November 2022 arXiv papers — page 98

Ioana A. Zelko, Douglas P. Finkbeiner, Albert Lee, Gregory Green

We present the first large-scale 3D map of interstellar dust temperature. We build upon existing 3D reddening maps derived from starlight absorption (Bayestar19), covering 3/4 of the sky. Starting with the column density for each of 500 million 3D voxels, we propose a temperature and emissivity power-law slope ($\beta$) for each of them, and integrate along

Sreekar Voleti, Koushik Pradhan, Subhro Bhattacharjee, Tanusri Saha-Dasgupta

Quantum materials with non-Kramers doublets are a fascinating venue to realize multipolar hidden orders. Impurity probes which break point group symmetries, such as implanted muons or substitutional impurities, split the non-Kramers degeneracy and exhibit a Janus-faced influence in such systems: they can destroy the very order they seek to probe. Here, we ex

Johannes Haller, Andreas Hoecker, Roman Kogler, Klaus Mönig

We present results from the global electroweak fit to precision measurements in the Standard Model (SM). The fit uses the latest theoretical calculations for observables on the $Z$ pole and the $W$ boson mass, yielding precise SM predictions for the effective weak mixing angle and the masses of the $W$ and Higgs bosons, as well as the top quark. We study the

Bahman Dehnadi, Ivan Novikov, Frank J. Tackmann

We present predictions for the photon energy spectrum in inclusive $B\to X_s\gamma$ decays mediated by the electromagnetic penguin operator $O_7$ to N$^3$LL$'$. We use soft-collinear effective theory (SCET) to resum the singular contributions in the peak region at large photon energy. In the tail region the resummed predictions are matched to fixed order at

Michael Nee

We discuss the production of dark matter with mass of order $10$-$100$ TeV and $\mathcal{O}(1)$ couplings to the standard model, a scenario we refer to as the `mesotuned WIMP'. Given the lack of new physics observed at the LHC, indicating we live in a world with some degree of fine-tuning, this scale is a natural scale to expect new particles to appear. Howe

Itamar J. Allali, Mark P. Hertzberg, Yi Lyu

In recent work we showed that the relic dark matter abundance of QCD axions can be altered when the Peccei-Quinn (PQ) field is coupled to very light scalar/s, rendering the effective axion mass dynamical in the early universe. In this work we develop this framework further, by introducing a new extended symmetry group to protect the new particles' mass. We f

Olaf Lechtenfeld, Maximilian Rupprecht

Adding a topological theta term to the action of $\mathcal{N}{=}\,1$ $D{=}4$ super Yang-Mills theory modifies its Nicolai map. For the BPS value of the theta angle a chiral version of the map emerges, which allows for a considerable simplification compared to the non-chiral formulation. We exhibit these improvements to all orders in perturbation theory and c

Mohammadreza Ayromlou, Dylan Nelson, Annalisa Pillepich

We explore three sets of cosmological hydrodynamical simulations, IllustrisTNG, EAGLE, and SIMBA, to investigate the physical processes impacting the distribution of baryons in and around haloes across an unprecedented mass range of $10^8<M_{\rm 200c}/{\rm M_{\odot}}<10^{15}$, from the halo centre out to scales as large as $30\,R_{\rm 200c}$. We demonstrate

Stefano Antonini, Brianna Grado-White, Shao-Kai Jian, Brian Swingle

According to holography, entanglement is the building block of spacetime; therefore, drastic changes of entanglement will lead to interesting transitions in the dual spacetime. In this paper, we study the effect of projective measurements on the Sachdev-Ye-Kitaev (SYK) model's thermofield double state, dual to an eternal black hole in Jackiw-Teitelboim (JT)

Suhail Dhawan, Stephen Thorp, Kaisey S. Mandel, Sam M. Ward

The local distance ladder estimate of the Hubble constant ($H_0$) is important in cosmology, given the recent tension with the early universe inference. We estimate $H_0$ from the Type Ia supernova (SN~Ia) distance ladder, inferring SN~Ia distances with the hierarchical Bayesian SED model, BayeSN. This method has a notable advantage of being able to continuo

J. F. Mahlmann, A. Vanthieghem, A. A. Philippov, A. Levinson

The radiation drag in photon-rich environments of cosmic explosions can seed kinetic instabilities by inducing velocity spreads between relativistically streaming plasma components. Such microturbulence is likely imprinted on the breakout signals of radiation-mediated shocks. However, large-scale, transverse magnetic fields in the deceleration region of the

Tomáš Šoltinský, James S. Bolton, Margherita Molaro, Nina Hatch

Enhanced ionizing radiation in close proximity to redshift $z\gtrsim 6$ quasars creates short windows of intergalactic Ly$\alpha$ transmission blueward of the quasar Ly$\alpha$ emission lines. The majority of these Ly$\alpha$ near-zones are consistent with quasars that have optically/UV bright lifetimes of $t_{\rm Q}\sim 10^{5}-10^{7}\rm\,yr$. However, lifet

Åsmund Folkestad, Aditya Dhumuntarao

We prove novel speed limits on the growth of entanglement, equal-time correlators, and spacelike Wilson loops in spatially uniform time-evolving states in strongly coupled CFTs with holographic duals. These bounds can also be viewed as quantum weak energy conditions. Several of the speed limits are valid for regions of arbitrary size and with multiple connec

Clay Cordova, Sungwoo Hong, Seth Koren, Kantaro Ohmori

We explore generalized global symmetries in theories of physics beyond the Standard Model. Theories of $Z'$ bosons generically contain 'non-invertible' chiral symmetries, whose presence indicates a natural paradigm to break this symmetry by an exponentially small amount in an ultraviolet completion. For example, in models of gauged lepton family difference s

Ananye Agarwal, Ashish Kumar, Jitendra Malik, Deepak Pathak

Animals are capable of precise and agile locomotion using vision. Replicating this ability has been a long-standing goal in robotics. The traditional approach has been to decompose this problem into elevation mapping and foothold planning phases. The elevation mapping, however, is susceptible to failure and large noise artifacts, requires specialized hardwar

Kenta Kiuchi, Sho Fujibayashi, Kota Hayashi, Koutarou Kyutoku

We perform a general-relativistic neutrino-radiation magnetohydrodynamic simulation of a one second-long binary neutron star merger on Japanese supercomputer Fugaku using about $72$ million CPU hours with $20,736$ CPUs. We consider an asymmetric binary neutron star merger with masses of $1.2$ and $1.5M_\odot$ and a `soft' equation of state SFHo. It results i

Yuxin Fang, Wen Wang, Binhui Xie, Quan Sun

We launch EVA, a vision-centric foundation model to explore the limits of visual representation at scale using only publicly accessible data. EVA is a vanilla ViT pre-trained to reconstruct the masked out image-text aligned vision features conditioned on visible image patches. Via this pretext task, we can efficiently scale up EVA to one billion parameters,

Dennis Melamed, Karnik Ram, Vivek Roy, Kris Kitani

Indoor localization systems often fuse inertial odometry with map information via hand-defined methods to reduce odometry drift, but such methods are sensitive to noise and struggle to generalize across odometry sources. To address the robustness problem in map utilization, we propose a data-driven prior on possible user locations in a map by combining learn

Mirac Suzgun, Luke Melas-Kyriazi, Dan Jurafsky

In open-ended natural-language generation, existing text decoding methods typically struggle to produce text which is both diverse and high-quality. Greedy and beam search are known to suffer from text degeneration and linguistic diversity issues, while temperature, top-k, and nucleus sampling often yield diverse but low-quality outputs. In this work, we pre

Holmfridur S. Hannesdottir, Andrew J. McLeod, Matthew D. Schwartz, Cristian Vergu

We present several classes of constraints on the discontinuities of Feynman integrals that go beyond the Steinmann relations. These constraints follow from a geometric formulation of the Landau equations that was advocated by Pham, in which the singularities of Feynman integrals correspond to critical points of maps between on-shell spaces. To establish our

C. Samanta, S. L. De Bonis, C. B. Møller, R. Tormo-Queralt

An open question in mechanics is whether mechanical resonators can be made nonlinear with vibrations approaching the quantum ground state. This requires engineering a mechanical nonlinearity far beyond what has been realized thus far. Here we discovered a mechanism to boost the Duffing nonlinearity by coupling the vibrations of a nanotube resonator to single

Manuel Alejandro Diaz-Zapata, Özgür Erkent, Christian Laugier, Jilles Dibangoye

Semantic grids are a useful representation of the environment around a robot. They can be used in autonomous vehicles to concisely represent the scene around the car, capturing vital information for downstream tasks like navigation or collision assessment. Information from different sensors can be used to generate these grids. Some methods rely only on RGB i

Emanuele D'Osualdo, Azalea Raad, Viktor Vafeiadis

There is an increasing body of literature proposing new and efficient persistent versions of concurrent data structures ensuring that a consistent state can be recovered after a power failure or a crash. Their correctness is typically stated in terms of \emph{durable linearizability} (DL), which requires that individual library operations appear to be execut

Suignard Philippe

In this contribution, we propose to analyze the statements coming from two ''civic tech'' platforms-the governmental platform, ''Grand D{\'e}bat National'' and, its political and algorithmic response proposed by a Yellow Vest collective, ''Vrai D{\'e}bat''-, by confronting two families of algorithms dedicated to text analysis. We propose to implement, on the

Jia He, Huanzhuo Wu, Xun Xiao, Riccardo Bassoli

In existing mobile network systems, the data plane (DP) is mainly considered a pipeline consisting of network elements end-to-end forwarding user data traffics. With the rapid maturity of programmable network devices, however, mobile network infrastructure mutates towards a programmable computing platform. Therefore, such a programmable DP can provide in-net

Michael E. Beverland, Prakash Murali, Matthias Troyer, Krysta M. Svore

While quantum computers promise to solve some scientifically and commercially valuable problems thought intractable for classical machines, delivering on this promise will require a large-scale quantum machine. Understanding the impact of architecture design choices for a scaled quantum stack for specific applications, prior to full realization of the quantu

Shuyue Stella Li, Kenton Murray

In this work, we focus on intrasentential code-mixing and propose several different Synthetic Code-Mixing (SCM) data augmentation methods that outperform the baseline on downstream sentiment analysis tasks across various amounts of labeled gold data. Most importantly, our proposed methods demonstrate that strategically replacing parts of sentences in the mat

Eric Chen, Zhang-Wei Hong, Joni Pajarinen, Pulkit Agrawal

State-of-the-art reinforcement learning (RL) algorithms typically use random sampling (e.g., $\epsilon$-greedy) for exploration, but this method fails on hard exploration tasks like Montezuma's Revenge. To address the challenge of exploration, prior works incentivize exploration by rewarding the agent when it visits novel states. Such intrinsic rewards (also

M. Andrecut

We discuss a class of cellular automata (CA) able to produce long random strings, starting from short "seed" strings. The approach uses two principles borrowed from cryptography: diffusion and confusion. We show numerically that the strings are pseudo-random using three approaches based on: Fourier transform, entropy estimation, and compression. An applicati

Junlin Han, Huangying Zhan, Jie Hong, Pengfei Fang

This paper studies the problem of measuring and predicting how memorable an image is to pattern recognition machines, as a path to explore machine intelligence. Firstly, we propose a self-supervised machine memory quantification pipeline, dubbed ``MachineMem measurer'', to collect machine memorability scores of images. Similar to humans, machines also tend t

Anxo Pérez, Neha Warikoo, Kexin Wang, Javier Parapar

Depressive disorders constitute a severe public health issue worldwide. However, public health systems have limited capacity for case detection and diagnosis. In this regard, the widespread use of social media has opened up a way to access public information on a large scale. Computational methods can serve as support tools for rapid screening by exploiting

Man-Chun Lee, Peter M. Topping

Recently it has been proved (Lee-Topping 2022, Deruelle-Schulze-Simon 2022, Lott 2019) that three-dimensional complete manifolds with non-negatively pinched Ricci curvature must be flat or compact, thus confirming a conjecture of Hamilton. In this paper we generalise our work on the existence of Ricci flows from non-compact pinched three-manifolds in order t

Ryan Donnelly, Sebastian Jaimungal

We study optimal control in models with latent factors where the agent controls the distribution over actions, rather than actions themselves, in both discrete and continuous time. To encourage exploration of the state space, we reward exploration with Tsallis Entropy and derive the optimal distribution over states - which we prove is $q$-Gaussian distribute

Ahmed Abbasi, Shuchin Aeron, Abiy Tasissa

Unlabeled sensing is a linear inverse problem with permuted measurements. We propose an alternating minimization (AltMin) algorithm with a suitable initialization for two widely considered permutation models: partially shuffled/$k$-sparse permutations and $r$-local/block diagonal permutations. Key to the performance of the AltMin algorithm is the initializat

Jichun Li, Yangpeng Zhang, Yangwen Zhang

In this paper, we address the well-known challenge in the numerical solution of time-fractional partial differential equations (TFPDEs), namely, that the dependence on all previous time levels leads to storage requirements that grow linearly with the number of time steps. To overcome this difficulty, we develop an efficient algorithm based on incremental sin

Soeren Becker, Kevin Styp-Rekowski, Oliver Vincent Leon Stoll, Odej Kao

Enabled by the increasing availability of sensor data monitored from production machinery, condition monitoring and predictive maintenance methods are key pillars for an efficient and robust manufacturing production cycle in the Industrial Internet of Things. The employment of machine learning models to detect and predict deteriorating behavior by analyzing

Natã Machado, Gilles G. de Castro

We define the full and reduced non-self-adjoint operator algebras associated with \'etale categories and restriction semigroups, answering a question posed by Kudryavtseva and Lawson in \cite{lawson}. Moreover, we define the semicrossed product algebra of an \'etale action of a restriction semigroup on a $C^*$-algebra, which turns out to be the key point whe

David Guevel, Andrew Beardmore, Kim L Page, Amy Lien

$\gamma$-ray observations of the Cygnus Cocoon, an extended source surrounding the Cygnus X star-forming region, suggest the presence of a cosmic ray accelerator reaching energies up to a few PeV. The very-high-energy (VHE; 0.1-100~TeV) $\gamma$-ray emission may be explained by the interaction of cosmic-ray hadrons with matter inside the Cocoon, but an origi

Patrick Gildersleve, Renaud Lambiotte, Taha Yasseri

The digital information landscape has introduced a new dimension to understanding how we collectively react to new information and preserve it at the societal level. This, together with the emergence of platforms such as Wikipedia, has challenged traditional views on the relationship between current events and historical accounts of events, with an ever-shri

Sagar Gubbi Venkatesh, Partha Talukdar, Srini Narayanan

Smartphone users often find it difficult to navigate myriad menus to perform common tasks such as "How to block calls from unknown numbers?". Currently, help documents with step-by-step instructions are manually written to aid the user. The user experience can be further enhanced by grounding the instructions in the help document to the UI and overlaying a t

Shengpu Tang, Felipe Vieira Frujeri, Dipendra Misra, Alex Lamb

Modern decision-making systems, from robots to web recommendation engines, are expected to adapt: to user preferences, changing circumstances or even new tasks. Yet, it is still uncommon to deploy a dynamically learning agent (rather than a fixed policy) to a production system, as it's perceived as unsafe. Using historical data to reason about learning algor

Onur Mutlu, Ataberk Olgun, A. Giray Yağlıkçı

We provide an overview of recent developments and future directions in the RowHammer vulnerability that plagues modern DRAM (Dynamic Random Memory Access) chips, which are used in almost all computing systems as main memory. RowHammer is the phenomenon in which repeatedly accessing a row in a real DRAM chip causes bitflips (i.e., data corruption) in physical

Maryam Saadati, Morteza Oveisiha

This article is devoted to investigate a nonsmooth/nonconvex uncertain multiobjective optimization problem with composition fields (CUP) for brevity) over arbitrary Asplund spaces. Employing some advanced techniques of variational analysis and generalized differentiation, we establish necessary optimality conditions for weakly robust efficient solutions of (

Pulak Malhotra, Ashwin Rao

Internet search engines have become an integral part of life, but for pop music, people still rely on textual search engines like Google. We propose Pied piper, a meta search engine for music. It can search for music lyrics, song metadata and song audio or a combination of any of these as the input query and efficiently return the relevant results.

Mu Chen, Zhedong Zheng, Yi Yang, Tat-Seng Chua

Unsupervised Domain Adaptation (UDA) aims to enhance the generalization of the learned model to other domains. The domain-invariant knowledge is transferred from the model trained on labeled source domain, e.g., video game, to unlabeled target domains, e.g., real-world scenarios, saving annotation expenses. Existing UDA methods for semantic segmentation usua

José Luis Montiel Olea, Cynthia Rush, Amilcar Velez, Johannes Wiesel

We study the classical problem of predicting an outcome variable, $Y$, using a linear combination of a $d$-dimensional covariate vector, $\mathbf{X}$. We are interested in linear predictors whose coefficients solve: % \begin{align*} \inf_{\boldsymbol{\beta} \in \mathbb{R}^d} \left( \mathbb{E}_{\mathbb{P}_n} \left[ \left(Y-\mathbf{X}^{\top}\beta \right)^r \ri

Sara Maleubre, Daniel J. Eisenstein, Lehman H. Garrison, Michael Joyce

We present a continuation of an analysis that aims to quantify resolution of $N$-body simulations by exploiting large (up to $N=4096^3$) simulations of scale-free cosmologies run using Abacus. Here we focus on radial pairwise velocities of the matter field, both by direct estimation and through the cumulative-2PCF (using the pair conservation equation). We f

Manuela Fischer, Yannic Maus, Magnús M. Halldórsson

We give a randomized $\Delta$-coloring algorithm in the LOCAL model that runs in $\text{poly} \log \log n$ rounds, where $n$ is the number of nodes of the input graph and $\Delta$ is its maximum degree. This means that randomized $\Delta$-coloring is a rare distributed coloring problem with an upper and lower bound in the same ballpark, $\text{poly}\log\log

Konstantin Malanchev, Matwey V. Kornilov, Maria V. Pruzhinskaya, Emille E. O. Ishida

We describe the SNAD Viewer, a web portal for astronomers which presents a centralized view of individual objects from the Zwicky Transient Facility's (ZTF) data releases, including data gathered from multiple publicly available astronomical archives and data sources. Initially built to enable efficient expert feedback in the context of adaptive machine lear

Davide Zorzenon, Nataliia Zaiets, Jörg Raisch

In this paper, an industrial bakery shop is modeled by switched max-plus linear-dual inequalities (SLDIs). SLDIs are timed discrete event systems suitable for describing flow shops with time-window constraints and switching operating modes, where each mode corresponds to a job type. We consider the scheduling problem of minimizing the makespan of the shop, a

Gal Metzer, Elad Richardson, Or Patashnik, Raja Giryes

Text-guided image generation has progressed rapidly in recent years, inspiring major breakthroughs in text-guided shape generation. Recently, it has been shown that using score distillation, one can successfully text-guide a NeRF model to generate a 3D object. We adapt the score distillation to the publicly available, and computationally efficient, Latent Di

Victor de la Torre, Jordi Marzo

In 2011, Armentano, Beltr\'an and Shub obtained in \cite{ABS11} a closed expression for the expected logarithmic energy of the random point process on the sphere given by the roots of random elliptic polynomials. We consider a different approach which allows us to extend the study to the Riesz energies and to compute the expected separation distance.

Aoumeur Daddi Hammou, Nicola Bartolo

We study the effect of quantum decoherence on the inflationary cosmological perturbations. This process might imprint specific observational signatures revealing the quantum nature of the inflationary mechanism being related to the longstanding issue of the quantum-to-classical transition of inflationary fluctuations. Several works have investigated the effe

Sujin Lee, Rijan Karkee, Azza Ben-Akacha, Derek Luong

Organic metal halide hybrids with low-dimensional structures at the molecular level have received great attention recently for their exceptional structural tunability and unique photophysical properties. Here we report for the first time the synthesis and characterization of a one-dimensional (1D) organic metal halide hybrid material, which contains metal ha

Yuxuan Ye, Edwin Simpson

This paper introduces a novel pipeline for summarising timelines of events reported by multiple news sources. Transformer-based models for abstractive summarisation generate coherent and concise summaries of long documents but can fail to outperform established extractive methods on specialised tasks such as timeline summarisation (TLS). While extractive sum

Charles-Philippe Diez

We combine the notion of free Stein kernel and the free Malliavin calculus to provide quantitative bounds under the free (quadratic) Wasserstein distance in the multivariate semicircular approximations for self-adjoint vector-valued multiple Wigner integrals. On the way, we deduce an HSI inequality for a modified non-microstates free entropy with respect to

Wouter Buijsman

This work identifies a solvable (in the sense that spectral correlation functions can be expressed in terms of orthogonal polynomials), rotationally invariant random matrix ensemble with a logarithmic weakly confining potential. The ensemble, which can be interpreted as a transformed Jacobi ensemble, is in the thermodynamic limit characterized by a Lorentzia

J. M. Borrero, A. Pastor Yabar

Electric currents play an important role in the energy balance of the plasma in the solar atmosphere. They are also indicative of non-potential magnetic fields and magnetic reconnection. Unfortunately, the direct measuring of electric currents has traditionally been riddled with inaccuracies. We study how accurately we can infer electric currents under diffe

Glenn Barnich, Kevin Nguyen, Romain Ruzziconi

The constrained Hamiltonian analysis of geometric actions is worked out before applying the construction to the extended Bondi-Metzner-Sachs group in four dimensions. For any Hamiltonian associated with an extended BMS$_4$ generator, this action provides a field theory in two plus one spacetime dimensions whose Poisson bracket algebra of Noether charges real

Justus-Jonas Erker, Stefan Schaffer, Gerasimos Spanakis

Inspired by the curvature of space-time (Einstein, 1921), we introduce Curved Contrastive Learning (CCL), a novel representation learning technique for learning the relative turn distance between utterance pairs in multi-turn dialogues. The resulting bi-encoder models can guide transformers as a response ranking model towards a goal in a zero-shot fashion by

Alexandre Tiard, Alex Wong, David Joon Ho, Yangchao Wu

We present a self-supervised algorithm for several classification tasks within hematoxylin and eosin (H&E) stained images of breast cancer. Our method is robust to stain variations inherent to the histology images acquisition process, which has limited the applicability of automated analysis tools. We address this problem by imposing constraints a learnt lat

Justin J. Michel, Jose Flores, Fengyuan Yang

Electrical switching of antiferromagnets (AFM) is critical for AFM spintronics. However, electrical pulse-induced Neel vector reorientation in AFM insulators, while predicted to occur at much faster timescales than ferromagnetic switching, has only been demonstrated in the quasi-DC regime. Here we report reliable current-induced AFM switching in Pt/$\alpha$-

Mohamed Gueye, Yazid Attabi, Maxime Dumas

Besides reproducing tabular data properties of standalone tables, synthetic relational databases also require modeling the relationships between related tables. In this paper, we propose the Row Conditional-Tabular Generative Adversarial Network (RC-TGAN), a novel generative adversarial network (GAN) model that extends the tabular GAN to support modeling and

Bakheet Aljedaani, Aakash Ahmad, Mansooreh Zahedi, M. Ali Babar

Mobile applications, mobile apps for short, have proven their usefulness in enhancing service provisioning across a multitude of domains that range from smart healthcare, to mobile commerce, and areas of context sensitive computing. In recent years, a number of empirically grounded, survey-based studies have been conducted to investigate secure development a

Ashwin Rao

The current approach to marking attendance in colleges is tedious and time consuming. I propose AttenFace, a standalone system to analyze, track and grant attendance in real time using face recognition. Using snapshots of class from live camera feed, the system identifies students and marks them as present in a class based on their presence in multiple snaps

Joakim Misund, Bob Briscoe

In the process of testing improvements to the Linux DCTCP code in various scenarios, we found different performance problems kept surfacing with no apparent pattern. This report records a systematic sequence of experiments designed to track down the causes of these problems, which were found to be due to a complex tangle of bugs and flaws. The report also pr

A. Anikin, T. Zalialiutdinov, D. Solovyev

The paper discusses the line profile asymmetry of the photon scattering process that arises naturally in quantum electrodynamics (QED). Based on precision spectroscopic experiments conducted on hydrogen atoms, we focus our attention on the two-photon $1s-2s$ transition. As one of the most precisely determined transition frequencies, it is a key pillar of opt

Temesgen Mehari, Nils Strodthoff

The field of deep-learning-based ECG analysis has been largely dominated by convolutional architectures. This work explores the prospects of applying the recently introduced structured state space models (SSMs) as a particularly promising approach due to its ability to capture long-term dependencies in time series. We demonstrate that this approach leads to

Simon Becker, Nilanjana Datta, Ludovico Lami, Cambyse Rouzé

In this article we develop a continuous variable (CV) shadow tomography scheme with wide ranging applications in quantum optics. Our work is motivated by the increasing experimental and technological relevance of CV systems in quantum information, quantum communication, quantum sensing, quantum simulations, quantum computing and error correction. We introduc

Yangyi Zhang, Sui Tang, Guo Yu

The Coronavirus Disease 2019 (COVID-19) has a profound impact on global health and economy, making it crucial to build accurate and interpretable data-driven predictive models for COVID-19 cases to improve policy making. The extremely large scale of the pandemic and the intrinsically changing transmission characteristics pose great challenges for effective C

P. R. S. Carvalho

In this Letter we validate experimentally the nonextensive statistical field theory, a new general field-theoretic approach introduced recently in the literature. With such an approach, we are capable of computing the critical properties of nonextensive systems undergoing continuous phase transitions, belonging to the new generalized O($N$)$_{q}$ universalit

Eleonora Fiorellino, Lukasz Tychoniec, Fernando Cruz-Saenz de Miera, Simone Antoniucci

Stars collect most of their mass during the protostellar stage, yet the accretion luminosity and stellar parameters, which are needed to compute the mass accretion rate, are poorly constrained for the youngest sources. The aim of this work is to fill this gap, computing the stellar properties and the accretion rates for a large sample of Class I protostars l

Krishanu Maulik, Manit Paul

We introduce and discuss a special type of feedback interacting urn model with deterministic interaction. This is a generalisation of the very well known Eggenberger and Polya (1923) urn model. In our model, balls are added to a particular urn depending on the replacement matrix of that urn and the color of ball chosen from some other urn. This urn model can

Anna Yesypenko, Per-Gunnar Martinsson

The paper describes a sparse direct solver for the linear systems that arise from the discretization of an elliptic PDE on a two dimensional domain. The scheme decomposes the domain into thin subdomains, or ``slabs'' and uses a two-level approach that is designed with parallelization in mind. The scheme takes advantage of $\mathcal H^2$-matrix structure emer

Che-Yi Liao, Gian-Gabriel P. Garcia, Kamran Paynabar, Zheng Dong

We develop a Spatio-TEMporal Mutually Exciting point process with Dynamic network (STEMMED), i.e., a point process network wherein each node models a unique community-drug event stream with a dynamic mutually-exciting structure, accounting for influences from other nodes. We show that STEMMED can be decomposed node-by-node, suggesting a tractable distributed

Gouranga Charan, Andrew Hredzak, Ahmed Alkhateeb

Millimeter wave (mmWave) and terahertz (THz) drones have the potential to enable several futuristic applications such as coverage extension, enhanced security monitoring, and disaster management. However, these drones need to deploy large antenna arrays and use narrow directive beams to maintain a sufficient link budget. The large beam training overhead asso

Rafael D. Benguria, Edgardo Stockmeyer, Cristóbal Vallejos, Hanne Van Den Bosch

We consider four-component Dirac operators on domains in the plane. With suitable boundary conditions, these operators describe graphene quantum dots. The most general boundary conditions are defined by a matrix depending on four real parameters. For operators with constant boundary parameters we show that the Hamiltonian is unitary equivalent to two copies

Benjamin Klopsch, Martyn Quick

Just infinite groups play a significant role in profinite group theory. For each $c \geq 0$, we consider more generally JNN$_c$F profinite (or, in places, discrete) groups that are Fitting-free; these are the groups $G$ such that every proper quotient of $G$ is virtually class-$c$ nilpotent whereas $G$ itself is not, and additionally $G$ does not have any no

Zelong Zeng, Fan Yang, Hong Liu, Shin'ichi Satoh

Recent deep metric learning (DML) methods typically leverage solely class labels to keep positive samples far away from negative ones. However, this type of method normally ignores the crucial knowledge hidden in the data (e.g., intra-class information variation), which is harmful to the generalization of the trained model. To alleviate this problem, in this

Yuru Jing

Cerebral stroke, the second most substantial cause of death universally, has been a primary public health concern over the last few years. With the help of machine learning techniques, early detection of various stroke alerts is accessible, which can efficiently prevent or diminish the stroke. Medical dataset, however, are frequently unbalanced in their clas

Gabriel Jung, Dionysios Karagiannis, Michele Liguori, Marco Baldi

We study primordial non-Gaussian signatures in the redshift-space halo field on non-linear scales, using a quasi-maximum likelihood estimator based on optimally compressed power spectrum and modal bispectrum statistics. We train and validate the estimator on a suite of halo catalogues constructed from the Quijote-PNG N-body simulations, which we release to a

Axel A. Araneda

This paper explores the capabilities of the Constant Elasticity of Variance model driven by a mixed-fractional Brownian motion (mfCEV) [Axel A. Araneda. The fractional and mixed-fractional CEV model. Journal of Computational and Applied Mathematics, 363:106-123, 2020] to address default-related financial problems, particularly the pricing of Credit Default S

Shuaifeng Jiang, Ahmed Hindy, Ahmed Alkhateeb

Reconfigurable intelligent surfaces (RISs) have attracted increasing interest due to their ability to improve the coverage, reliability, and energy efficiency of millimeter wave (mmWave) communication systems. However, designing the RIS beamforming typically requires large channel estimation or beam training overhead, which degrades the efficiency of these s

Alessandro Carones, Javier Carrón Duque, Domenico Marinucci, Marina Migliaccio

The angular power spectrum of the Cosmic Microwave Background (CMB) anisotropies is a key tool to study the Universe. However, it is blind to the presence of non--Gaussianities and deviations from statistical isotropy, which instead can be detected with other statistics such as Minkowski Functionals (MFs). These tools have been applied to CMB temperature and

Sourya Dey

Dynamic Mode Decomposition (DMD) is a technique to approximate generally non-linear dynamical systems using linear techniques, which are better understood and easier to analyze. Koopman theory extends DMD by transforming the original system into a new domain which facilitates linearization. This is a technical report on DMD and Koopman theory, with primary f

Barry L Zink

Research in spintronics often involves generation of heat in nanoscale magnetic systems. This heat generation can be intentional, as when studying effects created by an external applied temperature difference, or unintentional, coming as a consequence of driving relatively large charge currents through tiny structures. Understanding and controlling these the

Tom Young

The goal of building dialogue agents that can converse with humans naturally has been a long-standing dream of researchers since the early days of artificial intelligence. The well-known Turing Test proposed to judge the ultimate validity of an artificial intelligence agent on the indistinguishability of its dialogues from humans'. It should come as no surpr

Amanda Hirschi, Noah Porcelli

We find lower bounds on the number of intersection points between two relatively exact Hamiltonian isotopic Lagrangians. The bounds are given in terms of the cuplength of the Lagrangian in various multiplicative generalised cohomology theories. The intersection of the Lagrangians need not be transverse, however, we require certain orientation assumptions. Th

Sagnik Halder, George Michailidis

High dimensional Vector Autoregressions (VAR) have received a lot of interest recently due to novel applications in health, engineering, finance and the social sciences. Three issues arise when analyzing VAR's: (a) The high dimensional nature of the model in the presence of many time series that poses challenges for consistent estimation of its parameters; (

Bijit Mukherjee, Matthew D. Frye, Jeremy M. Hutson

Magnetically tunable Feshbach resonances exist in ultracold collisions between atoms in $^2$S and $^3$P$_0$ states, such as an alkali-metal atom colliding with Yb or Sr in a clock state. We investigate the mechanisms of these resonances and identify the terms in the collision Hamiltonian responsible for them. They involve indirect coupling between the open a

Mengfan Wu, Thomas Langerak, Otmar Hilliges, Juan Zarate

Tracking passive magnetic markers plays a vital role in advancing healthcare and robotics, offering the potential to significantly improve the precision and efficiency of systems. This technology is key to developing smarter, more responsive tools and devices, such as enhanced surgical instruments, precise diagnostic tools, and robots with improved environme

Sohail Ait Jmal, Mohamed Balli, Hamza Bouhani, Omar Mounkachi

In this paper, we investigate the influence of the rare earth element (R) on the physical properties of RVO3 (R = Er, Ho, Y, Lu). For this purpose, a theoretical work is reported in this study using ab initio method with the intent of enhancing our knowledge of the electronic, and magnetic properties of RVO3 (R = Er, Ho, Y, Lu) compounds. The electronic prop

Marc Fersztand, Vidit Nanda, Ulrike Tillmann

We introduce a sheaf-theoretic stability condition for finite acyclic quivers. Our main result establishes that for representations of affine type $\widetilde{\mathbb{A}}$ quivers, there is a precise relationship between the associated Harder-Narasimhan filtration and the barcode of the periodic zigzag persistence module obtained by unwinding the underlying

Benedikt Fesl, Andreas Faika, Nurettin Turan, Michael Joham

We consider channel estimation in systems equipped with a reconfigurable intelligent surface (RIS). In order to illuminate the additional cascaded channel as compared to systems without a RIS, commonly an unaffordable amount of pilot sequences has to be transmitted over different phase allocations at the RIS. However, for a given base station (BS) cell, ther

A. Albert, S. Alves, M. André, M. Ardid

By constantly monitoring at least one complete hemisphere of the sky, neutrino telescopes are well designed to detect neutrinos emitted by transient astrophysical events. Real-time searches with the ANTARES telescope have been performed to look for neutrino candidates coincident with gamma-ray bursts detected by the Swift and Fermi satellites, highenergy neu

Christophe Paul, Evangelos Protopapas

Many standard graph classes are known to be characterized by means of layouts (a permutation of its vertices) excluding some patterns. Important such graph classes are among others: proper interval graphs, interval graphs, chordal graphs, permutation graphs, (co-)comparability graphs. For example, a graph $G=(V,E)$ is a proper interval graph if and only if $

Ying Xu, Romane Gauriau, Anna Decker, Jacob Oppenheim

Understanding patterns of diagnoses, medications, procedures, and laboratory tests from electronic health records (EHRs) and health insurer claims is important for understanding disease risk and for efficient clinical development, which often require rules-based curation in collaboration with clinicians. We extended an unsupervised phenotyping algorithm, mix

Abror Pirnapasov, Rohil Prasad

We prove that a generic area-preserving diffeomorphism of a compact surface with non-empty boundary has an equidistributed set of periodic orbits. This implies that such a diffeomorphism has a dense set of periodic points, although we also give a self-contained proof of this "generic density'' theorem. One application of our results is the extension of mean

Xavier Lamy, Elio Marconi

We consider entropy solutions to the eikonal equation $|\nabla u|=1$ in two space dimensions. These solutions are motivated by a class of variational problems and fail in general to have bounded variation. Nevertheless they share with BV functions, several of their fine properties: we show in particular that the set of non-Lebesgue points has co-dimension at

Yiannis Giannakopoulos, Alexander Grosz, Themistoklis Melissourgos

We propose a unifying framework for smoothed analysis of combinatorial local optimization problems, and show how a diverse selection of problems within the complexity class PLS can be cast within this model. This abstraction allows us to identify key structural properties, and corresponding parameters, that determine the smoothed running time of local search