March 2023 arXiv papers — page 71

Simone Barattin, Christos Tzelepis, Ioannis Patras, Nicu Sebe

This work addresses the problem of anonymizing the identity of faces in a dataset of images, such that the privacy of those depicted is not violated, while at the same time the dataset is useful for downstream task such as for training machine learning models. To the best of our knowledge, we are the first to explicitly address this issue and deal with two m

Zhi Shiuh Lim, Changjian Li, Ping Yang, A. Ariando

We fabricated Pt/La0.5Ba0.5MnO3 ultrathin films with integration to the PbZr0.2Ti0.8O3 ferroelectric. Strong Topological Hall Effect can be measured across a wide temperature range, which can be turned on and off corresponding to the ferroelectric polarization switching driven by short voltage pulses, indicating creation and annihilation of magnetic Skyrmion

Omer Ben-Neria, Itay Kaplan, Tingxiang Zou

We study the logical properties of infinite geometric random graphs, introduced by Bonato and Janssen. These are graphs whose vertex set is a dense ``generic'' subset of a metric space, where two vertices are adjacent with probability $p>0$ provided the distance between them is bounded by some constant number. We prove that for a large class of metric spaces

Lukas Berns

In various high-energy physics contexts, such as neutrino-oscillation experiments, several assumptions underlying the typical asymptotic confidence interval construction are violated, such that one has to resort to computationally expensive methods like the Feldman-Cousins method for obtaining confidence intervals with proper statistical coverage. By constru

Benjamin Gess, Daniel Heydecker

The problem of deriving a gradient flow structure for the porous medium equation which is {\em thermodynamic}, in that it arises from the large deviations of some microscopic particle system, is studied. To this end, a rescaled zero-range process with jump rate $g(k)=k^\alpha, \alpha>1$ is considered, and its hydrodynamic limit and dynamical large deviations

Shoumik Saha, Wenxiao Wang, Yigitcan Kaya, Soheil Feizi

Machine Learning (ML) models have been utilized for malware detection for over two decades. Consequently, this ignited an ongoing arms race between malware authors and antivirus systems, compelling researchers to propose defenses for malware-detection models against evasion attacks. However, most if not all existing defenses against evasion attacks suffer fr

Helena Coggan, Clare E. Weeden, Philip Pearce, Mohit P. Dalwadi

Mutations in the epidermal growth factor receptor (EGFR) are common in non-small cell lung cancer (NSCLC), particularly in never-smoker patients. However, these mutations are not always carcinogenic, and have recently been reported in histologically normal lung tissue from patients with and without lung cancer. To investigate the outcome of EGFR mutation in

Chase Shimmin, Zhelun Li, Ema Smith

Many datasets in scientific and engineering applications are comprised of objects which have specific geometric structure. A common example is data which inhabits a representation of the group SO$(3)$ of 3D rotations: scalars, vectors, tensors, \textit{etc}. One way for a neural network to exploit prior knowledge of this structure is to enforce SO$(3)$-equiv

Eric Markley, Nathan Matsuda, Florian Schiffers, Oliver Cossairt

Computer generated holography has long been touted as the future of augmented and virtual reality (AR/VR) displays, but has yet to be realized in practice. Previous high-quality, color holographic displays have made either a 3$\times$ sacrifice on frame rate by using a sequential color illumination scheme or used more than one spatial light modulator (SLM) a

Thibaut Lemoine

We derive new formulas for the expectation and variance of Wilson loops for any contractible simple loop on a compact orientable surface of genus $1$ and higher, in the model of two-dimensional Yang--Mills theory with structure group $\mathrm{U}(N)$. They are written in terms of a Gaussian measure on the dual of $\mathrm{U}(N)$ introduced recently by the aut

J. M. Flynn, A. Jüttner, J. T. Tsang

We propose a model-independent framework for fitting hadronic form-factor data, which is often only available at discrete kinematical points, using parameterisations based on to unitarity and analyticity. In this novel approach the latter two properties of quantum-field theory regulate the ill-posed fitting problem and allow model-independent predictions ove

Evan Becker, Jingdong Gao, Ted Zadouri, Baharan Mirzasoleiman

We consider maximization of stochastic monotone continuous submodular functions (CSF) with a diminishing return property. Existing algorithms only guarantee the performance \textit{in expectation}, and do not bound the probability of getting a bad solution. This implies that for a particular run of the algorithms, the solution may be much worse than the prov

Stefano Pozza, Niel Van Buggenhout

We introduce a new method with spectral accuracy to solve linear non-autonomous ordinary differential equations (ODEs) of the kind $ \frac{d}{dt}\tilde{u}(t) = \tilde{f}(t) \tilde{u}(t)$, $\tilde{u}(-1)=1$, with $\tilde{f}(t)$ an analytic function. The method is based on a new expression for the solution $\tilde{u}(t)$ given in terms of a convolution-like op

Massimiliano Incudini, Michele Grossi, Andrea Ceschini, Antonio Mandarino

Quantum neural networks hold significant promise for numerous applications, particularly as they can be executed on the current generation of quantum hardware. However, due to limited qubits or hardware noise, conducting large-scale experiments often requires significant resources. Moreover, the output of the model is susceptible to corruption by quantum har

Jason M. Pittman

Port scanning refers to the systematic exploration of networked computing systems. The goal of port scanning is to identify active services and associated information. Although this technique is often employed by malicious actors to locate vulnerable systems within a network, port scanning is also a legitimate method employed by IT professionals to troublesh

Samuel Baguley, Tobias Friedrich, Aneta Neumann, Frank Neumann

Parameterized analysis provides powerful mechanisms for obtaining fine-grained insights into different types of algorithms. In this work, we combine this field with evolutionary algorithms and provide parameterized complexity analysis of evolutionary multi-objective algorithms for the $W$-separator problem, which is a natural generalization of the vertex cov

Jonathan M. Flynn, Ryan C. Hill, Andreas Jüttner, Amarjit Soni

Semileptonic $B_s \to K \ell \nu$ decays provide an alternative $b$-decay channel to determine the CKM matrix element $|V_{ub}|$, and to obtain a $R$-ratio to investigate lepton-flavor-universality violations. Results for the CKM matrix element may also shed light on the discrepancies seen between analyses of inclusive or exclusive decays. We calculate the d

Yiu Ming Chung, Hazem Youssef, Moritz Roidl

Autonomous navigation of mobile robots is a well studied problem in robotics. However, the navigation task becomes challenging when multi-robot systems have to cooperatively navigate dynamic environments with deadlock-prone layouts. We present a Distributed Timed Elastic Band (DTEB) Planner that combines Prioritized Planning with the online TEB trajectory Pl

Piyush Tiwary, Kumar Shubham, Vivek V. Kashyap, Prathosh A. P

Bayesian methods provide an elegant framework for estimating parameter posteriors and quantification of uncertainty associated with probabilistic models. However, they often suffer from slow inference times. To address this challenge, Bayesian Pseudo-Coresets (BPC) have emerged as a promising solution. BPC methods aim to create a small synthetic dataset, kno

Adriano Hernandez, Rumen Dangovski, Peter Y. Lu, Marin Soljacic

Model stitching (Lenc & Vedaldi 2015) is a compelling methodology to compare different neural network representations, because it allows us to measure to what degree they may be interchanged. We expand on a previous work from Bansal, Nakkiran & Barak which used model stitching to compare representations of the same shapes learned by differently seeded and/or

Mirko Consiglio, Jacopo Settino, Andrea Giordano, Carlo Mastroianni

The preparation of an equilibrium thermal state of a quantum many-body system on noisy intermediate-scale quantum (NISQ) devices is an important task in order to extend the range of applications of quantum computation. Faithful Gibbs state preparation would pave the way to investigate protocols such as thermalization and out-of-equilibrium thermodynamics, as

Guy Nir, Eran O. Ofek, Barak Zackay

Kuiper belt objects smaller than a few kilometers are difficult to observe directly. They can be detected when they randomly occult a background star. Close to the ecliptic plane, each star is occulted once every tens of thousands of hours, and occultations typically last for less than a second. We present an algorithm, and companion pipeline, for detection

Xianxian Zeng, Yanjun Zheng

With the explosive growth in the number of fine-grained images in the Internet era, it has become a challenging problem to perform fast and efficient retrieval from large-scale fine-grained images. Among the many retrieval methods, hashing methods are widely used due to their high efficiency and small storage space occupation. Fine-grained hashing is more ch

Shaina Raza, Syed Raza Bashir

Infectious diseases are a significant public health concern globally, and extracting relevant information from scientific literature can facilitate the development of effective prevention and treatment strategies. However, the large amount of clinical data available presents a challenge for information extraction. To address this challenge, this study propos

Alexander Davydov, Saber Jafarpour, Anton V. Proskurnikov, Francesco Bullo

While monotone operator theory is often studied on Hilbert spaces, many interesting problems in machine learning and optimization arise naturally in finite-dimensional vector spaces endowed with non-Euclidean norms, such as diagonally-weighted $\ell_{1}$ or $\ell_{\infty}$ norms. This paper provides a natural generalization of monotone operator theory to fin

Yuhan Liu, Anna Fang, Glen Moriarty, Robert Kraut

Online mental health communities (OMHCs) are an effective and accessible channel to give and receive social support for individuals with mental and emotional issues. However, a key challenge on these platforms is finding suitable partners to interact with given that mechanisms to match users are currently underdeveloped. In this paper, we collaborate with on

Denitsa Staicova

The use of the baryonic acoustic oscillations (BAO) datasets offers a unique opportunity to connect the early universe and the late one. In this proceeding, we discuss recent results that used a marginalised likelihood to remove the $H_0-r_d $ degeneracy and then tested it on different dark energy (DE) models. It was found that this approach which does not r

Verena A. Neufeld, Timothy C. Berkelbach

Coupled-cluster theory with single, double, and perturbative triple excitations (CCSD(T)) -- often considered the "gold standard" of main-group quantum chemistry -- is inapplicable to three-dimensional metals due to an infrared divergence, preventing its application to many important problems in materials science. We study the full, nonperturbative inclusion

Trenton Bricken, Xander Davies, Deepak Singh, Dmitry Krotov

Continual learning is a problem for artificial neural networks that their biological counterparts are adept at solving. Building on work using Sparse Distributed Memory (SDM) to connect a core neural circuit with the powerful Transformer model, we create a modified Multi-Layered Perceptron (MLP) that is a strong continual learner. We find that every componen

Ali M. Keshk, Robert Dyer

There are possible benefits and drawbacks to chaining methods together, as is often done in fluent APIs. A prior study investigated how Java developers chain methods in over 2.7k open-source projects. That study observed, for the dataset analyzed, that the use of method chaining in Java is popular and seems to be increasing over time. That study however was

Sohom Ghosh, Ankush Chopra, Sudip Kumar Naskar

Over the years, there has been a paradigm shift in how users access financial services. With the advancement of digitalization more users have been preferring the online mode of performing financial activities. This has led to the generation of a huge volume of financial content. Most investors prefer to go through these contents before making decisions. Eve

Md. Shafayat Hossain, M. K. Ma, Y. J. Chung, S. K. Singh

Fractional quantum Hall states (FQHSs) at even-denominator Landau level filling factors ($\nu$) are of prime interest as they are predicted to host exotic, topological states of matter. We report here the observation of a FQHS at $\nu=1/2$ in a two-dimensional electron system of exceptionally high quality, confined to a wide AlAs quantum well, where the elec

Jinlai Ning, Haoyan Guan, Michael Spratling

Tiny object detection has become an active area of research because images with tiny targets are common in several important real-world scenarios. However, existing tiny object detection methods use standard deep neural networks as their backbone architecture. We argue that such backbones are inappropriate for detecting tiny objects as they are designed for

Sonaldeep Halder, Chayan Patra, Dibyendu Mondal, Rahul Maitra

The recently developed Projective Quantum Eigensolver (PQE) has been demonstrated as an elegant methodology to compute the ground state energy of molecular systems in Noisy Intermdiate Scale Quantum (NISQ) devices. The iterative optimization of the ansatz parameters involves repeated construction of residues on a quantum device. The quintessential pattern of

Nathan Buskulic, Yvain Quéau, Jalal Fadili

Neural networks have become a prominent approach to solve inverse problems in recent years. Amongst the different existing methods, the Deep Image/Inverse Priors (DIPs) technique is an unsupervised approach that optimizes a highly overparametrized neural network to transform a random input into an object whose image under the forward model matches the observ

Jing Shuang Li, Carmen Amo Alonso

Recent advances in model predictive control (MPC) leverage local communication constraints to produce localized MPC algorithms whose complexities scale independently of total network size. However, no characterization is available regarding global performance, i.e. whether localized MPC (with communication constraints) performs just as well as global MPC (no

J. Chen, M. Cadelano, C. Pallanca, F. Ferraro

We report on the discovery of the companion star to the millisecond pulsar PSR J1835-3259B in the Galactic globular cluster NGC 6652. Taking advantage of deep photometric archival observations acquired through the Hubble Space Telescope in near-ultraviolet and optical bands, we identified a bright and blue object at a position compatible with that of the rad

Dieter Schweizer, Richard Rau, Can Deniz Bezek, Rahel A. Kubik-Huch

Speed-of-sound (SoS) is a novel imaging biomarker for assessing biomechanical characteristics of soft tissues. SoS imaging in pulse-echo mode using conventional ultrasound systems with hand-held transducers has the potential to enable new clinical uses. Recent work demonstrated diverging waves from single-element (SE) transmits to outperform plane-wave seque

Geraldo César Ferreira Gonçcalves, Sylvie Oliffson Kamphorst, Sônia Pinto-de-Carvalho

In this text we study billiards on ovals and investigate some consequences of a rotational symmetry of the boundary on the dynamics. As it simplifies some calculations, the symmetry helps to obtain the results. We focus on periodic orbits with the same symmetry of the boundary which always exist and prove that in general half of them are elliptic and Moser s

Colin Davalo

We study nearly geodesic immersions in higher rank symmetric spaces of non-compact type, which we define as immersions that satisfy a bound on their fundamental form, generalizing the notion of immersions in hyperbolic space with principal curvature in $(-1,1)$. This notion depends on the choice of a flag manifold embedded in the visual boundary, and immersi

Chun-Hui Liu, Haiping Hu, Shu Chen, Xiong-Jun Liu

The non-Hermitian skin effect under open boundary conditions is widely believed to originate from the intrinsic spectral topology under periodic boundary conditions. If the eigenspectra under periodic boundary conditions have no spectral windings (e.g., piecewise arcs) or a finite area on the complex plane, there will be no non-Hermitian skin effect with ope

Qi Chang, Patrick D. Byrnes, Danish Ahmad, Jennifer Toth

With lung cancer being the most fatal cancer worldwide, it is important to detect the disease early. A potentially effective way of detecting early cancer lesions developing along the airway walls (epithelium) is bronchoscopy. To this end, developments in bronchoscopy offer three promising noninvasive modalities for imaging bronchial lesions: white-light bro

Yuebing Liang, Fangyi Ding, Guan Huang, Zhan Zhao

Bike sharing is emerging globally as an active, convenient, and sustainable mode of transportation. To plan successful bike-sharing systems (BSSs), many cities start from a small-scale pilot and gradually expand the system to cover more areas. For station-based BSSs, this means planning new stations based on existing ones over time, which requires prediction

Charlie Blake, Douglas Orr, Carlo Luschi

We present unit scaling, a paradigm for designing deep learning models that simplifies the use of low-precision number formats. Training in FP16 or the recently proposed FP8 formats offers substantial efficiency gains, but can lack sufficient range for out-of-the-box training. Unit scaling addresses this by introducing a principled approach to model numerics

Nolan R. Wallach

In this paper the spherical case of the Whittaker Inversion Theorem is given a relatively self-contained proof. This special case can be used as a help in deciphering the handling of the continuous spectrum in the proof of the full theorem. It also leads directly to the solution of the quantum non-periodic Toda Lattice. This is also explained in detail in th

Priyank Oza

We consider a class of degenerate elliptic fully nonlinear equations with applications to Grad equations: \begin{align} \begin{cases} |Du|^\gamma \mathcal{M}_{\lambda,\Lambda}^+\big(D^2u(x)\big)=f\big(|u\geq u(x)|\big) &\text{ in }\Omega, u=g &\text{ on }\partial\Omega, \end{cases} \end{align} where $\gamma\geq 1$ is a constant, $\Omega$ is a bounded domain

R. S. Markiewicz, A. Bansil

We generalize the concept of vortex phase in a type II superconductor to textured phases, where certain phases can persist over an extended range of perturbations by confining competing phases on topological defects (the vortices in a superconductor). We apply this model to the pseudogap phase in cuprates, where the relevant topological defects are the antip

Youssef Mansour, Reinhard Heckel

Recently, self-supervised neural networks have shown excellent image denoising performance. However, current dataset free methods are either computationally expensive, require a noise model, or have inadequate image quality. In this work we show that a simple 2-layer network, without any training data or knowledge of the noise distribution, can enable high-q

Shinya Kanemura, Yushi Mura

We investigate a scenario of electroweak baryogenesis in the two Higgs doublet model with quark flavor mixing. In general, off-diagonal components of quark Yukawa interactions with additional Higgs bosons are strongly constrained by the data for flavor changing neutral currents. However, top-charm quark mixing is not the case, so that a large off-diagonal el

Jaehoon Yoo, Semin Kim, Doyup Lee, Chiheon Kim

Autoregressive transformers have shown remarkable success in video generation. However, the transformers are prohibited from directly learning the long-term dependency in videos due to the quadratic complexity of self-attention, and inherently suffering from slow inference time and error propagation due to the autoregressive process. In this paper, we propos

V. Laliena, S. A. Osorio, D. Bazo, S. Bustingorry

At low temperature and zero applied magnetic field, besides the equilibrium helical state, monoaxial chiral helimagnets have a continuum of helical states differing by the wave number of the modulation. The wave number of these states in units of the equilibrium state wave number is denoted here by p, and accordingly the corresponding states are called the p

Yotam Alexander, Nimrod De La Vega, Noam Razin, Nadav Cohen

The question of what makes a data distribution suitable for deep learning is a fundamental open problem. Focusing on locally connected neural networks (a prevalent family of architectures that includes convolutional and recurrent neural networks as well as local self-attention models), we address this problem by adopting theoretical tools from quantum physic

John T. Anderson, Linus Bergqvist, Kelly Bickel, Joseph A. Cima

We study Clark measures associated with general two-variable rational inner functions (RIFs) on the bidisk, including those with singularities, and with general $d$-variable rational inner functions with no singularities. We give precise descriptions of support sets and weights for such Clark measures in terms of level sets and partial derivatives of the ass

Erdem Koyuncu

Many empirical studies have demonstrated the performance benefits of conditional computation in neural networks, including reduced inference time and power consumption. We study the fundamental limits of neural conditional computation from the perspective of memorization capacity. For Rectified Linear Unit (ReLU) networks without conditional computation, it

Gianluca Grilletti, Davide Emilio Quadrellaro

We investigate in this article regular Heyting algebras by means of Esakia duality. In particular, we give a characterisation of Esakia spaces dual to regular Heyting algebras and we show that there are continuum-many varieties of Heyting algebras generated by regular Heyting algebras. We also study several logical applications of these classes of objects an

Roland Wiese, Klaus Kroy, Demian Levis

We numerically study the shear rheology of a binary mixture of soft Active Brownian Particles, from the fluid to the disordered solid regime. At low shear rates, we find a Newtonian regime, where a Green-Kubo relation with an effective temperature provides the linear viscosity. It is followed by a shear-thinning regime at larger shear rates. At high densitie

Juejing Liu, Zimeng Zhang, Xiaoxu Li, Meirong Zong

The controllable synthesis of iron oxides particles is a critical issue for materials science, energy storage, biomedical applications, environmental science, and earth science. However, synthesis of iron oxides with desired phase and size are still a time-consuming and trial-and-error process. This study presents solutions for two fundamental challenges in

Zhenyu Li, Zhipeng Zhang, Heng Fan, Yuan He

In this paper, we improve the challenging monocular 3D object detection problem with a general semi-supervised framework. Specifically, having observed that the bottleneck of this task lies in lacking reliable and informative samples to train the detector, we introduce a novel, simple, yet effective `Augment and Criticize' framework that explores abundant in

Yifan Shi, Yingqi Liu, Kang Wei, Li Shen

To defend the inference attacks and mitigate the sensitive information leakages in Federated Learning (FL), client-level Differentially Private FL (DPFL) is the de-facto standard for privacy protection by clipping local updates and adding random noise. However, existing DPFL methods tend to make a sharper loss landscape and have poorer weight perturbation ro

P. I. Cristofari, J. -F. Donati, C. P. Folsom, T. Masseron

We report first results on a method aimed at simultaneously characterising atmospheric parameters and magnetic properties of M dwarfs from high-resolution nIR spectra recorded with SPIRou in the framework of the SPIRou Legacy Survey. Our analysis relies on fitting synthetic spectra computed from MARCS model atmospheres to selected spectral lines, both sensit

Patrick Gerard, Nicholas Botzer, Tim Weninger

Formally announced to the public following former President Donald Trump's bans and suspensions from mainstream social networks in early 2022 after his role in the January 6 Capitol Riots, Truth Social was launched as an "alternative" social media platform that claims to be a refuge for free speech, offering a platform for those disaffected by the content mo

The-Gia Leo Nguyen, Lynton Ardizzone, Ullrich Köthe

Autoencoders are able to learn useful data representations in an unsupervised matter and have been widely used in various machine learning and computer vision tasks. In this work, we present methods to train Invertible Neural Networks (INNs) as (variational) autoencoders which we call INN (variational) autoencoders. Our experiments on MNIST, CIFAR and CelebA

N. V. Krylov

We prove the existence of weak solutions of It\^o's stochastic time dependent equations with irregular diffusion and drift terms of Morrey spaces. Weak uniqueness (generally conditional) and a conjecture pertaining to strong solutions are also discussed. Our results are new even if the drift term vanishes.

Olaf Müller

After calculating the Dushnik-Miller dimension of Minkowski spaces to be countable infinity, we define a novel notion of dimension for ordered spaces recovering the correct manifold dimension and obtain a corresponding obstruction for the existence of injective monotonous maps between Lorentzian length spaces. Furthermore we induce metrics on Cauchy subsets,

Dheeraj Pratap, Rizul Gautam, Amit Kumar Shaw, Vikas

Longer near-infrared wavelengths provide better penetration depth in biological tissues, so these are useful for plasmonic photothermal cancer therapeutics. In the context of nanoparticles for such applications, the absorption can be tuned for longer NIR wavelengths. However, on increasing the size of the nanoparticle, the scattering is enhanced and thus is

Tejaswini Medi, Jawad Tayyub, Muhammad Sarmad, Frank Lindseth

Implicit generative models have been widely employed to model 3D data and have recently proven to be successful in encoding and generating high-quality 3D shapes. This work builds upon these models and alleviates current limitations by presenting the first implicit generative model that facilitates the generation of complex 3D shapes with rich internal geome

Harsha Nori, Nicholas King, Scott Mayer McKinney, Dean Carignan

Large language models (LLMs) have demonstrated remarkable capabilities in natural language understanding and generation across various domains, including medicine. We present a comprehensive evaluation of GPT-4, a state-of-the-art LLM, on medical competency examinations and benchmark datasets. GPT-4 is a general-purpose model that is not specialized for medi

Maurits J. A. Houmes, Gabriele Baglioni, Makars Šiškins, Martin Lee

The temperature dependent order parameter provides important information on the nature of magnetism. Using traditional methods to study this parameter in two-dimensional (2D) magnets remains difficult, however, particularly for insulating antiferromagnetic (AF) compounds. Here, we show that its temperature dependence in AF MPS$_{3}$ (M(II) = Fe, Co, Ni) can

S. K. Tripathy, S. K. Pradhan, B. Barik, Z. Naik

We have studied a superbounce scenario in a set up of Brans-Dicke (BD) theory. The BD parameter is considered to be time dependent and is assumed to evolve with the Brans-Dicke scalar field. In the superbounce scenario, the model bounces at an epoch corresponding to a Big Crunch provided the ekpyrotic phase continues until that time. Within the given superbo

Hang Zhang, Ping Li

This paper considers the sparse recovery with shuffled labels, i.e., $\by = \bPitrue \bX \bbetatrue + \bw$, where $\by \in \RR^n$, $\bPi\in \RR^{n\times n}$, $\bX\in \RR^{n\times p}$, $\bbetatrue\in \RR^p$, $\bw \in \RR^n$ denote the sensing result, the unknown permutation matrix, the design matrix, the sparse signal, and the additive noise, respectively. Ou

Dishari Malakar, K Rajesh Nayak

We have developed a methodology to test the age-old cosmic censorship hypothesis in Kerr geometry. We have shown that the Kerr black hole can be overspun by particles captured from the innermost stable circular orbit. However, it appears that this does not happen for particles coming from infinity. We have also observed that overspinning becomes possible onl

Romain Bourneuf, Stéphan Thomassé

We show that every graph with twin-width $t$ has chromatic number $O(\omega ^{k_t})$ for some integer $k_t$, where $\omega$ denotes the clique number. This extends a quasi-polynomial bound from Pilipczuk and Soko{\l}owski and generalizes a result for bounded clique-width graphs by Bonamy and Pilipczuk. The proof uses the main ideas of the quasi-polynomial ap

Andrew Darlington

In 2020, Alabdali and Byott described the Hopf-Galois structures arising on Galois field extensions of squarefree degree. Extending to squarefree separable, but not necessarily normal, extensions $L/K$ is a natural next step. One must consider now the interplay between two Galois groups $G=\operatorname{Gal}(E/K)$ and $G'=\operatorname{Gal}(E/L)$, where $E$

Sanket Kachole, Xiaoqian Huang, Fariborz Baghaei Naeini, Rajkumar Muthusamy

Object segmentation for robotic grasping under dynamic conditions often faces challenges such as occlusion, low light conditions, motion blur and object size variance. To address these challenges, we propose a Deep Learning network that fuses two types of visual signals, event-based data and RGB frame data. The proposed Bimodal SegNet network has two distinc

J. de la Nuez González

We show that for any connected smooth manifold $M$ of dimension different from $3$ the restriction of the compact-open topology to the diffeomorphism group of $M$ is minimal, i.e. the group does not admit a strictly coarser Hausdorff group topology. This implies the minimality of the compact-open topology on the homeomorphism group of $M$ in all dimensions d

Léo Bénard, Yann Chaubet, Nguyen Viet Dang, Thomas Schick

In this paper, we compute special values of certain combinatorial zeta functions counting geodesic paths in the (n-1)-skeleton of a triangulation of a n-dimensional manifold. We show that they carry a topological meaning. As such, we recover the first Betti number and L2-Betti number of compact manifolds, and the linking number of pairs of null-homologous kn

Zenghao Chai, Tianke Zhang, Tianyu He, Xu Tan

3D Morphable Models (3DMMs) demonstrate great potential for reconstructing faithful and animatable 3D facial surfaces from a single image. The facial surface is influenced by the coarse shape, as well as the static detail (e,g., person-specific appearance) and dynamic detail (e.g., expression-driven wrinkles). Previous work struggles to decouple the static a

Charles Greenfield, Cami Collins, Workshop Participants

The US ITER Research Program Basic Research Needs Workshop, held over the course of several months in 2022 with over 400 participants, sought to identify steps to be taken to both maximize the return of the US investment in ITER construction and operation and to ensure US research priorities on ITER strengthen the domestic program aimed at the development of

Tobias Weber, Michael Ingrisch, Bernd Bischl, David Rügamer

While recent advances in large-scale foundational models show promising results, their application to the medical domain has not yet been explored in detail. In this paper, we progress into the realms of large-scale modeling in medical synthesis by proposing Cheff - a foundational cascaded latent diffusion model, which generates highly-realistic chest radiog

Sheetal Temara

ChatGPT is a generative pretrained transformer language model created using artificial intelligence implemented as chatbot which can provide very detailed responses to a wide variety of questions. As a very contemporary phenomenon, this tool has a wide variety of potential use cases that have yet to be explored. With the significant extent of information on

Ricardo Mokhtari, Azam Hamidinekoo, Daniel Sutton, Arthur Lewis

Crohn's Disease (CD) and Ulcerative Colitis (UC) are the two main Inflammatory Bowel Disease (IBD) types. We developed deep learning models to identify histological disease features for both CD and UC using only endoscopic labels. We explored fine-tuning and end-to-end training of two state-of-the-art self-supervised models for predicting three different end

Tierra M. Candelaria, E. A. C Mills, David S. Meier, Juergen Ott

The inner 300-500 pc of the Milky Way has some of the most extreme gas conditions in our Galaxy. Physical properties of the Central Molecular Zone (CMZ), including temperature, density, thermal pressure, and turbulent pressure, are key factors for characterizing gas energetics, kinematics, and evolution. The molecular gas in this region is more than an order

Tahiya Chowdhury

Using YouTube Kids as an example, in this work, we argue the need to understand a child's interaction process with AI and its broader implication on a child's emotional, social, and creative development. We present several design recommendations to create value-driven interaction in child-centric AI that can guide designing compelling, age-appropriate, benef

Alexander Heinrich, Sören Krollmann, Florentin Putz, Matthias Hollick

More and more consumer devices implement the IEEE Ultra-Wide Band (UWB) standard to perform distance measurements for sensitive tasks such as keyless entry and startup of modern cars, to find lost items using coin-sized trackers, and for smart payments. While UWB promises the ability to perform time-of-flight centimeter-accurate distance measurements between

Zongcheng Li, Xiaoxiao Long, Yusen Wang, Tuo Cao

We present a novel method, called NeTO, for capturing 3D geometry of solid transparent objects from 2D images via volume rendering. Reconstructing transparent objects is a very challenging task, which is ill-suited for general-purpose reconstruction techniques due to the specular light transport phenomena. Although existing refraction-tracing based methods,

Monika Kudlinska

We construct the first known infinite family of quasi-isometry classes of subgroups of hyperbolic groups which are not hyperbolic and are of type $\mathrm{FP}(\mathbb{Q})$. We give a simple criterion for producing many non-hyperbolic subgroups of hyperbolic groups with strong finiteness properties. We also observe that local hyperbolicity and algebraic fibri

Jean Prost, Antoine Houdard, Andrés Almansa, Nicolas Papadakis

In this paper, we propose to regularize ill-posed inverse problems using a deep hierarchical variational autoencoder (HVAE) as an image prior. The proposed method synthesizes the advantages of i) denoiser-based Plug \& Play approaches and ii) generative model based approaches to inverse problems. First, we exploit VAE properties to design an efficient algori

Volker Ziemann

We improve the algorithm to noninvasively update the response matrix using information from the orbit-feedback system, described in [1]. The new version is capable of adapting to slow changes of the lattice, albeit at the expense of limiting the accuracy.

Maxim Khomiakov, Alejandro Valverde Mahou, Alba Reinders Sánchez, Jes Frellsen

We present a novel pipeline for learning the conditional distribution of a building roof mesh given pixels from an aerial image, under the assumption that roof geometry follows a set of regular patterns. Unlike alternative methods that require multiple images of the same object, our approach enables estimating 3D roof meshes using only a single image for pre

Michael Baumgartner, Peter M. Full, Klaus H. Maier-Hein

The accurate detection of mediastinal lesions is one of the rarely explored medical object detection problems. In this work, we applied a modified version of the self-configuring method nnDetection to the Mediastinal Lesion Analysis (MELA) Challenge 2022. By incorporating automatically generated pseudo masks, training high capacity models with large patch si

Bishal Parajuli, Chih-Chun Chien

Introducing spatially tunable interactions to atomic Fermi gases makes it feasible to study two phenomena, the proximity effect and spatial Kibble-Zurek mechanism (KZM), in a unified platform. While the proximity effect of a superconductor adjacent to a normal metal corresponds to a step-function quench of the pairing interaction in real space, the spatial K

Vasiliki Stergiopoulou, Subhadip Mukherjee, Luca Calatroni, Laure Blanc-Féraud

The spatial resolution of images of living samples obtained by fluorescence microscopes is physically limited due to the diffraction of visible light, which makes the study of entities of size less than the diffraction barrier (around 200 nm in the x-y plane) very challenging. To overcome this limitation, several deconvolution and super-resolution techniques

Alessandro Coclite, Giuseppe Maria Coclite, Giuseppe Fanizza, Francesco Maddalena

In this paper we study the dispersive properties related to a model of peridynamic evolution, governed by a non local initial value problem, in the cases of two and three spatial dimensions. The features of the wave propagation characterized by the nontrivial interactions between nonlocality and the regimes of low and high frequencies are studied and suitabl

Diletta Burini, Nadia Chouhad

This paper deals with the micro-macro derivation of models from the underlying description provided by methods of the kinetic theory for active particles. We consider the so-called exotic models according to the definition proposed in in [9]. The first part of the presentation focuses on a survey and a critical analysis of some phenomenological models known

Collin Joseph Van Cuyk, Liang Hong

Data scientists are not mathematicians, but they make heavy use of mathematics in their daily work. While mathematicians can study a mathematical object which is inaccessible to our five senses, data scientists must deal with real-world data which are observable to us. This fine line suggests that a data scientist's philosophical position on mathematics migh

Philip Massey, Kathryn F. Neugent, Nidia I. Morrell

The WN3/O3 Wolf-Rayet (WR) stars were discovered as part of our survey for WRs in the Magellanic Clouds. The WN3/O3s show the emission lines of a high-excitation WN star and the absorption lines of a hot O-type star, but our prior work has shown that the absorption spectrum is intrinsic to the WR star. Their place in the evolution of massive stars remains un

Kada Williams

Consider two regions in the plane, bounded by an $n$-gon and an $m$-gon, respectively. At most how many connected components can there be in their intersection? This question was asked by Croft. We answer this asymptotically, proving the bounds $$\left\lfloor \frac{m}{2}\right\rfloor \cdot \left\lfloor \frac{n}{2}\right\rfloor\le f(n,m)\le \left\lfloor \frac

Mohamed Hibat-Allah, Roger G. Melko, Juan Carrasquilla

Recurrent neural networks (RNNs), originally developed for natural language processing, hold great promise for accurately describing strongly correlated quantum many-body systems. Here, we employ 2D RNNs to investigate two prototypical quantum many-body Hamiltonians exhibiting topological order. Specifically, we demonstrate that RNN wave functions can effect

Nina Kamčev, Mathias Schacht

R\"odl and Ruci\'nski (1990) established Ramsey's theorem for random graphs. In particular, for fixed integers $r$, $\ell\geq 2$ they showed that $\hat p_{K_\ell,r}(n)=n^{-\frac{2}{\ell+1}}$ is a threshold for the Ramsey property that every $r$-colouring of the edges of the binomial random graph $G(n,p)$ yields a monochromatic copy of $K_\ell$. We investigat

Youle Wang, Chenghong Zhu, Mingrui Jing, Xin Wang

Preparing the ground states of a many-body system is essential for evaluating physical quantities and determining the properties of materials. This work provides a quantum ground state preparation scheme with shallow variational warm-start to tackle the bottlenecks of current algorithms, i.e., demand for prior ground state energy information and lack of demo