March 2024 arXiv papers — page 103

Jingping Nie, Hanya Shao, Yuang Fan, Qijia Shao

Despite the global mental health crisis, access to screenings, professionals, and treatments remains high. In collaboration with licensed psychotherapists, we propose a Conversational AI Therapist with psychotherapeutic Interventions (CaiTI), a platform that leverages large language models (LLM)s and smart devices to enable better mental health self-care. Ca

Shibiao Xu, ShuChen Zheng, Wenhao Xu, Rongtao Xu

Infrared small object detection is an important computer vision task involving the recognition and localization of tiny objects in infrared images, which usually contain only a few pixels. However, it encounters difficulties due to the diminutive size of the objects and the generally complex backgrounds in infrared images. In this paper, we propose a deep le

Apurba Das

Let $H$ be a cocommutative Hopf algebra. The notion of Lie $H$-pseudoalgebra is a multivariable generalization of Lie conformal algebras. In this paper, we study some higher structures related to Lie $H$-pseudoalgebras where we increase the flexibility of the Jacobi identity. Namely, we first introduce $L_\infty$ $H$-pseudoalgebras (also called strongly homo

Sanjana Gautam, Pranav Narayanan Venkit, Sourojit Ghosh

With the widespread adoption of advanced generative models such as Gemini and GPT, there has been a notable increase in the incorporation of such models into sociotechnical systems, categorized under AI-as-a-Service (AIaaS). Despite their versatility across diverse sectors, concerns persist regarding discriminatory tendencies within these models, particularl

Makoto Sakamoto, Kazunori Takenaga

We develop a new formula called a mode recombination formula, and we can recast the effective potential at finite temperature in one-loop approximation for fermion and scalar fields on the $D$-dimensional spacetime, $S_{\tau}^1 \times R^{D-(p+1)}\times\prod_{i=1}^pS_i^1$ into a convenient form for discussing nonanalytic terms, which cannot be written in the

J. K. Lee, T. M. Chung

The rapid advancement of large language models (LLMs) has enabled natural language processing capabilities similar to those of humans, and LLMs are being widely utilized across various societal domains such as education and healthcare. While the versatility of these models has increased, they have the potential to generate subjective and normative language,

Christopher Kolios, Yeganeh Bahoo, Sajad Saeedi

We present DPPE, a dense pose estimation algorithm that functions over a Plenoxels environment. Recent advances in neural radiance field techniques have shown that it is a powerful tool for environment representation. More recent neural rendering algorithms have significantly improved both training duration and rendering speed. Plenoxels introduced a fully-d

Yifei He, Martin Kruczenski

Following the Gauge Theory Bootstrap method proposed in our previous work [arXiv:2309.12402], we compute pion scattering phase shifts for all partial waves with angular momentum $\ell\le 3$ up to 2 GeV and calculate the low energy $\chi$PT coefficients $\bar{\ell}_{1,2,4,6}$. The method looks for the most general S-matrix that matches at low energy the tree

Junyu Cao, Mohsen Bayati

A growing trend involves integrating human knowledge into learning frameworks, leveraging subtle human feedback to refine AI models. While these approaches have shown promising results in practice, the theoretical understanding of when and why such approaches are effective remains limited. This work takes steps toward developing a theoretical framework for a

Jincheng Gao, Lianyun Peng, Zheng-an Yao

In this paper, we consider the local existence and uniqueness result for the inhomogeneous Prandtl equations in dimension two by energy method. First of all, for the homogeneous case, the local-in-time well-posedness theory of unsteady Prandtl equations was obtained by [Alexandre, Wang, Xu, Yang, J. Am. Math. Soc., 28 (3), 745-784 (2015)] and [Masmoudi, Wong

Yuqiang Gao, Menno Bokdam, Paul J. Kelly

We add the magnetic degrees of freedom to the widely used Gaussian Approximation Potential of machine learning (ML) and present a model that describes the potential energy surface of a crystal based on the atomic coordinates as well as their noncollinear magnetic moments. Assuming an adiabatic approximation for the spin directions and magnitudes, the ML mode

Daniel Morton, Mark Cutkosky, Marco Pavone

ReachBot, a proposed robotic platform, employs extendable booms as limbs for mobility in challenging environments, such as martian caves. When attached to the environment, ReachBot acts as a parallel robot, with reconfiguration driven by the ability to detach and re-place the booms. This ability enables manipulation-focused scientific objectives: for instanc

Norikazu Yamada, Masahito Yamazaki, Ryuichiro Kitano

We apply the previously-developed sub-volume method to study the $\theta$-dependence of the four-dimensional SU(2) Yang-Mills theory at finite temperature. We calculate the first two coefficients, the topological susceptibility $\chi$ and the fourth cumulant $b_2$, in the $\theta$-expansion of the free energy density around the critical temperature ($T_c$) f

Krzysztof Kacprzyk, Samuel Holt, Jeroen Berrevoets, Zhaozhi Qian

Inferring unbiased treatment effects has received widespread attention in the machine learning community. In recent years, our community has proposed numerous solutions in standard settings, high-dimensional treatment settings, and even longitudinal settings. While very diverse, the solution has mostly relied on neural networks for inference and simultaneous

Siyao Liu, Yong Wang

In this paper, we extend the elliptic genus in [10] by the gauge group E_8 and the gauge group E_8*E_8. Then we prove that the generalized elliptic genus are the weak Jacobi forms. Using these elliptic genus, we obtain some SL_2(Z) modular forms and get some new anomaly cancellation formulas of characteristic forms for almost complex manifolds.

J. K. Lee, T. M. Chung

In this multi-task learning study on simultaneous analysis of emotions and their underlying causes in conversational contexts, deep neural network methods were employed to effectively process and train large labeled datasets. However, these approaches are typically limited to conducting context analyses across the entire corpus because they rely on one of th

Ronak Mehta, Jelena Diakonikolas, Zaid Harchaoui

We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses learning using $f$-DRO and spectral/$L$-risk minimization. We present Drago, a stochastic primal-dual algorithm that combines cyclic and randomized components with a carefully regularized primal update to achieve dual

Seif Ismail, Antonio Arbues, Ryan Cotterell, René Zurbrügg

The impressive capabilities of Large Language Models (LLMs) have led to various efforts to enable robots to be controlled through natural language instructions, opening exciting possibilities for human-robot interaction The goal is for the motor-control task to be performed accurately, efficiently and safely while also enjoying the flexibility imparted by LL

Jizhe Dou, Haotian Zhang, Guodong Sun

Recently there has been a growing interest in industry and academia, regarding the use of wireless chargers to prolong the operational longevity of unmanned aerial vehicles (commonly knowns as drones). In this paper we consider a charger-assisted drone application: a drone is deployed to observe a set points of interest, while a charger can move to recharge

Yoonyoung Cho, Junhyek Han, Yoontae Cho, Beomjoon Kim

Nonprehensile manipulation is essential for manipulating objects that are too thin, large, or otherwise ungraspable in the wild. To sidestep the difficulty of contact modeling in conventional modeling-based approaches, reinforcement learning (RL) has recently emerged as a promising alternative. However, previous RL approaches either lack the ability to gener

Phil Romero

The sheer number of nodes continues to increase in todays supercomputers, the first half of Trinity alone contains more than 9400 compute nodes. Since the speed of todays clusters are limited by the slowest nodes, it more important than ever to identify slow nodes, improve their performance if it can be done, and assure minimal usage of slower nodes during p

Jialin Hua, Liangqing Luo, Weiying Ping, Yan Liao

Open information extraction (OIE) aims to extract surface relations and their corresponding arguments from natural language text, irrespective of domain. This paper presents an innovative OIE model, APRCOIE, tailored for Chinese text. Diverging from previous models, our model generates extraction patterns autonomously. The model defines a new pattern form fo

Jordi Delgado, Enric Ventura

This text aims to provide a self-contained, comprehensive, and reasonably detailed presentation of the theory of Stallings automata and some of its main applications.

Shunsuke Tsubaki, Daisuke Niizumi, Daiki Takeuchi, Yasunori Ohishi

The aim of this research is to refine knowledge transfer on audio-image temporal agreement for audio-text cross retrieval. To address the limited availability of paired non-speech audio-text data, learning methods for transferring the knowledge acquired from a large amount of paired audio-image data to shared audio-text representation have been investigated,

Junpeng Jing, Ye Mao, Krystian Mikolajczyk

Dynamic stereo matching is the task of estimating consistent disparities from stereo videos with dynamic objects. Recent learning-based methods prioritize optimal performance on a single stereo pair, resulting in temporal inconsistencies. Existing video methods apply per-frame matching and window-based cost aggregation across the time dimension, leading to l

Jihoon Kim, Yongmin Kwon, Namwoo Kang

Generative Design (GD) has evolved as a transformative design approach, employing advanced algorithms and AI to create diverse and innovative solutions beyond traditional constraints. Despite its success, GD faces significant challenges regarding the manufacturability of complex designs, often necessitating extensive manual modifications due to limitations i

Lu Jingyu

As a bridge from virtuality to reality, Digital Twin has increased in popularity since proposed. Ideas have been proposed theoretical and practical for digital twins. From theoretical perspective, digital twin is fusion of data mapping between modalities; from practical point of view, digital twin is scenario implementation based on the Internet of Things an

Yizhou Gu, Xiaohu Yang, Jiaxin Han, Yirong Wang

In this paper, we set out to construct a set of reference mock galaxy redshift surveys (MGRSs) for the future Chinese Space-station Survey Telescope (CSST) observation, where subsequent survey selection effects can be added and evaluated. This set of MGRSs is generated using the dark matter subhalos extracted from a high-resolution Jiutian $N$-body simulatio

Marcos Medeiros, Uirá Kulesza, Roberta Coelho, Rodrigo Bonifácio

Developers often use crash reports to understand the root cause of bugs. However, locating the buggy source code snippet from such information is a challenging task, mainly when the log database contains many crash reports. To mitigate this issue, recent research has proposed and evaluated approaches for grouping crash report data and using stack trace infor

Zeraoulia Rafik, Alvaro Humberto Salas, Ayadi Souad

Here, we introduce a novel method for obtaining chaotic dynamics based on the Montgomery conjecture for the pair correlation of zeros of the Riemann zeta function. Motivated by the conjecture, we present a recursive relation that reveals chaotic behavior. Notably, we provide insights into the possible uses of this derived chaotic dynamics in electrical engin

J. A. Mendez-Bermudez, R. Aguilar-Sanchez

The Caputo fractional standard map (C-fSM) is a two-dimensional nonlinear map with memory given in action-angle variables $(I,\theta)$. It is parameterized by $K$ and $\alpha\in(1,2]$ which control the strength of nonlinearity and the fractional order of the Caputo derivative, respectively. In this work we perform a scaling study of the average squared actio

Sravan Kumar Ankireddy, Krishna Narayanan, Hyeji Kim

The design of reliable and efficient codes for channels with feedback remains a longstanding challenge in communication theory. While significant improvements have been achieved by leveraging deep learning techniques, neural codes often suffer from high computational costs, a lack of interpretability, and limited practicality in resource-constrained settings

Xiaochong Lan, Zhiguang Han, Yiming Cheng, Li Sheng

Limited access to mental healthcare resources hinders timely depression diagnosis, leading to detrimental outcomes. Social media platforms present a valuable data source for early detection, yet this task faces two significant challenges: 1) the need for medical knowledge to distinguish clinical depression from transient mood changes, and 2) the dual require

N. Sensharma, U. Garg, Q. B. Chen, S. Frauendorf

Chirality is a distinct signature that characterizes triaxial shapes in nuclei. We report the first observation of chirality in the nucleus $^{135}$Pr using a high-statistics Gammasphere experiment with the $^{123}$Sb($^{16}$O,4n)$^{135}$Pr reaction. Two chiral-partner bands with the configuration $\pi(1h_{11/2})^1\otimes\nu(1h_{11/2})^{-2}$ have been identi

Christophe Bonneville, Xiaolong He, April Tran, Jun Sur Park

Numerical solvers of partial differential equations (PDEs) have been widely employed for simulating physical systems. However, the computational cost remains a major bottleneck in various scientific and engineering applications, which has motivated the development of reduced-order models (ROMs). Recently, machine-learning-based ROMs have gained significant p

Petr Benes, Filip Blaschke, Y. M. Cho

The monopole production at LHC crucially depends on the monopole production mechanism. We show that, if the monopole production mechanism at LHC is the thermal fluctuation of the Higgs vacuum as the early universe did, it is practically impossible for LHC to produce the monopole, even with the future FCC energy upgrade. This is because the temperature of the

Gergely Szilvasy, Pierre-Emmanuel Mazaré, Matthijs Douze

In recent years, the dominant accuracy metric for vector search is the recall of a result list of fixed size (top-k retrieval), considering as ground truth the exact vector retrieval results. Although convenient to compute, this metric is distantly related to the end-to-end accuracy of a full system that integrates vector search. In this paper we focus on th

Joaquim Ortiz-Haro, Wolfgang Hönig, Valentin N. Hartmann, Marc Toussaint

Rapidly-exploring Random Trees (RRT) and its variations have emerged as a robust and efficient tool for finding collision-free paths in robotic systems. However, adding dynamic constraints makes the motion planning problem significantly harder, as it requires solving two-value boundary problems (computationally expensive) or propagating random control inputs

Zhiyi Tan, Bingkun Bao

In recent years, epidemic policy-making models are increasingly being used to provide reference for governors on prevention and control policies against catastrophic epidemics such as SARS, H1N1 and COVID-19. Existing studies are currently constrained by two issues: First, previous methods develop policies based on effect evaluation, since few of factors in

Yuki Izumida

The Curzon-Ahlborn (CA) cycle is a paradigmatic model of endoreversible heat engines, which yields the so-called CA efficiency as the efficiency at maximum power. Due to the arbitrariness of the relationship between the steady temperature and the time taken for the isothermal process of the CA cycle, the constructions of the CA cycle on the thermodynamic pla

Andrew B. Kahng, Zhiang Wang

Global placement is a fundamental step in VLSI physical design. The wide use of 2D processing element (PE) arrays in machine learning accelerators poses new challenges of scalability and Quality of Results (QoR) for state-of-the-art academic global placers. In this work, we develop DG-RePlAce, a new and fast GPU-accelerated global placement framework built o

Miki Horiguchi, Lu Tian, Kenneth L. Kehl, Hajime Uno

Delayed treatment effects on time-to-event outcomes have often been observed in randomized controlled studies of cancer immunotherapies. In the case of delayed onset of treatment effect, the conventional test/estimation approach using the log-rank test for between-group comparison and Cox's hazard ratio to estimate the magnitude of treatment effect is not op

Daniele Oriti

We outline the content and theoretical support for the proposal of "hydrodynamics on (mini)superspace" (or a non-linear extension of quantum cosmology) as an effective framework for quantum gravity in a cosmological context. The basis for the proposal is a general correspondence between hydrodynamics and cosmology, and a picture of the universe as a quantum

Noah Austin-Bingamon, Binod D. C., Yoichi Miyahara

Quality factor plays a fundamental role in dynamic mode atomic force microscopy. We present a technique to modify the quality factor of an atomic force microscopy cantilever within a Fabry-P\'erot optical interferometer. The experimental setup uses two separate laser sources to detect and excite the oscillation of the cantilever. While the intensity modulati

Z. L. Li, Y. J. Li

The nonadiabatic quantum Vlasov equation in spinor QED is derived, and its relation to the well-known adiabatic one is established by three methods. One is by an explicitly analytical expression, the second is by the Dirac equation in the V gauge, and the last is by introducing a turn-off electric field. Wherein what the first two of them are given is an ins

Andreas Savas-Halilaj, Knut Smoczyk

We consider the graphical mean curvature flow of maps ${\bf f}:\mathbb{R}^m\to\mathbb{R}^n$, $m\ge 2$, and derive estimates on the growth rates of the evolved graphs, based on a new version of the maximum principle for properly immersed submanifolds that extends the well-known maximum principle of Ecker and Huisken derived in their seminal paper [10]. In the

Zihan Zhang, Jason D. Lee, Yuxin Chen, Simon S. Du

A recent line of works showed regret bounds in reinforcement learning (RL) can be (nearly) independent of planning horizon, a.k.a.~the horizon-free bounds. However, these regret bounds only apply to settings where a polynomial dependency on the size of transition model is allowed, such as tabular Markov Decision Process (MDP) and linear mixture MDP. We give

Liupei Lu, Yufeng Yin, Yuming Gu, Yizhen Wu

Facial action unit (AU) detection is a fundamental block for objective facial expression analysis. Supervised learning approaches require a large amount of manual labeling which is costly. The limited labeled data are also not diverse in terms of gender which can affect model fairness. In this paper, we propose to use synthetically generated data and multi-s

Yuhan Zhao, Quanyan Zhu

Shared control allows the human driver to collaborate with an assistive driving system while retaining the ability to make decisions and take control if necessary. However, human-vehicle teaming and planning are challenging due to environmental uncertainties, the human's bounded rationality, and the variability in human behaviors. An effective collaboration

Nhan-Khanh Le, Erfaun Noorani, Sandra Hirche, John Baras

Real-world scenarios are characterized by timing uncertainties, e.g., delays, and disturbances. Algorithms with temporal robustness are crucial in guaranteeing the successful execution of tasks and missions in such scenarios. We study time-robust path planning for synthesizing robots' trajectories that adhere to spatial-temporal specifications expressed in S

Michał Kosiba

The aim of this paper is to study the Lipschitz normally embedded property for a set and its medial axis. We consider if and when a non-LNE set implies non-LNE medial axis and converse. We present an example a of Lipschitz normally set that has medial axis which is not Lipschitz normally emebedded. At the end we discuss special case when a set is a one dimen

Yuhan Zhao, Quanyan Zhu

Robot allocation plays an essential role in facilitating robotic service provision across various domains. Yet the increasing number of users and the uncertainties regarding the users' true service requirements have posed challenges for the service provider in effectively allocating service robots to users to meet their needs. In this work, we first propose

Zhiyong Wang, Jize Xie, Yi Chen, John C. S. Lui

We investigate the non-stationary stochastic linear bandit problem where the reward distribution evolves each round. Existing algorithms characterize the non-stationarity by the total variation budget $B_K$, which is the summation of the change of the consecutive feature vectors of the linear bandits over $K$ rounds. However, such a quantity only measures th

Anton Pelykh, Ozge Mercanoglu Sincan, Richard Bowden

Recent years have seen significant progress in human image generation, particularly with the advancements in diffusion models. However, existing diffusion methods encounter challenges when producing consistent hand anatomy and the generated images often lack precise control over the hand pose. To address this limitation, we introduce a novel approach to pose

Giorgio Morales, John Sheppard

In Precision Agriculture, the utilization of management zones (MZs) that take into account within-field variability facilitates effective fertilizer management. This approach enables the optimization of nitrogen (N) rates to maximize crop yield production and enhance agronomic use efficiency. However, existing works often neglect the consideration of respons

Austin Han, Robert Chiodi, Olivier Desjardins

A novel interface reconstruction strategy for volume of fluid (VOF) methods is introduced that represents the liquid-gas interface as two planes that co-exist within a single computational cell. In comparison to the piecewise linear interface calculation (PLIC), this new algorithm greatly improves the accuracy of the reconstruction, in particular when dealin

John C. Neu, Stephen W. Teitsworth

We study the fluctuational behavior of overdamped elastic filaments (e.g., strings or rods) driven by active matter which induces irreversibility. The statistics of discrete normal modes are translated into the continuum of the position representation which allows discernment of the spatial structure of dissipation and fluctuational work done by the active f

Víctor Becerril

The existence of the Gorenstein projective precovers over arbitrary rings is an open question. In this paper, we make use of three diferent techniques addressing intrinsic and homological properties of several classes of relative Gorenstein projective $R$-modules, among them including the Gorenstein projectives and Ding projectives, with the purpose of givin

Binqi Sun, Tomasz Kloda, Marco Caccamo

The rigid gang task model is based on the idea of executing multiple threads simultaneously on a fixed number of processors to increase efficiency and performance. Although there is extensive literature on global rigid gang scheduling, partitioned approaches have several practical advantages (e.g., task isolation and reduced scheduling overheads). In this pa

Marcin Szyniszewski

In systems undergoing localization-delocalization quantum phase transitions due to disorder or monitoring, there is a crucial need for robust methods capable of distinguishing phases and uncovering their intrinsic properties. In this work, we develop a process of finding a Slater determinant representation of free-fermion wave functions that accurately chara

Benjamin Budway, Mykhaylo Shkolnikov

Multilevel Dyson Brownian motions (MDBMs) combine Dyson Brownian motions of different dimensions into a single process in a canonical way. This paper completes the theory of MDBMs for $\beta\ge2$. Specifically, we use the superposition principle of Figalli and Trevisan to construct the MDBMs for all $\beta>2$ in a unified manner. This also extends their stoc

Jiayu Ding, Xulin Chen, Garret E. Katz, Zhenyu Gan

Quadrupedal robots exhibit a wide range of viable gaits, but generating specific footfall sequences often requires laborious expert tuning of numerous variables, such as touch-down and lift-off events and holonomic constraints for each leg. This paper presents a unified reinforcement learning framework for generating versatile quadrupedal gaits by leveraging

Toqi Tahamid Sarker, Taminul Islam, Khaled R Ahmed

Analyzing and detecting cannabis seed variants is crucial for the agriculture industry. It enables precision breeding, allowing cultivators to selectively enhance desirable traits. Accurate identification of seed variants also ensures regulatory compliance, facilitating the cultivation of specific cannabis strains with defined characteristics, ultimately imp

Gentian Muhaxheri, Christian D. Santangelo

While many materials exhibit a complex, hysteretic response to external driving, there has been a surge of interest in how the complex dynamics of internal materials states can be understood and designed to process and store information. We consider a system of connected rubber balloons that can be described by a Preisach model of non-interacting hysterons u

Ye Zhang, Mengran Zhu, Kailin Gui, Jiayue Yu

In this study, we explore the efficiency of the Monte Carlo Tree Search (MCTS), a prominent decision-making algorithm renowned for its effectiveness in complex decision environments, contingent upon the volume of simulations conducted. Notwithstanding its broad applicability, the algorithm's performance can be adversely impacted in certain scenarios, particu

Ziming Shao, Aileen Luo, Eti Barazani, Tao Zhou

Controlling the Mott transition through strain engineering is crucial for advancing the development and application of memristive and neuromorphic computing devices. Yet, Mott insulators are heterogeneous due to intrinsic phase boundaries and extrinsic defects, posing significant challenges to fully understanding the impact of local microscopic distortions o

Li-Qiang Sui, Mi-Xiang Lan

In the former studies, the time evolution information is missed in deducing the time-integrated polarizations of gamma-ray burst (GRB) prompt emission. Here, it is considered and the time-integrated polarizations is investigated through the summation of the time-resolved ones. The statistical properties of the distribution of the time-integrated polarization

Soumyadeep Pal, Yuguang Yao, Ren Wang, Bingquan Shen

Modern machine learning (ML) systems demand substantial training data, often resorting to external sources. Nevertheless, this practice renders them vulnerable to backdoor poisoning attacks. Prior backdoor defense strategies have primarily focused on the identification of backdoored models or poisoned data characteristics, typically operating under the assum

Luiz C. B. da Silva, Gilson S. Ferreira, José D. da Silva

In the last two decades, much effort has been dedicated to studying curves and surfaces according to their angle with a given direction. However, most findings were obtained using a case-by-case approach, and it is often unclear what are consequences of the specificities of the ambient manifold and what could be generic. In this work, we propose a theoretica

Michael Chiang, Austin Hopkins, Benjamin Loewe, Davide Marenduzzo

Multiphase field models have emerged as an important computational tool for understanding biological tissue while resolving single-cell properties. While they have successfully reproduced many experimentally observed behaviors of living tissue, the theoretical underpinnings have not been fully explored. We show that a two-dimensional version of the model, wh

Yassine El Ouahidi, Giulia Lioi, Nicolas Farrugia, Bastien Pasdeloup

In the context of Brain-Computer Interfaces, we propose an adaptive method that reaches offline performance level while being usable online without requiring supervision. Interestingly, our method does not require retraining the model, as it consists in using a frozen efficient deep learning backbone while continuously realigning data, both at input and late

Joshua Sparks

Inside the discipline of graph theory exists an extension known as the hypergraph. This generalization of graphs includes vertices along with hyperedges consisting of collections of two or more vertices. One well-studied application of this structure is that of the recursive tree, and we apply its framework within the context of hypergraphs to form hyperrecu

Zhichao Li, Ciril S. Prasad, Xielin Wang, Ding Zhang

Exceptional point (EP)-based optical sensors exhibit exceptional sensitivity but poor detectivity due to their acute sensitivity to perturbations such as noise. When the optical budget is limited as in applications on mobile platforms, high detectivity might be equally important as high sensitivity. In such scenarios, off-EP sensing is advantageous where a s

Felipe Zingali Meira

We classify Jacobian elliptic fibrations on K3 surfaces with a non-symplectic automorphism $\sigma$ of order 3 according to the action of $\sigma$ on their fibres, building on work by Garbagnati and Salgado for non-symplectic involutions. We determine the possible reducible fibres types and give Weierstrass equations for Jacobian elliptic fibration which are

Kevin Han Huang, Morgane Austern, Peter Orbanz

Gaussian universality results assert that the properties of many estimators remain unchanged when the input data are replaced by Gaussians. Such results have gained popularity in high-dimensional statistics and machine learning, as Gaussianity often substantially simplifies downstream analyses. Yet, an open question remains on when universality may cease to

Ihsan Ozan Yildirim, Murat Kuscu, Oguzhan Ozcan

The design of smart jewelry can be challenging as it requires technical knowledge and practice to explore form and function. Adressing this issue, we propose ProtoFlakes, a design speculation for a modular prototyping tool kit for smart jewelry design. ProtoFlakes builds upon the our prior work of Snowflakes, targeting designers with limited technical expert

Golrokh Nouri, Paul Tupper

Many drugs used therapeutically or recreationally induce tolerance: the effect of the substance decreases with repeated use. This phenomenon may reduce the efficacy of the substance unless dosage is increased beyond what is healthy for the individual. Restoring the effect of the substance can often be obtained by taking a break from consumption. We propose d

James B. Kennedy, Delio Mugnolo, Matthias Täufer

We examine diagonal combs, a recently identified class of infinite metric graphs whose properties depend on one parameter. These graphs exhibit a fascinating regime where they possess infinite volume while maintaining purely discrete spectrum for the Neumann Laplacian. In this regime, we establish polynomial upper and lower bounds on the $k$-th eigenvalue, r

Tunazzina Islam, Dan Goldwasser

Grasping the themes of social media content is key to understanding the narratives that influence public opinion and behavior. The thematic analysis goes beyond traditional topic-level analysis, which often captures only the broadest patterns, providing deeper insights into specific and actionable themes such as "public sentiment towards vaccination", "polit

Abigail Julian, Lars Ruthotto

Over the past decade, reversed Gradient Polarity (RGP) methods have become a popular approach for correcting susceptibility artifacts in Echo-Planar Imaging (EPI). Although several post-processing tools for RGP are available, their implementations do not fully leverage recent hardware, algorithmic, and computational advances, leading to correction times of s

Yigit Ege Bayiz, Arash Amini, Radu Marculescu, Ufuk Topcu

Social news websites, such as Reddit, have evolved into prominent platforms for sharing and discussing news. A key issue on social news websites sites is the formation of echo chambers, which often lead to the spread of highly biased or uncredible news. We develop a method to identify communities within a social news website that are prone to uncredible or h

Hakim Sidahmed, Samrat Phatale, Alex Hutcheson, Zhuonan Lin

While Reinforcement Learning from Human Feedback (RLHF) effectively aligns pretrained Large Language and Vision-Language Models (LLMs, and VLMs) with human preferences, its computational cost and complexity hamper its wider adoption. To alleviate some of the computational burden of fine-tuning, parameter efficient methods, like LoRA were introduced. In this

Garima Rajguru, L. Marcotulli, M. Ajello, A. Tramacere

Active galactic nuclei (AGNs) can power relativistic jets, which are called blazars when pointed close to our line of sight. Depending on the presence or absence of emission lines in their optical spectra, blazars are categorized into flat spectrum radio quasars (FSRQs) or BL Lacertae (BL Lacs) objects. According to the 'blazar sequence', as synchrotron peak

Urko Reinosa

In these lectures, we review the status of the Curci-Ferrari model as a phenomenological approach to Landau gauge-fixed YM theories in the infrared. More precisely, after discussing some of the general properties of the model, we explain in full detail the evaluation of the one-loop two-point correlators that allowed for a successful comparison to the corres

Eamon Duede, Kevin Davey

Can we acquire apriori knowledge of mathematical facts from the outputs of computer programs? People like Burge have argued (correctly in our opinion) that, for example, Appel and Haken acquired apriori knowledge of the Four Color Theorem from their computer program insofar as their program simply automated human forms of mathematical reasoning. However, unl

Yizhi Song, Zhifei Zhang, Zhe Lin, Scott Cohen

Generative object compositing emerges as a promising new avenue for compositional image editing. However, the requirement of object identity preservation poses a significant challenge, limiting practical usage of most existing methods. In response, this paper introduces IMPRINT, a novel diffusion-based generative model trained with a two-stage learning frame

Francesco Taioli, Stefano Rosa, Alberto Castellini, Lorenzo Natale

Vision-and-Language Navigation in Continuous Environments (VLN-CE) is one of the most intuitive yet challenging embodied AI tasks. Agents are tasked to navigate towards a target goal by executing a set of low-level actions, following a series of natural language instructions. All VLN-CE methods in the literature assume that language instructions are exact. H

Karolina Stańczak

Gender bias represents a form of systematic negative treatment that targets individuals based on their gender. This discrimination can range from subtle sexist remarks and gendered stereotypes to outright hate speech. Prior research has revealed that ignoring online abuse not only affects the individuals targeted but also has broader societal implications. T

Minh-Hao Van, Alycia N. Carey, Xintao Wu

Correctly classifying brain tumors is imperative to the prompt and accurate treatment of a patient. While several classification algorithms based on classical image processing or deep learning methods have been proposed to rapidly classify tumors in MR images, most assume the unrealistic setting of noise-free training data. In this work, we study a difficult

A. V. Smith Castelli, A. Cortesi, R. F. Haack, A. R. Lopes

The Fornax galaxy cluster is the richest nearby (D ~ 20 Mpc) galaxy association in the southern sky. As such, it provides a wealth of oportunities to elucidate on the processes where environment holds a key role in transforming galaxies. Although it has been the focus of many studies, Fornax has never been explored with contiguous homogeneous wide-field imag

Diganta Misra, Jay Gala, Antonio Orvieto

The strength of modern large-scale neural networks lies in their ability to efficiently adapt to new tasks with few examples. Although extensive research has investigated the transferability of Vision Transformers (ViTs) to various downstream tasks under diverse constraints, this study shifts focus to explore the transfer learning potential of [V]-Mamba. We

Yipeng Sun, Yixing Huang, Linda-Sophie Schneider, Mareike Thies

Computed Tomography (CT) image reconstruction is crucial for accurate diagnosis and deep learning approaches have demonstrated significant potential in improving reconstruction quality. However, the choice of loss function profoundly affects the reconstructed images. Traditional mean squared error loss often produces blurry images lacking fine details, while

Mir Faizal

We will highlight that despite there being various approaches to quantum gravity, there are universal approach-independent features of quantum gravity. The geometry of spacetime becomes an emergent structure, which emerges from some purely quantum gravitational degrees of freedom. We argue that these quantum gravitational degrees of freedom can be best under

M. A. Bastarrachea-Magnani, A. Camacho-Guardian

Due to the hybridization of charged excitons with cavity photons, trion-polaritons (TP) in microcavity semiconductors are a promising avenue for realizing strong polariton interactions and many-body polariton phases. We develop a quantum field theoretical formalism to study the formation of trion-polaritons in a microcavity semiconductor doped with itinerant

Kinjal Basu, Keerthiram Murugesan, Subhajit Chaudhury, Murray Campbell

Text-based games (TBGs) have emerged as an important collection of NLP tasks, requiring reinforcement learning (RL) agents to combine natural language understanding with reasoning. A key challenge for agents attempting to solve such tasks is to generalize across multiple games and demonstrate good performance on both seen and unseen objects. Purely deep-RL-b

Tomasz Limisiewicz, Terra Blevins, Hila Gonen, Orevaoghene Ahia

A major consideration in multilingual language modeling is how to best represent languages with diverse vocabularies and scripts. Although contemporary text encoding methods cover most of the world's writing systems, they exhibit bias towards the high-resource languages of the Global West. As a result, texts of underrepresented languages tend to be segmented

Cheng-Zhen Wang, John Guillamon, William Tuxbury, Ulrich Kuhl

We develop a Coherent Perfect Absorption (CPA) protocol for cases where scale invariance is violated due to the presence of nonlinear mechanisms. We demonstrate, using a microwave setting that lacks geometrical symmetries, that the nonlinearity offers new reconfigurable modalities: the destruction or formation of nonlinear CPAs (NL-CPAs), and their frequency

Namiko Saito, Joao Moura, Hiroki Uchida, Sethu Vijayakumar

Recognising the characteristics of objects while a robot handles them is crucial for adjusting motions that ensure stable and efficient interactions with containers. Ahead of realising stable and efficient robot motions for handling/transferring the containers, this work aims to recognise the latent unobservable object characteristics. While vision is common

Vaughn Hamilton, Gabriel Kaptchuk, Allison McDonald, Elissa M. Redmiles

Many people engage in digital intimacy: sex workers, their clients, and people who create and share intimate content recreationally. With this intimacy comes significant security and privacy risk, exacerbated by stigma. In this article, we present a commercial digital intimacy threat model and 10 research directions for safer digital intimacy

Krishna C. Kalvakala, Harsimran Singh, Pinaki Pal, Jorge P. Gonzalez

Computational fluid dynamics (CFD) simulations of a single-cylinder gasoline compression ignition engine are performed to investigate the impact of gasoline-ethanol blending on autoignition, nitrogen oxide (NOx), and soot emissions under low-load conditions. A four-component toluene primary reference fuel (TPRF) + ethanol (ETPRF) surrogate (with 10% ethanol