July 2022 arXiv papers — page 52

Tetsuya Shiromizu, Keisuke Izumi, Kangjae Lee, Diego Soligon

In accordance with current models of the accelerating Universe as a spacetime with a positive cosmological constant, new results about a cosmological upper bound for the area of stable marginally outer trapped surfaces are found taking into account angular momentum, gravitational waves and matter. Compared to previous results which take into account only som

Lingfeng Wang, Haocheng Li, Chunyin Liu

This paper describes our submission to the fourth Affective Behavior Analysis (ABAW) competition. We proposed a hybrid CNN-Transformer model for the Multi-Task-Learning (MTL) and Learning from Synthetic Data (LSD) task. Experimental results on validation dataset shows that our method achieves better performance than baseline model, which verifies that the ef

Aarash Feizi, Arantxa Casanova, Adriana Romero-Soriano, Reihaneh Rabbany

In this paper, we propose revisited versions for two recent hotel recognition datasets: Hotels50K and Hotel-ID. The revisited versions provide evaluation setups with different levels of difficulty to better align with the intended real-world application, i.e. countering human trafficking. Real-world scenarios involve hotels and locations that are not capture

Maria-Florina Balcan, Mikhail Khodak, Dravyansh Sharma, Ameet Talwalkar

An important unresolved challenge in the theory of regularization is to set the regularization coefficients of popular techniques like the ElasticNet with general provable guarantees. We consider the problem of tuning the regularization parameters of Ridge regression, LASSO, and the ElasticNet across multiple problem instances, a setting that encompasses bot

Dalton Anderson, Manav Shah, Linran Fan

Exceptional points, with simultaneous coalescence of eigen-values and eigen-vectors, can be realized with non-Hermitian photonic systems. With the enhanced response, exceptional points have been proposed to improve the performance of photonic sensing. Recently, there are intense debate about the actual sensing advantage of exceptional points. The major conce

Maxwell Freeman, Rhett Applestone, Wyatt Behn, Victor Brar

We demonstrate a cryogenic scanned probe microscope (SPM) that has been modified to be controlled with a haptic device, such that the operator can `feel' the surface of a sample under investigation. This system allows for direct tactile sensation of the atoms in and on top of a crystal, and we simulate, by using different SPM modalities, a sensation that is

Elshad Allahyarov, Hartmut Löwen

Structural correlations between colloids in a binary mixture of charged and uncharged spheres are calculated using computer simulations of the primitive model with explicit microions. For aqueous suspensions in a solvent of large dielectric constant, the traditional Derjaguin-Landau-Vervey-Overbeek (DLVO) theory of linear screening, supplemented with hard co

Alexander G. Abanov, Paul B. Wiegmann

We study flows of barotropic perfect fluid under the simultaneous action of the electromagnetic field and the axial-vector potential, the external field conjugate to the fluid helicity. We obtain the deformation of the Euler equation by the axial-vector potential and the deformations of various currents by two external fields. We show that the divergence of

Ru-Yu Lai, Gunther Uhlmann, Hanming Zhou

We consider the inverse problem for time-dependent semilinear transport equations. We show that time-independent coefficients of both the linear (absorption or scattering coefficients) and nonlinear terms can be uniquely determined, in a stable way, from the boundary measurements by applying a linearization scheme and Carleman estimates for the linear transp

Carl Boettiger

Encouraged by decision makers' appetite for future information on topics ranging from elections to pandemics, and enabled by the explosion of data and computational methods, model based forecasts have garnered increasing influence on a breadth of decisions in modern society. Using several classic examples from fisheries management, I demonstrate that selecti

Jonathan Stray, Alon Halevy, Parisa Assar, Dylan Hadfield-Menell

Recommender systems are the algorithms which select, filter, and personalize content across many of the worlds largest platforms and apps. As such, their positive and negative effects on individuals and on societies have been extensively theorized and studied. Our overarching question is how to ensure that recommender systems enact the values of the individu

Mingyue Hu, Yongcheng Qi

Consider the likelihood ratio test (LRT) statistics for the independence of sub-vectors from a $p$-variate normal random vector. We are devoted to deriving the limiting distributions of the LRT statistics based on a random sample of size $n$. It is well known that the limit is chi-square distribution when the dimension of the data or the number of the parame

Samuel Poncé, Miquel Royo, Massimiliano Stengel, Nicola Marzari

Charge transport plays a crucial role in manifold potential applications of two-dimensional materials, including field effect transistors, solar cells, and transparent conductors. At most operating temperatures, charge transport is hindered by scattering of carriers by lattice vibrations. Assessing the intrinsic phonon-limited carrier mobility is thus of par

E. Sevost'yanov, V. Targonskii

This article is devoted to the study of mappings defined in the region on the plane. Under certain conditions, the upper estimate of the distortion of the modulus of families of paths is obtained. Similarly, the upper estimate of the modulus of the families of paths in the pre-image under the mapping is also obtained.

Jiseok Youn, Jaehun Song, Hyung-Sin Kim, Saewoong Bahk

Deep neural network quantization with adaptive bitwidths has gained increasing attention due to the ease of model deployment on various platforms with different resource budgets. In this paper, we propose a meta-learning approach to achieve this goal. Specifically, we propose MEBQAT, a simple yet effective way of bitwidth-adaptive quantization aware training

Samuel Poncé, Miquel Royo, Marco Gibertini, Nicola Marzari

Despite considerable efforts, accurate computations of electron-phonon and carrier transport properties of low-dimensional materials from first principles have remained elusive. By building on recent advances in the description of long-range electrostatics, we develop a general approach to the calculation of electron-phonon couplings in two-dimensional mater

Meurig T Gallagher, Jackson C Kirkman-Brown, David J Smith

Flagellar motility is critical to natural and many forms of assisted reproduction. Rhythmic beating and wave propagation by the flagellum propels sperm through fluid and enables modulation between penetrative progressive motion, activated side-to-side yaw and hyperactivated motility associated with detachment from epithelial binding. These motility changes o

Joseph G. Makin

This work in progress aims to provide a unified introduction to statistical learning, building up slowly from classical models like the GMM and HMM to modern neural networks like the VAE and diffusion models. There are today many internet resources that explain this or that new machine-learning algorithm in isolation, but they do not (and cannot, in so brief

Peigen Cao, Bernhard Keller

In 2016, Leclerc constructed conjectural cluster structures on open Richardson varieties using representations of preprojective algebras. A variant with more explicit seeds was obtained by M\'enard in his thesis. We show that M\'enard's seeds do yield *upper* cluster algebra structures on open Richardson varieties and discuss the problems that remain in orde

Feng Liu, Xiaoming Liu

Recent research has shown that controllable image generation based on pre-trained GANs can benefit a wide range of computer vision tasks. However, less attention has been devoted to 3D vision tasks. In light of this, we propose a novel image-conditioned neural implicit field, which can leverage 2D supervisions from GAN-generated multi-view images and perform

Akshay Iyer, Clifford Lindsay, Hendrik Pretorius, Michael King

In this work, we develop a neural network-based method to convert a noisy motion signal generated from segmenting rebinned list-mode cardiac SPECT images, to that of a high-quality surrogate signal, such as those seen from external motion tracking systems (EMTs). This synthetic surrogate will be used as input to our pre-existing motion correction technique d

Brandon Carhuas-Torre, Ricardo Castillo, Miguel Loayza

We provide new conditions for the local existence of solutions to the time-weighted parabolic equation $ u_t - \Delta u = h(t)f(u) \mbox{ in } \Omega \times (0,T),$ where $ \Omega $ is a arbitrary smooth domain, $f\in C(\mathbb{R})$, $h\in C([0,\infty))$ and $u(0)\in L^r(\Omega)$. As consequence of our results, considering a suitable behavior of the non-nega

Siyuan Dong, Gilbert Hangel, Eric Z. Chen, Shanhui Sun

Magnetic Resonance Spectroscopic Imaging (MRSI) is an essential tool for quantifying metabolites in the body, but the low spatial resolution limits its clinical applications. Deep learning-based super-resolution methods provided promising results for improving the spatial resolution of MRSI, but the super-resolved images are often blurry compared to the expe

Matthias Eberl

We present a dynamic model theory that avoids the paradoxes stemming from completed infinities, but does not require any translation of formulae. The main adoption is the replacement of an actual infinite carrier set by a potential infinite one, and by a finitistic interpretation of the universal quantifier.

Feng Liu, Minchul Kim, Anil Jain, Xiaoming Liu

Although significant advances have been made in face recognition (FR), FR in unconstrained environments remains challenging due to the domain gap between the semi-constrained training datasets and unconstrained testing scenarios. To address this problem, we propose a controllable face synthesis model (CFSM) that can mimic the distribution of target datasets

Shikha Rathi, Lalita Sharma

In this study a comprehensive calculations of energies, hyperfine structure constants, Land\'e g$_J$ factors and isotope shifts have been performed for the lowest 71 states of Na-like Ar$^{7+}$, Kr$^{25+}$ and Xe$^{43+}$ ions. Radiative parameters viz., transition rates, wavelengths, oscillator strengths and lifetimes are also estimated for the electric and

S. Agayeva, V. Aivazyan, S. Alishov, M. Almualla

GRANDMA is a world-wide collaboration with the primary scientific goal of studying gravitational-wave sources, discovering their electromagnetic counterparts and characterizing their emission. GRANDMA involves astronomers, astrophysicists, gravitational-wave physicists, and theorists. GRANDMA is now a truly global network of telescopes, with (so far) 30 tele

S. Sabari, R. Kishor Kumar, R. Radha, P. Muruganandam

We theoretically explore the possibility of stabilizing the trapless polariton Bose-Einstein condensates (pBECs). Exploiting the variational method, we solve the associated nonlinear, complex Gross-Pitaevskii (cGP) equation and derive the equation of motion for the amplitude and width of the condensate. These variational results described by ordinary differe

Hilla De-Leon, Doron Gazit

The weak proton-proton fusion into a deuteron ($^2$H) is the driving reaction in the energy production in the Sun, as well as similar main sequence stars. Its reaction rate in the solar interior is determined only theoretically. Here, we provide a new determination of the rate of this reaction in solar conditions $S^{11}(0)$, and analyze theoretical and expe

Angelo Bratta, Michele Focchi, Niraj Rathod, Claudio Semini

Model Predictive Control (MPC) approaches are widely used in robotics, since they guarantee feasibility and allow the computation of updated trajectories while the robot is moving. They generally require heuristic references for the tracking terms and proper tuning of the parameters of the cost function in order to obtain good performance. For instance, when

Ji Zhang, Jean-Paul Ainam, Li-hui Zhao, Wenai Song

Scene classification has established itself as a challenging research problem. Compared to images of individual objects, scene images could be much more semantically complex and abstract. Their difference mainly lies in the level of granularity of recognition. Yet, image recognition serves as a key pillar for the good performance of scene recognition as the

Jingrui Wei, Ben Blaiszik, Aristana Scourtas, Dane Morgan

The information content of atomic resolution scanning transmission electron microscopy (STEM) images can often be reduced to a handful of parameters describing each atomic column, chief amongst which is the column position. Neural networks (NNs) are a high performance, computationally efficient method to automatically locate atomic columns in images, which h

Guodong Wang, Yunhong Wang, Jie Qin, Dongming Zhang

Video Anomaly Detection (VAD) is an important topic in computer vision. Motivated by the recent advances in self-supervised learning, this paper addresses VAD by solving an intuitive yet challenging pretext task, i.e., spatio-temporal jigsaw puzzles, which is cast as a multi-label fine-grained classification problem. Our method exhibits several advantages ov

Joseph Meleshko, Pascal Ochem, Jeffrey Shallit, Sonja Linghui Shan

We generalize the familiar notion of periodicity in sequences to a new kind of pseudoperiodicity, and we prove some basic results about it. We revisit the results of a 2012 paper of Shevelev and reprove his results in a simpler and more unified manner, and provide a complete answer to one of his previously unresolved questions. We consider finding words with

Tim Franzmeyer, Stephen McAleer, João F. Henriques, Jakob N. Foerster

Autonomous agents deployed in the real world need to be robust against adversarial attacks on sensory inputs. Robustifying agent policies requires anticipating the strongest attacks possible. We demonstrate that existing observation-space attacks on reinforcement learning agents have a common weakness: while effective, their lack of information-theoretic det

Aravinda Raman, Sameena Pathan, Tanweer Ali

Bone age assessment (BAA) is a standard method for determining the age difference between skeletal and chronological age. Manual processes are complicated and necessitate the expertise of experts. This is where deep learning comes into play. In this study, pre-trained models like VGG-16, InceptionV3, XceptionNet, and MobileNet are used to assess the bone age

Suvo Banik, Debdas Dhabal, Henry Chan, Sukriti Manna

Machine learning models and applications in materials design and discovery typically involve the use of feature representations or "descriptors" followed by a learning algorithm that maps them to a user-desired property of interest. Most popular mathematical formulation-based descriptors are not unique across atomic environments or suffer from transferabilit

Hana Haseljić, Soumick Chatterjee, Robert Frysch, Vojtěch Kulvait

Model-based reconstruction employing the time separation technique (TST) was found to improve dynamic perfusion imaging of the liver using C-arm cone-beam computed tomography (CBCT). To apply TST using prior knowledge extracted from CT perfusion data, the liver should be accurately segmented from the CT scans. Reconstructions of primary and model-based CBCT

M. Mezidi, A. Alekhin, G. D. Gu, D. Colson

We followed step by step the transition from an antiferromagnetic (AF) Mott insulator to a superconducting (SC) metal in the Bi$_2$Sr$_2$CaCu$_{2}$O$_{8+\delta}$ (Bi-2212) cuprate using the electronic Raman scattering spectroscopy. This was achieved by tracking the doping dependence of the spin singlet excitation originate from the AF Mott insulator, the nor

Steven Ehlert, Chien-Ting Chen, Doug A. Swartz, Ryan C. Hickox

We present a new statistical method for constructing background subtracted measurements from event list data gathered by X-ray and gamma ray observatories. This method was initially developed specifically to construct images that account for the high background fraction and low overall count rates observed in survey data from the Mikhail Pavlinsky ART-XC tel

Yuxuan Xia, Ángel F. García-Fernández, Florian Meyer, Jason L. Williams

In this paper, we propose a Poisson multi-Bernoulli (PMB) filter for extended object tracking (EOT), which directly estimates the set of object trajectories, using belief propagation (BP). The proposed filter propagates a PMB density on the posterior of sets of trajectories through the filtering recursions over time, where the PMB mixture (PMBM) posterior af

Shivaram Subramanian, Wei Sun, Youssef Drissi, Markus Ettl

With the abundance of available data, many enterprises seek to implement data-driven prescriptive analytics to help them make informed decisions. These prescriptive policies need to satisfy operational constraints, and proactively eliminate rule conflicts, both of which are ubiquitous in practice. It is also desirable for them to be simple and interpretable,

Xiutao Zhu, Yaojun Chen, Dániel Gerbner, Ervin Győri

The generalized Tur\'{a}n number $ex(n,K_s,H)$ is the maximum number of complete graph $K_s$ in an $H$-free graph on $n$ vertices. Let $F_k$ be the friendship graph consisting of $k$ triangles. Erd\H{o}s and S\'os (1976) determined the value of $ex(n,K_3,F_2)$. Alon and Shikhelman (2016) proved that $ex(n,K_3, F_k)\le (9k-15)(k+1)n.$ In this paper, by using

Brian Choi, Alejandro Aceves

We prove that the solutions to the discrete Nonlinear Schr\"odinger Equation (DNLSE) with non-local algebraically-decaying coupling converge strongly in $L^2(\mathbb{R}^2)$ to those of the continuum fractional Nonlinear Schr\"odinger Equation (FNLSE), as the discretization parameter tends to zero. The proof relies on sharp dispersive estimates that yield the

Maria-Veronica Ciocanel

Intracellular transport processes are essential to the healthy development of many organisms as well as more generally to healthy cellular function. The complex dynamics and interactions between protein molecules and filaments on different time and spatial scales generate many opportunities for mathematical modeling and analysis that can provide insights int

Ilya Galai, Andrei Tetenov

We find the conditions for bi-Lipschitz equivalence of self-similar Jordan arcs which are the attractors of self-similar zippers.

Huseyin Coskun, Alireza Zareian, Joshua L. Moore, Federico Tombari

Clustering is a ubiquitous tool in unsupervised learning. Most of the existing self-supervised representation learning methods typically cluster samples based on visually dominant features. While this works well for image-based self-supervision, it often fails for videos, which require understanding motion rather than focusing on background. Using optical fl

Neehar Kondapaneni, Pietro Perona, Oisin Mac Aodha

Each year, thousands of people learn new visual categorization tasks -- radiologists learn to recognize tumors, birdwatchers learn to distinguish similar species, and crowd workers learn how to annotate valuable data for applications like autonomous driving. As humans learn, their brain updates the visual features it extracts and attend to, which ultimately

Weiyang Liu, Zhen Liu, Liam Paull, Adrian Weller

This paper considers the problem of unsupervised 3D object reconstruction from in-the-wild single-view images. Due to ambiguity and intrinsic ill-posedness, this problem is inherently difficult to solve and therefore requires strong regularization to achieve disentanglement of different latent factors. Unlike existing works that introduce explicit regulariza

Arian Bakhtiarnia, Błażej Leporowski, Lukas Esterle, Alexandros Iosifidis

Images and video frames captured by cameras placed throughout smart cities are often transmitted over the network to a server to be processed by deep neural networks for various tasks. Transmission of raw images, i.e., without any form of compression, requires high bandwidth and can lead to congestion issues and delays in transmission. The use of lossy image

Gelareh Hajian, Evelyn Morin, Ali Etemad

Accurate force/torque estimation is essential for applications such as powered exoskeletons, robotics, and rehabilitation. However, force/torque estimation under dynamic conditions is a challenging due to changing joint angles, force levels, muscle lengths, and movement speeds. We propose a novel method to accurately model the generated force under isotonic,

Miguel Barrero

Global transfer systems are equivalent to global $N_\infty$-operads, which parametrize different levels of commutativity in globally equivariant homotopy theory, where objects have compatible actions by all compact Lie groups. In this paper we explicitly describe and completely classify global transfer systems for the family of all abelian compact Lie groups

Lavanya Singh

As we grant artificial intelligence increasing power and independence in contexts like healthcare, policing, and driving, AI faces moral dilemmas but lacks the tools to solve them. Warnings from regulators, philosophers, and computer scientists about the dangers of unethical artificial intelligence have spurred interest in automated ethics-i.e., the developm

Pablo Navarrete, York Schröder

We report on ongoing efforts to tackle an important open problem in QCD thermodynamics, namely an evaluation of the pressure to order $g^6$ in a weak-coupling expansion, corresponding to four loops. In particular, we identify a class of contributing Feynman sum-integrals with lower-loop factors, describe the formalism to tensor decompose those, and manage to

Xiao Gu, Yao Guo, Zeju Li, Jianing Qiu

Machine learning models fail to perform well on real-world applications when 1) the category distribution P(Y) of the training dataset suffers from long-tailed distribution and 2) the test data is drawn from different conditional distributions P(X|Y). Existing approaches cannot handle the scenario where both issues exist, which however is common for real-wor

Ciwan Ceylan, Kambiz Ghoorchian, Danica Kragic

Structural node embeddings, vectors capturing local connectivity information for each node in a graph, have many applications in data mining and machine learning, e.g., network alignment and node classification, clustering and anomaly detection. For the analysis of directed graphs, e.g., transactions graphs, communication networks and social networks, the ca

Gautam Salhotra, I-Chun Arthur Liu, Marcus Dominguez-Kuhne, Gaurav S. Sukhatme

We present a novel Learning from Demonstration (LfD) method, Deformable Manipulation from Demonstrations (DMfD), to solve deformable manipulation tasks using states or images as inputs, given expert demonstrations. Our method uses demonstrations in three different ways, and balances the trade-off between exploring the environment online and using guidance fr

Hamza Bouaziz, Yazid Delenda, Kamel Khelifa-Kerfa

We revisit the azimuthal decorrelation $\delta\phi$ between a jet and a $Z$ boson produced at hadron colliders. Employing different recombination schemes for the jets leads to significantly different NLL-resummed predictions for the distribution of this quantity. Specifically when the jets are reconstructed with the $E$-scheme (i.e., four-momentum addition)

Terrence George, Alexander Goncharov, Richard Kenyon

In 2015, Vladimir Fock proved that the spectral transform, associating to an element of a dimer cluster integrable system its spectral data, is birational by constructing an inverse map using theta functions on Jacobians of spectral curves. We provide an alternate construction of the inverse map that involves only rational functions in the spectral data.

D. E. Pelinovsky, J. Wei, Y. Wu

We consider positive and spatially decaying solutions to the Gross-Pitaevskii equation with a harmonic potential. For the energy-critical case, there exists a ground state if and only if the frequency belongs to (1,3) in three dimensions and in (0,d) in d dimensions. We give a precise description on asymptotic behaviors of the ground state up to the leading

Markus Eisenbach, Mariia Karabin, Massimiliano Lupo Pasini, Junqi Yin

The investigation of finite temperature properties using Monte-Carlo (MC) methods requires a large number of evaluations of the system's Hamiltonian to sample the phase space needed to obtain physical observables as function of temperature. DFT calculations can provide accurate evaluations of the energies, but they are too computationally expensive for routi

Nirmalya Thakur, Chia Y. Han

This paper presents the findings of an exploratory study on the continuously generating Big Data on Twitter related to the sharing of information, news, views, opinions, ideas, feedback, and experiences about the COVID-19 pandemic, with a specific focus on the Omicron variant, which is the globally dominant variant of SARS-CoV-2 at this time. A total of 1202

Sarra Habchi, Guillaume Haben, Jeongju Sohn, Adriano Franci

Flaky tests are defined as tests that manifest non-deterministic behaviour by passing and failing intermittently for the same version of the code. These tests cripple continuous integration with false alerts that waste developers' time and break their trust in regression testing. To mitigate the effects of flakiness, both researchers and industrial experts p

Dmitry E. Pelinovsky, Guido Schneider

We consider a scalar Fermi-Pasta-Ulam (FPU) system on a square 2D lattice. The Kadomtsev-Petviashvili (KP-II) equation can be derived by means of multiple scale expansions to describe unidirectional long waves of small amplitude with slowly varying transverse modulations. We show that the KP-II approximation makes correct predictions about the dynamics of th

Efthymios Tzinis, Scott Wisdom, Tal Remez, John R. Hershey

We introduce AudioScopeV2, a state-of-the-art universal audio-visual on-screen sound separation system which is capable of learning to separate sounds and associate them with on-screen objects by looking at in-the-wild videos. We identify several limitations of previous work on audio-visual on-screen sound separation, including the coarse resolution of spati

In-Koo Cho, Jonathan Libgober

This paper examines whether one can learn to play an optimal action while only knowing part of true specification of the environment. We choose the optimal pricing problem as our laboratory, where the monopolist is endowed with an underspecified model of the market demand, but can observe market outcomes. In contrast to conventional learning models where the

Akshay Iyer, Linh Nguyen, Shweta Khushu

Wind energy is expected to be one of the leading ways to achieve the goals of the Paris Agreement but it in turn heavily depends on effective management of its operations and maintenance (O&M) costs. Blade failures account for one-third of all O&M costs thus making accurate detection of blade damages, especially cracks, very important for sustained operation

Divay Garg, Kamana Porwal

The main aim of this article is to analyze mixed finite element method for the second order Dirichlet boundary control problem. Therein, we develop both a priori and a posteriori error analysis using the energy space based approach. We obtain optimal order a priori error estimates in the energy norm and $L^2$-norm with the help of auxiliary problems. The rel

Ryan B. Christianson, Ryan M. Pollyea, Robert B. Gramacy

The canonical technique for nonlinear modeling of spatial/point-referenced data is known as kriging in geostatistics, and as Gaussian Process (GP) regression for surrogate modeling and statistical learning. This article reviews many similarities shared between kriging and GPs, but also highlights some important differences. One is that GPs impose a process t

Rahul Rahaman, Dipika Singhania, Alexandre Thiery, Angela Yao

In temporal action segmentation, Timestamp supervision requires only a handful of labelled frames per video sequence. For unlabelled frames, previous works rely on assigning hard labels, and performance rapidly collapses under subtle violations of the annotation assumptions. We propose a novel Expectation-Maximization (EM) based approach that leverages the l

Miguel Berasategui, Pablo M. Berná, Hung Viet Chu

Partially greedy bases in Banach spaces were introduced by Dilworth et al. as a strictly weaker notion than the (almost) greedy bases. In this paper, we study two natural ways to strengthen the definition of partial greediness. The first way produces what we call the consecutive almost greedy property, which turns out to be equivalent to the almost greedy pr

Dino Osmanovic, Elisa Franco

Chemical reactions that couple to systems that phase separate have been implicated in diverse contexts from biology to materials science. However, how a particular set of chemical reactions (chemical reaction network, CRN) would affect the behaviors of a phase separating system is difficult to fully predict theoretically. In this paper, we analyze a mean fie

Renhai Wang, Yang Sun, Feng Zhang, Feng Zheng

We perform a high-throughput screening on phonon-mediated superconductivity in ternary metal diboride structure with alkali, alkaline earth, and transition metals. We find 17 ground states and 78 low-energy metastable phases. From fast calculations of zone-center electron-phonon coupling, 43 compounds are revealed to show electron-phonon coupling strength hi

W. H. S. Brandão, A. L. Aguiar, L. A. Ribeiro Júnior, D. S. Galvão

Studies aimed at designing new allotropic forms of carbon have received much attention. Recently, a new 2D graphene-like allotrope named Pentahexoctite was theoretically proposed. Pentahexoctite has a metallic signature, and its structure consists of continuous 5-6-8 rings of carbon atoms with sp2 hybridization. Here, we carried out fully-atomistic computati

Menny Aka

This is the text accompanying my Bourbaki seminar on the work of Einsiedler and Lindenstrauss on joinings. The first five sections surveys their proof of the classification of joinings of higher-rank torus actions on arithmetic quotients of semisimple or perfect algebraic groups. The last section surveys how this classification can be used to tackle arithmet

Fei Ye, Adrian G. Bors

Due to their inference, data representation and reconstruction properties, Variational Autoencoders (VAE) have been successfully used in continual learning classification tasks. However, their ability to generate images with specifications corresponding to the classes and databases learned during Continual Learning (CL) is not well understood and catastrophi

Gianni Franchi, Xuanlong Yu, Andrei Bursuc, Emanuel Aldea

Predictive uncertainty estimation is essential for deploying Deep Neural Networks in real-world autonomous systems. However, most successful approaches are computationally intensive. In this work, we attempt to address these challenges in the context of autonomous driving perception tasks. Recently proposed Deterministic Uncertainty Methods (DUM) can only pa

Mohammad Ali Sayed, Mohsen Ghafouri, Mourad Debbabi, Chadi Assi

Driven by the necessity to combat climate change, Electric Vehicles (EV) are being deployed to take advantage of their ability in reducing emissions generated by the transportation sector. This deployment has left the power grid vulnerable to attacks through the EV infrastructure. This paper is written from an attacker\'s perspective and proposes a dynamic l

Farimah Poursafaei, Shenyang Huang, Kellin Pelrine, Reihaneh Rabbany

Despite the prevalence of recent success in learning from static graphs, learning from time-evolving graphs remains an open challenge. In this work, we design new, more stringent evaluation procedures for link prediction specific to dynamic graphs, which reflect real-world considerations, to better compare the strengths and weaknesses of methods. First, we c

L. Doyle, H. M. Cegla, E. Bryant, D. Bayliss

In this paper, we present high-resolution spectroscopic transit observations from ESPRESSO of the super-Neptune WASP-166~b. In addition to spectroscopic ESPRESSO data, we analyse photometric data from {\sl TESS} of six WASP-166~b transits along with simultaneous NGTS observations of the ESPRESSO runs. These observations were used to fit for the planetary par

Stefana-Lucia Anita

This paper concerns an optimal control problem $(P)$ related to a nonlinear Fokker-Planck equation. The problem is deeply related to a stochastic optimal control problem $(P_S)$ for a McKean-Vlasov equation. The existence of an optimal control is obtained for the deterministic problem $(P)$. The existence of an optimal control is established and necessary op

Giuseppe De Laurentis, Ben Page

In these proceedings, we discuss the recent approach of Ref. [1] for the construction of compact Ans\"atze for scattering amplitudes. The method builds powerful constraints on the analytic structure of the rational functions in amplitudes from numerical tests of their behavior close to singularity surfaces. We discuss how we systematically understand these s

S. Schuldt, S. H. Suyu, R. Canameras, Y. Shu

Modeling of strongly gravitationally lensed galaxies is often required in order to use them as astrophysical or cosmological probes. With current and upcoming wide-field imaging surveys, the number of detected lenses is increasing significantly such that automated and fast modeling procedures for ground-based data are urgently needed. This is especially pert

Zhihang Zhong, Xiao Sun, Zhirong Wu, Yinqiang Zheng

We study the challenging problem of recovering detailed motion from a single motion-blurred image. Existing solutions to this problem estimate a single image sequence without considering the motion ambiguity for each region. Therefore, the results tend to converge to the mean of the multi-modal possibilities. In this paper, we explicitly account for such mot

Akhil Uniyal, Sara Kanzi, İzzet Sakallı

We study the greybody factors, quasinormal modes, and shadow of the higher dimensional de-Sitter (dS)/ anti de-Sitter (AdS) black hole spacetimes derived from the Einstein-bumblebee gravity theory within the Lorentz symmetry breaking (LSB) framework. We specifically apply the semi-analytical WKB method and the time domain approach to study the scalar and Dir

Aishwarya Kumar, Aziza Suleymanzade, Mark Stone, Lavanya Taneja

Long distance transmission of quantum information is a central ingredient of distributed quantum information processors for both computing and secure communication. Transmission between superconducting/solid-state quantum processors necessitates transduction of individual microwave photons to optical photons. Current approaches to transduction employ solid s

Davide Moltisanti, Jinyi Wu, Bo Dai, Chen Change Loy

Generative models for audio-conditioned dance motion synthesis map music features to dance movements. Models are trained to associate motion patterns to audio patterns, usually without an explicit knowledge of the human body. This approach relies on a few assumptions: strong music-dance correlation, controlled motion data and relatively simple poses and move

Silvio Dolfi, Emanuele Pacifici, Lucia Sanus, Victor Sotomayor

Let G be a finite group. Denoting by cd(G) the set of degrees of the irreducible complex characters of G, we consider the character degree graph of G: this is the (simple undirected) graph whose vertices are the prime divisors of the numbers in cd(G), and two distinct vertices p, q are adjacent if and only if pq divides some number in cd(G). In the series of

Johan Henriksson, Stefanos R. Kousvos, Marten Reehorst

Using numerical conformal bootstrap technology we perform a non-perturbative study of the Ising CFT and its spectrum from infinitesimal to finite values of $\varepsilon=4-d$. Exploiting the recent navigator bootstrap method in conjunction with the extremal functional method, we test various qualitative and quantitative features of the $\varepsilon$-expansion

Bastian B. Brandt, Amine Chabane, Volodymyr Chelnokov, Francesca Cuteri

Imaginary chemical potentials serve as a useful tool to constrain the QCD phase diagram and to gain insight into the thermodynamics of strongly interacting matter. In this study, we report on the first determination of the phase diagram for arbitrary imaginary baryon and isospin chemical potentials at high temperature using one-loop perturbation theory, reve

Yael Avni, David Andelman, Henri Orland

The electric conductivity of ionic solutions is well understood at low ionic concentrations of up to a few millimolar but becomes difficult to unravel at higher concentrations that are still common in nature and technological applications. A model for the conductivity at high concentrations was recently put forth for monovalent electrolytes at low electric f

Charlie Cresswell-Hogg, Daniel F. Litim

In the limit of many fermion flavors it is demonstrated that the sextic Gross-Neveu theory in three dimensions displays a line of interacting UV fixed points, characterised by an exactly marginal sextic interaction. We determine the conformal window of UV-complete theories, universal scaling dimensions, and the phase diagram using renormalisation group metho

Isuru Ratnayake, V. A. Samaranayake

A time-varying zero-inflated serially dependent Poisson process is proposed. The model assumes that the intensity of the Poisson Process evolves according to a generalized autoregressive conditional heteroscedastic (GARCH) formulation. The proposed model is a generalization of the zero-inflated Poisson Integer GARCH model proposed by Fukang Zhu in 2012, whic

Nirmalya Thakur

The COVID-19 Omicron variant, reported to be the most immune evasive variant of COVID-19, is resulting in a surge of COVID-19 cases globally. This has caused schools, colleges, and universities in different parts of the world to transition to online learning. As a result, social media platforms such as Twitter are seeing an increase in conversations related

Lun-Hui Hu, Rui-Xing Zhang

We show that lattice dislocations of topological iron-based superconductors such as FeTe$_{1-x}$Se$_x$ will intrinsically trap non-Abelian Majorana quasiparticles, in the absence of any external magnetic field. Our theory is motivated by the recent experimental observations of normal-state topology and surface magnetism that coexist with superconductivity in

Frank Schindler, Stepan S. Tsirkin, Titus Neupert, B. Andrei Bernevig

In insulating crystals, it was previously shown that defects with two fewer dimensions than the bulk can bind topological electronic states. We here further extend the classification of topological defect states by demonstrating that the corners of crystalline defects with integer Burgers vectors can bind 0D higher-order end (HEND) states with anomalous char

Cem Eröncel, Géraldine Servant

We show that very compact axion mini-clusters can form in models where axion-like-particle (ALP) dark matter is produced via the kinetic misalignment mechanism, which is well-motivated in pre-inflationary $U(1)$ symmetry breaking scenarios. This is due to ALP fragmentation. We predict denser halos than what has been obtained so far in the literature from sta

Konstantinos Zarvalis

We explore two properties of backward orbits under semigroups of holomorphic self-maps in the unit disk. First, we prove that regular backward orbits are quasi-geodesics for the hyperbolic distance of the unit disk. Then, we show that backward orbits satisfy a useful property, this time in Euclidean terms.

A. Abada, J. Kriewald, E. Pinsard, S. Rosauro-Alcaraz

Heavy neutral leptons are motivated by several extensions of the Standard Model and their presence induces modifications in the lepton mixing matrix, including new Dirac and Majorana CP violating phases. It has been recently shown that these phases play an important role in lepton number and lepton flavour violating decays and transitions, with a striking im

Thomas Gehrmann, Andreas von Manteuffel, Tong-Zhi Yang

Splitting functions govern the scale evolution of parton distribution functions. Through a Mellin transformation, they are related to anomalous dimensions of twist-two operators in the operator product expansion. We study off-shell operator matrix element, where the physical operators mix under renormalization with other gauge-variant operators of the same q