April 2024 arXiv papers — page 47

Jiangtao Wang, Jianrong Shi, Jianning Fu, Weikai Zong

Accurate determination of the stellar atmospheric parameters of RR Lyrae stars (RRLs) requires short individual exposures of the spectra to mitigate pulsation effects. We present improved template matching methods to determine the stellar atmospheric parameters of RRLs from single-epoch spectra of LAMOST (Large Sky Area Multi-Object Fiber Spectroscopic Teles

Mateus G. Machado, João G. Melo, Cleber Zanchettin, Pedro H. M. Braga

This work investigates the potential of Reinforcement Learning (RL) to tackle robot motion planning challenges in the dynamic RoboCup Small Size League (SSL). Using a heuristic control approach, we evaluate RL's effectiveness in obstacle-free and single-obstacle path-planning environments. Ablation studies reveal significant performance improvements. Our met

Gavin Brown, Jonathan Hayase, Samuel Hopkins, Weihao Kong

We present a sample- and time-efficient differentially private algorithm for ordinary least squares, with error that depends linearly on the dimension and is independent of the condition number of $X^\top X$, where $X$ is the design matrix. All prior private algorithms for this task require either $d^{3/2}$ examples, error growing polynomially with the condi

Rossella Gamba, Danilo Chiaramello, Sayan Neogi

Complete waveform models able to account for arbitrary non-planar orbits represent a holy grail in current gravitational-wave astronomy. Here, we take a step towards this direction and present a simple yet efficient prescription to obtain the evolution of the spin vectors and of the orbital angular momentum along non-circularized orbits, that can be applied

Davide Caffagni, Federico Cocchi, Nicholas Moratelli, Sara Sarto

Multimodal LLMs are the natural evolution of LLMs, and enlarge their capabilities so as to work beyond the pure textual modality. As research is being carried out to design novel architectures and vision-and-language adapters, in this paper we concentrate on endowing such models with the capability of answering questions that require external knowledge. Our

Dominik Winecki, Christopher S. Kochanek

We use a multilevel perceptron (MLP) neural network to obtain photometry of saturated stars in the All-Sky Automated Survey for Supernovae (ASAS-SN). The MLP can obtain fairly unbiased photometry for stars from g~4 to 14~mag, particularly compared to the dispersion (15%-85% 1sigma range around the median) of 0.12 mag for saturated (g<11.5 mag) stars. More im

Floor van Donkelaar, Lucio Mayer, Pedro R. Capelo, Tomas Tamfal

In this work, we address the following question: ``can we use the current cosmological simulations to identify intermediate-mass black holes (IMBHs) and quantify a putative population of wandering IMBHs?''. We compare wandering-IMBH counts in different simulations with different sub-grid methods and post-processing recipes, the ultimate goal being to aid fut

Amit Vikram, Laura Shou, Victor Galitski

We prove that the time required for sustained information scrambling in any Hamiltonian quantum system is universally at least logarithmic in the entanglement entropy of scrambled states. This addresses two foundational problems in nonequilibrium quantum dynamics. (1) It sets the earliest possible time for the applicability of equilibrium statistical mechani

Maximilian von Wietersheim-Kramsta, Kiyam Lin, Nicolas Tessore, Benjamin Joachimi

We present a simulation-based inference (SBI) cosmological analysis of cosmic shear two-point statistics from the fourth weak gravitational lensing data release of the ESO Kilo-Degree Survey (KiDS-1000). KiDS-SBI efficiently performs non-Limber projection of the matter power spectrum via Levin's method, and constructs log-normal random matter fields on the c

Yang Liu, Antonio Padilla, Paul M. Saffin, Robert G. C. Smith

We perform a thorough analysis of de Sitter vacua in O(d,d) invariant cosmologies. Starting with a homogeneous and isotropic framework we examine conditions for the existence of such vacua, non-perturbative in \alpha' in both the string frame and the Einstein frame. We elucidate the nature of the instability in the string frame vacuum. For the Einstein frame

Jacqueline Caminiti, Batia Friedman-Shaw, Alex May, Robert C. Myers

In the AdS/CFT correspondence, the causal structure of the bulk AdS spacetime is tied to entanglement in the dual CFT. This relationship is captured by the connected wedge theorem, which states that a bulk scattering process implies the existence of $O(1/G_N)$ entanglement between associated boundary subregions. In this paper, we study the connected wedge th

Katerina Slavicinska, Ewine F. van Dishoeck, Łukasz Tychoniec, Pooneh Nazari

This work aims to utilize the increased sensitivity and resolution of the JWST to quantify the HDO/H$_{2}$O ratio in ices toward young stellar objects (YSOs) and to determine if the HDO/H$_{2}$O ratios measured in the gas phase toward massive YSOs (MYSOs) are representative of the ratios in their ice envelopes. Two protostars observed in the Investigating Pr

Yifan Zhang, Rhine Samajdar, Sarang Gopalakrishnan

Nitrogen-vacancy centers are spatially resolved probes of current noise. So far, current noise sensing with NV centers has primarily been used as a way to probe equilibrium transport coefficients. We develop a framework for computing the spatiotemporal correlations of nonequilibrium current noise in the Boltzmann regime, and apply it to two-dimensional metal

Benjamin F. Schiffer, Adrian Franco Rubio, Rahul Trivedi, J. Ignacio Cirac

The propagation of errors severely compromises the reliability of quantum computations. The quantum adiabatic algorithm is a physically motivated method to prepare ground states of classical and quantum Hamiltonians. Here, we analyze the proliferation of a single error event in the adiabatic algorithm. We give numerical evidence using tensor network methods

Rebecca Diesing, Minghao Guo, Chang-Goo Kim, James Stone

The end of supernova remnant (SNR) evolution is characterized by a so-called "radiative" stage, in which efficient cooling of the hot bubble inside the forward shock slows expansion, leading to eventual shock breakup. Understanding SNR evolution at this stage is vital for predicting feedback in galaxies, since SNRs are expected to deposit their energy and mo

Sean M. O'Brien, Megan E. Schwamb, Samuel Gill, Christopher A. Watson

We present the results from the first two years of the Planet Hunters NGTS citizen science project, which searches for transiting planet candidates in data from the Next Generation Transit Survey (NGTS) by enlisting the help of members of the general public. Over 8,000 registered volunteers reviewed 138,198 light curves from the NGTS Public Data Releases 1 a

Xiangyu Xu, Lijuan Liu, Shuicheng Yan

Existing Transformers for monocular 3D human shape and pose estimation typically have a quadratic computation and memory complexity with respect to the feature length, which hinders the exploitation of fine-grained information in high-resolution features that is beneficial for accurate reconstruction. In this work, we propose an SMPL-based Transformer framew

Xuanhua He, Quande Liu, Shengju Qian, Xin Wang

Generating high-fidelity human video with specified identities has attracted significant attention in the content generation community. However, existing techniques struggle to strike a balance between training efficiency and identity preservation, either requiring tedious case-by-case fine-tuning or usually missing identity details in the video generation p

Matt Y Cheung, Tucker J Netherton, Laurence E Court, Ashok Veeraraghavan

Modern deep learning reconstruction algorithms generate impressively realistic scans from sparse inputs, but can often produce significant inaccuracies. This makes it difficult to provide statistically guaranteed claims about the true state of a subject from scans reconstructed by these algorithms. In this study, we propose a framework for computing provably

Mattia Bianchi, Sergio Grammatico

Distributed decision problems features a group of agents that can only communicate over a peer-to-peer network, without a central memory. In applications such as network control and data ranking, each agent is only affected by a small portion of the decision vector: this sparsity is typically ignored in distributed algorithms, while it could be leveraged to

Jingyang Lin, Yingda Xia, Jianpeng Zhang, Ke Yan

3D medical vision-language (VL) pretraining has shown potential in radiology by leveraging large-scale multimodal datasets with CT-report pairs. However, existing methods primarily rely on a global VL alignment directly adapted from 2D scenarios. The entire 3D image is transformed into one global embedding, resulting in a loss of sparse but critical semantic

Jonas Hjulstad

In this project, identifiability, observability and uncertainty properties of the deterministic and Chain Binomial stochastic SIR, SEIR and SEIAR epidemiological models are studied. Techniques for modeling overdispersion are investigated and used to compare simulated trajectories for moderately sized, homogenous populations. With the chosen model parameters

Wanrong Zhu, Jennifer Healey, Ruiyi Zhang, William Yang Wang

Recent advancements in instruction-following models have made user interactions with models more user-friendly and efficient, broadening their applicability. In graphic design, non-professional users often struggle to create visually appealing layouts due to limited skills and resources. In this work, we introduce a novel multimodal instruction-following fra

Ian Aupiais, Romain Grasset, Dmitri Daineka, Javier Briatico

Chiral engineering of TeraHertz (THz) light fields and the use of the handedness of light in THz light-matter interactions promise many novel opportunities for advanced sensing and control of matter in this frequency range. Unlike previously explored methods, this is achieved here by leveraging the chiral properties of highly confined THz surface plasmon mod

Ge Gao, Alexey Taymanov, Eduardo Salinas, Paul Mineiro

We study interactive learning of LLM-based language agents based on user edits made to the agent's output. In a typical setting such as writing assistants, the user interacts with a language agent to generate a response given a context, and may optionally edit the agent response to personalize it based on their latent preference, in addition to improving the

Arnab Roy, Jusak Tandean, German Valencia

Motivated by the LHCb measurement of the hyperon decay mode $\Sigma^+\to p\mu^+\mu^-$ and prospects for improvement, we revisit the estimates for the rate and muon forward-backward asymmetry within the standard model and beyond. The standard model prediction has a fourfold ambiguity, and we suggest ways to resolve it with other measurements, including possib

Zehuan Huang, Hongxing Fan, Lipeng Wang, Lu Sheng

Recent advancements in controllable human image generation have led to zero-shot generation using structural signals (e.g., pose, depth) or facial appearance. Yet, generating human images conditioned on multiple parts of human appearance remains challenging. Addressing this, we introduce Parts2Whole, a novel framework designed for generating customized portr

Simone Roncallo, Angela Rosy Morgillo, Chiara Macchiavello, Lorenzo Maccone

Classification is a central task in deep learning algorithms. Usually, images are first captured and then processed by a sequence of operations, of which the artificial neuron represents one of the fundamental units. This paradigm requires significant resources that scale (at least) linearly in the image resolution, both in terms of photons and computational

N. Emil J. Bjerrum-Bohr, Gang Chen, Yuchan Miao, Marcos Skowronek

We introduce a novel approach to compute Compton amplitudes involving a fermion pair inspired by Hopf algebra amplitude constructions. This approach features a recursive relation employing quasi-shuffle sets, directly verifiable by massive factorization properties. We derive results for minimal gauge invariant color-kinematic numerators with physical massive

Jiahe Li, Jiawei Zhang, Xiao Bai, Jin Zheng

Radiance fields have demonstrated impressive performance in synthesizing lifelike 3D talking heads. However, due to the difficulty in fitting steep appearance changes, the prevailing paradigm that presents facial motions by directly modifying point appearance may lead to distortions in dynamic regions. To tackle this challenge, we introduce TalkingGaussian,

Lahav Lipson, Jia Deng

We introduce a new system for Multi-Session SLAM, which tracks camera motion across multiple disjoint videos under a single global reference. Our approach couples the prediction of optical flow with solver layers to estimate camera pose. The backbone is trained end-to-end using a novel differentiable solver for wide-baseline two-view pose. The full system ca

Qian Xu, Kai Chen, Xueqi Shen, Lei Xing

By counting the number of pass/fail occurrences of a DUT (Device under Test) in the stirring process in a reverberation chamber (RC), the threshold electric field (E-field) level can be well estimated without tuning the input power and repeating the whole testing many times. The Monte-Carlo method is used to verify the results. Estimated values and uncertain

Sawyer Robertson, Zhengchao Wan, Alexander Cloninger

We study the linearization of a discrete transportation distance between probability distributions on finite weighted graphs originally due to Maas (``Gradient flows of the entropy for finite {M}arkov chains,'' J. Funct. Anal. 261(8), 2011) which demonstrates various connections to the underlying combinatorial structure of the graph. For a connected graph an

Cecilia Aragon, Melissa Vosen Callens, Stacy M. Branham, Cali Anicha

In the early stages of the COVID-19 pandemic, many events and conferences hastily converted to a virtual format, and many commercial ventures promptly developed tools promising seamless transitions to virtual spaces. In particular, efforts to expand and monetize augmented and virtual reality environments increased. While these spaces increased accessibility

Devin Tebbe, Meryem Barkallah, Braeden Burger, Douglas Zytko

Virtual reality (VR) dating introduces novel opportunities for romantic interactions, but it also raises concerns about new harms that typically occur separately in traditional dating apps and general-purpose social VR environments. Given the subjectivity in which VR dating experiences can be considered harmful it is imperative to involve user stakeholders i

Sebastian Cmentowski, Sukran Karaosmanoglu, Frank Steinicke

Digitalization and virtualization are parts of our everyday lives in almost all aspects ranging from work, education, and communication to entertainment. A novel step in this direction is the widespread interest in extended reality (XR) [2]. The newest consumer-ready head-mounted displays (HMD) such as Meta Quest 3 or Apple Vision Pro, have reached unprecede

Wangfan Li, Rohit Mallick, Carlos Toxtli-Hernandez, Christopher Flathmann

Recent developments in artificial intelligence (AI) have permeated through an array of different immersive environments, including virtual, augmented, and mixed realities. AI brings a wealth of potential that centers on its ability to critically analyze environments, identify relevant artifacts to a goal or action, and then autonomously execute decision-maki

Neelay Fruitwala, Gang Huang, Yilun Xu, Abhi Rajagopala

Quantum circuits utilizing real time feedback techniques (such as active reset and mid-circuit measurement) are a powerful tool for NISQ-era quantum computing. Such techniques are crucial for implementing error correction protocols, and can reduce the resource requirements of certain quantum algorithms. Realizing these capabilities requires flexible, low-lat

Cameron Smith, David Charatan, Ayush Tewari, Vincent Sitzmann

This paper introduces FlowMap, an end-to-end differentiable method that solves for precise camera poses, camera intrinsics, and per-frame dense depth of a video sequence. Our method performs per-video gradient-descent minimization of a simple least-squares objective that compares the optical flow induced by depth, intrinsics, and poses against correspondence

Erlend Grong, Karen Habermann, Stefan Sommer

Simulation of conditioned diffusion processes is an essential tool in inference for stochastic processes, data imputation, generative modelling, and geometric statistics. Whilst simulating diffusion bridge processes is already difficult on Euclidean spaces, when considering diffusion processes on Riemannian manifolds the geometry brings in further complicati

Niloofar Sayadi, Diego Gómez-Zará

The rapid evolution of social Virtual Reality (VR) platforms has significantly enhanced the way users interact and socialize in digital spaces, offering immersive experiences that closely mimic real-world interactions [1]. However, this technological advancement has brought new challenges, particularly in ensuring safety and preventing harassment [11]. Unlik

Syed Ali Asif, Emma Cao, Hang Chen, Chien-Chung Shen

The Metaverse, an immersive virtual world, has emerged as a shared space where people engage in various activities ranging from social interactions to commerce. Cryptocurrencies [3] and Non-Fungible Tokens (NFTs) [6] play pivotal roles within this virtual realm, reshaping interactions and transactions. Cryptocurrencies, utilizing cryptographic techniques for

Leandro M. Arancibia, Andrés I. Bertoni, Cristián G. Sánchez, Alejandro M. Lobos

We theoretically propose a polymer-based nano-device consisting of a single trans-polyacetylene (tPA) molecule capacitively coupled to external voltage gates. We model the integrated device using a Su-Schrieffer-Heeger (SSH)-like Hamiltonian, and we demonstrate the emergence of localized domain walls (DWs) with quantized charges (i.e., soliton excitations) l

Junli Ren, Yikai Liu, Yingru Dai, Junfeng Long

Legged navigation is typically examined within open-world, off-road, and challenging environments. In these scenarios, estimating external disturbances requires a complex synthesis of multi-modal information. This underlines a major limitation in existing works that primarily focus on avoiding obstacles. In this work, we propose TOP-Nav, a novel legged navig

Stefan Heimersheim, Neel Nanda

Activation patching is a popular mechanistic interpretability technique, but has many subtleties regarding how it is applied and how one may interpret the results. We provide a summary of advice and best practices, based on our experience using this technique in practice. We include an overview of the different ways to apply activation patching and a discuss

Yan-Ming Chiou, Bob Price, Chien-Chung Shen, Syed Ali Asif

Integrating mixed reality (MR) with artificial intelligence (AI) technologies, including vision, language, audio, reasoning, and planning, enables the AI-powered MR assistant [1] to substantially elevate human efficiency. This enhancement comes from situational awareness, quick access to essential information, and support in learning new skills in the right

Bin Wang, Zhuangcheng Gu, Guang Liang, Chao Xu

The paper introduces the UniMER dataset, marking the first study on Mathematical Expression Recognition (MER) targeting complex real-world scenarios. The UniMER dataset includes a large-scale training set, UniMER-1M, which offers unprecedented scale and diversity with one million training instances to train high-quality, robust models. Additionally, UniMER f

Xingguang Zhang, Chih-Hsien Chou

When deploying pre-trained video object detectors in real-world scenarios, the domain gap between training and testing data caused by adverse image conditions often leads to performance degradation. Addressing this issue becomes particularly challenging when only the pre-trained model and degraded videos are available. Although various source-free domain ada

Songsong Zhou, Andrew M. Rappe

Ferroelectric hafnia exhibits promising robust polarization and silicon compatibility for ferroelectric devices. Unfortunately, it suffers from difficult polarization switching. Methods to enable easier polarization switching are needed, and the underlying reason for this switching difficulty is not understood. Here, we investigated the 180$^\circ$ domain wa

Ruchi Panchanadikar

Technological advancements have undoubtedly revolutionized various aspects of human life, altering the ways we perceive the world, engage with others, build relationships, and conduct our daily work routines. Among the recent advancements, the proliferation of virtual and mixed reality technologies stands out as a significant leap forward, promising to eleva

Zinan Zhang, Xinning Gui, Yubo Kou

Virtual space offers innovative ways for individuals to engage with one another in a digital setting. Prominent virtual social platforms, such as Facebook Spaces, VR Chat, and AltspaceVR, facilitate social connections, allowing users to interact seamlessly. Additionally, certain video games, like Second Life and World of Warcraft, are set within these virtua

Raymond Chan, Benjamin W. J. Kwok, Adriel Yeo, Kan Chen

The "Darkverse" could be the negative harmful area of the Metaverse; a new virtual immersive environment for the facilitation of illicit activity such as misinformation, fraud, harassment, and illegal marketplaces. This paper explores the potential for inappropriate activities within the Metaverse, and the similarities between the Darkverse and the Dark Web.

Leslie Wöhler, Satoshi Ikehata, Kiyoharu Aizawa

In comparison to traditional footage, 360{\deg} videos can convey engaging, immersive experiences and even be utilized to create interactive virtual environments. Like regular recordings, these videos need to consider the privacy of recorded people and could be targets for video manipulations. However, due to their properties like enhanced presence, the effe

Joseph B. Schlenoff, Khalil Akkaoui

Strong changes in bulk properties, such as modulus and viscosity, are observed near the glass transition temperature, T_{g}, of amorphous materials. For more than a century, intense efforts have been made to define a microscopic origin for these macroscopic changes in properties. Using transition state theory, we delve into the atomic/molecular level picture

Eugene Kukshinov

This proposal highlights the potential real-world consequences of harmful experiences in immersive and embodied spaces due to presence, which moderates experiences, intensifying positive or negative content. While positive experiences enhance social interactions, negative content can lead to harm. Also, presence is not continuous and may break. Understanding

Keyan Guo, Freeman Guo, Hongxin Hu

The advancement in computing and hardware, like spatial computing and VR headsets (e.g., Apple's Vision Pro) [1], has boosted the popularity of social VR platforms (VRChat, Rec Room, Meta HorizonWorlds) [2, 3, 4]. Unlike traditional digital interactions, social VR allows for more immersive experiences, with avatars that mimic users' real-time movements and e

Liwei Tan, Minsheng Huang, Wenjun Ying

The kernel-free boundary integral (KFBI) method has successfully solved partial differential equations (PDEs) on irregular domains. Diverging from traditional boundary integral methods, the computation of boundary integrals in KFBI is executed through the resolution of equivalent simple interface problems on Cartesian grids, utilizing fast algorithms. While

Syed Ali Asif, Philip Gable, Chien-Chung Shen, Yan-Ming Chiou

Emotions are an integral part of being human, and experiencing a range of emotions is what makes life rich and vibrant. From basic emotions like anger, fear, happiness, and sadness to more complex ones like excitement and grief, emotions help us express ourselves and connect with the world around us. In recent years, researchers have begun adopting virtual r

Yujie Tao, Sean Follmer

The rise of Mixed Reality (MR) stimulates new interactive techniques that seamlessly blend the virtual and physical environments. Just as virtual content could be overlayed onto the physical world for providing adaptive user interfaces [5, 8], emergent techniques "repurpose" everyday environments and sensory cues to support the virtual content [7, 9, 13-15].

Jan-Christoph Kassing, Grigory Vartanyan, Jürgen Giesl

Dependency pairs are one of the most powerful techniques for proving termination of term rewrite systems (TRSs), and they are used in almost all tools for termination analysis of TRSs. Problem #106 of the RTA List of Open Problems asks for an adaption of dependency pairs for relative termination. Here, infinite rewrite sequences are allowed, but one wants to

Yifeng Ding, Jiawei Liu, Yuxiang Wei, Terry Yue Zhuo

We introduce XFT, a simple yet powerful training scheme, by simply merging upcycled Mixture-of-Experts (MoE) to unleash the performance limit of instruction-tuned code Large Language Models (LLMs). While vanilla sparse upcycling fails to improve instruction tuning, XFT introduces a shared expert mechanism with a novel routing weight normalization strategy in

Pál András Papp, Georg Anegg, Aikaterini Karanasiou, A. N. Yzelman

We study the problem of efficiently scheduling a computational DAG on multiple processors. The majority of previous works have developed and compared algorithms for this problem in relatively simple models; in contrast to this, we analyze this problem in a more realistic model that captures many real-world aspects, such as communication costs, synchronizatio

Austin Goddard, Kang Du, Yu Xiang

Making predictions in an unseen environment given data from multiple training environments is a challenging task. We approach this problem from an invariance perspective, focusing on binary classification to shed light on general nonlinear data generation mechanisms. We identify a unique form of invariance that exists solely in a binary setting that allows u

Manyi Yao, Abhishek Aich, Yumin Suh, Amit Roy-Chowdhury

Vision transformer based models bring significant improvements for image segmentation tasks. Although these architectures offer powerful capabilities irrespective of specific segmentation tasks, their use of computational resources can be taxing on deployed devices. One way to overcome this challenge is by adapting the computation level to the specific needs

Shuo Ling, Liwei Tan, Wenjun Ying

The Kernel-Free Boundary Integral (KFBI) method presents an iterative solution to boundary integral equations arising from elliptic partial differential equations (PDEs). This method effectively addresses elliptic PDEs on irregular domains, including the modified Helmholtz, Stokes, and elasticity equations. The rapid evolution of neural networks and deep lea

Santosh Karki Chhetri, Rabindra Basnet, Jian Wang, Krishna Pandey

Magnetic topological semimetals LnSbTe (Ln = Lanthanide) have attracted intensive attention because of the presence of interplay between magnetism, topological, and electron correlations depending on the choices of magnetic Ln elements. Recently, varying Sb-Te composition has been found to effectively control the electronic and magnetic states in LnSbxTe$_{2

Sebastian Debus, Andreas Kretschmer

A symmetric ideal is an ideal in a polynomial ring which is stable under all permutations of the variables. In this paper we initiate a global study of zero-dimensional symmetric ideals. By this we mean a geometric study of the invariant Hilbert schemes $\mathrm{Hilb}_{\rho}^{S_n}(\mathbb{C}^n)$ parametrizing symmetric subschemes of $\mathbb{C}^n$ whose coor

Hamida Khatri

This paper delves into the distressing prevalence of gender-based violence (GBV) and its deep-seated psychological ramifications, particularly among Muslim South Asian women living in diasporic communities. Despite the gravity of GBV, these women often face formidable barriers in voicing their experiences and accessing support. "Augmented Voices" emerges as

Weiyan Shi, Ryan Li, Yutong Zhang, Caleb Ziems

To enhance language models' cultural awareness, we design a generalizable pipeline to construct cultural knowledge bases from different online communities on a massive scale. With the pipeline, we construct CultureBank, a knowledge base built upon users' self-narratives with 12K cultural descriptors sourced from TikTok and 11K from Reddit. Unlike previous cu

Zhongyuan Huang, Ziwei Chen, Maolin Yang, Mihai Chu

Recently, considerable efforts have been made on research and improvement for Ni-rich lithium-ion batteries to meet the demand from vehicles and grid-level large-scale energy storage. Development of next-generation high-performance lithium-ion batteries requires a comprehensive understanding on the underlying electrochemical mechanisms associated with its st

Aidan Z. H. Yang, Sophia Kolak, Vincent J. Hellendoorn, Ruben Martins

Language models have improved by orders of magnitude with the recent emergence of Transformer-based Large Language Models (LLMs). LLMs have demonstrated their ability to generate natural code that is highly similar to code written by professional developers. One intermediate value an LLM can emit is entropy, which measures the naturalness of a token of code.

Vahideh Eshaghian, Sören Wilkening, Johan Åberg, David Gross

Many search-based quantum algorithms that achieve a theoretical speedup are not practically relevant since they require extraordinarily long coherence times, or lack the parallelizability of their classical counterparts.This raises the question of how to divide computational tasks into a collection of parallelizable sub-problems, each of which can be solved

Manisha, Nitin Kumar

Cancelable Biometric is repetitive distortion embedded in original Biometric image for keeping it secure from unauthorized access. In this paper, we have generated Cancelable Biometric templates with Reverse Boolean XOR technique. Three different methods have been proposed for generation of Cancelable Biometric templates based on Visual Secret Sharing scheme

Pedro C. Neto, Rafael M. Mamede, Carolina Albuquerque, Tiago Gonçalves

Face recognition applications have grown in parallel with the size of datasets, complexity of deep learning models and computational power. However, while deep learning models evolve to become more capable and computational power keeps increasing, the datasets available are being retracted and removed from public access. Privacy and ethical concerns are rele

Igor Bandos, Unai D. M. Sarraga

Recently we have constructed a completely supersymmetric nonlinear action possessing the properties expected from multiple D0-brane system. Its quantization should result in an interesting supersymmetric field theory in the (super)space with additional matrix coordinates which can provide an important insights in the study of String Theory. As a first stage

Thuy Nguyen Thi, Anh Nguyen Viet, Thin Dang Van, Ngan Nguyen Luu Thuy

This paper describes our systems for the sub-task I in the Software Mention Detection in Scholarly Publications shared-task. We propose three approaches leveraging different pre-trained language models (BERT, SciBERT, and XLM-R) to tackle this challenge. Our bestperforming system addresses the named entity recognition (NER) problem through a three-stage fram

William Giarè, Miguel A. Sabogal, Rafael C. Nunes, Eleonora Di Valentino

We investigate the implications of the Baryon Acoustic Oscillations measurement released by the Dark Energy Spectroscopic Instrument (DESI) for Interacting Dark Energy (IDE) models characterized by an energy-momentum flow from Dark Matter to Dark Energy. By combining Planck-2018 and DESI data, we observe a preference for interactions, leading to a non-vanish

Yong En Kok, Alexander Bentley, Andrew Parkes, Amanda J. Wright

Optical imaging quality can be severely degraded by system and sample induced aberrations. Existing adaptive optics systems typically rely on iterative search algorithm to correct for aberrations and improve images. This study demonstrates the application of convolutional neural networks to characterise the optical aberration by directly predicting the Zerni

Hannes Jakob, Maxwell Levine

Krueger showed that PFA implies that for all regular $\Theta \ge \aleph_2$, there are stationarily many $[H(\Theta)]^{\aleph_1}$ that are internally club but not internally approachable. From countably many Mahlo cardinals, we force a model in which, for all positive $n<\omega$ and $\Theta \ge \aleph_{n+1}$, there is a stationary subset of $[H(\Theta)]^{\ale

Metin Ozturk, Berk Çiloğlu, Görkem Berkay Koç, Halim Yanikomeroglu

It is crucial to deploy temporary non-terrestrial networks (NTN) in disaster situations where terrestrial networks are no longer operable. Deploying uncrewed aerial vehicle base stations (UAV-BSs) can provide a radio access network (RAN); however, the backhaul link may also be damaged and unserviceable in such disaster conditions. In this regard, high-altitu

Peter Kulits, Haiwen Feng, Weiyang Liu, Victoria Abrevaya

Inverse graphics -- the task of inverting an image into physical variables that, when rendered, enable reproduction of the observed scene -- is a fundamental challenge in computer vision and graphics. Successfully disentangling an image into its constituent elements, such as the shape, color, and material properties of the objects of the 3D scene that produc

Sankalp Gilda, Benedikt Heidrich, Franz Kiraly

In time series analysis, traditional bootstrapping methods often fall short due to their assumption of data independence, a condition rarely met in time-dependent data. This paper introduces tsbootstrap, a python package designed specifically to address this challenge. It offers a comprehensive suite of bootstrapping techniques, including Block, Residual, an

Abbas Maazallahi, Sreehari Thota, Naga Prasad Kondaboina, Vineetha Muktineni

This study analyzes crop yield prediction in India from 1997 to 2020, focusing on various crops and key environmental factors. It aims to predict agricultural yields by utilizing advanced machine learning techniques like Linear Regression, Decision Tree, KNN, Na\"ive Bayes, K-Mean Clustering, and Random Forest. The models, particularly Na\"ive Bayes and Rand

José Moran, Angelo Secchi, Jean-Philippe Bouchaud

We revisit granular models that represent the size of a firm as the sum of the sizes of multiple constituents or sub-units. Originally developed to address the unexpectedly slow reduction in volatility as firm size increases, these models also explain the shape of the distribution of firm growth rates. We introduce new theoretical insights regarding the rela

Junwon You, Eunwoo Heo, Jae-Hun Jung

Link prediction (LP), inferring the connectivity between nodes, is a significant research area in graph data, where a link represents essential information on relationships between nodes. Although graph neural network (GNN)-based models have achieved high performance in LP, understanding why they perform well is challenging because most comprise complex neur

Mona Alzahrani, Muhammad Usman, Salma Kammoun, Saeed Anwar

Human decision-making often relies on visual information from multiple perspectives or views. In contrast, machine learning-based object recognition utilizes information from a single image of the object. However, the information conveyed by a single image may not be sufficient for accurate decision-making, particularly in complex recognition problems. The u

Sean Prudhoe, Unnati Akhouri, Tommy Chin, Sarah Shandera

We derive a new constructive procedure to rapidly generate ensembles of phase-covariant dynamical maps that may be associated to the individual spins of a closed quantum system. We do this by first computing the single-spin dynamical maps in small XXZ networks and chains, specialized to the class of initial states that guarantees phase-covariant dynamics for

Mabel Lizzy Rajendran, Anna Zhigun

Local well-posedness is established for a highly nonlocal nonlinear diffusion-adhesion system for bounded initial values with small support. Macroscopic systems of this kind were previously obtained by the authors through upscaling in [32] and can account for the effect of microscopic receptor binding dynamics in cell-cell adhesion. The system analysed here

Robert E. Kent

The theory of institutions is framed as an indexed/fibered duality, where the indexed aspect specifies the fibered aspect. Tarski represented truth in terms of a satisfaction relation. The theory of institutions encodes satisfaction as its core architecture in the indexed aspect. Logical environments enrich this truth architecture by axiomatizing the truth a

Itamar J. Allali, Alessio Notari, Fabrizio Rompineve

We investigate the presence of extra relativistic degrees of freedom in the early Universe, contributing to the effective number of neutrinos $N_\text{eff}$, as $\Delta N_\text{eff}\equiv N_\text{eff}-3.044\geq 0$, in light of the recent measurements of Baryon Acoustic Oscillations (BAO) by the DESI collaboration. We analyze one-parameter extensions of the $

Brendan King, Jeffrey Flanigan

Training task-oriented dialogue systems typically requires turn-level annotations for interacting with their APIs: e.g. a dialogue state and the system actions taken at each step. These annotations can be costly to produce, error-prone, and require both domain and annotation expertise. With advances in LLMs, we hypothesize that unlabeled data and a schema de

Ehsan Amooghorban, Sareh Shahidani, Somaye Mohamadi Abdhvand

We study the geometric phase (GP) of a two-level atom coupled to an environment composed of free space and a dielectric nanosphere in thermal and out of thermal equilibrium. We analytically and numerically analyze the optical properties and loss of the dielectric medium, along with the non-equilibrium effects of the environment on the GP. In the weak couplin

Luke Snow, Vikram Krishnamurthy

We study a sequence of independent one-shot non-cooperative games where agents play equilibria determined by a tunable mechanism. Observing only equilibrium decisions, without parametric or distributional knowledge of utilities, we aim to steer equilibria towards social optimality, and to certify when this is impossible due to the game's structure. We develo

Márk Somorjai, Mihály Dobos-Kovács, Zsófia Ádám, Levente Bajczi

Constrained Horn Clauses (CHCs) have conventionally been used as a low-level representation in formal verification. Most existing solvers use a diverse set of specialized techniques, including direct state space traversal or under-approximating abstraction, necessitating purpose-built complex algorithms. Other solvers successfully simplified the verification

J. Tanner Slagel, Mariano Moscato, Lauren White, César A. Muñoz

Differential dynamic logic (dL) is a formal framework for specifying and reasoning about hybrid systems, i.e., dynamical systems that exhibit both continuous and discrete behaviors. These kinds of systems arise in many safety- and mission-critical applications. This paper presents a formalization of dL in the Prototype Verification System (PVS) that includes

Minoru Hirose, Toshiki Matsusaka, Shin-ichiro Seki

Recently, Maesaka, Watanabe, and the third author discovered a phenomenon where the iterated integral expressions of multiple zeta values become discretized. In this paper, we extend their result to the case of multiple polylogarithms and provide two proofs. The first proof uses the method of connected sums, while the second employs induction based on the di

Constanza Fierro, Jiaang Li, Anders Søgaard

The purpose of instruction tuning is enabling zero-shot performance, but instruction tuning has also been shown to improve chain-of-thought reasoning and value alignment (Si et al., 2023). Here we consider the impact on $\textit{consistency}$, i.e., the sensitivity of language models to small perturbations in the input. We compare 10 instruction-tuned LLaMA

Giovanni Brigati, Francesco Pedrotti

In this paper we derive estimates for the Hessian of the logarithm (log-Hessian) for solutions to the heat equation. For initial data in the form of log-Lipschitz perturbation of strongly log-concave measures, the log-Hessian admits an explicit, uniform (in space) lower bound. This yields a new estimate for the Lipschitz constant of a transport map pushing f

Adnan Malik, Amjad Hussain, M. Farasat Shamir, Ayesha Almas

The objective of our current study is to explore novel aspects of a stationary anisotropic relativistic hybrid compact star that consists of quark matter (QM) in its core and ordinary baryonic matter (OBM) in its crust. This study has been done by adopting separate equations of states (EoSs) for quark matter and baryonic matter. The MIT bag model equation of

Johann Gabriel Meyer, Andrea Zablah, Oleg Pronin

We present here a new geometry for laser amplifiers based on bulk gain media. The overlapped seed and pump beams are repetitively refocused into the gain medium with a Herriott-type multipass cell. Similar to a waveguide, this configuration allows for a confined propagation inside the gain medium over much longer lengths than in ordinary single pass bulk amp