May 2024 arXiv papers — page 2

Ivan Dubrovsky, Andrei Dmitrenko, Aleksei Dmitrenko, Nikita Serov

Creation of nanomaterials with specific morphology remains a complex experimental process, even though there is a growing demand for these materials in various industry sectors. This study explores the potential of AI to predict the morphology of nanoparticles within the data availability constraints. For that, we first generated a new multi-modal dataset th

Xiaolong Sun, Liushuai Shi, Le Wang, Sanping Zhou

Temporal sentence grounding is a challenging task that aims to localize the moment spans relevant to a language description. Although recent DETR-based models have achieved notable progress by leveraging multiple learnable moment queries, they suffer from overlapped and redundant proposals, leading to inaccurate predictions. We attribute this limitation to t

Sara Willhammar, Hiroki Iimori, Joao Vieira, Lars Sundström

In what ways could cellular massive MIMO be improved? This technology has already been shown to bring huge performance gains. However, coverage holes and difficulties to transmit multiple streams to multi-antenna users because of insufficient channel rank remain issues. Distributed MIMO, also known as cell-free massive MIMO, might be the ultimate solution. H

Michael Nelson

We investigate associativity of multiplications on chain complexes over commutative noetherian rings from two perspectives. First, we introduce a natural associator subcomplex and show how its homology can detect associativity. Second, we use Gr\"obner bases to compute associators.

Nicolau Andrés-Thió, Charles Audet, Miguel Diago, Aimen E. Gheribi

This work introduces solar, a collection of ten optimization problem instances for benchmarking blackbox optimization solvers. The instances present different design aspects of a concentrated solar power plant simulated by blackbox numerical models. The type of variables (discrete or continuous), dimensionality, and number and types of constraints (including

Cristina Ballantine, Amanda Welch

We consider the number of various partitions of $n$ with parts separated by parity and prove combinatorially several inequalities between these numbers. For example, we show that for $n\geq 5$ we have $p_{od}^{eu}(n)<p_{ed}^{ou}(n)$, where $p_{od}^{eu}(n)$ is the number of partitions of $n$ with odd parts distinct and even parts unrestricted and all odd part

Thomas Creutzig, Niklas Garner, Heeyeon Kim

We introduce a family of 3d $\mathcal{N} = 4$ superconformal field theories that have zero-dimensional Coulomb and Higgs branches and propose that the rational vertex operator algebras $W^{\text{min}}_{k - \scriptstyle{\frac{1}{2}}}(\mathfrak{sp}_{2N})$ and $L_{k}(\mathfrak{osp}_{1|2N})$ model the modular tensor categories of line operators in their topologi

Salvatore Greco, Roman Słowiński

The seven-valued logic considered in this paper naturally arises within the rough set framework, allowing to distinguish vagueness due to imprecision from ambiguity due to coarseness. Recently, we discussed its utility for reasoning about data describing multi-attribute classification of objects. We also showed that this logic contains, as a particular case,

Wojciech Brzezicki, Timo Hyart, Francesco Massel

The interplay between topology, dissipation and nonlinearities can give rise to a wealth of new phenomena and pave the way for novel topological lasers, sensors and other quantum devices. Along these lines, we propose here an optomechanical setup in which the concomitant presence of a spatially modulated external drive and dissipation gives rise to a topolog

Peter J. Haine

A source of difficulty in profinite homotopy theory is that the profinite completion functor does not preserve finite products. In this note, we provide a new, checkable criterion on prospaces $X$ and $Y$ that guarantees that the profinite completion of $X\times Y$ agrees with the product of the profinite completions of $X$ and $Y$. Using this criterion, we

Youssef Mohamed, Zeyad Youssef, Ahmed Heakl, Ahmed Zaky

Biometric identification is a reliable method to verify individuals based on their unique physical or behavioral traits, offering a secure alternative to traditional methods like passwords or PINs. This study focuses on ear biometric identification, exploiting its distinctive features for enhanced accuracy, reliability, and usability. While past studies typi

M. Renzo, J. A. Goldberg, A. Grichener, O. Gottlieb

Core collapse initial conditions are a bottleneck in understanding the explosion mechanism(s) of massive stars. Stellar evolution codes struggle after carbon burning, and either stop or adopt numerical simplifications missing crucial physics. The use of small nuclear reaction networks (NRN) that account for energy production but bypass weak reactions is typi

Ocheme Anthony Ekle, William Eberle

This survey paper presents a comprehensive and conceptual overview of anomaly detection using dynamic graphs. We focus on existing graph-based anomaly detection (AD) techniques and their applications to dynamic networks. The contributions of this survey paper include the following: i) a comparative study of existing surveys on anomaly detection; ii) a Dynami

Mohammed Amine Gharsallaoui, Bhupinderjeet Singh, Supriya Savalkar, Aryan Deshwal

Predicting the spatiotemporal variation in streamflow along with uncertainty quantification enables decision-making for sustainable management of scarce water resources. Process-based hydrological models (aka physics-based models) are based on physical laws, but using simplifying assumptions which can lead to poor accuracy. Data-driven approaches offer a pow

Lea S. Nahon, Nyx L. Ng, Bertram Gawronski

An analysis drawing on Signal Detection Theory suggests that people may fall for misinformation because they are unable to discern true from false information (truth insensitivity) or because they tend to accept information with a particular slant regardless of whether it is true or false (belief bias). Three preregistered experiments with participants from

Jack Kilgallen, Barak Pearlmutter, Jeffery Mark Siskind

Within neuroimgaing studies it is a common practice to perform repetitions of trials in an experiment when working with a noisy class of data acquisition system, such as electroencephalography (EEG) or magnetoencephalography (MEG). While this approach can be useful in some experimental designs, it presents significant limitations for certain types of analyse

Zhuo Chen, Rumen Dangovski, Charlotte Loh, Owen Dugan

We propose Quantum-informed Tensor Adaptation (QuanTA), a novel, easy-to-implement, fine-tuning method with no inference overhead for large-scale pre-trained language models. By leveraging quantum-inspired methods derived from quantum circuit structures, QuanTA enables efficient high-rank fine-tuning, surpassing the limitations of Low-Rank Adaptation (LoRA)-

Kevin Christian Wibisono, Yixin Wang

Large language models (LLMs) like transformers demonstrate impressive in-context learning (ICL) capabilities, allowing them to make predictions for new tasks based on prompt exemplars without parameter updates. While existing ICL theories often assume structured training data resembling ICL tasks (e.g., x-y pairs for linear regression), LLMs are typically tr

Lucía Ferrari, Gastón Folatelli, Keila Ertini, Hanindyo Kuncarayakti

Context. SN 2023ixf was discovered in Galaxy M101 in May 2023. Its proximity made it an extremely valuable opportunity for the scientific community to study the characteristics of the SN and its progenitor. A point source detected on archival images and hydrodynamical modelling of the bolometric light curve has been used to constrain the former star's proper

Zihni Kaan Baykara, Houri-Christina Tarazi, Cumrun Vafa

In this work we investigate a largely unexplored non-geometric corner of the string landscape: the quasicrystalline orbifolds. These exist at special points of the Narain moduli leading to frozen moduli and large quantum symmetries. Here we complete the classification and construction of quasicrystalline Narain lattices and use this to explore supersymmetric

Clifford Lam, Zetai Cen

We introduce the matrix-valued time-varying Main Effects Factor Model (MEFM). MEFM is a generalization to the traditional matrix-valued factor model (FM). We give rigorous definitions of MEFM and its identifications, and propose estimators for the time-varying grand mean, row and column main effects, and the row and column factor loading matrices for the com

Mark Lowell, Catharine Kastner

During neural network training, the sharpness of the Hessian matrix of the training loss rises until training is on the edge of stability. As a result, even nonstochastic gradient descent does not accurately model the underlying dynamical system defined by the gradient flow of the training loss. We use an exponential Euler solver to train the network without

M. T. Krilich, C. G. Díaz

The H II region N88a in the Small Magellanic Cloud (SMC) is a spherical region of $1.5~\mathrm{pc}$ diameter, with high concentration of gas and dust, and at least four massive stars within it. Previous studies suggest that the four known sources may be insufficient to ionize the region and explain the nebular emission. In this contribution we analyze the io

Robert Graf, Paul-Sören Platzek, Evamaria Olga Riedel, Constanze Ramschütz

Objectives: To present a publicly available deep learning-based torso segmentation model that provides comprehensive voxel-wise coverage, including delineations that extend to the boundaries of anatomical compartments. Materials and Methods: We extracted preliminary segmentations from TotalSegmentator, spine, and body composition models for Magnetic Resonanc

Valentin Vallucci-Goy, Ugo Lebreuilly, Patrick Hennebelle

The coupling between the magnetic field and the gas during the collapsing phase of star-forming cores is strongly affected by the dust size distribution, which is expected to evolve. We aim to investigate the influence of key parameters on the evolution of the dust distribution as well as on the magnetic resistivities during the protostellar collapse. We per

Mingyuan Meng, Dagan Feng, Lei Bi, Jinman Kim

Deformable image registration is a fundamental step for medical image analysis. Recently, transformers have been used for registration and outperformed Convolutional Neural Networks (CNNs). Transformers can capture long-range dependence among image features, which have been shown beneficial for registration. However, due to the high computation/memory loads

Corey Simmerer, Marisa Morakis, Lei Tian, Lia Gomez-Perez

Significance: Access to diagnostic eye care could be expanded with high-throughput and easy-to-use tools. Phase mask-based imaging may improve the fundus camera by enabling computational refocusing with no moving parts. While phase mask-based imaging has been demonstrated in a model eye, this approach has not been shown $\textit{in vivo}$. Aim: A computation

Tiancheng Shen, Jun Hao Liew, Long Mai, Lu Qi

Advances in text-based image generation and editing have revolutionized content creation, enabling users to create impressive content from imaginative text prompts. However, existing methods are not designed to work well with the oversimplified prompts that are often encountered in typical scenarios when users start their editing with only vague or abstract

Andrew C. Li, Zizhao Chen, Toryn Q. Klassen, Pashootan Vaezipoor

Reward Machines provide an automaton-inspired structure for specifying instructions, safety constraints, and other temporally extended reward-worthy behaviour. By exposing the underlying structure of a reward function, they enable the decomposition of an RL task, leading to impressive gains in sample efficiency. Although Reward Machines and similar formal sp

Melanie Schmidt-Wolf, Tyler Becker, Denielle Oliva, Monica Nicolescu

The environments in which the collaboration of a robot would be the most helpful to a person are frequently uncontrolled and cluttered with many objects present. Legible robot arm motion is crucial in tasks like these in order to avoid possible collisions, improve the workflow and help ensure the safety of the person. Prior work in this area, however, focuse

Katayoun Kobraei, Mehrdad Baradaran, Seyed Mohsen Sadeghi, Raziyeh Masumshah

Motivation: Unanticipated drug-drug interactions (DDIs) pose significant risks in polypharmacy, emphasizing the need for predictive methods. Recent advancements in computational techniques aim to address this challenge. Methods: We introduce ADEP, a novel approach integrating a discriminator and an encoder-decoder model to address data sparsity and enhance f

Dionysios Karagiannis, Roy Maartens, Shun Saito, José Fonseca

A major goal of cosmology is to understand the nature of the field(s) which drove primordial Inflation. Through future observations, the statistics of large-scale structure will allow us to probe primordial non-Gaussianity of the curvature perturbation at the end of Inflation. We show how a new correlation statistic can significantly improve these constraint

Eura Nofshin, Esther Brown, Brian Lim, Weiwei Pan

Explanations of an AI's function can assist human decision-makers, but the most useful explanation depends on the decision's context, referred to as the downstream task. User studies are necessary to determine the best explanations for each task. Unfortunately, testing every explanation and task combination is impractical, especially considering the many fac

Hanxian Huang, Zhenghan Lin, Zixuan Wang, Xin Chen

We explore the use of Large Language Models (LLMs) to generate high-quality Register-Transfer Level (RTL) code with minimal human interference. The traditional RTL design workflow requires human experts to manually write high-quality RTL code, which is time-consuming and error-prone. With the help of emerging LLMs, developers can describe their requirements

Bao Liu, Tianbao Liu, Zhongshuo Hu, Fei Ye

The advancement of industrialization has spurred the development of innovative swarm intelligence algorithms, with Lion Swarm Optimization (LSO) notable for its robustness, parallelism, simplicity, and efficiency. While LSO excels in single-objective optimization, its multi-objective variants face challenges such as poor initialization, local optima entrapme

Ciamac C. Moallemi, Dan Robinson

Milionis et al.(2023) studied the rate at which automated market makers leak value to arbitrageurs when block times are discrete and follow a Poisson process, and where the risky asset price follows a geometric Brownian motion. We extend their model to analyze another popular mechanism in decentralized finance for onchain trading: Dutch auctions. We compute

Alexandre M. Pombo, Lorenzo Pizzuti

Virial-like identities obtained through Derrick's scaling argument are powerful, multi-purpose tools to study general relativistic models. Applications comprise establishing no-go/hair theorems and numerical accuracy tests. In the presence of a horizon (\textit{aka} boundary), the spacetime can be divided into regions, each with its own identity. So far, suc

Edypo Melo, Diogo Souto, Katia Cunha, Verne V. Smith

Exoplanets hosting M dwarfs are the best targets to characterize Earth-like or super-Earth planetary atmospheres with the James Webb Space Telescope (JWST). We determine detailed stellar parameters ($T_{\rm eff}$, log$g$, and $\xi$) and individual abundances of twelve elements for four cool M dwarfs hosting exoplanets TOI-1685, GJ 436, GJ 3470, and TOI-2445,

Shenming Fu, Thomas Matheson, Aaron Meisner, Yuanyuan Zhang

We introduce a pipeline that performs rapid image subtraction and source selection to detect transients, with a focus on identifying gravitational wave optical counterparts using the Dark Energy Camera (DECam). In this work, we present the pipeline steps from processing raw data to identification of astrophysical transients on individual exposures. We proces

Bin Chen, Xiangliang Kong, Sijie Yu, Chengcai Shen

Where and how flares efficiently accelerate charged particles remains an unresolved question. Recent studies revealed that a "magnetic bottle" structure, which forms near the bottom of a large-scale reconnection current sheet above the flare arcade, is an excellent candidate for confining and accelerating charged particles. However, further understanding its

Mohammed Shafi, Edmund J. Copeland, Rafid Mahbub, Swagat S. Mishra

We investigate the formation and decay of oscillons during the post-inflationary reheating epoch from inflaton oscillations around asymptotically flat potentials $V(\varphi)$ in the presence of an external coupling of the form $\frac{1}{2}\, g^2 \, \varphi^2 \, \chi^2$. It is well-known that in the absence of such an external coupling, the attractive self-in

Thayne Currie

Recently, Biddle et al. (2024) claimed a non-detection of the protoplanet AB Aurigae b in Keck/NIRC2 Pa$\beta$ imaging. I reprocess these newly-public data and compare them to data from the extreme AO platform (SCExAO/CHARIS) used to discover AB Aur b. AB Aur b is decisively imaged with SCExAO/CHARIS at wavelengths covering Pa$\beta$. The Biddle et al. non d

Francisco M. Blanco

We consider a class of finite-dimensional dynamical systems whose equations of motion are derived from a non-local-in-time action principle. The action functional has a zeroth order piece derived from a local Hamiltonian and a perturbation in the form of a non-local functional of the trajectory on phase space. We prove that the dynamics of these systems admi

X. Zhang, E. Bulbul, N. Malavasi, V. Ghirardini

The properties of the warm-hot intergalactic medium (WHIM) in cosmic filaments are among the least quantified units in modern astrophysics. The Spectrum Roentgen Gamma/eROSITA All Sky Survey ((SRG/eRASS) provides a unique opportunity to study the X-ray emission of the WHIM. We applied both imaging and spectroscopic stacking techniques to the data of the firs

Samuel Duffield, Kaelan Donatella, Johnathan Chiu, Phoebe Klett

Although theoretically compelling, Bayesian learning with modern machine learning models is computationally challenging since it requires approximating a high dimensional posterior distribution. In this work, we (i) introduce posteriors, an easily extensible PyTorch library hosting general-purpose implementations making Bayesian learning accessible and scala

Shi-Fan Chen, Zvonimir Vlah, Martin White

We study the bispectrum in Lagrangian perturbation theory. Extending past results for the power spectrum, we describe a method to efficiently compute the bispectrum in LPT, focusing on the Zeldovich approximation, in which contributions due to linear displacements are captured to all orders in a manifestly infrared (IR) safe way. We then isolate the effects

Mireia Montes, Jorge Sánchez Almeida, Ignacio Trujillo

A long linear structure recently discovered could be the stellar wake produced by the passage of a runaway supermassive black hole (SMBH) or, alternatively, a bulgeless edge-on galaxy. We report on new very deep HST imaging that seems to be in tension with the SMBH runaway scenario but is consistent with the bulgeless edge-on galaxy scenario. The new observa

Jiaxuan Li, Jenny E. Greene, Scott G. Carlsten, Shany Danieli

It is well-known that almost all isolated dwarf galaxies are actively forming stars. We report the discovery of dw1322m2053 (nicknamed Hedgehog), an isolated quiescent dwarf galaxy at a distance of $2.40\pm0.15$ Mpc with a stellar mass of $M_\star \approx 10^{5.8}\, M_\odot$. The distance is measured using surface brightness fluctuations with both Legacy Sur

Charlie Cresswell-Hogg, Daniel F. Litim

We study the generation of fermion mass in a context where interactions break a discrete chiral symmetry. Then, fermion mass is not protected by a symmetry, no symmetry is broken by the generation of mass, and a vanishing mass no longer enhances a symmetry. We elaborate these scenarios for template fermionic and Yukawa theories in three dimensions where mass

David Stefanyszyn, Xi Tong, Yuhang Zhu

Cosmological correlators encode invaluable information about the wavefunction of the primordial universe. In this letter we present a duality between correlators and wavefunction coefficients that is valid to all orders in the loop expansion and manifests itself as a $\mathbb{Z}_4$ symmetry. To demonstrate the power of the duality, we derive a correlator-to-

Marcus Bintz, Vincent S. Liu, Johannes Hauschild, Ahmed Khalifa

We predict that the gapless $U(1)$ Dirac spin liquid naturally emerges in a two-dimensional array of quantum dipoles. In particular, we demonstrate that the dipolar XY model$\unicode{x2014}$realized in both Rydberg atom arrays and ultracold polar molecules$\unicode{x2014}$hosts a quantum spin liquid ground state on the kagome lattice. Large-scale density mat

S. Evcil, S. Adalali, N. Alan, R. Canbay

Eclipsing binary systems are significant objects for astrophysics in that direct observations can determine the fundamental parameters of stars. In this study, we determined precisely the fundamental parameters of the binary component stars obtained by simultaneous analysis of radial velocities and the {\it TESS} light curve using the Wilson and Devinney cod

Keisuke Inomata, Marc Kamionkowski, Celia M. Toral, Stephen R. Taylor

We present an efficient technique for calculating the angular two-point correlation functions (or ``overlap reduction functions'') induced by gravitational waves in both the pulse arrival times of pulsars and in the angular deflections of distant sources. In the most general case, there are six auto- and cross-correlations for the pulse arrival times and the

G. Bruno De Luca, Nicolò De Ponti, Andrea Mondino, Alessandro Tomasiello

For the Laplacian of an $n$-Riemannian manifold $X$, the Weyl law states that the $k$-th eigenvalue is asymptotically proportional to $(k/V)^{2/n}$, where $V$ is the volume of $X$. We show that this result can be derived via physical considerations by demanding that the gravitational potential for a compactification on $X$ behaves in the expected $(4+n)$-dim

Christophe Grojean, Jonathan Kley, Damien Leflot, Chang-Yuan Yao

Sixty years after the experimental discovery of CP violation in the quark sector, the existence of a similar CP violation in the lepton sector is still to be established. Actually, the structure of such a violation depends crucially on the origin of the neutrino masses. In an attempt at categorizing the leptonic sources of CP violation, we studied the $\nu$S

Jubee Sohn, Margaret J. Geller, Josh Borrow, Mark Vogelsberger

We derive the central stellar velocity dispersion function for quiescent galaxies in 280 massive clusters with $\log (M_{200} / M_{\odot}) > 14$ in IllustrisTNG300. The velocity dispersion function is an independent tracer of the dark matter mass distribution of subhalos in galaxy clusters. Based on the IllustrisTNG cluster catalog, we select quiescent membe

Zeyi Sun, Tong Wu, Pan Zhang, Yuhang Zang

Recent years have witnessed remarkable progress in multi-view diffusion models for 3D content creation. However, there remains a significant gap in image quality and prompt-following ability compared to 2D diffusion models. A critical bottleneck is the scarcity of high-quality 3D objects with detailed captions. To address this challenge, we propose Bootstrap

Chaoyou Fu, Yuhan Dai, Yongdong Luo, Lei Li

In the quest for artificial general intelligence, Multi-modal Large Language Models (MLLMs) have emerged as a focal point in recent advancements. However, the predominant focus remains on developing their capabilities in static image understanding. The potential of MLLMs in processing sequential visual data is still insufficiently explored, highlighting the

Xiao Zhang, William Gao, Seemandhar Jain, Michael Maire

Image relighting is the task of showing what a scene from a source image would look like if illuminated differently. Inverse graphics schemes recover an explicit representation of geometry and a set of chosen intrinsics, then relight with some form of renderer. However error control for inverse graphics is difficult, and inverse graphics methods can represen

Hrushikesh Sable, Nathan M. Myers, Vito W. Scarola

A topological quantum number, the Witten index, characterizes supersymmetric models by probing for zero energy modes and the possibility of supersymmetry breaking. We propose an averaging method to infer the Witten index in quantum analog simulators. Motivated by recent work on Rydberg atoms trapped in optical tweezer arrays, we consider a related supersymme

Philip Lynch, Vojtěch Witzany, Maarten van de Meent, Niels Warburton

Extreme mass ratio inspirals (EMRIs), where a compact object orbits a massive black hole, are a key source of gravitational waves for the future Laser Interferometer Space Antenna (LISA). Due to their small mass ratio, ($\epsilon \sim 10^{-4}$--$10^{-7}$), the binary evolves slowly and EMRI signals will be in-band for years. Additionally, astrophysical EMRIs

John Wendeborn, Catherine C. Espaillat, Thanawuth Thanathibodee, Connor E. Robinson

Classical T Tauri Stars (CTTSs) are young, low-mass stars which accrete material from their surrounding protoplanetary disk. To better understand accretion variability, we conducted a multi-epoch, multi-wavelength photometric monitoring campaign of four CTTSs: TW Hya, RU Lup, BP Tau, and GM Aur, in 2021 and 2022, contemporaneous with HST UV and optical spect

Xin Wen, Bingchen Zhao, Yilun Chen, Jiangmiao Pang

Severe data imbalance naturally exists among web-scale vision-language datasets. Despite this, we find CLIP pre-trained thereupon exhibits notable robustness to the data imbalance compared to supervised learning, and demonstrates significant effectiveness in learning generalizable representations. With an aim to investigate the reasons behind this finding, w

Jean-Marc Valin, Ahmed Mustafa, Jan Büthe

Neural vocoders are now being used in a wide range of speech processing applications. In many of those applications, the vocoder can be the most complex component, so finding lower complexity algorithms can lead to significant practical benefits. In this work, we propose FARGAN, an autoregressive vocoder that takes advantage of long-term pitch prediction to

Najoung Kim, Sebastian Schuster, Shubham Toshniwal

Recent work has provided indirect evidence that pretraining language models on code improves the ability of models to track state changes of discourse entities expressed in natural language. In this work, we systematically test this claim by comparing pairs of language models on their entity tracking performance. Critically, the pairs consist of base models

Kazimierz J. Borkowski, Stephen P. Reynolds, Robert Petre, David A. Green

We report results from 13 years of Chandra monitoring of nonthermal X-ray emission from the youngest Galactic supernova remnant G1.9+0.3, the only remnant known to be increasing in brightness. We confirm the spatially-integrated flux increase rate of $(1.2 \pm 0.2)$% yr$^{-1}$ between 1 and 7 keV, but find large spatial variations, from decreases of $-3$% yr

Katherine Van Koevering, Jon Kleinberg

One uniquely human trait is our inability to be random. We see and produce patterns where there should not be any and we do so in a predictable way. LLMs are supplied with human data and prone to human biases. In this work, we explore how LLMs approach randomness and where and how they fail through the lens of the well studied phenomena of generating binary

Hannah Lange, Guillaume Bornet, Gabriel Emperauger, Cheng Chen

Owing to their great expressivity and versatility, neural networks have gained attention for simulating large two-dimensional quantum many-body systems. However, their expressivity comes with the cost of a challenging optimization due to the in general rugged and complicated loss landscape. Here, we present a hybrid optimization scheme for neural quantum sta

Siyi Hu, Diego Martin Arroyo, Stephanie Debats, Fabian Manhardt

Generating realistic 3D scenes is an area of growing interest in computer vision and robotics. However, creating high-quality, diverse synthetic 3D content often requires expert intervention, making it costly and complex. Recently, efforts to automate this process with learning techniques, particularly diffusion models, have shown significant improvements in

Alan Bregazzi, James P. McGilligan, Paul F. Griffin, Erling Riis

Ultracold atoms are crucial for unlocking truly precise and accurate quantum metrology, and provide an essential platform for quantum computing, communication and memories. One of the largest ongoing challenges is the miniaturization of these quantum devices. Here, we show that the typically macroscopic optical lattice architecture at the heart of many ultra

Nicolas Zucchet, Antonio Orvieto

Recurrent neural networks (RNNs) notoriously struggle to learn long-term memories, primarily due to vanishing and exploding gradients. The recent success of state-space models (SSMs), a subclass of RNNs, to overcome such difficulties challenges our theoretical understanding. In this paper, we delve into the optimization challenges of RNNs and discover that,

Zhouxing Shi, Qirui Jin, Zico Kolter, Suman Jana

Branch-and-bound (BaB) is among the most effective techniques for neural network (NN) verification. However, existing works on BaB for NN verification have mostly focused on NNs with piecewise linear activations, especially ReLU networks. In this paper, we develop a general framework, named GenBaB, to conduct BaB on general nonlinearities to verify NNs with

Alexander Polishchuk, Eric Rains

We consider the ${\mathbb Z}^n$-graded algebra of global sections of line bundles generated by the standard line bundles $L_1,\ldots,L_n$ on $\bar{M}_{0,n}$. We find a simple presentation of this algebra by generators and quadratic relations. As an application we prove that the moduli space $\bar{M}_{0,n}[\psi]$ of $\psi$-stable curves of genus $0$ is Cohen-

Jianqing Liang, Min Chen, Jiye Liang

The Transformer architecture has recently gained considerable attention in the field of graph representation learning, as it naturally overcomes several limitations of Graph Neural Networks (GNNs) with customized attention mechanisms or positional and structural encodings. Despite making some progress, existing works tend to overlook external information of

Tri Dao, Albert Gu

While Transformers have been the main architecture behind deep learning's success in language modeling, state-space models (SSMs) such as Mamba have recently been shown to match or outperform Transformers at small to medium scale. We show that these families of models are actually quite closely related, and develop a rich framework of theoretical connections

Jingjing Wang, Dan Zhang, Feng Luo

Previous work has demonstrated that, in the Variance Preserving (VP) scenario, the nascent Directly Denoising Diffusion Models (DDDM) can generate high-quality images in one step while achieving even better performance in multistep sampling. However, the Pseudo-LPIPS loss used in DDDM leads to concerns about the bias in assessment. Here, we propose a unified

Matthias Rosenkranz, Eric Brunner, Gabriel Marin-Sanchez, Nathan Fitzpatrick

A fundamental step of any quantum algorithm is the preparation of qubit registers in a suitable initial state. Often qubit registers represent a discretization of continuous variables and the initial state is defined by a multivariate function. We develop protocols for preparing quantum states whose amplitudes encode multivariate functions by linearly combin

Christopher Eckner, Felipe Figueroa, Piotr Tourkine

Regge poles connect the analytic structure of scattering amplitudes, analytically continued in spin, to the high-energy limit in momentum space. Dual models are expected to have only Regge poles, and string theory suggests there should be an infinite number of them. In this study, we investigate the number of Regge trajectories these models may have. We prov

Deng Cao, Hongbo Zhang, Rajveer Dhillon

Organic weed control is a vital to improve crop yield with a sustainable approach. In this work, a directed energy weed control robot prototype specifically designed for organic farms is proposed. The robot uses a novel distributed array robot (DAR) unit for weed treatment. Soybean and corn databases are built to train deep learning neural nets to perform we

Susmita Haldar, Mary Pierce, Luiz Fernando Capretz

Formal software testing education is important for building efficient QA professionals. Various aspects of quality assurance approaches are usually covered in courses for training software testing students. Automated Test Tools is one of the core courses in the software testing post-graduate curriculum due to the high demand for automated testers in the work

Angela Adamo, Hakim Atek, Micaela B. Bagley, Eduardo Bañados

With stunning clarity, JWST has revealed the Universe's first billion years. The scientific community is analyzing a wealth of JWST imaging and spectroscopic data from that era, and is in the process of rewriting the astronomy textbooks. Here, 1.5 years into the JWST science mission, we provide a snapshot of the great progress made towards understanding the

Annette Huber, Martin Kalck

We apply the structure theory of finite dimensional algebras in order to deduce dimension formulas for spaces of period numbers, i.e., complex numbers defined by integrals of algebraic nature. We get a complete and conceptually clear answer in the case of $1$-periods, generalising classical results like Baker's theorem on the logarithms of algebraic numbers

David Fitzek, Yi Hong Teoh, Hin Pok Fung, Gebremedhin A. Dagnew

We introduce a generative pretained transformer (GPT) designed to learn the measurement outcomes of a neutral atom array quantum computer. Based on a vanilla transformer, our encoder-decoder architecture takes as input the interacting Hamiltonian, and outputs an autoregressive sequence of qubit measurement probabilities. Its performance is studied in the vic

Carsten Lemmen, Philipp Sebastian Sommer

Frequently in socio-environmental sciences, models are used as tools to represent, understand, project and predict the behaviour of these complex systems. Along the modelling chain, Good Modelling Practices have been evolving that ensure - amongst others - that models are transparent and their results replicable. Whenever such models are represented in softw

Xinxi Zhang, Song Wen, Ligong Han, Felix Juefei-Xu

Adapting large-scale pre-trained generative models in a parameter-efficient manner is gaining traction. Traditional methods like low rank adaptation achieve parameter efficiency by imposing constraints but may not be optimal for tasks requiring high representation capacity. We propose a novel spectrum-aware adaptation framework for generative models. Our met

Jim Fuller, Daichi Tsuna

The mass loss mechanism of red supergiant stars is not well understood, even though it has crucial consequences for their stellar evolution and the appearance of supernovae that occur upon core-collapse. We argue that outgoing shock waves launched near the photosphere can support a dense chromosphere between the star's surface and the dust formation radius a

Jiatao Gu, Ying Shen, Shuangfei Zhai, Yizhe Zhang

Diffusion models have emerged as a powerful tool for generating high-quality images from textual descriptions. Despite their successes, these models often exhibit limited diversity in the sampled images, particularly when sampling with a high classifier-free guidance weight. To address this issue, we present Kaleido, a novel approach that enhances the divers

Kanghee Park, Jiayu Wang, Taylor Berg-Kirkpatrick, Nadia Polikarpova

Large Language Models (LLMs) struggle with reliably generating highly structured outputs, such as program code, mathematical formulas, or well-formed markup. Constrained decoding approaches mitigate this problem by greedily restricting what tokens an LLM can output at each step to guarantee that the output matches a given constraint. Specifically, in grammar

Tengyang Xie, Dylan J. Foster, Akshay Krishnamurthy, Corby Rosset

Reinforcement learning from human feedback (RLHF) has emerged as a central tool for language model alignment. We consider online exploration in RLHF, which exploits interactive access to human or AI feedback by deliberately encouraging the model to produce diverse, maximally informative responses. By allowing RLHF to confidently stray from the pre-trained mo

Qinhua Jiang, Xishun Liao, Yaofa Gong, Jiaqi Ma

Work zone is one of the major causes of non-recurrent traffic congestion and road incidents. Despite the significance of its impact, studies on predicting the traffic impact of work zones remain scarce. In this paper, we propose a data integration pipeline that enhances the utilization of work zone and traffic data from diversified platforms, and introduce a

Houston Claure

The foundation of successful human collaboration is deeply rooted in the principles of fairness. As robots are increasingly prevalent in various parts of society where they are working alongside groups and teams of humans, their ability to understand and act according to principles of fairness becomes crucial for their effective integration. This is especial

Fengdi Che, Chenjun Xiao, Jincheng Mei, Bo Dai

We prove that the combination of a target network and over-parameterized linear function approximation establishes a weaker convergence condition for bootstrapped value estimation in certain cases, even with off-policy data. Our condition is naturally satisfied for expected updates over the entire state-action space or learning with a batch of complete traje

Kieran A. Murphy, Sam Dillavou, Dani S. Bassett

Probabilistic representation spaces convey information about a dataset and are shaped by factors such as the training data, network architecture, and loss function. Comparing the information content of such spaces is crucial for understanding the learning process, yet most existing methods assume point-based representations, neglecting the distributional nat

Santiago Hernández-Gómez, Takuya Isogawa, Alessio Belenchia, Amikam Levy

The Kirkwood-Dirac quasiprobability distribution, intimately connected with the quantum correlation function of two observables measured at distinct times, is becoming increasingly relevant for fundamental physics and quantum technologies. This quasiprobability distribution can take non-positive values, and its experimental reconstruction becomes challenging

Runsheng Yu, Yong Wang, Xiaoqi Jiao, Youzhi Zhang

Reinforcement Learning from Human Feedback (RLHF) has been commonly used to align the behaviors of Large Language Models (LLMs) with human preferences. Recently, a popular alternative is Direct Policy Optimization (DPO), which replaces an LLM-based reward model with the policy itself, thus obviating the need for extra memory and training time to learn the re

Pablo José Vega Esparza

The following article summarizes research where theorems and their respective demonstrations are postulated based on quadratic equations with special properties given by the Pythagorean triplets and the Fibonacci sequence given the second order of equations where integer solutions are found an environment in number theory and its applications to calculus.

John Wendeborn, Catherine C. Espaillat, Sophia Lopez, Thanawuth Thanathibodee

The Classical T Tauri Star (CTTS) stage is a critical phase of the star and planet formation process. In an effort to better understand the mass accretion process, which can dictate further stellar evolution and planet formation, a multi-epoch, multi-wavelength photometric and spectroscopic monitoring campaign of four CTTSs (TW Hya, RU Lup, BP Tau, and GM Au

Vincenzo Sapienza, Marco Miceli, Aya Bamba, Salvatore Orlando

Supernova (SN) 1987A is widely regarded as an excellent candidate for leveraging the capabilities of the freshly launched XRISM satellite. Recent researches indicate that the X-ray emission from SN 1987A will increasingly originate from its ejecta in the years to come. In a previous study, we thoroughly examined the proficiency of XRISM-Resolve in identifyin

Fabian Obster, Christian Heumann

This paper introduces a novel framework for reducing variable selection bias by balancing selection frequencies of base-learners in boosting and introduces the sgboost package in R, which implements this framework combined with sparse-group boosting. The group bias reduction algorithm employs a simulation-based approach to iteratively adjust the degrees of f

Jacek Karolczak, Jerzy Stefanowski

The need for interpreting machine learning models is addressed through prototype explanations within the context of tree ensembles. An algorithm named Adaptive Prototype Explanations of Tree Ensembles (A-PETE) is proposed to automatise the selection of prototypes for these classifiers. Its unique characteristics is using a specialised distance measure and a