arXiv papers from May 2024

Ananya Jain, Aviral Bhardwaj, Kaushik Murali, Isha Surani

Large language models, notably utilizing Transformer architectures, have emerged as powerful tools due to their scalability and ability to process large amounts of data. Dosovitskiy et al. expanded this architecture to introduce Vision Transformers (ViT), extending its applicability to image processing tasks. Motivated by this advancement, we fine-tuned two

Austin Hoover, Jonathan C. Wong

Particle accelerators generate charged-particle beams with tailored distributions in six-dimensional position-momentum space (phase space). Knowledge of the phase space distribution enables model-based beam optimization and control. In the absence of direct measurements, the distribution must be tomographically reconstructed from its projections. In this pap

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

A search for radiative decay of $B^0_s$ mesons to orbitally excited $K^+K^-$ states is performed using proton proton collisions recorded by the \mbox{LHCb}\xspace experiment, corresponding to an integrated luminosity of 9~fb$^{-1}$. The dikaon spectrum in the mass range $m_{KK}<2400$~{\ensuremath{\,\text{Me\kern -0.1em V\!/}c^2}\xspace} is dominated by the $

Ziyi Zhang, Yorie Nakahira, Guannan Qu

We study the problem of learning to stabilize unknown noisy Linear Time-Invariant (LTI) systems on a single trajectory. It is well known in the literature that the learn-to-stabilize problem suffers from exponential blow-up in which the state norm blows up in the order of $\Theta(2^n)$ where $n$ is the state space dimension. This blow-up is due to the open-l

Yu-Chien Lin, Yan Xin, Ta-Sung Lee, Charlie

Acquiring downlink channel state information (CSI) is crucial for optimizing performance in massive Multiple Input Multiple Output (MIMO) systems operating under Frequency-Division Duplexing (FDD). Most cellular wireless communication systems employ codebook-based precoder designs, which offer advantages such as simpler, more efficient feedback mechanisms an

De-Zhang Li, Xin Wang, Xiao-Bao Yang

Ising model is famous in condensed matter and statistical physics. In this work we present a free-fermion formulation of the two-dimensional classical Ising models on the honeycomb, triangular and Kagom\'e lattices. Each Ising model is studied in the cases of a zero field and of an imaginary field $i(\pi/2){k_B}T$. We employ the decorated lattice technique,

Tilmann Bruckhaus

Retrieval-Augmented Generation (RAG) improves the accuracy and relevance of large language model outputs by incorporating knowledge retrieval. However, implementing RAG in enterprises poses challenges around data security, accuracy, scalability, and integration. This paper explores the unique requirements for enterprise RAG, surveys current approaches and li

Yifan Zeng, Ojas Tendolkar, Raymond Baartmans, Qingyun Wu

Ranking passages by prompting a large language model (LLM) can achieve promising performance in modern information retrieval (IR) systems. A common approach to sort the ranking list is by prompting LLMs for a pairwise or setwise comparison which often relies on sorting algorithms. However, sorting-based methods require consistent comparisons to correctly sor

Caucher Birkar, Jia Jia, Artan Sheshmani

In this paper we build bridges between moduli theory of sheaf stable pairs on one hand and birational geometry on the other hand. We will in particular treat moduli of sheaf stable pairs on smooth projective curves in detail and present some calculations in low degrees. We will also outline problems in various directions.

Rocio Kiman, Timothy D. Brandt, Jacqueline K. Faherty, Mark Popinchalk

Measuring fundamental stellar parameters is key to fully comprehending the evolution of stars. However, current theoretical models over-predict effective temperatures, and under-predict radii, compared to observations of K and M dwarfs (radius inflation problem). In this work, we developed a model independent method to infer precise radii of single FGK and M

Yuchen Liu, Chenyang Xu

We prove the set of Koll\'{a}r valuations in the dual complex of a klt singularity with a fixed complement is path connected. We also classify the case when the dual complex is one dimensional.

Haoan Feng, Xin Xu, Leila De Floriani

Digital terrain models (DTMs) are pivotal in remote sensing, cartography, and landscape management, requiring accurate surface representation and topological information restoration. While topology analysis traditionally relies on smooth manifolds, the absence of an easy-to-use continuous surface model for a large terrain results in a preference for discrete

William Hogan, Jingbo Shang

Recent research efforts have explored the potential of leveraging natural language inference (NLI) techniques to enhance relation extraction (RE). In this vein, we introduce MetaEntailRE, a novel adaptation method that harnesses NLI principles to enhance RE performance. Our approach follows past works by verbalizing relation classes into class-indicative hyp

Ezgi Polat, Yilmaz Simsek

The main purpose and motivation of this article is to create a linear transformation on the polynomial ring of rational numbers. A matrix representation of this linear transformation based on standard fundamentals will be given. For some special cases of this matrix, matrix equations including inverse matrices, the Bell polynomials will be given. With the he

Kristi Doleh, Leonard Humphrey, Chandler M. Linseisen, Michael D. Kitcher

Domain wall (DW) devices have garnered recent interest for diverse applications including memory, logic, and neuromorphic primitives; fast, accurate device models are therefore imperative for large-scale system design and verification. Extant DW motion models are sub-optimal for large-scale system design either over-consuming compute resources with physics-h

Ian DeHaan, Kanstantsin Pashkovich

We study a class of Bayesian online selection problems with matroid constraints. Consider a vendor who has several items to sell, with the set of sold items being subject to some structural constraints, e.g., the set of sold items should be independent with respect to some matroid. Each item has an offer value drawn independently from a known distribution. G

Takeshi Torii

In this paper we give an example of duoidal $\infty$-categories. We introduce map $\mathcal{O}$-monoidales in an $\mathcal{O}$-monoidal $(\infty,2)$-category for an $\infty$-operad $\mathcal{O}^{\otimes}$. We show that the endomorphism mapping $\infty$-category of a map $\mathcal{O}$-monoidale is a coCartesian $(\Delta^{\rm op},\mathcal{O})$-duoidal $\infty$

Maximillian Chen, Ruoxi Sun, Tomas Pfister, Sercan Ö. Arık

Large language models (LLMs), optimized through human feedback, have rapidly emerged as a leading paradigm for developing intelligent conversational assistants. However, despite their strong performance across many benchmarks, LLM-based agents might still lack conversational skills such as disambiguation -- when they are faced with ambiguity, they often over

Gonzalo Navarro, Alejandro Pacheco

We introduce a data structure for counting pattern occurrences in texts compressed with any run-length context-free grammar. Our structure uses space proportional to the grammar size and counts the occurrences of a pattern of length $m$ in a text of length $n$ in time \(O(m\log^{2+\epsilon} n)\), for any constant \(\epsilon > 0\) chosen at indexing time. Thi

Jacob Bedrossian, Chi-Hao Wu

In this paper we derive a quantitative dichotomy for the top Lyapunov exponent of a class of non-dissipative SDEs on a compact manifold in the small noise limit. Specifically, we prove that in this class, either the Lyapunov exponent is zero for all noise strengths, or it is positive for all noise strengths and that the decay of the exponent in the small-noi

Bimsara Pathiraja, Caleb Liu, Ransalu Senanayake

The deployment of autonomous vehicles (AVs) is rapidly expanding to numerous cities. At the heart of AVs, the object detection module assumes a paramount role, directly influencing all downstream decision-making tasks by considering the presence of nearby pedestrians, vehicles, and more. Despite high accuracy of pedestrians detected on held-out datasets, the

Olcyr Sumensari

The Belle-II experiment has recently measured $\mathcal{B}(B^+\to K^+\nu\bar{\nu})$, which appears to be almost $3\sigma$ larger than its Standard Model (SM) prediction. In this talk, I will critically revisit the status of the SM predictions for the $B\to K^{(\ast)}\nu\bar{\nu}$ decays, and discuss the interpretation of the recent Belle-II measurement in te

Neelam Shukla, Artem G. Volosniev, Jeremy R. Armstrong

We study a modified three-dimensional Gross-Pitaevski equation that describes a static impurity in a dipolar Bose-Einstein condensate (BEC). Our focus is on the interplay between the shape of the impurity and the anisotropy of the medium manifested in the energy and the density of the system. Without external confinement, properties of the system are derived

Michail Mamalakis, Héloïse de Vareilles, Graham Murray, Pietro Lio

Explainability is a critical factor in enhancing the trustworthiness and acceptance of artificial intelligence (AI) in healthcare, where decisions directly impact patient outcomes. Despite advancements in AI interpretability, clear guidelines on when and to what extent explanations are required in medical applications remain lacking. We propose a novel categ

Jie JW Wu, Fatemeh H Fard

Large language models (LLMs) have significantly improved their ability to perform tasks in the field of code generation. However, there is still a gap between LLMs being capable coders and being top-tier software engineers. Based on the observation that top-level software engineers often ask clarifying questions to reduce ambiguity in both requirements and c

Peter Dotti, Yifei Bai, Toshihiko Shimasaki, Anna R. Dardia

The interplay of various localizing mechanisms is a central topic of modern condensed matter physics. In this work we experimentally explore the interplay between quasiperiodic disorder and periodic driving, each of which in isolation is capable of driving a metal-insulator phase transition. Using a 1D quasiperiodic cold-atom chain we measure transport acros

Kamesh Munagala, Govind S. Sankar

In this paper, we consider classic randomized low diameter decomposition procedures for planar graphs that obtain connected clusters which are cohesive in that close-by pairs of nodes are assigned to the same cluster with high probability. We require the additional aspect of individual fairness - pairs of nodes at comparable distances should be separated wit

Chen Feng, Duolikun Danier, Fan Zhang, Alex Mackin

Visual artifacts are often introduced into streamed video content, due to prevailing conditions during content production and delivery. Since these can degrade the quality of the user's experience, it is important to automatically and accurately detect them in order to enable effective quality measurement and enhancement. Existing detection methods often foc

Yufei Huang, Yulin Li, Andrea Matta, Mohsen Jafari

This paper presents a novel approach to improving autonomous vehicle control in environments lacking clear road markings by integrating a diffusion-based motion predictor within an Active Inference Framework (AIF). Using a simulated parking lot environment as a parallel to unmarked roads, we develop and test our model to predict and guide vehicle movements e

Jinchao Zhu, Yuxuan Wang, Siyuan Pan, Pengfei Wan

The Stable Diffusion Model (SDM) is a prevalent and effective model for text-to-image (T2I) and image-to-image (I2I) generation. Despite various attempts at sampler optimization, model distillation, and network quantification, these approaches typically maintain the original network architecture. The extensive parameter scale and substantial computational de

John T. Halloran, Manbir Gulati, Paul F. Roysdon

Mamba state-space models (SSMs) have recently outperformed state-of-the-art (SOTA) Transformer large language models (LLMs) in various tasks and been widely adapted. However, a major concern for stable learning in recurrent-based deep models (such as SSMs) is the sensitivity of their recurrent dynamics. Despite widespread adaptation, the sensitivity of Mamba

S. Y. Lou

The quest to reveal the physical essence of the infinitely many symmetries and conservation laws that are intrinsic to integrable systems has historically posed a significant challenge at the confluence of physics and mathematics. This scholarly investigation delves into five open problems related to these boundless symmetries within integrable systems by sc

Mario A. Acero, A. Oliveros

Considering a well-motivated $f(R)$ modified-gravity model, in which an exponential function of the curvature is included, in this paper we implement a statistical data analysis to set constraints on the parameters of the model, taking into account an analytic approximate solution for the expansion rate, $H(z)$. Using a Monte Carlo Markov Chain-based analysi

Andrey Smirnov

We consider a class of $q$-hypergeometric equations describing the quantum difference equation for the cotangent bundles over projective spaces $X=T^{*}\mathbb{P}^{n-1}$ . We show that over $\mathbb{Q}_p$ these equations are equipped with the Frobenius action $(q,z)\to (q^p,z^p)$. We obtain an explicit formula for the constant term of the Frobenius intertwin

Trevor Norton, Debswapna Bhattacharya

Diffusion probabilistic models have made their way into a number of high-profile applications since their inception. In particular, there has been a wave of research into using diffusion models in the prediction and design of biomolecular structures and sequences. Their growing ubiquity makes it imperative for researchers in these fields to understand them.

Theo F. Motta, Julian Bernhardt, Michael Buballa, Christian S. Fischer

In this work we continue our efforts to study the existence of a phase with an inhomogeneous, i.e., spatially varying, chiral condensate in QCD. To this end we employ a previously established method of stability analysis of the two-particle irreducible effective action in a truncation that corresponds to a rainbow-ladder approximation of the quark-gluon inte

Avanish Mishra, Sumit A. Suresh, Saryu J. Fensin, Nithin Mathew

Grain boundaries (GBs) govern critical properties of polycrystals. Although significant advancements have been made in characterizing minimum energy GBs, real GBs are seldom found in such states, making it challenging to establish structure-property relationships. This diversity of atomic arrangements in metastable states motivates using data-driven methods

Bartosz Biadasiewicz, Wojciech Dybalski

We describe a phase transition of infrared radiation, driven by quantum fluctuations, which takes place at the boundary of (the conformal diagram of) Minkowski spacetime. Specifically, we consider a family of states interpolating between the vacuum and the Kraus-Polley-Reents infravacuum. A state from this family can be imagined as a static source emitting f

Conor T. Curtin, Rossen I. Ivanov

The Lagrangian formulation for the irrotational wave motion is straightforward and follows from a Lagrangian functional which is the difference between the kinetic and the potential energy of the system. In the case of fluid with constant vorticity, which arises for example when a shear current is present, the separation of the energy into kinetic and potent

Adam E. A. Fouda, Stephen H. Southworth, Phay J. Ho

The fragmentation of molecular cations following inner-shell decay processes in molecules containing heavy elements underpins the x-ray damage effects observed in x-ray scattering measurements of biological and chemical materials, as well as in medical applications involving Auger-electron emitting radionuclides. Traditionally, these processes are modeled us

Blake Temple, Robin Young

We prove the existence of ``pure tone'' nonlinear sound waves of all frequencies. These are smooth, time periodic, oscillatory solutions of the $3\times3$ compressible Euler equations satisfying periodic or acoustic boundary conditions in one space dimension. This resolves a centuries old problem in the theory of Acoustics, by establishing that the pure mode

Gregory Schwartzman

We report that ChatGPT 4 and 4o are susceptible to a prompt injection attack that allows an attacker to exfiltrate users' personal data. It is applicable without the use of any 3rd party tools and all users are currently affected. This vulnerability is exacerbated by the recent introduction of ChatGPT's memory feature, which allows an attacker to command Cha

Ilya Shenbin, Sergey Nikolenko

We present ImplicitSLIM, a novel unsupervised learning approach for sparse high-dimensional data, with applications to collaborative filtering. Sparse linear methods (SLIM) and their variations show outstanding performance, but they are memory-intensive and hard to scale. ImplicitSLIM improves embedding-based models by extracting embeddings from SLIM-like mo

Qian Ruan, Ilia Kuznetsov, Iryna Gurevych

Collaborative review and revision of textual documents is the core of knowledge work and a promising target for empirical analysis and NLP assistance. Yet, a holistic framework that would allow modeling complex relationships between document revisions, reviews and author responses is lacking. To address this gap, we introduce Re3, a framework for joint analy

Jay Desai, Xiaobo Guo, Srinivasan H. Sengamedu

The performance of Large Language Models has achieved superhuman breadth with unprecedented depth. At the same time, the language models are mostly black box models and the underlying mechanisms for performance have been evaluated using synthetic or mechanistic schemes. We extend current mechanistic schemes to incorporate Logic, memory, and nuances of Langua

Yuhan Li, Yiding Zhang, Gu Mi, Ji Lin

The US FDA's Project Optimus initiative that emphasizes dose optimization prior to marketing approval represents a pivotal shift in oncology drug development. It has a ripple effect for rethinking what changes may be made to conventional pivotal trial designs to incorporate a dose optimization component. Aligned with this initiative, we propose a novel Seaml

Chien-Kun Huang, Yi-Ting Chang, Lun-Wei Ku, Cheng-Te Li

This paper provides an overview of the Fake-EmoReact 2021 Challenge, held at the 9th SocialNLP Workshop, in conjunction with NAACL 2021. The challenge requires predicting the authenticity of tweets using reply context and augmented GIF categories from EmotionGIF dataset. We offer the Fake-EmoReact dataset with more than 453k as the experimental materials, wh

Zhengang Li, Yan Kang, Yuchen Liu, Difan Liu

While AI-generated content has garnered significant attention, achieving photo-realistic video synthesis remains a formidable challenge. Despite the promising advances in diffusion models for video generation quality, the complex model architecture and substantial computational demands for both training and inference create a significant gap between these mo

Atul Mohan, Nat Gopalswamy, Anshu Kumari, Sachiko Akiyama

Decameter hectometric (DH; 1-14 MHz) type-IV radio bursts are produced by flare-accelerated electrons trapped in post-flare loops or the moving magnetic structures associated with the CMEs. From a space weather perspective, it is important to systematically compile these bursts, explore their spectro-temporal characteristics, and study the associated CMEs. W

Yanting Teng, Rhine Samajdar, Katherine Van Kirk, Frederik Wilde

Learning faithful representations of quantum states is crucial to fully characterizing the variety of many-body states created on quantum processors. While various tomographic methods such as classical shadow and MPS tomography have shown promise in characterizing a wide class of quantum states, they face unique limitations in detecting topologically ordered

Maressa P Sampaio, Renan G Alvim, Felipe K Kalil, Maria C O Aguiar

Motivated by a problem from the 2023 International Physicists' Tournament, we investigate the formation of particular patterns when light passes through glass. Experimentally, we use various glass plates, registering each reflected and transmitted outcome. Thus, we find the condition to form a common pattern, namely, randomly scratched plates produce the hal

Yundi Zhang, Nil Stolt-Ansó, Jiazhen Pan, Wenqi Huang

The prevailing deep learning-based methods of predicting cardiac segmentation involve reconstructed magnetic resonance (MR) images. The heavy dependency of segmentation approaches on image quality significantly limits the acceleration rate in fast MR reconstruction. Moreover, the practice of treating reconstruction and segmentation as separate sequential pro

E. Chaussidon, A. de Mattia, C. Yèche, J. Aguilar

The next generation of spectroscopic surveys is expected to achieve an unprecedented level of accuracy in the measurement of cosmological parameters. To avoid confirmation bias and thereby improve the reliability of these results, blinding procedures become a standard practice in the cosmological analyses of such surveys. Blinding is especially crucial when

Raissa Costa Barroso, Yves Lemière, François Mauger, Quentin Baghi

The Laser Interferometer Space Antenna (LISA) will be a space-borne gravitational wave (GW) detector to be launched in the next decade. Central to LISA data analysis is time-delay interferometry (TDI), a numerical procedure which drastically reduces otherwise overwhelming laser frequency noise. LISA data analysis is usually performed on sets of TDI variables

Dmitry Alexandrovsky, Kathrin Gerling, Merlin Steven Opp, Christopher Benjamin Hahn

The games research community has developed substantial knowledge on designing engaging experiences that draw players in. Surprisingly, less is known about player \textit{dis}engagement, with existing work predominantly addressing disengagement from the perspective of problematic play, and research exploring player disengagement from a constructive designer p

Ranjeet Kumar, Newton Nath, Rahul Srivastava

We introduce a framework for hybrid neutrino mass generation, wherein scotogenic dark sector particles, including dark matter, are charged non-trivially under the $A_4$ flavor symmetry. The spontaneous breaking of the $A_4$ group to residual $\mathcal{Z}_2$ subgroup results in the ``cutting" of the radiative loop. As a consequence the neutrinos acquire mass

Hang Yu, Fei Dai

WASP-107 b seems to be a poster child of the long-suspected high-eccentricity migration scenario. It is on a 5.7-day, polar orbit. The planet is Jupiter-like in radius but Neptune-like in mass with exceptionally low density. WASP-107 c is on a 1100-day, $e=0.28$ orbit with at least Saturn mass. Planet b may still have a residual eccentricity of $0.06\pm 0.04

Ritam Pal, Brandon Kemerling, Daniel Ryan, Sudhakar Bollapragada

Additive manufacturing, especially laser powder bed fusion (L-PBF), is widely used for fabricating metal parts with intricate geometries. However, parts produced via L-PBF suffer from varied surface roughness which affects the dynamic or fatigue properties. Accurate prediction of fatigue properties as a function of surface roughness is a critical requirement

Zihni Kaan Baykara, Houri-Christina Tarazi, Cumrun Vafa

In four decades of string theory research, only a handful of non-supersymmetric tachyon-free strings with only one neutral scalar at tree level were found. We construct new such non-supersymmetric tachyon-free string theories using asymmetric orbifolds that serve as the lower-dimensional counterparts to the $O(16) \times O(16)$ string in 4d, 6d, and 8d, each

Gabriele Tartero, Werner Krauth

In past decades, enormous effort has been expended to develop algorithms and even to construct special-purpose computers in order to efficiently evaluate total energies and forces for long-range-interacting particle systems, with the particle-mesh Ewald and the fast multipole methods as well as the 'Anton' series of supercomputers serving as examples for bio

Sebastien Röcken, Anton F. Burnet, Julija Zavadlav

Machine learning (ML) potentials are a powerful tool in molecular modeling, enabling ab initio accuracy for comparably small computational costs. Nevertheless, all-atom simulations employing best-performing graph neural network architectures are still too expensive for applications requiring extensive sampling, such as free energy computations. Implicit solv

Swathi Narashiman, Venkat A, Divyaratna Joshi, Deepak Sridhar

On the advent of the slow death of Moore's law, the silicon industry is moving towards a new era of chiplets. The automotive industry is experiencing a profound transformation towards software-defined vehicles, fueled by the surging demand for automotive compute chips, expected to reach 20-22 billion by 2030. High-performance compute (HPC) chips become instr

Huong Nguyen, Tri Nguyen, Lauri Lovén, Susanna Pirttikangas

This paper presents a fully coupled blockchain-assisted federated learning architecture that effectively eliminates single points of failure by decentralizing both the training and aggregation tasks across all participants. Our proposed system offers a high degree of flexibility, allowing participants to select shared models and customize the aggregation for

Jerin Yasmin, Jiale Wang, Yuan Tian, Bram Adams

The Workflows as Code paradigm is becoming increasingly essential to streamline the design and management of complex processes within data-intensive software systems. These systems require robust capabilities to process, analyze, and extract insights from large datasets. Workflow orchestration platforms such as Apache Airflow are pivotal in meeting these nee

Bernd Bohnet, Kevin Swersky, Rosanne Liu, Pranjal Awasthi

We explore the use of long-context capabilities in large language models to create synthetic reading comprehension data from entire books. Previous efforts to construct such datasets relied on crowd-sourcing, but the emergence of transformers with a context size of 1 million or more tokens now enables entirely automatic approaches. Our objective is to test t

Matthew Buican

Coulomb branches of vacua are the most universal moduli spaces that arise in local unitary interacting 4d $\mathcal{N}=2$ superconformal field theories (SCFTs). In these theories, $1/2$-BPS primaries parameterize the Coulomb branches and form (anti-)chiral rings. We define the notion of a Coulomb branch operator algebra, $\mathcal{A}_{\mathcal{C}}$, that con

Sebastian Otte

Automatic differentiation is a key feature of present deep learning frameworks. Moreover, they typically provide various ways to specify custom gradients within the computation graph, which is of particular importance for defining surrogate gradients in the realms of non-differentiable operations such as the Heaviside function in spiking neural networks (SNN

Graciana Puentes

We present an analytical and numerical study of a class of geometric phase induced by weak measurements. In particular, we analyze the dependence of the geometric phase on the winding ($W$) of the polar angle ($\varphi$), upon a sequence of $N$ weak measurements of increased magnitude ($c$), resulting in the appearance of a multiplicity of critical measureme

Fabrizio Del Monte, Pietro Longhi

This paper studies the space of monodromy data of second order $q$-difference equations through the framework of WKB analysis. We compute the connection matrices associated to the Stokes phenomenon of WKB wavefunctions and develop a general framework to parameterize monodromies of $q$-difference equations. Computations of monodromies are illustrated with exp

Benjamin Short, David M. Malaspina, Alexandros Chasapis, Jaye L. Verniero

Regions of magnetic field with near-radial, Parker Spiral-like geometry known as quiescent regions have been observed in Parker Solar Probe data. These regions have very low $\delta B / \langle |B| \rangle$ compared to non-quiescent solar wind at the same heliocentric distances. Quiescent regions are observed to have lower solar wind bulk speeds, lower proto

Archer Clayton, Paul Jenkins

Griffin, the second author, and Molnar studied coefficient duality for canonical bases for a broad range of spaces of weakly holomorphic modular forms, showing that the Fourier coefficients of canonical basis elements appear as negatives of Fourier coefficients for elements of a canonical basis of a related space of forms. We investigate the effect of the tr

Tea Temim, J. Martin Laming, P. J. Kavanagh, Nathan Smith

We present JWST observations of the Crab Nebula, the iconic remnant of the historical SN 1054. The observations include NIRCam and MIRI imaging mosaics, plus MIRI/MRS IFU spectra that probe two select locations within the ejecta filaments. We derive a high-resolution map of dust emission and show that the grains are concentrated in the innermost, high-densit

Erin S. Lamb, Tristan Kremp, David J. DiGiovanni, Paul S. Westbrook

Transmission matrix measurements of multimode fibers are now routinely performed in numerous labs, enabling control of the electric field at the distal end of the fiber and paving the way for the potential application to ultrathin medical endoscopes with high resolution. However, the process of building an experimental setup and developing the supporting cod

Ou Tan, Dongseok Choi, Aiyin Chen, David S. Greenfield

Purpose: To evaluate nerve fiber layer (NFL) reflectance for glaucoma diagnosis using a large dataset. Methods: Participants were imaged with 4.9mm ONH scans using spectral-domain optical coherence tomography (OCT). The NFL reflectance map was reconstructed from 13 concentric rings of optic nerve head(ONH) scan, then processed by an azimuthal filter to reduc

M. P. Morales Rodríguez, E. García Herrera, O. Magaña Loaiza, B. Perez-Garcia

We use the spatial degree of freedom of light modes to construct optical analogues of generalized quantum coherent states for Hermite- and Laguerre-Gauss modes. Our optical analogues preserve the statistical properties of their quantum counterparts, encoded in their amplitude and phase distributions. We explore three basic symmetries that provide generalized

Shengyu Chen, Peyman Givi, Can Zheng, Xiaowei Jia

The precise simulation of turbulent flows is of immense importance in a variety of scientific and engineering fields, including climate science, freshwater science, and the development of energy-efficient manufacturing processes. Within the realm of turbulent flow simulation, direct numerical simulation (DNS) is widely considered to be the most reliable appr

Kabir Hossain, Ou Tan, Po-Han Yeh, Jie Wang

Purpose: Reliability for Nerve Fiber Layer Reflectance Using Spectral Domain Optical Coherence Tomography (OCT) Methods: The study utilized OCT to scan participants with a cubic 6x6 mm disc scan. NFL reflectance were normalized by the average of bands below NFL and summarized. We selected several reference bands, including the pigment epithelium complex (PPE

J. Amette Estrada, M. Noseda, P. J. Cobelli, P. D. Mininni

We study density isolines in quantum turbulence under the Schramm-Loewner framework using direct numerical simulations of the truncated Gross-Pitaevskii equation, in both spherical and cylindrical traps with three-dimensional dynamics. Density isolines develop increasing complexity as turbulence matures. As the systems evolves towards a thermalized regime, i

Andrew Adamatzky, Nic Roberts, Raphael Fortulan, Noushin Raeisi Kheirabadi

The colloid cellular automata do not imitate the physical structure of colloids but are governed by logical functions derived from the colloids. We analyse the space-time complexity of Boolean circuits derived from the electrical responses of colloids: ZnO (zinc oxide, an inorganic compound also known as calamine or zinc white, which naturally occurs as the

Hong Qian, Zhongwei Shen

Entropy, its production, and its change in a dynamical system can be understood from either a fully stochastic dynamic description or from a deterministic dynamics exhibiting chaotic behavior. By taking the former approach based on the general diffusion process with diffusion $\tfrac{1}{\alpha}{\bf D}(\bf x)$ and drift $\bf b(\bf x)$, where $\alpha$ represen

Huixin Zhan, Zijun Zhang

Clinical variant classification of pathogenic versus benign genetic variants remains a challenge in clinical genetics. Recently, the proposition of genomic foundation models has improved the generic variant effect prediction (VEP) accuracy via weakly-supervised or unsupervised training. However, these VEPs are not disease-specific, limiting their adaptation

Pratik Harsh, Hongjian Sun, Debapriya Das, Goyal Awagan

The growing integration of distributed energy resources (DERs) into the power grid necessitates an effective coordination strategy to maximize their benefits. Acting as an aggregator of DERs, a virtual power plant (VPP) facilitates this coordination, thereby amplifying their impact on the transmission level of the power grid. Further, a demand response progr

Dao Thanh Hai, Minh Nguyen, Isaac Woungang

Inspired by the renaissance of optical computing recently, this poster presents a disruptive outlook on the possibility of seamless integration between optical communications and optical computing infrastructures, paving the way for achieving optical-layer intelligence and consequently boosting the capacity efficiency. This entails a paradigm shift in optica

Hanpeng Gao, Shengda Liu, Yu-Zhe Liu, Yucheng Wang

We define integrals for functions on finite-dimensional algebras, adapting methods from Leinster's research. This paper discusses the relationships between the integrals of functions defined on subsets $\mathbb{I}_1 \subseteq {\mathit{\Lambda}}_1$ and $\mathbb{I}_2 \subseteq {\mathit{\Lambda}}_2$ of two finite-dimensional algebras, under the influence of a m

Yongge Yang, Yu-Ching Lee, Po-An Chen, Chuang-Chieh Lin

This study is focused on periodic Fisher markets where items with time-dependent and stochastic values are regularly replenished and buyers aim to maximize their utilities by spending budgets on these items. Traditional approaches of finding a market equilibrium in the single-period Fisher market rely on complete information about buyers' utility functions a

Giosue Baggio, Elliot Murphy

In a recent paper, Mandelkern & Linzen (2024) - henceforth M&L - address the question of whether language models' (LMs) words refer. Their argument draws from the externalist tradition in philosophical semantics, which views reference as the capacity of words to "achieve 'word-to-world' connections". In the externalist framework, causally uninterrupted chain

Benjamin Brock, Robert Cohn, Suyash Bakshi, Tuomas Karna

Data structures and algorithms are essential building blocks for programs, and \emph{distributed data structures}, which automatically partition data across multiple memory locales, are essential to writing high-level parallel programs. While many projects have designed and implemented C++ distributed data structures and algorithms, there has not been widesp

Ali ArjomandBigdeli, Andrew Mata, Stanley Bak

Neural network controllers are currently being proposed for use in many safety-critical tasks. Most analysis methods for neural network control systems assume a fixed control period. In control theory, higher frequency usually improves performance. However, for current analysis methods, increasing the frequency complicates verification. In the limit, when ac

Sebastián Bahamondes, Ignacio Salazar Landea, Rodrigo Soto-Garrido

Holographic quantum matter exploits the AdS/CFT correspondence to study systems in condensed matter physics. An example of these systems are strongly correlated semimetals, which feature a rich phase diagram structure. In this work, we present a holographic model for a Dirac semimetal in $2+1$ dimensions that features a topological phase transition. Our cons

Slavek M. Rucinski

A re-examination of high-resolution spectral monitoring of the W UMa-type binaries AW UMa and Epsilon CrA casts doubt on the widely utilized Lucy (1968a, 1968b) model of contact binaries. The detection of the very faint profile of the secondary component in AW UMa leads to a new spectroscopic determination of the mass ratio, q(sp) = 0.092 +/- 0.007, which is

Sowmya Chandrasekaran, Thomas Bartz-Beielstein

Stochastic optimization algorithms have been successfully applied in several domains to find optimal solutions. Because of the ever-growing complexity of the integrated systems, novel stochastic algorithms are being proposed, which makes the task of the performance analysis of the algorithms extremely important. In this paper, we provide a novel ranking sche

Benjamin Thérien, Charles-Étienne Joseph, Boris Knyazev, Edouard Oyallon

Learned optimizers (LOs) have the potential to significantly reduce the wall-clock training time of neural networks. However, they can struggle to optimize unseen tasks (meta-generalize), especially when training networks wider than those seen during meta-training. To address this, we derive the Maximal Update Parametrization ($\mu$P) for two state-of-the-ar

Jiakai Li

Given a based link $(K,p)$, we define a "tilde"-version $\tilde{HMR}(K,p)$ of real monopole Floer homology and prove an unoriented skein exact triangle. We show the Euler characteristic of $\tilde{HMR}(K,p)$ is equal to Miyazawa's invariant $|deg(K)|$ arXiv:2312.02041 and examine some examples. Further, we construct a spectral sequence over $\mathbb{F}_2$ ab

Pavlo Bilous, Louis Thirion, Henri Menke, Maurits W. Haverkort

A deep-learning approach to optimize the selection of Slater determinants in configuration interaction calculations for condensed-matter quantum many-body systems is developed. We exemplify our algorithm on the discrete version of the single-impurity Anderson model with up to 299 bath sites. Employing a neural network classifier and active learning, our algo

Yilin Zheng, Atilla Eryilmaz

With the development of edge networks and mobile computing, the need to serve heterogeneous data sources at the network edge requires the design of new distributed machine learning mechanisms. As a prevalent approach, Federated Learning (FL) employs parameter-sharing and gradient-averaging between clients and a server. Despite its many favorable qualities, s

Jonathan Treviño-Marroquín

In this document, we propose a bridge between the graphs and the geometric realizations of their Vietoris Rips complexes, i.e. Graphs, with their canonical \v{C}ech closure structure, have the same homotopy type that the realization of their Vietoris Rips complex.

Eric Balkanski, Steven DiSilvio, Alan Kuhnle, ChunLi Peng

In this work, we study the classical problem of maximizing a submodular function subject to a matroid constraint. We develop deterministic algorithms that are very parsimonious with respect to querying the submodular function, for both the case when the submodular function is monotone and the general submodular case. In particular, we present a 1/4 approxima

Alireza Fallah, Michael I. Jordan, Annie Ulichney

We consider a dynamic mechanism design problem where an auctioneer sells an indivisible good to groups of buyers in every round, for a total of $T$ rounds. The auctioneer aims to maximize their discounted overall revenue while adhering to a fairness constraint that guarantees a minimum average allocation for each group. We begin by studying the static case (

Matej Božić, Marko Horvat

This article presents a review of typical techniques used in three distinct aspects of deep learning model development for audio generation. In the first part of the article, we provide an explanation of audio representations, beginning with the fundamental audio waveform. We then progress to the frequency domain, with an emphasis on the attributes of human

Charlie Dworaczek Guera, Karol K. Kozlowski

In 2000, Lukyanov conjectured that a certain ratio of $N$-fold integrals should provide access, in the large-$N$ regime, to the ground state expectation value of the exponential of the Sinh-Gordon quantum field in 1+1 dimensions and finite volume $R$. This work aims at rigorously constructing the fundamental objects necessary to address the large-$N$ analysi