November 2022 arXiv papers — page 159

A. Botteon, R. J. van Weeren, G. Brunetti, F. Vazza

The hot plasma within merging galaxy clusters is predicted to be filled with shocks and turbulence that may convert part of their kinetic energy into relativistic electrons and magnetic fields generating synchrotron radiation. Analyzing Low Frequency Array (LOFAR) observations of the galaxy cluster Abell 2255, we show evidence of radio synchrotron emission d

Ryan Peterson, Joseph Cavanaugh

The sparsity-ranked lasso (SRL) has been developed for model selection and estimation in the presence of interactions and polynomials. The main tenet of the SRL is that an algorithm should be more skeptical of higher-order polynomials and interactions *a priori* compared to main effects, and hence the inclusion of these more complex terms should require a hi

Tyler J. Slade, Nao Furukawa, Tanner R. Smith, Juan Schmidt

We present the growth and basic magnetic and transport properties of Cr$_{1+x}$Pt$_{5-x}$P. We show that single crystals can readily be grown from a high-temperature solution created by adding dilute quantities of Cr to Pt-P based melts. Like other 1-5-1 compounds, Cr$_{1+x}$Pt$_{5-x}$P adopts a tetragonal P4/mmm structure composed face-sharing CrPt$_3$ like

S. Basak, M. Alzate Banguero, L. Burzawa, F. Simmons

The capabilities of image probe experiments are rapidly expanding, providing new information about quantum materials on unprecedented length and time scales. Many such materials feature inhomogeneous electronic properties with intricate pattern formation on the observable surface. This rich spatial structure contains information about interactions, dimension

Usman A. Javid, Raymond Lopez-Rios, Jingwei Ling, Austin Graf

An efficient simulator for quantum systems is one of the original goals for the efforts to develop a quantum computer [1]. In recent years, synthetic dimension in photonics [2] have emerged as a potentially powerful approach for simulation that is free from the constraint of geometric dimensionality. Here we demonstrate a quantum-correlated synthetic crystal

Alvaro Andres Alvarez Peralta, Priya Desai, Somalee Datta

This manuscript explores linking real-world patient data with external death data in the context of research Clinical Data Warehouses (r-CDWs). We specifically present the linking of Electronic Health Records (EHR) data for Stanford Health Care (SHC) patients and data from the Social Security Administration (SSA) Limited Access Death Master File (LADMF) made

Xu Shen, Francesco Borrelli

We present a novel method to address the problem of multi-vehicle conflict resolution in highly constrained spaces. An optimal control problem is formulated to incorporate nonlinear, non-holonomic vehicle dynamics and exact collision avoidance constraints. A solution to the problem can be obtained by first learning configuration strategies with reinforcement

Zachary Zhou, Alisha Zachariah, Devin Conathan, Jeffery Kline

Natural language models are often summarized through a high-dimensional set of descriptive metrics including training corpus size, training time, the number of trainable parameters, inference times, and evaluation statistics that assess performance across tasks. The high dimensional nature of these metrics yields challenges with regard to objectively compari

R. Giampaoli, J. L. Figueiredo, J. D. Rodrigues, J. A. Rodrigues

The statistical properties of physical systems in thermal equilibrium are blatantly different from their far-from-equilibrium counterparts. In the latter, fluctuations often dominate the dynamics and might cluster in ordered patterns in the form of dissipative coherent structures. Here, we study the transition of a cold atomic cloud, driven close to a sharp

Xuan Shen, Zhenglun Kong, Minghai Qin, Peiyan Dong

The conventional lottery ticket hypothesis (LTH) claims that there exists a sparse subnetwork within a dense neural network and a proper random initialization method called the winning ticket, such that it can be trained from scratch to almost as good as the dense counterpart. Meanwhile, the research of LTH in vision transformers (ViTs) is scarcely evaluated

Ivica Zivkovic, Ravi Yadav, Jian-Rui Soh, ChangJiang Yi

Recent discovery of topologically non-trivial behavior in Co3Sn2S2 stimulated a notable interest in this itinerant ferromagnet (Tc = 174 K). The exact magnetic state remains ambiguous, with several reports indicating the existence of a second transition in the range 125 -- 130 K, with antiferromagnetic and glassy phases proposed to coexist with the ferromagn

Alireza Mohammadshahi, Thomas Scialom, Majid Yazdani, Pouya Yanki

Existing metrics for evaluating the quality of automatically generated questions such as BLEU, ROUGE, BERTScore, and BLEURT compare the reference and predicted questions, providing a high score when there is a considerable lexical overlap or semantic similarity between the candidate and the reference questions. This approach has two major shortcomings. First

Johannes Kruse, Eike Cramer, Benjamin Schäfer, Dirk Witthaut

The operation of power systems is affected by diverse technical, economic and social factors. Social behaviour determines load patterns, electricity markets regulate the generation and weather-dependent renewables introduce power fluctuations. Thus, power system dynamics must be regarded as a non-autonomous system whose parameters vary strongly with time. Ho

Aleksandra Kalinowska, Elnaz Davoodi, Florian Strub, Kory W Mathewson

While it is known that communication facilitates cooperation in multi-agent settings, it is unclear how to design artificial agents that can learn to effectively and efficiently communicate with each other. Much research on communication emergence uses reinforcement learning (RL) and explores unsituated communication in one-step referential tasks -- the task

Choiti Bandyopadhyay

In this article, we introduce and explore the notion of topological amenability in the broad setting of (locally compact) semihypergroups. We acquire several stationary, ergodic and Banach algebraic characterizations of the same in terms of convergence of certain probability measures, total variation of convolution with probability measures and translation o

Lorenzo Leone, Salvatore F. E. Oliviero, Lukasz Cincio, M. Cerezo

Variational Quantum Algorithms (VQAs) and Quantum Machine Learning (QML) models train a parametrized quantum circuit to solve a given learning task. The success of these algorithms greatly hinges on appropriately choosing an ansatz for the quantum circuit. Perhaps one of the most famous ansatzes is the one-dimensional layered Hardware Efficient Ansatz (HEA),

Nicola Peserico, Bhavin J. Shastri, Volker J. Sorger

The explosion of artificial intelligence and machine-learning algorithms, connected to the exponential growth of the exchanged data, is driving a search for novel application-specific hardware accelerators. Among the many, the photonics field appears to be in the perfect spotlight for this global data explosion, thanks to its almost infinite bandwidth capaci

P. Palma-Bifani, G. Chauvin, M. Bonnefoy, P. M. Rojo

We aim to revisit the system AB Pic which has a known companion at the exoplanet/ brown-dwarf boundary. We based this study on a rich set of observations to investigate the companion's orbit and atmosphere. We composed a spectrum of AB Pic b merging archival VLT/SINFONI K-band data, with published spectra at J and H-band (SINFONI) and Lp-band (Magellan-AO),

Ori Roth, Yossi Gil

Fluent API is an object-oriented pattern for elegant APIs and embedded DSLs. A smart fluent API can enforce the API protocol or DSL syntax at compile time. As fluent API designs typically rely on function overloading, they are hard to realize in functional programming languages. We show how to write functional fluent APIs using parametric polymorphism and ty

Joel Shor, Nick Johnston

Compression is essential to storing and transmitting medical videos, but the effect of compression on downstream medical tasks is often ignored. Furthermore, systems in practice rely on standard video codecs, which naively allocate bits between medically relevant frames or parts of frames. In this work, we present an empirical study of some deficiencies of c

Quan Vuong, Aviral Kumar, Sergey Levine, Yevgen Chebotar

In offline RL, constraining the learned policy to remain close to the data is essential to prevent the policy from outputting out-of-distribution (OOD) actions with erroneously overestimated values. In principle, generative adversarial networks (GAN) can provide an elegant solution to do so, with the discriminator directly providing a probability that quanti

Michał Praszałowicz, Maciej Kucab

One possible interpretation of two narrow $\Omega^0_c$ states reported by the LHCb Collaboration at CERN in 2017 is that they are pentaquarks belonging to a $\overline{\boldsymbol{15}}$ exotic SU(3) representation, as predicted by the Chiral Quark--Soliton Model. If so, there must exist a number of other exotic states since the model predicts three different

Dalton Bidleman, Luke Oeding

Restricted secant varieties of Grassmannians are constructed from sums of points corresponding to $k$-planes with the restriction that their intersection has a prescribed dimension. We study dimensions of restricted secant of Grassmannians and relate them to the analogous question for secants of Grassmannians via an incidence variety construction. We define

Lawrence Li, Sushant Sachdeva

We demonstrate that for expander graphs, for all $\epsilon > 0,$ there exists a data structure of size $\widetilde{O}(n\epsilon^{-1})$ which can be used to return $(1 + \epsilon)$-approximations to effective resistances in $\widetilde{O}(1)$ time per query. Short of storing all effective resistances, previous best approaches could achieve $\widetilde{O}(n\ep

Xinliang Frederick Zhang, Nick Beauchamp, Lu Wang

Stance detection is typically framed as predicting the sentiment in a given text towards a target entity. However, this setup overlooks the importance of the source entity, i.e., who is expressing the opinion. In this paper, we emphasize the need for studying interactions among entities when inferring stances. We first introduce a new task, entity-to-entity

Ben Kenwright

We present a novel approach for solving articulated inverse kinematic problems (e.g., character structures) by means of an iterative dual-quaternion and exponentialmapping approach. As dual-quaternions are a break from the norm and offer a straightforward and computationally efficient technique for representing kinematic transforms (i.e., position and transl

Matthew De Furio, Tyler Gardner, John Monnier, Michael R. Meyer

We present preliminary results from our long-baseline interferometry (LBI) survey to constrain the multiplicity properties of intermediate-mass A-type stars within 80pc. Previous multiplicity studies of nearby stars exhibit orbital separation distributions well-fitted with a log-normal with peaks > 15au, increasing with primary mass. The A-star multiplicity

Tommi Sottinen, Ercan Sönmez, Lauri Viitasaari

We study the existence and regularity of local times for general $d$-dimensional stochastic processes. We give a general condition for their existence and regularity properties. To emphasize the contribution of our results, we show that they include various prominent examples, among others solutions to stochastic differential equations driven by fractional B

Patrick Gresham, Brian Jeffers, Alexey Shashurin

In this paper, a coaxial pulsed plasma thruster was designed, built, and tested. This design confirmed PPT operation at substantially reduced discharge voltages of 100 to 200 V across the discharge plasma at a discharge current level of 10.4 kA similar to magnetoplasmadynamic arcs. The PPT cathode was imaged with an ICCD camera over a wide range of pressures

Yanyan Shi

In this paper, we study the charged-particle dynamics under strong magnetic field in a toroidal axi-symmetric geometry. Using modulated Fourier expansions of the exact and numerical solutions, the long-term drift motion of the exact solution in toroidal geometry is derived and the error analysis of the large-stepsize modified Boris algorithm over long time s

Luke Strgar, David Harwath

We apply transfer learning to the task of phoneme segmentation and demonstrate the utility of representations learned in self-supervised pre-training for the task. Our model extends transformer-style encoders with strategically placed convolutions that manipulate features learned in pre-training. Using the TIMIT and Buckeye corpora we train and test the mode

S. Allak, A. Akyuz, E. Sonbas, K. S. Dhuga

In this study, we use archival data from HST, Chandra, XMM-Newton, and Swift-XRT, to probe the nature of 9 (X1-X9) candidate ULXs in NGC 1672. Our study focuses on using the precise source positions obtained via improved astrometry based on {\it Chandra} and HST observations to search for and identify optical counterparts for these ULXs.Unique optical counte

Shivam Gupta, Bidisha Bhatt, Meenaxi Sharma, Krishnacharya Khare

Due to the slow dynamics of the wetting ridge, it is challenging to predict the wetting morphology of liquid drops on thin lubricant coated surfaces. It is hypothesized that when a drop sinks on a lubricated surface, quasi-static wetting morphology can be numerically computed only from the knowledge of interfacial energies, lubricant thickness, and drop volu

Néstor Espinoza, Leonardo Úbeda, Stephan M. Birkmann, Pierre Ferruit

We report on JWST commissioning observations of the transiting exoplanet HAT-P-14 b, obtained using the Bright Object Time Series (BOTS) mode of the NIRSpec instrument with the G395H/F290LP grating/filter combination ($3-5\mu$m). While the data were used primarily to verify that the NIRSpec BOTS mode is working as expected, and to enable it for general scien

Brian Yan, Matthew Wiesner, Ondrej Klejch, Preethi Jyothi

In this work, we seek to build effective code-switched (CS) automatic speech recognition systems (ASR) under the zero-shot setting where no transcribed CS speech data is available for training. Previously proposed frameworks which conditionally factorize the bilingual task into its constituent monolingual parts are a promising starting point for leveraging m

Cristian Tellez-Piñerez, Leonardo Trujillo, Andrés Gutiérrez-Rojas, Juan Sosa

We propose a novel methodology relating item response theory methods with small area estimation strategies in the presence of missing data. Specifically, we propose an unbiased estimator for the average ability parameter of three-parameter logistic models. Thus, we carry out an extensive simulation study in order to compare our estimator with the well-known

CMS Collaboration

A measurement of the jet mass distribution in hadronic decays of Lorentz-boosted top quarks is presented. The measurement is performed in the lepton+jets channel of top quark pair production ($\mathrm{t\bar{t}}$) events, where the lepton is an electron or muon. The products of the hadronic top quark decay are reconstructed using a single large-radius jet wit

Shawn Hymel, Colby Banbury, Daniel Situnayake, Alex Elium

Edge Impulse is a cloud-based machine learning operations (MLOps) platform for developing embedded and edge ML (TinyML) systems that can be deployed to a wide range of hardware targets. Current TinyML workflows are plagued by fragmented software stacks and heterogeneous deployment hardware, making ML model optimizations difficult and unportable. We present E

Carolin Benjamins, Elena Raponi, Anja Jankovic, Koen van der Blom

Bayesian Optimization (BO) is a powerful, sample-efficient technique to optimize expensive-to-evaluate functions. Each of the BO components, such as the surrogate model, the acquisition function (AF), or the initial design, is subject to a wide range of design choices. Selecting the right components for a given optimization task is a challenging task, which

Vishak Prasad C, Colin White, Paarth Jain, Sibasis Nayak

A majority of recent developments in neural architecture search (NAS) have been aimed at decreasing the computational cost of various techniques without affecting their final performance. Towards this goal, several low-fidelity and performance prediction methods have been considered, including those that train only on subsets of the training data. In this wo

Xingzhou Tang, Ali Mozaffari, Noe Atzin, Soumik Das

Solitons in nematic liquid crystals offer intriguing opportunities for transport and sensing in microfluidic systems. Little is known about the elementary conditions that are needed to create solitons in nematic materials. In this work, theory, simulations and experiments are used to study the generation and propagation of solitary waves (or "solitons") in n

Abbad Moussa, Houcine Aounallah, Prabir Rudra, Faizuddin Ahmed

In this work, we study the Klein-Gordon oscillator with equal scalar and vector potentials in a topologically charged Ellis-Bronnikov wormhole space-time background. The behaviour of a relativistic oscillator field is studied with a position-dependent mass via transformation $M^{2}\rightarrow (M+S(x))^{2}$ and vector potential through a minimal substitution

Dacheng Li, Rulin Shao, Hongyi Wang, Han Guo

Enabling private inference is crucial for many cloud inference services that are based on Transformer models. However, existing private inference solutions can increase the inference latency by more than 60x or significantly compromise the inference quality. In this paper, we design the framework MPCFORMER as a practical solution, using Secure Multi-Party Co

Swapnil Saha, Hafiz Imtiaz

Non-negative matrix factorization is a popular unsupervised machine learning algorithm for extracting meaningful features from data which are inherently non-negative. However, such data sets may often contain privacy-sensitive user data, and therefore, we may need to take necessary steps to ensure the privacy of the users while analyzing the data. In this wo

E. Victor Flynn, Kamal Khuri-Makdisi

We give the explicit equations for a P^3 x P^3 embedding of the Jacobian of a curve of genus 2, which gives a natural analog for abelian surfaces of the Edwards curve model of elliptic curves. This gives a much more succinct description of the Jacobian variety than the standard version in P^{15}. We also give a condition under which, as for the Edwards curve

Alex Golovin, Sabine Reffert, Andreas Just, Stefan Jordan

We present the compilation of the Fifth Catalogue of Nearby Stars (CNS5), based on astrometric and photometric data from Gaia EDR3 and Hipparcos, and supplemented with parallaxes from ground-based astrometric surveys carried out in the infrared. The aim of the CNS5 is to provide the most complete sample of objects in the solar neighbourhood. For all known st

Michał Fabisiak, Jan Peszek

We prove that certain types of measure-valued mappings are monokinetic i.e. the distribution of velocity is concentrated in a Dirac mass. These include weak measure-valued solutions to the strongly singular Cucker-Smale model with singularity of order $\alpha$ greater or equal to the dimension of the ambient space. Consequently, we are able to answer a coupl

L. M. de Sá, A. Bernardo, R. R. A. Bachega, J. E. Horvath

The lack of objects between $2\,M_{\odot}$ and $5\,M_{\odot}$ in the joint mass distribution of compact objects has been termed "mass gap", and attributed mainly to the characteristics of the supernova mechanism precluding their birth. However, recent observations show that a number of candidates reported to lie inside the "gap" may fill it, suggesting inste

João Machado de Freitas, Bernhard C. Geiger

Ensuring trustworthiness in machine learning -- by balancing utility, fairness, and privacy -- remains a critical challenge, particularly in representation learning. In this work, we investigate a family of closely related information-theoretic objectives, including information funnels and bottlenecks, designed to extract invariant representations from data.

A. Damineli, D. J. Hillier, F. Navarete, A. F. J. Moffat

Eta Carinae ($\eta$\,Car) exhibits a unique set of P Cygni profiles with both broad and narrow components. Over many decades, the spectrum has changed -- there has been an increase in observed continuum fluxes and a decrease in FeII and HI emission line equivalent widths. The spectrum is evolving towards that of a P Cygni star such as P~Cygni itself and HDE~

Prabhanjan Ananth, Aditya Gulati, Luowen Qian, Henry Yuen

Pseudorandom quantum states (PRS) are efficiently constructible states that are computationally indistinguishable from being Haar-random, and have recently found cryptographic applications. We explore new definitions, new properties and applications of pseudorandom states, and present the following contributions: 1. New Definitions: We study variants of pseu

Pasin Manurangsi

We study the complexity of computing (and approximating) VC Dimension and Littlestone's Dimension when we are given the concept class explicitly. We give a simple reduction from Maximum (Unbalanced) Biclique problem to approximating VC Dimension and Littlestone's Dimension. With this connection, we derive a range of hardness of approximation results and runn

Siyu Liu, Marija Ilic

This paper concerns the potential of corrective actions, such as generation and load dispatch on minimizing the effects of transmission line failures in electric power systems. Three loss functions (grid-centric, consumer-centric, and influence localization) are used to statistically evaluate the criticality of initial contingent failures. A learning scheme

Patrick Steinmüller, Tobias Schulz, Ferdinand Graf, Daniel Herr

Parametrized Quantum Circuits (PQCs) enable a novel method for machine learning (ML). However, from a computational point of view they present a challenge to existing eXplainable AI (xAI) methods. On the one hand, measurements on quantum circuits introduce probabilistic errors which impact the convergence of these methods. On the other hand, the phase space

Piotr Morawiecki, Philippe H. Trinh

The objective of this three-part work is to formulate and rigorously analyse a number of reduced mathematical models that are nevertheless capable of describing the hydrology at the scale of a river basin (i.e. catchment). Coupled surface and subsurface flows are considered. In this second part, we construct a benchmark catchment scenario and investigate the

Christoph Richard, Nicolae Strungaru

We show that any translate of a model set is a model set in some modified cut-and-project scheme. Restricting to Euclidean direct space, we show that any translate of an inter model set is a model set in some modified cut-and-project scheme with second countable internal space. In both cases, the window in the modified cut-and-project scheme inherits the top

Pawel Swietojanski, Stefan Braun, Dogan Can, Thiago Fraga da Silva

This work studies the use of attention masking in transformer transducer based speech recognition for building a single configurable model for different deployment scenarios. We present a comprehensive set of experiments comparing fixed masking, where the same attention mask is applied at every frame, with chunked masking, where the attention mask for each f

Aureliano M. Robles-Pérez, José Carlos Rosales

We study the extended Frobenius problem for sequences of the form $\{l_a\}\cup\{l_a+l_n\}_{n\in\mathbb{N}}$ and $\{l_a+l_n\}_{n\in\mathbb{N}}$, where $\{l_n\}_{n\in\mathbb{N}}$ is the Lucas series and $l_a$ is a Lucas number. As a consequence, we show that the families of numerical semigroups associated to both sequences satisfy the Wilf's conjecture.

Léa Ferellec, Colin Snodgrass, Alan Fitzsimmons, Agata Rożek

Main Belt Comets (MBCs) exhibit sublimation-driven activity while occupying asteroid-like orbits in the Main Asteroid Belt. MBCs and candidates show stronger clustering of their longitudes of perihelion around 15{\deg} than other objects from the Outer Main Belt (OMB). This potential property of MBCs could facilitate the discovery of new candidates by observ

Anton Tsitsulin, Davide Mottin, Panagiotis Karras, Alex Bronstein

We introduce a spectral notion of graph complexity derived from the Weyl's law. We experimentally demonstrate its correlation to how well the graph can be embedded in a low-dimensional Euclidean space.

Joseph C. Chapman, Nicholas A. Peters

Squeezed light, which is easily degraded by loss, could benefit from generation directly in optical fiber. Furthermore, highly nonlinear fiber could offer more efficient generation with lower pump power and shorter fiber lengths than standard single-mode fiber. We investigate non-polarization-maintaining highly nonlinear fiber (HNLF) for squeezed-light gener

Qin Dai, Benjamin Heinzerling, Kentaro Inui

Bi-encoder architectures for distantly-supervised relation extraction are designed to make use of the complementary information found in text and knowledge graphs (KG). However, current architectures suffer from two drawbacks. They either do not allow any sharing between the text encoder and the KG encoder at all, or, in case of models with KG-to-text attent

Håkon I. Røst, Federico Mazzola, Johannes Bakkelund, Anna Cecilie Åsland

We report energy renormalizations from electron-phonon and electron-magnon interactions in spin minority surface resonances on Ni(111). The different interactions are identified, disentangled, and quantified from the characteristic signatures they provide to the complex self-energy and the largely different binding energies at which they occur. The observed

Xingzhi Guo, Steven Skiena

Word and graph embeddings are widely used in deep learning applications. We present a data structure that captures inherent hierarchical properties from an unordered flat embedding space, particularly a sense of direction between pairs of entities. Inspired by the notion of \textit{distributional generality}, our algorithm constructs an arborescence (a direc

Daniel A. Spencer, David Bolin, Amanda F. Mejia

Task functional magnetic resonance imaging (fMRI) is a type of neuroimaging data used to identify areas of the brain that activate during specific tasks or stimuli. These data are conventionally modeled using a massive univariate approach across all data locations, which ignores spatial dependence at the cost of model power. We previously developed and valid

Stefano Piemontese, Tommaso Roscilde, Alioscia Hamma

In this paper we study the transitions of entanglement complexity in an exemplary family of states - the Rokhsar-Kivelson-sign wavefunctions - whose degree of entanglement is controlled by a single parameter. This family of states is known to feature a transition between a phase exhibiting volume-law scaling of entanglement entropy and a phase with sub-exten

Aditya Sanghi, Rao Fu, Vivian Liu, Karl Willis

Recent works have demonstrated that natural language can be used to generate and edit 3D shapes. However, these methods generate shapes with limited fidelity and diversity. We introduce CLIP-Sculptor, a method to address these constraints by producing high-fidelity and diverse 3D shapes without the need for (text, shape) pairs during training. CLIP-Sculptor

Julie Jiang, Ron Dotsch, Mireia Triguero Roura, Yozen Liu

Pictorial emojis and stickers are commonly used in online social networking to facilitate and aid communications. We delve into the use of Bitmoji stickers, a highly expressive form of pictorial communication using avatars resembling actual users. We collect a large-scale dataset of the metadata of 3 billion Bitmoji stickers shared among 300 million Snapchat

Lucía Pedraza, Juan Pablo Pinasco, Viktoriya Semeshenko, Pablo Balenzuela

Analytical approaches in models of opinion formation have been extensively studied either for an opinion represented as a discrete or a continuous variable. In this paper, we analyze a model which combines both approaches. The state of an agent is represented with an internal continuous variable (the leaning or propensity), that leads to a discrete public op

Meghana Killi, Darach Watson, Seiji Fujimoto, Hollis Akins

We present the first detection of the [N II] 122 $\mu$m and [O III] 52 $\mu$m lines for a reionisation-epoch galaxy. Based on these lines and previous [C II] 158 $\mu$m and [O III] 88 $\mu$m measurements, using two different radiative transfer models of the interstellar medium, we estimate an upper limit on electron density of $\lesssim$ 500 cm$^{-3}$ and a

Jean-Marc Deshouillers, Michael Drmota, Clemens Müllner, Andrei Shubin

The purpose of this paper is to study subsequences of synchronizing $k$-automatic sequences $a(n)$ along Piatetski-Shapiro sequences $\lfloor n^c \rfloor$ with non-integer $c>1$. In particular, we show that $a(\lfloor n^c \rfloor)$ satisfies a prime number theorem of the form $\sum_{n\le x} \Lambda(n)a(\lfloor n^c \rfloor) \sim C\, x$, and, furthermore, that

Tilman Plehn, Anja Butter, Barry Dillon, Theo Heimel

Depending on the point of view, modern machine learning is either providing an unprecedented boost to the numerical methods of particle physics, or it is transforming the way we do science with vast amounts of complex data. In any case, it is crucial for young researchers to stay on top of this development and apply cutting-edge methods and tools to all LHC

Zhe Ren, Jiang-Hao Yu

We present a complete and independent off-shell Green's basis of the dimension 8 operators in the Standard Model effective field theory (SMEFT). We propose an off-shell amplitude formalism such that this new kind of amplitudes has a one-to-one correspondence to general operators carrying all kinds of redundancies. The advantage of such formalism is that all

Kasia Budzik, Davide Gaiotto

We discuss the notion of translation-invariant vacua for 2d chiral algebras and relate it to the notion of the associated variety. The two-dimensional chiral algebra associated to four-dimensional ${\cal N}=4$ $U(N)$ SYM has a conjectural holographic dual involving the B-model topological string theory. We study the effect of non-zero vacuum expectation valu

Tetiana Parshakova, Fangzhao Zhang, Stephen Boyd

We consider the problem of minimizing a function that is a sum of convex agent functions plus a convex common public function that couples them. The agent functions can only be accessed via a subgradient oracle; the public function is assumed to be structured and expressible in a domain specific language (DSL) for convex optimization. We focus on the case wh

Paul Balister, Béla Bollobás, Robert Morris, Julian Sahasrabudhe

A covering system is a finite collection of arithmetic progressions whose union is the set of integers. The study of these objects was initiated by Erd\H{o}s in 1950, and over the following decades he asked many questions about them. Most famously, he asked whether there exist covering systems with distinct moduli whose minimum modulus is arbitrarily large.

Ruddarraju Amrutha, Pratyusha Chattopadhyay

A.A. Suslin proved a normality theorem for an elementary linear group, which says that an elementary linear group of size bigger than or equal to 3 over a commutative ring with unity is normal in the general linear group of same size. Subsequently, V.I. Kopeiko extended this result of Suslin for a symplectic group defined with respect to the standard skew-sy

Ivan Cherednik, Bradley Hicks

The main aim is to obtain integral formulas for DAHA coinvariants and the corresponding inner products for any values of the DAHA parameters. In the compact case, our approach is similar to the procedure of ``picking up residues" due to Arthur, Heckman, Opdam and others; the resulting formula is a sum of integrals over double affine residual subtori. A singl

Gissell Estrada-Rodriguez, Diane Peurichard, Xinran Ruan

In this work we first prove, by formal arguments, that the diffusion limit of nonlinear kinetic equations, where both the transport term and the turning operator are density-dependent, leads to volume-exclusion chemotactic equations. We generalise an asymptotic preserving scheme for such nonlinear kinetic equations based on a micro-macro decomposition. By pr

Prateek Anand, Ganesh Subramanian

We study the inertial migration of a torque-free neutrally buoyant sphere in wall-bounded plane Couette flow over a wide range of channel Reynolds numbers, $Re_c$, in the limit of small particle Reynolds number\,($Re_p\ll1$) and confinement ratio\,($\lambda\ll1$). Here, $Re_c = V_\text{wall}H/\nu$ where $H$ denotes the separation between the channel walls, $

Arnab Neelim Mazumder, Niall Lyons, Ashutosh Pandey, Avik Santra

Explainability of neural network prediction is essential to understand feature importance and gain interpretable insight into neural network performance. However, explanations of neural network outcomes are mostly limited to visualization, and there is scarce work that looks to use these explanations as feedback to improve model performance. In this work, mo

Jun Wang, Abhir Bhalerao, Terry Yin, Simon See

Radiology report generation (RRG) has gained increasing research attention because of its huge potential to mitigate medical resource shortages and aid the process of disease decision making by radiologists. Recent advancements in RRG are largely driven by improving a model's capabilities in encoding single-modal feature representations, while few studies ex

Cem Ates Musluoglu, Alexander Bertrand

Wireless sensor networks consist of sensor nodes that are physically distributed over different locations. Spatial filtering procedures exploit the spatial correlation across these sensor signals to fuse them into a filtered signal satisfying some optimality condition. However, gathering the raw sensor data in a fusion center to solve the problem in a centra

Eric Chen, Adam Ge, Andrew Kalashnikov, Tanya Khovanova

In this paper, we generalize a lot of facts from John Conway and Alex Ryba's paper, \textit{The extra Fibonacci series and the Empire State Building}, where we replace the Fibonacci sequence with the Tribonacci sequence. We study the Tribonacci array, which we also call \textit{the Trithoff array} to emphasize the connection to the Wythoff array. We describe

Fernando Marchesano, Luca Melotti

We revisit the Emergence Proposal in 4d ${\cal N}=2$ vector multiplet sectors that arise from type II string Calabi--Yau compactifications, with emphasis on the role of axionic fundamental strings, or EFT strings. We focus on large-volume type IIA compactifications, where EFT strings arise from NS5-branes wrapping internal four-cycles, and consider a set of

Mohamed Y. Nada, Tarek Mealy, Md Shafiqul Islam, Ilya Vitebskiy

We design a three-way silicon optical waveguide with the Bloch dispersion relation supporting a stationary inflection point (SIP). The SIP is a third order exceptional point of degeneracy (EPD) where three Bloch modes coalesce forming the frozen mode with greatly enhanced amplitude. The proposed design consists of a coupled resonators optical waveguide (CROW

Ilia Sucholutsky, Ruairidh M. Battleday, Katherine M. Collins, Raja Marjieh

Supervised learning typically focuses on learning transferable representations from training examples annotated by humans. While rich annotations (like soft labels) carry more information than sparse annotations (like hard labels), they are also more expensive to collect. For example, while hard labels only provide information about the closest class an obje

Samuel Dooley

One of the main promises of technology development is for it to be adopted by people, organizations, societies, and governments -- incorporated into their life, work stream, or processes. Often, this is socially beneficial as it automates mundane tasks, frees up more time for other more important things, or otherwise improves the lives of those who use the t

João C. Lobato, Isabela S. Matos, Maurício O. Calvão, Ioav Waga

We generalize to reduced Horndeski theories of gravity, where gravitational waves (GWs) travel at the speed of light, the expression of a statistically homogeneous and unpolarized stochastic gravitational wave background (SGWB) signal measured as the correlation between the individual signals detected by two interferometers in arbitrary configurations. We al

Jessica Craven, Mark Hughes, Vishnu Jejjala, Arjun Kar

We automate the process of machine learning correlations between knot invariants. For nearly 200,000 distinct sets of input knot invariants together with an output invariant, we attempt to learn the output invariant by training a neural network on the input invariants. Correlation between invariants is measured by the accuracy of the neural network predictio

Jie Gu, Marcos Marino

Topological string theory has multi-instanton sectors which lead to non-perturbative effects in the string coupling constant and control the large order behavior of the perturbative genus expansion. As proposed by Couso, Edelstein, Schiappa and Vonk, these sectors can be described by a trans-series extension of the BCOV holomorphic anomaly equations. In this

Ashish Kumar Meena, Wenlei Chen, Adi Zitrin, Patrick L. Kelly

We report the discovery of a transient seen in a strongly lensed arc at redshift $z_{\rm s}=1.2567$ in \emph{Hubble Space Telescope} imaging of the Abell 370 galaxy cluster. The transient is detected at $29.51\pm0.14$ AB mag in a WFC3/UVIS F200LP difference image made using observations from two different epochs, obtained in the framework of the \emph{Flashl

Gabriel E. Freedman, Aaron D. Johnson, Rutger van Haasteren, Sarah J. Vigeland

Pulsar timing arrays (PTAs) detect low-frequency gravitational waves (GWs) by looking for correlated deviations in pulse arrival times. Current Bayesian searches use Markov Chain Monte Carlo (MCMC) methods, which struggle to sample the large number of parameters needed to model the PTA and GW signals. As the data span and number of pulsars increase, this pro

Karim Zantout, Steffen Backes, Aleksandar Razpopov, Dominik Lessnich

In this work we present a multi-orbital form of the Two-Particle Self-Consistent approach (TPSC), here the effective local and static irreducible interaction vertices are determined by means of the Dynamical Mean-Field Theory (DMFT). This approach replaces the approximate ansatz equations for the double occupations $\langle n^{}_{\alpha,\sigma}n^{}_{\beta,\s

Thibault Damour, Piero Rettegno

We compare numerical relativity (NR) data on the scattering of equal-mass, non-spinning binary black holes to various analytical predictions based on post-Minkowskian (PM) results. While the usual sequence of PM-expanded scattering angles shows a rather poor convergence towards NR data, we find that a reformulation of PM information in terms of Effective-One

Hiroki Nagakura, Masamichi Zaizen

We present a systematic study of fast neutrino-flavor conversion (FFC) with both small-scale and large-scale numerical simulations in spherical symmetry. We find that FFCs can, in general, reach a quasi-steady state, and these features in the non-linear phase are not characterized by the growth rate of FFC instability but rather angular structures of electro

A. E. Faraggi, S. Groot Nibbelink, M. Hurtado-Heredia

This paper aims to shed light on what becomes of discrete torsion within heterotic orbifolds when they are resolved to smooth geometries. Gauged Linear Sigma Models (GLSMs) possessing (0,2) worldsheet supersymmetry are employed as interpolations between them. This question is addressed for resolutions of the non-compact C3/Z2xZ2 and the compact T6/Z2xZ2 orbi

Daniel Finstad, Laurel V. White, Duncan A. Brown

Gravitational-wave observations of neutron star mergers can probe the nuclear equation of state by measuring the imprint of the neutron star's tidal deformability on the signal. We investigate the ability of future gravitational-wave observations to produce a precise measurement of the equation of state from binary neutron star inspirals. Since measurability

Zirui Chen, Drummond B. Fielding, Greg L. Bryan

Turbulent Radiative Mixing Layers (TRMLs) form at the interface of cold, dense gas and hot, diffuse gas in motion with each other. TRMLs are ubiquitous in and around galaxies on a variety of scales, including galactic winds and the circumgalactic medium. They host the intermediate temperature gases that are efficient in radiative cooling, thus play a crucial

Upamanyu Moitra

We initiate a systematic study of continuously self-similar (CSS) gravitational dynamics in two dimensions, motivated by critical phenomena observed in higher dimensional gravitational theories. We consider CSS spacetimes admitting a homothetic Killing vector (HKV) field. For a general two-dimensional gravitational theory coupled to a dilaton field and Maxwe