January 2022 arXiv papers

Bogdan Batko

A recent generalization of the Conley index to discrete multivalued dynamical systems without a continuous selector is motivated by applications to data-driven dynamics. In the present paper we continue the program by studying attractor-repeller pairs and Morse decompositions in this setting. In particular, we prove Morse equation and Morse inequalities.

Anthony Ortiz, Dhaval Negandhi, Sagar R Mysorekar, Joseph Kiesecker

Rapid development of renewable energy sources, particularly solar photovoltaics (PV), is critical to mitigate climate change. As a result, India has set ambitious goals to install 500 gigawatts of solar energy capacity by 2030. Given the large footprint projected to meet renewables energy targets, the potential for land use conflicts over environmental value

J. Menezes, S. Batista, M. Tenorio, E. A. Triaca

Antipredator behaviour is a self-preservation strategy present in many biological systems, where individuals join the effort in a collective reaction to avoid being caught by an approaching predator. We study a nonhierarchical tritrophic system, whose predator-prey interactions are described by the rock-paper-scissors game rules. We performed a set of spatia

Xhevat Z. Krasniqi

In this paper, we have proved four theorems on the degree of approximation of continuous functions by matrix means of their Fourier series which is expressed in terms of the modulus of continuity and a non-negative mediate function.

Ben Adcock, Juan M. Cardenas, Nick Dexter

Many problems in computational science and engineering can be described in terms of approximating a smooth function of $d$ variables, defined over an unknown domain of interest $Ω\subset \mathbb{R}^d$, from sample data. Here both the curse of dimensionality ($d\gg 1$) and the lack of domain knowledge with $Ω$ potentially irregular and/or disconnected are con

Pedro H. Azevedo de Amorim

Much work has been done to give semantics to probabilistic programming languages. In recent years, most of the semantics used to reason about probabilistic programs fall in two categories: semantics based on Markov kernels and semantics based on linear operators. Both styles of semantics have found numerous applications in reasoning about probabilistic progr

Christis Katsouris

We revisit classical asymptotics when testing for a structural break in linear regression models by obtaining the limit theory of residual-based and Wald-type processes. First, we establish the Brownian bridge limiting distribution of these test statistics. Second, we study the asymptotic behaviour of the partial-sum processes in nonstationary (linear) time

Wojciech Górny

We study the set of possible traces of anisotropic least gradient functions. We show that even on the unit disk it changes with the anisotropic norm: for two sufficiently regular strictly convex norms the trace spaces coincide if and only if the norms coincide. The example of a function in exactly one of the trace spaces is given by a characteristic function

Yaren Bilge Kaya, Kayse Lee Maass, Geri L. Dimas, Renata Konrad

Recent estimates indicate that there are over 1 million runaway and homeless youth and young adults (RHY) in the United States (US). Exposure to trauma, violence, and substance abuse, coupled with a lack of community support services, puts homeless youth at high risk of being exploited and trafficked. Although access to safe housing and supportive services s

Alexey Lebedev, Ilya Vorobyev, Vladimir Lebedev, Christian Deppe

In this paper, we developed new coding strategies for the Z-channel. In particular, we look at the case with two-stage encoding. In this case, the encoder uses noiseless feedback once and adjusts the further encoding strategy based on the previous partial output of the channel. Nevertheless, the developed codes improve the known results with full feedback fo

William Zuluaga

In this paper, we show that in every coextensive variety V, the assignment that maps each algebra to its set of central elements is both functorial and representable. Furthermore, we prove that the full subcategory of finitely presented algebras in V is coextensive. Finally, we establish that if V is additionally (0, 1)-dense, the Gaeta topos classifies cent

Antony J. Speranza

Recently, Ciambelli, Leigh, and Pai (CLP) [arXiv:2111.13181] have shown that nonzero charges integrating Hamilton's equation can be defined for all diffeomorphisms acting near the boundary of a subregion in a gravitational theory. This is done by extending the phase space to include a set of embedding fields that parameterize the location of the boundary

Jeff Bilmes

In this manuscript, we offer a gentle review of submodularity and supermodularity and their properties. We offer a plethora of submodular definitions; a full description of a number of example submodular functions and their generalizations; example discrete constraints; a discussion of basic algorithms for maximization, minimization, and other operations; a

Blaine McLaughlin, David P. Lake, Matthew Mitchell, Paul E. Barclay

We demonstrate the simultaneous generation of second and third harmonic signals from a telecom wavelength pump in a gallium phosphide (GaP) microdisk. Using analysis of the power scaling of both the second and third harmonic outputs and calculations of nonlinear cavity mode coupling factors, we study contributions to the third harmonic signal from direct and

Anirudha Majumdar, Zhiting Mei, Vincent Pacelli

Our goal is to develop theory and algorithms for establishing fundamental limits on performance imposed by a robot's sensors for a given task. In order to achieve this, we define a quantity that captures the amount of task-relevant information provided by a sensor. Using a novel version of the generalized Fano inequality from information theory, we demon

Kun Zhang, Kwangmin Yu, Vladimir Korepin

Quantum search algorithm (also known as Grover's algorithm) lays the foundation for many other quantum algorithms. Although it is very simple, its implementation is limited on noisy intermediate-scale quantum (NISQ) processors. Grover's algorithm was designed without considering the physical resources, such as depth, in the real implementations. Ther

Zhaobidan Feng, Eric C. Rowell, Shuang Ming

Ocneanu rigidity implies that there are finitely many (braided) fusion categories with a given set of fusion rules. While there is no method for determining all such categories up to equivalence, there are a few cases for which can. For example, Kazhdan and Wenzl described all fusion categories with fusion rules isomorphic to those of $SU(N)_k$. In this pape

Aleksandr Perevalov, Dennis Diefenbach, Ricardo Usbeck, Andreas Both

The ability to have the same experience for different user groups (i.e., accessibility) is one of the most important characteristics of Web-based systems. The same is true for Knowledge Graph Question Answering (KGQA) systems that provide the access to Semantic Web data via natural language interface. While following our research agenda on the multilingual a

Omar Fawzi, Alexander Müller-Hermes, Ala Shayeghi

The threshold theorem is a fundamental result in the theory of fault-tolerant quantum computation stating that arbitrarily long quantum computations can be performed with a polylogarithmic overhead provided the noise level is below a constant level. A recent work by Fawzi, Grospellier and Leverrier (FOCS 2018) building on a result by Gottesman (QIC 2013) has

Stav Belogolovsky, Ido Greenberg, Danny Eitan, Shie Mannor

Neural differential equations predict the derivative of a stochastic process. This allows irregular forecasting with arbitrary time-steps. However, the expressive temporal flexibility often comes with a high sensitivity to noise. In addition, current methods model measurements and control together, limiting generalization to different control policies. These

Victor Montenegro, Gareth Siôn Jones, Sougato Bose, Abolfazl Bayat

Quantum sensors outperform their classical counterparts in their estimation precision, given the same amount of resources. So far, quantum-enhanced sensitivity has been achieved by exploiting the superposition principle. This enhancement has been obtained for particular forms of entangled states, adaptive measurement basis change, critical many-body systems,

Andrew Corbett, Dmitry Kangin

Continuous-depth neural networks, such as Neural ODEs, have refashioned the understanding of residual neural networks in terms of non-linear vector-valued optimal control problems. The common solution is to use the adjoint sensitivity method to replicate a forward-backward pass optimisation problem. We propose a new approach which explicates the network'

Pujan Biswas, Sudarshan Kumar, Neeraj Kumbhakarna

The primary target of this paper is to present novel compounds in view of their possible use as oxidizers in propulsion applications using molecular modeling calculations and supersonic flow simulations. Carbon-based heterocyclic compounds tend to have strained molecular structures leading to high heats of formation and energetic behavior. In the present wor

Anuj Mahajan, Mikayel Samvelyan, Tarun Gupta, Benjamin Ellis

Collective intelligence is a fundamental trait shared by several species of living organisms. It has allowed them to thrive in the diverse environmental conditions that exist on our planet. From simple organisations in an ant colony to complex systems in human groups, collective intelligence is vital for solving complex survival tasks. As is commonly observe

Mark Tygert

The author's recent research papers, "Cumulative deviation of a subpopulation from the full population" and "A graphical method of cumulative differences between two subpopulations" (both published in volume 8 of Springer's open-access "Journal of Big Data" during 2021), propose graphical methods and summary statistics, withou

Nicklas Sindlev Andersen, Marco Chiarandini, Kristian Debrabant

Given the counters of vehicles that traverse the roads of a traffic network, we reconstruct the travel demand that generated them expressed in terms of the number of origin-destination trips made by users. We model the problem as a bi-level optimization problem. At the inner-level, given a tentative demand, we solve a Dynamic Traffic Assignment (DTA) problem

Martin Lazar, Enrique Zuazua

In this paper we develop a procedure to deal with a family of parameter-dependent ill-posed problems, for which the exact solution in general does not exist. The original problems are relaxed by considering corresponding approximate ones, whose optimal solutions are well dfined, where the optimality is determined by the minimal norm requirement. The procedur

Amir Shirian, Krishna Somandepalli, Tanaya Guha

Large scale databases with high-quality manual annotations are scarce in audio domain. We thus explore a self-supervised graph approach to learning audio representations from highly limited labelled data. Considering each audio sample as a graph node, we propose a subgraph-based framework with novel self-supervision tasks that can learn effective audio repre

Bao Tran Truong, Oliver Melbourne Allen, Filippo Menczer

The spread of misinformation poses a threat to the social media ecosystem. Effective countermeasures to mitigate this threat require that social media platforms be able to accurately detect low-credibility accounts even before the content they share can be classified as misinformation. Here we present methods to infer account credibility from information dif

Yu Qiu, Xiaoting Zhang

We construct a geometric model for the root category $\mathcal{D}^b(Q)/[2]$ of any Dynkin diagram $Q$, which is an $h_Q$-gon $\mathbf{V}_Q$ with cores, where $h_Q$ is the Coxeter number and $\mathcal{D}^b(Q)$ is the bounded derived category associated to $Q$. As an application, we classify all spaces $\mathrm{ToSt}\mathcal{D}$ of total stability conditions o

Viet Quoc Vo, Ehsan Abbasnejad, Damith C. Ranasinghe

Despite our best efforts, deep learning models remain highly vulnerable to even tiny adversarial perturbations applied to the inputs. The ability to extract information from solely the output of a machine learning model to craft adversarial perturbations to black-box models is a practical threat against real-world systems, such as autonomous cars or machine

Agata Smoktunowicz

Let p be a prime number. We show that there is a one-to-one correspondence between the set of strongly nilpotent braces and the set of nilpotent pre-Lie rings of cardinality $p^{n}$, for sufficiently large p. Moreover, there is an injective mapping from the set of left nilpotent pre-Lie rings into the set of left nilpotent braces of cardinality $p^{n}$ for n

Clemens Berger, Mai Gehrke

We establish a duality between global sheaves on spectral spaces and right distributive bands. This is a sheaf-theoretical extension of classical Stone duality between spectral spaces and bounded distributive lattices. The topology of a spectral space admits a refinement, the so-called patch topology, giving rise to a patch monad on sheaves over a fixed spec

Mingfei Sun, Sam Devlin, Jacob Beck, Katja Hofmann

We present trust region bounds for optimizing decentralized policies in cooperative Multi-Agent Reinforcement Learning (MARL), which holds even when the transition dynamics are non-stationary. This new analysis provides a theoretical understanding of the strong performance of two recent actor-critic methods for MARL, which both rely on independent ratios, i.

Julian Gerstenberg, Ralph Neininger, Denis Spiegel

In distributional reinforcement learning not only expected returns but the complete return distributions of a policy are taken into account. The return distribution for a fixed policy is given as the solution of an associated distributional Bellman equation. In this note we consider general distributional Bellman equations and study existence and uniqueness

Nathan D. Bamberger, Dylan Dyer, Keshaba N. Parida, Tarek H. El-Asssad

Structure-function relationships constitute an important tool to investigate the fundamental principles of molecular electronics. Most commonly, this involves identifying a potentially important molecular structural element, followed by designing and synthesizing a set of related organic molecules, and finally interpretation of their experimental and/or comp

Chengzhuo Ni, Ruiqi Zhang, Xiang Ji, Xuezhou Zhang

Policy gradient (PG) estimation becomes a challenge when we are not allowed to sample with the target policy but only have access to a dataset generated by some unknown behavior policy. Conventional methods for off-policy PG estimation often suffer from either significant bias or exponentially large variance. In this paper, we propose the double Fitted PG es

Zihui Xue, Yuedong Yang, Mengtian Yang, Radu Marculescu

Graph Neural Networks (GNNs) have demonstrated a great potential in a variety of graph-based applications, such as recommender systems, drug discovery, and object recognition. Nevertheless, resource-efficient GNN learning is a rarely explored topic despite its many benefits for edge computing and Internet of Things (IoT) applications. To improve this state o

Tiangang Cui, Xin Tong, Olivier Zahm

Markov chain Monte Carlo (MCMC) methods form one of the algorithmic foundations of Bayesian inverse problems. The recent development of likelihood-informed subspace (LIS) methods offers a viable route to designing efficient MCMC methods for exploring high-dimensional posterior distributions via exploiting the intrinsic low-dimensional structure of the underl

Oleg Gutik, Oksana Prokhorenkova, Diana Sekh

It is proved that the semigroups $\mathrm{\mathbf{End}}(\boldsymbol{B}_ω)$ and $\mathrm{\mathbf{End}}(\boldsymbol{B}_{\mathbb{Z}})$ of the endomorphisms of the bicyclic semigroup $\boldsymbol{B}_ω$ and the endomorphisms of the extended bicyclic semigroup $\boldsymbol{B}_{\mathbb{Z}}$ are isomorphic to the semidirect products $(ω,+)\rtimes_φ(ω,*)$ and $\mathb

Shilpa Gupta, Gaurav Dwivedi

This paper aims to establish the existence of a weak solution for the non-local problem: \begin{equation*} \left\{\begin{array}{ll} -a\left(\int_Ω\mathcal{H}(x,|\nabla u|)dx \right) Δ_{\mathcal{H}}u &=f(x,u) \ \ \hbox{in} \ \ Ω, \ \ \ \\ \hspace{3.3cm} u &= 0 \ \ \hbox{on} \ \ \partial Ω, \end{array}\right. \end{equation*} where $Ω\subseteq \mathbb{R}^{N},\,

Anders Sundnes Løvlie, Astrid Waagstein, Peter Hyldgård

This article reports on a Research through Design study exploring how to design a tool for helping readers of science journalism understand the strength and uncertainty of scientific evidence in news stories about health science, using both textual and visual information. A central aim has been to teach readers about criteria for assessing scientific evidenc

Dmitry Kuklin

Existing approaches for the field measurements of the frequency-dependent soil properties take a significant amount of time, making it difficult to obtain new experimental data and study the electrical soil properties further. However, a relatively uncomplicated measurement device assembled from accessible electronic components can make the measurements almo

Moeen Mostafavi, Michael D. Porter, Dawn T. Robinson

Predicting the outcome of a process requires modeling the system dynamic and observing the states. In the context of social behaviors, sentiments characterize the states of the system. Affect Control Theory (ACT) uses sentiments to manifest potential interaction. ACT is a generative theory of culture and behavior based on a three-dimensional sentiment lexico

Xuezhou Zhang, Yuda Song, Masatoshi Uehara, Mengdi Wang

We present BRIEE (Block-structured Representation learning with Interleaved Explore Exploit), an algorithm for efficient reinforcement learning in Markov Decision Processes with block-structured dynamics (i.e., Block MDPs), where rich observations are generated from a set of unknown latent states. BRIEE interleaves latent states discovery, exploration, and e

Andrea Medaglia, Andrea Tosin, Mattia Zanella

In this paper, we focus on the construction of a hybrid scheme for the approximation of non-Maxwellian kinetic models with uncertainties. In the context of multiagent systems, the introduction of a kernel at the kinetic level is useful to avoid unphysical interactions. The methods here proposed, combine a direct simulation Monte Carlo (DSMC) in the phase spa

Jose Pablo Folch, Shiqiang Zhang, Robert M Lee, Behrang Shafei

Bayesian Optimization is a very effective tool for optimizing expensive black-box functions. Inspired by applications developing and characterizing reaction chemistry using droplet microfluidic reactors, we consider a novel setting where the expense of evaluating the function can increase significantly when making large input changes between iterations. We f

Md Rajib Hossen, Mohammad A. Islam, Kishwar Ahmed

Cloud applications are increasingly moving away from monolithic services to agile microservices-based deployments. However, efficient resource management for microservices poses a significant hurdle due to the sheer number of loosely coupled and interacting components. The interdependencies between various microservices make existing cloud resource autoscali

Chong Chen, Yingmin Liu, Orlando P. Simonetti, Matthew Tong

Background: Several studies have shown that both respiratory and cardiac motion can be extracted from the Pilot Tone (PT) signal successfully. However, most of these studies were performed in healthy volunteers. In addition, validating PT using ECG as a reference can be problematic because both PT and ECG tend to be unreliable in patients with arrhythmias. P

Iordanis Kerenidis, Anupam Prakash

We introduce a new approach for quantum linear algebra based on quantum subspace states and present three new quantum machine learning algorithms. The first is a quantum determinant sampling algorithm that samples from the distribution $\Pr[S]= det(X_{S}X_{S}^{T})$ for $|S|=d$ using $O(nd)$ gates and with circuit depth $O(d\log n)$. The state of art classica

Sam Nariman

We investigate a conjecture due to Haefliger and Thurston in the context of foliated manifold bundles. In this context, Haefliger-Thurston's conjecture predicts that every $M$-bundle over a manifold $B$ where $\text{dim}(B)\leq \text{dim}(M)$ is cobordant to a flat $M$-bundle. In particular, we study the bordism class of flat $M$-bundles over low dimensi

Daozhi Shen, HeeBong Yang, Christian Spudat, Tarun Patel

The ability to perform broadband optical spectroscopy with sub-diffraction-limit resolution is highly sought-after for a wide range of critical applications. However, sophisticated tip-enhanced techniques are currently required to achieve this goal. We bypass this challenge by demonstrating an extremely broadband photodetector based on a two-dimensional (2D)

Mo Zhou, Jianfeng Lu

We propose a single time-scale actor-critic algorithm to solve the linear quadratic regulator (LQR) problem. A least squares temporal difference (LSTD) method is applied to the critic and a natural policy gradient method is used for the actor. We give a proof of convergence with sample complexity $\mathcal{O}(\varepsilon^{-1} \log(\varepsilon^{-1})^2)$. The

Andrea Passamonti, Nils Andersson, Pantelis Pnigouras

We study the tidal response of a superfluid neutron star in a binary system, focussing on Newtonian models with superfluid neutrons present throughout the star's core and the inner crust. Within the two-fluid formalism, we consider the main aspects that arise from the presence of different regions inside the star, with particular focus on the various int

Stella Bounareli, Vasileios Argyriou, Georgios Tzimiropoulos

This paper is on face/head reenactment where the goal is to transfer the facial pose (3D head orientation and expression) of a target face to a source face. Previous methods focus on learning embedding networks for identity and pose disentanglement which proves to be a rather hard task, degrading the quality of the generated images. We take a different appro

Natasha Alkhatib, Maria Mushtaq, Hadi Ghauch, Jean-Luc Danger

Network Intrusion Detection Systems (NIDSs) are widely regarded as efficient tools for securing in-vehicle networks against diverse cyberattacks. However, since cyberattacks are always evolving, signature-based intrusion detection systems are no longer adopted. An alternative solution can be the deployment of deep learning based intrusion detection system wh

Artur Alho, José Natário, Paolo Pani, Guilherme Raposo

A foundational theorem due to Buchdahl states that, within General Relativity (GR), the maximum compactness $\mathcal{C}\equiv GM/(Rc^2)$ of a static, spherically symmetric, perfect fluid object of mass $M$ and radius $R$ is $\mathcal{C}=4/9$. As a corollary, there exists a compactness gap between perfect fluid stars and black holes (where $\mathcal{C}=1/2$)

Kasra Farain, Daniel Bonn

The question when and how dense granular materials start to flow under stress, despite many industrial and geophysical applications, remains largely unresolved. We develop and test a simple equation for the onset of quasi-static flows of granular materials which is based on the frictional aging of the granular packing. The result is a non-monotonic stress-st

Gabriel Cuomo, Zohar Komargodski, Márk Mezei, Avia Raviv-Moshe

We consider line defects with large quantum numbers in conformal field theories. First, we consider spin impurities, both for a free scalar triplet and in the Wilson-Fisher $O(3)$ model. For the free scalar triplet, we find a rich phase diagram that includes a perturbative fixed point, a new nonperturbative fixed point, and runaway regimes. To obtain these r

Aaron Landesman, Daniel Litt

We show that the minimum rank of a non-isotrivial local system of geometric origin, on a suitably general $n$-pointed curve of genus $g$, is at least $2\sqrt{g+1}$. We apply this result to resolve conjectures of Esnault-Kerz and Budur-Wang. The main input is an analysis of stability properties of flat vector bundles under isomonodromic deformation, which add

Xuejian Shen, Thejs Brinckmann, David Rapetti, Mark Vogelsberger

We perform cosmological zoom-in simulations of $19$ relaxed cluster-mass haloes with the inclusion of adiabatic gas in the cold dark matter (CDM) and self-interacting dark matter (SIDM) models. These clusters are selected as dynamically relaxed clusters from a parent simulation with $M_{\rm 200} \simeq 1\operatorname{-}3\times 10^{15}\,{\rm M}_{\odot}$. Both

Avik Banerjee, Diogo Buarque Franzosi, Gabriele Ferretti

Composite Higgs models, together with partial compositeness, predict the existence of new scalars and vector-like quarks (partners) at and above the TeV scale. Generically, the presence of these additional scalars opens up new decay topologies for the partners. In this paper we show how to systematically construct the general low energy Lagrangian to capture

Somnath Basu Roy Chowdhury, Snigdha Chaturvedi

Text representations learned by machine learning models often encode undesirable demographic information of the user. Predictive models based on these representations can rely on such information, resulting in biased decisions. We present a novel debiasing technique, Fairness-aware Rate Maximization (FaRM), that removes protected information by making repres

A. Sarkar, P. Deb, R. Bose

The critical endpoint (CEP) is a fundamental feature of the Quantum Chromodynamics (QCD) phase diagram, marking the boundary between quark-gluon plasma and hadronic matter. Heavy-ion collision experiments, such as the RHIC Beam Energy Scan, aim to probe the QCD phase diagram by varying collision energy. However, the short-lived nature of produced particles m

David Benisty, Eugene Vasiliev, N. Wyn Evans, Anne-Christine Davis

High accuracy proper motions (PMs) of M31 and other Local Group satellites have now been provided by the {\it Gaia} satellite. We revisit the Timing Argument to compute the total mass $M$ of the Local Group from the orbit of the Milky Way and M31, allowing for the Cosmological Constant. We rectify for a systematic effect caused by the presence of the Large M

Ashu Kushwaha, Sunil Malik, S. Shankaranarayanan

We present a novel model that may provide an interpretation for a class of non-repeating FRBs -- short ($<1~\rm{s}$), bright ($0.1 - 1000~\rm{Jy}$) bursts of MHz-GHz frequency radio waves. The model has three ingredients -- compact object, a progenitor with effective magnetic field strength around $10^{10}~{\rm Gauss}$, and high frequency (MHz-GHz) gravitati

Amulya Ratnakar, Sourin Das

A recent study have shown that it is possible to have enhancement, in contrast to an expected suppression, in tunneling density of states (TDOS) in a Luttinger liquid (LL) which is solely driven by the non-local density-density interactions. Also, it is well known that a LL in proximity to a superconductor (SC) shows enhancement in TDOS in the vicinity of th

Matteo Beccaria, Simone Giombi, Arkady Tseytlin

The generalized Wilson loop operator interpolating between the supersymmetric and the ordinary Wilson loop in ${\cal N}=4$ SYM theory provides an interesting example of renormalization group flow on a line defect: the scalar coupling parameter $ζ$ has a non-trivial beta function and may be viewed as a running coupling constant in a 1d defect QFT. In this pap

I. Sfaradi, A. Horesh, R. Fender, D. A. Green

We report here radio follow-up observations of the optical Tidal Disruption Event (TDE) AT 2019azh. Previously reported X-ray observations of this TDE showed variability at early times and a dramatic increase in luminosity, by a factor of $\sim 10$, about 8 months after optical discovery. The X-ray emission is mainly dominated by intermediate hard--soft X-ra

Konstantin Leyde, Simone Mastrogiovanni, Danièle A. Steer, Eric Chassande-Mottin

Gravitational wave (GW) standard sirens are well-established probes with which one can measure cosmological parameters, and are complementary to other probes like the cosmic microwave background or supernovae standard candles. Here we focus on dark GW sirens, specifically binary black holes (BBHs) for which there is only GW data. Our approach relies on the a

Maxim Fitkevich

We propose a family of dilaton gravity models possessing bouncing solutions with interiors connecting separate asymptotically flat regions. We demonstrate that inner Cauchy horizons are stable given certain initial conditions. We study causal structure and evaluate thermodynamic properties of black bounces using Euclidean methods. Extremal bounces have zero

Anthony J. Pahl, Alice Shapley, Charles C. Steidel, Naveen A. Reddy

The connection between the escape fraction of ionizing photons ($f_{\rm esc}$) and star-formation rate surface density ($Σ_{\rm SFR}$) is a key input for reionization models, but remains untested at high redshift. We analyse 35 z~3 galaxies from the Keck Lyman Continuum Survey (KLCS) covered by deep, rest far-UV spectra of the Lyman continuum (LyC) and high-

Aidan Zentner, Siddharth Dandavate, Oren Slone, Mariangela Lisanti

Galactic rotation curves exhibit a diverse range of inner slopes. Observational data indicates that explaining this diversity may require a mechanism that correlates a galaxy&#39;s surface brightness with the central-most region of its dark matter halo. In this work, we compare several concrete models that capture the relevant physics required to explain the

Mohamed Seif, Dung Nguyen, Anil Vullikanti, Ravi Tandon

The goal of community detection over graphs is to recover underlying labels/attributes of users (e.g., political affiliation) given the connectivity between users (represented by adjacency matrix of a graph). There has been significant recent progress on understanding the fundamental limits of community detection when the graph is generated from a stochastic

Stephen Bates, Edward Kennedy, Robert Tibshirani, Valerie Ventura

Standard regression adjustment gives inconsistent estimates of causal effects when there are time-varying treatment effects and time-varying covariates. Loosely speaking, the issue is that some covariates are post-treatment variables because they may be affected by prior treatment status, and regressing out post-treatment variables causes bias. More precisel

R. Albuquerque, S. Narison, D. Rabetiarivony

We present improved estimates of the couplings, masses and mass ratios of the Z_Q,X_Q and T_{QQqq} states using QCD Laplace sum rules (LSR), their ratios R and double ratios DRSR within stability criteria, where the NLO factorized PT QCD corrections are included which is important for giving a meaning on the running MS heavy quark mass used in the analysis.

Kevin R. McKee, Xuechunzi Bai, Susan T. Fiske

Interaction and cooperation with humans are overarching aspirations of artificial intelligence (AI) research. Recent studies demonstrate that AI agents trained with deep reinforcement learning are capable of collaborating with humans. These studies primarily evaluate human compatibility through &#34;objective&#34; metrics such as task performance, obscuring

Martin A. Reiss, Karin Muglach, Richard Mullinix, Maria M. Kuznetsova

Progress in space weather research and awareness needs community-wide strategies and procedures to evaluate our modeling assets. Here we present the activities of the Ambient Solar Wind Validation Team embedded in the COSPAR ISWAT initiative. We aim to bridge the gap between model developers and end-users to provide the community with an assessment of the st

Clemens Heuberger, Daniel Krenn, Gabriel F. Lipnik

In this note, we precisely elaborate the connection between recognisable series (in the sense of Berstel and Reutenauer) and $q$-regular sequences (in the sense of Allouche and Shallit) via their linear representations. In particular, we show that the minimisation algorithm for recognisable series can also be used to minimise linear representations of $q$-re

Alexandru Gheorghiu, Tony Metger, Alexander Poremba

Quantum mechanical effects have enabled the construction of cryptographic primitives that are impossible classically. For example, quantum copy-protection allows for a program to be encoded in a quantum state in such a way that the program can be evaluated, but not copied. Many of these cryptographic primitives are two-party protocols, where one party, Bob,

Max Ehrlich, Jon Barker, Namitha Padmanabhan, Larry Davis

Video compression is a central feature of the modern internet powering technologies from social media to video conferencing. While video compression continues to mature, for many compression settings, quality loss is still noticeable. These settings nevertheless have important applications to the efficient transmission of videos over bandwidth constrained or

Scott Davidson, Felix A. Pollock, Erik M. Gauger

We demonstrate that it is possible to effectively eliminate radiative losses during excitonic energy transport in systems with an intrinsic energy gradient. By considering chain-like systems of repeating `unit&#39; cells which can each consist of multiple sites, we show that tuning a single system parameter (the intra-unit-cell coupling) leads to efficient a

Phan Thành Nam, Julien Ricaud, Arnaud Triay

We consider the low density Bose gas in the thermodynamic limit with a three-body interaction potential. We prove that the leading order of the ground state energy of the system is determined completely in terms of the scattering energy of the interaction potential. The corresponding result for two-body interactions was proved in seminal papers of Dyson (195

Elina Pavlovska, Peter G. Silvestrov, Patrik Recher, Girts Barinovs

Experiments on collisions of isolated electrons guided along the edges in quantum Hall setups can mimic mixing of photons with the important distinction that electrons are charged fermions. In the so-called electronic Hong-Ou-Mandel (HOM) setup uncorrelated pairs of electrons are injected towards a beamsplitter. If the two electron wave packets were identica

Matt Menickelly, Yunsoo Ha, Matthew Otten

Variational quantum algorithms, which have risen to prominence in the noisy intermediate-scale quantum setting, require the implementation of a stochastic optimizer on classical hardware. To date, most research has employed algorithms based on the stochastic gradient iteration as the stochastic classical optimizer. In this work we propose instead using stoch

Philip Smith, Vitaliy Kurlin

Persistent homology is a popular and useful tool for analysing finite metric spaces, revealing features that can be used to distinguish sets of unlabeled points and as input into machine learning pipelines. The famous stability theorem of persistent homology provides an upper bound for the change of persistence in the bottleneck distance under perturbations

Matthew D. A. Orkney, Justin I. Read, Oscar Agertz, Andrew Pontzen

The Eridanus II (EriII) &#39;ultra-faint&#39; dwarf has a large ($15\,\text{pc}$) and low mass ($4.3\times10^3\,\text{M}_\odot$) star cluster (SC) offset from its centre by $23\pm3\,\text{pc}$ in projection. Its size and offset are naturally explained if EriII has a central dark matter core, but such a core may be challenging to explain in a $Λ$CDM cosmology

F. G. Ben, A. V. Giannini, M. V. T. Machado

Transverse momentum broadening of a parton propagating through a large nucleus is evaluated in the color dipole approach using different models for the dipole cross section or unintegrated gluon distribution, which lead to different values of the coefficient $C_{\mathcal{F}}(0,s)$. Numerical calculations are compared to data extracted from LHCb and ALICE exp

Aaron Strangfeld, Benjamin Wiegand, Julien Kluge, Matthias Schoch

Compactness, robustness and autonomy of optical frequency references are prerequisites for reliable operation in mobile systems, on ground as well as in space. We present a standalone plug and play optical frequency reference device based on frequency modulation spectroscopy of the D2-transition in rubidium at 780 nm. After a single button press the hand-siz

Honghao Fu, Daochen Wang, Qi Zhao

Self-testing is a fundamental feature of quantum mechanics that allows a classical verifier to force untrusted quantum devices to prepare certain states and perform certain measurements on them. The standard approach assumes at least two spatially separated devices. Recently, Metger and Vidick [Quantum, 2021] showed that a single EPR pair of a single quantum

Wei Zhao, Steffen Eger

Multilingual representations pre-trained with monolingual data exhibit considerably unequal task performances across languages. Previous studies address this challenge with resource-intensive contextualized alignment, which assumes the availability of large parallel data, thereby leaving under-represented language communities behind. In this work, we attribu

Anuj Pasricha, Yi-Shiuan Tung, Bradley Hayes, Alessandro Roncone

In this work, we introduce PokeRRT, a novel motion planning algorithm that demonstrates poking as an effective non-prehensile manipulation skill to enable fast manipulation of objects and increase the size of a robot&#39;s reachable workspace. We showcase poking as a failure recovery tactic used synergistically with pick-and-place for resiliency in cases whe

Armen Kostanyan, Arevik Harmandayan

This article discusses a particular case of the data clustering problem, where it is necessary to find groups of adjacent text segments of the appropriate length that match a fuzzy pattern represented as a sequence of fuzzy properties. To solve this problem, a heuristic algorithm for finding a sufficiently large number of solutions is proposed. The key idea

Denis Yarats, David Brandfonbrener, Hao Liu, Michael Laskin

Recent progress in deep learning has relied on access to large and diverse datasets. Such data-driven progress has been less evident in offline reinforcement learning (RL), because offline RL data is usually collected to optimize specific target tasks limiting the data&#39;s diversity. In this work, we propose Exploratory data for Offline RL (ExORL), a data-

Søren Fournais, Bernard Helffer, Ayman Kachmar

Experimentally observed magnetic fields with nanoscale variations are theoretically modeled by a piece-wise constant function with jump discontinuity along a smooth curve, the magnetic edge. Assuming the edge is a closed curve with an axis of symmetry and the field is sign changing and with exactly two distinct values, we prove that semi-classical tunneling

Kamran Pentland, Massimiliano Tamborrino, T. J. Sullivan, James Buchanan

Sequential numerical methods for integrating initial value problems (IVPs) can be prohibitively expensive when high numerical accuracy is required over the entire interval of integration. One remedy is to integrate in a parallel fashion, &#34;predicting&#34; the solution serially using a cheap (coarse) solver and &#34;correcting&#34; these values using an ex

Carlo Campajola, Marco D&#39;Errico, Claudio J. Tessone

We propose a novel framework to analyse the velocity of money in terms of the contribution (MicroVelocity) of each individual agent, and to uncover the distributional determinants of aggregate velocity. Leveraging on complete publicly available transactions data stored in blockchains from four cryptocurrencies, we empirically find that MicroVelocity i) is ve

Matteo Braglia, Juan Garcia-Bellido, Sachiko Kuroyanagi

Unresolved sources of gravitational waves (GWs) produced by the merger of a binary of black holes at cosmological distances combine into a stochastic background. Such a background is in the continuous or popcorn regime, depending on whether the GW rate is high enough so that two or more events overlap in the same frequency band. These two regimes respectivel

Or Feldman, Amit Boyarski, Shai Feldman, Dani Kogan

Graph isomorphism testing is usually approached via the comparison of graph invariants. Two popular alternatives that offer a good trade-off between expressive power and computational efficiency are combinatorial (i.e., obtained via the Weisfeiler-Leman (WL) test) and spectral invariants. While the exact power of the latter is still an open question, the for

Mathieu Dagréou, Pierre Ablin, Samuel Vaiter, Thomas Moreau

Bilevel optimization, the problem of minimizing a value function which involves the arg-minimum of another function, appears in many areas of machine learning. In a large scale empirical risk minimization setting where the number of samples is huge, it is crucial to develop stochastic methods, which only use a few samples at a time to progress. However, comp