November 2022 arXiv papers — page 69

Hongyu Zhou, Zheng Ge, Zeming Li, Xiangyu Zhang

This paper proposes an efficient multi-camera to Bird's-Eye-View (BEV) view transformation method for 3D perception, dubbed MatrixVT. Existing view transformers either suffer from poor transformation efficiency or rely on device-specific operators, hindering the broad application of BEV models. In contrast, our method generates BEV features efficiently with

Sebastian Eterović, Thomas Scanlon

We prove a general likely intersections theorem, a counterpart to the Zilber-Pink conjectures, under the assumption that the Ax-Schanuel property and some mild additional conditions are known to hold for a given category of complex quotient spaces definable in some fixed o-minimal expansion of the ordered field of real numbers. For an instance of our general

Bimalendu Mahapatra, Aditya Bandopadhyay

In computational models of microchannel flows, the Helmholtz-Smoluchowski slip velocity boundary condition is often used because it approximates the motion of the electric double layer without resolving the charge density profiles close to the walls while drastically reducing the computational effort needed for the flow model to be solved. Despite working we

Chenyang Qiu, Shanying Zhu, Zichong Ou, Jie Lu

In this paper, we propose a distributed stochastic second-order proximal method that enables agents in a network to cooperatively minimize the sum of their local loss functions without any centralized coordination. The proposed algorithm, referred to as St-SoPro, incorporates a decentralized second-order approximation into an augmented Lagrangian function, a

Jinho Chang, Jong Chul Ye

The recent success of large foundation models in artificial intelligence has prompted the emergence of chemical pre-trained models. Despite the growing interest in large molecular pre-trained models that provide informative representations for downstream tasks, attempts for multimodal pre-training approaches on the molecule domain were limited. To address th

Juan David Pabon, Shaikha Alkandari, Matthew C. Valenti, Xi Yu

Ground assets deployed in a cluttered environment with randomized obstacles (e.g., a forest) may experience line of sight (LoS) obstruction due to those obstacles. Air assets can be deployed in the vicinity to aid the communication by establishing two-hop paths between the ground assets. Obstacles that are taller than a position-dependent critical height may

Anjali Balagopal, Dan Nguyen, Ti Bai, Michael Dohopolski

When a pre-trained general auto-segmentation model is deployed at a new institution, a support framework in the proposed Prior-guided DDL network will learn the systematic difference between the model predictions and the final contours revised and approved by clinicians for an initial group of patients. The learned style feature differences are concatenated

Bimalendu Mahapatra, Aditya Bandopadhyay

In this study, geometrically modified microchannels fabricated using stereolithography technique are employed to analyze micromixing of polymeric solutions. Experimental and numerical analyses were conducted to evaluate the qualitative and quantitative validity of the Newtonian fluid flow inside the geometrically modified channels. An in-house image processi

Yueyang Zhang

We present a complete description of the form of transcendental meromorphic solutions of the second order differential equation \begin{equation}\tag{\dag} w''w-w'^2+a w'w+b w^2=\alpha w+\beta w'+\gamma, \end{equation} where $a$, $b$, $\alpha$, $\beta$ and $\gamma$ are all rational functions. Together with the Wiman--Valiron theory, we then show that any tran

Justin Cui, Ruochen Wang, Si Si, Cho-Jui Hsieh

Dataset Distillation is a newly emerging area that aims to distill large datasets into much smaller and highly informative synthetic ones to accelerate training and reduce storage. Among various dataset distillation methods, trajectory-matching-based methods (MTT) have achieved SOTA performance in many tasks, e.g., on CIFAR-10/100. However, due to exorbitant

Rodolfo Valiente, Mahdi Razzaghpour, Behrad Toghi, Ghayoor Shah

Autonomous vehicle (AV) navigation in the presence of Human-driven vehicles (HVs) is challenging, as HVs continuously update their policies in response to AVs. In order to navigate safely in the presence of complex AV-HV social interactions, the AVs must learn to predict these changes. Humans are capable of navigating such challenging social interaction sett

Antoine Labelle, Stoyan Dimitrov

Chess tableaux are a special kind of standard Young tableaux where, in the chessboard coloring of the Young diagram, even numbers always appear in white cells and odd numbers in black cells. If, for $\lambda$ a partition of $n$, $\text{Chess}(\lambda)$ denotes the number of chess tableaux of shape $\lambda$, then Chow, Eriksson and Fan observed that $\displa

Mohamed Naveed Gul Mohamed, Raman Goyal, Suman Chakravorty, Ran Wang

This paper considers the problem of system identification for linear time varying systems. We propose a new system realization approach that uses an "information-state" as the state vector, where the "information-state" is composed of a finite number of past inputs and outputs. The system identification algorithm uses input-output data to fit an autoregressi

Lifu Wang, Tianyu Wang, Shengwei Yi, Bo Shen

We study the learning ability of linear recurrent neural networks with Gradient Descent. We prove the first theoretical guarantee on linear RNNs to learn any stable linear dynamic system using any a large type of loss functions. For an arbitrary stable linear system with a parameter $\rho_C$ related to the transition matrix $C$, we show that despite the non-

Xuewu Lin, Tianwei Lin, Zixiang Pei, Lichao Huang

Bird-eye-view (BEV) based methods have made great progress recently in multi-view 3D detection task. Comparing with BEV based methods, sparse based methods lag behind in performance, but still have lots of non-negligible merits. To push sparse 3D detection further, in this work, we introduce a novel method, named Sparse4D, which does the iterative refinement

Ancheng Lin, Jun Li, Yusheng Xiang, Wei Bian

High-quality surface normal can help improve geometry estimation in problems faced by autonomous vehicles, such as collision avoidance and occlusion inference. While a considerable volume of literature focuses on densely scanned indoor scenarios, normal estimation during autonomous driving remains an intricate problem due to the sparse, non-uniform, and nois

Xue Li, Yuanzhi Cheng

In designing and applying graph neural networks, we often fall into some optimization pitfalls, the most deceptive of which is that we can only build a deep model by solving over-smoothing. The fundamental reason is that we do not understand how graph neural networks work. Stress graph drawing can offer a unique viewpoint to message iteration in the graph, s

Shancheng Fang, Zhendong Mao, Hongtao Xie, Yuxin Wang

Scene text spotting is of great importance to the computer vision community due to its wide variety of applications. Recent methods attempt to introduce linguistic knowledge for challenging recognition rather than pure visual classification. However, how to effectively model the linguistic rules in end-to-end deep networks remains a research challenge. In th

Byeongsu Yu, Laura Felicia Matusevich

We give a combinatorial description of local cohomology modules of a graded module over a semigroup ring, with support at the graded maximal ideal. This combinatorial framework yields Hochster-type formulas for the Hilbert series of such local cohomology modules in terms of the homology of finitely many polyhedral cell complexes. A Cohen--Macaulay criterion

Jinlu Li, Yanghai Yu, Weipeng Zhu

The aim of this paper is to answer the question left in \cite{GL} (Math. Z. (2015) 281). We prove that the zero-filter limit of the Camassa-Holm equation is the Burgers equation in the same topology of Sobolev spaces as the initial data.

Prashant Pandey

Microbubbles - used as contrast agents in ultrasound imaging - are important tools in biomedical research, having been used together with ultrasound to develop significant diagnostic and therapeutic techniques. It has been suggested that the dynamic behaviour of microbubbles is dependent on the surrounding fluid's ambient (hydrostatic) pressure, and the pote

L. Mars Gao, J. Nathan Kutz

Recent progress in autoencoder-based sparse identification of nonlinear dynamics (SINDy) under $\ell_1$ constraints allows joint discoveries of governing equations and latent coordinate systems from spatio-temporal data, including simulated video frames. However, it is challenging for $\ell_1$-based sparse inference to perform correct identification for real

Yixing Xu, Daniel Olsen, Bainan Xia, Dan Livengood

Some U.S. states have set clean energy goals and targets in an effort to decarbonize their electricity sectors. There are many reasons for such goals and targets, including the increasingly apparent effects of climate change. A handful of states (Washington, California, New York, and Virginia) are aiming for deep decarbonization by 2050 or earlier, a mere 30

Cauê M. Kersul, Rodrigo Benevides, Flávio Moraes, Gabriel H. M. de Aguiar

In this work, we study the effects of mechanical anisotropy in a 2D optomechanical crystal geometry. We fabricate and measure devices with different orientations, showing the dependence of the mechanical spectrum and the optomechanical coupling with the relative angle of the device to the crystallography directions of silicon. Our results show that the devic

Md Sultan Al Nahian, Spencer Frazier, Brent Harrison, Mark Riedl

Value alignment is the task of creating autonomous systems whose values align with those of humans. Past work has shown that stories are a potentially rich source of information on human values; however, past work has been limited to considering values in a binary sense. In this work, we explore the use of machine learning models for the task of normative pr

Bo Lu, Satoshi Ikegaya, Pablo Burset, Yukio Tanaka

The Josephson rectification effect, where the resistance is finite in one direction while zero in the other, has been recently realized experimentally. The resulting Josephson diode has many potential applications on superconducting devices, including quantum computers. Here, we theoretically show that a superconductor-normal metal-superconductor Josephson j

Chatchai Noytaptim

For rational numbers $c$, we present a trichotomy of the set of totally real (totally $p$-adic, respectively) preperiodic points for maps in the quadratic unicritical family $f_c(x)=x^2+c$. As a consequence, we classify quadratic polynomials $f_c$ with rational parameters $c\in\mathbb{Q}$ so that $f_c$ has only finitely many totally real (totally $p$-adic, r

Konstantin G. Zloshchastiev

A unified model of a dilute Bose-Einstein condensate is proposed, combining of the logarithmic and Gross-Pitaevskii nonlinear terms in a wave equation, where the Gross-Pitaevskii term describes two-body interactions, as suggested by standard perturbation theory; while the logarithmic term is essentially non-perturbative, and takes into account quantum vacuum

Sergio Giardino

The second quantization of the quaternionic fermionic field is undertaken using the real Hilbert space approach to quaternionic quantum mechanics ($\mathbbm H$QM). The solution responds to an open problem of quaternionic quantum theory, and launches the basis to the development of the quaternionic interaction theory.

Xinfa Zhu, Yi Lei, Kun Song, Yongmao Zhang

This paper aims to synthesize the target speaker's speech with desired speaking style and emotion by transferring the style and emotion from reference speech recorded by other speakers. We address this challenging problem with a two-stage framework composed of a text-to-style-and-emotion (Text2SE) module and a style-and-emotion-to-wave (SE2Wave) module, brid

Yao Zhang, Haokun Chen, Ahmed Frikha, Yezi Yang

Visual Question Answering (VQA) is a multi-discipline research task. To produce the right answer, it requires an understanding of the visual content of images, the natural language questions, as well as commonsense reasoning over the information contained in the image and world knowledge. Recently, large-scale Vision-and-Language Pre-trained Models (VLPMs) h

Christian Machado, Yuehan Yao, Emma Feldman, Joanna Aizenberg

To achieve efficient liquid collection, a surface must regulate incoming liquid accumulation with outgoing liquid transport. Often, this can be proposed to be achieved by functionalizing surfaces with non-wetting characteristics. Yet, there remain fundamental, practical limits to which non-wetting surfaces can effectively be employed. We instead utilize film

Iván López-Espejo, Ram C. M. C. Shekar, Zheng-Hua Tan, Jesper Jensen

In the context of keyword spotting (KWS), the replacement of handcrafted speech features by learnable features has not yielded superior KWS performance. In this study, we demonstrate that filterbank learning outperforms handcrafted speech features for KWS whenever the number of filterbank channels is severely decreased. Reducing the number of channels might

Mahmoud Salem, Mohamed Osama Ahmed, Frederick Tung, Gabriel Oliveira

ML models often operate within the context of a larger system that can adapt its response when the ML model is uncertain, such as falling back on safe defaults or a human in the loop. This commonly encountered operational context calls for principled techniques for training ML models with the option to abstain from predicting when uncertain. Selective neural

Xiang Wang, Yimin Yang, Zhichang Guo, Zhili Zhou

Deep Convolutional Neural Networks (DCNNs) have exhibited impressive performance on image super-resolution tasks. However, these deep learning-based super-resolution methods perform poorly in real-world super-resolution tasks, where the paired high-resolution and low-resolution images are unavailable and the low-resolution images are degraded by complicated

Evan P. G. Gale, Magdalena Zych

In this paper, we extend the Unruh-DeWitt (UDW) model to include a relativistic quantized center of mass (c.m.) for the detector, which traditionally has a classical c.m. and follows a classical trajectory. We develop a relativistic model of an inertial detector following two different approaches, starting from either a first- or second-quantized treatment,

Zhixiang Zhang, Biao Jie, Zhengdong Wang, Jie Zhou

Recent studies have applied deep learning methods such as convolutional recurrent neural networks (CRNs) and Transformers to brain disease classification based on dynamic functional connectivity networks (dFCNs), such as Alzheimer's disease (AD), achieving better performance than traditional machine learning methods. However, in CRNs, the continuous convolut

Tiago Nunes da Silva, David D. Chinellato, André V. Giannini, Maurício N. Ferreira

We extend our previous investigation of the effects of pre-hydrodynamic evolution on final-state observables in heavy-ion collisions to smaller systems. We use a state-of-the-art hybrid model for the numerical simulations with optimal parameters obtained from a previous Bayesian study. By studying p-Pb collisions, we find that the effects due to the assumpti

J. A. Hawkins, L. Baumann, H. M. Aktulga, D. Dault

Many integral equations used to analyze scattering, such as the standard combined field integral equation (CFIE), are not well-conditioned for a wide range of frequencies and multi-scale geometries. There has been significant effort to alleviate this problem. A more recent one is using a set of decoupled potential integral equations (DPIE). These equations h

Anatolii V. Mokshin, Roman A. Khabibullin

In this study, we present the original method for reconstructing the potential of interparticle interaction from statistically averaged structural data, namely, the radial distribution function of particles in many-particle system. This method belongs to a family of machine learning methods and is implemented through the differential evolution algorithm. As

Henry Kvinge, Grayson Jorgenson, Davis Brown, Charles Godfrey

While the last five years have seen considerable progress in understanding the internal representations of deep learning models, many questions remain. This is especially true when trying to understand the impact of model design choices, such as model architecture or training algorithm, on hidden representation geometry and dynamics. In this work we present

Mingzhe Sun, Aaron Zhou, Naize Yang, Yaqian Xu

Closed-loop sleep modulation is an emerging research paradigm to treat sleep disorders and enhance sleep benefits. However, two major barriers hinder the widespread application of this research paradigm. First, subjects often need to be wire-connected to rack-mount instrumentation for data acquisition, which negatively affects sleep quality. Second, conventi

Julia Chuzhoy

Expander graphs play a central role in graph theory and algorithms. With a number of powerful algorithmic tools developed around them, such as the Cut-Matching game, expander pruning, expander decomposition, and algorithms for decremental All-Pairs Shortest Paths (APSP) in expanders, to name just a few, the use of expanders in the design of graph algorithms

Fei Feng, Yifan Zhou, Peng Zhang

Quantum power flow (QPF) provides inspiring directions for tackling power flow's computational burdens leveraging quantum computing. However, existing QPF methods are mainly based on noise-sensitive quantum algorithms, whose practical utilization is significantly hindered by the limited capability of today's noisy-intermediate-scale quantum (NISQ) devices. T

Huyuan Chen, Huihuan Peng, Yanqing Sun

Our purpose of this paper is to investigate positive solutions of the elliptic equation with regional fractional Laplacian $$ ( - \Delta )_{B_1}^s u +u= h(x,u) \quad {\rm in} \ \, B_1,\qquad u\in C_0(B_1), $$ where $( - \Delta )_{B_1}^s$ with $s\in(0,\frac12]$ is the regional fractional Laplacian and $h$ is the nonlinearity. Ordinarily, positive solutions va

Gilly Elor, Alfredo Walter Mario Guerrera

Production of the matter-antimatter asymmetry in the $B$-Mesogenesis mechanism is directly related to the branching fraction of seemingly baryon number violating decays of $B$ mesons into a light Standard Model baryon and missing energy. Achieving the observed baryon asymmetry requires that the branching fraction for such decays be greater than about $10^{-7

Edwin S. Kite, Axel Noblet

Mars' wet-to-dry transition is a major environmental catastrophe, yet the spatial pattern, tempo, and cause of drying are poorly constrained. We built a globally-distributed database of constraints on Mars late-stage paleolake size relative to catchment area (aridity index), and found evidence for climate zonation as Mars was drying out. Aridity increased ov

Jialiang Wang, Daniel Scharstein, Akash Bapat, Kevin Blackburn-Matzen

We present the design of a productionized end-to-end stereo depth sensing system that does pre-processing, online stereo rectification, and stereo depth estimation with a fallback to monocular depth estimation when rectification is unreliable. The output of our depth sensing system is then used in a novel view generation pipeline to create 3D computational p

Clément Bonnet, Laurence Midgley, Alexandre Laterre

Meta-gradient Reinforcement Learning (RL) allows agents to self-tune their hyper-parameters in an online fashion during training. In this paper, we identify a bias in the meta-gradient of current meta-gradient RL approaches. This bias comes from using the critic that is trained using the meta-learned discount factor for the advantage estimation in the outer

Zhabiz Gharibshah, Xingquan Zhu

Contrastive self-supervised learning has been successfully used in many domains, such as images, texts, graphs, etc., to learn features without requiring label information. In this paper, we propose a new local contrastive feature learning (LoCL) framework, and our theme is to learn local patterns/features from tabular data. In order to create a niche for lo

Ian M. Nugent

We present a modified spin algorithm, including spin correlations, which has been implemented in {\tt ee$\in$MC} for the simulation of $e^{+}e^{-} \to \tau^{+}\tau^{-} (\gamma)$ events with an arbitrary initial spin configuration. This algorithm is suitable for the proposed BELLE-II polarization upgrade to SuperKEKB, both for the ideal case and for the case

Luis E. Nieto-Barajas

In the biology field of botany, leaf shape recognition is an important task. One way of characterising the leaf shape is through the centroid contour distances (CCD). Each CCD path might have different resolution, so normalisation is done by associating each contour to a circular density. Densities are rotated by subtracting the mean or mode preferred direct

Sarwan Ali

The SARS-CoV-2 coronavirus is the cause of the COVID-19 disease in humans. Like many coronaviruses, it can adapt to different hosts and evolve into different lineages. It is well-known that the major SARS-CoV-2 lineages are characterized by mutations that happen predominantly in the spike protein. Understanding the spike protein structure and how it can be p

I. Stetcu, A. Baroni, J. Carlson

We present an efficient method to prepare states of a many-body system on quantum hardware, first isolating individual quantum numbers and then using time evolution to isolate the energy. Our method in its simplest form requires only one additional auxiliary qubit. The total time evolved for an accurate solution is proportional to the ratio of the spectrum r

Shubham Srivastav, S. J. Smartt, M. E. Huber, G. Dimitriadis

We present observations and analysis of the host-less and luminous type Ia supernova 2022ilv, illustrating it is part of the 2003fg-like family, often referred to as super-Chandrasekhar (Ia-SC) explosions. The ATLAS light curve shows evidence of a short-lived, pulse-like early excess, similar to that detected in another luminous type Ia supernova (SN 2020hvf

Mikheil Sekania, Shota Garuchava, Jamal Berakdar, George I. Japaridze

Ground state (GS) phase diagram of the one dimensional repulsive Hubbard model with both nearest neighbor ($t$) and next-nearest-neighbor ($t^{\prime}$) hopping and a staggered potential ($\Delta$) is determined in the case of half-filled band and zero net magnetization within the mean-field theory. The model may be realized by cold atoms in engineered optic

D. Vijay Anand, Anass B El-Yaagoubi, Hernando Ombao, Moo K. Chung

We propose to analyze dynamically changing brain networks by decomposing them into three orthogonal components through the Hodge decomposition. We propose to quantify the magnitude and relative strength of each component. We performed extensive simulation studies with known ground truth. The Hodge decomposition is then applied to the dynamically changing hum

Hanwen Huang, Peng Zeng, Qinglong Yang

We study the problem of estimating a $k$-sparse signal ${\mbox{$\beta$}}_0\in{\bf R}^p$ from a set of noisy observations ${\bf y}\in{\bf R}^n$ under the model ${\bf y}={\bf X}{\mbox{$\beta$}}+{\bf w}$, where ${\bf X}\in{\bf R}^{n\times p}$ is the measurement matrix the row of which is drawn from distribution $N(0,{\mbox{$\Sigma$}})$. We consider the class of

Loïc Hélouët, Pranay Agrawal

In time Petri nets (TPNs), time and control are tightly connected: time measurement for a transition starts only when all resources needed to fire it are available. Further, upper bounds on duration of enabledness can force transitions to fire (this is called urgency). For many systems, one wants to decouple control and time, i.e. start measuring time as soo

Wim Boes, Hugo Van hamme

We study the impact of visual assistance for automated audio captioning. Utilizing multi-encoder transformer architectures, which have previously been employed to introduce vision-related information in the context of sound event detection, we analyze the usefulness of incorporating a variety of pretrained features. We perform experiments on a YouTube-based

Ilaria Ermolli, Fabrizio Giorgi, Theodosios Chatzistergos

The Rome Precision Solar Photometric Telescope (Rome/PSPT) is a ground-based telescope engaged in precision solar photometry. It has a 27-year database of full-disk images of the photosphere and chromosphere beginning in 1996 and continuing to 2022. The solar images have been obtained daily, weather permitting, with approximately 2 arcsec/pixel scale in Ca I

Seyf Eddine Ghenimi, Abdelmouhcene Sengouga

We study the small vibrations of an axially travelling string with a dashpoint damping at one end. The string is modelled by a wave equation in a time-dependent interval with two endpoints moving at a constant speed $v$. For the undamped case, we obtain a conserved functional equivalent to the energy of the solution. We derive precise upper and lower estimat

G. Monteiro, G. Guerrero, F. Del Sordo, A. Bonanno

Magnetic fields have been observed in massive Ap/Bp stars and presumably are also present in the radiative zone of solar-like stars. Yet, to date there is no clear understanding of the dynamics of the magnetic field in stably stratified layers. A purely toroidal magnetic field configuration is known to be unstable, developing mainly non-axisymmetric modes. R

Simon E. T. Smith, Rachel Friesen, Antoine Marchal, Jaime E. Pineda

Filamentary structures have been found nearly ubiquitously in molecular clouds and yet their formation and evolution is still poorly understood. We examine a segment of Taurus Molecular Cloud 1 (TMC-1) that appears as a single, narrow filament in continuum emission from dust. We use the Regularized Optimization for Hyper-Spectral Analysis (ROHSA), a Gaussian

Xu Feng, Guo-Yang Li, Seok-Hyun Yun

Visualizing elastic waves by noninvasive imaging has been useful for analyzing the mechanical properties of materials and tissues. However, the maximum wave frequency of elastography has been limited to ~10 kHz due to the finite sensitivity to small vibration and finite imaging speed. Here, we present an optical coherence elastography technique that extends

Lance Kavalsky, Vinay I. Hegde, Eric Muckley, Matthew S. Johnson

The implementation of automation and machine learning surrogatization within closed-loop computational workflows is an increasingly popular approach to accelerate materials discovery. However, the scale of the speedup associated with this paradigm shift from traditional manual approaches remains an open question. In this work, we rigorously quantify the acce

Dom Jebbia, Xiaojun Wang, Yasin Bakis, Henry L. Bart

Flexible metadata pipelines are crucial for supporting the FAIR data principles. Despite this need, researchers seldom report their approaches for identifying metadata standards and protocols that support optimal flexibility. This paper reports on an initiative targeting the development of a flexible metadata pipeline for a collection containing over 300,000

Xiang Wang, Yimin Yang, Qixiang Pang, Xiao Lu

Face super-resolution is a domain-specific image super-resolution, which aims to generate High-Resolution (HR) face images from their Low-Resolution (LR) counterparts. In this paper, we propose a novel face super-resolution method, namely Semantic Encoder guided Generative Adversarial Face Ultra-Resolution Network (SEGA-FURN) to ultra-resolve an unaligned ti

V. Denysiuk

Under consideration methods of constructing trigonometric interpolation splines of two variables on rectangular areas. These methods are easily generalized to the case of trigonometric interpolation splines of several variables on such domains. A numerical example illustrating the main theoretical propositions is considered. The given methods of constructing

Shubham Kumar Bharti, Xuezhou Zhang, Adish Singla, Xiaojin Zhu

We propose a provable defense mechanism against backdoor policies in reinforcement learning under subspace trigger assumption. A backdoor policy is a security threat where an adversary publishes a seemingly well-behaved policy which in fact allows hidden triggers. During deployment, the adversary can modify observed states in a particular way to trigger unex

Karol Kowalski, Nicholas P. Bauman

We present an extension of many-body downfolding methods to reduce the resources required in the quantum phase estimation (QPE) algorithm. In this paper, we focus on the Schrieffer--Wolff (SW) transformation of the electronic Hamiltonians for molecular systems that provides significant simplifications of quantum circuits for simulations of quantum dynamics.

Mengmeng Xu, Yanghao Li, Cheng-Yang Fu, Bernard Ghanem

This paper deals with the problem of localizing objects in image and video datasets from visual exemplars. In particular, we focus on the challenging problem of egocentric visual query localization. We first identify grave implicit biases in current query-conditioned model design and visual query datasets. Then, we directly tackle such biases at both frame a

Ju-Hyung Lee, Omer Gokalp Serbetci, Dheeraj Panneer Selvam, Andreas F. Molisch

Pathloss prediction is an essential component of wireless network planning. While ray tracing based methods have been successfully used for many years, they require significant computational effort that may become prohibitive with the increased network densification and/or use of higher frequencies in 5G/B5G (beyond 5G) systems. In this paper, we propose and

Haoran You, Yunyang Xiong, Xiaoliang Dai, Bichen Wu

Vision Transformers (ViTs) have shown impressive performance but still require a high computation cost as compared to convolutional neural networks (CNNs), one reason is that ViTs' attention measures global similarities and thus has a quadratic complexity with the number of input tokens. Existing efficient ViTs adopt local attention (e.g., Swin) or linear at

Sidharth Gupta, Konik Kothari, Valentin Debarnot, Ivan Dokmanić

We propose a differentiable imaging framework to address uncertainty in measurement coordinates such as sensor locations and projection angles. We formulate the problem as measurement interpolation at unknown nodes supervised through the forward operator. To solve it we apply implicit neural networks, also known as neural fields, which are naturally differen

Hossein Mohammadi, Vuk Marojevic, Bodong Shang

This paper introduces the deployment of unmanned aerial vehicles (UAVs) as lightweight wireless access points that leverage the fixed infrastructure in the context of the emerging open radio access network (O-RAN). More precisely, we propose an aerial radio unit that dynamically serves an under served area and connects to the distributed unit via a wireless

Mohammed Amin Amri

In the present article, we formulate a conjectural uniform error term in the Chebotarev-Sato-Tate distribution for abelian surfaces $\mathbb{Q}$-isogenous to a product of not $\overline{\mathbb{Q}}$-isogenous non-CM-elliptic curves, established by the author in \cite[Theorem 1.1]{Amri22}. As a consequence, we provide a direct proof to the generalized Lang-Tr

Stefano Gariazzo, Pablo Martínez-Miravé, Olga Mena, Sergio Pastor

Deviations from unitarity in the three-neutrino mixing canonical picture are expected in many physics scenarios beyond the Standard Model. The mixing of new heavy neutral leptons with the three light neutrinos would in principle modify the strength and flavour structure of charged-current and neutral-current interactions with matter. Non-unitarity effects wo

Naijia Liu, Jan Rozendaal, Liang Song, Lixin Yan

We introduce the Hardy spaces for Fourier integral operators on Riemannian manifolds with bounded geometry. We then use these spaces to obtain improved local smoothing estimates for Fourier integral operators satisfying the cinematic curvature condition, and for wave equations on compact manifolds. The estimates are essentially sharp, for all $2<p<\infty$ an

Trevor D. Wooley

For every finite abelian group $G$, there are positive integers $n$ and $d$ such that $G$ is isomorphic to the multiplicative group of $d$-th powers of reduced residues modulo $n$.

Virginia Ajani, Joachim Harnois-Déraps, Valeria Pettorino, Jean-Luc Starck

We present a first application to photometric galaxy clustering and weak lensing of wavelet based multi-scale higher order summary statistics: starlet peak counts and starlet $\ell_1$-norm. Peak counts are the local maxima in the map and the $\ell_1$-norm is computed via the sum of the absolute values of the starlet (wavelet) decomposition coefficients of a

Muyuan Chen, Bogdan Toader, Roy Lederman

Resolving the structural variability of proteins is often key to understanding the structure-function relationship of those macromolecular machines. Single particle analysis using Cryogenic electron microscopy (CryoEM), combined with machine learning algorithms, provides a way to reveal the dynamics within the protein system from noisy micrographs. Here, we

Theodor Cimpeanu, Alessandro Di Stefano, Cedric Perret, The Anh Han

Institutions and investors are constantly faced with the challenge of appropriately distributing endowments. No budget is limitless and optimising overall spending without sacrificing positive outcomes has been approached and resolved using several heuristics. To date, prior works have failed to consider how to encourage fairness in a population where social

Hongbo Kang, Yiwei Zhao, Guy E. Blelloch, Laxman Dhulipala

The performance of today's in-memory indexes is bottlenecked by the memory latency/bandwidth wall. Processing-in-memory (PIM) is an emerging approach that potentially mitigates this bottleneck, by enabling low-latency memory access whose aggregate memory bandwidth scales with the number of PIM nodes. There is an inherent tension, however, between minimizing

Daniel Jarrett, Corentin Tallec, Florent Altché, Thomas Mesnard

Consider the problem of exploration in sparse-reward or reward-free environments, such as in Montezuma's Revenge. In the curiosity-driven paradigm, the agent is rewarded for how much each realized outcome differs from their predicted outcome. But using predictive error as intrinsic motivation is fragile in stochastic environments, as the agent may become tra

Patrick Del Vecchio, Oussama Moutanabbir

The selective confinement of light-holes (LHs) is demonstrated by introducing a low-dimensional system consisting of highly tensile-strained Ge quantum well enabling the design of an ultrafast gate-defined spin qubit under the electric dipole spin resonance. The qubit size-dependent $g$-factor and dipole moment are mapped, and the parameters inducing their m

Mohammad Mahdi Altakach, Priyanka Lamba, Fabio Maltoni, Kentarou Mawatari

We introduce a methodology and investigate the feasibility of measuring quantum properties of tau lepton pairs in the $H \to \tau^+ \tau^-$ decay at future lepton colliders. In particular, observation of entanglement, steerability and violation of Bell inequalities are examined for the ILC and FCC-ee. We find that detecting quantum correlation crucially reli

Pedro Bicudo, José Emílio Ribeiro

We study the meson spectra in the excited QCD vacua, denominated replicas. We find all mesons have real masses, with no tachyons, thus showing the QCD replicas are indeed metastable.

Igor Melnyk, Pierre Dognin, Payel Das

In this work we propose a novel end-to-end multi-stage Knowledge Graph (KG) generation system from textual inputs, separating the overall process into two stages. The graph nodes are generated first using pretrained language model, followed by a simple edge construction head, enabling efficient KG extraction from the text. For each stage we consider several

Violetta Sagun, Edoardo Giangrandi, Oleksii Ivanytskyi, Costança Providência

We study the impact of asymmetric bosonic dark matter on neutron star properties, including possible changes of tidal deformability, maximum mass, radius, and matter distribution inside the star. The conditions at which dark matter particles tend to condensate in the star's core or create an extended halo are presented. We show that dark matter condensed in

Vaibhav Joshi, Sricharan V, Preejith SP, Mohanasankar Sivaprakasam

An electroencephalogram (EEG) signal is currently accepted as a standard for automatic sleep staging. Lately, Near-human accuracy in automated sleep staging has been achievable by Deep Learning (DL) based approaches, enabling multi-fold progress in this area. However, An extensive and expensive clinical setup is required for EEG based sleep staging. Addition

Peter A. Forsyth, Kenneth R. Vetzal, G. Westmacott

We consider the holder of an individual tontine retirement account, with maximum and minimum withdrawal amounts (per year) specified. The tontine account holder initiates the account at age 65, and earns mortality credits while alive, but forfeits all wealth in the account upon death. The holder desires to maximize total withdrawals, and minimize the expecte

Shu Hu, George H. Chen

We propose a general approach for training survival analysis models that minimizes a worst-case error across all subpopulations that are large enough (occurring with at least a user-specified minimum probability). This approach uses a training loss function that does not know any demographic information to treat as sensitive. Despite this, we demonstrate tha

Adam Brown, Aditi Laddha, Madhusudhan Pittu, Mohit Singh

Determinant maximization provides an elegant generalization of problems in many areas, including convex geometry, statistics, machine learning, fair allocation of goods, and network design. In an instance of the determinant maximization problem, we are given a collection of vectors $v_1,\ldots, v_n \in \mathbb{R}^d$, and the goal is to pick a subset $S\subse

Alexander C. DeRieux, Walid Saad, Wangda Zuo, Rachmawan Budiarto

Rapid global urbanization is a double-edged sword, heralding promises of economical prosperity and public health while also posing unique environmental and humanitarian challenges. Smart and connected communities (S&CCs) apply data-centric solutions to these problems by integrating artificial intelligence (AI) and the Internet of Things (IoT). This coupling

Çağkan Yapar, Ron Levie, Gitta Kutyniok, Giuseppe Caire

In this article, we present a collection of radio map datasets in dense urban setting, which we generated and made publicly available. The datasets include simulated pathloss/received signal strength (RSS) and time of arrival (ToA) radio maps over a large collection of realistic dense urban setting in real city maps. The two main applications of the presente

Thomas Barthelmé, Steven Frankel, Kathryn Mann

We prove a classification theorem for transitive Anosov and pseudo-Anosov flows on closed 3-manifolds, up to orbit equivalence. In many cases, flows on a 3-manifold $M$ are completely determined by the set of free homotopy classes of their (unoriented) periodic orbits. The exceptional cases are flows with a special structure in their orbit space called a ``t

Haradhan Adhikary

The existence and location of the QCD critical point is an object of both experimental and theoretical studies. The comprehensive data collected by NA61/SHINE during a two-dimensional scan in beam momentum (13A-150A GeV/c) and system size (p+p, p+Pb, Be+Be, Ar+Sc, Xe+La, Pb+Pb) allows for a systematic search for the critical point-a search for a non-monotoni

Petr Cagas, James Juno, Ammar Hakim, Andrew LaJoie

This work studies the interaction between colliding plasma jets to understand regimes in which jet merging results in shock formation versus regimes in which the shock formation is mitigated due to the collisionless interpenetration of the jets. A kinetic model is required for this study because fluid models will always produce a shock upon the collision of

Víctor Blanco, Alberto Japón, Justo Puerto, Peter Zhang

This paper introduces Weighted Optimal Classification Forests (WOCFs), a new family of classifiers that takes advantage of an optimal ensemble of decision trees to derive accurate and interpretable classifiers. We propose a novel mathematical optimization-based methodology which simultaneously constructs a given number of trees, each of them providing a pred