July 2022 arXiv papers — page 77

Cheng-Ming Li, Shu-Yu Zuo, Ya-Peng Zhao, Hui-Jun Mu

In this work, a modified Nambu-Jona-Lasinio (NJL) model with proper-time regularization is employed to study the structure of nonstrange quark stars. The coupling constant of four-fermion interaction in the conventional NJL model is modified as $G=G_1+G_2\langle\bar{\psi}\psi\rangle$ to highlight the feedback of quark propagator to gluon propagator. To study

Antonio Tadeu Azevedo Gomes, Larissa Miguez da Silva, Frederic Valentin

Physics-Informed Neural Networks (PINNs) are machine learning tools that approximate the solution of general partial differential equations (PDEs) by adding them in some form as terms of the loss/cost function of a Neural Network. Most pieces of work in the area of PINNs tackle non-linear PDEs. Nevertheless, many interesting problems involving linear PDEs ma

Donald A. Robinson, Michael E. Foster, Christopher H. Bennett, Austin Bhandarkar

Emerging concepts for neuromorphic computing, bioelectronics, and brain-computer interfacing inspire new research avenues aimed at understanding the relationship between oxidation state and conductivity in unexplored materials. Here, we present ruthenium Prussian blue analogue (RuPBA), a mixed valence coordination compound with an open framework structure an

Arash Amini, Cong Zheng, Qiyu Sun, Nader Motee

The Carleman linearization is one of the mainstream approaches to lift a finite-dimensional nonlinear dynamical system into an infinite-dimensional linear system with the promise of providing accurate approximations of the original nonlinear system over larger regions around the equilibrium for longer time horizons with respect to the conventional first-orde

Kyoung Min Yoo, May Hlaing, Sourabh Jain, James Fan

A micro-ring-resonator (MRR) optical biosensor based on the evanescent field sensing mechanism has been extensively studied due to its high sensitivity and compact device size. However, a suitable on-chip integrated spectrometer device has to be demonstrated for the lab-on-a-chip applications, which can read the resonance wavelength shift from MRR biosensors

Peter Boyvalenkov, Peter Dragnev, Douglas Hardin, Edward Saff

In this article we investigate the $N$-point min-max and the max-min polarization problems on the sphere for a large class of potentials in $\mathbb{R}^n$. We derive universal lower and upper bounds on the polarization of spherical designs of fixed dimension, strength, and cardinality. The bounds are universal in the sense that they are a convex combination

Jeroen Van Der Donckt, Jonas Van Der Donckt, Emiel Deprost, Nicolas Vandenbussche

Over the last few years, research in automatic sleep scoring has mainly focused on developing increasingly complex deep learning architectures. However, recently these approaches achieved only marginal improvements, often at the expense of requiring more data and more expensive training procedures. Despite all these efforts and their satisfactory performance

L. Mahy, H. Sana, T. Shenar, K. Sen

Aims. To search for these rare objects, we study 32 Galactic O-type stars that were reported as SB1s in the literature. In our sample we include Cyg X-1, which is known to host an accreting stellar-mass BH, and HD 74194, a supergiant fast X-ray transient, in order to validate our methodology. The final goal is to characterise the nature of the unseen compani

Lishuo Pan, Sandeep Manjanna, M. Ani Hsieh

The multi-robot adaptive sampling problem aims at finding trajectories for a team of robots to efficiently sample the phenomenon of interest within a given endurance budget of the robots. In this paper, we propose a robust and scalable approach using Multi-Agent Reinforcement Learning for cooperated Adaptive Sampling (MARLAS) of quasi-static environmental pr

Rubab Amin, James Greenberg, Brendan Heffernan, Tadao Nagatsuma

Spectral purity of any millimeter wave (mmW) source is of the utmost interest in low-noise applications. Optical synthesis via photomixing is an attractive source for such mmWs, which usually involves expensive spectrally pure lasers with narrow linewidths approaching monochromaticity due to their inherent fabrication costs or specifications. Here, we report

Md Masudur Rahman, Yexiang Xue

Deep Reinforcement Learning (RL) agents often overfit the training environment, leading to poor generalization performance. In this paper, we propose Thinker, a bootstrapping method to remove adversarial effects of confounding features from the observation in an unsupervised way, and thus, it improves RL agents' generalization. Thinker first clusters experie

Ioannis Chatzigeorgiou, Francisco A. Monteiro

Guessing random additive noise decoding (GRAND) is a noise-centric decoding method, which is suitable for ultra-reliable low-latency communications, as it supports high-rate error correction codes that generate short-length codewords. GRAND estimates transmitted codewords by guessing the error patterns that altered them during transmission. The guessing proc

Paul Terwilliger

This survey paper contains a tutorial introduction to distance-regular graphs, with an emphasis on the subconstituent algebra and the $Q$-polynomial property.

Karthik R. Peddireddy, Ryan Clairmont, Rae M. Robertson-Anderson

Mixtures of polymers of varying topologies and stiffnesses display complex emergent rheological properties that often cannot be predicted from their single-component counterparts. For example, entangled blends of ring and linear polymers have been shown to exhibit enhanced shear thinning and viscosity, as well as prolonged relaxation timescales, compared to

Congying Wang, Jia Yu, Zhuoyue Zhao

Although spatial indexes shorten the query response time, they rely on complex tree structures to narrow down the search space. Such structures in turn yield additional storage overhead and take a toll on index maintenance. Recently, there have been a flurry of efforts attempting to leverage Machine-Learning (ML) models to simplify the index structures. Howe

Patrick S. Eastham, Hadi Mohammadigoushki, Kourosh Shoele

An axisymmetric squirmer in a Bingham viscoplastic fluid is studied numerically to determine the effect of a yield stress environment on locomotion. The nonlinearity of the governing equations necessitates numerical methods, which is accomplished by solving a variable-viscosity Stokes equation with a Finite Element approach. The effects of stroke modes, both

Chuang Niu, Ge Wang

Minimum redundancy among different elements of an embedding in a latent space is a fundamental requirement or major preference in representation learning to capture intrinsic informational structures. Current self-supervised learning methods minimize a pair-wise covariance matrix to reduce the feature redundancy and produce promising results. However, such r

Jan Docekal, Jakub Rozlivek, Jiri Matas, Matej Hoffmann

We study the performance of state-of-the-art human keypoint detectors in the context of close proximity human-robot interaction. The detection in this scenario is specific in that only a subset of body parts such as hands and torso are in the field of view. In particular, (i) we survey existing datasets with human pose annotation from the perspective of clos

Paul Terwilliger

The positive part $U^+_q$ of $U_q(\hat{\mathfrak{sl}}_2)$ has a presentation with two generators $W_0$, $W_1$ and two relations called the $q$-Serre relations. The algebra $U^+_q$ contains some elements, said to be alternating. There are four kinds of alternating elements, denoted $\lbrace W_{-k}\rbrace_{k\in \mathbb N}$, $\lbrace W_{k+1}\rbrace_{k\in \mathb

Quoc Hung Ngo, Tahar Kechadi, Nhien-An Le-Khac

In recent years, data science has evolved significantly. Data analysis and mining processes become routines in all sectors of the economy where datasets are available. Vast data repositories have been collected, curated, stored, and used for extracting knowledge. And this is becoming commonplace. Subsequently, we extract a large amount of knowledge, either d

Chen Liu, Xiaomeng Dong, Michael Potter, Hsi-Ming Chang

Focal Loss has reached incredible popularity as it uses a simple technique to identify and utilize hard examples to achieve better performance on classification. However, this method does not easily generalize outside of classification tasks, such as in keypoint detection. In this paper, we propose a novel adaptation of Focal Loss for keypoint detection task

Hector G. T. Torres

With the advent of Transformative Artificial Intelligence, it is now more important than ever to be able to both measure and forecast the transformative impact/potential of innovation. However, current methods fall short when faced with this task. This paper introduces the Transform-o-meter; a methodology that can be used to achieve the aforementioned goal,

Gudrun Hiller, Tim Höhne, Daniel F. Litim, Tom Steudtner

We address the notorious metastability of the standard model (SM) and promote it to a model building task: What are the new ingredients required to stabilize the SM up to the Planck scale without encountering subplanckian Landau poles? Using the SM extended by vector-like fermions (VLFs), we chart out the corresponding landscape of Higgs stability. We find t

James Gleeson, Daniel Snider, Yvonne Yang, Moshe Gabel

Reinforcement learning (RL) workloads take a notoriously long time to train due to the large number of samples collected at run-time from simulators. Unfortunately, cluster scale-up approaches remain expensive, and commonly used CPU implementations of simulators induce high overhead when switching back and forth between GPU computations. We explore two optim

Mick Gielen, Walter D. van Suijlekom

We study the duals of a certain class of finite-dimensional operator systems, namely the class of operator systems associated to tolerance relations on finite sets or equivalently the class of operator systems that are associated with graphs. In the case where the graphs associated with these operator systems are chordal we are able to find concrete realizat

Haiyi Mao, Minxue Jia, Jason Xiaotian Dou, Haotian Zhang

Metacells are disjoint and homogeneous groups of single-cell profiles, representing discrete and highly granular cell states. Existing metacell algorithms tend to use only one modality to infer metacells, even though single-cell multi-omics datasets profile multiple molecular modalities within the same cell. Here, we present \textbf{C}ross-M\textbf{O}dal \te

Makana Silva, Christopher Hirata

Extreme mass ratio inspirals (EMRIs) -- systems with a compact object orbiting a much more massive (e.g., galactic center) black hole -- are of interest both as a new probe of the environments of galactic nuclei, and their waveforms are a precision test of the Kerr metric. This work focuses on the effects of an external perturbation due to a third body aroun

Sébastien Lachapelle, Simon Lacoste-Julien

Disentanglement via mechanism sparsity was introduced recently as a principled approach to extract latent factors without supervision when the causal graph relating them in time is sparse, and/or when actions are observed and affect them sparsely. However, this theory applies only to ground-truth graphs satisfying a specific criterion. In this work, we intro

Amit Jena, Tong Huang, S. Sivaranjani, Dileep Kalathil

This paper considers the problem of characterizing the stability region of a large-scale networked system comprised of dissipative nonlinear subsystems, in a distributed and computationally tractable way. One standard approach to estimate the stability region of a general nonlinear system is to first find a Lyapunov function for the system and characterize i

Jorge A. Mendez, Eric Eaton

A major goal of artificial intelligence (AI) is to create an agent capable of acquiring a general understanding of the world. Such an agent would require the ability to continually accumulate and build upon its knowledge as it encounters new experiences. Lifelong or continual learning addresses this setting, whereby an agent faces a continual stream of probl

Savita V. Kendre, Gus. T. Teran, Lauryn Whiteside, Tyler Looney

The COVID-19 pandemic has revealed the importance of digital fabrication to enable online learning, which remains a challenge for robotics courses. We introduce a teaching methodology that allows students to participate remotely in a hands-on robotics course involving the design and fabrication of robots. Our methodology employs 3D printing techniques with f

Sanjay Gosain, Jack Harvey, Valentin Martinez-Pillet, Tom Woods

Designing compact instruments is the key for the scientific exploration by smaller spacecrafts such as cubesats or by deep space missions. Such missions require compact instrument designs to have minimal instrument mass. Here we present a proof of concept for miniaturization of the Global Oscillation Network Group GONG instrument. GONG instrument routinely o

Vidya Setlur, Michael Correll, Sarah Battersby

Binning is applied to categorize data values or to see distributions of data. Existing binning algorithms often rely on statistical properties of data. However, there are semantic considerations for selecting appropriate binning schemes. Surveys, for instance, gather respondent data for demographic-related questions such as age, salary, number of employees,

Estelle Guéville, David Joseph Wrisley

This article focuses on the transcription of medieval manuscripts. Whereas problems of transcription have long interested medievalists, few workable options in the era of printed editions were available besides normalisation. The automation of this process, known as handwritten text recognition (HTR), has made new kinds of digital text creation possible, but

Bruno Murta, Pedro M. Q. Cruz, J. Fernández-Rossier

Quantum state preparation is a key step in all digital quantum simulation algorithms. Here we propose methods to initialize on a gate-based quantum computer a general class of quantum spin wave functions, the so-called Valence-Bond-Solid (VBS) states, that are important for two reasons. First, VBS states are the exact ground states of a class of interacting

Jason T. Parker, Jessica DeBerardinis, Simo A. Mäkiharju

Tracer particles designed specifically for X-ray particle tracking and imaging velocimetry (XPTV and XPIV) are necessary to widen the range of flows that can be studied with these techniques. In this study, we demonstrate in-lab XPTV using new, custom-designed $O$(50 $\mu$m) diameter tungsten-coated hollow carbon spheres and a single energy threshold photon

Yunyi Li, Maria De-Arteaga, Maytal Saar-Tsechansky

An increased awareness concerning risks of algorithmic bias has driven a surge of efforts around bias mitigation strategies. A vast majority of the proposed approaches fall under one of two categories: (1) imposing algorithmic fairness constraints on predictive models, and (2) collecting additional training samples. Most recently and at the intersection of t

Sanjana Das, Hannah Lang, Hamilton Wan, Nancy Xu

We consider the summatory function of the totient function after applications of a suitable smoothing operator and study the limiting behavior of the associated error term. Under several conditional assumptions, we show that the smoothed error term possesses a limiting logarithmic distribution through a framework consolidated by Akbary--Ng--Shahabi. To obtai

Tucker McElroy, Anindya Roy, Gaurab Hore

Guaranteeing privacy in released data is an important goal for data-producing agencies. There has been extensive research on developing suitable privacy mechanisms in recent years. Particularly notable is the idea of noise addition with the guarantee of differential privacy. There are, however, concerns about compromising data utility when very stringent pri

Eric Setterqvist, Natan Kruglyak, Robert Forchheimer

We suggest a novel classification algorithm that is based on local approximations and explain its connections with Artificial Neural Networks (ANNs) and Nearest Neighbour classifiers. We illustrate it on the datasets MNIST and EMNIST of images of handwritten digits. We use the dataset MNIST to find parameters of our algorithm and apply it with these paramete

Masih Haseli, Jorge Cortés

Extended Dynamic Mode Decomposition (EDMD) is a popular data-driven method to approximate the action of the Koopman operator on a linear function space spanned by a dictionary of functions. The accuracy of EDMD model critically depends on the quality of the particular dictionary's span, specifically on how close it is to being invariant under the Koopman ope

Nasrallah F. Nasrallah, Karl Schilcher

We present a calculation of the masses of the isovector mesons ( vector, scalar and pseudoscalar including the established recurrences) using a new method of finite energy QCD sum rules. The method is based on the idea of choosing a suitable integration kernel which minimizes the occurring integral over the cut in the complex energy (squared) plane. We obtai

Tom Coates, Johannes Hofscheier, Alexander Kasprzyk

We use machine learning to predict the dimension of a lattice polytope directly from its Ehrhart series. This is highly effective, achieving almost 100% accuracy. We also use machine learning to recover the volume of a lattice polytope from its Ehrhart series, and to recover the dimension, volume, and quasi-period of a rational polytope from its Ehrhart seri

Michael Desmond, Evelyn Duesterwald, Vatche Isahagian, Vinod Muthusamy

Most business process automation is still developed using traditional automation technologies such as workflow engines. These systems provide domain specific languages that require both business knowledge and programming skills to effectively use. As such, business users often lack adequate programming skills to fully leverage these code oriented environment

Diogo L. Pires, Mark Broom

Evolutionary game theory has proved to be a powerful tool to probe the self-organisation of collective behaviour by considering frequency-dependent fitness in evolutionary processes. It has shown that the stability of a strategy depends not only on the payoffs received after each encounter but also on the population's size. Here, we study $2\times2$ games in

Botao Li, Yoshihiko Nishikawa, Philipp Hoellmer, Louis Carillo

We discuss historic pressure computations for the hard-disk model performed since 1953, and compare them to results that we obtain with a powerful event-chain Monte Carlo and a massively parallel Metropolis algorithm. Like other simple models in the sciences, such as the Drosophila model of biology, the hard-disk model has needed monumental effort to be unde

Gabriel Pena, Verónica Moreno, Nestor Barraza

We propose to use the mean time between infections (MTBI) metric as obtained from a recently introduced non-homogeneous Markov stochastic model. Different types of parameter calibration are performed. We estimate the MTBI using data from different time windows and from the whole stage history and compare the results. In order to detect waves and stages in th

Zhengyi Wang, Ji Yao, Xiangkun Liu, Dezi Liu

We present a forecast study on the cross-correlation between cosmic shear tomography from the Chinese Survey Space Telescope (CSST) and CMB lensing from Ali CMB Polarization Telescope (AliCPT-1) in Tibet. The correlated galaxy and CMB lensing signals were generated from Gaussian realizations based on inputted auto- and cross-spectra. To account for the error

Jason Wu, Titus Barik, Xiaoyi Zhang, Colin Lea

Touch is the primary way that users interact with smartphones. However, building mobile user interfaces where touch interactions work well for all users is a difficult problem, because users have different abilities and preferences. We propose a system, Reflow, which automatically applies small, personalized UI adaptations, called refinements -- to mobile ap

Aitor Alaña, Nicolò Antolini, Giulio Biagioni, Iñigo L. Egusquiza

We provide a theoretical characterization of the dynamical crossing of the superfluid-supersolid phase transition for a dipolar condensate confined in an elongated trap, as observed in the recent experiment by G. Biagioni et al. [Phys. Rev. X 12, 021019 (2022)]. By means of the extended Gross-Pitaevskii theory, which includes the Lee-Huang-Yang quantum fluct

Eric Yeh, Pedro Sequeira, Jesse Hostetler, Melinda Gervasio

We present a novel generative method for producing unseen and plausible counterfactual examples for reinforcement learning (RL) agents based upon outcome variables that characterize agent behavior. Our approach uses a variational autoencoder to train a latent space that jointly encodes information about the observations and outcome variables pertaining to an

Jin Won Kim

This thesis is concerned with the stochastic filtering problem for a hidden Markov model (HMM) with the white noise observation model. For this filtering problem, we make three types of original contributions: (1) dual controllability characterization of stochastic observability, (2) dual minimum variance optimal control formulation of the stochastic filteri

Pierre Bergé, Édouard Bonnet, Hugues Déprés, Rémi Watrigant

For any $\varepsilon > 0$, we give a polynomial-time $n^\varepsilon$-approximation algorithm for Max Independent Set in graphs of bounded twin-width given with an $O(1)$-sequence. This result is derived from the following time-approximation trade-off: We establish an $O(1)^{2^q-1}$-approximation algorithm running in time $\exp(O_q(n^{2^{-q}}))$, for every in

Weipeng Wu, Sergi Lendinez, Mojtaba Taghipour Kaffash, Richard D. Schaller

Terahertz (THz) sciences and technologies have contributed to a rapid development of a wide range of applications and expanded the frontiers in fundamental science. Spintronic terahertz emitters offer conceptual advantages since the spin orientation in the magnetic layer can be easily controlled either by the externally applied magnetic field or by the inter

Shounak Naik, Rajaswa Patil, Swati Agarwal, Veeky Baths

Representational Similarity Analysis is a method from cognitive neuroscience, which helps in comparing representations from two different sources of data. In this paper, we propose using Representational Similarity Analysis to probe the semantic grounding in language models of code. We probe representations from the CodeBERT model for semantic grounding by u

Zachary Burns, Zhaowei Liu

In recent years there has been great interest in using deep neural networks (DNN) for super-resolution image reconstruction including for structured illumination microscopy (SIM). While these methods have shown very promising results, they all rely on data-driven, supervised training strategies that need a large number of ground truth images, which is experi

Ian P. Swift, Sana Ebrahimi, Azade Nova, Abolfazl Asudeh

Content spread inequity is a potential unfairness issue in online social networks, disparately impacting minority groups. In this paper, we view friendship suggestion, a common feature in social network platforms, as an opportunity to achieve an equitable spread of content. In particular, we propose to suggest a subset of potential edges (currently not exist

Hossam Farag, Cedomir Stefanovic, Mikael Gidlund

Delay and Age-of-Information (AoI) are two crucial performance metrics for emerging time-sensitive applications in Industrial Internet of Things (IIoT). In order to achieve optimal performance, studying the inherent interplay between these two parameters in non-trivial task. In this work, we consider a Device-to-Device (D2D)-based heterogeneous IIoT network

Junichi Koganemaru, Ian Tice

This paper concerns the construction of traveling wave solutions to the free boundary incompressible Navier-Stokes system. We study a single layer of viscous fluid in a strip-like domain that is bounded below by a flat rigid surface and above by a moving surface. The fluid is acted upon by a bulk force and a surface stress that are stationary in a coordinate

E. Rabinovici, A. Sánchez-Garrido, R. Shir, J. Sonner

We apply a notion of quantum complexity, called "Krylov complexity", to study the evolution of systems from integrability to chaos. For this purpose we investigate the integrable XXZ spin chain, enriched with an integrability breaking deformation that allows one to interpolate between integrable and chaotic behavior. K-complexity can act as a probe of the in

Karolina Bogacka, Katarzyna Wasielewska-Michniewska, Marcin Paprzycki, Maria Ganzha

Federated learning (FL) was proposed to facilitate the training of models in a distributed environment. It supports the protection of (local) data privacy and uses local resources for model training. Until now, the majority of research has been devoted to "core issues", such as adaptation of machine learning algorithms to FL, data privacy protection, or deal

Denis Horvath, Cyril Slabý, Zoltán Tomori, Andrej Hovan

This study aims to examine experimental conditions in which active particles are forced by their surroundings to move forward and backward in a continuous oscillatory manner. The experimental design is based on using a vibrating self-propelled toy-robot called hexbug, which is placed inside a narrow channel closed on one end by a rigid moving wall. Using the

Vesa Kaarnioja, Andreas Rupp

In this study, we consider the development of tailored quasi-Monte Carlo (QMC) cubatures for non-conforming discontinuous Galerkin (DG) approximations of elliptic partial differential equations (PDEs) with random coefficients. We consider both the affine and uniform and the lognormal models for the input random field, and investigate the use of QMC cubatures

Shishir G. Patil, Paras Jain, Prabal Dutta, Ion Stoica

Fine-tuning models on edge devices like mobile phones would enable privacy-preserving personalization over sensitive data. However, edge training has historically been limited to relatively small models with simple architectures because training is both memory and energy intensive. We present POET, an algorithm to enable training large neural networks on mem

Marissa Masden

We algorithmically determine the regions and facets of all dimensions of the canonical polyhedral complex, the universal object into which a ReLU network decomposes its input space. We show that the locations of the vertices of the canonical polyhedral complex along with their signs with respect to layer maps determine the full facet structure across all dim

Jos Stam

At a fundamental level most physical equations are time reversible. In this paper we propose an integrator that preserves this property at the discrete computational level. Our simulations can be run forward and backwards and trace the same path exactly bitwise. We achieve this by implementing theoretically reversible integrators using a mix of fixed and flo

Shibashis Guha, Ismaël Jecker, Karoliina Lehtinen, Martin Zimmermann

Parikh automata extend finite automata by counters that can be tested for membership in a semilinear set, but only at the end of a run, thereby preserving many of the desirable algorithmic properties of finite automata. Here, we study the extension of the classical framework onto infinite inputs: We introduce reachability, safety, B\"uchi, and co-B\"uchi Par

Sujeong Kim, Abhinav Garlapati, Jonah Lubin, Amir Tamrakar

We present a series of two studies conducted to understand user's affective states during voice-based human-machine interactions. Emphasis is placed on the cases of communication errors or failures. In particular, we are interested in understanding "confusion" in relation with other affective states. The studies consist of two types of tasks: (1) related to

José Edson Sampaio

In this article, we study the Lipschitz Geometry at infinity of complex analytic sets and we obtain results on algebraicity of analytic sets and on Bernstein's problem. Moser's Bernstein Theorem says that a minimal hypersurface which is a graph of an entire Lipschitz function must be a hyperplane. H. B. Lawson, Jr. and R. Osserman presented examples showing

Eric J. Murphy

The bulk of the present-day stellar mass was formed in galaxies when the universe was less than half its current age (i.e., $1 \lesssim z \lesssim 3$). While this likely marks one of the most critical time periods for galaxy evolution, we currently do not have a clear picture on the radial extent and distribution of cold molecular gas and associated star for

Ítalo Santana, Breno Serrano, Maximilian Schiffer, Thibaut Vidal

The classical hinge-loss support vector machines (SVMs) model is sensitive to outlier observations due to the unboundedness of its loss function. To circumvent this issue, recent studies have focused on non-convex loss functions, such as the hard-margin loss, which associates a constant penalty to any misclassified or within-margin sample. Applying this loss

Alexander G. Sboev, Nikolay A. Kudryshov, Ivan A. Moloshnikov, Saveliy V. Zavertyaev

Currently, the evolution of Covid-19 allows researchers to gather the datasets accumulated over 2 years and to use them in predictive analysis. In turn, this makes it possible to assess the efficiency potential of more complex predictive models, including neural networks with different forecast horizons. In this paper, we present the results of a consistent

Christopher Branner-Augmon, Narek Galstyan, Sam Kumar, Emmanuel Amaro

Using memory located on remote machines, or far memory, as a swap space is a promising approach to meet the increasing memory demands of modern datacenter applications. Operating systems have long relied on prefetchers to mask the increased latency of fetching pages from swap space to main memory. Unfortunately, with traditional prefetching heuristics, perfo

Sahil, Sohail, Sibasish Ghosh

In this work, we derive Robertson-Heisenberg like uncertainty relation for two incompatible observables in a pre- and post-selected (PPS) system. The newly defined standard deviation and the uncertainty relation in the PPS system have physical meanings which we present here. We demonstrate two unusual properties in the PPS system using our uncertainty relati

Gabriele Bogo, Younes Nikdelan

In the first part of the paper we characterize certain systems of first order nonlinear differential equations whose space of solutions is an $\mathfrak{sl}_2(\mathbb{C})$-module. We prove that such systems, called Ramanujan systems of Rankin-Cohen type, have a special shape and are precisely the ones whose solution space admits a Rankin-Cohen structure. In

Jiashu Wang, Tianyi Wang, Mykhaylo Ozerov, Zhan Zhang

Topological surface-states can acquire an energy gap when time-reversal symmetry is broken by interfacing with a magnetic insulator. This gap has yet to be measured. Such topological-magnetic insulator heterostructures can host a quantized anomalous Hall effect and can allow the control of the magnetic state of the insulator in a spintronic device. In this w

Yiwei Hu, Paul Guerrero, Miloš Hašan, Holly Rushmeier

Graph-based procedural materials are ubiquitous in content production industries. Procedural models allow the creation of photorealistic materials with parametric control for flexible editing of appearance. However, designing a specific material is a time-consuming process in terms of building a model and fine-tuning parameters. Previous work [Hu et al. 2022

Colin Geniet, Stéphan Thomassé

We characterise the classes of tournaments with tractable first-order model checking. For every hereditary class of tournaments $\mathcal T$, first-order model checking is either fixed parameter tractable or $\textrm{AW}[*]$-hard. This dichotomy coincides with the fact that $\mathcal T$ has either bounded or unbounded twin-width, and that the growth of $\mat

Lachlan Elam, Mónica C. Quiñones-Frías, Ying Zhang, Avital A. Rodal

The transport of particles in cells is influenced by the properties of intracellular networks they traverse while searching for localized target regions or reaction partners. Moreover, given the rapid turnover in many intracellular structures, it is crucial to understand how temporal changes in the network structure affect diffusive transport. In this work,

J. Burke, D. A. Howell, D. J. Sand, R. C. Amaro

If Type Ia supernovae (SNe~Ia) result from a white dwarf being ignited by Roche lobe overflow from a nondegenerate companion, then as the supernova explosion runs into the companion star its ejecta will be shocked, causing an early blue excess in the lightcurve. A handful of these excesses have been found in single-object studies, but inferences about the po

Chao Duan, Takashi Nishikawa, Deniz Eroglu, Adilson E. Motter

A central issue in the study of large complex network systems, such as power grids, financial networks, and ecological systems, is to understand their response to dynamical perturbations. Recent studies recognize that many real networks show nonnormality and that nonnormality can give rise to reactivity--the capacity of a linearly stable system to amplify it

Andrey V. Sadofyev, Matthew D. Sievert, Ivan Vitev

In a recent study [1] we have extended the opacity expansion approach to describe jet-medium interactions including medium motion effects in the context of heavy-ion collisions. We have computed color field of the in-medium sources, including the effects of the transverse field components and the energy transfer between the medium and jet. The corresponding

Roman R. Rafikov

Resolved observations of debris discs can be used to derive radial profiles of Azimuthally-averaged Surface Density (ASD), which carries important information about the disc structure even in presence of non-axisymmetric features and has improved signal-to-noise characteristics. We develop a (semi-)analytical formalism allowing one to relate ASD to the under

Paola Arias, Nicolás Bernal, Jacek K. Osiński, Leszek Roszkowski

We consider the production of axion dark matter through the misalignment mechanism in the context of a nonstandard cosmological history involving early matter domination by a scalar field with a time-dependent decay rate. In cases where the temperature of the Universe experiences a temporary period of increase, Hubble friction can be restored in the evolutio

Edgar Eggert, Boris Leistedt

A non-negligible source of systematic bias in cosmological analyses of galaxy surveys is the on-sky modulation caused by foregrounds and variable image characteristics such as observing conditions. Standard mitigation techniques perform a regression between the observed galaxy density field and sky maps of the potential contaminants. Such maps are ad-hoc, lo

Tomer Shenar, Hugues Sana, Laurent Mahy, Kareem El-Badry

Stellar-mass black holes are the final remnants of stars born with more than 15 solar masses. Billions are expected to reside in the Local Group, yet only few are known, mostly detected through X-rays emitted as they accrete material from a companion star. Here, we report on VFTS 243: a massive X-ray faint binary in the Large Magellanic Cloud. With an orbita

Tomer Shenar, Hugues Sana, Laurent Mahy, Jesus Maiz Apellaniz

We aim to hunt for massive binaries hosting a black hole companion (OB+BH) and establish the natal mass-ratio distribution of massive stars at the subsolar metallicity environment of the Large Magellanic Cloud (LMC). We use the shift-and-add grid disentangling technique to characterize the hidden companions in 51 SB1 O-type and evolved B-type binaries in the

Boris Leistedt, Justin Alsing, Hiranya Peiris, Daniel Mortlock

We present a Bayesian hierarchical framework to analyze photometric galaxy survey data with stellar population synthesis (SPS) models. Our method couples robust modeling of spectral energy distributions with a population model and a noise model to characterize the statistical properties of the galaxy populations and real observations, respectively. By self-c

Anh Dung Le, Alfred H. Mueller, Stéphane Munier

In the framework of a stochastic picture for the one-dimensional branching Brownian motion, we compute the probability density of the number of particles near the rightmost one at a time $T$, that we take very large, when this extreme particle is conditioned to arrive at a predefined position $x_T$ chosen far ahead of its expected position $m_T$. We recover

Sophia K. Domokos, Andrew B. Royston

Four-dimensional N=4 super Yang-Mills, with a codimension-one defect breaking half of the supersymmetry, arises as the field theory description of the D3/D5 intersection in the holographic limit. This is one of the earliest, most extensively studied, and commonly used systems in holography. In this note we give the full R-symmetry-covariant supersymmetry var

David Berardo, Julien DeWit

When studying transiting exoplanets it is common to assume a spherical planet shape. However short rotational periods can cause a planet to bulge at its equator, as is the case with Saturn whose equatorial radius is almost 10% larger than its polar radius. As a new generation of instruments comes online, it is important to continually assess the underlying a

Joachim Brod, Sandra Kvedaraite, Zachary Polonsky, Ahmed Youssef

We calculate the leading-logarithmic and next-to-leading-logarithmic electroweak corrections to the charm-top-quark contribution to the effective $|\Delta S| = 2$ Lagrangian, relevant for the parameter $\epsilon_K$. We find that these corrections lead to a $-0.5\%$ shift in the corresponding Wilson coefficient. Moreover, our calculation removes an implicit a

De-Shuang Meng, Chen Yuan, Qing-guo Huang

As one of the promising candidates of cold dark matter (DM), primordial black holes (PBHs) were formed due to the collapse of over-densed regions generated by the enhanced curvature perturbations during the radiation-dominated era. The enhanced curvature perturbations are expected to be non-Gaussian in some relevant inflation models and hence the higher-orde

Surajit Kalita, Banibrata Mukhopadhyay

Over the last couple of decades, there are direct and indirect evidences for massive compact objects than their conventional counterparts. A couple of such examples are super-Chandrasekhar white dwarfs and massive neutron stars. The observations of more than a dozen peculiar over-luminous type Ia supernovae predict their origins from super-Chandrasekhar whit

David R. M. Arvidsson-Shukur, Aidan G. McConnell, Nicole Yunger Halpern

We construct a metrology experiment in which the metrologist can sometimes amend her input state by simulating a closed timelike curve, a worldline that travels backward in time. The existence of closed timelike curves is hypothetical. Nevertheless, they can be simulated probabilistically by quantum-teleportation circuits. We leverage such simulations to pin

Daniel Miller, Daniel Loss, Ivano Tavernelli, Hermann Kampermann

The Shor-Laflamme distribution (SLD) of a quantum state is a collection of local unitary invariants that quantify $k$-body correlations. We show that the SLD of graph states can be derived by solving a graph-theoretical problem. In this way, the mean and variance of the SLD are obtained as simple functions of efficiently computable graph properties. Furtherm

Li Gan, Stéphane Ouvry, Alexios P. Polychronakos

We relate the combinatorics of periodic generalized Dyck and Motzkin paths to the cluster coefficients of particles obeying generalized exclusion statistics, and obtain explicit expressions for the counting of paths with a fixed number of steps of each kind at each vertical coordinate. A class of generalized compositions of the integer path length emerges in

Mattia Trama, Vittorio Cataudella, Carmine Antonio Perroni, Francesco Romeo

Converting charge current into spin current is one of the main mechanisms exploited in spintronics. One prominent example is the Edelstein effect, namely the generation of a magnetization in response to an external electric field, which can be realized in systems with lack of inversion symmetry. If a system has electrons with an orbital angular momentum char

Francesca Chadha-Day, Björn Garbrecht, Jamie McDonald

Superradiance in black holes is well-understood but a general treatment for superradiance in stars has until now been lacking. This is surprising given the ease with which we can observe isolated neutron stars and the array of signatures which would result from stellar superradiance. In this work, we present the first systematic pipeline for computing superr

Daniel J. Schultz, SangEun Han, Yong Baek Kim

Classification and understanding of quantum phase transitions and critical phenomena in itinerant electron systems are outstanding questions in quantum materials research. Recent experiments on heavy fermion systems with higher-rank multipolar local moments provide a new platform to study such questions. In particular, experiments on $\text{Ce}_{3}\text{Pd}_