May 2023 arXiv papers — page 50

Chi Chen, Peng Li, Maosong Sun, Yang Liu

Weakly supervised vision-and-language pre-training (WVLP), which learns cross-modal representations with limited cross-modal supervision, has been shown to effectively reduce the data cost of pre-training while maintaining decent performance on downstream tasks. However, current WVLP methods use only local descriptions of images, i.e., object tags, as cross-

Abhishek Verma, Shahid Rauf, Kallol Bera, D. Sydorenko

Low-pressure multi-frequency capacitively coupled plasmas are used for numerous etch and deposition applications in the semiconductor industry. Pulsing of the radio-frequency (RF) sources enables control of neutral and charged species in the plasma on a millisecond timescale. The synchronous (i.e., simultaneous, in-phase) pulsing of both power sources in a d

C. E. Harris, Sumit K. Sarbadhicary, L. Chomiuk, Anthony L. Piro

Type Ia supernovae (SNe Ia) are important cosmological tools, probes of binary star evolution, and contributors to cosmic metal enrichment; yet, a definitive understanding of the binary star systems that produce them remains elusive. In this work we present early-time (first observation within 10 days post-explosion) radio observations of six nearby (within

Twesh Upadhyaya, William F. Braasch,, Gabriel T. Landi, Nicole Yunger Halpern

We extend entropy production to a deeply quantum regime involving noncommuting conserved quantities. Consider a unitary transporting conserved quantities ("charges") between two systems initialized in thermal states. Three common formulae model the entropy produced. They respectively cast entropy as an extensive thermodynamic variable, as an information-theo

Filippo Ferrari, Luca Gravina, Debbie Eeltink, Pasquale Scarlino

We define quantum chaos and integrability in open quantum many-body systems as a dynamical property of single stochastic realizations, referred to as quantum trajectories. This definition relies on the predictions of random matrix theory applied to the subset of the Liouvillian spectrum involved in each quantum trajectory. Our approach, which we name spectra

Jean-Baptiste Salomon, Noam Libeskind, Yehuda Hoffman

From high resolution cosmological simulations of the Local Group in realistic environment, namely HESTIA simulations, we study the position and kinematic deviations that may arise between the disc of a Milky Way (or Andromeda)-like galaxy and its halo. We focus on the 3-dimensional analysis of the centres of mass (COM). The study presents two parts. We first

Russell Deitrick, Colin Goldblatt

Molecular oxygen in our atmosphere has increased from less than a part per million in the Archean Eon, to a fraction of a percent in the Proterozoic, and finally to modern levels during the Phanerozoic. The ozone layer formed with the early Proterozoic oxygenation. While oxygen itself has only minor radiative and climatic effects, the accompanying ozone has

Rahul Poddar

We explore $T \overline T$ deformations of Warped Conformal Field Theories (WCFTs) in two dimensions as examples of $T\overline T$ deformed non-relativistic quantum field theories. WCFTs are quantum field theories with a Virasoro$\times$U(1) Kac-Moody symmetry. We compute the deformed symmetry algebra of a $T\overline T$ deformed holographic WCFT, using the

Ryotaro Suzuki, Jonas Haferkamp, Jens Eisert, Philippe Faist

Recently, the dynamics of quantum systems that involve both unitary evolution and quantum measurements have attracted attention due to the exotic phenomenon of measurement-induced phase transitions. The latter refers to a sudden change in a property of a state of $n$ qubits, such as its entanglement entropy, depending on the rate at which individual qubits a

Botond C. Nagy, Márton Kormos, Gábor Takács

The sine-Gordon model is a paradigmatic quantum field theory that provides the low-energy effective description of many gapped 1D systems. Despite this fact, its complete thermodynamic description in all its regimes has been lacking. Here we fill this gap and derive the framework that captures its thermodynamics and serves as the basis of its hydrodynamic de

Arpan Bhattacharyya, Saptaswa Ghosh, Sounak Pal

We investigate the correction to the potential that gives rise to the bound orbits and radiation from non-spinning inspiralling binary black holes in a dark matter environment consisting of axion-like particles and dark photons using the techniques of Worldline Effective Field Theory. We compute the conservative dynamics up to $1$PN order for gravitational,

Andrew C. Yuan, Yaar Vituri, Erez Berg, Boris Spivak

The lowest order Josephson coupling, $J_1(\theta)\cos(\phi)$, between two d-wave superconductors with phase-difference $\phi$ across the junction vanishes when their relative orientation is rotated by $\theta=\pi/4$. However, in the presence of inhomogeneity, $J_{1}(\mathbf{r})$ is non-zero locally, with a sign that fluctuates in space. We show that such a r

Yi-Ting Tu, DinhDuy Vu, Sankar Das Sarma

A generalization of the Aubry-Andr\'e model, the non-interacting GPD model introduced in S. Ganeshan et al.,[ Phys. Rev. Lett. 114, 146601 (2015)], is known analytically to possess a mobility edge, allowing both extended and localized eigenstates to coexist. This mobility edge has been hypothesized to survive in closed many-body interacting systems, giving r

Tim Meier, Stijn J. van Tongeren

We construct actions for four dimensional noncommutative Yang-Mills theory with star-gauge symmetry, with non-constant noncommutativity, to all orders in the noncommutativity. Our construction covers all noncommutative spaces corresponding to Drinfel'd twists based on the Poincar\'e algebra, including nonabelian ones, whose $r$ matrices are unimodular. This

Haoyu Hu, Yi Jiang, Dumitru Călugăru, Xiaolong Feng

Kagome Materials with flat bands exhibit wildly different physical properties depending on symmetry group, and electron number. For the case of ScV$_6$Sn$_6$ in space group 191, we investigate the existence of a charge density wave (CDW) at vector $\bar{K}=(\frac{1}{3},\frac{1}{3},\frac{1}{3})$ and its relationship with the phonon behavior. The experimental

Nemanja Kaloper

We point out that time's arrow is naturally induced by quantum mechanical evolution, whenever the systems have a very large number ${\cal N}$ of non-degenerate states and a Hamiltonian bounded from below. When ${\cal N}$ is finite, the arrow is imperfect, since evolution can resurrect past states. In the limit ${\cal N} \rightarrow \infty$ the arrow is fixed

Carlos A. R. Herdeiro, Eugen Radu

General Relativity minimally coupled to a massive, free, complex scalar field, is shown to allow asymptotically flat solutions, non-singular on and outside the event horizon, describing two spinning black holes (2sBHs) in equilibrium, with co-axial, aligned angular momenta. The 2sBHs configurations bifurcate from solutions describing dipolar spinning boson s

Timothée Schaeffer, Sambit K. Giri, Aurel Schneider

In this study, we introduce BEoRN (Bubbles during the Epoch of Reionisation Numerical Simulator), a publicly available Python code that generates three-dimensional maps of the 21-cm signal from the cosmic dawn and the epoch of reionisation. Built upon N-body simulation outputs, BEoRN populates haloes with stars and galaxies based on a flexible source model.

Belén Gavela, Pablo Quílez, Maria Ramos

We demonstrate that the true QCD axion that solves the strong CP problem can be found in all generality outside the customary standard QCD band, with QCD being the sole source of Peccei-Quinn breaking. The essential reason is that the basis of axion-gluon interactions does not need to coincide with the mass basis. Specifically, we consider the case in which

Rhine Samajdar, Ewan McCulloch, Vedika Khemani, Romain Vasseur

We present a scalable protocol for measuring full counting statistics (FCS) in experiments or tensor-network simulations. In this method, an ancilla in the middle of the system acts as a turnstile, with its phase keeping track of the time-integrated particle flux. Unlike quantum gas microscopy, the turnstile protocol faithfully captures FCS starting from num

Sarang Gopalakrishnan, Romain Vasseur

The Heisenberg spin chain is a canonical integrable model. As such, it features stable ballistically propagating quasiparticles, but spin transport is sub-ballistic at any nonzero temperature: an initially localized spin fluctuation spreads in time $t$ to a width $t^{2/3}$. This exponent, as well as the functional form of the dynamical spin correlation funct

Selim C. Hotinli, Elena Pierpaoli, Simone Ferraro, Kendrick Smith

An observer that is moving towards a high-density region sees, on average, a higher matter density and more foreground-emitting sources ahead than behind themself. Consequently, the average abundance and luminosity of objects producing cosmological signals around an in-falling dark matter halo is larger in the direction of the halo's motion. In this Letter,

Rebecca von Kuk, Johannes K. L. Michel, Zhiquan Sun

We initiate the study of transverse momentum-dependent (TMD) fragmentation functions for heavy quarks, demonstrate their factorization in terms of novel nonperturbative matrix elements in heavy-quark effective theory (HQET), and prove new TMD sum rules that arise from heavy-quark spin symmetry. We discuss the phenomenology of heavy-quark TMD FFs at $B$ facto

Giovanni Maria Tomaselli, Thomas F. M. Spieksma, Gianfranco Bertone

Due to superradiant instabilities, clouds of ultralight bosons can spontaneously grow around rotating black holes, creating so-called "gravitational atoms". In this work, we study their dynamical effects on binary systems. We first focus on open orbits, showing that the presence of a cloud can increase the cross section for the dynamical capture of a compact

Charles L. Steinhardt, Albert Sneppen, Thorbjørn Clausen, Harley Katz

Recent studies have reported tension between the presence of luminous, high-redshift galaxies and the halo mass functions predicted by standard cosmology. Here, an improved test is proposed using the presence of high-redshift Balmer breaks to probe the formation of early $10^4 - 10^5 M_\odot$ baryonic minihalos. Unlike previous tests, this does not depend up

Akos Bogdan, Andy Goulding, Priyamvada Natarajan, Orsolya Kovacs

Observations of quasars reveal that many supermassive black holes (BHs) were in place less than 700 million years after the Big Bang. However, the origin of the first BHs remains a mystery. Seeds of the first BHs are postulated to be either light (i.e., $10-100~\rm{M_{\odot}})$, remnants of the first stars or heavy (i.e., $10^4-10^5~\rm{M_{\odot}})$, origina

Marco Fedele, Monika Blanke, Andreas Crivellin, Syuhei Iguro

Form factors are crucial theory input in order to extract $|V_{cb}|$ from $B \to D^{(*)}\ell\nu$ decays, to calculate the Standard Model prediction for ${\cal R}(D^{(*)})$ and to assess the impact of New Physics. In this context, the Dispersive Matrix approach, a first-principle calculation of the form factors, using no experimental data but rather only latt

Guhao Feng, Bohang Zhang, Yuntian Gu, Haotian Ye

Recent studies have discovered that Chain-of-Thought prompting (CoT) can dramatically improve the performance of Large Language Models (LLMs), particularly when dealing with complex tasks involving mathematics or reasoning. Despite the enormous empirical success, the underlying mechanisms behind CoT and how it unlocks the potential of LLMs remain elusive. In

Brandon Smith, Miguel Farinha, Siobhan Mackenzie Hall, Hannah Rose Kirk

Vision-language models are growing in popularity and public visibility to generate, edit, and caption images at scale; but their outputs can perpetuate and amplify societal biases learned during pre-training on uncurated image-text pairs from the internet. Although debiasing methods have been proposed, we argue that these measurements of model bias lack vali

Antón Baleato Lizancos, Martin White

A leading way to constrain physical theories from cosmological observations is to test their predictions for the angular clustering statistics of matter tracers, a technique that is set to become ever more central with the next generation of large imaging surveys. Interpretation of this clustering requires knowledge of the projection kernel, or the redshift

Anuj Diwan, Anirudh Srinivasan, David Harwath, Eunsol Choi

Existing speech-to-speech translation (S2ST) models fall into two camps: they either leverage text as an intermediate step or require hundreds of hours of parallel speech data. Both approaches are incompatible with textless languages or language pairs with limited parallel data. We present PFB, a framework for training textless S2ST models that require just

Johan Edstedt, Qiyu Sun, Georg Bökman, Mårten Wadenbäck

Feature matching is an important computer vision task that involves estimating correspondences between two images of a 3D scene, and dense methods estimate all such correspondences. The aim is to learn a robust model, i.e., a model able to match under challenging real-world changes. In this work, we propose such a model, leveraging frozen pretrained features

Rongjie Huang, Huadai Liu, Xize Cheng, Yi Ren

Direct speech-to-speech translation (S2ST) aims to convert speech from one language into another, and has demonstrated significant progress to date. Despite the recent success, current S2ST models still suffer from distinct degradation in noisy environments and fail to translate visual speech (i.e., the movement of lips and teeth). In this work, we present A

Rundi Wu, Ruoshi Liu, Carl Vondrick, Changxi Zheng

Synthesizing novel 3D models that resemble the input example has long been pursued by graphics artists and machine learning researchers. In this paper, we present Sin3DM, a diffusion model that learns the internal patch distribution from a single 3D textured shape and generates high-quality variations with fine geometry and texture details. Training a diffus

Lorenzo Leone, Salvatore F. E. Oliviero, Alioscia Hamma

In this paper, we present a learning algorithm aimed at learning states obtained from computational basis states by Clifford circuits doped with a finite number $t$ of $T$-gates. The algorithm learns an exact tomographic description of $t$-doped stabilizer states in terms of Pauli observables. This is possible because such states are countable and form a dis

Jian-Wei Qiu, Zhite Yu

The $x$ dependence of hadrons' generalized parton distributions (GPDs) $\mathcal{F}(x,\xi,t)$ is the most difficult to extract from the existing known processes, while the $\xi$ and $t$ dependence are uniquely determined by the kinematics of the scattered hadron. We study the single diffractive hard exclusive processes for extracting GPDs in the photoproduct

Hsinlin Tan

Vehicle technology has developed rapidly these years, however, the security measures for in-vehicle network does not keep up with the trend. Controller area network(CAN) is the most used protocol in the in-vehicle network. With the characteristic of CAN, there exists many vulnerabilities including lacks of integrity and confidentiality, and hence CAN is vuln

Linwei Sang, Yinliang Xu, Huan Long, Wenchuan Wu

Prediction plays a vital role in the active distribution network voltage regulation under the high penetration of photovoltaics. Current prediction models aim at minimizing individual prediction errors but overlook their collective impacts on downstream decision-making. Hence, this paper proposes a safety-aware semi-end-to-end coordinated decision model to b

Daniël Vos, Jelle Vos, Tianyu Li, Zekeriya Erkin

Decision trees are interpretable models that are well-suited to non-linear learning problems. Much work has been done on extending decision tree learning algorithms with differential privacy, a system that guarantees the privacy of samples within the training data. However, current state-of-the-art algorithms for this purpose sacrifice much utility for a sma

Weixi Feng, Wanrong Zhu, Tsu-jui Fu, Varun Jampani

Attaining a high degree of user controllability in visual generation often requires intricate, fine-grained inputs like layouts. However, such inputs impose a substantial burden on users when compared to simple text inputs. To address the issue, we study how Large Language Models (LLMs) can serve as visual planners by generating layouts from text conditions,

Patrick Pietzonka, Francesco Coghi

We analytically derive universal bounds that describe the trade-off between thermodynamic cost and precision in a sequence of events related to some internal changes of an otherwise hidden physical system. The precision is quantified by the fluctuations in either the number of events counted over time or the times between successive events. Our results are v

Yuval Alaluf, Elad Richardson, Gal Metzer, Daniel Cohen-Or

A key aspect of text-to-image personalization methods is the manner in which the target concept is represented within the generative process. This choice greatly affects the visual fidelity, downstream editability, and disk space needed to store the learned concept. In this paper, we explore a new text-conditioning space that is dependent on both the denoisi

Benjamin T. Afflerbach, Carter Francis, Lane E. Schultz, Janine Spethson

We use a random forest model to predict the critical cooling rate (RC) for glass formation of various alloys from features of their constituent elements. The random forest model was trained on a database that integrates multiple sources of direct and indirect RC data for metallic glasses to expand the directly measured RC database of less than 100 values to

Gili Lior, Gabriel Stanovsky

Spurious correlations were found to be an important factor explaining model performance in various NLP tasks (e.g., gender or racial artifacts), often considered to be ''shortcuts'' to the actual task. However, humans tend to similarly make quick (and sometimes wrong) predictions based on societal and cognitive presuppositions. In this work we address the qu

Marziyeh Soltani, Mahtab Mirmohseni, Rahim Tafazolli

This paper aims to analyze the stochastic performance of a multiple input multiple output (MIMO) integrated sensing and communication (ISAC) system in a downlink scenario, where a base station (BS) transmits a dual-functional radar-communication (DFRC) signal matrix, serving the purpose of transmitting communication data to the user while simultaneously sens

Avi Caciularu, Matthew E. Peters, Jacob Goldberger, Ido Dagan

The integration of multi-document pre-training objectives into language models has resulted in remarkable improvements in multi-document downstream tasks. In this work, we propose extending this idea by pre-training a generic multi-document model from a novel cross-document question answering pre-training objective. To that end, given a set (or cluster) of t

Kaushal Santosh Bhogale, Sai Sundaresan, Abhigyan Raman, Tahir Javed

Improving ASR systems is necessary to make new LLM-based use-cases accessible to people across the globe. In this paper, we focus on Indian languages, and make the case that diverse benchmarks are required to evaluate and improve ASR systems for Indian languages. To address this, we collate Vistaar as a set of 59 benchmarks across various language and domain

Michaela Ennis

The great behavioral heterogeneity observed between individuals with the same psychiatric disorder and even within one individual over time complicates both clinical practice and biomedical research. However, modern technologies are an exciting opportunity to improve behavioral characterization. Existing psychiatry methods that are qualitative or unscalable,

Tatyana Yakhno, Vladimir Yakhno

The presence of microparticles (clusters of micron size) of unknown origin in the volume of water, including highly purified water (bidistilled, deionized), has been repeatedly demonstrated by various methods of physical analysis. Various assumptions have been made about the nature of these microparticles, but none of them has become generally accepted. The

Khaled Eldowa, Emmanuel Esposito, Tommaso Cesari, Nicolò Cesa-Bianchi

In this work, we improve on the upper and lower bounds for the regret of online learning with strongly observable undirected feedback graphs. The best known upper bound for this problem is $\mathcal{O}\bigl(\sqrt{\alpha T\ln K}\bigr)$, where $K$ is the number of actions, $\alpha$ is the independence number of the graph, and $T$ is the time horizon. The $\sqr

Colin Rothgang, Florian Rabe, Christoph Benzmüller

Higher-order logic HOL offers a very simple syntax and semantics for representing and reasoning about typed data structures. But its type system lacks advanced features where types may depend on terms. Dependent type theory offers such a rich type system, but has rather substantial conceptual differences to HOL, as well as comparatively poor proof automation

Khalid Alnajjar, Mika Hämäläinen, Jack Rueter

In this paper, we present an approach for translating word embeddings from a majority language into 4 minority languages: Erzya, Moksha, Udmurt and Komi-Zyrian. Furthermore, we align these word embeddings and present a novel neural network model that is trained on English data to conduct sentiment analysis and then applied on endangered language data through

Yuto Hosaka, Ramin Golestanian, Andrej Vilfan

The Lorentz reciprocal theorem -- that is used to study various transport phenomena in hydrodynamics -- is violated in chiral active fluids that feature odd viscosity with broken time-reversal and parity symmetries. Here we show that the theorem can be generalized to fluids with odd viscosity by choosing an auxiliary problem with the opposite sign of the odd

William Giarè, Supriya Pan, Eleonora Di Valentino, Weiqiang Yang

The cosmic microwave background (CMB) temperature and polarization anisotropies, as observed by independent astronomical missions such as WMAP, Planck, and most recently the Atacama Cosmology Telescope and the South Pole Telescope have played a vital role in accurately constraining cosmological theories and models, establishing cosmic inflation as the most w

Yan Liu, Xiaokang Chen, Yan Gao, Zhe Su

With the popularity of automatic code generation tools, such as Copilot, the study of the potential hazards of these tools is gaining importance. In this work, we explore the social bias problem in pre-trained code generation models. We propose a new paradigm to construct code prompts and successfully uncover social biases in code generation models. To quant

Gabriel Guo, Judah Goldfeder, Aniv Ray, Tony Dear

Collision detection is essential to virtually all robotics applications. However, traditional geometric collision detection methods generally require pre-existing workspace geometry representations; thus, they are unable to infer the collision detection function from sampled data when geometric information is unavailable. Learning-based approaches can overco

Carl C. Cowen, William Johnston, Rebecca G. Wahl

Given an n by n matrix A over the complex numbers and an invariant subspace L, this paper gives a straightforward formula to construct an n by n matrix N that commutes with A and has L equal to the kernel of N. For Q a matrix putting A into Jordan canonical form J = RAQ with R the inverse of Q, we get N = RM$ where the kernel of M is an invariant subspace fo

Trung Hoang Le, Huiping Cao, Tran Cao Son

A plethora of approaches have been proposed for joint entity-relation (ER) extraction. Most of these methods largely depend on a large amount of manually annotated training data. However, manual data annotation is time consuming, labor intensive, and error prone. Human beings learn using both data (through induction) and knowledge (through deduction). Answer

Tamoghna Barik, Jay D. Sau

Recent scanning tunneling spectroscopy along crystalline domain-walls associated with a half unit cell shift have revealed sub-gap density of states that are expected to arise from helical Majorana modes. Such propagating Majorana modes have been proposed to exist on the surface state of topological materials similar to FeTe$_{\text{1-x}}$Se$_\text{x}$ (FTS)

A. Emin Orhan, Brenden M. Lake

Young children develop sophisticated internal models of the world based on their visual experience. Can such models be learned from a child's visual experience without strong inductive biases? To investigate this, we train state-of-the-art neural networks on a realistic proxy of a child's visual experience without any explicit supervision or domain-specific

Samar Hadou, Navid NaderiAlizadeh, Alejandro Ribeiro

Algorithm unrolling has emerged as a learning-based optimization paradigm that unfolds truncated iterative algorithms in trainable neural-network optimizers. We introduce Stochastic UnRolled Federated learning (SURF), a method that expands algorithm unrolling to federated learning in order to expedite its convergence. Our proposed method tackles two challeng

Emanuele Costa, Rosario Fazio, Sebastiano Pilati

Density functional theory (DFT) is routinely employed in material science and in quantum chemistry to simulate weakly correlated electronic systems. Recently, deep learning (DL) techniques have been adopted to develop promising functionals for the strongly correlated regime. DFT can be applied to quantum spin models too, but functionals based on DL have not

Ruth Britto

We propose a construction of generalized cuts of Feynman integrals as an operation on the domain of the Feynman parametric integral. A set of on-shell conditions removes the corresponding boundary components of the integration domain, in favor of including a boundary component from the second Symanzik polynomial. Hence integration domains are full-dimensiona

Abdel-Rahman Tawil, Muhidin Mohamed, Xavier Schmoor, Konstantinos Vlachos

The adoption of data science brings vast benefits to Small and Medium-sized Enterprises (SMEs) including business productivity, economic growth, innovation and jobs creation. Data Science can support SMEs to optimise production processes, anticipate customers' needs, predict machinery failures and deliver efficient smart services. Businesses can also harness

Meirbek Mussatayev, Qiuji Yi, Mark Fitzgerald, Vincent K. Maes

Real-time monitoring of carbon fibre composites during Automated Fibre Placement (AFP) manufacturing remains a challenge for non-destructive evaluation (NDE) techniques. An directional eddy-current (EC) probe with asymmetric transmit and differential receive (Tx-dRx) coils is designed, constructed and characterized to evaluate the detectability of out-of-pla

Yunxiang Li, Meixu Chen, Kai Wang, Jun Ma

Image translation has wide applications, such as style transfer and modality conversion, usually aiming to generate images having both high degrees of realism and faithfulness. These problems remain difficult, especially when it is important to preserve content structures. Traditional image-level similarity metrics are of limited use, since the content struc

Janina Nicolini

Weak decays of heavy-quark baryons offer an attractive laboratory to search for effects beyond the Standard Model (SM), complementary to searches in meson decays. However, the properties of these baryons are not well understood. In particular, the $\Omega^-_b$ baryon is the least studied weakly decaying $b$ baryon. Therefore, the most precise determination o

Mahla Abdolahnejad, Justin Lee, Hannah Chan, Alex Morzycki

Burn injuries can result from mechanisms such as thermal, chemical, and electrical insults. A prompt and accurate assessment of burns is essential for deciding definitive clinical treatments. Currently, the primary approach for burn assessments, via visual and tactile observations, is approximately 60%-80% accurate. The gold standard is biopsy and a close se

Hanchao Liu, Dena Firoozi, Michèle Breton

We develop a variational approach to address risk-sensitive optimal control problems with an exponential-of-integral cost functional in a general linear-quadratic-Gaussian (LQG) single-agent setup, offering new insights into such problems. Our analysis leads to the derivation of a nonlinear necessary and sufficient condition of optimality, expressed in terms

Joey Hejna, Dorsa Sadigh

Reward functions are difficult to design and often hard to align with human intent. Preference-based Reinforcement Learning (RL) algorithms address these problems by learning reward functions from human feedback. However, the majority of preference-based RL methods na\"ively combine supervised reward models with off-the-shelf RL algorithms. Contemporary appr

Juan-Juan Niu, Jing-Bo Li, Huan-Yu Bi, Hong-Hao Ma

In the framework of nonrelativistic QCD, the excited doubly heavy baryons are thoroughly studied via the channel $e^{+} e^{-}\rightarrow \langle QQ^{\prime}\rangle[n] \rightarrow \Xi_{QQ^{\prime}} +\bar{Q^{\prime}} +\bar{Q}$, which takes place at the collision energy $Z$-pole. $Q^{(\prime)}$ represents $b$ or $c$ quark for the production of $\Xi_{cc}$, $\Xi_

Geremias Polanco

In this paper we express the difference of two complementary Beatty sequences, as the sum of two Beatty sequences closely related to them. In the process we introduce a new Algorithm that generalizes the well known Minimum Excluded algorithm and provides a method to generate combinatorially any pair of complementary Beatty sequences.

Vladimir Lifschitz

This paper describes a generalization of Clark's completion that is applicable to logic programs containing arithmetic operations and produces syntactically simple, natural looking formulas. If a set of first-order axioms is equivalent to the completion of a program then we may be able to find standard models of these axioms by running an answer set solver.

Shadi Rahimian, Raouf Kerkouche, Ina Kurth, Mario Fritz

Kaplan-Meier estimators are essential tools in survival analysis, capturing the survival behavior of a cohort. Their accuracy improves with large, diverse datasets, encouraging data holders to collaborate for more precise estimations. However, these datasets often contain sensitive individual information, necessitating stringent data protection measures that

Luca Di Liello, Siddhant Garg, Alessandro Moschitti

Answer Sentence Selection (AS2) is a core component for building an accurate Question Answering pipeline. AS2 models rank a set of candidate sentences based on how likely they answer a given question. The state of the art in AS2 exploits pre-trained transformers by transferring them on large annotated datasets, while using local contextual information around

Yiyang Ma, Huan Yang, Wenhan Yang, Jianlong Fu

Diffusion models, as a kind of powerful generative model, have given impressive results on image super-resolution (SR) tasks. However, due to the randomness introduced in the reverse process of diffusion models, the performances of diffusion-based SR models are fluctuating at every time of sampling, especially for samplers with few resampled steps. This inhe

Bartosz Bieganowski, Tomasz Cieślak, Jakub Siemianowski

The present note is devoted to the studies of the relation of the time-zero limits of Kaden's spirals and the 2D Euler equation. It is shown that the time-zero limits of Kaden's spirals satisfy inhomogeneous 2D Euler in a weak sense. As a corollary, the necessity of both, the decay of spherical averages around the origin of the spiral as well as the velocity

Krystal A. Jackson

Developments in artificial intelligence (AI) are likely to affect social engineering and change cyber defense operations. The broad and sweeping nature of AI impact means that many aspects of social engineering could be automated, potentially giving adversaries an advantage. In this review, we assess the ways phishing and spear-phishing might be affected by

Donatella Romano, Francesco R. Ferraro, Livia Origlia, Simon Portegies Zwart

Terzan 5 is a heavily obscured stellar system located in the inner Galaxy. It has been postulated to be a stellar relic, a Bulge Fossil Fragment witnessing the complex history of the assembly of the Milky Way bulge. In this paper, we follow the chemical enrichment of a set of putative progenitors of Terzan 5 to assess whether the chemical properties of this

Feifei Shao, Yawei Luo, Lei Chen, Ping Liu

Contemporary weakly-supervised object localization (WSOL) methods have primarily focused on addressing the challenge of localizing the most discriminative region while largely overlooking the relatively less explored issue of biased activation -- incorrectly spotlighting co-occurring background with the foreground feature. In this paper, we conduct a thoroug

Hannes Kath, Bengt Lüers, Thiago S. Gouvêa, Daniel Sonntag

Deep learning is ubiquitous, but its lack of transparency limits its impact on several potential application areas. We demonstrate a virtual reality tool for automating the process of assigning data inputs to different categories. A dataset is represented as a cloud of points in virtual space. The user explores the cloud through movement and uses hand gestur

Y. Jennifer Sun, Stephen Newman, Elad Hazan

Linear Quadratic Regulator (LQR) and Linear Quadratic Gaussian (LQG) control are foundational and extensively researched problems in optimal control. We investigate LQR and LQG problems with semi-adversarial perturbations and time-varying adversarial bandit loss functions. The best-known sublinear regret algorithm of \cite{gradu2020non} has a $T^{\frac{3}{4}

Yulle G. F. Borges, Vinícius L. de Lima, Flávio K. Miyazawa, Lehilton L. C. Pedrosa

This paper presents theoretical and practical results for the bin packing problem with scenarios, a generalization of the classical bin packing problem which considers the presence of uncertain scenarios, of which only one is realized. For this problem, we propose an absolute approximation algorithm whose ratio is bounded by the square root of the number of

Jose Miguel Ladino, Carlos A. Benavides-Gallego, Eduard Larrañaga, Javlon Rayimbaev

In the present work, we aimed to investigate the dynamics of spinning charged and magnetized test particles around both electrically and magnetically charged quantum-improved black holes. We derive the equations of motion for charged spinning test particles using the Mathisson-Papapetrou-Dixon equations with the Lorentz coupling term. The radius of innermost

Kyurae Kim, Jisu Oh, Kaiwen Wu, Yi-An Ma

We provide the first convergence guarantee for full black-box variational inference (BBVI), also known as Monte Carlo variational inference. While preliminary investigations worked on simplified versions of BBVI (e.g., bounded domain, bounded support, only optimizing for the scale, and such), our setup does not need any such algorithmic modifications. Our re

John Nguyen, Sid Wang, Ke Li, Carole-Jean Wu

Fine-tuning large-scale Transformers has led to the explosion of many AI applications across Natural Language Processing and Computer Vision tasks. However, fine-tuning all pre-trained model parameters becomes impractical as the model size and number of tasks increase. Parameter-efficient transfer learning (PETL) methods aim to address these challenges. Whil

Junyi Zhang, Charles Herrmann, Junhwa Hur, Luisa Polania Cabrera

Text-to-image diffusion models have made significant advances in generating and editing high-quality images. As a result, numerous approaches have explored the ability of diffusion model features to understand and process single images for downstream tasks, e.g., classification, semantic segmentation, and stylization. However, significantly less is known abo

Jorryt Matthee, Christopher Golling, Ruari Mackenzie, Gabriele Pezzulli

Observationally mapping the relation between galaxies and the intergalactic medium (IGM) is of key interest for studies of cosmic reionization. Diffuse hydrogen gas has typically been observed in HI Lyman-$\alpha$ (Ly$\alpha$) absorption in the spectra of bright background quasars. However, it is important to extend these measurements to background galaxies

Domenico Di Sante, Chiara Bigi, Philipp Eck, Stefan Enzner

Kagome materials have emerged as a setting for emergent electronic phenomena that encompass different aspects of symmetry and topology. It is debated whether the XV$_6$Sn$_6$ kagome family (where X is a rare earth element), a recently discovered family of bilayer kagome metals, hosts a topologically non-trivial ground state resulting from the opening of spin

Matteo Gabburo, Siddhant Garg, Rik Koncel-Kedziorski, Alessandro Moschitti

Recent studies show that sentence-level extractive QA, i.e., based on Answer Sentence Selection (AS2), is outperformed by Generation-based QA (GenQA) models, which generate answers using the top-k answer sentences ranked by AS2 models (a la retrieval-augmented generation style). In this paper, we propose a novel training paradigm for GenQA using supervision

Chiranjit Dutta, Nalini Ravishanker, Sumanta Basu

In this paper we propose univariate volatility models for irregularly spaced financial time series by modifying the regularly spaced stochastic volatility models. We also extend this approach to propose multivariate stochastic volatility (MSV) models for multiple irregularly spaced time series by modifying the MSV model that was used with daily data. We use

Mélina Verger, Sébastien Lallé, François Bouchet, Vanda Luengo

Predictive student models are increasingly used in learning environments due to their ability to enhance educational outcomes and support stakeholders in making informed decisions. However, predictive models can be biased and produce unfair outcomes, leading to potential discrimination against some students and possible harmful long-term implications. This h

Pablo A. Cano, Alexander Deich, Nicolás Yunes

Near-future, space-based, radio- and gravitational-wave interferometry missions will enable us to rigorously test whether the Kerr solution of general relativity accurately describes astrophysical black holes, or if it requires some kind of modification. At the same time, recent work has greatly improved our understanding of theories of gravity that modify t

Arnau Quera-Bofarull, Ayush Chopra, Anisoara Calinescu, Michael Wooldridge

Agent-based modelling (ABMing) is a powerful and intuitive approach to modelling complex systems; however, the intractability of ABMs' likelihood functions and the non-differentiability of the mathematical operations comprising these models present a challenge to their use in the real world. These difficulties have in turn generated research on approximate B

K. K. Abdurasulov, Sh. A. Ayupov, B. B. Yusupov

This paper is devoted to the study of local and 2-local derivations of nullfiliform, filiform and naturally graded quasi-filiform associative algebras. We prove that these algebras as a rule admit local derivations which are not derivations. We show that filiform and naturally graded quasi-filiform associative algebras admit 2-local derivations which are not

Shufan Wang, Sebastien Jean, Sailik Sengupta, James Gung

In executable task-oriented semantic parsing, the system aims to translate users' utterances in natural language to machine-interpretable programs (API calls) that can be executed according to pre-defined API specifications. With the popularity of Large Language Models (LLMs), in-context learning offers a strong baseline for such scenarios, especially in dat

Hannes Kath, Thiago S. Gouvêa, Daniel Sonntag

The impact of machine learning (ML) in many fields of application is constrained by lack of annotated data. Among existing tools for ML-assisted data annotation, one little explored tool type relies on an analogy between the coordinates of a graphical user interface and the latent space of a neural network for interaction through direct manipulation. In the

P. V. Sai Charan, Hrushikesh Chunduri, P. Mohan Anand, Sandeep K Shukla

This research article critically examines the potential risks and implications arising from the malicious utilization of large language models(LLM), focusing specifically on ChatGPT and Google's Bard. Although these large language models have numerous beneficial applications, the misuse of this technology by cybercriminals for creating offensive payloads and

J. D. Wagenveld, H-R. Klöckner, D. J. Schwarz

The cosmic radio dipole is an anisotropy in the number counts of radio sources, analogous to the dipole seen in the cosmic microwave background (CMB). Measurements of source counts of large radio surveys have shown that though the radio dipole is generally consistent in direction with the CMB dipole, the amplitudes are in tension. These observations present

Shishir G. Patil, Tianjun Zhang, Xin Wang, Joseph E. Gonzalez

Large Language Models (LLMs) have seen an impressive wave of advances recently, with models now excelling in a variety of tasks, such as mathematical reasoning and program synthesis. However, their potential to effectively use tools via API calls remains unfulfilled. This is a challenging task even for today's state-of-the-art LLMs such as GPT-4, largely due