March 2023 arXiv papers — page 15

Rajesh Kumar, Veronica Dexheimer, Johannes Jahan, Jorge Noronha

This review aims at providing an extensive discussion of modern constraints relevant for dense and hot strongly interacting matter. It includes theoretical first-principle results from lattice and perturbative QCD, as well as chiral effective field theory results. From the experimental side, it includes heavy-ion collision and low-energy nuclear physics resu

Torben Krüger, Yuriy Nemish

For random matrices with block correlation structure we show that the fluctuations of linear eigenvalue statistics are Gaussian on all mesoscopic scales with universal variance which coincides with that of the Gaussian unitary or Gaussian orthogonal ensemble, depending on the symmetry class of the model. The main tool used for determining this variance is a

Johann Rudi, Max Heldman, Emil M. Constantinescu, Qi Tang

In this work we consider a relativistic drift-kinetic model for runaway electrons along with a Fokker-Planck operator for small-angle Coulomb collisions, a radiation damping operator, and a secondary knock-on (Boltzmann) collision source. We develop a new scalable fully implicit solver utilizing finite volume and conservative finite difference schemes and dy

Daniel Bresnahan, Nicholas Hippen, Yuliya Lierler

Answer set programming is a declarative logic programming paradigm geared towards solving difficult combinatorial search problems. While different logic programs can encode the same problem, their performance may vary significantly. It is not always easy to identify which version of the program performs the best. We present the system Predictor (and its algo

Miguel Campercholi, Mauricio Tellechea, Pablo Ventura

This work deals with the definability problem by quantifier-free first-order formulas over a finite algebraic structure. We show the problem to be coNP-complete and present two decision algorithms based on a semantical characterization of definable relations as those preserved by isomorphisms of substructures, the second one also providing a formula in the p

Pierre Nazé

There is evidence that taking the time average of the work performed by a thermally isolated system effectively "transforms" the adiabatic process into an isothermal one. This approach allows inherent quantities of adiabatic processes to be accessed through the definitions of isothermal processes. A fluctuation theorem is then established, linking the time-a

Ziya Erkoç, Fangchang Ma, Qi Shan, Matthias Nießner

Implicit neural fields, typically encoded by a multilayer perceptron (MLP) that maps from coordinates (e.g., xyz) to signals (e.g., signed distances), have shown remarkable promise as a high-fidelity and compact representation. However, the lack of a regular and explicit grid structure also makes it challenging to apply generative modeling directly on implic

Yuan Hu, W. Brent Lindquist, Svetlozar T. Rachev, Frank J. Fabozzi

Motivated by the Corns-Satchell, continuous time, option pricing model, we develop a binary tree pricing model with underlying asset price dynamics following It\^o-Mckean skew Brownian motion. While the Corns-Satchell market model is incomplete, our discrete time market model is defined in the natural world; extended to the risk neutral world under the no-ar

Eric Hedlin, Jinfan Yang, Nicholas Vining, Kwang Moo Yi

We introduce CN-DHF (Compact Neural Double-Height-Field), a novel hybrid neural implicit 3D shape representation that is dramatically more compact than the current state of the art. Our representation leverages Double-Height-Field (DHF) geometries, defined as closed shapes bounded by a pair of oppositely oriented height-fields that share a common axis, and l

Osoro B Ogutu, Edward J Oughton, Kai Zeng, Brian L. Mark

Operating securely over 5G (and legacy) infrastructure is a challenge. In non-cooperative networks, malicious actors may try to decipher, block encrypted messages, or specifically jam wireless radio systems. Such activities can disrupt operations, from causing minor inconvenience, through to fully paralyzing the functionality of critical infrastructure. Whil

Colin G. West

ChatGPT is built on a large language model trained on an enormous corpus of human text to emulate human conversation. Despite lacking any explicit programming regarding the laws of physics, recent work has demonstrated that GPT-3.5 could pass an introductory physics course at some nominal level and register something close to a minimal understanding of Newto

Sihao Hu, Zhen Zhang, Bingqiao Luo, Shengliang Lu

As various forms of fraud proliferate on Ethereum, it is imperative to safeguard against these malicious activities to protect susceptible users from being victimized. While current studies solely rely on graph-based fraud detection approaches, it is argued that they may not be well-suited for dealing with highly repetitive, skew-distributed and heterogeneou

Ameesh Shah, Jonathan DeCastro, John Gideon, Beyazit Yalcinkaya

Advancements in simulation and formal methods-guided environment sampling have enabled the rigorous evaluation of machine learning models in a number of safety-critical scenarios, such as autonomous driving. Application of these environment sampling techniques towards improving the learned models themselves has yet to be fully exploited. In this work, we int

Igor Zingman, Sergio Frayle, Ivan Tankoyeu, Segrey Sukhanov

Image-to-image translation (I2I) methods allow the generation of artificial images that share the content of the original image but have a different style. With the advances in Generative Adversarial Networks (GANs)-based methods, I2I methods enabled the generation of artificial images that are indistinguishable from natural images. Recently, I2I methods wer

Brajagopal Das, Lena Wysocki, Jörg Schöpf, Lin Yang

Controlling magnetic anisotropy (MA) is important in a variety of applications including magnetic memories, spintronic sensors, and skyrmion-based data distribution. The perovskite manganite family provides a fertile playground for complex, intricate, and potentially useful structure-magnetism relations. Here we report on the MA that emerges in 10% Ru substi

DUNE Collaboration, A. Abed Abud, B. Abi, R. Acciarri

A primary goal of the upcoming Deep Underground Neutrino Experiment (DUNE) is to measure the $\mathcal{O}(10)$ MeV neutrinos produced by a Galactic core-collapse supernova if one should occur during the lifetime of the experiment. The liquid-argon-based detectors planned for DUNE are expected to be uniquely sensitive to the $\nu_e$ component of the supernova

Saranya Venkatraman, He He, David Reitter

Humans tend to follow the Uniform Information Density (UID) principle by distributing information evenly in utterances. We study if decoding algorithms implicitly follow this UID principle, and under what conditions adherence to UID might be desirable for dialogue generation. We generate responses using different decoding algorithms with GPT-2 on the Persona

Lin Zhao, Mingxi Zhou, Brice Loose

Robotic underwater systems, e.g., Autonomous Underwater Vehicles (AUVs) and Remotely Operated Vehicles (ROVs), are promising tools for collecting biogeochemical data at the ice-water interface for scientific advancements. However, state estimation, i.e., localization, is a well-known problem for robotic systems, especially, for the ones that travel underwate

Frank Lu, Kevin Ren, Dawei Shen, Siki Wang

In this paper, we investigate the relationship between Temperley-Lieb immanants, which were introduced by Rhoades and Skandera, and %-immanants, an immanant based on a concept introduced by Chepuri and Sherman-Bennett. Our main result is a classification of when a Temperley-Lieb immanant can be written as a linear combination of %-immanants. This result uses

H. Güven, J. Margueron, K. Bozkurt, E. Khan

We investigate the question of the nature of compact stars, considering they may be neutron stars or hybrid stars containing a quark core, within the present constraints given by gravitational waves, radio-astronomy, X-ray emissions from millisecond pulsars and nuclear physics. A Bayesian framework is used to combine together all these constraints and to pre

Desnes Nunes, Ricardo Primi, Ramon Pires, Roberto Lotufo

The present study aims to explore the capabilities of Language Models (LMs) in tackling high-stakes multiple-choice tests, represented here by the Exame Nacional do Ensino M\'edio (ENEM), a multidisciplinary entrance examination widely adopted by Brazilian universities. This exam poses challenging tasks for LMs, since its questions may span into multiple fie

Will J. Roper, Stephen M. Wilkins, Stephen Riggs, Jessica Pilling

We present the first study of galaxy evolution in $\ddot{\mu}$ based cosmologies. We find that recent JWST observations of massive galaxies at extremely high redshifts are consistent with such a cosmology. However, the low redshift Universe is entirely divergent from the $\ddot{\mu}$ cosmic star formation rate density. We thus propose that our Universe was a

Eitan Rosen, Paulina Hoyos, Xiuyuan Cheng, Joe Kileel

Graph Laplacian based algorithms for data lying on a manifold have been proven effective for tasks such as dimensionality reduction, clustering, and denoising. In this work, we consider data sets whose data points lie on a manifold that is closed under the action of a known unitary matrix Lie group G. We propose to construct the graph Laplacian by incorporat

Lane G. Gunderman

Traditional stabilizer codes operate over prime power local-dimensions. In this work we extend the stabilizer formalism using the local-dimension-invariant setting to import stabilizer codes from these standard local-dimensions to other cases. In particular, we show that any traditional stabilizer code can be used for analog continuous-variable codes, and co

Zhiyi Li, Douglas Orr, Valeriu Ohan, Godfrey Da costa

Reducing the computational cost of running large scale neural networks using sparsity has attracted great attention in the deep learning community. While much success has been achieved in reducing FLOP and parameter counts while maintaining acceptable task performance, achieving actual speed improvements has typically been much more difficult, particularly o

Jialin Dong, Lin F. Yang

Recently, the study of linear misspecified bandits has generated intriguing implications of the hardness of learning in bandits and reinforcement learning (RL). In particular, Du et al. (2020) show that even if a learner is given linear features in $\mathbb{R}^d$ that approximate the rewards in a bandit or RL with a uniform error of $\varepsilon$, searching

Luke Postle, Evelyne Smith-Roberge

We generalize a framework of list colouring results to correspondence colouring. Correspondence colouring is a generalization of list colouring wherein we localize the meaning of the colours available to each vertex. As pointed out by Dvo\v{r}\'ak and Postle, both of Thomassen's theorems on the 5-choosability of planar graphs and 3-choosability of planar gra

Alexandre Wagemakers, Alvar Daza, Miguel A. F. Sanjuán

Bifurcation theory is the usual analytic approach to study the parameter space of a dynamical system. Despite the great power of prediction of these techniques, fundamental limitations appear during the study of a given problem. Nonlinear dynamical systems often hide their secrets and the ultimate resource is the numerical simulations of the equations. This

Dhruv Mubayi, Andrew Suk

One formulation of the Erdos-Szekeres monotone subsequence theorem states that for any red/blue coloring of the edge set of the complete graph on $\{1, 2, \ldots, N\}$, there exists a monochromatic red $s$-clique or a monochromatic blue increasing path $P_n$ with $n$ vertices, provided $N >(s-1)(n-1)$. %We had previously shown that a suitable generalization

Rachel Woods-Robinson, Yihuang Xiong, Jimmy-Xuan Shen, Nicholas Winner

Many semiconductors present weak or forbidden transitions at their fundamental band gaps, inducing a widened region of transparency. This occurs in high-performing n-type transparent conductors (TCs) such as Sn-doped In2O3 (ITO), however thus far the presence of forbidden transitions has been neglected in searches for new p-type TCs. To address this, we firs

Yujin Wu, Mohamed Daoudi, Ali Amad

Recently, wearable emotion recognition based on peripheral physiological signals has drawn massive attention due to its less invasive nature and its applicability in real-life scenarios. However, how to effectively fuse multimodal data remains a challenging problem. Moreover, traditional fully-supervised based approaches suffer from overfitting given limited

Gelareh Farahi, Cheng-Li Chiu, Xiaomeng Liu, Zlatko Papic

Interacting electrons in flat bands give rise to a variety of quantum phases. One fundamental aspect of such states is the ordering of the various flavours - such as spin or valley - that the electrons can undergo and the excitation spectrum of the broken symmetry states that they form. These properties cannot be probed directly with electrical transport mea

Adir Rahamim, Yonatan Belinkov

Recent work has compared neural network representations via similarity-based analyses to improve model interpretation. The quality of a similarity measure is typically evaluated by its success in assigning a high score to representations that are expected to be matched. However, existing similarity measures perform mediocrely on standard benchmarks. In this

Wayne A. Johnson

We use the ordinary Euler operator to compute the Ehrhart series for an arbitrary lattice polytope. The resulting formula involves the coefficients of the Ehrhart polynomial, combined via Eulerian numbers. We use this to compute $h^*_{d-1}$ in terms of the coefficients of the Ehrhart polynomial, resulting in a new linear inequality satisfied by the coefficen

Brian Chen, Nina Shvetsova, Andrew Rouditchenko, Daniel Kondermann

Spatio-temporal grounding describes the task of localizing events in space and time, e.g., in video data, based on verbal descriptions only. Models for this task are usually trained with human-annotated sentences and bounding box supervision. This work addresses this task from a multimodal supervision perspective, proposing a framework for spatio-temporal ac

Patrick Chadbourne, Nasir Eisty

Causal inference is a study of causal relationships between events and the statistical study of inferring these relationships through interventions and other statistical techniques. Causal reasoning is any line of work toward determining causal relationships, including causal inference. This paper explores the relationship between causal reasoning and variou

Daniela Calvetti, Erkki Somersalo

Bayesian hierarchical models have been demonstrated to provide efficient algorithms for finding sparse solutions to ill-posed inverse problems. The models comprise typically a conditionally Gaussian prior model for the unknown, augmented by a hyperprior model for the variances. A widely used choice for the hyperprior is a member of the family of generalized

Simon Müller, Andrés González de Castilla, Christoph Taeschler, Andreas Klein

The predictive electrolyte model COSMO-RS-ES is refined to improve the description of systems at 25{\deg}C in which strong ion pairing is expected due to a low static permittivity of the liquid phase. Furthermore, the short-range ion energy interaction equations have been modified to better describe the misfit and energy interaction terms between ions and so

Kamran Shafafi, Eduardo Nuno Almeida, André Coelho, Helder Fontes

Unmanned Aerial Vehicles (UAVs) offer promising potential as communications node carriers, providing on-demand wireless connectivity to users. While existing literature presents various wireless channel models, it often overlooks the impact of UAV heading. This paper provides an experimental characterization of the Air-to-Ground (A2G) and Ground-to-Air (G2A)

Colin Leong, Herumb Shandilya, Bonaventure F. P. Dossou, Atnafu Lambebo Tonja

Many natural language processing (NLP) tasks make use of massively pre-trained language models, which are computationally expensive. However, access to high computational resources added to the issue of data scarcity of African languages constitutes a real barrier to research experiments on these languages. In this work, we explore the applicability of low-c

CMS Collaboration

Quasireal photons exchanged in relativistic heavy ion interactions are powerful probes of the gluonic structure of nuclei. The coherent J/$\psi$ photoproduction cross section in ultraperipheral lead-lead collisions is measured as a function of photon-nucleus center-of-mass energies per nucleon (W$^\text{Pb}_{\gamma\text{N}}$), over a wide range of 40 $\lt$ W

Pedro dos Santos, Paulo Oliveira

This paper proposes an integrated architecture for Thrust Vector Control (TVC) and state estimation for low-cost small-scale launchers, naturally unstable, and propelled by a solid motor. The architecture is based on a non-linear, six-degrees-of-freedom model for the generic thrust-vector-controlled launcher dynamics and kinematics, deduced and implemented i

Shawn Priore, Meeko Oishi

While techniques have been developed for chance constrained stochastic optimal control using sample disturbance data that provide a probabilistic confidence bound for chance constraint satisfaction, far less is known about how to use sample data in a manner that can provide almost surely guarantees of chance constraint satisfaction. In this paper, we develop

Simon Müller, Christoph Taeschler, Andreas Klein, Irina Smirnova

The new predictive electrolyte model COSMO-RS-ES is evaluated and refined for the calculation of solubilities of salts in mixed solvent systems. It is demonstrated that the model is capable of predicting solid-liquid equilibria at 25 {\deg}C for ammonium and alkali metal salts quite accurately in a wide variety of solvent mixtures. Furthermore, through the i

Najib Idrissi, Renato Vasconcellos Vieira

The Swiss-Cheese operads, which encode actions of algebras over the little $n$-cubes operad on algebras over the little $(n-1)$-cubes operad, comes in several variants. We prove that the variant in which open operations must have at least one open input is not formal in characteristic zero. This is slightly stronger than earlier results of Livernet and Willw

Alejandro Garrote-Márquez, Lucas Lodeiro, Rahul Suresh, Norge Cruz Hernández

Hydrogen bonds (HBs) play an important role in the rotational dynamics of organic cations in hybrid organic/inorganic halide perovskites, affecting the structural and electronic properties of the perovskites. However, the properties and even the existence of HBs in these perovskites are not well established. We investigate HBs in perovskites MAPbBr$_3$ (MA$^

Devin S. Chu, Tuan Do, Andrea Ghez, Abhimat K. Gautam

We present the results of the first systematic search for spectroscopic binaries within the central 2 x 3 arcsec$^2$ around the supermassive black hole at the center of the Milky Way galaxy. This survey is based primarily on over a decade of adaptive optics-fed integral-field spectroscopy (R$\sim$4000), obtained as part of the Galactic Center Orbits Initiati

Vishal Asnani, Xi Yin, Tal Hassner, Xiaoming Liu

Advancements in the generation quality of various Generative Models (GMs) has made it necessary to not only perform binary manipulation detection but also localize the modified pixels in an image. However, prior works termed as passive for manipulation localization exhibit poor generalization performance over unseen GMs and attribute modifications. To combat

Rishi Hazra, Brian Chen, Akshara Rai, Nitin Kamra

To enable progress towards egocentric agents capable of understanding everyday tasks specified in natural language, we propose a benchmark and a synthetic dataset called Egocentric Task Verification (EgoTV). The goal in EgoTV is to verify the execution of tasks from egocentric videos based on the natural language description of these tasks. EgoTV contains pa

Mitchell DeHaven, Stephen Scott

Automatic fact verification has become an increasingly popular topic in recent years and among datasets the Fact Extraction and VERification (FEVER) dataset is one of the most popular. In this work we present BEVERS, a tuned baseline system for the FEVER dataset. Our pipeline uses standard approaches for document retrieval, sentence selection, and final clai

Organizers Of QueerInAI, :, Anaelia Ovalle, Arjun Subramonian

We present Queer in AI as a case study for community-led participatory design in AI. We examine how participatory design and intersectional tenets started and shaped this community's programs over the years. We discuss different challenges that emerged in the process, look at ways this organization has fallen short of operationalizing participatory and inter

Dirk Tasche

Sparse joint shift (SJS) was recently proposed as a tractable model for general dataset shift which may cause changes to the marginal distributions of features and labels as well as the posterior probabilities and the class-conditional feature distributions. Fitting SJS for a target dataset without label observations may produce valid predictions of labels a

Tobias H. Piotrowiak, Rico Zehl, Ellen Suhr, Benedikt Kohnen

Compositionally complex perovskites provide the opportunity to develop stable and active catalysts for electrochemical applications. The challenge lies in the identification of single-phase perovskites with optimized composition for high electrical conductivity. Leveraging a recently discovered effect of self-organized thin film growth during reactive sputte

Ashesh Ghosh

We theoretically study the importance of many body correlations on the collective Debye Waller (DW) factor in the context of the Nonlinear Langevin Equation (NLE) single particle activated dynamics theory of glass transition and its extension to include collective elasticity (ECNLE theory). This microscopic force-based approach envisions structural alpha rel

Dat T. Tran, Nam H. Le, Ha T. N. Tran

In this paper, we find criteria for when cyclic cubic and cyclic quartic fields have well-rounded ideal lattices. We show that every cyclic cubic field has at least one well-rounded ideal. We also prove that there exist families of cyclic quartic fields which have well-rounded ideals and explicitly construct their minimal bases. In addition, for a given prim

Wiktor Piotrowski, Yoni Sher, Sachin Grover, Roni Stern

This paper studies how a domain-independent planner and combinatorial search can be employed to play Angry Birds, a well established AI challenge problem. To model the game, we use PDDL+, a planning language for mixed discrete/continuous domains that supports durative processes and exogenous events. The paper describes the model and identifies key design dec

Zheng Gong, Michael J. Quin, Simon Bohlen, Christoph H. Keitel

Employing colliding-pulse injection has been shown to enable high-quality electron beams to be generated from laser-plasma accelerators. Here by leveraging test particle simulations, Hamiltonian analysis, and multidimensional particle-in-cell (PIC) simulations, we lay the theoretical framework of spin-polarized electron beam generation in the colliding-pulse

G. Guedes, P. Olgoso, J. Santiago

Effective field theories offer a rationale to classify new physics models based on the size of their contribution to the effective Lagrangian, and therefore to experimental observables. A complete classification can be obtained, at a fixed order in perturbation theory, in the form of IR/UV dictionaries. We report on the first step towards the calculation of

Francesco Garosi, David Marzocca, Sokratis Trifinopoulos

The emission of collinear radiation off an elementary lepton can be factorised from the hard scattering process by introducing Parton Distribution Functions of a Lepton (LePDF), which, contrary to protons, can be derived from first principles. In case of multi-TeV lepton colliders, such as the muon colliders currently being proposed, the complete structure o

Md Yousuf Harun, Jhair Gallardo, Tyler L. Hayes, Christopher Kanan

Supervised Continual learning involves updating a deep neural network (DNN) from an ever-growing stream of labeled data. While most work has focused on overcoming catastrophic forgetting, one of the major motivations behind continual learning is being able to efficiently update a network with new information, rather than retraining from scratch on the traini

José Pombal, Pedro Saleiro, Mário A. T. Figueiredo, Pedro Bizarro

Data valuation is a ML field that studies the value of training instances towards a given predictive task. Although data bias is one of the main sources of downstream model unfairness, previous work in data valuation does not consider how training instances may influence both performance and fairness of ML models. Thus, we propose Fairness-Aware Data vauatiO

Noah E. Wolfe, Carla Frohlich, Jonah M. Miller, Alejandro Torres-Forne

Core-collapse supernovae are predicted to produce gravitational waves (GWs) that may be detectable by Advanced LIGO/Virgo. These GW signals carry information from the heart of these catacylsmic events, where matter reaches nuclear densities. Recent studies have shown that it may be possible to infer properties of the proto-neutron star (PNS) via gravitationa

Rita Jiménez Rolland, Porfirio L. León Álvarez, Luis Jorge Sánchez Saldaña

Let $\mathrm{Mod}(S)$ be the mapping class group of a compact connected orientable surface $S$, possibly with punctures and boundary components, with negative Euler characteristic. We prove that for any infinite virtually abelian subgroup $H$ of $\mathrm{Mod}(S)$, there is a subgroup $H'$ commensurable with $H$ such that the commensurator of $H$ equals the n

Jean C. Peyen, Leonid V. Bogachev, Paul P. Martin

The paper is concerned with the asymptotic analysis of a family of Boltzmann (multiplicative) distributions over the set $\check{\varLambda}^{q}$ of strict integer partitions (i.e., with unequal parts) into perfect $q$-th powers. A combinatorial link is provided via a suitable conditioning by fixing the partition weight (the sum of parts) and length (the num

Robert P. Finemensch, Gerald X. Gilbert-Thorple

These twenty-two lectures, with exercises, comprise the extent of what was meant to be a full-year graduate-level course on the strong interactions and QCD, given at Pactech in 19$xx$-$xy$. The course was cut short by the illness that led to Finemensch's death. Several of the lectures were finalized in collaboration with Finemensch for an anticipated monogra

Haoran Geng, Ziming Li, Yiran Geng, Jiayi Chen

Learning a generalizable object manipulation policy is vital for an embodied agent to work in complex real-world scenes. Parts, as the shared components in different object categories, have the potential to increase the generalization ability of the manipulation policy and achieve cross-category object manipulation. In this work, we build the first large-sca

Nikolaos Masios, Andreas Irmler, Tobias Schäfer, Andreas Grüneis

Coupled-cluster theories can be used to compute ab initio electronic correlation energies of real materials with systematically improvable accuracy. However, the widely-used coupled cluster singles and doubles plus perturbative triples (CCSD(T)) method is only applicable to insulating materials. For zero-gap materials the truncation of the underlying many-bo

Jan Glaubitz, Anne Gelb

We present a hierarchical Bayesian learning approach to infer jointly sparse parameter vectors from multiple measurement vectors. Our model uses separate conditionally Gaussian priors for each parameter vector and common gamma-distributed hyper-parameters to enforce joint sparsity. The resulting joint-sparsity-promoting priors are combined with existing Baye

Peijun Li, Ying Liang, Yuliang Wang

We propose a data-assisted two-stage method for solving an inverse random source problem of the Helmholtz equation. In the first stage, the regularized Kaczmarz method is employed to generate initial approximations of the mean and variance based on the mild solution of the stochastic Helmholtz equation. A dataset is then obtained by sampling the approximate

Tanmay Gautam, Samuel Pfrommer, Somayeh Sojoudi

Conventional optimization methods in machine learning and controls rely heavily on first-order update rules. Selecting the right method and hyperparameters for a particular task often involves trial-and-error or practitioner intuition, motivating the field of meta-learning. We generalize a broad family of preexisting update rules by proposing a meta-learning

Michael D. Zollars, David J. Grymin, Isaac E. Weintraub

This work develops feasible path trajectories for a coordinated strike with multiple aircraft in a constrained environment. Using direct orthogonal collocation methods, the two-point boundary value optimal control problem is transcribed into a nonlinear programming problem. A coordinate transformation is performed on the state variables to leverage the benef

Garrett Louie, Zilin Chen, Tejas Deshpande, Timothy Kovachy

Multi-photon Bragg diffraction is a powerful method for fast, coherent momentum transfer of atom waves. However, laser noise, Doppler detunings, and cloud expansion limit its efficiency in large momentum transfer (LMT) pulse sequences. We present simulation studies of robust Bragg pulses developed through numerical quantum optimal control. Optimized pulse pe

Zhiang Xie, Dongwei Chen, Puxi Li

Climate change is one of the key topics in climate science. However, previous research has predominantly concentrated on changes in mean values, and few research examines changes in Probability Distribution Function (PDF). In this study, a novel method called Wasserstein Stability Analysis (WSA) is developed to identify PDF changes, especially the extreme ev

Irfansha Shaik, Jaco van de Pol

Encoding 2-player games in QBF correctly and efficiently is challenging and error-prone. To enable concise specifications and uniform encodings of games played on grid boards, like Tic-Tac-Toe, Connect-4, Domineering, Pursuer-Evader and Breakthrough, we introduce Board-game Domain Definition Language (BDDL), inspired by the success of PDDL in the planning do

Anni Li, Andres S. Chavez Armijos, Christos G. Cassandras

We derive time and energy-optimal control policies for a Connected Autonomous Vehicle (CAV) to complete lane change maneuvers in mixed traffic. The interaction between CAVs and Human-Driven Vehicles (HDVs) requires designing the best possible response of a CAV to actions by its neighboring HDVs. This interaction is formulated using a bilevel optimization set

Eloghosa Ikponmwoba, Ope Owoyele

We present an approach for designing swarm-based optimizers for the global optimization of expensive black-box functions. In the proposed approach, the problem of finding efficient optimizers is framed as a reinforcement learning problem, where the goal is to find optimization policies that require a few function evaluations to converge to the global optimum

Congpei Qiu, Tong Zhang, Wei Ke, Mathieu Salzmann

Dense Self-Supervised Learning (SSL) methods address the limitations of using image-level feature representations when handling images with multiple objects. Although the dense features extracted by employing segmentation maps and bounding boxes allow networks to perform SSL for each object, we show that they suffer from coupling and positional bias, which a

Valérie Bettaque, Brian Swingle

Motivated by the ground state structure of quantum models with all-to-all interactions such as mean-field quantum spin glass models and the Sachdev-Ye-Kitaev (SYK) model, we propose a tensor network architecture which can accomodate volume law entanglement and a large ground state degeneracy. We call this architecture the non-local renormalization ansatz (No

Leonardo Pedroso, W. P. M. H. Heemels, Mauro Salazar

Within mobility systems, the presence of self-interested users can lead to aggregate routing patterns that are far from the societal optimum which could be achieved by centrally controlling the users' choices. In this paper, we design a fair incentive mechanism to steer the selfish behavior of the users to align with the societally optimal aggregate routing.

Jonas Haferkamp

We prove new lower bounds on the growth of robust quantum circuit complexity -- the minimal number of gates $C_{\delta}(U)$ to approximate a unitary $U$ up to an error of $\delta$ in operator norm distance. More precisely we show two bounds for random quantum circuits with local gates drawn from a subgroup of $SU(4)$. First, for $\delta=\Theta(2^{-n})$, we p

James Rodi, Pietro Ubertini

The gamma-ray burst (GRB) 221009A, with its extreme brightness, has provided the opportunity to explore GRB prompt and afterglow emission behavior on short time scales with high statistics. In conjunction with detection up to very high-energy gamma-rays, studies of this event shed light on the emission processes at work in the initial phases of GRBs emission

Steffen Gielen, Robert Santacruz

Group field theory (GFT) models for quantum gravity coupled to a massless scalar field give rise to cosmological models that reproduce the (expanding or contracting) dynamics of homogeneous and isotropic spacetimes in general relativity at low energies, while including high-energy corrections that lead to singularity resolution by a "bounce." Here we investi

Henri M. J. Boffin

The enigmatic open clusters serve as a constant reminder of the mysteries of the universe, helping to confront astronomical theories. Unknown to many, these clusters often possess tails with inappropriate labels, serving as the tell-tale signs of their historical journey. But unlike typical tails, these extensions can either precede or follow the body, yet t

James Giroux, Martin Bouchard, Robert Laganiere

Object detection utilizing Frequency Modulated Continous Wave radar is becoming increasingly popular in the field of autonomous systems. Radar does not possess the same drawbacks seen by other emission-based sensors such as LiDAR, primarily the degradation or loss of return signals due to weather conditions such as rain or snow. However, radar does possess t

Antoine Bourget, Julius F. Grimminger, Amihay Hanany, Rudolph Kalveks

We study particular families of bad 3d $\mathcal{N}=4$ quiver gauge theories, whose Higgs branches consist of many cones. We show the role of a novel brane configuration in realizing the Higgs moduli for each distinct cone. Through brane constructions, magnetic quivers, Hasse diagrams, and Hilbert series computations we study the intricate structure of the c

Vasco Lopes, Bruno Degardin, Luís A. Alexandre

Neural Architecture Search (NAS) benchmarks significantly improved the capability of developing and comparing NAS methods while at the same time drastically reduced the computational overhead by providing meta-information about thousands of trained neural networks. However, tabular benchmarks have several drawbacks that can hinder fair comparisons and provid

Emma R. Beasor, Nathan Smith, Jennifer E. Andrews

Yellow hypergiants (YHGs) are often presumed to represent a transitional post-red supergiant (RSG) phase for stars $\sim$30-40 \msun. Here we present visual-wavelength echelle spectra of six YHG candidates in the Galactic cluster Westerlund 1, and we compare them to known YHGs, IRC +10420 and Hen3-1979. We find that the six YHG candidates do not exhibit any

Alan Kahan, Leonardo Ermann, Marcos Saraceno, Cecilia Cormick

We complement previous studies of an ion coupled with an optical cavity in the dispersive regime, for a model which exhibits bistability of different configurations in the semiclassical description. Our approach is based on a truncated evolution in phase space and is intended to explore an especially interesting parameter region where the fully quantum-mecha

Horacio Casini, Ignacio Salazar Landea, Gonzalo Torroba

We first present an analysis of infinitesimal null deformations for the entanglement entropy, which leads to a major simplification of the proof of the $C$, $F$ and $A$-theorems in quantum field theory. Next, we study the quantum null energy condition (QNEC) on the light-cone for a CFT. Finally, we combine these tools in order to establish the irreversibilit

Javiera Parada, Jeremy Heyl, Harvey Richer, Paul Ripoche

In the second paper of this series, we developed a new distance determination method using the median $J$ magnitude of carbon-rich asymptotic giant branch stars (CS) as standard candles and the Magellanic Clouds as the fundamental calibrators. The $J$-band CS luminosity function was modeled using a modified Lorentzian distribution whose parameters were used

Sayak Dutta, Sowgat Muzahid, Joop Schaye, Sapna Mishra

We present a detailed study of cool, neutral gas traced by Lya around 4595 z<0.5 galaxies using stacks of background quasar spectra. The galaxies are selected from our MUSEQuBES low-z survey along with data from the literature. These galaxies, with a median stellar mass of log (M*/Msun)= 10.0, are probed by 184 background quasars giving rise to 5054 quasar-g

Rebekka Koch, Alvise Bastianello

We revisit the exact thermodynamic description of the classical sine-Gordon field theory, a notorious integrable model. We found that existing results in the literature based on the soliton-gas picture did not correctly take into account light, but extended, solitons and thus led to incorrect results. This issue is regularized upon requantization: we derive

Joseph J. Cuozzo, Wei Pan, Javad Shabani, Enrico Rossi

In superconducting systems in which inversion and time-reversal symmetry are simultaneously broken the critical current for positive and negative current bias can be different. For superconducting systems formed by Josephson junctions (JJs) this effect is termed Josephson diode effect. In this work, we study the Josephson diode effect for a superconducting q

Mattia Di Mauro, Judit Pérez-Romero, Miguel A. Sánchez-Conde, Nicolao Fornengo

Clusters of galaxies are the largest gravitationally-bound systems in the Universe. Their dynamics are dominated by dark matter (DM), which makes them among the best targets for indirect DM searches. We analyze 12 years of data collected by the Fermi Large Area Telescope (Fermi-LAT) in the direction of 49 clusters of galaxies selected for their proximity to

Diptimoy Ghosh, Kushan Panchal, Farman Ullah

Boostless Bootstrap techniques have been applied by many in the literature to compute pure scalar and graviton correlators. In this paper, we focus primarily on mixed graviton and scalar correlators. We start by developing an EFT of Inflation (EFToI) with some general assumptions, clarifying various subtleties related to power counting. We verify explicitly

Tomas Hale, David Kubiznak, Ota Svitek, Tayebeh Tahamtan

The regularized Maxwell theory is a recently discovered theory of non-linear electrodynamics that admits many important gravitating solutions within the Einstein theory. Namely, it was originally derived as the unique non-linear electrodynamics (that depends only on the field invariant $F_{\mu\nu}F^{\mu\nu}$) whose radiative solutions can be found in the Rob

Yutaka Fujita, Takuma Izumi, Nozomu Kawakatu, Hiroshi Nagai

Massive molecular clouds have been discovered in massive elliptical galaxies at the center of galaxy clusters. Some of this cold gas is expected to flow in the central supermassive black holes and activate galactic nucleus (AGN) feedback. In this study, we analyze archival ALMA data of 9 massive elliptical galaxies, focusing on CO line emissions, to explore

Shahar Hod

Recent numerical studies have revealed the physically intriguing fact that charged black holes whose charge-to-mass ratios are larger than the critical value $(Q/M)_{\text{crit}}=\sqrt{2(9+\sqrt{6})}/5$ can support hairy matter configurations which are made of scalar fields with a non-minimal negative coupling to the Gauss-Bonnet invariant of the curved spac

H. Kuncarayakti, J. Sollerman, L. Izzo, K. Maeda

We report on our study of supernova (SN) 2022xxf based on observations obtained during the first four months of its evolution. The light curves (LCs) display two humps of similar maximum brightness separated by 75 days, unprecedented for a broad-lined (BL) Type Ic supernova (SN IcBL). SN 2022xxf is the most nearby SN IcBL to date (in NGC 3705, $z = 0.0037$,

Zhi-Chao Li, H. Lu

We construct regular black holes and stars that are geodesically complete and satisfy the dominant energy condition from Einstein-$f(F^2)$ gravities with several classes of analytic $f(F^2)$ functions that can be viewed as perturbations to Maxwell's theory in weak field limit. We establish that regular black holes with special static metric ($g_{tt} g_{rr}=-