March 2023 arXiv papers — page 89

C. A. Dong-Páez, M. Volonteri, R. S. Beckmann, Y. Dubois

Massive black-hole (BH) mergers are predicted to be powerful sources of low-frequency gravitational waves (GWs). Coupling the detection of GWs with an electromagnetic (EM) detection can provide key information about merging BHs and their environments. We study the high-resolution cosmological radiation-hydrodynamics simulation OBELISK, run to redshift $z=3.5

David J. Lagattuta, Johan Richard, Harald Ebeling, Quentin Basto

We present the first strong-gravitational-lensing analysis of the galaxy cluster RXJ0437.1+0043 (RXJ0437; z = 0.285). Newly obtained, deep MUSE observations, Keck/MOSFIRE near-infrared spectroscopy, and Hubble Space Telescope SNAPshot imaging reveal 13 multiply imaged background galaxies, three of them (at z=1.98, 2.97, and 6.02, respectively) in hyperbolic

Erez Y. Urbach

String stars, or Horowitz-Polchinski solutions, are Euclidean string theory saddles with a normalizable condensate of thermal winding strings. String stars were suggested as a possible description of stringy (Euclidean) black holes close to the Hagedorn temperature. In this work, we continue the study initiated in arXiv:2202.06966 by investigating the thermo

Julian Heeck, Mikheil Sokhashvili

Non-topological solitons are localized classical field configurations stabilized by a Noether charge. Friedberg, Lee, and Sirlin proposed a simple renormalizable soliton model in their seminal 1976 paper, consisting of a complex scalar field that carries the Noether charge and a real-scalar mediator. We revisit this model, point out commonalities and differe

Tiankai Hang, Shuyang Gu, Chen Li, Jianmin Bao

Denoising diffusion models have been a mainstream approach for image generation, however, training these models often suffers from slow convergence. In this paper, we discovered that the slow convergence is partly due to conflicting optimization directions between timesteps. To address this issue, we treat the diffusion training as a multi-task learning prob

Tsun-Hsuan Wang, Pingchuan Ma, Andrew Everett Spielberg, Zhou Xian

While significant research progress has been made in robot learning for control, unique challenges arise when simultaneously co-optimizing morphology. Existing work has typically been tailored for particular environments or representations. In order to more fully understand inherent design and performance tradeoffs and accelerate the development of new breed

Konstantinos Tertikas, Despoina Paschalidou, Boxiao Pan, Jeong Joon Park

Impressive progress in generative models and implicit representations gave rise to methods that can generate 3D shapes of high quality. However, being able to locally control and edit shapes is another essential property that can unlock several content creation applications. Local control can be achieved with part-aware models, but existing methods require 3

Justin Kerr, Chung Min Kim, Ken Goldberg, Angjoo Kanazawa

Humans describe the physical world using natural language to refer to specific 3D locations based on a vast range of properties: visual appearance, semantics, abstract associations, or actionable affordances. In this work we propose Language Embedded Radiance Fields (LERFs), a method for grounding language embeddings from off-the-shelf models like CLIP into

Andrei Paleyes, Siyuan Guo, Bernhard Schölkopf, Neil D. Lawrence

Component-based development is one of the core principles behind modern software engineering practices. Understanding of causal relationships between components of a software system can yield significant benefits to developers. Yet modern software design approaches make it difficult to track and discover such relationships at system scale, which leads to gro

Yi Wei, Linqing Zhao, Wenzhao Zheng, Zheng Zhu

3D scene understanding plays a vital role in vision-based autonomous driving. While most existing methods focus on 3D object detection, they have difficulty describing real-world objects of arbitrary shapes and infinite classes. Towards a more comprehensive perception of a 3D scene, in this paper, we propose a SurroundOcc method to predict the 3D occupancy w

A. Salch

In this note, for each odd prime $p$, we show that the orders of the $KU$-local homotopy groups of the mod $p$ Moore spectrum are equal to denominators of special values of certain quotients of Dedekind zeta-functions of totally real number fields. With this observation in hand, we give a cute topological proof of the Leopoldt conjecture for those number fie

Guilherme Brando, Kazuya Koyama, Hans A. Winther

We revisit a method to incorporate the Vainshtein screening mechanism in N-body simulations proposed by R. Scoccimarro in~\cite{Scoccimarro:2009eu}. We further extend this method to cover a subset of Horndeski theories that evade the bound on the speed of gravitational waves set by the binary neutron star merger GW170817. The procedure consists of the comput

M. Deckers, O. Graur, K. Maguire, L. Shingles

We present an in-depth study of the late-time near-infrared plateau in Type Ia supernovae (SNe Ia), which occurs between 70-500 d. We double the existing sample of SNe Ia observed during the late-time near-infrared plateau with new observations taken with the Hubble Space Telescope, Gemini, New Technology Telescope, the 3.5m Calar Alto Telescope, and the Nor

Tim Rolling

Let $A_t$ be an $\alpha$-stable symmetric process, $0<\alpha\leq 2$, on $\mathbb{R}^d$ and $D\subset \mathbb{R}^d$ be a bounded domain. This paper presents a proof, based on the classical Brascamp-Lieb-Luttinger inequalities for multiple integrals, that the distribution of the first exit time of $A_t$ from $D$ increases under Steiner symmetrization. Further,

Valery V. Ryzhikov, Jean-Paul Thouvenot

Answering Vershik's question we show that quasi-similarity does not conserve the entropy, proving quasi-similarity of all Bernoulli actions of a countable infinite group. We prove also the following generalization of Pinsker's theorem: the actions with zero Kirillov-Kushnirenko $P$-entropy and the actions with completely positive $P$-entropy are disjoint. Po

Zijie J. Wang, Duen Horng Chau

As machine learning (ML) is increasingly integrated into our everyday Web experience, there is a call for transparent and explainable web-based ML. However, existing explainability techniques often require dedicated backend servers, which limit their usefulness as the Web community moves toward in-browser ML for lower latency and greater privacy. To address

Douglas Lundholm

An overview is given of the 2D many-anyon gas, including its definition (both for ideal and certain less-than-ideal particles, as well as for abelian and nonabelian braid group representations), its corresponding known properties starting out from the intricate relationship between exchange and exclusion, as well as its emergence from bosons and/or fermions

Prajakta Rajmane, Jayvant Patade, M. T. Gophane

The differential equation (DE) with proportional delay is a particular case of the time-dependent delay differential equation (DDE). In this paper, we solve non-linear DEs with proportional delay using the successive approximation method (SAM). We prove the existence, uniqueness of theorems, and stability for DEs with proportional delay using SAM. We derive

Damien Raynal, Timothée de Guillebon, David Guéry-Odelin, Emmanuel Trizac

We report on speeding up equilibrium recovery in the previously unexplored general case of the underdamped regime using an optically levitated particle. We accelerate the convergence towards equilibrium by an order of magnitude compared to the natural relaxation time. We then discuss the efficiency of the studied protocols, especially for a multidimensional

Zhenzhen Weng, Laura Bravo-Sánchez, Serena Yeung-Levy

Recent text-to-image generative models have exhibited remarkable abilities in generating high-fidelity and photo-realistic images. However, despite the visually impressive results, these models often struggle to preserve plausible human structure in the generations. Due to this reason, while generative models have shown promising results in aiding downstream

Amro Abbas, Kushal Tirumala, Dániel Simig, Surya Ganguli

Progress in machine learning has been driven in large part by massive increases in data. However, large web-scale datasets such as LAION are largely uncurated beyond searches for exact duplicates, potentially leaving much redundancy. Here, we introduce SemDeDup, a method which leverages embeddings from pre-trained models to identify and remove semantic dupli

Howie Joress, Bruce Ravel, Elaf Anber, Jonathan Hollenbach

Short range order is a critical driver of properties (e.g. corrosion resistance and tensile strength) in multicomponent alloys such as complex concentrated alloys (CCAs). Extended x-ray absorption fine structure (EXAFS) is a powerful technique well suited for quantifying this short range order.Here, we described in detail the characteristics of CCAs that mak

Efekan Kökcü, Daan Camps, Lindsay Bassman Oftelie, Wibe A. de Jong

Recently we developed a local and constructive algorithm based on Lie algebraic methods for compressing Trotterized evolution under Hamiltonians that can be mapped to free fermions. The compression algorithm yields a circuit which scales linearly in the number of qubits, has a depth independent of evolution time and compresses time-dependent Hamiltonians. Th

Renaud Gauthier

We formalize an abstraction of Grothendieck's philosophy of motives and construct a category of derived motivic spectra in the Segal category $\mathbb{R} \underline{\text{Hom}} ((\text{dSt}_k)^{\text{op}}_{/F}, \text{Top})$ ($\text{dSt}_k$ the Segal category of derived stacks on s$k$-Alg, Top = $L \text{Set}_{\Delta}$ the Segal category of simplicial sets),

Wele Gedara Chaminda Bandara, Vishal M. Patel

Remote Sensing Change Detection (RS-CD) aims to detect relevant changes from Multi-Temporal Remote Sensing Images (MT-RSIs), which aids in various RS applications such as land cover, land use, human development analysis, and disaster response. The performance of existing RS-CD methods is attributed to training on large annotated datasets. Furthermore, most o

Chenyang Qi, Xiaodong Cun, Yong Zhang, Chenyang Lei

The diffusion-based generative models have achieved remarkable success in text-based image generation. However, since it contains enormous randomness in generation progress, it is still challenging to apply such models for real-world visual content editing, especially in videos. In this paper, we propose FateZero, a zero-shot text-based editing method on rea

Mai Nishimura, Shohei Nobuhara, Ko Nishino

We introduce an on-ground Pedestrian World Model, a computational model that can predict how pedestrians move around an observer in the crowd on the ground plane, but from just the egocentric-views of the observer. Our model, InCrowdFormer, fully leverages the Transformer architecture by modeling pedestrian interaction and egocentric to top-down view transfo

Stepan Alexandrov

In this note, we improve Nikulin's inequality in the case of right-angled hyperbolic polyhedra. The new inequality allows to give much shorter proofs of the known dimension bounds. We also improve Nonaka's lower bound on the number of ideal vertices for right-angled hyperbolic polyhedra.

Belinda Tzen, Anant Raj, Maxim Raginsky, Francis Bach

Mirror descent, introduced by Nemirovski and Yudin in the 1970s, is a primal-dual convex optimization method that can be tailored to the geometry of the optimization problem at hand through the choice of a strongly convex potential function. It arises as a basic primitive in a variety of applications, including large-scale optimization, machine learning, and

Xinwei Zhang, Mingyi Hong, Jie Chen

Vertical federated learning (VFL) is a distributed learning paradigm, where computing clients collectively train a model based on the partial features of the same set of samples they possess. Current research on VFL focuses on the case when samples are independent, but it rarely addresses an emerging scenario when samples are interrelated through a graph. Fo

Johannes Kopp, Dominik Kellner, Aldi Piroli, Klaus Dietmayer

Radar sensors employed for environment perception, e.g. in autonomous vehicles, output a lot of unwanted clutter. These points, for which no corresponding real objects exist, are a major source of errors in following processing steps like object detection or tracking. We therefore present two novel neural network setups for identifying clutter. The input dat

Kaidi Zhang, Yun Yu, Stephen Carr, Mohammad Babar

Interfacial electron-transfer (ET) reactions underpin the interconversion of electrical and chemical energy. Pioneering experiments showed that the ET rate depends on the Fermi Dirac distribution of the electronic density of states (DOS) of the electrode, formalized in the Marcus Hush Chidsey (MHC) model. Here, by controlling interlayer twists in well-define

Amin Falah, Shibashis Guha, Ashutosh Trivedi

Continuous-time Markov decision processes (CTMDPs) are canonical models to express sequential decision-making under dense-time and stochastic environments. When the stochastic evolution of the environment is only available via sampling, model-free reinforcement learning (RL) is the algorithm-of-choice to compute optimal decision sequence. RL, on the other ha

Zhenhuan Yang, Yingqiang Ge, Congzhe Su, Dingxian Wang

Recently, there has been an increasing adoption of differential privacy guided algorithms for privacy-preserving machine learning tasks. However, the use of such algorithms comes with trade-offs in terms of algorithmic fairness, which has been widely acknowledged. Specifically, we have empirically observed that the classical collaborative filtering method, t

Sergey Victor Ludkovski

This article is devoted to investigations of a structure and homomorphisms of microbundles. Microbundles are generalizations of manifolds. For manifolds it was studied when their families of homomorphism can be supplied with the manifold structure. But for microbundles this problem was not yet investigated. Continuous homomorphisms of microbundles are studie

Saverio Bocini, Maurizio Fagotti

We argue that, in a basis common to all one-site shift invariant conserved charges, there is no eigenstate of a noninteracting local spin-1/2 chain Hamiltonian that satisfies the area law if the ground state has half-integer central charge. That is to say, in those models all (quasi)local one-site shift invariant conserved operators are gapless. From the sta

Arindam Khan, Aditya Lonkar, Saladi Rahul, Aditya Subramanian

Set cover and hitting set are fundamental problems in combinatorial optimization which are well-studied in the offline, online, and dynamic settings. We study the geometric versions of these problems and present new online and dynamic algorithms for them. In the online version of set cover (resp. hitting set), $m$ sets (resp.~$n$ points) are give $n$ points

Somoballi Ghoshal, Shremoyee Goswami, Amlan Chakrabarti, Susmita Sur-Kolay

Magnetic Resonance Imaging (MRI) is a technology for non-invasive imaging of anatomical features in detail. It can help in functional analysis of organs of a specimen but it is very costly. In this work, methods for (i) virtual three-dimensional (3D) reconstruction from a single sequence of two-dimensional (2D) slices of MR images of a human spine and brain

Andrey Voynov, Qinghao Chu, Daniel Cohen-Or, Kfir Aberman

We introduce an Extended Textual Conditioning space in text-to-image models, referred to as $P+$. This space consists of multiple textual conditions, derived from per-layer prompts, each corresponding to a layer of the denoising U-net of the diffusion model. We show that the extended space provides greater disentangling and control over image synthesis. We f

Marcelo Campos, Simon Griffiths, Robert Morris, Julian Sahasrabudhe

The Ramsey number $R(k)$ is the minimum $n \in \mathbb{N}$ such that every red-blue colouring of the edges of the complete graph $K_n$ on $n$ vertices contains a monochromatic copy of $K_k$. We prove that \[ R(k) \leqslant (4 - \varepsilon)^k \] for some constant $\varepsilon > 0$. This is the first exponential improvement over the upper bound of Erd\H{o}s a

Lolian Shtembari, Allen Caldwell

Finding upper limits on the rate of events from a proposed process in the presence of unknown backgrounds is an often encountered problem in the search for rare processes. Methods based on unusually large "gaps", or spacings, in the event distribution allow to set limits on the rate of the proposed signal distribution. In this paper, we present two novel spa

Bobby Huggins, Chengkun Li, Marlon Tobaben, Mikko J. Aarnos

PyVBMC is a Python implementation of the Variational Bayesian Monte Carlo (VBMC) algorithm for posterior and model inference for black-box computational models (Acerbi, 2018, 2020). VBMC is an approximate inference method designed for efficient parameter estimation and model assessment when model evaluations are mildly-to-very expensive (e.g., a second or mo

Sepehr Janghorbani, Gerard de Melo

Recent breakthroughs in self supervised training have led to a new class of pretrained vision language models. While there have been investigations of bias in multimodal models, they have mostly focused on gender and racial bias, giving much less attention to other relevant groups, such as minorities with regard to religion, nationality, sexual orientation,

S. P. O'Neil, I. Khalid, A. A. Rompokos, C. A. Weidner

Recent achievements in quantum control have resulted in advanced techniques for designing controllers for applications in quantum communication, computing, and sensing. However, the susceptibility of such systems to noise and uncertainties necessitates robust controllers that perform effectively under these conditions to realize the full potential of quantum

Yukihiro Kanda, Nobuhiro Maekawa

Since it has been pointed out that physics beyond the Standard Model may be constrained by gravitational waves from cosmic strings, it has been more important to clarify in what cases cosmic strings are formed. We study the stability of the embedded string which is formed when $SU(N)\times U(1)_X$ gauge symmetries are broken to $SU(N-1)\times U(1)_Q$, and fi

Lester Buxton, Marc-Thomas Russo, Jim Al-Khalili, Andrea Rocco

We model a quantum system coupled to an environment of damped harmonic oscillators by following the approach of Caldeira-Leggett and adopting the Caldirola-Kanai Lagrangian for the bath oscillators. In deriving the master equation of the quantum system of interest (a particle in a general potential), we show that the potential is modified non-trivially by a

Shubham Aggarwal, Muhammad Aneeq uz Zaman, Melih Bastopcu, Tamer Başar

This paper studies an $N$--agent cost-coupled game where the agents are connected via an unreliable capacity constrained network. Each agent receives state information over that network which loses packets with probability $p$. A Base station (BS) actively schedules agent communications over the network by minimizing a weighted Age of Information (WAoI) base

Nicolás Ayobi, Alejandra Pérez-Rondón, Santiago Rodríguez, Pablo Arbeláez

We propose Masked-Attention Transformers for Surgical Instrument Segmentation (MATIS), a two-stage, fully transformer-based method that leverages modern pixel-wise attention mechanisms for instrument segmentation. MATIS exploits the instance-level nature of the task by employing a masked attention module that generates and classifies a set of fine instrument

Alexander E. Siemenn, Eunice Aissi, Fang Sheng, Armi Tiihonen

High-throughput materials synthesis methods have risen in popularity due to their potential to accelerate the design and discovery of novel functional materials, such as solution-processed semiconductors. After synthesis, key material properties must be measured and characterized to validate discovery and provide feedback to optimization cycles. However, wit

Jonathan Joe, Matt Noble

For $d > 0$, define $G(\mathbb{Q}^3, d)$ to be the graph whose set of vertices is the rational space $\mathbb{Q}^3$, where two vertices are adjacent if and only if they are a Euclidean distance $d$ apart. Let $\chi(\mathbb{Q}^3, d)$ be the chromatic number of such a graph or, in other words, the minimum number of colors needed to color the points of $\mathbb

Jose Acevedo, Grigoriy Blekherman, Sebastian Debus, Cordian Riener

We study the geometry of the image of the nonnegative orthant under the power-sum map and the elementary symmetric polynomials map. After analyzing the image in finitely many variables, we concentrate on the limit as the number of variables approaches infinity. We explain how the geometry of the limit plays a crucial role in undecidability results in nonnega

James Chin-Jen Pang, Hessam Mahdavifar, S. Sandeep Pradhan

In this paper, we leverage polar codes and the well-established channel polarization to design capacity-achieving codes with a certain constraint on the weights of all the columns in the generator matrix (GM) while having a low-complexity decoding algorithm. We first show that given a binary-input memoryless symmetric (BMS) channel $W$ and a constant $s \in

William D. Banks

We show that the Generalized Riemann Hypothesis for all Dirichlet L-functions is a consequence of certain conjectural properties of the zeros of the Riemann zeta function. Conversely, we prove that the zeros of $\zeta(s)$ satisfy those properties under GRH.

Scott Harper

A generating set for a finite group $G$ is said to be minimal if no proper subset generates $G$, and $m(G)$ denotes the maximal size of a minimal generating set for $G$. We prove a conjecture of Lucchini, Moscatiello and Spiga by showing that there exist $a,b > 0$ such that any finite group $G$ satisfies $m(G) \leq a \cdot \delta(G)^b$, for $\delta(G) = \sum

Duolikun Danier, Fan Zhang, David Bull

Existing works on video frame interpolation (VFI) mostly employ deep neural networks that are trained by minimizing the L1, L2, or deep feature space distance (e.g. VGG loss) between their outputs and ground-truth frames. However, recent works have shown that these metrics are poor indicators of perceptual VFI quality. Towards developing perceptually-oriente

G. Fabbris, D. Meyers, Y. Shen, V. Bisogni

Ruddlesden-Popper and reduced Ruddlesden-Popper nickelates are intriguing candidates for mimicking the properties of high-temperature superconducting cuprates. The degree of similarity between these nickelates and cuprates has been the subject of considerable debate. Resonant inelastic x-ray scattering (RIXS) has played an important role in exploring their e

Francesco Grotto, Leonardo Maini, Anna Paola Todino

We consider polynomial transforms (polyspectra) of Berry's model -- the Euclidean Random Wave model -- and of Random Hyperspherical Harmonics. We determine the asymptotic behavior of variance for polyspectra of any order in the high-frequency limit. In particular, we are able to treat polyspectra of any odd order $q\geq 5$, whose asymptotic behavior was left

Guo Chuan Thiang

We provide an elementary proof and refinement of a well-known idea from physics: a chiral-symmetric local Hamiltonian on a half-space has the same signed number of edge-localized states with energies in the bulk band gap, as its bulk winding number. The requirement of non-elementary methods to relate generic and non-generic cases is emphasized. Our hands-on

Rachael Boyd, Ruth Charney, Rose Morris-Wright, Sarah Rees

In this paper we investigate properties of the Artin monoid Cayley graph. This is the Cayley graph of an Artin group $A_\Gamma$ with respect to the (infinite) generating set given by the associated Artin monoid $A^+_\Gamma$. In a previous paper, the first three authors introduced a monoid Deligne complex and showed that this complex is contractible for all A

Jonathan Timcheck, Sumit Bam Shrestha, Daniel Ben Dayan Rubin, Adam Kupryjanow

A critical enabler for progress in neuromorphic computing research is the ability to transparently evaluate different neuromorphic solutions on important tasks and to compare them to state-of-the-art conventional solutions. The Intel Neuromorphic Deep Noise Suppression Challenge (Intel N-DNS Challenge), inspired by the Microsoft DNS Challenge, tackles a ubiq

Cormac J. K. Larkin, Ville Lundén, Leonard Schulz, Markus Baumgartner-Steinleitner

Mars, lacking an intrinsic dynamo, is an ideal laboratory to comparatively study induced magnetospheres, which can be found in other terrestrial bodies as well as comets. Additionally, Mars is of particular interest to further exploration due to its loss of habitability by atmospheric escape and possible future human exploration. In this context, we propose

L. Fossati, I. Pillitteri, I. F. Shaikhislamov, A. Bonfanti

We aim to narrow down the origin of the non-detection of the metastable HeI triplet at about 10830 A obtained for the hot Jupiter WASP-80b. We measure the X-ray flux of WASP-80 from archival observations and use it as input to scaling relations accounting for the coronal [Fe/O] abundance ratio to infer the extreme-ultraviolet (EUV) flux in the 200-504 A rang

Nils Kohring, Fabian R. Pieroth, Martin Bichler

Understanding and analyzing markets is crucial, yet analytical equilibrium solutions remain largely infeasible. Recent breakthroughs in equilibrium computation rely on zeroth-order policy gradient estimation. These approaches commonly suffer from high variance and are computationally expensive. The use of fully differentiable simulators would enable more eff

Wooyeon Kim, Constantin Kogler

We prove effective density of random walks on homogeneous spaces, assuming that the underlying measure is supported on matrices generating a dense subgroup and having algebraic entries. The main novelty is an argument passing from high dimension to effective equidistribution in the setting of random walks on homogeneous spaces, exploiting spectral gap of the

Krisztian Balog, Filip Radlinski, Andrey Petrov

Despite the potential impact of explanations on decision making, there is a lack of research on quantifying their effect on users' choices. This paper presents an experimental protocol for measuring the degree to which positively or negatively biased explanations can lead to users choosing suboptimal recommendations. Key elements of this protocol include a p

Ziyue Huang, Lujuan Dang, Yuqing Xie, Wentao Ma

State-of-health (SOH) estimation is a key step in ensuring the safe and reliable operation of batteries. Due to issues such as varying data distribution and sequence length in different cycles, most existing methods require health feature extraction technique, which can be time-consuming and labor-intensive. GRU can well solve this problem due to the simple

Franquiz Caraballo Alba

The linear orbit of a degree d hypersurface in $\mathbb{P}^n$ is its orbit under the natural action of PGL(n+1), in the projective space of dimension $N =\binom{n+d}{d} - 1$ parameterizing such hypersurfaces. This action restricted to a specific hypersurface $X$ extends to a rational map from the projectivization of the space of matrices to $\mathbb{P}^N$. T

Yiming Li, Qi Fang, Jiamu Bai, Siheng Chen

Multiple robots could perceive a scene (e.g., detect objects) collaboratively better than individuals, although easily suffer from adversarial attacks when using deep learning. This could be addressed by the adversarial defense, but its training requires the often-unknown attacking mechanism. Differently, we propose ROBOSAC, a novel sampling-based defense st

Eddardaa Ben Loussaief, Hatem Rashwan, Mohammed Ayad, Mohammed Zakaria Hassan

With numerous medical tasks, the performance of deep models has recently experienced considerable improvements. These models are often adept learners. Yet, their intricate architectural design and high computational complexity make deploying them in clinical settings challenging, particularly with devices with limited resources. To deal with this issue, Know

Sijie Sun, Nan Xue, Stefano Aime, Hyoungsoo Kim

Addition of particles to a viscoelastic suspension dramatically alters the properties of the mixture, particularly when it is sheared or otherwise processed. Shear-induced stretching of the polymers results in elastic stress that causes a substantial increase in measured viscosity with increasing shear, and an attractive interaction between particles, leadin

G. Panotopoulos, G. Barnert, L. E. Campusano

We study dynamical dark energy models within Einstein's theory by means of matter perturbations and the growth index $\gamma$. Within four-dimensional General Relativity, we assume that dark energy does not cluster, and we adopt a linear ansatz for the growth index to investigate its impact on the deceleration parameter, $q$, and on the dark energy equation-

M. Cerezo, Guillaume Verdon, Hsin-Yuan Huang, Lukasz Cincio

At the intersection of machine learning and quantum computing, Quantum Machine Learning (QML) has the potential of accelerating data analysis, especially for quantum data, with applications for quantum materials, biochemistry, and high-energy physics. Nevertheless, challenges remain regarding the trainability of QML models. Here we review current methods and

Tanushree Shah

We classify tight contact structures with zero Giroux torsion on some Seifert-fibered manifolds with four exceptional fibers. We get the lower bound by constructing contact structures using Legendrian surgery. We use convex surface theory to obtain the upper bound.

Michael Zhang, Khaled K. Saab, Michael Poli, Tri Dao

Time series modeling is a well-established problem, which often requires that methods (1) expressively represent complicated dependencies, (2) forecast long horizons, and (3) efficiently train over long sequences. State-space models (SSMs) are classical models for time series, and prior works combine SSMs with deep learning layers for efficient sequence mode

Yelena Mejova, Kyrieki Kalimeri, Gianmarco De Francisci Morales

Face masks are one of the cheapest and most effective non-pharmaceutical interventions available against airborne diseases such as COVID-19. Unfortunately, they have been met with resistance by a substantial fraction of the populace, especially in the U.S. In this study, we uncover the latent moral values that underpin the response to the mask mandate, and p

Ronan Herry, Dominique Malicet, Guillaume Poly

We study the regularity of the law of a quadratic form $Q(X,X)$, evaluated in a sequence $X = (X_{i})$ of independent and identically distributed random variables, when $X_{1}$ can be expressed as a sufficiently smooth function of a Gaussian field. This setting encompasses a large class of important and frequently used distributions, such as, among others, G

Evelyne Hubert, Tobias Metzlaff, Philippe Moustrou, Cordian Riener

Trigonometric polynomials are usually defined on the lattice of integers.We consider the larger class of weight and root lattices with crystallographic symmetry.This article gives a new approach to minimize trigonometric polynomials, which are invariant under the associated reflection group.The invariance assumption allows us to rewrite the objective functio

Carl Johan Peter Johansson, Massimo Sorella

We positively answer Question 2.2 and Question 2.3 in [Bru\`e, De Lellis, 2023] in dimension $4$ by building new examples of solutions to the forced $4d$ incompressible Navier-Stokes equations, which exhibit anomalous dissipation, related to the zeroth law of turbulence [K41]. We also prove that the unique smooth solution $v_\nu$ of the $4d$ Navier--Stokes e

Chloe Smith, Maxfield Kouzel, Xugui Zhou, Homa Alemzadeh

Significant advancements in type 1 diabetes treatment have been made in the development of state-of-the-art Artificial Pancreas Systems (APS). However, lapses currently exist in the timely treatment of unsafe blood glucose (BG) levels, especially in the case of rebound hyperglycemia. We propose a machine learning (ML) method for predictive BG scenario catego

Juan Pardo-Montero, Miguel Pombar, Antonio Gómez-Caamaño, Simona Giordanengo

Purpose: To present a methodology to analyze the variation of RBE with fractionation from clinical data of tumor control probability (TCP) and to apply it to study the response of prostate cancer to proton therapy. M&M: We analyzed the dependence of the RBE on the dose per fraction by using the LQ model and the Poisson TCP formalism. Clinical TCPs for prosta

Nima Hatami, Laura Mechtouff, David Rousseau, Tae-Hee Cho

Patient outcome prediction is critical in management of ischemic stroke. In this paper, a novel machine learning model is proposed for stroke outcome prediction using multimodal Magnetic Resonance Imaging (MRI). The proposed model consists of two serial levels of Autoencoders (AEs), where different AEs at level 1 are used for learning unimodal features from

Sanghwan Kim, Lorenzo Noci, Antonio Orvieto, Thomas Hofmann

In contrast to the natural capabilities of humans to learn new tasks in a sequential fashion, neural networks are known to suffer from catastrophic forgetting, where the model's performances on old tasks drop dramatically after being optimized for a new task. Since then, the continual learning (CL) community has proposed several solutions aiming to equip the

Kai Bergermann, Martin Stoll

We consider the solution of large stiff systems of ordinary differential equations with explicit exponential Runge--Kutta integrators. These problems arise from semi-discretized semi-linear parabolic partial differential equations on continuous domains or on inherently discrete graph domains. A series of results reduces the requirement of computing linear co

Stefano Bonetti, Michele Botti, Ilario Mazzieri, Paola F. Antonietti

We present and analyze a high-order discontinuous Galerkin method for the space discretization of the wave propagation model in thermo-poroelastic media. The proposed scheme supports general polytopal grids. Stability analysis and $hp$-version error estimates in suitable energy norms are derived for the semi-discrete problem. The fully-discrete scheme is the

Luiz Frederic Wagner

In this paper, we first explore holomorphic Hamiltonian systems. In particular, we define action functionals for those systems and show that holomorphic trajectories obey an action principle, i.e., that they can be understood - in some sense - as critical points of these action functionals. As an application, we use holomorphic Hamiltonian systems to establi

F. J. Iguaz, L. Bombelli, S. Meo, F. Orsini

DANTE is a new Digital Pulse Processor (DPP) developed for fluorescence detectors, like silicon drift detectors (SDDs) or High Purity Germanium detectors (HPGe), used in X-ray Fluorescence (XRF) and X-ray Absorption Spectroscopy (XAS) experiments at synchrotron facilities. Its main features are its optimal energy resolution and peak stability for detector co

Chris Lu, Sebastian Towers, Jakob Foerster

Meta-learning, the notion of learning to learn, enables learning systems to quickly and flexibly solve new tasks. This usually involves defining a set of outer-loop meta-parameters that are then used to update a set of inner-loop parameters. Most meta-learning approaches use complicated and computationally expensive bi-level optimisation schemes to update th

Rishi Veerapaneni, Muhammad Suhail Saleem, Maxim Likhachev

Graph search planning algorithms for navigation typically rely heavily on heuristics to efficiently plan paths. As a result, while such approaches require no training phase and can directly plan long horizon paths, they often require careful hand designing of informative heuristic functions. Recent works have started bypassing hand designed heuristics by usi

Muhammad Shehab, Abdullateef Almohamad, Mohamed Elsayed, Ahmed Badawy

We investigate THz communication uplink multiple access using cascaded intelligent reflecting surfaces (IRSs) assuming correlated channels. Two independent objectives to be achieved via adjusting the phases of the cascaded IRSs: 1) maximizing the received rate of a desired user under interference from the second user and 2) maximizing the sum rate of both us

Iulia Cristian, Juan J. L. Velázquez

In this work, we study a particular system of coagulation equations characterized by two values, namely volume $v$ and surface area $a$. Compared to the standard one-dimensional models, this model incorporates additional information about the geometry of the particles. We describe the coagulation process as a combination between collision and fusion of parti

Antoine Bodin, Nicolas Macris

In this work, we present a new approach to analyze the gradient flow for a positive semi-definite matrix denoising problem in an extensive-rank and high-dimensional regime. We use recent linear pencil techniques of random matrix theory to derive fixed point equations which track the complete time evolution of the matrix-mean-square-error of the problem. The

Ute Lisenfeld, Patrick M. Ogle, Philip N. Appleton, Thomas H. Jarrett

At the highest stellar masses (log(\mstar) $\gtrsim$ 11.5 \msun), only a small fraction of galaxies are disk-like and actively star-forming objects. These so-called `super spirals' are ideal objects to better understand how galaxy evolution proceeds and to extend our knowledge about the relation between stars and gas to a higher stellar mass regime. We prese

Bin Xia, Yulun Zhang, Shiyin Wang, Yitong Wang

Diffusion model (DM) has achieved SOTA performance by modeling the image synthesis process into a sequential application of a denoising network. However, different from image synthesis, image restoration (IR) has a strong constraint to generate results in accordance with ground-truth. Thus, for IR, traditional DMs running massive iterations on a large model

K. Victor Sam Moses Babu, Divyanshi Dwivedi, Pratyush Chakraborty, Pradeep Kumar Yemula

The adoption of distributed energy resources (DERs) such as solar panels and wind turbines is transforming the traditional energy grid into a more decentralized system, where microgrids are emerging as a key concept. Peer-to-Peer (P2P) energy sharing in microgrids enhances the efficiency and flexibility of the overall system by allowing the exchange of surpl

Farooq Ahmad Wani, Maria Sofia Bucarelli, Fabrizio Silvestri

We introduce a novel method for training machine learning models in the presence of noisy labels, which are prevalent in domains such as medical diagnosis and autonomous driving and have the potential to degrade a model's generalization performance. Inspired by established literature that highlights how deep learning models are prone to overfitting to noisy

Laya Ghodrati, Victor M. Panaretos

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of $\mathbb{R}$. An order-$1$ autoregressive model in this context is to be understood as a Markov chain, where one specifies a certain structure (regression) for the one-step conditional Fr\'echet mean with respect to a nat

Rémy Degenne

In fixed budget bandit identification, an algorithm sequentially observes samples from several distributions up to a given final time. It then answers a query about the set of distributions. A good algorithm will have a small probability of error. While that probability decreases exponentially with the final time, the best attainable rate is not known precis

Lucas Ertzbischoff, Daniel Han-Kwan

In this paper, we prove the local in time well-posedness of thick spray equations in Sobolev spaces, for initial data satisfying a Penrose-type stability condition. This system is a coupling between particles described by a kinetic equation and a surrounding fluid governed by compressible Navier-Stokes equations. In the thick spray regime, the volume fractio

Christopher Shepard, Dillon Yost, Yosuke Kanai

The lack of molecular-level understanding for the electronic excitation response of DNA to charged particle radiation, such as high-energy protons, remains a fundamental scientific bottleneck in advancing proton and other ion beam cancer therapies. In particular, the dependence of different types of DNA damage on high-energy protons represents a significant

Xuyang Zhou, Prince Mathews, Benjamin Berkels, Saba Ahmad

Phase transformations and crystallographic defects are two essential tools to drive innovations in materials. Bulk materials design via tuning chemical compositions has been systematized using phase diagrams. We show here that the same thermodynamic concept can be applied to understand the chemistry at defects. We present a combined experimental and modellin