November 2022 arXiv papers — page 76

Zhihao Duan, Fengqing Zhu

Optimizing computation in an edge-cloud system is an important yet challenging problem. In this paper, we consider a three-way trade-off between bit rate, classification accuracy, and encoding complexity in an edge-cloud image classification system. Our method includes a new training strategy and an efficient encoder architecture to improve the rate-accuracy

Ema Becirovic, Emil Björnson, Erik G. Larsson

Many real-world scenarios for massive machine-type communication involve sensors monitoring a physical phenomenon. As a consequence, the activity pattern of these sensors will be correlated. In this letter, we study how the correlation of user activities can be exploited to improve detection performance in grant-free random access systems where the users tra

Fatemeh Mahmoudi, Xuewen Lu

Semi-competing risks data arise when both non-terminal and terminal events are considered in a model. Such data with multiple events of interest are frequently encountered in medical research and clinical trials. In this framework, terminal event can censor the non-terminal event but not vice versa. It is known that variable selection is practical in identif

Veronica Piccialli, Dolores Romero Morales, Cecilia Salvatore

Counterfactual Explanations are becoming a de-facto standard in post-hoc interpretable machine learning. For a given classifier and an instance classified in an undesired class, its counterfactual explanation corresponds to small perturbations of that instance that allows changing the classification outcome. This work aims to leverage Counterfactual Explanat

Jan Steiner, Kire Micev, Asude Aydin, Jörg Rieckermann

Hydrometers that can measure size and velocity distributions of precipitation are needed for research and corrections of rainfall estimates from weather radars and microwave links. Existing video disdrometers measure drop size distributions, but underestimate small raindrops and are impractical for widespread always-on IoT deployment. We propose an innovativ

Ting-Yao Hsu, Yoshi Suhara, Xiaolan Wang

Community-based Question Answering (CQA), which allows users to acquire their desired information, has increasingly become an essential component of online services in various domains such as E-commerce, travel, and dining. However, an overwhelming number of CQA pairs makes it difficult for users without particular intent to find useful information spread ov

Anja Schmiedt, Christian Weiß

In any dimension $d \geq 2$, there is no known example of a low-discrepancy sequence which possess Poisssonian pair correlations. This is in some sense rather surprising, because low-discrepancy sequences always have $\beta$-Poissonian pair correlations for all $0 < \beta < \tfrac{1}{d}$ and are therefore arbitrarily close to having Poissonian pair correlati

Jane R. Rigby, Paul A. Lightsey, Macarena García Marín, Charles W. Bowers

We describe the sources of stray light and thermal background that affect JWST observations, report actual backgrounds as measured from commissioning and early-science observations, compare these background levels to prelaunch predictions, estimate the impact of the backgrounds on science performance, and explore how the backgrounds probe the achieved config

Adrián Andrada, María Laura Barberis

We give a characterization of almost abelian Lie groups carrying left invariant hypercomplex structures and we show that the corresponding Obata connection is always flat. We determine when such Lie groups admit HKT metrics and study the corresponding Bismut connection. We obtain the classification of hypercomplex almost abelian Lie groups in dimension 8 and

Son-Tung Tran, Joshua V. Stough, Xiaoyan Zhang, Christopher M. Haggerty

Bayesian Optimization (BO) is a well-studied hyperparameter tuning technique that is more efficient than grid search for high-cost, high-parameter machine learning problems. Echocardiography is a ubiquitous modality for evaluating heart structure and function in cardiology. In this work, we use BO to optimize the architectural and training-related hyperparam

Leevi Kerkelä, Kiran Seunarine, Filip Szczepankiewicz, Chris A. Clark

Diffusion magnetic resonance imaging is sensitive to the microstructural properties of brain tissue. However, estimating clinically and scientifically relevant microstructural properties from the measured signals remains a highly challenging inverse problem that machine learning may help solve. This study investigated if recently developed rotationally invar

Haroldo C. D. Lima Junior, Carolina L. Benone, Luís C. B. Crispino

We study the scattering of monochromatic planar scalar waves in a geometry that interpolates between the Schwarzschild solution, regular black holes and traversable wormhole spacetimes. We employ the partial waves approach to compute the differential scattering cross section of the regular black hole, as well as of the wormhole solutions. We compare our full

Davood Dar, Saswata Roy, Neepa T. Maitra

The adiabatic approximation in time-dependent density functional theory (TDDFT) is known to give an incorrect pole structure in the quadratic response function, leading to unphysical divergences in excited state-to-state transition probabilities and hyperpolarizabilties. We find the form of the exact quadratic response kernel and derive a practical and accur

Paul Bouteyre, Dung Xuan Nguyen, Loïc Malgrey, Zhiyi Yuan

Bound states in the continuum (BICs) in photonic slabs and metasurfaces appear as polarization singularities in momentum space, characterized by an integer winding number. This winding is widely treated as a robust topological label, preserved under smooth deformations of the structure. Here we show that this robustness fails under band inversion. Using a ge

José Ángel Castellanos-Reyes, Jesús Castrejón-Figueroa, Alejandro Reyes-Coronado

Electron beams in scanning transmission electron microscopy (STEM) exert forces and torques on study samples, with magnitudes that allow the controlled manipulation of nanoparticles (a technique called electron tweezers). Related theoretical research has mostly focused on the study of forces and linear momentum transfers from swift electrons (like those used

K. Ieki, T. Iwamoto, S. Kobayashi, Toshinori Mori

In the MEG II experiment, the liquid xenon gamma-ray detector uses Multi-Pixel Photon Counters (MPPC) which are sensitive to vacuum ultraviolet (VUV) light under a high-intensity muon beam environment. In the commissioning phase of the detector with the beam, a significant degradation in the photon detection efficiency (PDE) for VUV light was found, while th

Michael Strickland

In this proceedings contribution, I review recent work that aims to provide a more comprehensive and systematic understanding of bottomonium dynamics in the quark-gluon plasma using an open quantum system (OQS) approach that is applied in the framework of the potential non-relativistic QCD (pNRQCD) effective field theory and coupled to realistic hydrodynamic

N. Alexia Raharinirina, Konstantin Fackeldey, Marcus Weber

We consider two disjoint sets of points. If at least one of the sets can be embedded into an Euclidean space, then we provide sufficient conditions for the two sets to be jointly embedded in one Euclidean space. In this joint Euclidean embedding, the distances between the points are generated by a specific relation-preserving function. Consequently, the mutu

J. Zou, S. D. Hogan

Electric-field-controlled F\"orster resonance energy transfer (FRET) between Rydberg helium (He) atoms and ground-state ammonia (NH$_3$) molecules has been studied at translational temperatures below 100 mK. The experiments were performed in an intrabeam collision apparatus with pulsed supersonic beams of NH$_3$ seeded in He. A range of F\"orster resonances,

Aukosh Jagannath, Patrick Lopatto

We study the free energy of a mean-field spin glass whose coupling distribution has power law tails. Under the assumption that the couplings have infinite variance and finite mean, we show that the thermodynamic limit of the quenched free energy exists, and that the free energy is self-averaging.

Joël Tang, Marina Fomicheva, Lucia Specia

Neural conditional language generation models achieve the state-of-the-art in Neural Machine Translation (NMT) but are highly dependent on the quality of parallel training dataset. When trained on low-quality datasets, these models are prone to various error types, including hallucinations, i.e. outputs that are fluent, but unrelated to the source sentences.

Sophie Marques, Leandro Boonzaaier

With this paper, we gain a better understanding of the set of near-field structures on a fixed scalar group. If we were able to describe all near-field structures on a fixed scalar group, we could describe all near-vector spaces. The near-field structures induced by isomorphisms of canonical near-vector spaces differ by quasi-multiplicative bijections while

Phillipe Gille, Robert Guralnick

We consider semi-continuity of certain dimensions on group schemes.

David Rügamer, Florian Pfisterer, Bernd Bischl, Bettina Grün

In this work, we propose an efficient implementation of mixtures of experts distributional regression models which exploits robust estimation by using stochastic first-order optimization techniques with adaptive learning rate schedulers. We take advantage of the flexibility and scalability of neural network software and implement the proposed framework in mi

Ethan Cotterill, Vinícius Lima, Renato Vidal Martins, Alexandre Reis

In a previous paper, the first three authors formulated a precise conjecture about the dimension of the {\it generalized Severi variety} $M^n_{d,g; {\rm S}, {\bf k}}$ of degree-$d$ holomorphic maps $\mathbb{P}^1 \rightarrow \mathbb{P}^n$ whose images' singularities are singleton cusps with value semigroups ${\rm S}$ and ramification profiles ${\bf k}$. In th

Coralia Cartis, Jaroslav Fowkes, Zhen Shao

We propose a general random subspace framework for unconstrained nonconvex optimization problems that requires a weak probabilistic assumption on the subspace gradient, which we show to be satisfied by various random matrix ensembles, such as Gaussian and sparse sketching, using Johnson-Lindenstrauss embedding properties. We show that, when safeguarded with

Alessandra F. Lütz, Marco Antonio Amaral, Ian Braga, Lucas Wardil

The Stag-hunt game is a prototype for social contracts. Adopting a new and better social contract is usually challenging because the current one is already widely adopted and stable due to deviants' sanctions. Thus, how does a population shift from the current social contract to a better one? In other words, how can a social system leave a local social optim

Michael E. Crenshaw

The form of the energy-momentum tensor when a quasimonochromatic field propagates into and through an antireflection-coated, sourceless, transparent, continuous, linear magneto-dielectric medium, initially at rest in the local frame, remains controversial. The Minkowski energy-momentum tensor is the main component of the electromagnetic conservation law. It

Cynthia Stoner

For graphs $G$ and $H$, what relations can be determined between $t(G,W)$ and $t(H,W)$ for a general graph $W$? We study this problem through the framework of the density domination exponent, which is defined to be the smallest constant $c$ such that $t(G,W)\ge t(H,W)^c$ for every graph $W$. This broad generalization encompasses the Sidorenko conjecture, the

Titas Anciukevičius, Zexiang Xu, Matthew Fisher, Paul Henderson

Diffusion models currently achieve state-of-the-art performance for both conditional and unconditional image generation. However, so far, image diffusion models do not support tasks required for 3D understanding, such as view-consistent 3D generation or single-view object reconstruction. In this paper, we present RenderDiffusion, the first diffusion model fo

Sinem Güler, Bülent Ünal

In this paper, we mainly study gradient $\rho$-Einstein solitons on doubly warped product manifolds. More explicitly, we obtain necessary and sufficient conditions for a doubly warped product manifold to be a gradient $\rho$-Einstein soliton. We also apply our main result to warped product spacetime models such as generalized Robertson-Walker and standard st

Albert Zhu, Simon Batzner, Albert Musaelian, Boris Kozinsky

Deep learning has emerged as a promising paradigm to give access to highly accurate predictions of molecular and materials properties. A common short-coming shared by current approaches, however, is that neural networks only give point estimates of their predictions and do not come with predictive uncertainties associated with these estimates. Existing uncer

Thomas J. Misa

This article combines humanistic "data critique" with informed inspection of big data analysis. It measures gender bias when gender prediction software tools (Gender API, Namsor, and Genderize.io) are used in historical big data research. Gender bias is measured by contrasting personally identified computer science authors in the well-regarded DBLP dataset (

Kyle Deeds, Dan Suciu, Magda Balazinska

Recent work has reemphasized the importance of cardinality estimates for query optimization. While new techniques have continuously improved in accuracy over time, they still generally allow for under-estimates which often lead optimizers to make overly optimistic decisions. This can be very costly for expensive queries. An alternative approach to estimation

Nabarun Chakrabarty, Indrani Chakraborty

A Two-Higgs doublet model (2HDM) can predict the observed muon $g-2$ for an appropriately light pseudoscalar that now faces tight constraints. However, It was shown in past that augmenting the 2HDM by an additional inert doublet can lead to an explanation to the muon $g-2$ anomaly for a much heavier pseudoscalar. In this study, we probe such a framework at t

Anastasiya Belyaeva, Joel Shor, Daniel E. Cook, Kishwar Shafin

Accurate genome sequencing can improve our understanding of biology and the genetic basis of disease. The standard approach for generating DNA sequences from PacBio instruments relies on HMM-based models. Here, we introduce Distilled DeepConsensus - a distilled transformer-encoder model for sequence correction, which improves upon the HMM-based methods with

Trung X. Pham, Axi Niu, Zhang Kang, Sultan Rizky Madjid

Self-supervised learning (SSL) approaches have shown promising capabilities in learning the representation from unlabeled data. Amongst them, momentum-based frameworks have attracted significant attention. Despite being a great success, these momentum-based SSL frameworks suffer from a large gap in representation between the online encoder (student) and the

Aviraj Sinha, Elena R. Henderson, Jessie M. Henderson, Mitchell A. Thornton

Emerging quantum algorithms that process data require that classical input data be represented as a quantum state. These data-processing algorithms often follow the gate model of quantum computing--which requires qubits to be initialized to a basis state, typically $\lvert 0 \rangle$--and thus often employ state generation circuits to transform the initializ

Sahil Singla, Atoosa Malemir Chegini, Mazda Moayeri, Soheil Feiz

Deep neural networks can be unreliable in the real world when the training set does not adequately cover all the settings where they are deployed. Focusing on image classification, we consider the setting where we have an error distribution $\mathcal{E}$ representing a deployment scenario where the model fails. We have access to a small set of samples $\math

Xiaoshan Zhou, Pin-Chao Liao

A transfer learning paradigm is proposed for "knowledge" transfer between the human brain and convolutional neural network (CNN) for a construction hazard categorization task. Participants' brain activities are recorded using electroencephalogram (EEG) measurements when viewing the same images (target dataset) as the CNN. The CNN is pretrained on the EEG dat

Jianwei Zhang, Julie Liss, Suren Jayasuriya, Visar Berisha

Approximately 1.2% of the world's population has impaired voice production. As a result, automatic dysphonic voice detection has attracted considerable academic and clinical interest. However, existing methods for automated voice assessment often fail to generalize outside the training conditions or to other related applications. In this paper, we propose a

Brian Freidin

We study the degrees of homogeneous harmonic maps between simplicial cones. Such maps have been used to model the local behavior of harmonic maps between singular spaces, where the degrees of homogeneous approximations describe the regularity of harmonic maps. In particular the degrees of homogeneous harmonic maps are related to eigenvalues of discrete norma

Adrianna Janik, Maria Torrente, Luca Costabello, Virginia Calvo

Background: Stratifying cancer patients according to risk of relapse can personalize their care. In this work, we provide an answer to the following research question: How to utilize machine learning to estimate probability of relapse in early-stage non-small-cell lung cancer patients? Methods: For predicting relapse in 1,387 early-stage (I-II), non-small-ce

Yining Lu, Jingxi Qiu, Gaurav Gupta

Subjective answer evaluation is a time-consuming and tedious task, and the quality of the evaluation is heavily influenced by a variety of subjective personal characteristics. Instead, machine evaluation can effectively assist educators in saving time while also ensuring that evaluations are fair and realistic. However, most existing methods using regular ma

Zihao Liang, Jason King Ching Lo

Control barrier function (CBF) has recently started to serve as a basis to develop approaches for enforcing safety requirements in control systems. However, constructing such function for a general system is a non-trivial task. This paper proposes an iterative, optimization-based framework to obtain a CBF from a given user-specified set for a general control

Matheus A. Tunes, Hi T. Vo, Jon K. S. Baldwin, Tarik A. Saleh

Two new refractory amorphous high-entropy alloys (RAHEAs) within the W--Ta--Cr--V and W--Ta--Cr--V--Hf systems were herein synthesized using magnetron-sputtering and tested under high-temperature annealing and displacing irradiation using \textit{in situ} Transmission Electron Microscopy. While the WTaCrV RAHEA was found to be unstable under such tests, addi

Jane S. Greaves, Janusz J. Petkowski, Anita M. S. Richards, Clara Sousa-Silva

Searches for phosphine in Venus' atmosphere have sparked a debate. Cordiner et al. 2022 analyse spectra from the Stratospheric Observatory For Infrared Astronomy (SOFIA) and infer <0.8 ppb of PH3. We noticed that some spectral artefacts arose from non-essential calibration-load signals. By-passing these signals allows simpler post-processing and a 5.7{\sigma

Itai Linial, Re'em Sari

We discuss the formation and evolution of systems comprised of a low-mass ($M_\star \lesssim 4 \, \rm M_\odot$) main sequence star, orbiting a $10^5-10^7 \, \rm M_\odot$ supermassive black hole with an orbital period of order $\sim$hours, and a mild eccentricity ($e\approx0.1-0.2$), episodically shedding mass at each pericenter passage. We argue that the res

Lorenzo Catani, Matthew Leifer, Giovanni Scala, David Schmid

Interference phenomena are often claimed to resist classical explanation. However, such claims are undermined by the fact that the specific aspects of the phenomenology upon which they are based can in fact be reproduced in a noncontextual ontological model [Catani et al., Quantum 7, 1119 (2023)]. This raises the question of what other aspects of the phenome

F. A. Driessen, N. D. Kee

The winds of hot, massive stars are variable from processes happening on both large and small spatial scales. A particular case of such wind variability is 'discrete-absorption components' (DACs) that manifest themselves as outward moving density features in UV resonance line spectra. Such DACs are believed to be caused by large-scale spiral-shaped density s

Mario Renteria-Pinon, Xiaochen Tang, Jaime Ramirez-Angulo, Wei Tang

This paper presents a fully integrated second-order level-crossing sampling data converter for real-time data compression and feature extraction. Compared with level-sampling ADCs which sample at fixed voltage levels, the proposed circuits updates tracking thresholds using linear extrapolation, which forms a second-order level-crossing sampling ADC that has

H. L. Shashirekha, F. Balouchzahi, M. D. Anusha, G. Sidorov

The task of automatically identifying a language used in a given text is called Language Identification (LI). India is a multilingual country and many Indians especially youths are comfortable with Hindi and English, in addition to their local languages. Hence, they often use more than one language to post their comments on social media. Texts containing mor

Guodong Jiang, Yafis Barlas

A band-projection formalism is developed for calculating the superfluid weight in two-dimensional multi-orbital superconductors with an orbital-dependent pairing. It is discovered that, in this case, the band geometric superfluid stiffness tensor can be locally non-positive-definite in some regions of the Brillouin zone. When these regions are large enough o

Xiaoming Wang, Yanfa Yan

Within the framework of independent particle approximation, the optical activity tensor of solids is formulated as from different contributions: the magnetic dipole, electric quadrupole, and band dispersion terms. The first two terms have similar counterparts in the theory of finite systems, while the last term is unique for crystals. The magnetic dipole and

Kamran Keykhosravi, Musa Furkan Keskin, Gonzalo Seco-Granados, Petar Popovski

Reconfigurable intelligent surface (RIS) is a promising technological enabler for the 6th generation (6G) of wireless systems with applications in localization and communication. In this paper, we consider the problem of positioning a single-antenna user in 3D space based on the received signal from a single-antenna base station and reflected signal from an

A. S. Lobão

In this work, we investigate braneworld models in mimetic gravity, with the source field having generalized dynamics. We present the mathematical description necessary to study the generalized model and consider the first-order formalism to solve the equations of motion. We also investigate the linear stability of the gravitational sector to verify that the

Víctor Sotomayor

We report on recent progress concerning the relationship that exists between the algebraic structure of a finite group and certain features of its class-size prime graph.

Adolfo O. Fumega, D. Wong, C. Schulz, F. Rodríguez

We investigate the electronic structure of Cs$_2$CuCl$_4$, a material discussed in the framework of a frustrated quantum antiferromagnet, by means of resonant inelastic x-ray scattering (RIXS) and Density Functional Theory (DFT). From the non-dispersive highly localized dd excitations, we resolve the crystal field splitting of the Cu$^{2+}$ ions in a strongl

Kyle Franson, Brendan P. Bowler, Mariangela Bonavita, Timothy D. Brandt

Benchmark brown dwarf companions with well-determined ages and model-independent masses are powerful tools to test substellar evolutionary models and probe the formation of giant planets and brown dwarfs. Here, we report the independent discovery of HIP~21152~B, the first imaged brown dwarf companion in the Hyades, and conduct a comprehensive orbital and atm

E. Vanzella, A. Claeyssens, B. Welch, A. Adamo

Star cluster formation in the early universe and their contribution to reionization remains to date largely unconstrained. Here we present JWST/NIRCam imaging of the most highly magnified galaxy known at z ~ 6, the Sunrise arc. We identify six young massive star clusters (YMCs) with measured radii spanning ~ 20 pc down to ~ 1 pc (corrected for lensing magnif

C. J. Baker, W. Bertsche, A. Capra, C. L. Cesar

The ALPHA Collaboration, based at the CERN Antiproton Decelerator, has recently implemented a novel beamline for low-energy ($\lesssim$ 100 eV) positron and antiproton transport between cylindrical Penning traps that have strong axial magnetic fields. Here, we describe how a combination of semianalytical and numerical calculations were used to optimise the l

Giuliano Chiriacò, Andrew J. Millis

We investigate the possibility of a striped inhomoegenous phase occurring as an electronic system with an order parameter linearly coupled to the elastic degrees of freedom is tuned through the electronic phase transition. We find that in finite systems where boundary conditions create an eleastic incompatibility, a stripe pattern may emerge in the vicinity

Mirko Arienzo, Markus Heinrich, Ingo Roth, Martin Kliesch

Properties of quantum systems can be estimated using classical shadows, which implement measurements based on random ensembles of unitaries. Originally derived for global Clifford unitaries and products of single-qubit Clifford gates, practical implementations are limited to the latter scheme for moderate numbers of qubits. Beyond local gates, the accurate i

James Munday, T. R. Marsh, Mark Hollands, Ingrid Pelisoli

The shortest-period binary star system known to date, RX J0806.3+1527 (HM Cancri), has now been observed in the optical for more than two decades. Although it is thought to be a double degenerate binary undergoing mass transfer, an early surprise was that its orbital frequency, $f_0$, is currently increasing as the result of gravitational wave radiation. Thi

Belle Collaboration, F. Meier, A. Vossen, I. Adachi

We report measurements of the ratios of branching fractions for $B \to \bar{D}^{(*)}\pi\ell^+\nu_\ell$ and $B \to \bar{D}^{(*)}\pi^+\pi^-\ell^+\nu_\ell$ relative to $B \to \bar{D}^*\ell^+\nu_\ell$ decays with $\ell = e, \mu$. These results are obtained from a data sample that contains $772 \times 10^6 B\bar{B}$ pairs collected near the $\Upsilon(4S)$ resonan

Bo Chang, Alexandros Karatzoglou, Yuyan Wang, Can Xu

Sequential recommender models are essential components of modern industrial recommender systems. These models learn to predict the next items a user is likely to interact with based on his/her interaction history on the platform. Most sequential recommenders however lack a higher-level understanding of user intents, which often drive user behaviors online. I

Carlo Nipoti

Fragmentation of rotating gaseous systems via gravitational instability is believed to be a crucial mechanism in several astrophysical processes, such as formation of planets in protostellar discs, of molecular clouds in galactic discs, and of stars in molecular clouds. Gravitational instability is fairly well understood for infinitesimally thin discs. Howev

Shrishmoy Ray, Sasha Hinkley, Steph Sallum, Mariangela Bonavita

JWST promises to be the most versatile infrared observatory for the next two decades. The Near Infrared and Slitless Spectrograph (NIRISS) instrument, when used in the Aperture Masking Interferometry (AMI) mode, will provide an unparalleled combination of angular resolution and sensitivity compared to any existing observatory at mid-infrared wavelengths. Usi

Alan Goodman, Katia Camacho Mata, Sophia A Henneberg, Rogerio Jorge

We present a novel method for numerically finding quasi-isodynamic stellarator magnetic fields with excellent fast-particle confinement and extremely small neoclassical transport. The method works particularly well in configurations with only one field period. We examine the properties of these newfound quasi-isodynamic configurations, including their bootst

J. A. Fernández-Ontiveros, X. López-López, A. Prieto

The disappearance of the accretion disc in low-luminosity active galactic nuclei (LLAGN) leaves behind a faint optical nuclear continuum whose nature has been largely debated, mainly due to serious observational limitations in the IR to UV range. We combine multi-wavelength sub-arcsecond resolution observations -- able to isolate the genuine nuclear continuu

Ralph Kraft, Maxim Markevitch, Caroline Kilbourne, Joseph S. Adams

The Line Emission Mapper (LEM) is an X-ray Probe for the 2030s that will answer the outstanding questions of the Universe's structure formation. It will also provide transformative new observing capabilities for every area of astrophysics, and to heliophysics and planetary physics as well. LEM's main goal is a comprehensive look at the physics of galaxy form

Thomas Bouley, Philip Sørensen, Tien-Tien Yu

Ultralight dark matter is a compelling dark matter candidate. In this work, we examine the impact of quadratically-coupled ultralight dark matter on the predictions of Big Bang Nucleosynthesis. The presence of ultralight dark matter can modify the effective values of fundamental constants during Big Bang Nucleosynthesis, modifying the predicted abundances of

Talar Yontan

We performed detailed photometric and astrometric analyses of the open star clusters Berkeley 68 and Stock 20. This was based on ground-based CCD UBV photometric data complemented by space-based Gaia Data Release 3 photometry and astrometry. 198 stars were identified as likely cluster members for Berkeley 68 and 51 for Stock 20. Two-color diagrams were used

Mohammad Akhond, Andrea Legramandi, Carlos Nunez, Leonardo Santilli

We study five-dimensional ${\cal N}=1$ Superconformal Field Theories of the linear quiver type. These are deformed by a relevant operator, corresponding to a homogeneous mass term for certain matter fields. The free energy is calculated at arbitrary values of the mass parameter. After a careful regularisation procedure, the result can be put in correspondenc

Muldrow Etheredge, Ben Heidenreich

We derive formulas for the leading mass, entropy, and long-range self-force corrections to extremal black holes due to higher-derivative operators. These formulas hold for black holes with arbitrary couplings to gauge fields and moduli, provided that the leading-order solutions are static, spherically-symmetric, extremal, and have nonzero horizon area. To us

Andrew W. Boyle, Luke G. Bouma

On the pre-main-sequence, the rotation rates of Sun-like stars are dictated by the interplay between the protostellar disk and the star's contraction. At ages exceeding 100 million years (Myr), magnetic spin-down erases the initial stellar spin rate and enables rotation-based age dating (gyrochronology). The exact time at which the transition between these t

Michael Hughes, Axel U. J. Lode, Dieter Jaksch, Paolo Molignini

With rapid progress in control and manipulation of ultracold magnetic atoms and dipolar molecules, the quantum simulation of lattice models with strongly interacting dipole-dipole interactions (DDI) and high densities is now within experimental reach. This rapid development raises the issue about the validity of quantum simulation in such regimes. In this st

Aurélien Dersy, Andrei Khmelnitsky, Riccardo Rattazzi

We consider the field theory that defines a perfect incompressible 2D fluid. One distinctive property of this system is that the quadratic action for fluctuations around the ground state features neither mass nor gradient term. Quantum mechanically this poses a technical puzzle, as it implies the Hilbert space of fluctuations is not a Fock space and perturba

Siddharth Gururani, Arun Mallya, Ting-Chun Wang, Rafael Valle

Animating portraits using speech has received growing attention in recent years, with various creative and practical use cases. An ideal generated video should have good lip sync with the audio, natural facial expressions and head motions, and high frame quality. In this work, we present SPACE, which uses speech and a single image to generate high-resolution

Hao Li, Jinguo Zhu, Xiaohu Jiang, Xizhou Zhu

Despite the remarkable success of foundation models, their task-specific fine-tuning paradigm makes them inconsistent with the goal of general perception modeling. The key to eliminating this inconsistency is to use generalist models for general task modeling. However, existing attempts at generalist models are inadequate in both versatility and performance.

Weijie Su, Xizhou Zhu, Chenxin Tao, Lewei Lu

To effectively exploit the potential of large-scale models, various pre-training strategies supported by massive data from different sources are proposed, including supervised pre-training, weakly-supervised pre-training, and self-supervised pre-training. It has been proved that combining multiple pre-training strategies and data from various modalities/sour

Fabien H Wagner, Ricardo Dalagnol, Celso HL Silva-Junior, Griffin Carter

Monitoring changes in tree cover for rapid assessment of deforestation is considered the critical component of any climate mitigation policy for reducing carbon. Here, we map tropical tree cover and deforestation between 2015 and 2022 using 5 m spatial resolution Planet NICFI satellite images over the state of Mato Grosso (MT) in Brazil and a U-net deep lear

Ben Bartlett, Olivia Y. Long, Avik Dutt, Shanhui Fan

Synthetic dimensions have generated great interest for studying many types of topological, quantum, and many-body physics, and they offer a flexible platform for simulation of interesting physical systems, especially in high dimensions. In this Letter, we describe a programmable photonic device capable of emulating the dynamics of a broad class of Hamiltonia

Sanjib Kumar Das, Sourav Manna, Bitan Roy

Topological classification of quantum solids often (if not always) groups all trivial atomic or normal insulators (NIs) into the same featureless family. As we argue here, this is not necessarily the case always. In particular, when the global phase diagram of electronic crystals harbors topological insulators with the band inversion at various time-reversal

Ethan Lake, Shankar Balasubramanian, Soonwon Choi

We explore the relationship between renormalization group (RG) flow and error correction by constructing quantum algorithms that exactly recognize 1D symmetry-protected topological (SPT) phases protected by finite internal Abelian symmetries. For each SPT phase, our algorithm runs a quantum circuit which emulates RG flow: an arbitrary input ground state wave

Shibo Xu, Zheng-Zhi Sun, Ke Wang, Liang Xiang

Non-Abelian anyons are exotic quasiparticle excitations hosted by certain topological phases of matter. They break the fermion-boson dichotomy and obey non-Abelian braiding statistics: their interchanges yield unitary operations, rather than merely a phase factor, in a space spanned by topologically degenerate wavefunctions. They are the building blocks of t

Per Berglund, Giorgi Butbaia, Tristan Hübsch, Vishnu Jejjala

Finding Ricci-flat (Calabi-Yau) metrics is a long standing problem in geometry with deep implications for string theory and phenomenology. A new attack on this problem uses neural networks to engineer approximations to the Calabi-Yau metric within a given K\"ahler class. In this paper we investigate numerical Ricci-flat metrics over smooth and singular K3 su

Tim Brooks, Aleksander Holynski, Alexei A. Efros

We propose a method for editing images from human instructions: given an input image and a written instruction that tells the model what to do, our model follows these instructions to edit the image. To obtain training data for this problem, we combine the knowledge of two large pretrained models -- a language model (GPT-3) and a text-to-image model (Stable

Xinyu Zhang, Jiahui Chen, Junkun Yuan, Qiang Chen

Masked image modeling (MIM) learns visual representation by masking and reconstructing image patches. Applying the reconstruction supervision on the CLIP representation has been proven effective for MIM. However, it is still under-explored how CLIP supervision in MIM influences performance. To investigate strategies for refining the CLIP-targeted MIM, we stu

Ali G. Moghaddam, Kevin Geishendorf, Richard Schlitz, Jorge I. Facio

Time-reversal symmetry breaking allows for a rich set of magneto-transport properties related to electronic topology. Focusing on the magnetic Weyl semimetal Co$_3$Sn$_2$S$_2$, we prepared micro-ribbons and investigated their transverse and longitudinal transport properties from 100 K to 180 K in magnetic fields $\mu_0 H$ up to 2T. We establish the presence

Saikat Nandy, Scott H. Holan, Jonathan R. Bradley, Christopher K. Wikle

We introduce Bayesian hierarchical models for predicting high-dimensional tabular survey data which can be distributed from one or multiple classes of distributions (e.g., Gaussian, Poisson, Binomial, etc.). We adopt a Bayesian implementation of a Hierarchical Generalized Transformation (HGT) model to deal with the non-conjugacy of non-Gaussian data models w

Sascha Caron, Christopher Eckner, Luc Hendriks, Guðlaugur Jóhannesson

The Galactic Center Excess (GCE) in GeV gamma rays has been debated for over a decade, with the possibility that it might be due to dark matter annihilation or undetected point sources such as millisecond pulsars (MSPs). This study investigates how the gamma-ray emission model ($\gamma$EM) used in Galactic center analyses affects the interpretation of the GC

Eliahu Horwitz, Yedid Hoshen

Diffusion models have become the go-to method for many generative tasks, particularly for image-to-image generation tasks such as super-resolution and inpainting. Current diffusion-based methods do not provide statistical guarantees regarding the generated results, often preventing their use in high-stakes situations. To bridge this gap, we construct a confi

Ron Mokady, Amir Hertz, Kfir Aberman, Yael Pritch

Recent text-guided diffusion models provide powerful image generation capabilities. Currently, a massive effort is given to enable the modification of these images using text only as means to offer intuitive and versatile editing. To edit a real image using these state-of-the-art tools, one must first invert the image with a meaningful text prompt into the p

Michele Pernice

In this work, we introduce the moduli stack $\widetilde{\mathcal{M}}_{g,n}^r$ of $n$-pointed, $A_r$-stable curves of genus $g$ and use it to compute the Chow ring of $\overline{\mathcal{M}}_3$. As a byproduct, we also compute the Chow ring of $\widetilde{\mathcal{M}}_3^7$. All the Chow rings are assumed to be with coefficients in $\mathbb{Z}[1/6]$.

Alexander Soloviev

In these proceedings, I will discuss collisions of poles in the complex plane as a signature of phase transitions for theories relevant to the quark gluon plasma. I will begin with an illustrative example, namely the chiral phase transition, which can be characterized by colliding poles as a function of temperature. Then, recognizing the interplay between we

Yuang Zhang, Tiancai Wang, Xiangyu Zhang

In this paper, we propose MOTRv2, a simple yet effective pipeline to bootstrap end-to-end multi-object tracking with a pretrained object detector. Existing end-to-end methods, MOTR and TrackFormer are inferior to their tracking-by-detection counterparts mainly due to their poor detection performance. We aim to improve MOTR by elegantly incorporating an extra

James Seale Smith, Paola Cascante-Bonilla, Assaf Arbelle, Donghyun Kim

Recently, large-scale pre-trained Vision-and-Language (VL) foundation models have demonstrated remarkable capabilities in many zero-shot downstream tasks, achieving competitive results for recognizing objects defined by as little as short text prompts. However, it has also been shown that VL models are still brittle in Structured VL Concept (SVLC) reasoning,

Hasan Bahcivan, David J. Brady, Gordon C. Hageman

We consider sampling and detection strategies for solar illuminated space debris. We argue that the lowest detectable debris cross section may be reduced by 10-100x by analysis of stacks of image frames collected at high rates rather than single frame data. In particular, instead of a pixel as a spatial region, the analysis is based on a "phase-space-pixel"

Shoufa Chen, Peize Sun, Yibing Song, Ping Luo

We propose DiffusionDet, a new framework that formulates object detection as a denoising diffusion process from noisy boxes to object boxes. During the training stage, object boxes diffuse from ground-truth boxes to random distribution, and the model learns to reverse this noising process. In inference, the model refines a set of randomly generated boxes to