February 2024 arXiv papers — page 123

Hideaki Takahashi, Alex Fukunaga

Concealing an intermediate point on a route or visible from a route is an important goal in some transportation and surveillance scenarios. This paper studies the Transit Obfuscation Problem, the problem of traveling from some start location to an end location while "covering" a specific transit point that needs to be concealed from adversaries. We propose t

Md Musfiqur Rahman, Matt Jordan, Murat Kocaoglu

Causal inference from observational data plays critical role in many applications in trustworthy machine learning. While sound and complete algorithms exist to compute causal effects, many of them assume access to conditional likelihoods, which is difficult to estimate for high-dimensional (particularly image) data. Researchers have alleviated this issue by

Sangwoo Shin, Minjong Yoo, Jeongwoo Lee, Honguk Woo

This work explores the zero-shot adaptation capability of semantic skills, semantically interpretable experts' behavior patterns, in cross-domain settings, where a user input in interleaved multi-modal snippets can prompt a new long-horizon task for different domains. In these cross-domain settings, we present a semantic skill translator framework SemTra whi

Weijie Tu, Weijian Deng, Dylan Campbell, Stephen Gould

Vision-Language Models (VLMs) have emerged as the dominant approach for zero-shot recognition, adept at handling diverse scenarios and significant distribution changes. However, their deployment in risk-sensitive areas requires a deeper understanding of their uncertainty estimation capabilities, a relatively uncharted area. In this study, we explore the cali

Alfio Bonanno, Amir-Pouyan Khosravi, Frank Saueressig

We reply to the ``Comment'' on ``Regular evaporating black holes with stable cores'' by R. Carballo-Rubio, F. Di Filippo, S. Liberati, C. Pacilio, and M. Visser. As a key result, we show that the regime of mass-inflation identified in the comment connects smoothly to the late-time attractors discovered in our works [A. Bonanno et. al., Regular black holes wi

Justin Davis, Mehmet E. Belviranli

In recent years, deep neural networks (DNNs) have gained widespread adoption for continuous mobile object detection (OD) tasks, particularly in autonomous systems. However, a prevalent issue in their deployment is the one-size-fits-all approach, where a single DNN is used, resulting in inefficient utilization of computational resources. This inefficiency is

Sraboni Dey, Kirandas P S, Deepshikha Jaiswal Nagar, Joy Mitra

Epsilon near zero (ENZ) materials can contribute significantly to the advancement of spectrally selective coatings aimed at enhancing efficient use of solar radiation and thermal energy management. Here, we demonstrate a subwavelength thick, multilayer optical coating that imparts a spectrally "step function" like reflectivity onto diverse surfaces, from sta

Terrence George

We prove a correspondence between Ising models in a torus and the algebro-geometric data of a Harnack curve with a certain symmetry and a point in the real part of its Prym variety, extending the correspondence between dimer models and Harnack curves and their Jacobians due to Kenyon and Okounkov.

Siyuan Li, Shijie Han, Yingnan Zhao, By Liang

Reinforcement learning (RL) has shown its strength in challenging sequential decision-making problems. The reward function in RL is crucial to the learning performance, as it serves as a measure of the task completion degree. In real-world problems, the rewards are predominantly human-designed, which requires laborious tuning, and is easily affected by human

Grant C. Forbes, Nitish Gupta, Leonardo Villalobos-Arias, Colin M. Potts

Recently there has been a proliferation of intrinsic motivation (IM) reward-shaping methods to learn in complex and sparse-reward environments. These methods can often inadvertently change the set of optimal policies in an environment, leading to suboptimal behavior. Previous work on mitigating the risks of reward shaping, particularly through potential-base

Weijie Tu, Weijian Deng, Tom Gedeon

Contrastive Language-Image Pre-training (CLIP) models have demonstrated remarkable generalization capabilities across multiple challenging distribution shifts. However, there is still much to be explored in terms of their robustness to the variations of specific visual factors. In real-world applications, reliable and safe systems must consider other safety

Nathaniel Smith, Alim Sukhtayev

We derive a counting formula for the eigenvalues of Schr\"odinger operators with self-adjoint boundary conditions on quantum star graphs. More specifically, we develop techniques using Evans functions to reduce full quantum graph eigenvalue problems into smaller subgraph eigenvalue problems. These methods provide a simple way to calculate the spectra of oper

Mingrui Ma, Lansheng Han, Chunjie Zhou

The frequent occurrence of cyber-attacks has made webshell attacks and defense gradually become a research hotspot in the field of network security. However, the lack of publicly available benchmark datasets and the over-reliance on manually defined rules for webshell escape sample generation have slowed down the progress of research related to webshell esca

Chudamani Poudyal

There are several ways to establish the asymptotic normality of $L$-statistics, which depend on the choice of the weights-generating function and the cumulative distribution selection of the underlying model. In this study, we focus on stablishing computational formulas for the asymptotic variance of two robust $L$-estimators: the method of trimmed moments (

Xiao Zhang, Ruoyu Xiang, Chenhan Yuan, Duanyu Feng

Despite Spanish's pivotal role in the global finance industry, a pronounced gap exists in Spanish financial natural language processing (NLP) and application studies compared to English, especially in the era of large language models (LLMs). To bridge this gap, we unveil Tois\'on de Oro, the first bilingual framework that establishes instruction datasets, fi

Igor Svoboda, Dmytro Lande

Our study presents a new framework that incorporates the Analytic Hierarchy Process (AHP) and Generative Pre-trained Transformer 4 (GPT-4) large language model (LLM), bringing novel approaches to cybersecurity Multiple-criteria Decision Making (MCDA). By utilizing the capabilities of GPT-4 autonomous agents as virtual experts, we automate the decision-making

Shiyi Wang, Yang Nan, Felder Federico N, Sheng Zhang

Each medical segmentation task should be considered with a specific AI algorithm based on its scenario so that the most accurate prediction model can be obtained. The most popular algorithms in medical segmentation, 3D U-Net and its variants, can directly implement the task of lung trachea segmentation, but its failure to consider the special tree-like struc

Yuanyuan Mao, Xin Lin, Qin Ni, Liang He

As a foundational component of cognitive intelligence, theory of mind (ToM) can make AI more closely resemble human thought processes, thereby enhancing their interaction and collaboration with human. In particular, it can significantly improve a model's comprehension of videos in complex scenes. However, current video question answer (VideoQA) datasets focu

Kyungha Kim, Sangyun Lee, Kung-Hsiang Huang, Hou Pong Chan

Fact-checking research has extensively explored verification but less so the generation of natural-language explanations, crucial for user trust. While Large Language Models (LLMs) excel in text generation, their capability for producing faithful explanations in fact-checking remains underexamined. Our study investigates LLMs' ability to generate such explan

Hsi-An Pan, Lihwai Lin, Sara L. Ellison, Mallory D. Thorp

Star formation quenching is one of the key processes that shape the evolution of galaxies. In this study, we investigate the changes in molecular gas and star formation properties as galaxies transit from the star-forming main sequence to the passive regime. Our analysis reveals that as galaxies move away from the main sequence towards the green valley the r

Liu Chen, Zhiyong Qiu, Fulvio Zonca

Using the slab plasma as a paradigm model, we have derived analytically equations for the nonlinear generation of zero-frequency zonal flows by electron drift waves including, on the same footing, both the beat-driven and spontaneous excitations. It is found that the beat-driven zonal flow tends to reduce the frequency mismatch between the electron drift wav

Dongsheng Zhu, Xunzhu Tang, Weidong Han, Jinghui Lu

This paper presents VisLingInstruct, a novel approach to advancing Multi-Modal Language Models (MMLMs) in zero-shot learning. Current MMLMs show impressive zero-shot abilities in multi-modal tasks, but their performance depends heavily on the quality of instructions. VisLingInstruct tackles this by autonomously evaluating and optimizing instructional texts t

George Boateng

Across the African continent, students grapple with various educational challenges, including limited access to essential resources such as computers, internet connectivity, reliable electricity, and a shortage of qualified teachers. Despite these challenges, recent advances in AI such as BERT, and GPT-4 have demonstrated their potential for advancing educat

Yunyan Lee, Ian R. Petersen, Daoyi Dong

This article presents a robust control strategy using Time-Optimal Model Predictive Control (TOMPC) for a two-level quantum system subject to bounded uncertainties. In this method, the control field is optimized over a finite horizon using a nominal quantum system as the reference and then the optimal control for the first time interval is applied and a proj

Amirsiavosh Bashardoust, Stefan Feuerriegel, Yash Raj Shrestha

Generative artificial intelligence (AI) presents large risks for society when it is used to create fake news. A crucial factor for fake news to go viral on social media is that users share such content. Here, we aim to shed light on the sharing behavior of users across human-generated vs. AI-generated fake news. Specifically, we study: (1) What is the percei

Namitha Issac, Anindya Saha, Saanika Choudhary, Aakash Chaudhary

We present a detailed structural and gas kinematic study of the star-forming complex W5-NW. A cloud-cloud collision scenario unravels with evidences of collision induced star and cluster formation. Various signatures of cloud-cloud collision such as "complementary distribution" and "bridging-features" are explored. At the colliding region, the two clouds hav

Meng Zhang, Dennis Yin, Nicholas Gangi, Amir Begović

Electronic-photonic computing systems offer immense potential in energy-efficient artificial intelligence (AI) acceleration tasks due to the superior computing speed and efficiency of optics, especially for real-time, low-energy deep neural network (DNN) inference tasks on resource-restricted edge platforms. However, current optical neural accelerators based

Sang in Shim, Yongsun Kim, Seung-il Nam

We utilize the effective Lagrangian method within the tree-level Born approximation to explore $\phi$-meson photoproduction, i.e., $\gamma p \to \phi p$. Our analysis encompasses contributions from various sources, including the Pomeron, $f_1$-Regge, pseudoscalar particles ($\pi$, $\eta$), scalar particles ($a_0$, $f_0$), protons, and three-nucleon resonance

Ningfei Chen, Liu Chen, Fulvio Zonca, Zhiyong Qiu

In this work, gyrokinetic theory of drift waves (DWs) self-regulation via the forced driven zonal flow (ZF) is presented, and finite diamagnetic drift frequency due to plasma nonuniformity is shown to play dominant role in ZF forced generation. The obtained nonlinear DW equation is a nonlinear Schr\"odinger equation, in which the linear dispersiveness, linea

Evan Ma, Chang Liu

Metallurgists have long been accustomed to a trade-off between yield strength and tensile ductility. Extending previously known strain-hardening mechanisms, the emerging multi-principal-element alloys (MPEAs) offer additional help in promoting the strength-ductility synergy, towards gigapascal yield strength simultaneously with pure-metal-like tensile ductil

Yuetian Luo, Rina Foygel Barber

Algorithm evaluation and comparison are fundamental questions in machine learning and statistics -- how well does an algorithm perform at a given modeling task, and which algorithm performs best? Many methods have been developed to assess algorithm performance, often based around cross-validation type strategies, retraining the algorithm of interest on diffe

Xing Wei, D. N. C. Lin

We study the magnetic and tidal interactions of a gas-giant exoplanet with its host star and with its exomoons, and focus on their retention. We briefly revisit the scaling law for planetary dynamo in terms of its mass, radius and luminosity. Based on the virial theorem, we construct an evolution law for planetary magnetic field and find that its initial ent

Qingkai Zeng, Yuyang Bai, Zhaoxuan Tan, Shangbin Feng

Automatic taxonomy induction is crucial for web search, recommendation systems, and question answering. Manual curation of taxonomies is expensive in terms of human effort, making automatic taxonomy construction highly desirable. In this work, we introduce Chain-of-Layer which is an in-context learning framework designed to induct taxonomies from a given set

Zainab Zaidi, Tansu Alpcan, Christopher Leckie, Sarah Efrain

This paper presents a novel and efficient wireless channel estimation scheme based on a tapped delay line (TDL) model of wireless signal propagation, where a data-driven machine learning approach is used to estimate the path delays and gains. The key motivation for our novel channel estimation model is to gain environment awareness, i.e., detecting changes i

Jiarui Zhang, Jinyi Hu, Mahyar Khayatkhoei, Filip Ilievski

Multimodal Large Language Models (MLLMs) have recently shown remarkable perceptual capability in answering visual questions, however, little is known about the limits of their perception. In particular, while prior works have provided anecdotal evidence of MLLMs' sensitivity to object size, this phenomenon and its underlying causes have not been explored com

Naoyuki Kanda, Xiaofei Wang, Sefik Emre Eskimez, Manthan Thakker

Laughter is one of the most expressive and natural aspects of human speech, conveying emotions, social cues, and humor. However, most text-to-speech (TTS) systems lack the ability to produce realistic and appropriate laughter sounds, limiting their applications and user experience. While there have been prior works to generate natural laughter, they fell sho

Mark Mandelkern

A classical theory of Desarguesian geometry, originating with D. Hilbert in his 1899 treatise, Grundlagen der Geometrie, leads from axioms to the construction of a division ring from which coordinates may be assigned to points, and equations to lines; this theory is highly nonconstructive. The present paper develops this coordinatization theory constructivel

Mesut Toka, Byungju Lee, Jaehyup Seong, Aryan Kaushik

Low-Earth orbit (LEO) satellite systems have been deemed a promising key enabler for current 5G and the forthcoming 6G wireless networks. Such LEO satellite constellations can provide worldwide three-dimensional coverage, high data rate, and scalability, thus enabling truly ubiquitous connectivity. On the other hand, another promising technology, reconfigura

Courtney L. Crawford, Timothy R. Bedding, Yaguang Li, Dennis Stello

When low- and intermediate-mass stars evolve off the main sequence, they expand and cool into the red giant stages of evolution, which include those associated with shell H burning (the red giant branch), core He burning (the red clump), and shell He burning (the asymptotic giant branch). The majority of red giants have masses $< 2 M_\odot$, and red giants m

Linwei Sang, Yinliang Xu, Hongbin Sun, Qiuwei Wu

Regulating the proper carbon-aware intervention policy is one of the keys to emission alleviation in the distribution network, whose basis lies in effectively attributing the emission responsibility using emission factors. This paper establishes the distribution locational marginal emission (DLME) to calculate the marginal change of emission from the margina

Kohei Ishizaki, Ikuo Ide, Masaki Fujita, Hiroki Hotta

Producing a polarized lanthanum (La) target with high polarization and long relaxation time is crucial for realizing time-reversal violation experiments using polarized neutron beams. We use a LaAlO3 crystal doped with a small amount of Nd3+ ions for the polarized lanthanum target. Optimizing the amount of Nd3+ ions is considerably important because the achi

T. E. Gureyev, C. Hall, B. Arhatari, D. Pelliccia

Young double-slit experiments using monochromatic hard X-rays with the energy of 25 keV are presented. The experiments were performed at a synchrotron source with a distance of 110 m between the interferometer and the detector to produce an interference pattern with a sufficiently broad period that could be adequately sampled by a photon-counting detector wi

Rundong Luo, Hong-Xing Yu, Jiajun Wu

We study inferring 3D object-centric scene representations from a single image. While recent methods have shown potential in unsupervised 3D object discovery from simple synthetic images, they fail to generalize to real-world scenes with visually rich and diverse objects. This limitation stems from their object representations, which entangle objects' intrin

Qizhao Chen, Ziqi Hu, Junyi Geng, Dongwei Bai

Capabilities of long-range flight and vertical take-off and landing (VTOL) are essential for Urban Air Mobility (UAM). Tiltrotor VTOLs have the advantage of balancing control simplicity and system complexity due to their redundant control authority. Prior work on controlling these aircraft either requires separate controllers and switching modes for differen

Ying Li, Zhichen Jing, De-Chao Song, Qiao Li

The white-light continuum emissions in solar flares (i.e., white-light flares) are usually observed on the solar disk but, in a few cases, off the limb. Here we present on-disk as well as off-limb continuum emissions at 3600 {\AA} (in the Balmer continuum) in an X2.1 flare (SOL2023-03-03T17:52) and an X1.5 flare (SOL2023-08-07T20:46), respectively, observed

Hang Yin, Abolfazl Safikhani, George Michailidis

The paper introduces a flexible model for the analysis of multivariate nonlinear time series data. The proposed Functional Coefficients Network Autoregressive (FCNAR) model considers the response of each node in the network to depend in a nonlinear fashion to each own past values (autoregressive component), as well as past values of each neighbor (network co

D. Rosales Herrera, J. R. Alvarado García, A. Fernández Téllez, J. E. Ramírez

We investigate the transverse momentum distribution (TMD) statistics from three different theoretical approaches. In particular, we explore the framework used for string models, wherein the particle production is given by the Schwinger mechanism. The thermal distribution arises from the Gaussian fluctuations of the string tension. The hard part of the TMD ca

Xijun Wang, Santiago López-Tapia, Aggelos K. Katsaggelos

Atmospheric turbulence, a common phenomenon in daily life, is primarily caused by the uneven heating of the Earth's surface. This phenomenon results in distorted and blurred acquired images or videos and can significantly impact downstream vision tasks, particularly those that rely on capturing clear, stable images or videos from outdoor environments, such a

Jaeseong Lee, Junha Hyung, Sohyun Jeong, Jaegul Choo

Face swapping has gained significant attention for its varied applications. Most previous face swapping approaches have relied on the seesaw game training scheme, also known as the target-oriented approach. However, this often leads to instability in model training and results in undesired samples with blended identities due to the target identity leakage pr

Tonglong Wei, Youfang Lin, Shengnan Guo, Yan Lin

Trajectory data is essential for various applications as it records the movement of vehicles. However, publicly available trajectory datasets remain limited in scale due to privacy concerns, which hinders the development of trajectory data mining and trajectory-based applications. To address this issue, some methods for generating synthetic trajectories have

Gabriel Simmons, Vladislav Savinov

This study evaluates the ability of Large Language Model (LLM)-based Subpopulation Representative Models (SRMs) to generalize from empirical data, utilizing in-context learning with data from the 2016 and 2020 American National Election Studies. We explore generalization across response variables and demographic subgroups. While conditioning with empirical d

Liming Jiang

Mini-applications, commonly referred to as mini-apps, are compact software programs embedded within larger applications or platforms, offering targeted functionality without the need for separate installations. Typically web-based or cloud-hosted, these mini-apps streamline user experiences by providing focused services accessible through web browsers or mob

Patrick Diehl, Steven R. Brandt, Gregor Daiß, Hartmut Kaiser

Cloud computing for high performance computing resources is an emerging topic. This service is of interest to researchers who care about reproducible computing, for software packages with complex installations, and for companies or researchers who need the compute resources only occasionally or do not want to run and maintain a supercomputer on their own. Th

Wei Xu, An Liu, Yiting Zhang, Vincent Lau

Efficient learning and model compression algorithm for deep neural network (DNN) is a key workhorse behind the rise of deep learning (DL). In this work, we propose a message passing based Bayesian deep learning algorithm called EM-TDAMP to avoid the drawbacks of traditional stochastic gradient descent (SGD) based learning algorithms and regularization-based

Mathieu Laurière, Ludovic Tangpi, Xuchen Zhou

Graphon games have been introduced to study games with many players who interact through a weighted graph of interaction. By passing to the limit, a game with a continuum of players is obtained, in which the interactions are through a graphon. In this paper, we focus on a graphon game for optimal investment under relative performance criteria, and we propose

Alex Ushveridze

The fact that accurately predicted information can serve as an energy source paves the way for new approaches to autonomous learning. The energy derived from a sequence of successful predictions can be recycled as an immediate incentive and resource, driving the enhancement of predictive capabilities in AI agents. We propose that, through a series of straigh

Yuli Rudyak

Let TC$_n$(X) denote the n-th topological complexity of a topological space X. It is known that TC$_n$(X) does not exceed n-1 for non-contractible X, and so it makes sense to describe spaces X with TC$_n$(X) =n-1. Grant--Lupton--Oprea proved the following: If X is a nilpotent space with TC$_n$(X)=n-1 then X is homotopy equivalent to an odd-dimensional sphere

Rachitesh Kumar, Jon Schneider, Balasubramanian Sivan

Learning to bid in repeated first-price auctions is a fundamental problem at the interface of game theory and machine learning, which has seen a recent surge in interest due to the transition of display advertising to first-price auctions. In this work, we propose a novel concave formulation for pure-strategy bidding in first-price auctions, and use it to an

Venkitesh Ayyar, LSD collaboration

Several SU(N) gauge theories have been explored as candidates for producing stable dark matter particles that can explain their relative abundance, while also evading current constraints from direct, indirect and collider searches. In this talk, I will present the confinement and spectral properties of a new model we name "Hyper Stealth Dark Matter", which i

Jin Xu

The four-color conjecture has puzzled mathematicians for over 170 years and has yet to be proven by purely mathematical methods. This series of articles provides a purely mathematical proof of the four-color conjecture, consisting of two parts: the transformation step and the decycle step. The transformation step uses two innovative tools, contracting and ex

Konstantin N. Nesterov, Ivan V. Pechenezhskiy

The dispersive-readout scheme enables quantum nondemolition measurement of superconducting qubits. An increased readout power can shorten the readout time and reduce the state discrimination error but can promote qubit transitions into higher noncomputational states. The ability to predict the onset of these measurement-induced state transitions can aid the

Wei Feng, Yanmin Wang, Yunfei Chen, Ning Ge

The upcoming sixth generation (6G) wireless communication network is envisioned to cover space, air, and maritime areas, in addition to urban-centered terrestrial coverage by the fifth generation (5G) network, to support intelligent Internet of Things (IoT). Towards this end, we investigate structured integration of satellites, unmanned aerial vehicles (UAVs

Yasutaka Ooga, Yuki Nishida, Yoshihide Watanabe

The max-plus algebra $\mathbb{R}\cup \{-\infty \}$ is defined in terms of a combination of the following two operations: addition, $a \oplus b := \max(a,b)$, and multiplication, $a \otimes b := a + b$. In this study, we propose a new method to characterize the set of all solutions of a max-plus two-sided linear system $A \otimes x = B \otimes x$. We demonstr

Luben M. C. Cabezas, Mateus P. Otto, Rafael Izbicki, Rafael B. Stern

Predictive models make mistakes. Hence, there is a need to quantify the uncertainty associated with their predictions. Conformal inference has emerged as a powerful tool to create statistically valid prediction regions around point predictions, but its naive application to regression problems yields non-adaptive regions. New conformal scores, often relying u

Danil Akhtiamov, David Bosch, Reza Ghane, K Nithin Varma

A celebrated result by Gordon allows one to compare the min-max behavior of two Gaussian processes if certain inequality conditions are met. The consequences of this result include the Gaussian min-max (GMT) and convex Gaussian min-max (CGMT) theorems which have had far-reaching implications in high-dimensional statistics, machine learning, non-smooth optimi

Yunbum Kook, Matthew S. Zhang, Sinho Chewi, Murat A. Erdogdu

We study the complexity of sampling from the stationary distribution of a mean-field SDE, or equivalently, the complexity of minimizing a functional over the space of probability measures which includes an interaction term. Our main insight is to decouple the two key aspects of this problem: (1) approximation of the mean-field SDE via a finite-particle syste

Tianyi Ren, Abhishek Sharma, Juampablo Heras Rivera, Harshitha Rebala

Identification of tumor margins is essential for surgical decision-making for glioblastoma patients and provides reliable assistance for neurosurgeons. Despite improvements in deep learning architectures for tumor segmentation over the years, creating a fully autonomous system suitable for clinical floors remains a formidable challenge because the model pred

Sriram Gopalakrishnan, Vincent Neiger, Mohab Safey El Din

Given polynomials $g$ and $f_1,\dots,f_p$, all in $\Bbbk[x_1,\dots,x_n]$ for some field $\Bbbk$, we consider the problem of computing the critical points of the restriction of $g$ to the variety defined by $f_1=\cdots=f_p=0$. These are defined by the simultaneous vanishing of the $f_i$'s and all maximal minors of the Jacobian matrix associated to $(g,f_1, \l

Shonal Chaudhry, Anuraganand Sharma

The order of training samples can have a significant impact on the performance of a classifier. Curriculum learning is a method of ordering training samples from easy to hard. This paper proposes the novel idea of a curriculum learning approach called Data Distribution-based Curriculum Learning (DDCL). DDCL uses the data distribution of a dataset to build a

Joseph Toombs, Chi Chung Li, Nour Akiki, Yaxuan Sun

Additive manufacturing (AM) has revolutionized the fabrication of devices with precisely controlled optical, fluidic, mechanical, and filtering properties, offering greater design freedom than conventional manufacturing methods. Tomographic volumetric additive manufacturing (TVAM) has many advantages compared to other AM methods including smooth layer-less s

Valentina Alberti, Cinzia Cocco, Sergio Consoli, Valentina Montalto

Cultural gems is a web application conceived by the European Commission's Joint Research Centre (DG JRC), which aims at engaging people and organisations across Europe to create a unique repository of cultural and creative places. The main goal is to provide a vision of European culture in order to strengthen a sense of identity within a single European cult

Alice Cai, Ian Arawjo, Elena L. Glassman

The vast majority of discourse around AI development assumes that subservient, "moral" models aligned with "human values" are universally beneficial -- in short, that good AI is sycophantic AI. We explore the shadow of the sycophantic paradigm, a design space we term antagonistic AI: AI systems that are disagreeable, rude, interrupting, confrontational, chal

Leah South, Matthew Sutton

This chapter describes several control variate methods for improving estimates of expectations from MCMC.

Hendrik De Bie, Pan Lian

The higher step Grushin operators $\Delta_{\alpha}$ are a family of sub-elliptic operators which degenerate on a sub-manifold of $\mathbb{R}^{n+m}$. This paper establishes Carleman-type inequalities for these operators. It is achieved by deriving a weighted $L^{p}-L^{q}$ estimate for the Grushin-harmonic projector. The crucial ingredient in the proof is the

Yunzhe Xue, Usman Roshan

Recent work has shown the defense of 01 loss sign activation neural networks against image classification adversarial attacks. A public challenge to attack the models on CIFAR10 dataset remains undefeated. We ask the following question in this study: are 01 loss sign activation neural networks hard to deceive with a popular black box text adversarial attack

Qingru Hu, Malena Rice, Xian-Yu Wang, Songhu Wang

TOI-677 b is part of an emerging class of ``tidally-detached'' gas giants ($a/R_\star \gtrsim 11$) that exhibit large orbital eccentricities and yet low stellar obliquities. Such sources pose a challenge for models of giant planet formation, which must account for the excitation of high eccentricities without large changes in the orbital inclination. In this

Vincent Neiger, Clément Pernet, Gilles Villard

Krylov methods rely on iterated matrix-vector products $A^k u_j$ for an $n\times n$ matrix $A$ and vectors $u_1,\ldots,u_m$. The space spanned by all iterates $A^k u_j$ admits a particular basis -- the \emph{maximal Krylov basis} -- which consists of iterates of the first vector $u_1, Au_1, A^2u_1,\ldots$, until reaching linear dependency, then iterating sim

Zongliang Ji, Anna Goldenberg, Rahul G. Krishnan

Scheduling laboratory tests for ICU patients presents a significant challenge. Studies show that 20-40% of lab tests ordered in the ICU are redundant and could be eliminated without compromising patient safety. Prior work has leveraged offline reinforcement learning (Offline-RL) to find optimal policies for ordering lab tests based on patient information. Ho

Maxim Kontsevich, Yan Soibelman

In the first of the series of papers devoted to our project ``Holomorphic Floer Theory" we discuss exponential integrals and related wall-crossing structures. We emphasize two points of view on the subject: the one based on the ideas of deformation quantization and the one based on the ideas of Floer theory. Their equivalence is a corollary of our generalize

Priyan Vaithilingam, Ian Arawjo, Elena L. Glassman

We ideate a future design workflow that involves AI technology. Drawing from activity and communication theory, we attempt to isolate the new value large AI models can provide design compared to past technologies. We arrive at three affordances -- dynamic grounding, constructive negotiation, and sustainable motivation -- that summarize latent qualities of na

Kwang-Sung Jun, Jungtaek Kim

Adapting to a priori unknown noise level is a very important but challenging problem in sequential decision-making as efficient exploration typically requires knowledge of the noise level, which is often loosely specified. We report significant progress in addressing this issue for linear bandits in two respects. First, we propose a novel confidence set that

Suqi Liu, Morgane Austern

We study the graph matching problem in the presence of vertex feature information using shallow graph neural networks. Specifically, given two graphs that are independent perturbations of a single random geometric graph with sparse binary features, the task is to recover an unknown one-to-one mapping between the vertices of the two graphs. We show under cert

Oscar Araque, Luca Barbaglia, Francesco Berlingieri, Marco Colagrossi

After decades of improvements in the employment conditions of females in Spain, this process came to a sudden stop with the Great Spanish Recession of 2008. In this contribution, we analyse a large longitudinal corpus of national and regional news outlets employing advanced Natural Language Processing techniques to capture the valence of mentions of gender i

Joshua Krinsky, Alan Bettis, Qiuyu Tang, Daniel Moreira

The social media-fuelled explosion of fake news and misinformation supported by tampered images has led to growth in the development of models and datasets for image manipulation detection. However, existing detection methods mostly treat media objects in isolation, without considering the impact of specific manipulations on viewer perception. Forensic datas

Doron Shafrir

If $M$ is a submonoid of a finitely generated nilpotent group $G$, and $MG'$ is a finite index subgroup of $G$, then $M$ itself is a finite index subgroup of $G$. If $MG'=G$, then $M=G$. This generalizes a well-known theorem for subgroups of finitely generated nilpotent groups. As a result, we give an algorithm for the Identity Problem in nilpotent groups.

Andrea De Domenico, Ali Farjami, Krishna Manoorkar, Alessandra Palmigiano

We further develop the abstract algebraic logic approach to input/output logic initiated in \cite{wollic22}, where the family of selfextensional logics was proposed as a general background environment for input/output logics. In this paper, we introduce and discuss the generalizations of several types of permission (negative, dual negative, static, dynamic),

Sergio Consoli, Valentina Alberti, Cinzia Cocco, Francesco Panella

The recovery and resilience of the cultural and creative sectors after the COVID-19 pandemic is a current topic with priority for the European Commission. Cultural gems is a crowdsourced web platform managed by the Joint Research Centre of the European Commission aimed at creating community-led maps as well as a common repository for cultural and creative pl

Andrea Lodi, Jasone Ramírez-Ayerbe

In this paper, we consider the problem of generating a set of counterfactual explanations for a group of instances, with the one-for-many allocation rule, where one explanation is allocated to a subgroup of the instances. For the first time, we solve the problem of minimizing the number of explanations needed to explain all the instances, while considering s

Karl Pelka, Matteo Aquilina, André Xuereb

Entanglement, one of the clearest manifestations of non-classical physics, holds significant promise for technological applications such as more secure communications and faster computations. In this paper we explore the use of non-reciprocal transport in a network of continuous-variable systems to route entanglement in one direction through the network. We

Esther Banaian, Anh Trong Nam Hoang, Elizabeth Kelley, Weston Miller

We construct a bijection between certain Deodhar components of a braid variety constructed from an affine Kac-Moody group of type $A_{n-1}$ and vertex-labeled trees on $n$ vertices. By an argument of Galashin, Lam, and Williams using Opdam&#39;s trace formula in the affine Hecke algebra and an identity due to Haglund, we obtain an elaborate new proof for the

Borjan Geshkovski, Cyril Letrouit, Yury Polyanskiy, Philippe Rigollet

Viewing Transformers as interacting particle systems, we describe the geometry of learned representations when the weights are not time dependent. We show that particles, representing tokens, tend to cluster toward particular limiting objects as time tends to infinity. Cluster locations are determined by the initial tokens, confirming context-awareness of re

Catriona Maclean

Huayi Chen introduces the notion of an approximable graded algebra, which he uses to prove a Fujita-type theorem in the arithmetic setting, and asked if any such algebra is the graded ring of a big line bundle on a projective variety. This was proved to be false in a previous paper of the author&#39;s, who subsequently proved that any such algebra is associa

Pierre Tholoniat, Huseyin A. Inan, Janardhan Kulkarni, Robert Sim

This position paper investigates the integration of Differential Privacy (DP) in the training of Mixture of Experts (MoE) models within the field of natural language processing. As Large Language Models (LLMs) scale to billions of parameters, leveraging expansive datasets, they exhibit enhanced linguistic capabilities and emergent abilities. However, this gr

Raffaele Moretta, Fabio Pascariello, Giovanni Petraglia, Maurizio Feo

In this paper, a new sampling scheme of the near field radiated by a planar source is proposed and assessed. More in detail, the paper shows a uniform sampling criterion that allows representing the near field over a plane with a number of measurements lower than the classical half-wavelength sampling. At first, a discretization strategy of the near field ba

Pranav Subramaniam, Sanjay Krishnan

In every enterprise database, administrators must define an access control policy that specifies which users have access to which tables. Access control straddles two worlds: policy (organization-level principles that define who should have access) and process (database-level primitives that actually implement the policy). Assessing and enforcing process com

Barış Can Esmer, Jacob Focke, Dániel Marx, Paweł Rzążewski

It is known for many algorithmic problems that if a tree decomposition of width $t$ is given in the input, then the problem can be solved with exponential dependence on $t$. A line of research by Lokshtanov, Marx, and Saurabh [SODA 2011] produced lower bounds showing that in many cases known algorithms achieve the best possible exponential dependence on $t$,

Binyan Hu, A. K. Qin

Medical image segmentation (MIS) plays an instrumental role in medical image analysis, where considerable effort has been devoted to automating the process. Currently, mainstream MIS approaches are based on deep neural networks (DNNs), which are typically trained on a dataset with annotations produced by certain medical experts. In the medical domain, the an

Caner Hazirbas, Alicia Sun, Yonathan Efroni, Mark Ibrahim

Despite the remarkable performance of foundation vision-language models, the shared representation space for text and vision can also encode harmful label associations detrimental to fairness. While prior work has uncovered bias in vision-language models' (VLMs) classification performance across geography, work has been limited along the important axis of ha

J. Alvarez, A. Rivera, R. Gonzalez-Arrabal, D. Garoz

The European HiPER project aims to demonstrate commercial viability of inertial fusion energy within the following two decades. This goal requires an extensive Research & Development program on materials for different applications (e.g., first wall, structural components and final optics). In this paper we will discuss our activities in the framework of HiPE

Carlos E. Arreche, Hari P. Sitaula

In 2012 Chen and Singer introduced the notion of discrete residues for rational functions as a complete obstruction to rational summability. More explicitly, for a given rational function f(x), there exists a rational function g(x) such that f(x) = g(x+1) - g(x) if and only if every discrete residue of f(x) is zero. Discrete residues have many important furt