September 2023 arXiv papers

Jiani Ding, Piero Madau, J. Xavier Prochaska

We present new measurements of the mean transmitted flux in the hydrogen Ly$\rm α$ and a relative transmitted flux measurement in Ly$\rm β$ using 27,008 quasar spectra from the Fourteenth Data Release (DR14) of the Extended Baryon Oscillation Spectroscopic Survey (eBOSS). Individual spectra are first combined into 16 composites with mean redshifts in the ran

Hailan Ma, Zhenhong Sun, Daoyi Dong, Dong Gong

Quantum state tomography (QST) is the process of reconstructing the complete state of a quantum system (mathematically described as a density matrix) through a series of different measurements. These measurements are performed on a number of identical copies of the quantum system, with outcomes gathered as frequencies. QST aims to recover the density matrix

Luana Barros, Levy Chaves, Sandra Avila

Melanoma is the most severe type of skin cancer due to its ability to cause metastasis. It is more common in black people, often affecting acral regions: palms, soles, and nails. Deep neural networks have shown tremendous potential for improving clinical care and skin cancer diagnosis. Nevertheless, prevailing studies predominantly rely on datasets of white

Salim Sazzed, Sharif Ullah

Malware poses a significant security risk to individuals, organizations, and critical infrastructure by compromising systems and data. Leveraging memory dumps that offer snapshots of computer memory can aid the analysis and detection of malicious content, including malware. To improve the efficacy and address privacy concerns in malware classification system

Tamás Kátay, László Márton Tóth, Zoltán Vidnyánszky

We study Constraint Satisfaction Problems (CSPs) in an infinite context. We show that the dichotomy between easy and hard problems -- established already in the finite case -- presents itself as the strength of the corresponding De Bruijin-Erdős-type compactness theorem over ZF. More precisely, if $\mathcal{D}$ is a structure, let $K_\mathcal{D}$ stand for t

Chelsea Edmonds, Lawrence C. Paulson

Formalised libraries of combinatorial mathematics have rapidly expanded over the last five years, but few use one of the most important tools: probability. How can often intuitive probabilistic arguments on the existence of combinatorial structures, such as hypergraphs, be translated into a formal text? We present a modular framework using locales in Isabell

Golam Md Muktadir

This paper presents a comprehensive exploration of the evolution of prompt engineering and generation in the field of natural language processing (NLP). Starting from the early language models and information retrieval systems, we trace the key developments that have shaped prompt engineering over the years. The introduction of attention mechanisms in 2015 r

Lawk Mineh

Let $G$ be a relatively hyperbolic group and let $Q$ and $R$ be relatively quasiconvex subgroups. It is known that there are many pairs of finite index subgroups $Q' \leqslant_f Q$ and $R' \leqslant_f R$ such that the subgroup join $\langle Q', R' \rangle$ is also relatively quasiconvex, given suitable assumptions on the profinite topology of

Mohammad Mahdi Derakhshani, Ivona Najdenkoska, Cees G. M. Snoek, Marcel Worring

We present Self-Context Adaptation (SeCAt), a self-supervised approach that unlocks few-shot abilities for open-ended classification with small visual language models. Our approach imitates image captions in a self-supervised way based on clustering a large pool of images followed by assigning semantically-unrelated names to clusters. By doing so, we constru

Jake Ketchum, Sophia Schiffer, Muchen Sun, Pranav Kaarthik

Gait generation for soft robots is challenging due to the nonlinear dynamics and high dimensional input spaces of soft actuators. Limitations in soft robotic control and perception force researchers to hand-craft open loop controllers for gait sequences, which is a non-trivial process. Moreover, short soft actuator lifespans and natural variations in actuato

Behzad Tahmasebzadeh, Ling Zhu, Juntai Shen, Dimitri A. Gadotti

We apply the barred Schwarzschild method developed by Tahmasebzadeh et al. (2022) to a barred S0 galaxy, NGC 4371, observed by IFU instruments from the TIMER and ATLAS3D projects. We construct the gravitational potential by combining a fixed black hole mass, a spherical dark matter halo, and stellar mass distribution deprojected from $3.6$ $μ$m S$^4$G image

Cameron Shinn, Collin McCarthy, Saurav Muralidharan, Muhammad Osama

We introduce the Sparsity Roofline, a visual performance model for evaluating sparsity in neural networks. The Sparsity Roofline jointly models network accuracy, sparsity, and theoretical inference speedup. Our approach does not require implementing and benchmarking optimized kernels, and the theoretical speedup becomes equal to the actual speedup when the c

Chris Kapulkin, Nathan Kershaw

We show that the category of (reflexive) graphs and graph maps carries exactly two closed symmetric monoidal products: the box product and the categorical product.

Xuansheng Wu, Wenlin Yao, Jianshu Chen, Xiaoman Pan

Large Language Models (LLMs) have achieved remarkable success, where instruction tuning is the critical step in aligning LLMs with user intentions. In this work, we investigate how the instruction tuning adjusts pre-trained models with a focus on intrinsic changes. Specifically, we first develop several local and global explanation methods, including a gradi

Gaurav Jain, Basel Hindi, Zihao Zhang, Koushik Srinivasula

Blind and low-vision (BLV) people rely on GPS-based systems for outdoor navigation. GPS's inaccuracy, however, causes them to veer off track, run into obstacles, and struggle to reach precise destinations. While prior work has made precise navigation possible indoors via hardware installations, enabling this outdoors remains a challenge. Interestingly, m

Tianxiang Zhao, Wenchao Yu, Suhang Wang, Lu Wang

Imitation learning, which learns agent policy by mimicking expert demonstration, has shown promising results in many applications such as medical treatment regimes and self-driving vehicles. However, it remains a difficult task to interpret control policies learned by the agent. Difficulties mainly come from two aspects: 1) agents in imitation learning are u

Young In Kim, Andrea Agiollo, Pratiksha Agrawal, Johannes O. Royset

Optimization algorithms that seek flatter minima, such as Sharpness-Aware Minimization (SAM), are credited with improved generalization and robustness to noise. We ask whether such gains impact membership privacy. Surprisingly, we find that SAM is more prone to Membership Inference Attacks (MIA) than classical SGD across multiple datasets and attack methods,

Claude Duhr, Yu Jiao Zhu

We report on the analytic computation of the 2-loop amplitude for Bhabha scattering in QED. We study the analytic structure of the amplitude, and reveal its underlying connections to hyperbolic Coxeter groups and arithmetic geometries of elliptic curves.

Artem Lopatin, Pedro Antonio Muniz Martins

We described a minimal separating set for the algebra of $O(F_q)$-invariant polynomial functions of $m$-tuples of two-dimensional vectors over a finite field $F_q$.

Karl Royen, Suman Mondal, Frank Pollmann, Fabian Heidrich-Meisner

In the study of the thermalization of closed quantum systems, the role of kinetic constraints on the temporal dynamics and the eventual thermalization is attracting significant interest. Kinetic constraints typically lead to long-lived metastable states depending on initial conditions. We consider a model of interacting hardcore bosons with an additional kin

Chak Lam Shek, Xiyang Wu, Wesley A. Suttle, Carl Busart

Navigating robots through unstructured terrains is challenging, primarily due to the dynamic environmental changes. While humans adeptly navigate such terrains by using context from their observations, creating a similar context-aware navigation system for robots is difficult. The essence of the issue lies in the acquisition and interpretation of context inf

Hejia Geng, Boxun Xu, Peng Li

Large Language Models (LLMs) have demonstrated impressive inferential capabilities, with numerous research endeavors devoted to enhancing this capacity through prompting. Despite these efforts, a unified epistemological foundation is still conspicuously absent. Drawing inspiration from Kant's a priori philosophy, we propose the UPAR prompting framework,

Artem Lopatin

The algebra of ${\rm GL}_n$-invariants of $m$-tuples of $n\times n$ matrices with respect to the action by simultaneous conjugation is a classical topic in case of infinite base field. On the other hand, in case of a finite field generators of polynomial invariants even in case of a pair of $2\times 2$ matrices are not known. Working over an arbitrary field

Antoine Marot, Noureddine Henka, Benjamin Donnot, Sami Tazi

Flexible grid topology has become a key enabler of flexibility in modern power grids, particularly for congestion management. Studying the effects of combinatorial topological changes is therefore of significant interest, though it remains computationally intensive in most cases. To address this, we revisit the superposition theorem, which has served as the

Trager Joswig-Jones, Baosen Zhang

Grid-interfacing inverters act as the interface between renewable resources and the electric grid, and have the potential to offer fast and programmable controls compared to synchronous generators. With this flexibility there has been significant research efforts into determining the best way to control these inverters. Inverters are limited in their maximum

Fırtına Küçük

The main objective of this article is to establish the $p$-adic Artin formalism for the algebraic $p$-adic $L$-functions attached to the adjoint representations of Coleman families of modular forms. In particular, we prove a factorization formula involving the determinants of appropriate Selmer complexes, using tools from rigid geometry, homological algebra,

Erman Cineli, Viktor L. Ginzburg, Basak Z. Gurel

The main result of the paper is that for any closed symplectic manifold the spectral norm of the iterates of a Hamiltonian diffeomorphism is locally uniformly bounded away from zero $C^\infty$-generically.

Mustafa O. Karabag, Sophia Smith, Negar Mehr, David Fridovich-Keil

When interacting with other decision-making agents in non-adversarial scenarios, it is critical for an autonomous agent to have inferable behavior: The agent's actions must convey their intention and strategy. We model the inferability problem using Stackelberg games with observations where a leader and a follower repeatedly interact. During the interact

Ian Rabago, Zhaohuan Zhu, Stephen Lubow, Rebecca G. Martin

Disc warping, and possibly disc breaking, has been observed in protoplanetary discs around both single and multiple stars. Large warps can break the disc, producing multiple observational signatures. In this work, we use comparisons of disc timescales to derive updated formulae for disc breaking, with better predictions as to when and where a disc is expecte

Melvyn Tyloo, Marc Vuffray, Andrey Y. Lokhov

Malfunctioning equipment, erroneous operating conditions or periodic load variations can cause periodic disturbances that would persist over time, creating an undesirable transfer of energy across the system -- an effect referred to as forced oscillations. Wide-area oscillations may damage assets, trigger inadvertent tripping or control actions, and be the c

P. Kumari, S. Rani, S. Kar, T. Mukherjee

The Quantum Materials group at Indian Institute of Technology Patna is working on a range of topics relating to nanoelectronics, spintronics, clean energy and memory design etc. The PI has past experiences of working extensively with superconducting systems like cuprates [1, 2], ruthanate [3], pnictide [4, 5], thin film heterostructures [6, 7] etc and magnet

Fadillah Maani, Asim Ukaye, Nada Saadi, Numan Saeed

Echocardiography has become an indispensable clinical imaging modality for general heart health assessment. From calculating biomarkers such as ejection fraction to the probability of a patient's heart failure, accurate segmentation of the heart structures allows doctors to assess the heart's condition and devise treatments with greater precision and

Loren E. Held, George Mamatsashvili, Martin E. Pessah

The discovery of the first binary neutron star merger, GW170817, has spawned a plethora of global numerical relativity simulations. These simulations are often ideal (with dissipation determined by the grid) and/or axisymmetric (invoking ad hoc mean-field dynamos). However, binary neutron star mergers (similar to X-ray binaries and active galactic nuclei inn

Oem Trivedi, Simran Kaur Saggu, Pankaj S. Joshi

The pursuit of unraveling the true essence of dark energy has become an immensely captivating endeavor in modern cosmology. Alongside the conventional cosmological constant approach, a diverse range of ideas has been proposed, encompassing scalar field-based models and various modified gravity approaches. A particularly intriguing notion involves exploring s

Shashank Shekhar, Asif Salim, Adesh Bansode, Vivaswan Jinturkar

Counterfactual explanations (CFE) are methods that explain a machine learning model by giving an alternate class prediction of a data point with some minimal changes in its features. It helps the users to identify their data attributes that caused an undesirable prediction like a loan or credit card rejection. We describe an efficient and an actionable count

Saaketh Medepalli, Naren Doraiswamy

Meta-learning frameworks for few-shot learning aims to learn models that can learn new skills or adapt to new environments rapidly with a few training examples. This has led to the generalizability of the developed model towards new classes with just a few labelled samples. However these networks are seen as black-box models and understanding the representat

Babak Taheri, Daniel K. Molzahn

Many power system operation and planning problems use the DC power flow approximation to address computational challenges from the nonlinearity of the AC power flow equations. The DC power flow simplifies the AC power flow equations to a linear form that relates active power flows to phase angle differences across branches, parameterized by coefficients base

Douglas P. Wiens

We present a result according to which certain functions of covariance matrices are maximized at scalar multiples of the identity matrix. This is used to show that experimental designs that are optimal under an assumption of independent, homoscedastic responses can be minimax robust, in broad classes of alternate covariance structures. In particular it can j

Mahmud Hasan, Mathias Muia

Information Maximizing Generative Adversarial Network (infoGAN) can be understood as a minimax problem involving two neural networks: discriminators and generators with mutual information functions. The infoGAN incorporates various components, including latent variables, mutual information, and objective function. This research demonstrates the Generalizatio

H. Hawley-Herrera

The Scintillating Bubble Chamber (SBC) collaboration is constructing a 10~kg liquid argon (LAr) bubble chamber at SNOLAB called SBC-SNOLAB having the main objective of detecting dark matter. One of the most novel aspects of SBC-SNOLAB is the scintillation system, consisting of LAr doped with on the order of 10~ppm Xe, 48 FBK VUV silicon photomultipliers (SiP

Sadib Fardin, Md. Jawarul Moresalein, Toushiqul Islam, Abrar Faiyad

In recent years, lead-free solder alloys based on tin, silver, or copper have gained popularity over lead-based solder alloys due to their improved mechanical and electrical properties and their non-toxic nature. In our previous studies, we examined the stress-strain behavior of SAC305 under varying cooling rates. This study extends our investigation to vari

Zhiyao Sun, Tian Lv, Sheng Ye, Matthieu Lin

The generation of stylistic 3D facial animations driven by speech presents a significant challenge as it requires learning a many-to-many mapping between speech, style, and the corresponding natural facial motion. However, existing methods either employ a deterministic model for speech-to-motion mapping or encode the style using a one-hot encoding scheme. No

Snehal Jauhri, Sophie Lueth, Georgia Chalvatzaki

Mobile Manipulation (MoMa) systems incorporate the benefits of mobility and dexterity, due to the enlarged space in which they can move and interact with their environment. However, even when equipped with onboard sensors, e.g., an embodied camera, extracting task-relevant visual information in unstructured and cluttered environments, such as households, rem

Christian Koke, Abhishek Saroha, Yuesong Shen, Marvin Eisenberger

It is by now a well known fact in the graph learning community that the presence of bottlenecks severely limits the ability of graph neural networks to propagate information over long distances. What so far has not been appreciated is that, counter-intuitively, also the presence of strongly connected sub-graphs may severely restrict information flow in commo

Quentin Bertrand, Avishek Joey Bose, Alexandre Duplessis, Marco Jiralerspong

Deep generative models have made tremendous progress in modeling complex data, often exhibiting generation quality that surpasses a typical human's ability to discern the authenticity of samples. Undeniably, a key driver of this success is enabled by the massive amounts of web-scale data consumed by these models. Due to these models' striking perform

Yu Hua

Persistence is the first principle of big memory systems. We comprehensively analyze the vertical and horizontal extensions of existing memory hierarchy. Networks are flattening traditional storage hierarchies. We present the state-of-the-art studies upon the big memory systems, together with design methodology and implementations. We discuss the full-stack

Junsong Chen, Jincheng Yu, Chongjian Ge, Lewei Yao

The most advanced text-to-image (T2I) models require significant training costs (e.g., millions of GPU hours), seriously hindering the fundamental innovation for the AIGC community while increasing CO2 emissions. This paper introduces PIXART-$α$, a Transformer-based T2I diffusion model whose image generation quality is competitive with state-of-the-art image

Ankit Bhojak, Surjeet Singh Choudhary, Saurabh Shrivastava, Kalachand Shuin

In this article, we address endpoint issues for the bilinear spherical maximal functions. We obtain borderline restricted weak type estimates for the well studied bilinear spherical maximal function $$\mathfrak{M}(f,g)(x):=\sup_{t>0}\left|\int_{\mathbb S^{2d-1}}f(x-ty_1)g(x-ty_2)\;dσ(y_1,y_2)\right|,$$ in dimensions $d=1,2$ and as an application, we deduce s

Yang Sun, Gwang-Ho Lee, Ann I. Zabludoff, K. Decker French

We measure velocity offsets in the NaI $λ\lambda5890, 5896$ (Na D) interstellar medium absorption lines to track how neutral galactic winds change as their host galaxies evolve. Our sample of $\sim$80,000 SDSS spectra at $0.010 < z < 0.325$ includes starburst, post-starburst, and quiescent galaxies, forming an evolutionary sequence of declining star formatio

Ohidul Islam, M. Hussayeen Khan Anik, Shakhawat Hossain Shakib, Nahid Hasan Niloy

This work examines a symmetry-protected bound state in the continuum (BIC) supported unique all-dielectric dome-shaped metasurface (MS). The simple unit MS is made up of four dome-shaped nanobars of silicon in the top layer and silica glass as the substrate. Two strong transmission dips denoted as an electric dipole (ED) and magnetic dipole (MD) quasi-BIC wi

Jinlong Wei, Junhao Hu, Chenggui Yuan

By using the Itô-Tanaka trick, we prove the unique strong solvability as well as the gradient estimates for stochastic differential equations with irregular drifts in low regularity Lebesgue-Hölder space $L^q(0,T;{\mathcal C}_b^α({\mathbb R}^d))$ with $α\in(0,1)$ and $q\in (2/(1+α),2$). As applications, we show the unique weak and strong solvability for stoc

Kushal Chakrabarti, Mayank Baranwal

This paper considers solving distributed optimization problems in peer-to-peer multi-agent networks. The network is synchronous and connected. By using the proportional-integral (PI) control strategy, various algorithms with fixed stepsize have been developed. Two notable among them are the PI algorithm and the PI consensus algorithm. Although the PI algorit

Aakash Tripathi, Asim Waqas, Kavya Venkatesan, Yasin Yilmaz

The advancements in data acquisition, storage, and processing techniques have resulted in the rapid growth of heterogeneous medical data. Integrating radiological scans, histopathology images, and molecular information with clinical data is essential for developing a holistic understanding of the disease and optimizing treatment. The need for integrating dat

Daxun Wang

A generalized Baumslag-Solitar group is a finitely generated group that acts on a tree with infinite-cyclic vertex and edge stabilizers. In this paper, we show that the isomorphism problem is solvable for small rose non-ascending generalized Baumslag- Solitar groups.

Zu-Cheng Chen, Qing-Guo Huang, Chang Liu, Lang Liu

Recently, multiple pulsar timing array collaborations have presented compelling evidence for a stochastic signal at nanohertz frequencies, potentially originating from cosmic strings. Cosmic strings are linear topological defects that can arise during phase transitions in the early Universe or as fundamental strings in superstring theory. This paper focuses

Xihao He

This paper focuses on a mean-field optimal stopping problem with non-Markov dynamics and common noise, inspired by Talbi, Touzi, and Zhang \cite{TalbiTouziZhang1,TalbiTouziZhang3}. The goal is to establish the limit theory and demonstrate the equivalence of the value functions between weak and strong formulations. The difference between the strong and weak f

Daniel M. Baczkowski, Dursun A. Bulutoglu

We describe how previously known methods for determining the number of decimation classes of density $δ$ binary vectors can be extended to nonnegative integer vectors, where the vectors are indexed by a finite abelian group $G$ of size $\ell$ and exponent $\ell^*$ such that $δ$ is relatively prime to $\ell^*$. We extend the previously discovered theory of mu

Jiongkang Ni, Xiaoliang Xu, Yuxiang Wang, Can Li

Given a vector dataset $\mathcal{X}$ and a query vector $\vec{x}_q$, graph-based Approximate Nearest Neighbor Search (ANNS) aims to build a graph index $G$ and approximately return vectors with minimum distances to $\vec{x}_q$ by searching over $G$. The main drawback of graph-based ANNS is that a graph index would be too large to fit into the memory especial

Jose Andres Millan-Romera, Hriday Bavle, Muhammad Shaheer, Martin R. Oswald

Recent works on SLAM extend their pose graphs with higher-level semantic concepts like Rooms exploiting relationships between them, to provide, not only a richer representation of the situation/environment but also to improve the accuracy of its estimation. Concretely, our previous work, Situational Graphs (S-Graphs+), a pioneer in jointly leveraging semanti

Gyubin Park, Jiwoo Choi, Da Hoon Jeong, Jong-Han Kim

Most of the optimal guidance problems can be formulated as nonconvex optimization problems, which can be solved indirectly by relaxation, convexification, or linearization. Although these methods are guaranteed to converge to the global optimum of the modified problems, the obtained solution may not guarantee global optimality or even the feasibility of the

Jiwoo Choi, Jong-Han Kim

This paper introduces a first-order method for solving optimal powered descent guidance (PDG) problems, that directly handles the nonconvex constraints associated with the maximum and minimum thrust bounds with varying mass and the pointing angle constraints on thrust vectors. This issue has been conventionally circumvented via lossless convexification (LCvx

Azra Seyyedi, Sina Dortaj, Mahdi Bohlouli, SeyedEhsan Nedaaee Oskoee

Internet of Things has pervaded every area of modern life. From a research and industry standpoint, there has been an increasing demand and desire in recent years to develop Internet of Things networks with distributed structure. Wireless communication under emergency circumstances is one of the important applications that distributed Internet of Things can

Yulu Gan, Sungwoo Park, Alexander Schubert, Anthony Philippakis

Recent advances in generative diffusion models have enabled text-controlled synthesis of realistic and diverse images with impressive quality. Despite these remarkable advances, the application of text-to-image generative models in computer vision for standard visual recognition tasks remains limited. The current de facto approach for these tasks is to desig

Shushu Ruan, Xinglong Yu, Zhenjie Shen, Xincheng Wang

We developed a magneto-optical trap reaction microscope (MOTREMI) for strontium atoms by combining the multi-particle coincident detection with laser cooling technique. Present compact injection system can provide cold Sr atoms in three modes of 2D MOT, molasses and 3D MOT, delivering targets with adjustable densities and ratios of the ground state $5s^2$ ($

Anand Utsav Kapoor, Maarten Baes, Arjen van der Wel, Andrea Gebek

We present and publicly release a new star-forming regions emission library TODDLERS (Time evolution of Observables including Dust Diagnostics and Line Emission from Regions containing young Stars) for the publicly available radiative transfer code SKIRT. The library generation involves the spherical evolution of a homogeneous gas cloud around a young stella

Yihang Chen, Lukas Mauch

Generative Flow Networks (GFlowNets) have been introduced as a method to sample a diverse set of candidates with probabilities proportional to a given reward. However, GFlowNets can only be used with a predefined scalar reward, which can be either computationally expensive or not directly accessible, in the case of multi-objective optimization (MOO) tasks fo

Fei Zhao, Taotian Pang, Zhen Wu, Zheng Ma

In-context learning (ICL) is a new paradigm for natural language processing (NLP), where a large language model (LLM) observes a small number of demonstrations and a test instance as its input, and directly makes predictions without updating model parameters. Previous studies have revealed that ICL is sensitive to the selection and the ordering of demonstrat

Georgios Akrivis, Sören Bartels, Christian Palus

We devise a projection-free iterative scheme for the approximation of harmonic maps that provides a second-order accuracy of the constraint violation and is unconditionally energy stable. A corresponding error estimate is valid under a mild but necessary discrete regularity condition. The method is based on the application of a BDF2 scheme and the considered

Geng Tian, Zhizhang Xie, Guoliang Yu

In this article, we prove a strong relative Novikov conjecture for any pair of groups that are coarsely embeddable into Hilbert space.

Zhaowei Zhang, Fengshuo Bai, Jun Gao, Yaodong Yang

Personal values are a crucial factor behind human decision-making. Considering that Large Language Models (LLMs) have been shown to impact human decisions significantly, it is essential to make sure they accurately understand human values to ensure their safety. However, evaluating their grasp of these values is complex due to the value&#39;s intricate and a

Deog Ki Hong, Jie Jiang, Dong-han Yeom

In this study, we generalize the Fokker-Planck equation to two-dimensional cases, including potential functions with periodic boundary conditions and piecewise-defined structures, to analyze the probability distribution in multi-field inflationary models. We employ the spectral method for spatial derivatives and the Crank-Nicolson method for the time evoluti

Guy Boyde

We show how to formulate some recent results from homological stability of algebras in Graham and Lehrer&#39;s language of cellular algebras. The aim is to begin to connect the new results from topology to well-established representation theoretic understandings of these objects.

Juha Kinnunen, Kim Myyryläinen

This paper discusses parabolic reverse Hölder inequalities and their connections to parabolic Muckenhoupt weights. The main result gives several characterizations for this class of weights. There are challenging features related to the parabolic geometry and the time lag, for example, in covering and chaining arguments. We also prove a Gehring type self-impr

Gousia Habib, Tausifa Jan Saleem, Ishfaq Ahmad Malik, Brejesh Lall

With the rapid development of computer vision, Vision Transformers (ViTs) offer the tantalising prospect of unified information processing across visual and textual domains due to the lack of inherent inductive biases in ViTs. ViTs require enormous datasets for training. We introduce an innovative ensemble-based distillation approach that distils inductive b

Shijie Bao, Qi&#39;an Guan, Zhitong Mi, Zheng Yuan

In this article, we obtain a class of tame maximal weights (Zhou weights). Using Tian functions (the function of jumping numbers with respect to the exponents of a holomorphic function or the multiples of a plurisubharmonic function) as a main tool, we establish an expression of relative types (Zhou numbers) to these tame maximal weights in integral form, wh

Jonas Belouadi, Anne Lauscher, Steffen Eger

Generating bitmap graphics from text has gained considerable attention, yet for scientific figures, vector graphics are often preferred. Given that vector graphics are typically encoded using low-level graphics primitives, generating them directly is difficult. To address this, we propose the use of TikZ, a well-known abstract graphics language that can be c

Ugur Yildiran

Inverted pendulums constitute one of the popular systems for benchmarking control algorithms. Several methods have been proposed for the control of this system, the majority of which rely on the availability of a mathematical model. However, deriving a mathematical model using physical parameters or system identification techniques requires manual effort. Mo

A. Ilker Topuz

A major part of cosmic rays consists of the primary protons, and this portion plays a crucial role in the space applications such as shielding of spacecrafts. In this study, the proton flux values measured at the top of the atmosphere through the BESS-TeV spectrometer in 2004 are introduced into the GEANT4 simulations by using a probability grid that generat

Pedram Rabiee, Jesse B. Hoagg

This paper presents a new approach for guaranteed safety subject to input constraints (e.g., actuator limits) using a composition of multiple control barrier functions (CBFs). First, we present a method for constructing a single CBF from multiple CBFs, which can have different relative degrees. This construction relies on a soft minimum function and yields a

Shanmin Yang, Hui Guo, Shu Hu, Bin Zhu

Deepfake technology poses a significant threat to security and social trust. Although existing detection methods have shown high performance in identifying forgeries within datasets that use the same deepfake techniques for both training and testing, they suffer from sharp performance degradation when faced with cross-dataset scenarios where unseen deepfake

Qi Wang

We classify the $τ$-tilting finite, silting-discrete, and derived-discrete Borel--Schur algebras over an algebraically closed field of arbitrary characteristic. We further prove that derived-discreteness is equivalent to silting-discreteness within this family and determine precisely when these equivalent conditions are strictly stronger than $τ$-tilting fin

Xiao Zhang, Michael Maire

Within the framework of generative adversarial networks (GANs), we propose objectives that task the discriminator for self-supervised representation learning via additional structural modeling responsibilities. In combination with an efficient smoothness regularizer imposed on the network, these objectives guide the discriminator to learn to extract informat

Abdelbasset Djeniah, Mohamed Chaouch, Amina Angelika Bouchentouf

This paper explores the nonparametric estimation of the volatility component in a heteroscedastic scalar-on-function regression model, where the underlying discrete-time process is ergodic and subject to a missing-at-random mechanism. We first propose a simplified estimator for the regression and volatility operators, constructed solely from the observed dat

Javier Pérez de Frutos, Ragnhild Holden Helland, Shreya Desai, Line Cathrine Nymoen

Background: Dental caries diagnosis requires the manual inspection of diagnostic bitewing images of the patient, followed by a visual inspection and probing of the identified dental pieces with potential lesions. Yet the use of artificial intelligence, and in particular deep-learning, has the potential to aid in the diagnosis by providing a quick and informa

Mathias-Felipe de-Lima-Santos, Isabella Gonçalves, Marcos G. Quiles, Lucia Mesquita

In today&#39;s digital age, images have emerged as powerful tools for politicians to engage with their voters on social media platforms. Visual content possesses a unique emotional appeal that often leads to increased user engagement. However, research on visual communication remains relatively limited, particularly in the Global South. This study aims to br

Shaina Raza, Oluwanifemi Bamgbose, Veronica Chatrath, Shardul Ghuge

Bias detection in text is crucial for combating the spread of negative stereotypes, misinformation, and biased decision-making. Traditional language models frequently face challenges in generalizing beyond their training data and are typically designed for a single task, often focusing on bias detection at the sentence level. To address this, we present the

Haoyu Ma, Jialong Wu, Ningya Feng, Chenjun Xiao

Model-based reinforcement learning (MBRL) holds the promise of sample-efficient learning by utilizing a world model, which models how the environment works and typically encompasses components for two tasks: observation modeling and reward modeling. In this paper, through a dedicated empirical investigation, we gain a deeper understanding of the role each ta

Ao-Yan Cheng, Hao Cai, Shi Chen, Tian-Lu Chen

The HADAR experiment, which will be constructed in Tibet, China, combines the wide-angle advantages of traditional EAS array detectors with the high sensitivity advantages of focused Cherenkov detectors. Its physics objective is to observe transient sources such as gamma-ray bursts and counterparts of gravitational waves. The aim of this study is to utilize

Mehfuz A Rahman, Khushal Das, Jiju Poovvancheri, Neil London

A vast majority of augmented reality devices come equipped with depth and color cameras. Despite their advantages, extracting both photometric and depth features simultaneously in real-time remains challenging due to inherent differences between depth and color images. Furthermore, standard convolution operations are insufficient for extracting information d

Teddy Lazebnik

Sexually transmitted diseases (STDs) are a group of pathogens infecting new hosts through sexual interactions. Due to its social and economic burden, multiple models have been proposed to study the spreading of pathogens. In parallel, in the ever-evolving landscape of digital social interactions, the pervasive utilization of dating apps has become a prominen

Martin Brenncke

Dark patterns have become increasingly pervasive in online choice architectures, encompassing practices like subscription traps, hiding information about fees, pre-selecting options by default, nagging, and drip pricing. Regulators around the world have started to express concerns that such practices are causing substantial consumer detriment. This Article f

Badr Ben Elallid, Hamza El Alaoui, Nabil Benamar

In this paper, we explore the challenges associated with navigating complex T-intersections in dense traffic scenarios for autonomous vehicles (AVs). Reinforcement learning algorithms have emerged as a promising approach to address these challenges by enabling AVs to make safe and efficient decisions in real-time. Here, we address the problem of efficiently

Xiang Liu, Liangxi Liu, Feiyang Ye, Yunheng Shen

Efficiently aggregating trained neural networks from local clients into a global model on a server is a widely researched topic in federated learning. Recently, motivated by diminishing privacy concerns, mitigating potential attacks, and reducing communication overhead, one-shot federated learning (i.e., limiting client-server communication into a single rou

Andrej Bogdanov, Krishnamoorthy Dinesh, Yuval Filmus, Yuval Ishai

We consider the following question of bounded simultaneous messages (BSM) protocols: Can computationally unbounded Alice and Bob evaluate a function $f(x,y)$ of their inputs by sending polynomial-size messages to a computationally bounded Carol? The special case where $f$ is the mod-2 inner-product function and Carol is bounded to AC$^0$ has been studied in

Sjoerd Dirksen, Patrick Finke, Martin Genzel

In practice, deep neural networks are often able to easily interpolate their training data. To understand this phenomenon, many works have aimed to quantify the memorization capacity of a neural network architecture: the largest number of points such that the architecture can interpolate any placement of these points with any assignment of labels. For real-w

Chengdong Ma, Ziran Yang, Hai Ci, Jun Gao

The primary challenge in deploying Large Language Model (LLM) is ensuring its harmlessness. Red team can identify vulnerabilities by attacking LLM to attain safety. However, current efforts heavily rely on single-round prompt designs and unilateral red team optimizations against fixed blue teams. These static approaches lead to significant reductions in gene

Rédempteur Ntawiratsa, David Niyukuri, Irène Irakoze, Menus Nkurunziza

The term structure of interest rates (yield curve) is a critical facet of financial analytics, impacting various investment and risk management decisions. It is used by the central bank to conduct and monitor its monetary policy. That instrument reflects the anticipation of inflation and the risk by investors. The rates reported on yield curve are the corner

K. Narayan

We develop further the investigations in arXiv:2210.12963 [hep-th] on de Sitter space, extremal surfaces and time entanglement. We discuss the no-boundary de Sitter extremal surface areas as certain analytic continuations from $AdS$ while also amounting to space-time rotations. The structure of the extremal surfaces suggests a geometric picture of the time-e

Federico Campanini, Francesca Fedele

Torsion theories play an important role in abelian categories and they have been widely studied in the last sixty years. In recent years, with the introduction of pretorsion theories, the definition has been extended to general (non-pointed) categories. Many examples have been investigated in several different contexts, such as topological spaces and topolog

Jon Asier Bárcena-Petisco, Enrique Zuazua

We study the tracking or sidewise controllability of the heat equation. More precisely, we seek for controls that, acting on part of the boundary of the domain where the heat process evolves, aim to assure that the normal trace or flux on the complementary set tracks a given trajectory. The dual equivalent observability problem is identified. It consists on