May 2023 arXiv papers — page 42

Churna Bhandari, Cüneyt Şahin, Durga Paudyal, Michael E. Flatté

We present a first-principles study of defect formation and electronic structure of erbium (Er)-doped yttria (Y$_2$O$_3$). This is an emerging material for spin-photon interfaces in quantum information science due to the narrow linewidth optical emission from Er dopants at standard telecommunication wavelengths and their potential for quantum memories and tr

Matthias Thamm, Bernd Rosenow

Majorana zero modes in superconductor-nanowire hybrid structures are a promising candidate for topologically protected qubits with the potential to be used in scalable structures. Currently, disorder in such Majorana wires is a major challenge, as it can destroy the topological phase and thus reduce the yield in the fabrication of Majorana devices. We study

Marcel Menner, Karl Berntorp

This paper considers a stochastic control framework, in which the residual model uncertainty of the dynamical system is learned using a Gaussian Process (GP). In the proposed formulation, the residual model uncertainty consists of a nonlinear function and state-dependent noise. The proposed formulation uses a posterior-GP to approximate the residual model un

Yanay Florshaim, Elad Zohar, David Zeev Koplovich, Ilan Meltzer

Spatial adiabatic passage (SAP) is a process that facilitates the transfer of a wave packet between two localized modes that are not directly coupled, but rather interact through an intermediate third mode. By employing a counter-intuitive adiabatic pulse sequence, this technique achieves minimal population in the intermediate state and high transfer efficie

Michael Plainer, Felix Dietrich, Ioannis G. Kevrekidis

Even the best scientific equipment can only partially observe reality. Recorded data is often lower-dimensional, e.g., two-dimensional pictures of the three-dimensional world. Combining data from multiple experiments then results in a marginal density. This work shows how to transport such lower-dimensional marginal densities into a more informative, higher-

Kahan Dare, Jannek J. Hansen, Iurie Coroli, Aisling Johnson

Light-matter interaction in the ultrastrong coupling regime can be used to generate exotic ground states with two-mode squeezing and may be of use for quantum enhanced sensing. Current demonstrations of ultrastrong coupling have been performed in fundamentally nonlinear systems. We report a cavity optomechanical system that operates in the linear coupling re

Yuxin Zhang, Weiming Dong, Fan Tang, Nisha Huang

Personalizing generative models offers a way to guide image generation with user-provided references. Current personalization methods can invert an object or concept into the textual conditioning space and compose new natural sentences for text-to-image diffusion models. However, representing and editing specific visual attributes such as material, style, an

Igor Klep, Tea Štrekelj, Aljaž Zalar

An $n\times n$ symmetric matrix $A$ is copositive if the quadratic form $x^TAx$ is nonnegative on the nonnegative orthant. The cone of copositive matrices strictly contains the cone of completely positive matrices, i.e., all matrices of the form $BB^T$ for some (possibly rectangular) matrix $B$ with nonnegative entries. The main result, proved using Blekherm

Xingqian Xu, Jiayi Guo, Zhangyang Wang, Gao Huang

Text-to-image (T2I) research has grown explosively in the past year, owing to the large-scale pre-trained diffusion models and many emerging personalization and editing approaches. Yet, one pain point persists: the text prompt engineering, and searching high-quality text prompts for customized results is more art than science. Moreover, as commonly argued: "

Reza Shirkavand, Liang Zhan, Heng Huang, Li Shen

Recent advancements in the acquisition of various brain data sources have created new opportunities for integrating multimodal brain data to assist in early detection of complex brain disorders. However, current data integration approaches typically need a complete set of biomedical data modalities, which may not always be feasible, as some modalities are on

Juyoung Lee, Sanghyuk Lee, Sewook Oh

We study the elliptic maximal functions defined by averages over ellipses and rotated ellipses which are multi-parametric variants of the circular maximal function. We prove that those maximal functions are bounded on $L^p$ for some $p\neq \infty$. For this purpose, we obtain some sharp multi-parameter local smoothing estimates.

Yihao Huang, Yue Cao, Tianlin Li, Felix Juefei-Xu

Segment anything model (SAM) has presented impressive objectness identification capability with the idea of prompt learning and a new collected large-scale dataset. Given a prompt (e.g., points, bounding boxes, or masks) and an input image, SAM is able to generate valid segment masks for all objects indicated by the prompts, presenting high generalization ac

Aleksandr V. Pukhlikov

In this paper we prove the birational rigidity of Fano-Mori fibre spaces $\pi\colon V\to S$, every fibre of which is a Fano complete intersection of index 1 and codimension $k\geqslant 3$ in the projective space ${\mathbb P}^{M+k}$ for $M$ sufficiently high, satisfying certain natural conditions of general position, in the assumption that the fibre space $V/

Daichi Matsuzuki

In this paper, we show that $\infty$-adic multiple zeta values associated to the function field of an algebraic curve of higher genus over a finite field are not zero, under certain assumption on the gap sequence associated to the rational point $\infty$ on the given curve. Using arguments and results of Sheats and Thakur for the case of the projective line,

Yachen Kang, Diyuan Shi, Jinxin Liu, Li He

This study focuses on the topic of offline preference-based reinforcement learning (PbRL), a variant of conventional reinforcement learning that dispenses with the need for online interaction or specification of reward functions. Instead, the agent is provided with fixed offline trajectories and human preferences between pairs of trajectories to extract the

Zhenxi Zhang, Ran Ran, Chunna Tian, Heng Zhou

Semi-supervised medical image segmentation offers a promising solution for large-scale medical image analysis by significantly reducing the annotation burden while achieving comparable performance. Employing this method exhibits a high degree of potential for optimizing the segmentation process and increasing its feasibility in clinical settings during trans

Petar Bevanda, Max Beier, Armin Lederer, Stefan Sosnowski

Many machine learning approaches for decision making, such as reinforcement learning, rely on simulators or predictive models to forecast the time-evolution of quantities of interest, e.g., the state of an agent or the reward of a policy. Forecasts of such complex phenomena are commonly described by highly nonlinear dynamical systems, making their use in opt

Zhenxi Zhang, Ran Ran, Chunna Tian, Heng Zhou

Consistency learning plays a crucial role in semi-supervised medical image segmentation as it enables the effective utilization of limited annotated data while leveraging the abundance of unannotated data. The effectiveness and efficiency of consistency learning are challenged by prediction diversity and training stability, which are often overlooked by exis

Zhengyi Wang, Cheng Lu, Yikai Wang, Fan Bao

Score distillation sampling (SDS) has shown great promise in text-to-3D generation by distilling pretrained large-scale text-to-image diffusion models, but suffers from over-saturation, over-smoothing, and low-diversity problems. In this work, we propose to model the 3D parameter as a random variable instead of a constant as in SDS and present variational sc

Kenny Ballou, Elena Sherman

Value-based static analysis techniques express computed program invariants as logical formula over program variables. Researchers and practitioners use these invariants to aid in software engineering and verification tasks. When selecting abstract domains, practitioners weigh the cost of a domain against its expressiveness. However, an abstract domain's expr

Gerald E. Marsh

While there are no known analytic solutions for force-free fields in toroidal coordinates, with a reasonable boundary condition it is possible to find a solution for the surface field and, with a restriction on the form of the field, to the interior of the torus as well.

Anna Meyer, Fabian Brand, André Kaup

We present an end-to-end trainable wavelet video coder based on motion-compensated temporal filtering (MCTF). Thereby, we introduce a different coding scheme for learned video compression, which is currently dominated by residual and conditional coding approaches. By performing discrete wavelet transforms in temporal, horizontal, and vertical dimension, we o

Meghna Sharma, Naveen Kumar Jain, Sushil Kumar

Let $\mathcal{P}$ denote the Carath\'{e}odory class accommodating all the analytic functions $p$ having positive real part and satisfying $p(0)=1$. In this paper, the second coefficient of the normalized analytic function $f$ defined on the open unit disc is constrained to define new classes of analytic functions. The classes are characterised by the functio

Dinesh Parthasarathy, Georgios Kontes, Axel Plinge, Christopher Mutschler

The Constrained Markov Decision Process (CMDP) formulation allows to solve safety-critical decision making tasks that are subject to constraints. While CMDPs have been extensively studied in the Reinforcement Learning literature, little attention has been given to sampling-based planning algorithms such as MCTS for solving them. Previous approaches perform c

J. McDonald

Higgs Inflation via a metastable Standard Model Higgs Potential is possible if the effective Planck mass in the Jordan frame increases after inflation ends. Here we consider the predictions of this model independently of the dynamics responsible for the Planck mass transition. The classical predictions are the same as for conventional Higgs Inflation. The qu

John B. Etnyre, Hyunki Min, Lisa Piccirillo, Agniva Roy

We present a handlebody construction of small symplectic caps, and hence of small closed symplectic 4-manifolds. We use this to construct handlebody descriptions of symplectic embeddings of rational homology balls in $\mathbb{C}\mathrm{P}^2$, and thereby provide the first examples of (infinitely many) symplectic handlebody decompositions of a closed symplect

Adrian Makowski, Michał Dąbrowski, Ivan Michel Antolovic, Claudio Bruschini

Reprogrammable linear optical circuits are essential elements of photonic quantum technology implementations. Integrated optics provides a natural platform for tunable photonic circuits, but faces challenges when high dimensions and high connectivity are involved. Here, we implement high-dimensional linear transformations on spatial modes of photons using wa

Federico Botta, Robin Lovelace, Laura Gilbert, Arthur Turrell

The effective and ethical use of data to inform decision-making offers huge value to the public sector, especially when delivered by transparent, reproducible, and robust data processing workflows. One way that governments are unlocking this value is through making their data publicly available, allowing more people and organisations to derive insights. Howe

Robert F. Wilson, Thomas Barclay, Brian P. Powell, Joshua Schlieder

The Nancy Grace Roman Space Telescope (Roman) is NASA's next astrophysics flagship mission, expected to launch in late 2026. As one of Roman's core community science surveys, the Galactic Bulge Time Domain Survey (GBTDS) will collect photometric and astrometric data for over 100 million stars in the Galactic bulge to search for microlensing planets. To asses

Fengming Zhu, Fangzhen Lin

Multi-agent routing problems have gained significant attention recently due to their wide range of industrial applications, ranging from logistics warehouse automation to indoor service robots. Conventionally, they are modeled as classical planning problems. In this paper, we argue that it can be beneficial to formulate them as universal planning problems, p

Louis Bethune, Thomas Massena, Thibaut Boissin, Yannick Prudent

State-of-the-art approaches for training Differentially Private (DP) Deep Neural Networks (DNN) face difficulties to estimate tight bounds on the sensitivity of the network's layers, and instead rely on a process of per-sample gradient clipping. This clipping process not only biases the direction of gradients but also proves costly both in memory consumption

Matthias Laeuter, Tobias Kramer, Martin Rubin, Kathrin Altwegg

The relation between ice composition in the nucleus of comet 67P/Churyumov-Gerasimenko on the one hand and relative abundances of volatiles in the coma on the other hand is important for the interpretation of density measurements in the environment of the cometary nucleus. For the 2015 apparition, in situ measurements from the two ROSINA (Rosetta Orbiter Spe

Caitlin C. Doughty, Joseph F. Hennawi, Frederick B. Davies, Zarija Lukić

The Ly$\alpha$ forest (LAF) at $z>5$ probes the thermal and reionization history of the intergalactic medium (IGM) and the nature of dark matter, but its interpretation requires comparison to cosmological hydrodynamical simulations. At high-$z$, convergence of these simulations is more exacting since transmission is dominated by underdense voids that are cha

Raymond Li, Felipe González-Pizarro, Linzi Xing, Gabriel Murray

The standard approach for neural topic modeling uses a variational autoencoder (VAE) framework that jointly minimizes the KL divergence between the estimated posterior and prior, in addition to the reconstruction loss. Since neural topic models are trained by recreating individual input documents, they do not explicitly capture the coherence between topic wo

Christos Kourris, Matthias Vojta

The formation of a heavy Fermi liquid in metals with local moments is characterized by multiple energy and temperature scales, most prominently the Kondo temperature and the coherence temperature, characterizing the onset of Kondo screening and the emergence of Fermi-liquid coherence, respectively. In the standard setting of a wide conduction band, both scal

Yuandong Tian, Yiping Wang, Beidi Chen, Simon Du

Transformer architecture has shown impressive performance in multiple research domains and has become the backbone of many neural network models. However, there is limited understanding on how it works. In particular, with a simple predictive loss, how the representation emerges from the gradient \emph{training dynamics} remains a mystery. In this paper, for

Freddy A. Cisneros, Nikta Fakhri, Jordan M. Horowitz

We propose and investigate a method for identifying timescales of dissipation in nonequilibrium steady states modeled as discrete-state Markov jump processes. The method is based on how the irreversibility-measured by the statistical breaking of time-reversal symmetry-varies under temporal coarse-graining. We observe a sigmoidal-like shape of the irreversibi

Marion Neumeier, Andreas Tollkühn, Sebastian Dorn, Michael Botsch

For automotive applications, the Graph Attention Network (GAT) is a prominently used architecture to include relational information of a traffic scenario during feature embedding. As shown in this work, however, one of the most popular GAT realizations, namely GATv2, has potential pitfalls that hinder an optimal parameter learning. Especially for small and s

Ali Raza, Hadia Sultan Raja, Usman Maratib

Abstractive summary generation is a challenging task that requires the model to comprehend the source text and generate a concise and coherent summary that captures the essential information. In this paper, we explore the use of an encoder/decoder approach for abstractive summary generation in the Urdu language. We employ a transformer-based model that utili

Federico Laudisa

John S. Bell introduced the notion of beable, as opposed to the standard notion of observable, in order to emphasize the need for an unambiguous formulation of quantum mechanics. In the paper I show that Bell formulated in fact two different theories of beables. The first is somehow reminiscent of the Bohr views on quantum mechanics but, at the same time, is

Pasquale Bosso, Giuseppe Gaetano Luciano, Luciano Petruzziello, Fabian Wagner

According to a number of arguments in quantum gravity, both model-dependent and model-independent, Heisenberg's uncertainty principle is modified when approaching the Planck scale. This deformation is attributed to the existence of a minimal length. The ensuing models have found entry into the literature under the term Generalized Uncertainty Principle (GUP)

Stefan Hödl, William Robinson, Yoram Bachrach, Wilhelm Huck

Explainability techniques are crucial in gaining insights into the reasons behind the predictions of deep learning models, which have not yet been applied to chemical language models. We propose an explainable AI technique that attributes the importance of individual atoms towards the predictions made by these models. Our method backpropagates the relevance

Pedro Orvalho, Vasco Manquinho, Ruben Martins

It has been shown that Maximum Satisfiability (MaxSAT) problem instances can be effectively solved by partitioning the set of soft clauses into several disjoint sets. The partitioning methods can be based on clause weights (e.g., stratification) or based on graph representations of the formula. Afterwards, a merge procedure is applied to guarantee that an op

Thomas Bergamaschi, William I. Jay, Patrick R. Oare

We present a method for analytic continuation of retarded Green functions, including Euclidean Green functions computed using lattice QCD. The method is based on conformal maps and construction of an interpolation function which is analytic in the upper half plane. A novel aspect of our method is rigorous bounding of systematic uncertainties, which are handl

Ali Siahkoohi, Rudy Morel, Randall Balestriero, Erwan Allys

Unsupervised source separation involves unraveling an unknown set of source signals recorded through a mixing operator, with limited prior knowledge about the sources, and only access to a dataset of signal mixtures. This problem is inherently ill-posed and is further challenged by the variety of timescales exhibited by sources in time series data from plane

Renaud Detcherry, Efstratia Kalfagianni, Adam S. Sikora

The proof of Witten's finiteness conjecture established that the Kauffman bracket skein modules of closed $3$-manifolds are finitely generated over $\mathbb Q(A)$. In this paper, we develop a novel method for computing these skein modules. We show that if the skein module $S(M,\mathbb Q[A^{\pm 1}])$ of $M$ is tame (e.g. finitely generated over $\mathbb Q[A^{

Guozheng Ma, Linrui Zhang, Haoyu Wang, Lu Li

Data augmentation (DA) is a crucial technique for enhancing the sample efficiency of visual reinforcement learning (RL) algorithms. Notably, employing simple observation transformations alone can yield outstanding performance without extra auxiliary representation tasks or pre-trained encoders. However, it remains unclear which attributes of DA account for i

Joao Henrique Quintino Palhares, Yann Beilliard, Jury Sandrini, Franck Arnaud

In this work we report a study and a co-design methodology of an analog SNN crossbar output circuit designed in a 28nm FD-SOI technology node that comprises a tunable current attenuator and a leak-integrate and fire neurons that would enable the integration of emerging non-volatile memories (eNVMs) for synaptic arrays based on various technologies including

David Martínez-Rubio, Christophe Roux, Christopher Criscitiello, Sebastian Pokutta

In this work, we study optimization problems of the form $\min_x \max_y f(x, y)$, where $f(x, y)$ is defined on a product Riemannian manifold $\mathcal{M} \times \mathcal{N}$ and is $\mu_x$-strongly geodesically convex (g-convex) in $x$ and $\mu_y$-strongly g-concave in $y$, for $\mu_x, \mu_y \geq 0$. We design accelerated methods when $f$ is $(L_x, L_y, L_{

Philip Dienstbier, Lennart Seiffert, Timo Paschen, Andreas Liehl

Solids exposed to intense electric fields release electrons through tunnelling. This fundamental quantum process lies at the heart of various applications, ranging from high brightness electron sources in DC operation to petahertz vacuum electronics in laser-driven operation. In the latter process, the electron wavepacket undergoes semiclassical dynamics in

Dilip Kumar Sahoo, Nabin Kumar Meher

In this paper, for any positive integer $\ell\geq2,$ we define $\ell$-generalized Fibonacci zeta function. We then study its analytic continuation to the whole complex plane $\mathbb{C}.$ Further, we compute a possible list of singularities and residues of the function at these simple poles. Moreover, we deduce that the special values of $\ell$-generalized F

Andrew Kyle Lampinen, Stephanie C Y Chan, Ishita Dasgupta, Andrew J Nam

What can be learned about causality and experimentation from passive data? This question is salient given recent successes of passively-trained language models in interactive domains such as tool use. Passive learning is inherently limited. However, we show that purely passive learning can in fact allow an agent to learn generalizable strategies for determin

Kotaro Shimizu, Gia-Wei Chern

We investigate the phase ordering kinetics of skyrmion lattice (SkL) in a metallic magnet. The SkL can be viewed as a superposition of magnetic stripes whose periods are determined by the quasi-nesting wave vectors of the underlying Fermi surface. An effective magnetic Hamiltonian that describes the electron-mediated spin-spin interaction is obtained for a t

Zainab Awad, Alaa Ali

We addressed the physical and kinematical properties of Wolf -- Rayet [WR] central stars (CSs) and their hosting planetary nebulae (PNe). The studied sample comprises all [WR] CSs that are currently known. The analysis is based on recent observations of the parallax, proper motion, and color index of [WR] CSs from the Gaia space mission's early third release

Suraj Kumar Rai, Vivek Kumar Tiwari

The Quantum Chromodynamics (QCD) phase structure has been studied using the Polyakov-loop augmented quark-meson model (PQM) in the extended mean field approximation (e-MFA) where the quark one-loop vacuum term is included.~When the divergent vacuum term is regularized in the minimal subtraction scheme and the curvature meson masses are used to fix the parame

Tian-Le Yang, Joe Suzuki

This study demonstrates that double descent can be mitigated by adding a dropout layer adjacent to the fully connected linear layer. The unexpected double-descent phenomenon garnered substantial attention in recent years, resulting in fluctuating prediction error rates as either sample size or model size increases. Our paper posits that the optimal test erro

Luca Candelori, Vladimir Y. Chernyak, John R. Klein, Nick Rekuski

We calculate the field of rational local unitary invariants for mixed states of two qubits, by employing methods from algebraic geometry. We prove that this field is rational (i.e. purely transcendental), and that it is generated by nine algebraically independent polynomial invariants. We do so by constructing a relative section, in the sense of invariant th

Ehsan Ebrahimian, Chethan Krishnan, Ranjini Mondol, M. M. Sheikh-Jabbari

Dipole cosmology is the maximally Copernican generalization of the FLRW paradigm that can incorporate bulk flows in the cosmic fluid. In this paper, we first discuss how multiple fluid components with independent flows can be realized in this set up. This is the necessary step to promote ``tilted" Bianchi cosmologies to a viable framework for cosmological mo

Daneng Yang, Ethan O. Nadler, Hai-Bo Yu, Yi-Ming Zhong

We propose a parametric model for studying self-interacting dark matter (SIDM) halos. The model uses an analytical density profile, calibrated using a controlled N-body SIDM simulation that covers the entire gravothermal evolution, including core-forming and -collapsing phases. By normalizing the calibrated density profile, we obtain a universal description

Claudio Battiloro, Indro Spinelli, Lev Telyatnikov, Michael Bronstein

Latent Graph Inference (LGI) relaxed the reliance of Graph Neural Networks (GNNs) on a given graph topology by dynamically learning it. However, most of LGI methods assume to have a (noisy, incomplete, improvable, ...) input graph to rewire and can solely learn regular graph topologies. In the wake of the success of Topological Deep Learning (TDL), we study

Blaise Delattre, Quentin Barthélemy, Alexandre Araujo, Alexandre Allauzen

Since the control of the Lipschitz constant has a great impact on the training stability, generalization, and robustness of neural networks, the estimation of this value is nowadays a real scientific challenge. In this paper we introduce a precise, fast, and differentiable upper bound for the spectral norm of convolutional layers using circulant matrix theor

Juncheng Li, David J. Cappelleri

This paper presents Sim-Suction, a robust object-aware suction grasp policy for mobile manipulation platforms with dynamic camera viewpoints, designed to pick up unknown objects from cluttered environments. Suction grasp policies typically employ data-driven approaches, necessitating large-scale, accurately-annotated suction grasp datasets. However, the gene

Xiaomeng Yang, Zhi Qiao, Jin Wei, Dongbao Yang

Scene Text Recognition (STR) is difficult because of the variations in text styles, shapes, and backgrounds. Though the integration of linguistic information enhances models' performance, existing methods based on either permuted language modeling (PLM) or masked language modeling (MLM) have their pitfalls. PLM's autoregressive decoding lacks foresight into

Anubha Kabra, Emmy Liu, Simran Khanuja, Alham Fikri Aji

Figurative language permeates human communication, but at the same time is relatively understudied in NLP. Datasets have been created in English to accelerate progress towards measuring and improving figurative language processing in language models (LMs). However, the use of figurative language is an expression of our cultural and societal experiences, maki

Torben Ferber, Alexander Grohsjean, Felix Kahlhoefer

The Large Hadron Collider (LHC) has confirmed the Higgs mechanism to be responsible for generating mass in the Standard Model (SM), making it attractive to also consider spontaneous symmetry breaking as the origin of mass for new particles in a dark sector extension of the SM. Such a dark Higgs mechanism may in particular give mass to a dark matter candidate

Cordell Hammon, Jonathan Meddaugh, Jasmin Mohn, Brian Raines

In this paper, we demonstrate conditions under which a Lindel\"{o}f dynamical system exhibits $\omega$-chaos. In particular, if a system exhibits a generalized version of the specification property and has at least three points with mutually separated orbit closures, then the system exhibits dense $\omega$-chaos.

Tijs W. Alleman, Koen Schoors, Jan M. Baetens

This work validates a dynamic production network model, used to quantify the impact of economic shocks caused by COVID-19 in the UK, using data for Belgium. Because the model was published early during the 2020 COVID-19 pandemic, it relied on several assumptions regarding the magnitude of the observed economic shocks, for which more accurate data have become

Daniel Beltita, Fernand Pelletier

We investigate the quotients of Banach manifolds with respect to free actions of pseudogroups of local diffeomorphisms. These quotient spaces are called H-manifolds since the corresponding simply transitive action of the pseudogroup on its orbits is regarded as a homogeneity condition. The importance of these structures stems from the fact that for every reg

Xuming Hu, Zhijiang Guo, Zhiyang Teng, Irwin King

Multimodal relation extraction (MRE) is the task of identifying the semantic relationships between two entities based on the context of the sentence image pair. Existing retrieval-augmented approaches mainly focused on modeling the retrieved textual knowledge, but this may not be able to accurately identify complex relations. To improve the prediction, this

Travis Adams, Andrea Ajello, Diego Silva, Francisco Vazquez-Grande

We build a new measure of credit and financial market sentiment using Natural Language Processing on Twitter data. We find that the Twitter Financial Sentiment Index (TFSI) correlates highly with corporate bond spreads and other price- and survey-based measures of financial conditions. We document that overnight Twitter financial sentiment helps predict next

Thomas Laurent, James H. von Brecht, Xavier Bresson

We formalize and study a phenomenon called feature collapse that makes precise the intuitive idea that entities playing a similar role in a learning task receive similar representations. As feature collapse requires a notion of task, we leverage a simple but prototypical NLP task to study it. We start by showing experimentally that feature collapse goes hand

Leonhard Hochfilzer

We use the circle method to count the number of integer solutions to systems of bihomogeneous equations of bidegree $(1,1)$ and $(2,1)$ of bounded height in lopsided boxes. Previously, adjusting Birch's techniques to the bihomogeneous setting, Schindler showed an asymptotic formula provided the number of variables grows at least quadratically with the number

Shotaro Ishihara

As the deployment of pre-trained language models (PLMs) expands, pressing security concerns have arisen regarding the potential for malicious extraction of training data, posing a threat to data privacy. This study is the first to provide a comprehensive survey of training data extraction from PLMs. Our review covers more than 100 key papers in fields such a

Zhihao Wang, Longyue Wang, Jinsong Su, Junfeng Yao

In real-world systems, scaling has been critical for improving the translation quality in autoregressive translation (AT), which however has not been well studied for non-autoregressive translation (NAT). In this work, we bridge the gap by systematically studying the impact of scaling on NAT behaviors. Extensive experiments on six WMT benchmarks over two adv

Giuseppe Milazzo, Manuel Giuseppe Catalano, Antonio Bicchi, Giorgio Grioli

This study introduces an innovative design for a Variable Stiffness 3 Degrees of Freedom actuated wrist capable of actively and continuously adjusting its overall stiffness by modulating the active length of non-linear elastic elements. This modulation is akin to human muscular cocontraction and is achieved using only four motors. The mechanical configuratio

Longwen Zhou, Da-Jian Zhang

The past few years have witnessed a surge of interest in non-Hermitian Floquet topological matters due to their exotic properties resulting from the interplay between driving fields and non-Hermiticity. The present review sums up our studies on non-Hermitian Floquet topological matters in one and two spatial dimensions. We first give a bird's-eye view of the

Vishal Pallagani, Bharath Muppasani, Keerthiram Murugesan, Francesca Rossi

Automated planning is concerned with developing efficient algorithms to generate plans or sequences of actions to achieve a specific goal in a given environment. Emerging Large Language Models (LLMs) can answer questions, write high-quality programming code, and predict protein folding, showcasing their versatility in solving various tasks beyond language-ba

Jean-Yves Franceschi, Mike Gartrell, Ludovic Dos Santos, Thibaut Issenhuth

Particle-based deep generative models, such as gradient flows and score-based diffusion models, have recently gained traction thanks to their striking performance. Their principle of displacing particle distributions using differential equations is conventionally seen as opposed to the previously widespread generative adversarial networks (GANs), which invol

David Lindner, Xin Chen, Sebastian Tschiatschek, Katja Hofmann

We propose Convex Constraint Learning for Reinforcement Learning (CoCoRL), a novel approach for inferring shared constraints in a Constrained Markov Decision Process (CMDP) from a set of safe demonstrations with possibly different reward functions. While previous work is limited to demonstrations with known rewards or fully known environment dynamics, CoCoRL

Marcel Novaes

We present a semiclassical approach for time delay statistics in quantum chaotic systems, in the presence of absorption, for broken time-reversal symmetry. We derive three kinds of expressions for Schur-moments of the time delay operator: as a power series in inverse channel number, $1/M$, whose coefficients are rational functions of absorption time, $\tau_a

Marek J. Szczepańczyk, Yanyan Zheng, Javier M. Antelis, Michael Benjamin

We present the results from a search for gravitational-wave transients associated with core-collapse supernovae observed optically within 30 Mpc during the third observing run of Advanced LIGO and Advanced Virgo. No gravitational wave associated with a core-collapse supernova has been identified. We then report the detection efficiency for a variety of possi

Pankaj Kushwaha

Optical-UV synchrotron spectrum has been argued to be the primary driver of the majority of X-ray spectral changes in the BL Lacertae object OJ 287 during its low and intermediate X-ray flux state. Here, we focus on the lowest recorded X-ray flux state of OJ 287 by the Swift facility and report the finding of a power-law optical-UV spectrum with a photon spe

Jiangtao Kong, Zhenyu Zong, Tianyi Zhou, Huajie Shao

Incremental learning (IL) suffers from catastrophic forgetting of old tasks when learning new tasks. This can be addressed by replaying previous tasks' data stored in a memory, which however is usually prone to size limits and privacy leakage. Recent studies store only class centroids as prototypes and augment them with Gaussian noises to create synthetic da

Srdjan Stavrić, Paolo Barone, Silvia Picozzi

Bilayer CrI$_3$ attracted much attention owing to peculiar switching between the layered ferromagnetic and antiferromagnetic order upon stacking alternation. This finding pointed out the importance of the apparently small interlayer exchange, yet, existing literature addresses only its isotropic part. To fill this gap, we combine the density functional theor

Rafael Navarro-Carrera, Pierluigi Rinaldi, Karina I. Caputi, Edoardo Iani

We analyze a sample of 3300 galaxies between redshifts z~3.5 and z~8.5 selected from JWST images in the Hubble Ultra Deep Field (HUDF) and UKIDSS Ultra Deep Survey field, including objects with stellar masses as low as ~ 10^8 Msun up to z~8. The depth and wavelength coverage of the JWST data allow us, for the first time, to derive robust stellar masses for s

Imed Basdouri, Esmael Peyghan, Mohamed Amin Sadraoui

In this paper, we introduce an algebra structure denoted by InvDer algebra whose which we twist an algebra thanks to an invertible derivation, where its inverse is also a derivation. We define InvDer Lie algebras, InvDer associated algebras, InvDer zinbiel algebras and InvDer dendriforme algebras. We also study the relations between these structures using th

Jiawei Qin, Takuru Shimoyama, Xucong Zhang, Yusuke Sugano

Along with the recent development of deep neural networks, appearance-based gaze estimation has succeeded considerably when training and testing within the same domain. Compared to the within-domain task, the variance of different domains makes the cross-domain performance drop severely, preventing gaze estimation deployment in real-world applications. Among

David Barreiro-Villaverde, Anne Gosset, Marcos Lema, Miguel Alfonso Mendez

Paints and coatings often feature interfacial defects due to disturbances during the deposition process which, if they persist until solidification, worsen the product quality. In this article, we investigate the stability of a thin liquid film dragged by a vertical substrate moving against gravity, a flow configuration found in a variety of coating processe

Mingxiao Song, Yunsong Liu, Agam Shah, Sudheer Chava

The Non-Fungible-Token (NFT) market has experienced explosive growth in recent years. According to DappRadar, the total transaction volume on OpenSea, the largest NFT marketplace, reached 34.7 billion dollars in February 2023. However, the NFT market is mostly unregulated and there are significant concerns about money laundering, fraud and wash trading. The

Ethan Wilson, Frederick Shic, Eakta Jain

Face swapping combines one face's identity with another face's non-appearance attributes (expression, head pose, lighting) to generate a synthetic face. This technology is rapidly improving, but falls flat when reconstructing some attributes, particularly gaze. Image-based loss metrics that consider the full face do not effectively capture the perceptually i

Włodzimierz Drabent

We discuss how to implement backjumping (or intelligent backtracking) in Prolog by using the built-ins throw/1 and catch/3. We show that it is impossible in a general case, contrary to a claim that ``backjumping is exception handling". We provide two solutions. One works for binary programs; in a general case it imposes a restriction on where backjumping may

Adrián A. Budini

Open quantum systems are inherently coupled to their environments, which in turn also obey quantum dynamical rules. By restricting to dissipative dynamics, here we propose a measure that quantifies how far the environment action on a system departs from the influence of classical noise fluctuations. It relies on the lack of commutativity between the initial

Mingxing Hu, Yunhong Zhou

Signer-anonymity is the central feature of ring signatures, which enable a user to sign messages on behalf of an arbitrary set of users, called the ring, without revealing exactly which member of the ring actually generated the signature. Strong and long-term signer-anonymity is a reassuring guarantee for users who are hesitant to leak a secret, especially i

Maya Karanouh

The exponential growth in user acquisition and popularity of OpenAIs ChatGPT, an artificial intelligence(AI) powered chatbot, was accompanied by widespread mainstream media coverage. This article presents a quantitative data analysis of the early trends and sentiments revealed by conducting text mining and NLP methods onto a corpus of 10,902 mainstream news

E. Sellentin, A. Loureiro, L. Whiteway, J. S. Lafaurie

Inference in cosmology often starts with noisy observations of random fields on the celestial sphere, such as maps of the microwave background radiation, continuous maps of cosmic structure in different wavelengths, or maps of point tracers of the cosmological fields. Almanac uses Hamiltonian Monte Carlo sampling to infer the underlying all-sky noiseless map

Yuntao Wang, Yanghe Pan, Miao Yan, Zhou Su

With the widespread use of large artificial intelligence (AI) models such as ChatGPT, AI-generated content (AIGC) has garnered increasing attention and is leading a paradigm shift in content creation and knowledge representation. AIGC uses generative large AI algorithms to assist or replace humans in creating massive, high-quality, and human-like content at

Zhiyu Tan, Zichao Dong, Cheng Zhang, Weikun Zhang

Semantic occupancy prediction aims to infer dense geometry and semantics of surroundings for an autonomous agent to operate safely in the 3D environment. Existing occupancy prediction methods are almost entirely trained on human-annotated volumetric data. Although of high quality, the generation of such 3D annotations is laborious and costly, restricting the

Ehtibar N. Dzhafarov

This paper provides a systematic account of the hidden variable models (HVMs) formulated to describe systems of random variables with mutually exclusive contexts. Any such system can be described either by a model with free choice but generally context-dependent mapping of the hidden variables into observable ones, or by a model with context-independent mapp

Alexander P. Antonov, Yuanjian Zheng, Benno Liebchen, Hartmut Löwen

We introduce a framework for engineering active quantum matter that involves mimicking the role of self-propulsion through an external trapping potential that is moving along imposed trajectories traced by classical active dynamics. This approach in the presence of dissipation, not only recovers essential dynamical behavior of classical activity, including t

Jack Merullo, Carsten Eickhoff, Ellie Pavlick

A primary criticism towards language models (LMs) is their inscrutability. This paper presents evidence that, despite their size and complexity, LMs sometimes exploit a simple vector arithmetic style mechanism to solve some relational tasks using regularities encoded in the hidden space of the model (e.g., Poland:Warsaw::China:Beijing). We investigate a rang