November 2022 arXiv papers — page 160

Surajit Paul, Ruta Kale, Abhirup Datta, Aritra Basu

Diffuse radio emission has been detected in a considerable number of galaxy clusters and groups, revealing the presence of pervasive cosmic magnetic fields, and of relativistic particles in the large-scale structure (LSS) of the Universe. Since cluster radio emission is faint and steep spectrum, its observations are largely limited by the instrument sensitiv

Jonah Kudler-Flam

We study a proper definition of R\'enyi mutual information (RMI) in quantum field theory as defined via the Petz R\'enyi relative entropy. Unlike the standard definition, the RMI we compute is a genuine measure of correlations between subsystems, as evidenced by its non-negativity and monotonicity under local operations. Furthermore, the RMI is UV finite and

Xingchen Li, Isaac Shlosman, Daniel Pfenniger, Clayton Heller

The buckling process in stellar bars is full of unsolved issues. We analyze the origin of the buckling instability in stellar bars using high-resolution N-body simulations. Previous studies have promoted the nonresonant firehose instability to be responsible for the vertical buckling. We have analyzed the buckling process in terms of the resonant excitation

Erez Berg, Gaël Grissonnanche, Steven A. Kivelson, Chaitanya Murthy

A variety of low-temperature, normal-state properties of optimally and overdoped cuprate superconductors, including the DC and optical transport responses, are sufficiently anomalous that they might seem to be inconsistent with any quasiparticle description. However, we show by explicit construction that the most salient phenomena can be accounted for in a s

Alessandro Trinca, Raffaella Schneider, Roberto Maiolino, Rosa Valiante

In this paper we provide predictions for the BH population that would be observable with planned JWST surveys at $5 \le z \le 15$. We base our study on the recently developed Cosmic Archaeology Tool (CAT), which allows us to model BH seeds formation and growth, while being consistent with the general population of AGNs and galaxies observed at $4 \le z \le 7

Yi-Ming Wu, Ronny Thomale, S. Raghu

Motivated by the observation of a pair density wave (PDW) in the kagome metal CsV${}_3$Sb${}_5$, we consider the fate of electrons near a p-type van Hove singularity (vHS) in the presence of local repulsive interactions. We study the effect of such interactions on Fermi surface "patches" at the vHS. We show how a feature unique to the Kagome lattice known as

Marco Guzzi, T. J. Hobbs, Keping Xie, Joey Huston

The question of the existence and possible magnitude of nonperturbative (often called "intrinsic") charm in the proton has long confounded attempts to cleanly isolate such a contribution in global analyses of high-energy experiments. In this letter, we show that the available (non)perturbative QCD theory and hadronic data have still not developed to a suffic

Guanda Lin, Gang Yang

The double-copy construction for form factors was reported in our previous work, in which a novel mechanism of turning spurious poles in Yang-Mills theory into physical poles in gravity is observed. This paper is the first of a series of two papers providing the details as well as various generalizations on the double-copy construction of tree-level form fac

Gary T. Horowitz, Maciej Kolanowski, Jorge E. Santos

In holography, the IR behavior of a quantum system at nonzero density is described by the near horizon geometry of an extremal charged black hole. It is commonly believed that for systems on $S^3$, this near horizon geometry is $AdS_2\times S^3 $. We show that this is not the case: generic static, nonspherical perturbations of $AdS_2\times S^3 $ blow up at t

Shaunak Modak, Shany Danieli, Jenny E. Greene

Globular Clusters (GCs) provide valuable insight into the properties of their host galaxies' dark matter halos. Using N-body simulations incorporating semianalytic dynamical friction and GC-GC merger prescriptions, we study the evolution of GC radial distributions and mass functions in cuspy and cored dark matter halos. Modeling the dynamics of the GC-rich s

André Melo, Nathan Earnest-Noble, Francesco Tacchino

Quantum machine learning algorithms based on parameterized quantum circuits are promising candidates for near-term quantum advantage. Although these algorithms are compatible with the current generation of quantum processors, device noise limits their performance, for example by inducing an exponential flattening of loss landscapes. Error suppression schemes

Mingyu Li, Zheng Cai, Fuyan Bian, Xiaojing Lin

We present a study of the mass-metallicity relation (MZR) of 51 dwarf galaxies ($M_\star\approx 10^{6.5} - 10^{9.5}~M_\odot$) at $z = 2-3$ from the Abell 2744 and SMACS J0723-3732 galaxy cluster fields. These dwarf galaxies are identified and confirmed by deep JWST/NIRISS imaging and slitless grism spectroscopic observations. By taking advantage of the super

Rahul Sahay, Ashvin Vishwanath, Ruben Verresen

While many-body systems can host long-ranged entangled quantum spin liquids (QSLs), the ingredients for realizing these as ground states can be prohibitively difficult. In many circumstances, one requires (i) a constrained Hilbert space and (ii) an extensive quantum superposition. The paradigmatic example is the toric code, or $\mathbb{Z}_2$ spin liquid, whi

Jesse Osborne, Ian P. McCulloch, Bing Yang, Philipp Hauke

A major driver of quantum-simulator technology is the prospect of probing high-energy phenomena in synthetic quantum matter setups at a high level of control and tunability. Here, we propose an experimentally feasible realization of a large-scale $2+1$D $\mathrm{U}(1)$ gauge theory with dynamical matter and gauge fields in a cold-atom quantum simulator with

Jason Aebischer, Marko Pesut, Zachary Polonsky

We study the contribution from dipole operators to one-loop Fierz identities and provide the resulting QCD and QED shifts to the tree-level relations for all four-fermion operators. The results simplify one-loop basis changes as well as matching computations and allow one to consistently eliminate operators from an operator basis which give rise to complicat

Adam M. Dillamore, Vasily Belokurov, N. Wyn Evans, Andreea S. Font

We report a correlation between the presence of a Gaia-Sausage-Enceladus (GSE) analogue and dark matter halo spin in the ARTEMIS simulations of Milky Way-like galaxies. The haloes which contain a large population of accreted stars on highly radial orbits (like the GSE) have lower spin on average than their counterparts with more isotropic stellar velocity di

Ryan Dungee, Jennifer van Saders, Eric Gaidos, Mark Chun

We present stellar rotation periods for late K- and early M-dwarf members of the 4 Gyr old open cluster M67 as calibrators for gyrochronology and tests of stellar spin-down models. Using Gaia EDR3 astrometry for cluster membership and Pan-STARRS (PS1) photometry for binary identification, we build this set of rotation periods from a campaign of monitoring M6

Ruben Verresen, Umberto Borla, Ashvin Vishwanath, Sergej Moroz

Where in the landscape of many-body phases of matter do we place the Higgs condensate of a gauge theory? On the one hand, the Higgs phase is gapped, has no local order parameter, and for fundamental Higgs fields is adiabatically connected to the confined phase. On the other hand, Higgs phases such as superconductors display rich phenomenology. In this work,

Yutong Chen, Ronglai Zuo, Fangyun Wei, Yu Wu

Sign languages are visual languages using manual articulations and non-manual elements to convey information. For sign language recognition and translation, the majority of existing approaches directly encode RGB videos into hidden representations. RGB videos, however, are raw signals with substantial visual redundancy, leading the encoder to overlook the ke

Aaron Hui, Brian Skinner

A resistor at finite temperature produces white noise fluctuations of the current called Johnson-Nyquist noise. Measuring the amplitude of this noise provides a powerful primary thermometry technique to access the electron temperature. In practical situations, however, one needs to generalize the Johnson-Nyquist theorem to handle spatially inhomogeneous temp

Sara Chahid, Serafim Teknowijoyo, Iris Mowgood, Armen Gulian

The superconducting diode effect has been recently reported in a variety of systems and different symmetry breaking mechanisms have been examined. However, the frequency range of these potentially important devices still remains obscure. We investigated superconducting micro-bridges of Nb$_{3}$Sn in out-of-plane magnetic fields; optimum magnetic fields of $\

Di Luo, Jiayu Shen, Rumen Dangovski, Marin Soljačić

Quantum optimization, a key application of quantum computing, has traditionally been stymied by the linearly increasing complexity of gradient calculations with an increasing number of parameters. This work bridges the gap between Koopman operator theory, which has found utility in applications because it allows for a linear representation of nonlinear dynam

Julius Berner, Lorenz Richter, Karen Ullrich

We establish a connection between stochastic optimal control and generative models based on stochastic differential equations (SDEs), such as recently developed diffusion probabilistic models. In particular, we derive a Hamilton-Jacobi-Bellman equation that governs the evolution of the log-densities of the underlying SDE marginals. This perspective allows to

Fabio De Marco, Sara Savatović, Ronan Smith, Vittorio Di Trapani

Wavefront-marking X-ray imaging techniques use e.g., sandpaper or a grating to generate intensity fluctuations, and analyze their distortion by the sample in order to retrieve attenuation, phase-contrast, and dark-field information. Phase contrast yields an improved visibility of soft-tissue specimens, while dark-field reveals small-angle scatter from sub-re

Joachim Ott, Shih-Chii Liu

This work proposes a model for continual learning on tasks involving temporal sequences, specifically, human motions. It improves on a recently proposed brain-inspired replay model (BI-R) by building a biologically-inspired conditional temporal variational autoencoder (BI-CTVAE), which instantiates a latent mixture-of-Gaussians for class representation. We i

Liam Cronin, Soheil Sadeghi Eshkevari, Thomas J. Matarazzo, Sebastiano Milardo

The knowledge gap in the expected and actual conditions of bridges has created worldwide deficits in infrastructure service and funding challenges. Despite rapid advances over the past four decades, sensing technology is still not a part of bridge inspection protocols. Every time a vehicle with a mobile device passes over a bridge, there is an opportunity to

Philippa S. Cole, Gianfranco Bertone, Adam Coogan, Daniele Gaggero

Future gravitational wave interferometers such as LISA, Taiji, DECIGO, and TianQin, will enable precision studies of the environment surrounding black holes. In this paper, we study intermediate and extreme mass ratio binary black hole inspirals, and consider three possible environments surrounding the primary black hole: accretion disks, dark matter spikes,

Umur A. Ciftci, Gokturk Yuksek, Ilke Demir

Recently, productization of face recognition and identification algorithms have become the most controversial topic about ethical AI. As new policies around digital identities are formed, we introduce three face access models in a hypothetical social network, where the user has the power to only appear in photos they approve. Our approach eclipses current ta

Han Li, Wei Li, Gang Su

The pursuit of quantum spin liquid (QSL) in the Kitaev honeycomb magnets has drawn intensive attention recently. In particular, $\alpha$-RuCl$_3$ has been widely recognized as a promising candidate for the Kitaev QSL. Although the compound exhibits an antiferromagnetic order under zero field, it is believed to be endowed with fractionalized excitations, and

Mikkel Abrahamsen, Nichlas Langhoff Rasmussen

We study the problem of partitioning a given simple polygon $P$ into a minimum number of connected polygonal pieces, each of bounded size. We describe a general technique for constructing such partitions that works for several notions of `bounded size,' namely that each piece must be contained in an axis-aligned or arbitrarily rotated unit square or a unit d

Neil James Cook, Étienne Artigau, René Doyon, Melissa Hobson

With the maturation of near-infrared high-resolution spectroscopy, especially when used for precision radial velocity, data reduction has faced unprecedented challenges in terms of how one goes from raw data to calibrated, extracted, and corrected data with required precisions of thousandths of a pixel. Here we present APERO (A PipelinE to Reduce Observation

Zakaria Mhammedi, Khashayar Gatmiry

The aim of this paper is to design computationally-efficient and optimal algorithms for the online and stochastic exp-concave optimization settings. Typical algorithms for these settings, such as the Online Newton Step (ONS), can guarantee a $O(d\ln T)$ bound on their regret after $T$ rounds, where $d$ is the dimension of the feasible set. However, such algo

Sajad Movahedi, Azadeh Shakery

While deep learning in the form of recurrent neural networks (RNNs) has caused a significant improvement in neural language modeling, the fact that they are extremely prone to overfitting is still a mainly unresolved issue. In this paper we propose a regularization method based on generative adversarial networks (GANs) and adversarial training (AT), that can

Parag Gupta, Radostin D. Simitev, David MacTaggart

Magnetic helicity is a fundamental constraint in both ideal and resistive magnetohydrodynamics. Measurements of magnetic helicity density on the Sun and other stars are used to interpret the internal behaviour of the dynamo generating the global magnetic field. In this note, we study the behaviour of the global relative magnetic helicity in three self-consis

Anna Currey, Maria Nădejde, Raghavendra Pappagari, Mia Mayer

As generic machine translation (MT) quality has improved, the need for targeted benchmarks that explore fine-grained aspects of quality has increased. In particular, gender accuracy in translation can have implications in terms of output fluency, translation accuracy, and ethics. In this paper, we introduce MT-GenEval, a benchmark for evaluating gender accur

Md Tahmid Rahman Laskar, Cheng Chen, Xue-Yong Fu, Shashi Bhushan TN

Telephone transcription data can be very noisy due to speech recognition errors, disfluencies, etc. Not only that annotating such data is very challenging for the annotators, but also such data may have lots of annotation errors even after the annotation job is completed, resulting in a very poor model performance. In this paper, we present an active learnin

Guanghui Fu, Gabriel Jimenez, Sophie Loizillon, Rosana El Jurdi

Early and accurate diagnosis of parkinsonian syndromes is critical to provide appropriate care to patients and for inclusion in therapeutic trials. The red nucleus is a structure of the midbrain that plays an important role in these disorders. It can be visualized using iron-sensitive magnetic resonance imaging (MRI) sequences. Different iron-sensitive contr

Naiyuan J. Zhang, Yibang Wang, Kenji Watanabe, Takashi Taniguchi

Due to its potential connection with nematicity, electronic anisotropy has been the subject of intense research effort on a wide variety of material platforms. The emergence of spatial anisotropy not only offers a characterization of material properties of metallic phases, which cannot be accessed via conventional transport techniques, but it also provides a

A. Saldana-Lopez, D. Schaerer, J. Chisholm, A. Calabrò

To better understand the ionizing properties of galaxies in the EoR, we investigate deep, rest-frame ultraviolet (UV) spectra of $\simeq 500$ star-forming galaxies at $3 \leq z \leq 5$ selected from the public ESO-VANDELS spectroscopic survey. The absolute ionizing photon escape fraction ($f_{\rm esc}^{\rm abs}$) is derived by combining absorption line measu

T. J. Jopek, M. Hajdukova, R. Rudawska, M. Koseki

The Shower Database (SD) of the Meteor Data Center (MDC) had been operating on the basis of stream-naming rules which were too complex and insufficiently precise for 15 years. With a gradual increase in the number of discovered meteor showers, the procedure for submitting new showers to the database and naming them lead to situations that were inconsistent w

M. Crespo-Ballesteros, A. B. Matsko, M. Sumetsky

The formation of optical frequency combs (OFCs) by the parametric modulation of optical microresonators is commonly described by lumped-parameter models. However, these models do not consider the actual spatial distribution of the parametric modulation (SDPM). Here, we show that the effect of the SDPM becomes of special importance for an elongated SNAP bottl

Alexander Lytchak

We discuss solutions of several questions concerning the geometry of conformal planes.

Richard T. Baillie, Francis X. Diebold, George Kapetanios, Kun Ho Kim

We suggest a new single-equation test for Uncovered Interest Parity (UIP) based on a dynamic regression approach. The method provides consistent and asymptotically efficient parameter estimates, and is not dependent on assumptions of strict exogeneity. This new approach is asymptotically more efficient than the common approach of using OLS with HAC robust st

Zikang Leng, Yash Jain, Hyeokhyen Kwon, Thomas Plötz

Previous work has demonstrated that virtual accelerometry data, extracted from videos using cross-modality transfer approaches like IMUTube, is beneficial for training complex and effective human activity recognition (HAR) models. Systems like IMUTube were originally designed to cover activities that are based on substantial body (part) movements. Yet, life

Georgios V. Pitsiladis, Petros S. Stefaneas

In this paper, we address program development by multiple different programmers (or programming teams), each working in different settings (programming languages or reasoning frameworks), but following a common specification; in particular, we examine at an abstract level the problem of translatability between their produced programs. To this end, after cons

Ahmad M. Alghamdi, Sadek Gala, Maria Alessandra Ragusa

In this paper, we study regularity of weak solutions to the incompressible Navier-Stokes equations in $\mathbb{R}^{3}\times (0,T)$. The main goal is to establish the regularity criterion via the gradient of one velocity component in multiplier spaces.

Randall Balestriero, Yann LeCun

Deep Neural Networks (DNNs) outshine alternative function approximators in many settings thanks to their modularity in composing any desired differentiable operator. The formed parametrized functional is then tuned to solve a task at hand from simple gradient descent. This modularity comes at the cost of making strict enforcement of constraints on DNNs, e.g.

An Yang, Junshu Pan, Junyang Lin, Rui Men

The tremendous success of CLIP (Radford et al., 2021) has promoted the research and application of contrastive learning for vision-language pretraining. In this work, we construct a large-scale dataset of image-text pairs in Chinese, where most data are retrieved from publicly available datasets, and we pretrain Chinese CLIP models on the new dataset. We dev

Hoang Ky Nguyen

Some sixty years ago Buchdahl pioneered a program in search of static spherically symmetric vacua for pure $R^{2}$ gravity (Nuovo Cimento, Vol 23, No 1, pp 141-157 (1962); [https://link.springer.com/article/10.1007/BF02733549]). Surpassing several obstacles, his work culminated in a non-linear second-order ordinary differential equation (ODE) which required

Konstantinos Klapsas, Karolos Nikitaras, Nikolaos Ellinas, June Sig Sung

A large part of the expressive speech synthesis literature focuses on learning prosodic representations of the speech signal which are then modeled by a prior distribution during inference. In this paper, we compare different prior architectures at the task of predicting phoneme level prosodic representations extracted with an unsupervised FVAE model. We use

Dong Yeap Kang, Tom Kelly, Daniela Kühn, Deryk Osthus

For all integers $n \geq k > d \geq 1$, let $m_{d}(k,n)$ be the minimum integer $D \geq 0$ such that every $k$-uniform $n$-vertex hypergraph $\mathcal H$ with minimum $d$-degree $\delta_{d}(\mathcal H)$ at least $D$ has an optimal matching. For every fixed integer $k \geq 3$, we show that for $n \in k \mathbb{N}$ and $p = \Omega(n^{-k+1} \log n)$, if $\mathc

Yogesh Balaji, Seungjun Nah, Xun Huang, Arash Vahdat

Large-scale diffusion-based generative models have led to breakthroughs in text-conditioned high-resolution image synthesis. Starting from random noise, such text-to-image diffusion models gradually synthesize images in an iterative fashion while conditioning on text prompts. We find that their synthesis behavior qualitatively changes throughout this process

Kai Packhäuser, Lukas Folle, Florian Thamm, Andreas Maier

The availability of large-scale chest X-ray datasets is a requirement for developing well-performing deep learning-based algorithms in thoracic abnormality detection and classification. However, biometric identifiers in chest radiographs hinder the public sharing of such data for research purposes due to the risk of patient re-identification. To counteract t

Diptimoy Ghosh, Rajat Sharma, Farman Ullah

We derive the causality and unitarity constraints on dimension 6 and dimension 8 Gluon field strength operators in the Standard Model Effective Field Theory (SMEFT). In the first part of the paper, we use the 'amplitude analysis' i.e. dispersion relation for $2\rightarrow2$ scattering in the forward limit, to put bounds on the Wilson coefficients. We show th

Lukas Broers, Ludwig Mathey

We demonstrate the robustness of the recently established Floquet-assisted superradiant phase of the parametrically driven dissipative Dicke model, inspired by light-induced dynamics in graphene. In particular, we show the robustness of this state against key imperfections and argue for the feasibility of utilizing it for laser operation. We consider the eff

Luca Accornero, Francesco Cattafi

This work is a spin-off of an on-going programme which aims at revisiting the original studies of Lie and Cartan on pseudogroups and geometric structures from a modern perspective. Within the framework of Lie groupoids equipped with a special multiplicative form - called Pfaffian groupoids - we focus on principal bibundles and Morita equivalences. In particu

Yun-Ning Hung, Chao-Han Huck Yang, Pin-Yu Chen, Alexander Lerch

Transfer learning (TL) approaches have shown promising results when handling tasks with limited training data. However, considerable memory and computational resources are often required for fine-tuning pre-trained neural networks with target domain data. In this work, we introduce a novel method for leveraging pre-trained models for low-resource (music) cla

Miel Sharf, Daniel Zelazo

We study cluster assignment in homogeneous diffusive multi-agent networks. Given the number of clusters and agents within each cluster, we design the network graph ensuring the system will converge to the prescribed cluster configuration. Using recent results linking clustering and symmetries, we show that it is possible to design an oriented graph for which

Shawqi Al-Maliki, Faissal El Bouanani, Mohamed Abdallah, Junaid Qadir

Data distribution shift is a common problem in machine learning-powered smart city applications where the test data differs from the training data. Augmenting smart city applications with online machine learning models can handle this issue at test time, albeit with high cost and unreliable performance. To overcome this limitation, we propose to endow test-t

Manash Dey, Pralay Chakraborty, Subhankar Roy

We propose an elegant neutrino mass matrix texture entitled $\mu$-$\tau$ mixed symmetry highlighting two simple correlations among its elements and a detailed analysis is carried out to see its phenomenological implications. The proposed texture is motivated in the framework of Seesaw mechanism in association with $A_4$ symmetry

Joel Grodstein, Michael Levin

Morphogenesis, the establishment and repair of emergent complex anatomy by groups of cells, is a fascinating and biomedically-relevant problem. One of its most fascinating aspects is that a developing embryo can reliably recover from disturbances, such as splitting into twins. While this reliability implies some type of goal-seeking error minimization over a

Mikhail Sodin, Aron Wennman, Oren Yakir

Consider a random planar point process whose law is invariant under planar isometries. We think of the process as a random distribution of point charges and consider the electric field generated by the charge distribution. In Part I of this work, we found a condition on the spectral side which characterizes when the field itself is invariant with a well-defi

Jacopo Guidolin, Vyacheslav Kungurtsev, Ondřej Kuželka

Bayesian methods of sampling from a posterior distribution are becoming increasingly popular due to their ability to precisely display the uncertainty of a model fit. Classical methods based on iterative random sampling and posterior evaluation such as Metropolis-Hastings are known to have desirable long run mixing properties, however are slow to converge. G

Sovan Biswas, Anthony Rhodes, Ramesh Manuvinakurike, Giuseppe Raffa

Recent temporal action segmentation approaches need frame annotations during training to be effective. These annotations are very expensive and time-consuming to obtain. This limits their performances when only limited annotated data is available. In contrast, we can easily collect a large corpus of in-domain unannotated videos by scavenging through the inte

Kai Huang, Mingfei Cheng, Yang Wang, Bochen Wang

Few-shot segmentation (FSS) aims to segment objects of unseen classes given only a few annotated support images. Most existing methods simply stitch query features with independent support prototypes and segment the query image by feeding the mixed features to a decoder. Although significant improvements have been achieved, existing methods are still face cl

Bhavani Shankar M. R., Kumar Vijay Mishra, Mohammad Alaee-Kerahroodi

The previous chapters have discussed the canvas of joint radar-communications (JRC), highlighting the key approaches of radar-centric, communications-centric and dual-function radar-communications systems. Several signal processing and related aspects enabling these approaches including waveform design, resource allocation, privacy and security, and intellig

Viswanathan Palaniappan, S. Ramanan, Michael Urban

In this work, we calculate the ground state energy of pure neutron matter using the renormalization group based low-momentum effective interaction $V_{\text{low-}k}$ in Bogoliubov many-body perturbation theory (BMBPT), which is a perturbative expansion around the Hartree-Fock-Bogoliubov (HFB) ground state. In order to capture the low-density behavior of neut

Fengjun Wang, Sarai Mizrachi, Moran Beladev, Guy Nadav

Multi-label image classification is a foundational topic in various domains. Multimodal learning approaches have recently achieved outstanding results in image representation and single-label image classification. For instance, Contrastive Language-Image Pretraining (CLIP) demonstrates impressive image-text representation learning abilities and is robust to

Noah Halberstam, Tom Hutchcroft

We compute the precise logarithmic corrections to Alexander-Orbach behaviour for various quantities describing the geometric and spectral properties of the four-dimensional uniform spanning tree. In particular, we prove that the volume of an intrinsic $n$-ball in the tree is $n^2 (\log n)^{-1/3+o(1)}$, that the typical intrinsic displacement of an $n$-step r

David D. K. Chow

We examine properties of the Concrete (or Gumbel-softmax) distribution on the simplex. Using the natural vector space structure of the simplex, the Concrete distribution can be regarded as a transformation of the uniform distribution through a reflection and a location-scale transformation. The Fisher information is computed and the corresponding information

A. Zare, R. Wirth, C. A. Haselby, H. Hergert

In this work, we explore modewise Johnson-Lindenstrauss embeddings (JLEs) as a tool to reduce the computational cost and memory requirements of nuclear many-body methods. JLEs are randomized projections of high-dimensional data tensors onto low-dimensional subspaces that preserve key structural features. Such embeddings allow for the oblivious and incrementa

Justin Janquart, Tomasz Baka, Anuradha Samajdar, Tim Dietrich

In the coming years, third-generation detectors such as Einstein Telescope and Cosmic Explorer will enter the network of ground-based gravitational-wave detectors. Their current design predicts a significantly improved sensitivity band with a lower minimum frequency than existing detectors. This, combined with the increased arm length, leads to two major eff

R. Molina, E. Oset

We look at the mass distribution of the $D_s^+ \pi^-$ in the $B^0 \to \bar{D}^0 D_s^+ \pi^-$ decay, where a peak has been observed in the region of the $D^*_s \rho$, $D^* K^*$ thresholds. By creating these two channels together with a $\bar{D}^0$ in $B^0$ decay and letting them interact as coupled channels, we obtain a structure around their thresholds, shor

Simon Martin Breum, Bojan Kostic, Michael Szell

Contemporary street design prioritizes vehicular traffic flow and assumes compliant road users. However, actual human behavior is typically neglected, especially of cyclists, leading to streets with inadequate wayfinding and protection from vehicular traffic. To improve planning, here we develop a computational method to detect cyclist trajectories from vide

Yu. S. Orlov, S. V. Nikolaev, S. G. Ovchinnikov

Ultrafast quantum dynamics relaxation of a photoexcited state in a strongly correlated spin crossover system LaCoO3 under a sudden perturbation is considered with the density matrix generalized master equation. The magnetization and cobalt-oxygen bond length oscillations were found. The evolution of the electronic band structure during relaxation is calculat

Zexu Pan, Gordon Wichern, François G. Germain, Aswin Subramanian

Speaker diarization is well studied for constrained audios but little explored for challenging in-the-wild videos, which have more speakers, shorter utterances, and inconsistent on-screen speakers. We address this gap by proposing an audio-visual diarization model which combines audio-only and visual-centric sub-systems via late fusion. For audio, we show th

Miel Sharf, Bart Besselink, Karl Henrik Johansson

Designing large-scale control systems to satisfy complex specifications is hard in practice, as most formal methods are limited to systems of modest size. Contract theory has been proposed as a modular alternative to formal methods in control, in which specifications are defined by assumptions on the input to a component and guarantees on its output. However

Jiayi Chen, Wen Wu, Liye Shi, Yu Ji

Sequential Recommendation is a prominent topic in current research, which uses user behavior sequence as an input to predict future behavior. By assessing the correlation strength of historical behavior through the dot product, the model based on the self-attention mechanism can capture the long-term preference of the sequence. However, it has two limitation

Kumar Vijay Mishra, Ahmet M. Elbir, Amir I. Zaghloul

Metasurfaces (MTSs) are increasingly emerging as enabling technologies to meet the demands for multi-functional, small form-factor, efficient, reconfigurable, tunable, and low-cost radio-frequency (RF) components because of their ability to manipulate waves in a sub-wavelength thickness through modified boundary conditions. They enable the design of reconfig

Jasper van Doornmalen, Christopher Hojny

Handling symmetries in optimization problems is essential for devising efficient solution methods. In this article, we present a general framework that captures many of the already existing symmetry handling methods. While these methods are mostly discussed independently from each other, our framework allows to apply different methods simultaneously and thus

Xishun Liao, Xuanpeng Zhao, Ziran Wang, Zhouqiao Zhao

Connected and automated vehicles (CAVs) are supposed to share the road with human-driven vehicles (HDVs) in a foreseeable future. Therefore, considering the mixed traffic environment is more pragmatic, as the well-planned operation of CAVs may be interrupted by HDVs. In the circumstance that human behaviors have significant impacts, CAVs need to understand H

Jiayuan Wang, Q. M. Jonathan Wu, Farhad Pourpanah

Magnetic resonance (MR) and computer tomography (CT) images are two typical types of medical images that provide mutually-complementary information for accurate clinical diagnosis and treatment. However, obtaining both images may be limited due to some considerations such as cost, radiation dose and modality missing. Recently, medical image synthesis has aro

Danni Liu, Jan Niehues

The cornerstone of multilingual neural translation is shared representations across languages. Given the theoretically infinite representation power of neural networks, semantically identical sentences are likely represented differently. While representing sentences in the continuous latent space ensures expressiveness, it introduces the risk of capturing of

Christina Ovezik, Dimitris Karakostas, Aggelos Kiayias

Decentralization has been touted as the principal security advantage which propelled blockchain systems at the forefront of developments in the financial technology space. Its exact semantics nevertheless remain highly contested and ambiguous, with proponents and critics disagreeing widely on the level of decentralization offered by existing systems. To addr

Maciej Eder

In this paper, I introduce a simple method of computing relative word frequencies for authorship attribution and similar stylometric tasks. Rather than computing relative frequencies as the number of occurrences of a given word divided by the total number of tokens in a text, I argue that a more efficient normalization factor is the total number of relevant

Shikhar Murty, Pratyusha Sharma, Jacob Andreas, Christopher D. Manning

When trained on language data, do transformers learn some arbitrary computation that utilizes the full capacity of the architecture or do they learn a simpler, tree-like computation, hypothesized to underlie compositional meaning systems like human languages? There is an apparent tension between compositional accounts of human language understanding, which a

Iris Mowgood, Serafim Teknowijoyo, Sara Chahid, Armen Gulian

Based on a time-dependent Ginzburg-Landau system of equations and finite element modeling, we present novel results related with the physics of phase-slippage in superconducting wires surrounded by a non-superconductive environment. These results are obtained within our previously reported approach related to superconducting rings and superconductive gravita

B. Ananthanarayan, Sumit Banik, Souvik Bera, Sudeepan Datta

In the Lee-Pomeransky representation, Feynman integrals can be identified as a subset of Euler-Mellin integrals, which are known to satisfy Gel'fand-Kapranov-Zelevinsky (GKZ) system of partial differential equations. Here we present an automated package to derive the associated GKZ system for a given Feynman diagram and solve it in terms of hypergeometric fu

Luca Banetta, Francesco Leone, Carmine Anzivino, Michael S. Murillo

We present a theoretical framework to investigate the microscopic structure of concentrated hard-sphere colloidal suspensions under strong shear flows by fully taking into account the boundary-layer structure of convective diffusion. We solve the pair Smoluchowski equation with shear separately in the compressing and extensional sectors of the solid angle, b

Teodor Strömberg, Peter Schiansky, Marco Túlio Quintino, Michael Antesberger

In the macroscopic world, time is intrinsically asymmetric, flowing in a specific direction, from past to future. However, the same is not necessarily true for quantum systems, as some quantum processes produce valid quantum evolutions under time reversal. Supposing that such processes can be probed in both time directions, we can also consider quantum proce

Chengxiao Luo, Yiming Li, Yong Jiang, Shu-Tao Xia

Recent studies revealed that deep neural networks (DNNs) are exposed to backdoor threats when training with third-party resources (such as training samples or backbones). The backdoored model has promising performance in predicting benign samples, whereas its predictions can be maliciously manipulated by adversaries based on activating its backdoors with pre

Valeria Banica, Georg Maierhofer, Katharina Schratz

We introduce a numerical approach to computing the Schr\"odinger map (SM) based on the Hasimoto transform which relates the SM flow to a cubic nonlinear Schr\"odinger (NLS) equation. In exploiting this nonlinear transform we are able to introduce the first fully explicit unconditionally stable symmetric integrators for the SM equation. Our approach consists

Soumyaditya Das, Soumyajyoti Biswas

In the systems showing critical behavior, various response functions have a singularity at the critical point. Therefore, as the driving field is tuned towards its critical value, the response functions change drastically, typically diverging with universal critical exponents. In this work, we quantify the inequality of response functions with measures tradi

Guillaume Wang, Lénaïc Chizat

We consider the problem of computing mixed Nash equilibria of two-player zero-sum games with continuous sets of pure strategies and with first-order access to the payoff function. This problem arises for example in game-theory-inspired machine learning applications, such as distributionally-robust learning. In those applications, the strategy sets are high-d

Alessandra Feo, Emanuele Scanferla, Fulvio Celico

The contamination impact and the migration of the contaminant into the surrounding environment due to the presence of a spilled oil pipeline will cause significant damage to the natural ecosystem. For this reason, it is decisive to develop a rapid response strategy that might include accurate predictions of oil migration trajectories from numerical simulatio

David Müller-Bender, Rahil N. Valani, Günter Radons

In finite-dimensional, chaotic, Lorenz-like wave-particle dynamical systems one can find diffusive trajectories, which share their appearance with that of laminar chaotic diffusion [Phys. Rev. Lett. 128, 074101 (2022)] known from delay systems with lag-time modulation. Applying, however, to such systems a test for laminar chaos, as proposed in [Phys. Rev. E

Fabio Giovanneschi, Kumar Vijay Mishra, Maria Antonia Gonzalez-Huici

Traditional GPR target recognition methods include pre-processing the data by removal of noisy signatures, dewowing (high-pass filtering to remove low-frequency noise), filtering, deconvolution, migration (correction of the effect of survey geometry), and can rely on the simulation of GPR responses. The techniques usually suffer from the loss of information,

Jonathan Minz, Axel Kleidon, Nsilulu T. Mbungu, Lee M. Miller

Energy scenarios and transition pathways need estimates of technical wind energy potentials. However, the standard policy-side approach uses observed wind speeds, thereby neglecting the effects of kinetic energy (KE) removal by the wind turbines that depletes the regional wind resource, lowers wind speeds, and reduces capacity factors. The standard approach

Lei Feng, Or Katz, Casey Haack, Mohammad Maghrebi

One-dimensional systems exhibiting a continuous symmetry can host quantum phases of matter with true long-range order only in the presence of sufficiently long-range interactions. In most physical systems, however, the interactions are short-ranged, hindering the emergence of such phases in one dimension. Here we use a one-dimensional trapped-ion quantum sim

A. Lamura

The dynamical response of a tethered semiflexible polymer with self-attractive interactions and subjected to an external force field is numerically investigated by varying stiffness and self-interaction strength. The chain is confined in two spatial dimensions and placed in contact with a heat bath described by the Brownian multiparticle collision method. Fo