naturally has many ben

Cheyenne S. Mitchell, Mikael P. Backlund

We generalize the theory of resolving a mixture of two closely spaced spontaneous emission lifetimes to include pure dephasing contributions to decoherence, leading to the resurgence of Rayleigh's Curse at small lifetime separations. Considerable resolution enhancement remains possible when lifetime broadening is more significant than that due to pure dephas

Jadie Adams, Shireen Elhabian

Statistical shape modeling (SSM) enables population-based quantitative analysis of anatomical shapes, informing clinical diagnosis. Deep learning approaches predict correspondence-based SSM directly from unsegmented 3D images but require calibrated uncertainty quantification, motivating Bayesian formulations. Variational information bottleneck DeepSSM (VIB-D

Steve Schulze, Claes Fransson, Alexandra Kozyreva, Ting-Wan Chen

Abridged - Stars with ZAMS masses between 140 and $260 M_\odot$ are thought to explode as pair-instability supernovae (PISNe). During their thermonuclear runaway, PISNe can produce up to several tens of solar masses of radioactive nickel, resulting in luminous transients similar to some superluminous supernovae (SLSNe). Yet, no unambiguous PISN has been disc

James Miller S. T. da Silva

Completely positive and trace preserving (CPT) maps are important for Quantum Information Theory, because they describe a broad class of of transformations of quantum states. There are also two other related classes of maps, the unital completely positive (UCP) maps and the unital completely positive and trace preserving (UCPT) maps. For these three classes,

Fabiano F. Santos, Behnam Pourhassan, Emmanuel N. Saridakis

We present general solutions of Horndeski-like gravity that can interpolate between the de Sitter and anti-de Sitter regimes. In particular, we develop the first-order formalism with two scalar fields, and considering a black hole ansatz with flat slicing we investigate three different cases, namely exponential, vacuum, and smooth superpotential solutions, w

Daniel J Oliver, Aaron D Johnson, Joel Berrier, Kostas Glampedakis

Scattering events around the center of massive galaxies will occasionally toss a stellar-mass compact object into an orbit around the massive black hole at the center, beginning an extreme mass ratio inspiral. The early stages of such a highly eccentric orbit are not likely to produce detectable gravitational waves, as the source will only be in a suitable f

James Schmidt

High complexity models are notorious in machine learning for overfitting, a phenomenon in which models well represent data but fail to generalize an underlying data generating process. A typical procedure for circumventing overfitting computes empirical risk on a holdout set and halts once (or flags that/when) it begins to increase. Such practice often helps

Anil Bilgin, Ian Hammock, Jeremy Estes, Yu Jin

We propose a quantum science platform utilizing the dipole-dipole coupling between donor-acceptor pairs (DAPs) in wide bandgap semiconductors to realize optically controllable, long-range interactions between defects in the solid state. We carry out calculations based on density functional theory (DFT) to investigate the electronic structure and interactions

Delaney Farrell, Pierre Baldi, Jordan Ott, Aishik Ghosh

The interiors of neutron stars reach densities and temperatures beyond the limits of terrestrial experiments, providing vital laboratories for probing nuclear physics. While the star's interior is not directly observable, its pressure and density determine the star's macroscopic structure which affects the spectra observed in telescopes. The relationship bet

Charles E. Romero, Massimo Gaspari, Gerrit Schellenberger, Tanay Bhandarkar

The galaxy cluster Zwicky 3146 is a sloshing cool core cluster at $z{=}0.291$ that in SZ imaging does not appear to exhibit significant pressure substructure in the intracluster medium (ICM). We perform a surface brightness fluctuation analysis via Fourier amplitude spectra on SZ (MUSTANG-2) and X-ray (XMM-Newton) images of this cluster. These surface bright

Tushar Kataria, Beatrice Knudsen, Shireen Elhabian

Semantic segmentation is a critical step in automated image interpretation and analysis where pixels are classified into one or more predefined semantically meaningful classes. Deep learning approaches for semantic segmentation rely on harnessing the power of annotated images to learn features indicative of these semantic classes. Nonetheless, they often fai

Z. Li, A. Arhrib, R. Benbrik, M. Krab

Most of the current experimental searches for charged Higgs bosons at the Large Hadron Collider (LHC) concentrate upon the $tb$ and $\tau\nu$ decay channels. In the present study, we analyze instead the feasibility of the bosonic decay channel $W^{\pm *} h$, with the charged gauge boson being off-shell and $h$ being a neutral light Higgs boson, which decays

Kana Watanabe, Tomoki Nakamura, Tomoyo Morita

Reproduction experiments of radial pyroxene (RP) chondrules were carried out using an Ar-$\mathrm{H_2}$ or Ar gas-jet levitation system in a reducing atmosphere in order to simulate chondrule formation in the protoplanetary disk. The experiments reproduced RP-chondrule textures, consisting of sets of thin pyroxene crystals and mesostasis glass between crysta

Rahul Agrawal, Ahmed Elnahhas, Parviz Moin

Wall-modeled large-eddy simulations (WMLES) are conducted for the flow over the Sandia Axisymmetric Transonic Bump (ATB) at bump chord Reynolds number, Re = 1x10^6 and Mach number, Ma = 0.875. Utilizing various subgrid-scale and wall models, comparisons are made between the simulations and experiments for quantities of engineering interest, such as the skin

Tanvir Hassan, A. Ben Hamza

In human pose estimation methods based on graph convolutional architectures, the human skeleton is usually modeled as an undirected graph whose nodes are body joints and edges are connections between neighboring joints. However, most of these methods tend to focus on learning relationships between body joints of the skeleton using first-order neighbors, igno

Alexey Piunovskiy, Yi Zhang

We consider an optimization problem in a convex space $E$ with an affine objective function, subject to $J$ constraints in the forms of inequalities on some other affine functions, where $J$ is a given nonnegative integer. Under suitable conditions, we apply the Feinberg-Shwartz lemma in finite dimensional convex analysis to show that there exists an optimal

P. N. Best, R. Kondapally, W. L. Williams, R. K. Cochrane

Source classifications, stellar masses and star formation rates are presented for 80,000 radio sources from the first data release of the Low Frequency Array Two-metre Sky Survey (LoTSS) Deep Fields, which represents the widest deep radio survey ever undertaken. Using deep multi-wavelength data spanning from the ultraviolet to the far-infrared, spectral ener

Ian M. Morris, Kai Klink, Jaideep T. Singh, Jose L. Mendoza-Cortes

Detecting a non-zero electric dipole moment (EDM) in a particle would unambiguously signify physics beyond the Standard Model. A potential pathway towards this is the detection of a nuclear Schiff moment, the magnitude of which is enhanced by the presence of nuclear octupole deformation. However, due to the low production rate of isotopes featuring such "pea

Deniss Strods, Alan F. Smeaton

Gaps, dropouts and short clips of corrupted audio are a common problem and particularly annoying when they occur in speech. This paper uses machine learning to regenerate gaps of up to 320ms in an audio speech signal. Audio regeneration is translated into image regeneration by transforming audio into a Mel-spectrogram and using image in-painting to regenerat

Claudius Kienle, David Petri

Depth cameras are frequently used in robotic manipulation, e.g. for visual servoing. The quality of small and compact depth cameras is though often not sufficient for depth reconstruction, which is required for precise tracking in and perception of the robot's working space. Based on the work of Shabanov et al. (2021), in this work, we present a self-supervi

Kevin Galligan, Peihong Yuan, Muriel Médard, Ken R. Duffy

Guessing Random Additive Noise Decoding (GRAND) is a family of hard- and soft-detection error correction decoding algorithms that provide accurate decoding of any moderate redundancy code of any length. Here we establish a method through which any soft-input GRAND algorithm can provide soft output in the form of an accurate a posteriori estimate of the likel

Mihnea-Alexandru Tomita, Bruno Ferrarini, Michael Milford, Klaus McDonald-Maier

Images incorporate a wealth of information from a robot's surroundings. With the widespread availability of compact cameras, visual information has become increasingly popular for addressing the localisation problem, which is then termed as Visual Place Recognition (VPR). While many applications use high-resolution cameras and high-end systems to achieve opt

Ovishake Sen, Baibhab Chatterjee

In this paper, we propose and evaluate a method of generating low-cost device signatures for distributed wireless brain implants, using a Pseudo-Random Binary Sequence (PRBS) Generator that utilizes a modified Ring-Oscillator-based Physical Unclonable Function (RO-PUF). The modified RO-PUF's output is used as a seed for the PRBS generator, which creates a mu

Kansei Inamura, Kantaro Ohmori

We construct (2+1)-dimensional lattice systems, which we call fusion surface models. These models have finite non-invertible symmetries described by general fusion 2-categories. Our method can be applied to build microscopic models with, for example, anomalous or non-anomalous one-form symmetries, 2-group symmetries, or non-invertible one-form symmetries tha

Travis Munyer, Abdullah Tanvir, Arjon Das, Xin Zhong

The rapid advancement of Large Language Models (LLMs) has significantly enhanced the capabilities of text generators. With the potential for misuse escalating, the importance of discerning whether texts are human-authored or generated by LLMs has become paramount. Several preceding studies have ventured to address this challenge by employing binary classifie

Bernhard A. Moser, Michael Lunglmayr

In order to ease the analysis of error propagation in neuromorphic computing and to get a better understanding of spiking neural networks (SNN), we address the problem of mathematical analysis of SNNs as endomorphisms that map spike trains to spike trains. A central question is the adequate structure for a space of spike trains and its implication for the de

Vera Hagemann, Lara Watermann, Florian Klonek, Christiane Heinicke

Astronaut crews and ground control support teams are highly interdependent teams that need to communicate effectively to achieve a safe mission - despite being separated by large distances. Team communication quality with its facets clarity of objectives and information flow is a key coordination process to achieve high team performance and task satisfaction

Brian J. Vaughn

It is typical in the accelerator field to model machine components, especially RF cavities, as parallel RLC resonators. To properly model wake-fields, knowledge of the time-domain voltage resulting from beam excitation is often necessary. While analytical and quasi-analytical expressions are available to accomplish this for common bunch distributions such as

William Brown

This paper investigates how the speed of code review is affected by the code quality, activity and usage in the context of MediaWiki extensions. The median time to merge is compared against several other variables which are collected using a variety of manual methods and APIs. The results are graphed where possible and statistical analysis is used to determi

Chen Liu, Xiangxiong Zhang

In this paper, we are interested in constructing a scheme solving compressible Navier--Stokes equations, with desired properties including high order spatial accuracy, conservation, and positivity-preserving of density and internal energy under a standard hyperbolic type CFL constraint on the time step size, e.g., $\Delta t=\mathcal O(\Delta x)$. Strang spli

Žiga Babnik, Peter Peer, Vitomir Štruc

Modern face recognition (FR) models excel in constrained scenarios, but often suffer from decreased performance when deployed in unconstrained (real-world) environments due to uncertainties surrounding the quality of the captured facial data. Face image quality assessment (FIQA) techniques aim to mitigate these performance degradations by providing FR models

J. Xavier Prochaska, Peter C. Cornillon, David M. Reiman

We perform an out-of-distribution analysis of ~12,000,000 semi-independent 128x128 pixel^2 SST regions, which we define as cutouts, from all nighttime granules in the MODIS R2019 Level-2 public dataset to discover the most complex or extreme phenomena at the ocean surface. Our algorithm (Ulmo) is a probabilistic autoencoder, which combines two deep learning

Yang Zhao, Shang Wu, Jingqun Zhang, Sixu Li

Instant on-device Neural Radiance Fields (NeRFs) are in growing demand for unleashing the promise of immersive AR/VR experiences, but are still limited by their prohibitive training time. Our profiling analysis reveals a memory-bound inefficiency in NeRF training. To tackle this inefficiency, near-memory processing (NMP) promises to be an effective solution,

Majid Farhadloo, Arun Sharma, Shashi Shekhar, Svetomir N. Markovic

We consider the problem of reducing the time needed by healthcare professionals to understand patient medical history via the next generation of biomedical decision support. This problem is societally important because it has the potential to improve healthcare quality and patient outcomes. However, navigating electronic health records is challenging due to

Alexander Nietner, Marios Ioannou, Ryan Sweke, Richard Kueng

In this work, we show that learning the output distributions of brickwork random quantum circuits is average-case hard in the statistical query model. This learning model is widely used as an abstract computational model for most generic learning algorithms. In particular, for brickwork random quantum circuits on $n$ qubits of depth $d$, we show three main r

Olof Nebrin

In calculations of the ionization state, one is often forced to choose between the Case A recombination coefficient $\alpha_{\rm A}$ (sum over recombinations to all hydrogen states) or the Case B recombination coefficient $\alpha_{\rm B}$ (sum over all hydrogen states except the ground state). If the cloud is optically thick to ionizing photons, $\alpha_{\rm

Nadja Willenborg, Anna-Lena Horlemann, Violetta Weger

We consider $t$-Lee-error-correcting codes of length $n$ over the residue ring $\mathbb{Z}_m := \mathbb{Z}/m\mathbb{Z}$ and determine upper and lower bounds on the number of $t$-Lee-error-correcting codes. We use two different methods, namely estimating isolated nodes on bipartite graphs and the graph container method. The former gives density results for co

Ignacio Escanuela Romana, Clara Escanuela Nieves

We pose the estimation and predictability of stock market performance. Three cases are taken: US, Japan, Germany, the monthly index of the value of realized investment in stocks, prices plus the value of dividend payments (OECD data). Once deflated and trend removed, harmonic analysis is applied. The series are taken with and without the periods with evidenc

Luca Lussardi, Anderson Melchor Hernandez, Marco Morandotti

In this work, we demonstrate that a functional modeling the self-aggregation of stochastically distributed lipid molecules can be obtained as the $\Gamma$-limit of a family of discrete energies driven by a sequence of independent and identically distributed random variables. These random variables are intended to describe the asymptotic behavior of lipid mol

Homayoon Farrahi, A. Rupam Mahmood

Continuous-time reinforcement learning tasks commonly use discrete steps of fixed cycle times for actions. As practitioners need to choose the action-cycle time for a given task, a significant concern is whether the hyper-parameters of the learning algorithm need to be re-tuned for each choice of the cycle time, which is prohibitive for real-world robotics.

Yachuan Liu, Bohan Zhang, Qiaozhu Mei, Paramveer Dhillon

Recent work has shown that standard training via empirical risk minimization (ERM) can produce models that achieve high accuracy on average but low accuracy on underrepresented groups due to the prevalence of spurious features. A predominant approach to tackle this group robustness problem minimizes the worst group error (akin to a minimax strategy) on the t

Clément Cosco, Ofer Zeitouni

Let $W_N(\beta) = \mathrm{E}_0\left[e^{ \sum_{n=1}^N \beta \omega(n,S_n) - N\beta^2/2}\right]$ be the partition function of a two-dimensional directed polymer in a random environment, where $\omega(i,x), i\in \mathbb{N}, x\in \mathbb{Z}^2$ are i.i.d. standard normal and $\{S_n\}$ is the path of a random walk. With $\beta=\beta_N=\widehat{\beta} \sqrt{\pi/\lo

Samuel Kittle

In this paper we provide a sufficient condition for a Furstenberg measure generated by a finitely supported measure to be absolutely continuous. Using this, we give a very broad class of examples of absolutely continuous Furstenberg measures including examples generated by measures supported on two points.

Emmanuel V. C. Lopes, Rogerio J. Baierle, Roberto H. Miwa, Tome M. Schmidt

In this work we predict a family of noncentrosymmetric two-dimensional (2D) Weyl semimetals composed by porous Ge and SiGe structures. These systems are energetically stable graphenylene-like structures with a buckling, spontaneously breaking the inversion symmetry. The nontrivial topological phase for these 2D systems occurs just below the Fermi level, resu

Pierre-Antoine Guihéneuf, Patrice Le Calvez, Alejandro Passeggi

Let $S$ be a closed surface of genus $g\geq 2$, furnished with a Borel probability measure $\lambda$ with total support. We show that if $f$ is a $\lambda$-preserving homeomorphism isotopic to the identity such that the rotation vector $\mathrm{rot}_f(\lambda)\in H_1(S,\mathbb R)$ is a multiple of an element of $H_1(S,\mathbb Z)$, then $f$ has infinitely man

Zhengxiang Shi, Jerome Ramos, To Eun Kim, Xi Wang

In collaborative tasks, effective communication is crucial for achieving joint goals. One such task is collaborative building where builders must communicate with each other to construct desired structures in a simulated environment such as Minecraft. We aim to develop an intelligent builder agent to build structures based on user input through dialogue. How

Faye Jackson, Misheel Otgonbayar

Let $k, t$ be coprime integers, and let $1 \leq r \leq t$. We let $D_k^\times(r,t;n)$ denote the total number of parts among all $k$-indivisible partitions (i.e., those partitions where no part is divisible by $k$) of $n$ which are congruent to $r$ modulo $t$. In previous work of the authors, an asymptotic estimate for $D_k^\times(r,t;n)$ was shown to exhibi

Ryan Yee, Sameer K. Deshpande

We introduce a three-step framework to determine at which pitches Major League batters should swing. Unlike traditional plate discipline metrics, which implicitly assume that all batters should always swing at (resp. take) pitches inside (resp. outside) the strike zone, our approach explicitly accounts not only for the players and umpires involved in the pit

Stanisław Drożdż, Jarosław Kwapień, Marcin Wątorek

In relation to the traditional financial markets, the cryptocurrency market is a recent invention and the trading dynamics of all its components are readily recorded and stored. This fact opens up a unique opportunity to follow the multidimensional trajectory of its development since inception up to the present time. Several main characteristics commonly rec

Mohammad Hasan Ahmadilivani, Mahdi Taheri, Jaan Raik, Masoud Daneshtalab

Artificial Intelligence (AI) and, in particular, Machine Learning (ML) have emerged to be utilized in various applications due to their capability to learn how to solve complex problems. Over the last decade, rapid advances in ML have presented Deep Neural Networks (DNNs) consisting of a large number of neurons and layers. DNN Hardware Accelerators (DHAs) ar

Matias Moreno, Paola Rioseco, Hanne Van Den Bosch

We study the linearized Vlasov-Poisson equation in the gravitational case around steady states that are decreasing and continuous functions of the energy. We identify the absolutely continuous spectrum and give criteria for the existence of oscillating modes and estimate their number. Our method allows us to take into account an attractive external potential

Paolo Tirotta, Akira Yuasa, Masashi Morita

Representing text into a multidimensional space can be done with sentence embedding models such as Sentence-BERT (SBERT). However, training these models when the data has a complex multilevel structure requires individually trained class-specific models, which increases time and computing costs. We propose a two step approach which enables us to map sentence

Hildeberto Jardón-Kojakhmetov, Christian Kuehn, Iacopo P. Longo

We study synchronization for linearly coupled temporal networks of heterogeneous time-dependent nonlinear agents via the convergence of attracting trajectories of each node. The results are obtained by constructing and studying the stability of a suitable linear nonautonomous problem bounding the evolution of the synchronization errors. Both, the case of the

Andrew Katz, Siqing Wei, Gaurav Nanda, Christopher Brinton

Teamwork is a critical component of many academic and professional settings. In those contexts, feedback between team members is an important element to facilitate successful and sustainable teamwork. However, in the classroom, as the number of teams and team members and frequency of evaluation increase, the volume of comments can become overwhelming for an

Jesper Lykke Jacobsen, Hubert Saleur

In a recent paper, Gorbenko and Zan [arXiv:2005.07708] observed that $O(n)$ symmetry alone does not protect the well-known renormalization group flow from the dilute to the dense phase of the two-dimensional $O(n)$ model under thermal perturbations. We show in this paper that the required "extra protection" is topological in nature, and is related to the exi

Yanina Y. Shkel, Anuj Kumar Yadav

Given two jointly distributed random variables $(X,Y)$, a functional representation of $X$ is a random variable $Z$ independent of $Y$, and a deterministic function $g(\cdot, \cdot)$ such that $X=g(Y,Z)$. The problem of finding a minimum entropy functional representation is known to be equivalent to the problem of finding a minimum entropy coupling where, gi

Jason D. Lotay, Goncalo Oliveira

In this article we consider the Lagrangian mean curvature flow of compact, circle-invariant, almost calibrated Lagrangian surfaces in hyperk\"ahler 4-manifolds with circle symmetry. We show that this Lagrangian mean curvature flow can be continued for all time, through finite time singularities, and converges to a chain of special Lagrangians, thus verifying

Alex Durkin, Miao Guo

Wastewater systems are transitioning towards integrative process systems to recover multiple resources whilst simultaneously satisfying regulations on final effluent quality. This work contributes to the literature by bringing a systems-thinking approach to resource recovery from wastewater, harnessing surrogate modelling and mathematical optimisation techni