August 2022 arXiv papers — page 142

Jun Shern Chan, Michael Pieler, Jonathan Jao, Jérémy Scheurer

Prior work on language models (LMs) shows that training on a large number of diverse tasks improves few-shot learning (FSL) performance on new tasks. We take this to the extreme, automatically extracting 413,299 tasks from internet tables - orders of magnitude more than the next-largest public datasets. Finetuning on the resulting dataset leads to improved F

Mahdi Nazeri, Ali Mollahosseini, Iman Izadi

Information spread is an intriguing topic to study in network science, which investigates how information, influence, or contagion propagate through networks. Graph burning is a simplified deterministic model for how information spreads within networks. The complicated NP-complete nature of the problem makes it computationally difficult to solve using exact

Davide Cassani, Alejandro Ruipérez, Enrico Turetta

We study four-derivative corrections to five-dimensional minimal gauged supergravity. We evaluate the on-shell action of the AdS$_5$ black hole solution with two independent angular momenta and one electric charge at linear order in the corrections. After imposing supersymmetry, we are able to recast the action in terms of the supersymmetric chemical potenti

Ratish Puduppully, Parag Jain, Nancy F. Chen, Mark Steedman

In Multi-Document Summarization (MDS), the input can be modeled as a set of documents, and the output is its summary. In this paper, we focus on pretraining objectives for MDS. Specifically, we introduce a novel pretraining objective, which involves selecting the ROUGE-based centroid of each document cluster as a proxy for its summary. Our objective thus doe

Christopher Sun, Jay Nimbalkar, Ravnoor Bedi

Mosquito habitat ranges are projected to expand due to climate change. This investigation aims to identify future mosquito habitats by analyzing preferred ecological conditions of mosquito larvae. After assembling a data set with atmospheric records and larvae observations, a neural network is trained to predict larvae counts from ecological inputs. Time ser

Thaïs Baudon, Carsten Fuhs, Laure Gonnord

We revisit parallel-innermost term rewriting as a model of parallel computation on inductive data structures and provide a corresponding notion of runtime complexity parametric in the size of the start term. We propose automatic techniques to derive both upper and lower bounds on parallel complexity of rewriting that enable a direct reuse of existing techniq

Mohit Pal

The concept of differential uniformity was recently extended to the $c$-differential uniformity. An interesting problem in this area is the construction of functions with low $c$-differential uniformity and a lot of research has been done in this direction in the recent past. Here, we present three classes of (almost) perfect $c$-nonlinear permutations over

Francesco Cagnetta, Alessandro Favero, Matthieu Wyart

Understanding how convolutional neural networks (CNNs) can efficiently learn high-dimensional functions remains a fundamental challenge. A popular belief is that these models harness the local and hierarchical structure of natural data such as images. Yet, we lack a quantitative understanding of how such structure affects performance, e.g., the rate of decay

Nicholas Connolly, Vivien Londe, Anthony Leverrier, Nicolas Delfosse

We propose a decoder for the correction of erasures with hypergraph product codes, which form one of the most popular families of quantum LDPC codes. Our numerical simulations show that this decoder provides a close approximation of the maximum likelihood decoder that can be implemented in O(N^2) bit operations where N is the length of the quantum code. A pr

Nicola Apollonio, Lorenzo Balzotti

Path graphs are intersection graphs of paths in a tree. We start from the characterization of path graphs by Monma and Wei [C.L.~Monma,~and~V.K.~Wei, Intersection Graphs of Paths in a Tree, J. Combin. Theory Ser. B, 41:2 (1986) 141--181] and we reduce it to some 2-colorings subproblems, obtaining the first characterization that directly leads to a polynomial

Souvik Bera

We present a new methodology, suitable for implementation on computer, to perform the $\epsilon$-expansion of hypergeometric functions with linear $\epsilon$ dependent Pochhammer parameters in any number of variables. Our approach allows one to perform Taylor as well as Laurent series expansion of multivariable hypergeometric functions. Each of the coefficie

Fethi Ayaz, Marc Kegel, Klaus Mohnke

We present a simple proof of the surface classification theorem using normal curves. This proof is analogous to Kneser's and Milnor's proof of the existence and uniqueness of the prime decomposition of 3-manifolds. In particular, we do not need any invariants from algebraic topology to distinguish surfaces.

Julien H. Girard, Jarron Leisenring, Jens Kammerer, Mario Gennaro

In a cold and stable space environment, the James Webb Space Telescope (JWST or "Webb") reaches unprecedented sensitivities at wavelengths beyond 2 microns, serving most fields of astrophysics. It also extends the parameter space of high-contrast imaging in the near and mid-infrared. Launched in late 2021, JWST underwent a six month commissioning period. In

Brian Weber

The symplectic reduction of a complete toric K\"ahler manifold need not be closed or even be a polygon. Sharp differences in behavior occur between those complete toric K\"ahler 4-manifolds with closed and with non-closed reductions. This paper establishes geometric criteria for these reductions to be closed, and classifies all asymptotic geometries possible

Jens Kammerer, Julien Girard, Aarynn L. Carter, Marshall D. Perrin

The James Webb Space Telescope (JWST) will revolutionize the field of high-contrast imaging and enable both the direct detection of Saturn-mass planets and the characterization of substellar companions in the mid-infrared. While JWST will feature unprecedented sensitivity, angular resolution will be the key factor when competing with ground-based telescopes.

Luke C. Rhodes, Weronika Osmolska, Carolina A. Marques, Peter Wahl

Quasiparticle Interference (QPI) imaging is a powerful tool for the study of the low energy electronic structure of quantum materials. However, the measurement of QPI by scanning tunneling microscopy (STM) is restricted to surfaces and is thus inherently constrained to two dimensions. This has proved immensely successful for the study of materials that exhib

Duncan Neill, Rebecca Preston, William G. Newton, David Tsang

Much effort is devoted to measuring the nuclear symmetry energy through neutron star (NS) and nuclear observables. Since matter in the NS core may be non-hadronic, observables like radii and tidal deformability may not provide reliable constraints on properties of nucleonic matter. We demonstrate that coincident timing of a resonant shattering flare (RSF) an

Yifei Ren, Jian Lou, Li Xiong, Joyce C Ho

Tensor factorization has received increasing interest due to its intrinsic ability to capture latent factors in multi-dimensional data with many applications such as recommender systems and Electronic Health Records (EHR) mining. PARAFAC2 and its variants have been proposed to address irregular tensors where one of the tensor modes is not aligned, e.g., diff

Aishrila Mazumder, Arnab Chakraborty, Abhirup Datta

Studying the spatial distribution of extragalactic source populations is vital in understanding the matter distribution in the Universe. It also enables understanding the cosmological evolution of dark matter density fields and the relationship between dark matter and luminous matter. Clustering studies are also required for EoR foreground studies since it a

A. Brollo, A. Veronese, L. Salasnich

Anyons are particles with intermediate quantum statistics whose wavefunction acquires a phase $e^{i\theta}$ by particle exchange. Inspired by proposals of simulating anyons using ultracold atoms trapped in optical lattices, we study a two-site anyonic Josephson junction, i.e. anyons confined in a one-dimensional double-well potential. We show, analytically a

Francesco Bastianelli, Nicola Picoco

Given an irreducible projective variety $X$, the covering gonality of $X$ is the least gonality of an irreducible curve $E\subset X$ passing through a general point of $X$. In this paper we study the covering gonality of the $k$-fold symmetric product $C^{(k)}$ of a smooth complex projective curve $C$ of genus $g\geq k+1$. It follows from a previous work of

Vitonofrio Crismale, Simone Del Vecchio, Stefano Rossi

The ergodic properties of the shift on both full and $m$-truncated $t$-free $C^*$-algebras are analyzed. In particular, the shift is shown to be uniquely ergodic with respect to the fixed-point algebra. In addition, for every $m\geq 1$, the invariant states of the shift acting on the $m$-truncated $t$-free $C^*$-algebra are shown to yield a $m+1$-dimensional

J. Humberto Ramos, Jaejeong Shin, Kyle Volle, Paul Buzaud

In the absence of an absolute positioning system, such as GPS, autonomous vehicles are subject to accumulation of positional error which can interfere with reliable performance. Improved navigational accuracy without GPS enables vehicles to achieve a higher degree of autonomy and reliability, both in terms of decision making and safety. This paper details th

Z. Guo, C. Chen, E. S. Chng

The performances of automatic speech recognition (ASR) systems degrade drastically under noisy conditions. Explicit distortion modelling (EDM), as a feature compensation step, is able to enhance ASR systems under such conditions by simulating the in-domain noisy speeches from the clean counterparts. Yet, existing distortion models are either non-trainable or

Mihaela Curmei, Andreas Haupt, Dylan Hadfield-Menell, Benjamin Recht

Designing recommendation systems that serve content aligned with time varying preferences requires proper accounting of the feedback effects of recommendations on human behavior and psychological condition. We argue that modeling the influence of recommendations on people's preferences must be grounded in psychologically plausible models. We contribute a met

A. C. Carnall, D. J. McLeod, R. J. McLure, J. S. Dunlop

We report a robust sample of 10 massive quiescent galaxies at redshift, $z > 3$, selected using the first data from the JWST CEERS programme. Three of these galaxies are at $4 < z < 5$, constituting the best evidence to date for quiescent galaxies significantly before $z=4$. These extreme galaxies have stellar masses in the range log$_{10}(M_*/$M$_\odot) = 1

Tony J. Puthenpurakal, Sudeshna Roy

Let $A$ be a Dedekind domain of characteristic zero such that its localization at every maximal ideal has mixed characteristic with finite residue field. Let $R=A[X_1,\ldots, X_n]$ be a polynomial ring and $I=(a_1U_1, \ldots, a_c U_c)\subseteq R$ an ideal, where $a_j \in A$ (not necessarily units) and $U_j$'s are monomials in $X_1, \ldots, X_n$. We call such

J. Fowler, Maaike A. M. Van Kooten, Rebecca Jensen-Clem

Ground-based high contrast exoplanet imaging requires state-of-the-art adaptive optics (AO) systems in order to detect extremely faint planets next to their brighter host stars. For such extreme AO systems (with high actuator count deformable mirrors over a small field of view), the lag time of the correction (which can impact our system by the amount the wa

JETSCAPE collaboration, W. Fan, G. Vujanovic, S. A. Bass

Parton energy-momentum exchange with the quark gluon plasma (QGP) is a multi-scale problem. In this work, we calculate the interaction of charm quarks with the QGP within the higher twist formalism at high virtuality and high energy using the MATTER model, while the low virtuality and high energy portion is treated via a (linearized) Boltzmann Transport (LBT

Kaiyuan Yang, Li Xia, Y. C. Tay

We present a novel method to estimate the dominant eigenvalue and eigenvector pair of any non-negative real matrix via graph infection. The key idea in our technique lies in approximating the solution to the first-order matrix ordinary differential equation (ODE) with the Euler method. Graphs, which can be weighted, directed, and with loops, are first conver

Eric Vincent

A spin glass is a diluted magnetic material in which the magnetic moments are randomly interacting, with a huge number of metastable states which prevent reaching equilibrium. Spin-glass models are conceptually simple, but require very sophisticated treatments. These models have become a paradigm for the understanding of glassy materials and also for the sol

Zhouyou Fan, Huey-Wen Lin, Matthew Zeilbeck

We present the nonperturbatively renormalized nucleon gluon momentum fraction using ensembles with $2+1+1$ flavors of highly improved staggered quarks (HISQ), generated by MILC Collaboration. The calculation is done using clover fermions for the valence action with three pion masses, $220$, $310$ and $690$ MeV and three lattice spacings, 0.09, 0.12, and 0.15

Lu Zhang, Lu Qi, Xu Yang, Hong Qiao

This paper tackles the problem of novel category discovery (NCD), which aims to discriminate unknown categories in large-scale image collections. The NCD task is challenging due to the closeness to the real-world scenarios, where we have only encountered some partial classes and images. Unlike other works on the NCD, we leverage the prototypes to emphasize t

Sergio Iguri, Nicolas Kovensky

We provide a novel local definition for spectrally flowed vertex operators in the SL(2,$\mathbb{R}$)-WZW model, generalising the proposal of Maldacena and Ooguri in [arXiv:hep-th/0111180] for the singly-flowed case to all $\omega > 1$. This allows us to establish the precise connection between the computation of correlators using the so-called spectral flow

Saber Rostamzadeh, Mustafa Sarisaman

Modifying the hexagonal lattices of graphene enables the repositioning and merging of the Dirac cones which proves to be a key element in the use of these materials for alternative electronic applications such as valleytronics. Here we study the nonequilibrium transport of carriers within a system containing two Dirac cones in both standard graphene and semi

Stephen M. Wilkins, Aswin P. Vijayan, Christopher C. Lovell, William J. Roper

The star formation and metal enrichment histories of galaxies - at any epoch - constitute one of the key properties of galaxies, and their measurement is a core aim of observational extragalactic astronomy. The lack of deep rest-frame optical coverage at high-redshift has made robust constraints elusive, but this is now changing thanks to the \emph{James Web

Brian Weber

On Kahler 4-manifolds, not necessarily compact or of finite topological type, we obtain relationships between the fundamental group of compact embedded Levi-flat or pseudoconvex submanifold and the fundamental group of the ambient manifold $M^4$. When a Levi-flat submanifold $V^3$ has finite fundamental group then $\pi_1(M^4)=\iota_*\pi_1(V^3)$; when a non-s

Raghav Mehta, Changjian Shui, Brennan Nichyporuk, Tal Arbel

Large, annotated datasets are not widely available in medical image analysis due to the prohibitive time, costs, and challenges associated with labelling large datasets. Unlabelled datasets are easier to obtain, and in many contexts, it would be feasible for an expert to provide labels for a small subset of images. This work presents an information-theoretic

Nathan Steinle, Benjamin P. Gompertz, Matt Nicholl

A black-hole neutron-star binary merger can lead to an electromagnetic counterpart called a kilonova if the neutron star is disrupted prior to merger. The observability of a kilonova depends on the amount of neutron star ejecta, which is sensitive to the aligned component of the black hole spin. We explore the dependence of the ejected mass on two main mecha

Gaetan Bakalli, Stéphane Guerrier, Olivier Scaillet

We develop a penalized two-pass regression with time-varying factor loadings. The penalization in the first pass enforces sparsity for the time-variation drivers while also maintaining compatibility with the no-arbitrage restrictions by regularizing appropriate groups of coefficients. The second pass delivers risk premia estimates to predict equity excess re

George Miloshevich, Bastien Cozian, Patrice Abry, Pierre Borgnat

Understanding extreme events and their probability is key for the study of climate change impacts, risk assessment, adaptation, and the protection of living beings. Forecasting the occurrence probability of extreme heatwaves is a primary challenge for risk assessment and attribution, but also for fundamental studies about processes, dataset and model validat

S. Maddali, T. D. Frazer, N. Delegan, K. J. Harmon

We present a general analytic approach to spatially resolve the nano-scale lattice distortion field of strained and defected compact crystals with Bragg coherent x-ray diffraction imaging (BCDI). Our approach relies on fitting a differentiable forward model simultaneously to multiple BCDI datasets corresponding to independent Bragg reflections from the same

Renjan Rajan John, Sujoy Mahato, Madhusudhan Raman

When placed on four-manifolds, $ \mathcal{N} = 2 $ gauge theories couple to topological invariants of the background via two functions $ A $ and $ B $. General considerations allow for these functions to be fixed in terms of the Coulomb moduli and other parameters in the theory, but only up to multiplicative factors about which little is known. We extend ear

Atanu Koley

Measurement of chemical and kinematic structures in prestellar cores is essential for better understanding the star formation process. Here, we study the three prestellar cores (TMC-1C, L1544, and TMC-1) of the Taurus molecular cloud by means of the thioxoethenylidene (CCS) radical and ammonia (NH3) molecule observed with Karl G. Jansky Very Large Array tele

Aakash A. Sahai, M. Golkowski, T. Katsouleas, G. Andonian

A new class of strongly excited plasmonic modes that open access to unprecedented Petavolts per meter electromagnetic fields promise wide-ranging, transformative impact. These modes are constituted by large amplitude oscillations of the ultradense, delocalized free electron Fermi gas which is inherent in conductive media. Here structured semiconductors with

Sarah I Chastain, Alexander J van der Horst, Dario Carbone

Several new radio facilities have a field of view and sensitivity well suited for transient searches. This makes it more important than ever to accurately determine transient rates in radio surveys. The work presented here seeks to do this task by using Monte-Carlo simulations. In particular, the user inputs either a real or simulated observational setup, an

Philipp Windischhofer, Werner Riegler

In this report we study the dynamics of passive quenching in a single-photon avalanche diode. Our discussion is based on a microscopic description of the electron-hole avalanche coupled to the equivalent circuit of the device, consisting of the quench resistor and the junction capacitance. Analytic expressions for the resulting signal shape are derived from

Jonathan Stieber, Moritz Fuchs, Anirban Mukhopadhyay

FrOoDo is an easy-to-use and flexible framework for Out-of-Distribution detection tasks in digital pathology. It can be used with PyTorch classification and segmentation models, and its modular design allows for easy extension. The goal is to automate the task of OoD Evaluation such that research can focus on the main goal of either designing new models, new

Maria Carvalho, Fagner B. Rodrigues, Paulo Varandas

We prove that the homeomorphisms of a compact manifold with dimension one have zero topological emergence, whereas in dimension greater than one the topological emergence of a C^0-generic conservative homeomorphism is maximal, equal to the dimension of the manifold. Moreover, we show that the metric emergence of continuous self-maps on compact metric spaces

Wenjie Yao, Francesc Verdugo, Henry O. Everitt, Rasmus E. Christiansen

We present a general framework for inverse design of nanopatterned surfaces that maximize spatially averaged surface-enhanced Raman (SERS) spectra from molecules distributed randomly throughout a material or fluid, building upon a recently proposed trace formulation for optimizing incoherent emission. This leads to radically different designs than optimizing

Jieru Ren, Bubo Ma, Lirong Liu, Wenqing Wei

We report on charge state measurements of laser-accelerated carbon ions in the energy range of several MeV penetrating a dense partially ionized plasma. The plasma was generated by irradiation of a foam target with laser-induced hohlraum radiation in the soft X-ray regime. We used the tri-cellulose acetate (C$_{9}$H$_{16}$O$_{8}$) foam of 2 mg/cm$^{-3}$ dens

Charles L. Bérubé, Frédérique Baron

Mechanistic induced polarization (IP) models describe the relationships between the intrinsic properties of geomaterials and their frequency-dependent complex conductivity spectra. However, the uncertainties associated with estimating petrophysical properties from IP data are still poorly understood. Therefore, practitioners rarely use mechanistic models to

Bin Shi, Guodong Sun

In this paper, we propose a sampling algorithm based on state-of-the-art statistical machine learning techniques to obtain conditional nonlinear optimal perturbations (CNOPs), which is different from traditional (deterministic) optimization methods.1 Specifically, the traditional approach is unavailable in practice, which requires numerically computing the g

Xiao Han, Kam Woh Ng, Sauradip Nag, Zhiyu Qu

Large-scale weakly supervised product retrieval is a practically useful yet computationally challenging problem. This paper introduces a novel solution for the eBay Visual Search Challenge (eProduct) held at the Ninth Workshop on Fine-Grained Visual Categorisation workshop (FGVC9) of CVPR 2022. This competition presents two challenges: (a) E-commerce is a dr

Elías Baro, José F. Fernando, Daniel Palacín

We study some model-theoretic notions in NIP by means of spectral topology. In the o-minimal setting we relate the o-minimal spectrum with other topological spaces such as the real spectrum and the space of infinitesimal types of Peterzil and Starchenko. In particular, we prove for definably compact groups that the space of closed points is homeomorphic to t

Yusuf Brima, Marcellin Atemkeng

Deep learning shows promise for medical image analysis but lacks interpretability, hindering adoption in healthcare. Attribution techniques that explain model reasoning may increase trust in deep learning among clinical stakeholders. This paper aimed to evaluate attribution methods for illuminating how deep neural networks analyze medical images. Using adapt

Beatrice Franzolini, Alexandros Beskos, Maria De Iorio, Warrick Poklewski Koziell

Reliable estimates of volatility and correlation are fundamental in economics and finance for understanding the impact of macroeconomics events on the market and guiding future investments and policies. Dependence across financial returns is likely to be subject to sudden structural changes, especially in correspondence with major global events, such as the

Liyun Zhao, Zhenyou Wang, Zhirong Su

Let $\mu$ be a positive Borel measure on the interval $[0,1)$. For $\gamma>0$, the Hankel matrix $\mathcal{H}_{\mu,\gamma}=(\mu_{n,k})_{n,k\geq0}$ with entries $\mu_{n,k}=\mu_{n+k}$, where $\mu_{n+k}=\int_{0}^{\infty}t^{n+k}d\mu(t)$. formally induces the operator $$\mathcal{H}_{\mu,\gamma}=\sum_{n=0}^{\infty}\left(\sum_{k=0}^{\infty}\mu_{n,k}a_k\right)\frac{

Thomas Eboli, Jean-Michel Morel, Gabriele Facciolo

The optics of any camera degrades the sharpness of photographs, which is a key visual quality criterion. This degradation is characterized by the point-spread function (PSF), which depends on the wavelengths of light and is variable across the imaging field. In this paper, we propose a two-step scheme to correct optical aberrations in a single raw or JPEG im

Stephan J. Garbin, Marek Kowalski, Virginia Estellers, Stanislaw Szymanowicz

The recent increase in popularity of volumetric representations for scene reconstruction and novel view synthesis has put renewed focus on animating volumetric content at high visual quality and in real-time. While implicit deformation methods based on learned functions can produce impressive results, they are `black boxes' to artists and content creators, t

Cristian L. Cortes, A. Eugene DePrince, Stephen K. Gray

Quantum subspace diagonalization (QSD) algorithms have emerged as a competitive family of algorithms that avoid many of the optimization pitfalls associated with parameterized quantum circuit algorithms. While the vast majority of the QSD algorithms have focused on solving the eigenpair problem for ground, excited-state, and thermal observable estimation, th

Charles Collot, Helge Dietert, Pierre Germain

The kinetic wave equation arises in wave turbulence to describe the Fourier spectrum of solutions to the cubic Schroedinger equation. The equation has two Kolmogorov-Zakharov steady states corresponding to out-of-equilibrium cascades transferring for the first solution mass from infinity to zero (small spatial scales to large scales) and for the other soluti

Xing Zhao, Haoran Liang, Peipei Li, Guodao Sun

Previous methods based on 3DCNN, convLSTM, or optical flow have achieved great success in video salient object detection (VSOD). However, they still suffer from high computational costs or poor quality of the generated saliency maps. To solve these problems, we design a space-time memory (STM)-based network, which extracts useful temporal information of the

Zijin Wu, Xingyi Li, Juewen Peng, Hao Lu

Neural Radiance Field (NeRF) and its variants have exhibited great success on representing 3D scenes and synthesizing photo-realistic novel views. However, they are generally based on the pinhole camera model and assume all-in-focus inputs. This limits their applicability as images captured from the real world often have finite depth-of-field (DoF). To mitig

I. Riabenko, S. Prokhorenko, E. Klimova, K. Beloshenko

This study investigates the application of surface-enhanced Raman spectroscopy (SERS) as a diagnostic tool for myasthenia gravis, a severe neuromuscular disorder characterized by muscle weakness and fatigue. Blood serum samples were analyzed using SERS substrates embedded with 5 nm gold nanoparticles, resulting in significant Raman signal enhancement and the

Modjtaba Shokrian Zini, Adam R. Brown, Michael Freedman

The co-emergence of locality between the Hamiltonian and initial state of the universe is studied in a simple toy model. We hypothesize a fundamental loss functional for the combined Hamiltonian and quantum state and minimize it by gradient descent. This minimization yields a tensor product structure simultaneously respected by both the Hamiltonian and the s

J. M. De Sousa, L. D. Machado, C. F. Woellner, M. Medina

In this work, we investigated the mechanical response and fracture dynamics of boron nitride nanotubes (BNNTs)-peapods under ultrasonic velocity impacts (from 1 km/s to 6 km/s) against a solid target. BNNT-peapods are BNNTs containing an encapsulated linear arrangement of C60 molecules. We carried out fully atomistic reactive (ReaxFF) molecular dynamics simu

Hady Moussa, Gengyu Xu, Shixiong Yin, Emanuele Galiffi

Time-reflection is a uniform inversion of the temporal evolution of a signal, which arises when an abrupt change in the properties of the host material occurs uniformly in space. At such a time-interface, a portion of the input signal is time-reversed, and its frequency spectrum is homogeneously translated while its momentum is conserved, forming the tempora

Xuecong Ji, Jiacheng Gao, Changming Yue, Zhijun Wang

The quadratic band crossing points (QBCPs) at Fermi level in two-dimension have been proposed to be unstable under electron-electron interaction. The possible interaction driven states include quantum anomalous Hall (QAH) state and various nematic ordered states. In this work, motivated by the discovery of ferromagnetic van der Waals layered metal-organic fr

Simon-Christian Klein

A novel approach for the stabilization of the discontinuous Galerkin method based on the Dafermos entropy rate crition is presented. The approach is centered around the efficient solution of linear or nonlinear optimization problems in every timestep as a correction to the basic discontinuous Galerkin scheme. The thereby enforced Dafermos criterion results i

Dahlia Malkhi, Pawel Szalachowski

Many cryptocurrency platforms are vulnerable to Maximal Extractable Value (MEV) attacks, where a malicious consensus leader can inject transactions or change the order of user transactions to maximize its profit. A promising line of research in MEV mitigation is to enhance the Byzantine fault tolerance (BFT) consensus core of blockchains by new functionaliti

A. Montorsi, U. Bhattacharya, Daniel González-Cuadra, M. Lewenstein

Higher-order topological crystalline phases in low-dimensional interacting quantum systems represent a challenging and largely unexplored research topic. Here, we derive a Hamiltonian describing fermions interacting through correlated hopping processes that break chiral invariance, but preserve both inversion and time-reversal symmetries. In this way, we sho

Todor M. Mishonov, Nedelcho I. Zahariev, Hassan Chamati, Albert M. Varonov

We analyze the conditions for observation of zero sound in layered perovskites with transition metal ion on chalcogenide oxidizer. We conclude that propagation of zero sound is possible only for anti-ferromagnetic sign of the $s$-$d$ interaction. If the $s$-$d$ exchange integral $J_{sd}$ has antiferromagnetic sign, as it is perhaps in the case for layered cu

George Sterman, Werner Vogelsang

We study the power corrections for electroweak boson production that are implied by threshold resummation, which we have extended to massive particles produced at measured transverse momentum, $p_T$, and rapidity. Power corrections in the resulting expressions arise from ambiguities in the low-scale behavior of the perturbative running coupling. Arguing for

Todor M. Mishonov, Nedelcho I. Zahariev, Hassan Chamati, Albert M. Varonov

We perform a thorough theoretical study of the electron properties of a generic CuO$_2$ plane in the framework of Shubin-Kondo-Zener $s$-$d$ exchange interaction that simultaneously describes the correlation between $T_c$ and the Cu4$s$ energy. To this end, we apply the Pokrovsky theory [J. Exp. Theor. Phys. 13, 447-450 (1961)] for anisotropic gap BCS superc

Jia Qi Yip, Dianwen Ng, Bin Ma, Konstantin Pervushin

Nuclear Magnetic Resonance (NMR) is used in structural biology to experimentally determine the structure of proteins, which is used in many areas of biology and is an important part of drug development. Unfortunately, NMR data can cost thousands of dollars per sample to collect and it can take a specialist weeks to assign the observed resonances to specific

AJ Piergiovanni, Kairo Morton, Weicheng Kuo, Michael S. Ryoo

Video question answering is a challenging task that requires understanding jointly the language input, the visual information in individual video frames, as well as the temporal information about the events occurring in the video. In this paper, we propose a novel multi-stream video encoder for video question answering that uses multiple video inputs and a n

Stephan Kim, Shiming Lei, Leslie M. Schoop, R. J. Cava

In a topological insulator the metallic surface states are easily distinguished from the insulating bulk states (FuKane07). By contrast, in a topological superconductor (FuKane08,Qi,FuBerg,Oppen), much less is known about the relationship between an edge supercurrent and the bulk pair condensate. Can we force their pairing symmetries to be incompatible? In t

Yousef Altaher, Ali Fadel, Mazen Alotaibi, Mazen Alyazidi

Masader (Alyafeai et al., 2021) created a metadata structure to be used for cataloguing Arabic NLP datasets. However, developing an easy way to explore such a catalogue is a challenging task. In order to give the optimal experience for users and researchers exploring the catalogue, several design and user experience challenges must be resolved. Furthermore,

Maryam Khalid, Edward W. Knightly

Uncontrolled emissions of gases from industrial accidents and disasters result in huge loss of life and property. Such extreme events require a quick and reliable survey of the site for effective rescue strategy planning. To achieve these goals, a network of unmanned aerial vehicles can be deployed that survey the affected region and identify safe and danger

Andrew Jackson, Theodoros Kapourniotis, Animesh Datta

We present two algorithms, one quantum and one classical, for estimating partition functions of quantum spin Hamiltonians. The former is a DQC1 (Deterministic quantum computation with one clean qubit) algorithm, and the first such for complex temperatures. The latter, for real temperatures, achieves performance comparable to a state-of-the-art DQC1 algorithm

Jorge Martín-Pérez, Nuria Molner, Francesco Malandrino, Carlos Jesús Bernardos

Open Radio Access Network (O-RAN) is an emerging paradigm, whereby virtualized network infrastructure elements from different vendors communicate via open, standardized interfaces. A key element therein is the RAN Intelligent Controller (RIC), an Artificial Intelligence (AI)-based controller. Traditionally, all data available in the network has been used to

Cheng Kang, Jindich Prokop, Lei Tong, Huiyu Zhou

This paper presents a new model architecture, gate with inhibition MLP (giMLP).The gate with inhibition on CycleMLP (gi-CycleMLP) can produce equal performance on the ImageNet classification task, and it also improves the BERT, Roberta, and DeBERTaV3 models depending on two novel techniques. The first is the gating MLP, where matrix multiplications between t

Weijia Li, Yawen Lai, Linning Xu, Yuanbo Xiangli

This paper presents OmniCity, a new dataset for omnipotent city understanding from multi-level and multi-view images. More precisely, the OmniCity contains multi-view satellite images as well as street-level panorama and mono-view images, constituting over 100K pixel-wise annotated images that are well-aligned and collected from 25K geo-locations in New York

Chenjing Bu

We compute Joyce's (arXiv:2111.04694) enumerative invariants $[\mathcal{M}^{\mathrm{ss}}_{(r,d)}]_{\mathrm{inv}}$ for semistable rank $r$ degree $d$ coherent sheaves on a complex projective curve. These invariants are a generalization of the fundamental class of the moduli of semistable sheaves. We express the invariants as a regularized sum, which is a way

Thijs van Ommen, Mathias Drton

The observational characteristics of a linear structural equation model can be effectively described by polynomial constraints on the observed covariance matrix. However, these polynomials can be exponentially large, making them impractical for many purposes. In this paper, we present a graphical notation for many of these polynomial constraints. The express

Konstantinos Panagiotou, Leon Ramzews

We develop a simple and unified approach to investigate several aspects of the cluster statistics of random expansive (multi-)sets. In particular, we determine the limiting distribution of the size of the smallest and largest clusters, we establish all moments of the distribution of the number of clusters, and we prove a local limit theorem for that distribu

CMS Collaboration

Measurements of the inclusive and normalised differential cross sections are presented for the production of single top quarks in association with a W boson in proton-proton collisions at a centre-of-mass energy of 13 TeV. The data used were recorded with the CMS detector at the LHC during 2016-2018, and correspond to an integrated luminosity of 138 fb$^{-1}

Jonathan Schilhan

We show that it is consistent relative to ZF, that there is no well-ordering of $\mathbb{R}$ while a wide class of special sets of reals such as Hamel bases, transcendence bases, Vitali sets or Bernstein sets exists. To be more precise, we can assume that every projective hypergraph on $\mathbb{R}$ has a maximal independent set, among a few other things. For

Andreas Bluhm, Ángela Capel, Paul Gondolf, Antonio Pérez-Hernández

Based on the proofs of the continuity of the conditional entropy by Alicki, Fannes, and Winter, we introduce in this work the almost locally affine (ALAFF) method. This method allows us to prove a great variety of continuity bounds for the derived entropic quantities. First, we apply the ALAFF method to the Umegaki relative entropy. This way, we recover know

Antoine Dufour, Yohan Poirier-Ginter, Alexandre Lessard, Ryan Smith

Adaptive instance normalization (AdaIN) has become the standard method for style injection: by re-normalizing features through scale-and-shift operations, it has found widespread use in style transfer, image generation, and image-to-image translation. In this work, we present a generalization of AdaIN which relies on the whitening and coloring transformation

Maurizio Consoli, Leonardo Cosmai, Fabrizio Fabbri

Theoretical arguments and lattice simulations suggest that, beside the known resonance of mass $m_h=$ 125 GeV, the Higgs field might exhibit a second resonance with a larger mass $(M_H)^{\rm theor} = 690 \pm 10 ~({\rm stat}) \pm 20 ~({\rm sys})~ {\rm GeV}$ which, however, would couple to longitudinal W's with the same typical strength as the low-mass state a

Felix Ott, Nisha Lakshmana Raichur, David Rügamer, Tobias Feigl

Visual-inertial localization is a key problem in computer vision and robotics applications such as virtual reality, self-driving cars, and aerial vehicles. The goal is to estimate an accurate pose of an object when either the environment or the dynamics are known. Absolute pose regression (APR) techniques directly regress the absolute pose from an image inpu

Philippe Gaucher

Using the notion of tame regular $d$-path of the topological $n$-cube, we introduce the tame regular realization of a precubical set as a multipointed $d$-space. Its execution paths correspond to the nonconstant tame regular $d$-paths in the geometric realization of the precubical set. The associated Moore flow gives rise to a functor from precubical sets to

Yu Li, Bill Wang, William Li, Ruwen Qin

The Autonomous Truck-Mounted Attenuator (ATMA) system is a lead-follower vehicle system based on autonomous driving and connected vehicle technologies. The lead truck performs maintenance tasks on the road, and the unmanned follower truck alerts passing vehicles about the moving work zone and protects workers and the equipment. While the ATMA has been under

Qi Hou, Jianping Jiang, Charles M. Newman

We consider the zeros of the partition function of the Ising model with ferromagnetic pair interactions and complex external field. Under the assumption that the graph with strictly positive interactions is connected, we vary the interaction (denoted by $t$) at a fixed edge. It is already known that each zero is monotonic (either increasing or decreasing) in

Gerry Chen, Seth Hutchinson, Frank Dellaert

We present a locally optimal tracking controller for Cable Driven Parallel Robot (CDPR) control based on a time-varying Linear Quadratic Gaussian (TV-LQG) controller. In contrast to many methods which use fixed feedback gains, our time-varying controller computes the optimal gains depending on the location in the workspace and the future trajectory. Meanwhil

Ryosuke Hirai, Philipp Podsiadlowski

We perform 3D hydrodynamical simulations of new-born neutron stars (NSs) colliding with main-sequence binary companions after supernova explosions. Based on those hydrodynamical models, we construct a semi-analytical formula that describes the drag force inside stars with steep density gradients. We then compute the outcome of NS--companion collisions over a

E. Rodriguez-Fernandez, S. N. Santalla, M. Castro, R. Cuerno

Until very recently, the asymptotic occurrence of intrinsic anomalous scaling has been expected to require concomitant effects for kinetically rough interfaces, like quenched disorder or morphological instabilities. However, counterexamples have been recently reported for simpler situations dominated by time-dependent noise, as in the discrete growth system

Pabbathi Sri Charan, Saksham Gupta, Satvik Agrawal, Gadupudi Sahithi Sindhu

Computers today aren't just confined to laptops and desktops. Mobile gadgets like mobile phones and laptops also make use of it. However, one input device that hasn't changed in the last 50 years is the QWERTY keyboard. Users of virtual keyboards can type on any surface as if it were a keyboard thanks to sensor technology and artificial intelligence. In this