April 2023 arXiv papers — page 135

Fredrik Dahlqvist, Renato Neves

Modern programming frequently requires generalised notions of program equivalence based on a metric or a similar structure. Previous work addressed this challenge by introducing the notion of a V-equation, i.e. an equation labelled by an element of a quantale V, which covers inter alia (ultra-)metric, classical, and fuzzy (in)equations. It also introduced a

Filipe Fernandes, Cláudia Werner

Blockchain technologies for rewards in education are gaining attraction as a promising approach to motivate student learning and promote academic achievement. By providing tangible rewards for educational attainment and engagement, such as digital tokens, educators can motivate learners to take a more active role in their learning and increase their sense of

Gabriela Kováčová, Firdevs Ulus

It is possible to solve unbounded convex vector optimization problems (CVOPs) in two phases: (1) computing or approximating the recession cone of the upper image and (2) solving the equivalent bounded CVOP where the ordering cone is extended based on the first phase (Wagner et al., 2023). In this paper, we consider unbounded CVOPs and propose an alternative

Vladislav Lialin, Stephen Rawls, David Chan, Shalini Ghosh

Scaling up weakly-supervised datasets has shown to be highly effective in the image-text domain and has contributed to most of the recent state-of-the-art computer vision and multimodal neural networks. However, existing large-scale video-text datasets and mining techniques suffer from several limitations, such as the scarcity of aligned data, the lack of di

Ramya M. Anche, Jaren N. Ashcraft, Sebastiaan Y. Haffert, Maxwell A. Millar-Blanchaer

Next-generation large segmented mirror telescopes are expected to perform direct imaging and characterization of Earth-like rocky planets, which requires contrast limits of $10^{-7}$ to $10^{-8}$ at wavelengths from I to J band. One critical aspect affecting the raw on-sky contrast are polarization aberrations arising from the reflection from the telescope's

Zexing Li

We consider the mass supercritical (NLS) in dimension $d\ge 1$ in the mass-supercritical range. The existence of self-similar blow up dyamics is known [Merle-Rapha\"el-Szeftel, 2010], and suitable self-similar blow up profiles were constructed [Bahri-Martel-Rapha\"el, 2021]. In this work, we prove the finite codimensional nonlinear asymptotic stability of a

Ludovic Stephan, Yizhe Zhu

We consider the problem of low-rank rectangular matrix completion in the regime where the matrix $M$ of size $n\times m$ is ``long", i.e., the aspect ratio $m/n$ diverges to infinity. Such matrices are of particular interest in the study of tensor completion, where they arise from the unfolding of a low-rank tensor. In the case where the sampling probability

MicroBooNE collaboration, P. Abratenko, O. Alterkait, D. Andrade Aldana

MicroBooNE is a neutrino experiment located in the Booster Neutrino Beamline (BNB) at Fermilab, which collected data from 2015 to 2021. MicroBooNE's liquid argon time projection chamber (LArTPC) is accompanied by a photon detection system consisting of 32 photomultiplier tubes used to measure the argon scintillation light and determine the timing of neutrino

Nikhil Angad Bakshi, Tejus Gupta, Ramina Ghods, Jeff Schneider

Robotic solutions for quick disaster response are essential to ensure minimal loss of life, especially when the search area is too dangerous or too vast for human rescuers. We model this problem as an asynchronous multi-agent active-search task where each robot aims to efficiently seek objects of interest (OOIs) in an unknown environment. This formulation ad

Clarence Lewis Protin

PyLog is a minimal experimental proof assistant based on linearised natural deduction for intuitionistic and classical first-order logic extended with a comprehension operator. PyLog is interesting as a tool to be used in conjunction with other more complex proof assistants and formal mathematics projects (such as Coq and Coq-based projects). Proof assistant

Ian Curtis, Sean Griswold, Abigail Halverson, Eric Stilwell

Recently, two stronger versions of dynamical properties have been introduced and investigated: strong topological transitivity, which is a stronger version of the topological transitivity property, and hypermixing, which is a stronger version of the mixing property. We continue the investigation of these notions with two main results. First, we show there ar

W. Z. Jia, M. T. Yu

We study the properties of bound states in waveguide-QED systems consisting of multiple giant atoms coupled to a coupled-resonator waveguide. Based on the general analytical expressions for these states and the corresponding energy spectra, we analyze in detail the threshold conditions for the appearance of bound states and the photon-mediated interactions b

Nicolas Lenzing, David Krüger, Michael Potthoff

The geometrical spin torque mediates an indirect interaction of magnetic moments, which are weakly exchange coupled to a system of itinerant electrons. It originates from a finite spin-Berry curvature and leads to a non-Hamiltonian magnetic-moment dynamics. We demonstrate that there is an unprecedentedly strong geometrical spin torque in case of an electron

Minoosh Hemmat, Sabrine Ayari, Martin Micica, Hadrien Vergnet

Platinum diselenide (PtSe2) is a promising two-dimensional (2D) material for the terahertz (THz) range as, unlike other transition metal dichalcogenides (TMDs), its bandgap can be uniquely tuned from a semiconductor in the near-infrared to a semimetal with the number of atomic layers. This gives the material unique THz photonic properties that can be layer-e

Tiago A. Coimbra, Rodrigo Bloot, Jorge H. Faccipieri

In seismic imaging, understanding the relationship between wavefront-propagation velocity and time-interval velocity is crucial for achieving optimal resolution. However, this task becomes even more challenging when considering anisotropic situations. To accurately account for the influence of anisotropy on wavefronts, it is essential to have a solid grasp o

Vade Shah, Jason R. Marden

In competitive resource allocation environments, agents often choose to form alliances; however, for some agents, doing so may not always be beneficial. Is there a method of forming alliances that always reward each of their members? We study this question using the framework of the coalitional Blotto game, in which two players compete against a common adver

M. A. De Andrade, L. G. Ferreira Filho, C. Neves

The Planck's radiation law for the blackbody radiation spectrum was capable to explain the experimentally-measured blackbody spectrum. In order to get this result, Planck proposed his radiation law in a two-fold way: 1) by an \textit{ad hoc} modification of the assumed connection between energy and entropy for thermal radiation; 2) by assuming that the calcu

Lucio Mayer, Pedro R. Capelo, Lorenz Zwick, Tiziana Di Matteo

We present the results of the first fully cosmological hydrodynamical simulations studying the merger-driven model for massive black hole (BH) seed formation via direct collapse. Using the zoom-in technique as well as particle splitting, we achieve a final spatial resolution of $2$ pc. We show that the major merger of two massive galaxies at redshift $z \sim

Leonardo A. Pedraza Perez, Gonzalo A. Alvarez

Magnetic resonance imaging is a powerful, non invasive tool for medical diagnosis. The low sensitivity for detecting the nuclear spin signals, typically limits the image resolution to several tens of micrometers in preclinical systems and millimeters in clinical scanners. Other sources of information, derived from diffusion processes of intrinsic molecules a

Qi Chen, Mario Marchand

We use information-theoretic tools to derive a novel analysis of Multi-source Domain Adaptation (MDA) from the representation learning perspective. Concretely, we study joint distribution alignment for supervised MDA with few target labels and unsupervised MDA with pseudo labels, where the latter is relatively hard and less commonly studied. We further provi

Anupam Gupta, Gregory Kehne, Roie Levin

In the stochastic set cover problem (Grandoni et al., FOCS '08), we are given a collection $\mathcal{S}$ of $m$ sets over a universe $\mathcal{U}$ of size $N$, and a distribution $D$ over elements of $\mathcal{U}$. The algorithm draws $n$ elements one-by-one from $D$ and must buy a set to cover each element on arrival; the goal is to minimize the total cost

J. H. Adler, D. B. Emerson

This paper derives an a posteriori error estimator for the nonlinear first-order optimality conditions associated with the electrically and flexoelectrically coupled Frank-Oseen model of liquid crystals, building on previous results for elastic systems. The estimator is proposed for a penalty approach to imposing the unit-length constraint required by the mo

Aymen Mir, Xavier Puig, Angjoo Kanazawa, Gerard Pons-Moll

We introduce a method to synthesize animator guided human motion across 3D scenes. Given a set of sparse (3 or 4) joint locations (such as the location of a person's hand and two feet) and a seed motion sequence in a 3D scene, our method generates a plausible motion sequence starting from the seed motion while satisfying the constraints imposed by the provid

Zhanduo Tang, Ralf Rapp

We extend a previously constructed T-matrix approach to the quark-gluon plasma (QGP) to include the effects of spin-dependent interactions between partons. Following earlier work within the relativistic quark model, the spin-dependent interactions figure as relativistic corrections to the Cornell potential. When applied to the vacuum spectroscopy of quarkoni

Philip C. Myers, Michael M. Dunham, Ian W. Stephens

The opening angles of some protostellar outflows appear too narrow to match the expected core-star mass efficiency SFE = 0.3-0.5 if outflow cavity volume traces outflow mass, with a conical shape and a maximum opening angle near 90 deg. However, outflow cavities with paraboloidal shape and wider angles are more consistent with observed estimates of the SFE.

Luyao Niu, Abdullah Al Maruf, Andrew Clark, J. Sukarno Mertoguno

Interconnected systems such as power systems and chemical processes are often required to satisfy safety properties in the presence of faults and attacks. Verifying safety of these systems, however, is computationally challenging due to nonlinear dynamics, high dimensionality, and combinatorial number of possible faults and attacks that can be incurred by th

M. Waidele, Junwei Zhao, I. N. Kitiashvili

Previous studies analyzing the evanescent nature of acoustic waves in the lower solar atmosphere, up to 300\,km above the photosphere, have shown an unexpected phase shift of an order of 1\,s between different heights. Those studies investigated the spectral line \ion{Fe}{1} 6173.3\,\AA, commonly used for helioseismic measurements. Such phase-shifts can cont

Savannah P. Hays, Lianrui Zuo, Yuli Wang, Mark G. Luciano

Development of MR harmonization has enabled different contrast MRIs to be synthesized while preserving the underlying anatomy. In this paper, we use image harmonization to explore the impact of different T1-w MR contrasts on a state-of-the-art ventricle parcellation algorithm VParNet. We identify an optimal operating contrast (OOC) for ventricle parcellation

Sribalaji C. Anand, André M. H. Teixeira

This article considers the problem of risk-optimal allocation of security measures when the actuators of an uncertain control system are under attack. We consider an adversary injecting false data into the actuator channels. The attack impact is characterized by the maximum performance loss caused by a stealthy adversary with bounded energy. Since the impact

Kyle Finner, Andreas Faisst, Ranga-Ram Chary, M. James Jee

Utilizing the James Webb Space Telescope Early Release NIRCam Observations, we perform a weak-lensing analysis of the massive galaxy cluster SMACS J0723.3-7327 ($z=0.39$). We investigate the spatial variation of the PSF from the stars in the mosaic image. Our measurements show that the PSF for both modules has very small spatial and temporal variation with a

Sho Sugiura, Arkopal Dutt, William J. Munro, Sina Zeytinoğlu

In many natural and engineered systems, unknown quantum channels act on a subsystem that cannot be directly controlled and measured, but is instead learned through a controllable subsystem that weakly interacts with it. We study quantum channel discrimination (QCD) under these restrictions, which we call hidden system QCD (HQCD). We find that sequential prot

Ethan K. Gordon, Rana Soltani Zarrin

When using a tool, the grasps used for picking it up, reposing, and holding it in a suitable pose for the desired task could be distinct. Therefore, a key challenge for autonomous in-hand tool manipulation is finding a sequence of grasps that facilitates every step of the tool use process while continuously maintaining force closure and stability. Due to the

Alberto Baldrati, Davide Morelli, Giuseppe Cartella, Marcella Cornia

Fashion illustration is used by designers to communicate their vision and to bring the design idea from conceptualization to realization, showing how clothes interact with the human body. In this context, computer vision can thus be used to improve the fashion design process. Differently from previous works that mainly focused on the virtual try-on of garmen

Theodoros Ilias, Dayou Yang, Susana F. Huelga, Martin B. Plenio

We propose and analyze a driven-dissipative quantum sensor that is continuously monitored close to a dissipative critical point. The sensor relies on the critical open Rabi model with the spin and phonon degrees of freedom of a single trapped ion to achieve criticality-enhanced sensitivity. Effective continuous monitoring of the sensor is realized via a co-t

Samuele Poppi, Sara Sarto, Marcella Cornia, Lorenzo Baraldi

Machine Unlearning is an emerging paradigm for selectively removing the impact of training datapoints from a network. Unlike existing methods that target a limited subset or a single class, our framework unlearns all classes in a single round. We achieve this by modulating the network's components using memory matrices, enabling the network to demonstrate se

Sergei V. Kalinin, Debangshu Mukherjee, Kevin M. Roccapriore, Ben Blaiszik

Machine learning (ML) has become critical for post-acquisition data analysis in (scanning) transmission electron microscopy, (S)TEM, imaging and spectroscopy. An emerging trend is the transition to real-time analysis and closed-loop microscope operation. The effective use of ML in electron microscopy now requires the development of strategies for microscopy-

Zeshan Haider, Shahid Qamar, Muhammad Irfan

We study multiphoton blockade effects in a single-mode cavity interacting with two three-level atoms in $\Lambda$-configuration having position-dependent atom-field coupling. We consider the effects of dipole-dipole interaction (DDI) between the three-level atoms and show how the presence of DDI strongly influences the multiphoton blockade. For symmetric cou

Payaswini Saikia, David M. Russell, Saarah F. Pirbhoy, M. C. Baglio

We present long-term (2012-2022) optical monitoring of the candidate black hole X-ray binary Swift J1910.2-0546 with the Faulkes Telescopes and Las Cumbres Observatory (LCO) network. Following its initial bright 2012 outburst, we find that the source displayed a series of at least 7 quasi-periodic, high amplitude (~3 mags) optical reflares in 2013, with a re

Marcy Best, Antranik A. Sefilian, Cristobal Petrovich

The formation of a cold Jupiter (CJ) is expected to quench the influx of pebbles and the migration of cores interior to its orbit, thus limiting the efficiency of rocky planet formation either by pebble accretion and/or orbital migration. Observations, however, show that the presence of outer CJs ( >1 au and >0.3 Jupiter masses) correlates with the presence

Helong Huang, Chris W. Ormel

Due to their aerodynamical coupling with gas, pebbles in protoplanetary discs can drift over large distances to support planet growth in the inner disc. In the past decade, this pebble accretion has been studied extensively for aerodynamically small pebbles (Stokes number St < 1). However, accretion can also operate in the St > 1 mode, e.g., when planetesima

Paolo Aluffi

We consider polynomials expressing the cohomology classes of subvarieties of products of projective spaces, and limits of positive real multiples of such polynomials. We study the relation between these covolume polynomials and Lorentzian polynomials. While these are distinct notions, we prove that, like Lorentzian polynomials, covolume polynomials have M-co

Belle II Collaboration, I. Adachi, L. Aggarwal, H. Ahmed

We propose a new algorithm for the identification of the production flavor of neutral $D$ mesons in the Belle II experiment. The algorithm exploits the correlation between the flavor of a reconstructed neutral $D$ meson (signal $D$ meson) and the electric charges of particles reconstructed in the rest of the $e^+e^-\to c \bar{c}$ event. These include those o

Jianrui Li, Bjorn H. C. Emonts, Zheng Cai, Jianan Li

To understand how massive galaxies at high-$z$ co-evolve with enormous reservoirs of halo gas, it is essential to study the coldest phase of the circum-galactic medium (CGM), which directly relates to stellar growth. The SUPERCOLD-CGM survey is the first statistical survey of cold molecular gas on CGM scales. We present ALMA+ACA observations of CO(4-3) and c

Sebastian Schulz

We introduce grlic, a publicly available Python tool for generating glass-like point distributions with a radial density profile $n(r)$ as it is observed in large-scale surveys of galaxy distributions on the past light cone. Utilising these glass-like catalogues, we assess the bias and variance of the Landy-Szalay (LS) estimator of the first three two-point

Jaime Redondo-Yuste, Vitor Cardoso, Caio F. B. Macedo, Maarten van de Meent

The two-body problem is extensively studied in open systems and asymptotically flat spacetimes. However, there are many systems where radiation is trapped: they range from radiating charges in cavities to low-energy excitations of massive degrees of freedom, to anti-de Sitter spacetimes. Here, we study the problem of motion of a pointlike particle orbiting a

Prakash Gaikwad, Martin G. Haehnelt, Frederick B. Davies, Sarah E. I. Bosman

We measure the mean free path ($\lambda_{\rm mfp,HI}$), photo-ionization rate ($\langle \Gamma_{\rm HI} \rangle$) and neutral fraction ($\langle f_{\rm HI} \rangle$) of hydrogen in 12 redshift bins at $4.85<z<6.05$ from a large sample of moderate resolution XShooter and ESI QSO absorption spectra. The fluctuations in ionizing radiation field are modeled by p

Cosima Eibensteiner, Frank Bigiel, Adam K. Leroy, Eric W. Koch

We present new HI observations of the nearby massive spiral galaxy M83, taken with the VLA at $21^{\prime\prime}$ angular resolution ($\approx500$ pc) of an extended ($\sim$1.5 deg$^2$) 10-point mosaic combined with GBT single dish data. We study the super-extended HI disk of M83 (${\sim}$50 kpc in radius), in particular disc kinematics, rotation and the tur

M. Krumpe, T. Miyaji, A. Georgakakis, A. Schulze

This is the fifth paper in a series of investigations of the clustering properties of luminous, broad-emission-line active galactic nuclei (AGN) identified in the ROSAT All-Sky Survey (RASS) and Sloan Digital Sky Survey (SDSS). In this work we measure the cross-correlation function (CCF) between RASS/SDSS DR14 AGN with the SDSS CMASS galaxy sample at $0.44<z

Uddipta Bhardwaj, James Alvey, Benjamin Kurt Miller, Samaya Nissanke

The current and upcoming generations of gravitational wave experiments represent an exciting step forward in terms of detector sensitivity and performance. For example, key upgrades at the LIGO, Virgo and KAGRA facilities will see the next observing run (O4) probe a spatial volume around four times larger than the previous run (O3), and design implementation

Emily Dinan, Sho Yaida, Susan Zhang

We perform an effective-theory analysis of forward-backward signal propagation in wide and deep Transformers, i.e., residual neural networks with multi-head self-attention blocks and multilayer perceptron blocks. This analysis suggests particular width scalings of initialization and training hyperparameters for these models. We then take up such suggestions,

Nikhil Arora, Stéphane Courteau, Connor Stone, Andrea V. Macció

We present a catalogue of dynamical properties for 2368 late-type galaxies from the MaNGA survey. The latter complements the catalogue of photometric properties for the same sample based on deep optical DESI photometry processed with AutoProf. Rotation curves (RCs), extracted by model fitting H$\alpha$ velocity maps from the MaNGA Data Analysis Pipeline, ext

James Alvey, Mathis Gerdes, Christoph Weniger

Stellar streams are potentially a very sensitive observational probe of galactic astrophysics, as well as the dark matter population in the Milky Way. On the other hand, performing a detailed, high-fidelity statistical analysis of these objects is challenging for a number of key reasons. Firstly, the modelling of streams across their (potentially billions of

P. S. Bhupal Dev, Bhaskar Dutta, Tao Han, Doojin Kim

We point out a new mechanism giving rise to anomalous tau neutrino appearance at the near detectors of beam-focused neutrino experiments, without extending the neutrino sector. The charged mesons ($\pi^\pm, K^\pm$) produced and focused in the target-horn system can decay to a (neutrino-philic) light mediator via the helicity-unsuppressed three-body decays. I

Manoj K. Mandal, Pierpaolo Mastrolia, Hector O. Silva, Raj Patil

We present a gravitoelectric quadrupolar dynamical tidal-interaction Hamiltonian for a compact binary system, that is valid to second order in the post-Newtonian expansion. Our derivation uses the diagrammatic effective field theory approach, and involves Feynman integrals up to two loops, evaluated with the dimensional regularization scheme. We also derive

Mor Rozner, Hagai B. Perets

Most stars, binaries, and higher multiplicity systems are thought to form in stellar clusters and associations, which later dissociate. Very wide binaries can be easily disrupted in clusters due to dynamical evaporation (soft binaries) and/or due to tidal disruption by the gravitational potential of the cluster. Nevertheless, wide binaries are quite frequent

Tristan L. Smith, John T. Giblin, Mustafa A. Amin, Mary Gerhardinger

We study the nonlinear effects of minimally coupled, massless, cosmological scalar fields on the cosmic microwave background (CMB). These fields can exhibit post-recombination parametric resonance and subsequent nonlinear evolution leading to novel contributions to the gravitational potential. We compute the resulting contributions to the CMB temperature ani

Diogo P. L. Bragança

We build a self-consistent relativistic scalar theory of gravitation on a flat Minkowski spacetime from a general field Lagrangian. It is shown that, for parameters that satisfy the Equivalence Principle, this theory predicts the same outcome as general relativity for every classical solar-system test. This theory also admits gravitational waves that propaga

P. A. Oesch, G. Brammer, R. P. Naidu, R. J. Bouwens

We present the JWST Cycle 1 53.8hr medium program FRESCO, short for "First Reionization Epoch Spectroscopically Complete Observations". FRESCO covers 62 arcmin$^2$ in each of the two GOODS/CANDELS fields for a total area of 124 arcmin$^2$ exploiting JWST's powerful new grism spectroscopic capabilities at near-infrared wavelengths. By obtaining ~2 hr deep NIR

Yinlin Deng, Chunqiu Steven Xia, Chenyuan Yang, Shizhuo Dylan Zhang

Deep Learning (DL) library bugs affect downstream DL applications, emphasizing the need for reliable systems. Generating valid input programs for fuzzing DL libraries is challenging due to the need for satisfying both language syntax/semantics and constraints for constructing valid computational graphs. Recently, the TitanFuzz work demonstrates that modern L

Shih-Yang Su, Timur Bagautdinov, Helge Rhodin

High-fidelity human 3D models can now be learned directly from videos, typically by combining a template-based surface model with neural representations. However, obtaining a template surface requires expensive multi-view capture systems, laser scans, or strictly controlled conditions. Previous methods avoid using a template but rely on a costly or ill-posed

Qiushan Guo, Chuofan Ma, Yi Jiang, Zehuan Yuan

Learning image classification and image generation using the same set of network parameters is a challenging problem. Recent advanced approaches perform well in one task often exhibit poor performance in the other. This work introduces an energy-based classifier and generator, namely EGC, which can achieve superior performance in both tasks using a single ne

Pavol Harar, Lukas Herrmann, Philipp Grohs, David Haselbach

In cryo-electron microscopy, accurate particle localization and classification are imperative. Recent deep learning solutions, though successful, require extensive training data sets. The protracted generation time of physics-based models, often employed to produce these data sets, limits their broad applicability. We introduce FakET, a method based on Neura

Qiushan Guo, Yizhou Yu, Yi Jiang, Jiannan Wu

In this work, we present Multi-Level Contrastive Learning for Dense Prediction Task (MCL), an efficient self-supervised method for learning region-level feature representation for dense prediction tasks. Our method is motivated by the three key factors in detection: localization, scale consistency and recognition. To explicitly encode absolute position and s

Paul-Edouard Sarlin, Daniel DeTone, Tsun-Yi Yang, Armen Avetisyan

Humans can orient themselves in their 3D environments using simple 2D maps. Differently, algorithms for visual localization mostly rely on complex 3D point clouds that are expensive to build, store, and maintain over time. We bridge this gap by introducing OrienterNet, the first deep neural network that can localize an image with sub-meter accuracy using the

Rémi Pautrat, Iago Suárez, Yifan Yu, Marc Pollefeys

Line segments are powerful features complementary to points. They offer structural cues, robust to drastic viewpoint and illumination changes, and can be present even in texture-less areas. However, describing and matching them is more challenging compared to points due to partial occlusions, lack of texture, or repetitiveness. This paper introduces a new ma

Jordan Van Nest, Ferah Munshi, Charlotte Christensen, Alyson M. Brooks

We study satellite counts and quenched fractions for satellites of Milky Way analogs in Romulus25, a large-volume cosmological hydrodynamic simulation. Depending on the definition of a Milky Way analog, we have between 66 and 97 Milky Way analogs in Romulus25, a 25 Mpc per-side uniform volume simulation. We use these analogs to quantify the effect of environ

Alex B. Akins, Tatiana M. Bocanegra-Bahamón, Kuo-Nung Wang, Panagiotis Vergados

The EnVision and VERITAS missions to Venus will fly with X and Ka band telecommunications channels which can be used to conduct radio occultation studies of Venus' atmosphere. While link attenuation measurements during prior S and X band occultation experiments have been used to determine vertical profiles of H$_2$SO$_4$ vapor abundance, the addition of the

Abdullah Al Maruf, Luyao Niu, Bhaskar Ramasubramanian, Andrew Clark

Autonomous cyber and cyber-physical systems need to perform decision-making, learning, and control in unknown environments. Such decision-making can be sensitive to multiple factors, including modeling errors, changes in costs, and impacts of events in the tails of probability distributions. Although multi-agent reinforcement learning (MARL) provides a frame

Isak Svensson, Andreas Ekström, Christian Forssén

We sample the posterior probability distributions of the low-energy constants (LECs) in $\Delta$-full chiral effective field theory ($\chi$EFT) up to third order. We use eigenvector continuation for fast and accurate emulation of the likelihood and Hamiltonian Monte Carlo to draw effectively independent samples from the posteriors. Our Bayesian inference is

Faizan Bhat, Ahmadullah Zahed

We prove a precise form of AdS bulk locality by deriving analytical two-sided bounds on bulk Wilson coefficients. Our bounds are on the Wilson coefficients themselves, rather than their ratios, as is typically found in the literature. Inspired by the Celestial amplitudes program in flat space, we perform a Celestial transform of the CFT Mellin amplitude of f

Paulo Padrao, Jose Fuentes, Tero Kaarlela, Alfredo Bayuelo

Efficient and intuitive Human-Robot interfaces are crucial for expanding the user base of operators and enabling new applications in critical areas such as precision agriculture, automated construction, rehabilitation, and environmental monitoring. In this paper, we investigate the design of human-robot interfaces for the teleoperation of dynamical systems.

Zhengming Yu, Wei Cheng, Xian Liu, Wayne Wu

Animating virtual avatars with free-view control is crucial for various applications like virtual reality and digital entertainment. Previous studies have attempted to utilize the representation power of the neural radiance field (NeRF) to reconstruct the human body from monocular videos. Recent works propose to graft a deformation network into the NeRF to f

Velleda Baldoni, Michèle Vergne

As shown by P-E Paradan, the set of orbits contained in the sum of two holomorphic orbits in the Lie algebra of U(p,q) is determined by a set of inequalities similar to the Horn inequalities for the sum of conjugacy classes of two Hermitian matrices. We give another proof of these inequalities using representations of quivers. We also discuss the implication

Mengping Yang, Ceyuan Yang, Yichi Zhang, Qingyan Bai

A good metric, which promises a reliable comparison between solutions, is essential for any well-defined task. Unlike most vision tasks that have per-sample ground-truth, image synthesis tasks target generating unseen data and hence are usually evaluated through a distributional distance between one set of real samples and another set of generated samples. T

Calder Morton-Ferguson

We explicitly identify the algebra generated by symplectic Fourier-Deligne transforms (i.e. convolution with Kazhdan-Laumon sheaves) acting on the Grothendieck group of perverse sheaves on the basic affine space $G/U$, answering a question originally raised by A. Polishchuk. We show it is isomorphic to a distinguished subalgebra, studied by I. Marin, of the

Philippa S. Cole, Andrew D. Gow, Christian T. Byrnes, Subodh P. Patil

All single-field inflationary models invoke varying degrees of tuning in order to account for cosmological observations. Mechanisms that generate primordial black holes (PBHs) from enhancement of primordial power at small scales posit inflationary potentials that transiently break scale invariance and possibly adiabaticity over a range of modes. This require

Zhuo Chen, Laker Newhouse, Eddie Chen, Di Luo

Quantum many-body physics simulation has important impacts on understanding fundamental science and has applications to quantum materials design and quantum technology. However, due to the exponentially growing size of the Hilbert space with respect to the particle number, a direct simulation is intractable. While representing quantum states with tensor netw

Dawood Kothawala

All our observations that characterise space and time are expressed in terms of non-local, bi-tensorial objects such as geodesic intervals between events and two-point (Green) functions. In this contribution, I highlight the importance of characterising spacetime geometry in terms of such non-local objects, focusing particularly on two important bi-tensors t

Brian Moser, Stanislav Frolov, Federico Raue, Sebastian Palacio

This paper presents a novel Diffusion-Wavelet (DiWa) approach for Single-Image Super-Resolution (SISR). It leverages the strengths of Denoising Diffusion Probabilistic Models (DDPMs) and Discrete Wavelet Transformation (DWT). By enabling DDPMs to operate in the DWT domain, our DDPM models effectively hallucinate high-frequency information for super-resolved

Francisco D. Mazzitelli, Leonardo G. Trombetta

We consider the effect of stochastic fluctuations of the gravitational coupling G on the evolution of binary systems. We work at an elementary level, in the Newtonian limit, and focusing mainly on laser ranging. We show that, due to cumulative effects, observational data may be used to put bounds on the stochastic fluctuations. We also reanalyse previous res

Amandeep Kumar, Ankan kumar Bhunia, Sanath Narayan, Hisham Cholakkal

In this work, we propose a few-shot colorectal tissue image generation method for addressing the scarcity of histopathological training data for rare cancer tissues. Our few-shot generation method, named XM-GAN, takes one base and a pair of reference tissue images as input and generates high-quality yet diverse images. Within our XM-GAN, a novel controllable

Karl D. Gordon, Geoffrey C. Clayton, Marjorie Decleir, E. L. Fitzpatrick

Dust extinction is one of the fundamental measurements of dust grain sizes, compositions, and shapes. Most of the wavelength dependent variations seen in Milky Way extinction are strongly correlated with the single parameter R(V)=A(V)/E(B-V). Existing R(V) dependent extinction relationships use a mixture of spectroscopic and photometry observations, hence do

Jialin Liu, Ning Miao, Chongzhou Fang, Houman Homayoun

The Electrocardiogram (ECG) measures the electrical cardiac activity generated by the heart to detect abnormal heartbeat and heart attack. However, the irregular occurrence of the abnormalities demands continuous monitoring of heartbeats. Machine learning techniques are leveraged to automate the task to reduce labor work needed during monitoring. In recent y

Priyanka Kaswan, Sennur Ulukus

We study the version age of information in a multi-hop multi-cast cache-enabled network, where updates at the source are marked with incrementing version numbers, and the inter-update times on the links are not necessarily exponentially distributed. We focus on the set of non-arithmetic distributions, which includes continuous probability distributions as a

Bharat Joshi, Hunter Damron, Sharmin Rahman, Ioannis Rekleitis

This paper addresses the robustness problem of visual-inertial state estimation for underwater operations. Underwater robots operating in a challenging environment are required to know their pose at all times. All vision-based localization schemes are prone to failure due to poor visibility conditions, color loss, and lack of features. The proposed approach

R. F. Sawyer

Investigations of dense neutrino cloud evolution through quantum kinetic equations led to the possibility of ``fast flavor" (FF) processes. It is shown here that the usual quantum kinetic equations, while signaling the instabilities that make some instances of FF possible, are being erroneously interpreted. Approaching the subject directly from the quantum f

Ziming Wang, Yujiang Liu, Yifan Duan, Xingchen Li

In this paper, we present the USTC FLICAR Dataset, which is dedicated to the development of simultaneous localization and mapping and precise 3D reconstruction of the workspace for heavy-duty autonomous aerial work robots. In recent years, numerous public datasets have played significant roles in the advancement of autonomous cars and unmanned aerial vehicle

Mudit Jain, Mustafa A. Amin, Jonathan Thomas, Wisha Wanichwecharungruang

Using wave kinetics, we estimate the emergence time-scale of gravitating Bose-Einstein condensates/Bose stars in the kinetic regime for a general multicomponent Schr\"{o}dinger-Poisson (SP) system. We identify some effects of the diffusion and friction pieces in the wave-kinetic Boltzmann equation (at leading order in perturbation theory) and provide estimat

Anne van Delft, Andrew J. Blumberg

We introduce a new framework to analyze shape descriptors that capture the geometric features of an ensemble of point clouds. At the core of our approach is the point of view that the data arises as sampled recordings from a metric space-valued stochastic process, possibly of nonstationary nature, thereby integrating geometric data analysis into the realm of

Selma Franca, Adolfo G. Grushin

Topological insulators are described by topological invariants that can be computed by integrals over momentum space, but also as traces over local, real-space topological markers. These markers are useful to detect topological insulating phases in disordered crystals, quasicrystals and amorphous systems. Among these markers, only the spectral localizer oper

Jinhyuk Lee, Zhuyun Dai, Sai Meher Karthik Duddu, Tao Lei

Multi-vector retrieval models such as ColBERT [Khattab and Zaharia, 2020] allow token-level interactions between queries and documents, and hence achieve state of the art on many information retrieval benchmarks. However, their non-linear scoring function cannot be scaled to millions of documents, necessitating a three-stage process for inference: retrieving

LHCb collaboration, R. Aaij, A. S. W. Abdelmotteleb, C. Abellan Beteta

The very rare $D^{*}(2007)^0\to\mu^+\mu^-$ decay is searched for by analysing $B^-\to\pi^-\mu^+\mu^-$ decays. The analysis uses a sample of beauty mesons produced in proton-proton collisions collected with the LHCb detector between 2011 and 2018, corresponding to an integrated luminosity of 9 fb$^{-1}$. The signal signature corresponds to simultaneous peaks

Christoph Braun, Raphaël Saint-Jalm, Alexander Hesse, Johannes Arceri

Conventional topological insulators exhibit exotic gapless edge or surface states, as a result of non-trivial bulk topological properties. In periodically-driven systems the bulk-boundary correspondence is fundamentally modified and knowledge about conventional bulk topological invariants is insufficient. While ultracold atoms provide excellent settings for

J. Nolan Faught, Mark Kempton, Adam Knudson

For a graph $G$ on $n$ vertices with normalized Laplacian eigenvalues $0 = \lambda_1(G) \leq \lambda_2(G) \leq \cdots \leq \lambda_n(G)$ and graph complement $G^c$, we prove that \begin{equation*} \max\{\lambda_2(G),\lambda_2(G^c)\}\geq \frac{2}{n^2}. \end{equation*} We do this by way of lower bounding $\max\{i(G), i(G^c)\}$ and $\max\{h(G), h(G^c)\}$ where

Ryan Boldi, Alexander Lalejini, Thomas Helmuth, Lee Spector

We present an analysis of the loss of population-level test coverage induced by different down-sampling strategies when combined with lexicase selection. We study recorded populations from the first generation of genetic programming runs, as well as entirely synthetic populations. Our findings verify the hypothesis that informed down-sampling better maintain

Gonzalo Arias, Swann Marx, Guilherme Mazanti

This paper deals with the exponential stability of systems made of a hyperbolic PDE coupled with an ODE with different time scales, the dynamics of the PDE being much faster than that of the ODE. Such a difference of time scales is modeled though a small parameter $\varepsilon$ multiplying the time derivative in the PDE, and our stability analysis relies on

Niklas Dahlquist, Björn Lindqvist, Akshit Saradagi, George Nikolakopoulos

This article presents an architecture for multi-agent task allocation and task execution, through the unification of a market-inspired task-auctioning system with Behavior Trees for managing and executing lower level behaviors. We consider the scenario with multi-stage tasks, such as 'pick and place', whose arrival times are not known a priori. In such a sce

Steve Sibomanaa, Kelly Joelle Gatore Sinigirira, Paterne Gahungu, David Niyukuri

Tuberculosis (TB) is among the main public health challenges in Burundi. The literature lacks mathematical models for key parameter estimates of TB transmission dynamics in Burundi. In this paper, the supectible-exposed-infected-recovered (SEIR) model is used to investigate the transmission dynamics of tuberculosis in Burundi. Using the next generation metho

Jian Zhu, Ilya Valmianski, Anitha Kannan

AI-driven medical history-taking is an important component in symptom checking, automated patient intake, triage, and other AI virtual care applications. As history-taking is extremely varied, machine learning models require a significant amount of data to train. To overcome this challenge, existing systems are developed using indirect data or expert knowled

Piotr Teterwak, Kuniaki Saito, Theodoros Tsiligkaridis, Kate Saenko

Domain Generalization (DG) aims to develop classifiers that can generalize to new, unseen data distributions, a critical capability when collecting new domain-specific data is impractical. A common DG baseline minimizes the empirical risk on the source domains. Recent studies have shown that this approach, known as Empirical Risk Minimization (ERM), can outp