March 2024 arXiv papers

Yujuan Fu, Giridhar Kaushik Ramachandran, Nicholas J Dobbins, Namu Park

Social determinants of health (SDoH) play a critical role in shaping health outcomes, particularly in pediatric populations where interventions can have long-term implications. SDoH are frequently studied in the Electronic Health Record (EHR), which provides a rich repository for diverse patient data. In this work, we present a novel annotated corpus, the Pe

Nolan Alexander, William Scherer

We propose a novel method to improve estimation of asset returns for portfolio optimization. This approach first performs a monthly directional market forecast using an online decision tree. The decision tree is trained on a novel set of features engineered from portfolio theory: the efficient frontier functional coefficients. Efficient frontiers can be deco

Clara Lage, Nelly Pustelnik, Benjamin Audit, Jean-Michel Arbona

DNA replication stands as one of the fundamental biological processes crucial for cellular functioning. Recent experimental developments enable the study of replication dynamics at the single-molecule level for complete genomes, facilitating a deeper understanding of its main parameters. In these new data, replication dynamics is reported by the incorporatio

Katsunori Kubo

A tight-binding model for $e_g$ orbitals on a square lattice is investigated. We consider only the nearest-neighbor hopping and the model is characterized by two hopping parameters, $t_1$ and $t_2$. There are Dirac points in the electronic band structure and the type of the Dirac points (type-I or type-II) depends on the ratio $t_2/t_1$. For the case of the

Francisco J. Perez-Reche

The process of infection of a host is complex, influenced by factors such as microbial variation within and between hosts as well as differences in dose across hosts. This study uses dose-response and within-host microbial infection models to delve into the impact of these factors on infection probability. It is rigorously demonstrated that within-host heter

Sandro Maludze, Mahdi Mirzakhani, William Baker, Matthew Lee

The phonon-mediated hybrid detector is made out of a monolithic silicon crystal characterized by two interconnected regions linked through a narrow neck. Operating solely on phonon signal measurements, the hybrid design facilitates the differentiation between electron recoil and nuclear recoil events, effectively discerning two types of interaction down to l

Arturo Erdely, Manuel Rubio-Sanchez

Scatter plots are widely recognized as fundamental tools for illustrating the relationship between two numerical variables. Despite this, based on solid theoretical foundations, scatter plots generated from pairs of continuous random variables may not serve as reliable tools for assessing dependence. Sklar's Theorem implies that scatter plots created fro

Sihao Wu, Weijie Du, Xingbo Zhao, James P. Vary

We present an efficient and precise framework to quantum simulate the dynamics of the ultra-relativistic quark-nucleus scattering. This framework employs the eigenbasis of the asymptotic scattering system and implements a compact scheme for encoding this basis upon lattice discretization. It exploits the operator structure of the light-front Hamiltonian of t

Huiling Zhong, Fanrong Xu, Hai-Yang Cheng

We perform a global fit to the experimental data of two-body charmed baryon decays based on the topological diagrammatic approach (TDA) and take into account the phase shifts between $S$- and $P$-wave amplitudes as inspired by the recent BESIII measurement of the decay asymmetry in the decay $Λ_c^+\to Ξ^0K^+$. The TDA has the advantage that it is more intuit

Alexey A. Kovalev, Bo Li, Edward Schwartz

We study superfluidlike spin transport facilitated by thermal diffusion of magnetic domain walls, where the positive and negative chiralities of domain walls act as opposite topological charges. The topological charge conservation leads to algebraic decay of spin current carried by domain walls, allowing for the transport of spin over extended distances. We

Yuno Iwasaki, Andi Tan, Christopher G. Tully

The novel electron spectrometry method proposed by the PTOLEMY relic neutrino experiment requires a real-time, non-destructive estimate of the parallel and transverse momentum splits of tritium $β$-decay electrons. The collaboration has proposed to obtain this estimate using cyclotron-radiation emission spectroscopy (CRES), in which the kinetic energy of a c

Zhibo Chu, Zichong Wang, Wenbin Zhang

Large Language Models (LLMs) have demonstrated remarkable success across various domains. However, despite their promising performance in numerous real-world applications, most of these algorithms lack fairness considerations. Consequently, they may lead to discriminatory outcomes against certain communities, particularly marginalized populations, prompting

Haoxi Ran, Vitor Guizilini, Yue Wang

Diffusion models (DMs) excel in photo-realistic image synthesis, but their adaptation to LiDAR scene generation poses a substantial hurdle. This is primarily because DMs operating in the point space struggle to preserve the curve-like patterns and 3D geometry of LiDAR scenes, which consumes much of their representation power. In this paper, we propose LiDAR

Aditya Singh, Zeyuan Feng, Somil Bansal

Hamilton-Jacobi (HJ) reachability analysis is a widely adopted verification tool to provide safety and performance guarantees for autonomous systems. However, it involves solving a partial differential equation (PDE) to compute a safety value function, whose computational and memory complexity scales exponentially with the state dimension, making its direct

Adedibu Sunny Akingboye

This comprehensive review examines electrical and seismic refraction methods, emphasizing their advanced applications in electrical resistivity tomography (ERT) and seismic refraction tomography (SRT). These techniques are crucial for understanding surface-subsurface crustal dynamics, offering critical insights into resistivity and velocity structures for ge

Archer Amon, Zhipeng Yin, Zichong Wang, Avash Palikhe

Generative AI is becoming increasingly prevalent in creative fields, sparking urgent debates over how current copyright laws can keep pace with technological innovation. Recent controversies of AI models generating near-replicas of copyrighted material highlight the need to adapt current legal frameworks and develop technical methods to mitigate copyright in

Jaehoon Kim, Alexandr Kostochka, Ruth Luo

The famous Dirac&#39;s Theorem states that for each $n\geq 3$ every $n$-vertex graph $G$ with minimum degree $δ(G)\geq n/2$ has a hamiltonian cycle. When $δ(G)< n/2$, this cannot be guaranteed, but the existence of some other specific subgraphs can be provided. Gargano, Hell, Stacho and Vaccaro proved that every connected $n$-vertex graph $G$ with $δ(G)\geq

Marta Lazzaretti, Claudio Estatico, Alejandro Melero, Luca Calatroni

Off-the-grid regularisation has been extensively employed over the last decade in the context of ill-posed inverse problems formulated in the continuous setting of the space of Radon measures $\mathcal{M}(\mathcal{X})$. These approaches enjoy convexity and counteract the discretisation biases as well the numerical instabilities typical of their discrete coun

Rushang Karia, Jayesh Nagpal, Daksh Dobhal, Pulkit Verma

Understanding how robots plan and execute tasks is crucial in today&#39;s world, where they are becoming more prevalent in our daily lives. However, teaching non-experts, such as K-12 students, the complexities of robot planning can be challenging. This work presents an open-source platform, JEDAI.Ed, that simplifies the process using a visual interface that

Sara Fish, Yannai A. Gonczarowski, Ran I. Shorrer

We conduct experiments with algorithmic pricing agents based on Large Language Models (LLMs). In oligopoly settings, LLM-based pricing agents quickly and autonomously reach supracompetitive prices and profits. Variation in seemingly innocuous phrases in LLM instructions (&#34;prompts&#34;) substantially influence the degree of supracompetitive pricing. We de

Marie-Claude Arnaud, Vincent Humilière, Claude Viterbo

We extend to higher dimensions the notion of Birkhoff attractor of a dissipative map. We prove that this notion coincides with the classical Birkhoff attractor. We prove that for the dissipative system associated to the discounted Hamilton-Jacobi equation the graph of a solution is contained in the Birkhoff attractor. We also study what happens when we pertu

Qiong Wang, Anan Ghrayeb, SeongHyeon Kim, Liuyang Cheng

Twisted and coiled polymer actuators (TCPAs) generate large contractile mechanical work mimicking natural muscles, which makes them suitable for robotics and health-assistive devices. Understanding the mechanism of nylon TCPA remains challenging due to the interplay between their intricate geometry, chirality, residual stresses, and material microstructure.

Ye Liu, Jixuan He, Wanhua Li, Junsik Kim

Video temporal grounding (VTG) is a fine-grained video understanding problem that aims to ground relevant clips in untrimmed videos given natural language queries. Most existing VTG models are built upon frame-wise final-layer CLIP features, aided by additional temporal backbones (e.g., SlowFast) with sophisticated temporal reasoning mechanisms. In this work

Eric Guiffo Kaigom

Metarobotics aims to combine next generation wireless communication, multi-sense immersion, and collective intelligence to provide a pervasive, itinerant, and non-invasive access and interaction with distant robotized applications. Industry and society are expected to benefit from these functionalities. For instance, robot programmers will no longer travel w

Bibek Poudel, Weizi Li, Shuai Li

Human-driven vehicles (HVs) exhibit complex and diverse behaviors. Accurately modeling such behavior is crucial for validating Robot Vehicles (RVs) in simulation and realizing the potential of mixed traffic control. However, existing approaches like parameterized models and data-driven techniques struggle to capture the full complexity and diversity. To addr

Lourens Touwen, Doina Bucur, Remco van der Hofstad, Alessandro Garavaglia

We propose a novel model-selection method for dynamic networks. Our approach involves training a classifier on a large body of synthetic network data. The data is generated by simulating nine state-of-the-art random graph models for dynamic networks, with parameter range chosen to ensure exponential growth of the network size in time. We design a conceptuall

Ernest Y. -Z. Tan, Ramona Wolf

One of the main challenges in device-independent quantum key distribution (DIQKD) is achieving the required Bell violation over long distances, as the channel losses result in low overall detection efficiencies. Recent works have explored the concept of certifying nonlocal correlations over extended distances through the use of a local Bell test. Here, an ad

Jakaria Rabbi, Johannes Kiechle, Christian Beaulieu, Nilanjan Ray

This paper presents a comprehensive study focused on disentangling hippocampal shape variations from diffusion tensor imaging (DTI) datasets within the context of neurological disorders. Leveraging a Mesh Variational Autoencoder (VAE) enhanced with Supervised Contrastive Learning, our approach aims to improve interpretability by disentangling two distinct la

Eric Guiffo Kaigom

As a digital environment of interconnected virtual ecosystems driven by measured and synthesized data, the Metaverse has so far been mostly considered from its gaming perspective that closely aligns with online edutainment. Although it is still in its infancy and more research as well as standardization efforts remain to be done, the Metaverse could provide

Mohamed Elsayed, A. Rupam Mahmood

Deep representation learning methods struggle with continual learning, suffering from both catastrophic forgetting of useful units and loss of plasticity, often due to rigid and unuseful units. While many methods address these two issues separately, only a few currently deal with both simultaneously. In this paper, we introduce Utility-based Perturbed Gradie

Shudi Weng, Chengxi Li, Ming Xiao, Mikael Skoglund

Stragglers&#39; effects are known to degrade FL performance. In this paper, we investigate federated learning (FL) over wireless networks in the presence of communication stragglers, where the power-constrained clients collaboratively train a global model by iteratively optimizing a local objective function with their local datasets and transmitting local mo

Jalil Varela-Manjarres, Ali Kefayati, M. Benjamin Jungfleisch, John Q. Xiao

The surprising discovery of ultrafast demagnetization -- where electric field of femtosecond laser pulse couples to electrons of a ferromagnetic (FM) layer causing its magnetization vector {\em to shrink while not rotating}, is also assumed to be accompanied by generation of spin current in the direction orthogonal to electric field. However, understanding o

Weihua Hu, Yiwen Yuan, Zecheng Zhang, Akihiro Nitta

We present PyTorch Frame, a PyTorch-based framework for deep learning over multi-modal tabular data. PyTorch Frame makes tabular deep learning easy by providing a PyTorch-based data structure to handle complex tabular data, introducing a model abstraction to enable modular implementation of tabular models, and allowing external foundation models to be incorp

Maarten Grachten

An increasing number of generative music models can be conditioned on an audio prompt that serves as musical context for which the model is to create an accompaniment (often further specified using a text prompt). Evaluation of how well model outputs adhere to the audio prompt is often done in a model or problem specific manner, presumably because no generic

Krishna Jalan, Roji Pius, Manish Ramchander

The island paradigm for an AdS$_2$ eternal black hole in the Hartle-Hawking state coupled to a finite temperature non-gravitating bath asserts that after the Page time the operators in the black hole interior can be reconstructed using the bath degrees of freedom. We demonstrate this assertion by consideing a special operator $\mathbb{U}_{bath}$ that has non

Zhancheng Yao, Martin Sandberg, David W. Abraham, David J. Bishop

Building a modular architecture with superconducting quantum computing chips is one of the means to achieve qubit scalability, allowing the screening, selection, replacement, and integration of individual qubit modules into large quantum systems. However, the nondestructive replacement of modules within a compact architecture remains a challenge. Liquid meta

Siming He, Yuezhan Tao, Igor Spasojevic, Vijay Kumar

Active perception approaches select future viewpoints by using some estimate of the information gain. An inaccurate estimate can be detrimental in critical situations, e.g., locating a person in distress. However the true information gained can only be calculated post hoc, i.e., after the observation is realized. We present an approach to estimate the discre

Theo Diamandis, Guillermo Angeris, Alan Edelman

We introduce a general framework for flow problems over hypergraphs. In our problem formulation, which we call the convex flow problem, we have a concave utility function for the net flow at every node and a concave utility function for each edge flow. The objective is to maximize the sum of these utilities, subject to constraints on the flows allowed at eac

Jianqing Jia, Ashley Prater-Bennette, Lixin Shen, Erin E. Tripp

This paper introduces a nonconvex approach for sparse signal recovery, proposing a novel model termed the $τ_2$-model, which utilizes the squared $\ell_1/\ell_2$ norms for this purpose. Our model offers an advancement over the $\ell_0$ norm, which is often computationally intractable and less effective in practical scenarios. Grounded in the concept of effec

Gopal Yadav

In this paper, we construct the wedge holography for the de-Sitter space as a bulk theory. First, we discuss a more general mathematical construction of wedge holography in parallel with wedge holography construction for the AdS bulk, and then we construct the wedge holography in the extended static patch. In the first case, we prove that one can construct w

Cheng Wen, Jialun Cao, Jie Su, Zhiwu Xu

Formal verification provides a rigorous and systematic approach to ensure the correctness and reliability of software systems. Yet, constructing specifications for the full proof relies on domain expertise and non-trivial manpower. In view of such needs, an automated approach for specification synthesis is desired. While existing automated approaches are lim

K. Narayan, Hitesh K. Saini, Gopal Yadav

We study holographic volume complexity for various families of holographic cosmologies with Kasner-like singularities, in particular with $AdS$, hyperscaling violating and Lifshitz asymptotics. We find through extensive numerical studies that the complexity surface always bends in the direction away from the singularity and transitions from spacelike near th

Mikhail G. Katz, Stephane Sabourau

We show that every closed nonpositively curved surface satisfies Loewner&#39;s systolic inequality. The proof relies on a combination of the Gauss-Bonnet formula with an averaging argument using the invariance of the Liouville measure under the geodesic flow. This enables us to find a disk with large total curvature around its center yielding a large area.

José F. Fontanari

Evolutionary game theory has impacted many fields of research by providing a mathematical framework for studying the evolution and maintenance of social and moral behaviors. This success is owed in large part to the demonstration that the central equation of this theory - the replicator equation - is the deterministic limit of a stochastic imitation (social

Jordan Bryan, Haibo Zhou, Didong Li

In the heteroscedastic linear model, the weighted least squares (WLS) estimate of the model coefficients is more efficient than the ordinary least squares (OLS) esti- mate. However, the practical application of WLS is challenging because it requires knowledge of the error variances. Feasible weighted least squares (FLS) estimates, which use approximations of

Paula Rescala, Manoel Horta Ribeiro, Tiancheng Hu, Robert West

The capabilities of large language models (LLMs) have raised concerns about their potential to create and propagate convincing narratives. Here, we study their performance in detecting convincing arguments to gain insights into LLMs&#39; persuasive capabilities without directly engaging in experimentation with humans. We extend a dataset by Durmus and Cardie

Claudio Quadrelli

Let $p$ be a prime. Following Snopce-Tanushevski, a pro-$p$ group $G$ is called Frattini-resistant if the function $H\mapstoΦ(H)$, from the poset of all closed finitely-generated subgroups of $G$ into itself, is a poset embedding. We prove that for an oriented right-angled Artin pro-$p$ group (oriented pro-$p$ RAAG) $G$ associated to a directed graph the fol

Luca Amendola, Vrund Patel, Ziad Sakr, Elena Sellentin

The ratio of Bayesian evidences is a popular tool in cosmology to compare different models. There are however several issues with this method: Bayes&#39; ratio depends on the prior even in the limit of non-informative priors, and Jeffrey&#39;s scale, used to assess the test, is arbitrary. Moreover, the standard use of Bayes&#39; ratio is often criticized for

Tao Chen, Chenxi Huang, Bryce Gadway, Jacob P. Covey

Coherent dynamics of interacting quantum particles plays a central role in the study of strongly correlated quantum matter and the pursuit of quantum information processors. Here, we present the state-space of interacting Rydberg atoms as a synthetic landscape on which to control and observe coherent and correlated dynamics. With full control of the coupling

Sheng-Yang Kevin Ho

Let $\mathfrak{n} = \mathfrak{p}^r$ be a prime power ideal of $\mathbb{F}_q[T]$ with $r \geq 2$. We study the rational torsion subgroup $\mathcal{T}(\mathfrak{p}^r)$ of the Drinfeld modular Jacobian $J_0(\mathfrak{p}^r)$. We prove that the prime-to-$q(q-1)$ part of $\mathcal{T}(\mathfrak{p}^r)$ is equal to that of the rational cuspidal divisor class group $\

Chang Liu, AmirEmad Ghassami

Influence function (IF)-based estimators are widely used in mediation analysis due to their modeling flexibility, but standard implementations require direct estimation of the distribution functions of the mediator and treatment variables. Since these functions appear in the denominator of IF-based estimators, they can induce significant instability, particu

Thejus Mary S., Avijit Hazra, Neelesh Sharma, Rohit Dhir

We present a comprehensive analysis of weak transition form factors, semileptonic decays, and nonleptonic decays of $B_c$ meson involving pseudoscalar ($P$) and vector ($V$) meson for bottom-conserving and bottom-changing decay modes. We employ self-consistent covariant light-front quark model (CLFQM), termed as Type-II correspondence, to calculate the $B_c$

Fernando Palafox, Jesse Milzman, Dong Ho Lee, Ryan Park

We present a mathematical framework for modeling two-player noncooperative games in which one player is uncertain of the other player&#39;s costs but can preemptively allocate information-gathering resources to reduce this uncertainty. We refer to the players as the uncertain player (UP) and the certain player (CP), respectively. We obtain UP&#39;s decisions

Katy Blumer, Kate Donahue, Katie Fritz, Kate Ivanovich

In this paper, we study the highly competitive arena of baby naming. Through making several Extremely Reasonable Assumptions (namely, that parents are myopic, perfectly knowledgeable agents who pick a name based solely on its uniqueness), we create a model which is not only tractable and clean, but also perfectly captures the real world. We then extend our i

Xuan-Qian Bao, Xue-Dong Tian, Dong-Xiao Li, Yi-Mou Liu

We propose a theoretical scheme for dipole exchange-induced grating (DEIG) based on a hybrid system consisting of ultra-cold Rubidium ($^{87}$Rb) atomic ensemble and movable Rydberg spin atoms. The optical response of the grating appears as a superposition of three- and four-level configurations, similar to the cooperative optical nonlinear effect caused by

Ian Porada, Xiyuan Zou, Jackie Chi Kit Cheung

All state-of-the-art coreference resolution (CR) models involve finetuning a pretrained language model. Whether the superior performance of one CR model over another is due to the choice of language model or other factors, such as the task-specific architecture, is difficult or impossible to determine due to lack of a standardized experimental setup. To reso

Michael Hassid, Tal Remez, Jonas Gehring, Roy Schwartz

It is a common belief that large language models (LLMs) are better than smaller-sized ones. However, larger models also require significantly more time and compute during inference. This begs the question: what happens when both models operate under the same budget? (e.g., compute, run-time). To address this question, we analyze code generation LLMs of vario

Jia Guo, Haonan Han, Shuai Lu, Weihang Zhang

Conventional unsupervised anomaly detection (UAD) methods build separate models for each object category. Recent studies have proposed to train a unified model for multiple classes, namely model-unified UAD. However, such methods still implement the unified model separately on each class during inference with respective anomaly decision thresholds, which hin

Chih-Chung Hsu, Chia-Ming Lee, Yi-Shiuan Chou

In recent years, Vision Transformer-based approaches for low-level vision tasks have achieved widespread success. Unlike CNN-based models, Transformers are more adept at capturing long-range dependencies, enabling the reconstruction of images utilizing non-local information. In the domain of super-resolution, Swin-transformer-based models have become mainstr

Naoki Hiraiwa, Mai Bando, Isaia Nisoli, Yuzuru Sato

Modern space missions with uncrewed spacecraft require robust trajectory design to connect multiple chaotic orbits by small controls. To address this issue, we propose a control scheme to design robust trajectories by leveraging a geometrical structure in chaotic zones, known as a {\it lobe}. Our scheme shows that appropriately selected lobes reveal possible

Carlos Araújo Álvarez, Henry W. Y. Wong, Anna Liu, Juan Calderón Bustillo

Pair-instability supernova (PISN) prevents black-hole formation from stellar collapse within the approximate mass range $M\in [65,130]M_\odot$. However, such black holes may form hierarchically through merging ancestral black holes, whose properties determine those of the ``child&#39;&#39; one: mass, spin, and recoil velocity. Crucially, the child will leave

Zi-Kui Liu, Shun-Li Shang

A coherent theory for the superconductivity of both conventional and unconventional superconductors is currently lacking. Here we show that superconductivity arises from the formation of a symmetry-broken superconducting configuration (SCC) due to atomic perturbation of the normal conducting configuration (NCC). This electron-phonon interaction creates strai

Mohd Ali, Vardarajan Suneeta

In this paper, we show a local generalized second law (the generalized entropy is nondecreasing) in crossed product constructions for maximally extended static and Kerr black holes using modular theory. The new ingredient is the use of results from a recent paper discussing the entropy of the algebra of operators in subregions of arbitrary spacetimes. These

Haibao Yu, Wenxian Yang, Jiaru Zhong, Zhenwei Yang

Cooperatively utilizing both ego-vehicle and infrastructure sensor data via V2X communication has emerged as a promising approach for advanced autonomous driving. However, current research mainly focuses on improving individual modules, rather than taking end-to-end learning to optimize final planning performance, resulting in underutilized data potential. I

Jesse Atuhurra, Hidetaka Kamigaito

Natural language processing (NLP) has grown significantly since the advent of the Transformer architecture. Transformers have given birth to pre-trained large language models (PLMs). There has been tremendous improvement in the performance of NLP systems across several tasks. NLP systems are on par or, in some cases, better than humans at accomplishing speci

Yan Zhuang, Qi Liu, Haoyang Bi, Zhenya Huang

Computerized Adaptive Testing (CAT) offers an efficient and personalized method for assessing examinee proficiency by dynamically adjusting test questions based on individual performance. Compared to traditional, non-personalized testing methods, CAT requires fewer questions and provides more accurate assessments. As a result, CAT has been widely adopted acr

Michael Allen, Brian Grove, Ling Long, Fang-Ting Tu

The theories of hypergeometric functions and modular forms are highly intertwined. For example, particular values of truncated hypergeometric functions and hypergeometric character sums are often congruent or equal to Fourier coefficients of modular forms. In this series of papers, we develop and explore an explicit &#34;Hypergeometric-Modularity&#34; method

Peter Morfe, Felix Otto, Christian Wagner

This note is about a drift-diffusion process $X$ with a time-independent, divergence-free drift $b$, where $b$ is a smooth Gaussian field that decorrelates over large scales. In two space dimensions, this just fails to fall into the standard theory of stochastic homogenization, and leads to a borderline super-diffusive behavior. In a previous paper by Chatzi

Anna Musilová, Aleš Nekvinda, Dalimil Peša, Hana Turčinová

This paper explores some important aspects of the theory of rearrangement-invariant quasi-Banach function spaces. We focus on two main topics. Firstly, we prove an analogue of the Luxemburg representation theorem for rearrangement-invariant quasi-Banach function spaces over resonant measure spaces. Secondly, we develop the theory of fundamental functions and

Shuai Yang, Zhiyu Dong, Yan Chen

The behavior of strongly interacting electrons in bands with Berry curvature is a problem of wide interest. In this paper, we study this problem by numerically studying a fluxed Hubbard-type model on square lattice. Using this model, we demonstrate a metallic ferromagnet in electron bands equipped with Berry curvature can develop non-coplanar spin order in w

Kamran Razavi, Saeid Ghafouri, Max Mühlhäuser, Pooyan Jamshidi

Mobile and IoT applications increasingly adopt deep learning inference to provide intelligence. Inference requests are typically sent to a cloud infrastructure over a wireless network that is highly variable, leading to the challenge of dynamic Service Level Objectives (SLOs) at the request level. This paper presents Sponge, a novel deep learning inference s

Boris Botvinnik, Jonathan Rosenberg

Let $(M,L)$ be a (compact) non-spin spin$^c$ manifold. Fix a Riemannian metric $g$ on $M$ and a connection $A$ on $L$, and let $D_L$ be the associated spin$^c$ Dirac operator. Let $R^{tw}_{(g,A)}:=R_g + 2ic(Ω)$ be the twisted scalar curvature (which takes values in the endomorphisms of the spinor bundle), where $R_g$ is the scalar curvature of $g$ and $2ic(Ω

Wenlin Zhang, Chuhan Wu, Xiangyang Li, Yuhao Wang

The lack of training data gives rise to the system cold-start problem in recommendation systems, making them struggle to provide effective recommendations. To address this problem, Large Language Models (LLMs) can model recommendation tasks as language analysis tasks and provide zero-shot results based on their vast open-world knowledge. However, the large s

Omri Nisan Solan

We show that for every nonarithmetic lattice $Γ<{\rm SL}_2(\mathbb{C})$ there is a gap $\varepsilon_Γ>0$ such that for every $g\in {\rm SL}_2(\mathbb{C})$ the intersection ${\rm SL}_2(\mathbb{R})\cap gΓg^{-1}$ is either a lattice in ${\rm SL}_2(\mathbb{R})$ or has critical exponent $δ({\rm SL}_2(\mathbb{R})\cap gΓg^{-1}) \leq 1 - \varepsilon_Γ$.

Mathieu Ravaut, Bosheng Ding, Fangkai Jiao, Hailin Chen

With the rise of Large Language Models (LLMs) in recent years, abundant new opportunities are emerging, but also new challenges, among which contamination is quickly becoming critical. Business applications and fundraising in Artificial Intelligence (AI) have reached a scale at which a few percentage points gained on popular question-answering benchmarks cou

Hajime Otsuka, Ryo Yokokura

We study primordial electromagnetic fields in effective actions of string theory. In contrast to a conventional scenario of producing primordial electromagnetic fields induced by the axion inflation, we deal with the Dirac-Born-Infeld action as a non-linear generation of Maxwell theory. It turns out that the intensity of generated electromagnetic fields is b

Jingyue Wang, Junwei Huang, Daniel Kaplan, Xuehan Zhou

In the presence of high magnetic field, quantum Hall systems usually host both even- and odd-integer quantized states because of lifted band degeneracies. Selective control of these quantized states is challenging but essential to understand the exotic ground states and manipulate the spin textures. Here, we study the quantum Hall effect in Bi2O2Se thin film

Zhuoyi Pang, Fei Yao, Jian-Hui Zhang

We propose two approaches for extracting the total gluon helicity contribution to proton spin from lattice QCD, one from local operator matrix elements in a fixed gauge accessible on lattice with feasible renormalization, and the other from gauge-invariant nonlocal gluon correlators. Neither of these approaches requires a matching procedure when converted to

Xiujie Deng, Alexander Wu Chao, Wenhui Huang, Zizheng Li

In this paper, three specific scenarios of a novel accelerator light source mechanism called steady-state micro-bunching (SSMB) have been studied, i.e., longitudinal weak focusing, longitudinal strong focusing and generalized longitudinal strong focusing (GLSF). At present, GLSF is the most promising among them in realizing high-power short-wavelength cohere

Meisam Kabiri, Claudio Cimarelli, Hriday Bavle, Jose Luis Sanchez-Lopez

5G New Radio Time of Arrival (ToA) data has the potential to revolutionize indoor localization for micro aerial vehicles (MAVs). However, its performance under varying network setups, especially when combined with IMU data for real-time localization, has not been fully explored so far. In this study, we develop an error state Kalman filter (ESKF) and a pose

Gerald X. Gilbert-Thorple, Jôsé J. Jesus

An outstanding mystery of dark matter physics is the lack of direct detection signals to date. We suggest that dark matter is scatophobic: due to a repulsive long-range interaction, it is repelled by objects with a large net scat charge, such as the Earth, and is therefore not able to reach direct detection experiments. This represents the first step in a br

Weimin Zhang

Here we consider the following fractional Hamiltonian system \begin{equation*} \begin{cases} \begin{aligned} (-Δ)^{s} u&=H_v(u,v) \;\;&&\text{in}~Ω,\\ (-Δ)^{s} v&=H_u(u,v) &&\text{in}~Ω,\\ u &= v = 0 &&\text{in} ~ \mathbb{R}^N\setminusΩ, \end{aligned} \end{cases} \end{equation*} where $s\in (0,1)$, $N>2s$, $H \in C^1(\mathbb{R}^2, \mathbb{R})$ and $Ω\subset

Srinjoy Roy, Swagatam Das

Accounting for the uncertainty of value functions boosts exploration in Reinforcement Learning (RL). Our work introduces Maximum Mean Discrepancy Q-Learning (MMD-QL) to improve Wasserstein Q-Learning (WQL) for uncertainty propagation during Temporal Difference (TD) updates. MMD-QL uses the MMD barycenter for this purpose, as MMD provides a tighter estimate o

Michael Timothy Bennett

Simplicity is held by many to be the key to general intelligence. Simpler models tend to &#34;generalise&#34;, identifying the cause or generator of data with greater sample efficiency. The implications of the correlation between simplicity and generalisation extend far beyond computer science, addressing questions of physics and even biology. Yet simplicity

Santiago Cuervo, Ricard Marxer

Speech Language Models (SLMs) aim to learn language from raw audio, without textual resources. Despite significant advances, our current models exhibit weak syntax and semantic abilities. However, if the scaling properties of neural language models hold for the speech modality, these abilities will improve as the amount of compute used for training increases

Yang Luo, Zhineng Chen, Peng Zhou, Zuxuan Wu

Images suffer from heavy spatial redundancy because pixels in neighboring regions are spatially correlated. Existing approaches strive to overcome this limitation by reducing less meaningful image regions. However, current leading methods rely on supervisory signals. They may compel models to preserve content that aligns with labeled categories and discard c

Yassir Bendou, Giulia Lioi, Bastien Pasdeloup, Lukas Mauch

We consider the problem of zero-shot one-class visual classification, extending traditional one-class classification to scenarios where only the label of the target class is available. This method aims to discriminate between positive and negative query samples without requiring examples from the target class. We propose a two-step solution that first querie

Wenxiao Cai, Xinyue Lei, Xinyu He, Junming Leo Chen

We present Knowledge NeRF to synthesize novel views for dynamic scenes. Reconstructing dynamic 3D scenes from few sparse views and rendering them from arbitrary perspectives is a challenging problem with applications in various domains. Previous dynamic NeRF methods learn the deformation of articulated objects from monocular videos. However, qualities of the

Thanh Tam Nguyen, Thanh Trung Huynh, Zhao Ren, Thanh Toan Nguyen

As the adoption of explainable AI (XAI) continues to expand, the urgency to address its privacy implications intensifies. Despite a growing corpus of research in AI privacy and explainability, there is little attention on privacy-preserving model explanations. This article presents the first thorough survey about privacy attacks on model explanations and the

Itai Kreisler, Maor Ivgi, Oliver Hinder, Yair Carmon

We propose a method that achieves near-optimal rates for smooth stochastic convex optimization and requires essentially no prior knowledge of problem parameters. This improves on prior work which requires knowing at least the initial distance to optimality d0. Our method, U-DoG, combines UniXGrad (Kavis et al., 2019) and DoG (Ivgi et al., 2023) with novel it

Santosh Kumar Chaudhary, Nitin Gupta, Achintya Roy

In this paper, we introduce the cumulative past information generating function (CPIG) and relative cumulative past information generating function (RCPIG). We study its properties. We establish its relation with generalized cumulative past entropy (GCPE). We defined CPIG stochastic order and its relation with dispersive order. We provide the results for the

Vladimir Kozlov

We consider solitary water waves on a rotational, unidirectional flow in a two-dimensional channel of finite depth. Ovsyannikov has conjectured in 1983 that the solitary wave is uniquely determined by the Bernoulli constant, mass flux and by the flow force. This conjecture was disproved by Plotnikov in 1992 for the ir-rotational flow. In this paper we show t

Yingjin Bi

In this paper, we give a geometrization of semicanonical bases of quantum groups via Grothendieck groups of the derived categories of Lusztig&#39;s nilpotent varieties. Meanwhile, we describe the dual semicanonical bases in terms of Serre polynomials of Grassmannians of modules over preprojective algebras.

René Rühr

We associate to each unit volume lattice of $\R^d$ the Ising model with bond variables equal to the inverse successive minima of that lattice. This induces the notion of a critical temperature for a random lattice for which integrability exponents are proven.

Jihua Peng, Yanghong Zhou, P. Y. Mok

This paper presents a novel Kinematics and Trajectory Prior Knowledge-Enhanced Transformer (KTPFormer), which overcomes the weakness in existing transformer-based methods for 3D human pose estimation that the derivation of Q, K, V vectors in their self-attention mechanisms are all based on simple linear mapping. We propose two prior attention modules, namely

Shujie Hu, Long Zhou, Shujie Liu, Sanyuan Chen

The recent advancements in large language models (LLMs) have revolutionized the field of natural language processing, progressively broadening their scope to multimodal perception and generation. However, effectively integrating listening capabilities into LLMs poses significant challenges, particularly with respect to generalizing across varied contexts and

Osamu Seto, Takashi Shimomura, Yoshiki Uchida

We consider the gauged $U(1)_{B-L}$ model and examine the situation where the sterile neutrino is a dark matter candidate produced by the freeze-in mechanism. In our model, the dark matter $N$ is mainly produced by the decay of a $U(1)_{B-L}$ breaking scalar boson $ϕ$. We point out that the on-shell production of $ϕ$ through annihilation of the $U(1)_{B-L}$

Sergey Rybakov

We refine and generalize the results of K. E. Lauter and E. W. Howe on principal polarizations on products of abelian varieties over finite fields. Firstly, we study the reasons for the absence of an irreducible principal polarization in the isogeny class of the product of an ordinary and a supersingular abelian variety. Secondly, we provide a necessary cond

Xiaorui Huang, Gen Luo, Chaoyang Zhu, Bo Tong

Recently, Segment Anything Model (SAM) has become a research hotspot in the fields of multimedia and computer vision, which exhibits powerful yet versatile capabilities on various (un) conditional image segmentation tasks. Although SAM can support different types of segmentation prompts, we note that, compared to point- and box-guided segmentations, it perfo

Sergey Volkov

The total 5-loop quantum electrodynamics universal contribution to the anomalous magnetic moments of the leptons was calculated by the author. The obtained value $A_1^{(10)}=5.891(61)$ provides the first complete verification of the previously known value obtained by T. Aoyama, M. Hayakawa, T. Kinoshita, M. Nio (AHKN). The discrepancy is $5 σ$. The computati