February 2024 arXiv papers — page 81

Yasaman Jafari, Dheeraj Mekala, Rose Yu, Taylor Berg-Kirkpatrick

RL-based techniques can be employed to search for prompts that, when fed into a target language model, maximize a set of user-specified reward functions. However, in many target applications, the natural reward functions are in tension with one another -- for example, content preservation vs. style matching in style transfer tasks. Current techniques focus o

Jia Xu, Mona Diab

Minimizing social bias strengthens societal bonds, promoting shared understanding and better decision-making. We revisit the definition of bias by discovering new bias types (e.g., societal status) in dynamic environments and describe them relative to context, such as culture, region, time, and personal background. Our framework includes eight hypotheses abo

Shuzhou Yuan, Ercong Nie, Michael Färber, Helmut Schmid

Large Language Models (LLMs) exhibit strong In-Context Learning (ICL) capabilities when prompts with demonstrations are used. However, fine-tuning still remains crucial to further enhance their adaptability. Prompt-based fine-tuning proves to be an effective fine-tuning method in low-data scenarios, but high demands on computing resources limit its practical

Samuel L. Krushkal

The paper continues the author's research in the problem of quantitative investigation of basic curvelinear quasiinvariants of quasiconformal curves. It concerns polygons with infinite number of vertices and provides various distortion estimates in terms of intrinsic geometric characteristics of polygons. In particular, this implies the coarse upper and lowe

Shahan Ali Memon, Jevin D. West

In this commentary, we discuss the evolving nature of search engines, as they begin to generate, index, and distribute content created by generative artificial intelligence (GenAI). Our discussion highlights challenges in the early stages of GenAI integration, particularly around factual inconsistencies and biases. We discuss how output from GenAI carries an

Mark Hughes, Seungwon Kim, Maggie Miller

We prove that the double branched cover of a twist-roll spun knot in $S^4$ is smoothly preserved when four twists are added, and that the double branched cover of a twist-roll spun knot connected sum with a trivial projective plane is preserved after two twists are added. As a consequence, we conclude that the members of a family of homotopy $\mathbb{CP}^2$s

Quanjun Lang, Jianfeng Lu

We introduce a novel approach for learning memory kernels in Generalized Langevin Equations. This approach initially utilizes a regularized Prony method to estimate correlation functions from trajectory data, followed by regression over a Sobolev norm-based loss function with RKHS regularization. Our method guarantees improved performance within an exponenti

Jacob Finkenrath

This review gives an overview on the research of algorithms for dynamical fermions used in large scale lattice QCD simulations. First a short overview on the state-of-the-art of ensemble generation at the physical point is given. Followed by an overview on necessary steps towards simulation of large lattices with the Hybrid Monte Carlo algorithm. Here, the s

Adrian Miranda

We analyse compatibility between monads and monoidal structures in the two-dimensional setting. We describe sufficient conditions for monoidal structures to lift to the Eilenberg-Moore pseudoalgebras. We then extend these results to braids, syllapses and symmetries. To achieve these results we define the Gray-tensor product of pseudomonads, and examine its i

A-Ming Liu, Wenbin Guo, Vasily G. Safonov, Alexander N. Skiba

We characterize some classes of finite soluble groups. In particular, we prove that: a finite group $G$ is supersoluble if and only if $G$ has a normal subgroup $D$ such that $G/D$ is supersoluble and $D$ avoids every chief factor of $G$ between $V^{G}$ and $V_{G}$ for every maximal subgroup $V$ of the generalized Fitting subgroup $F^{*}(G)$ of $G$; a finite

Kailun Jin, Chung-Yu Wang, Hung Viet Pham, Hadi Hemmati

Large language models (LLMs) have demonstrated notable proficiency in code generation, with numerous prior studies showing their promising capabilities in various development scenarios. However, these studies mainly provide evaluations in research settings, which leaves a significant gap in understanding how effectively LLMs can support developers in real-wo

Roberto C. Alamino

As powerful as machine learning (ML) techniques are in solving problems involving data with large dimensionality, explaining the results from the fitted parameters remains a challenging task of utmost importance, especially in physics applications. This work shows how this can be accomplished for the ferromagnetic Ising model, the main target of several ML s

Shuzhou Yuan, Ercong Nie, Bolei Ma, Michael Färber

Large Language Models (LLMs) possess outstanding capabilities in addressing various natural language processing (NLP) tasks. However, the sheer size of these models poses challenges in terms of storage, training and inference due to the inclusion of billions of parameters through layer stacking. While traditional approaches such as model pruning or distillat

Johannes Nicaise

Let $\Gamma$ be a divisible subgroup of $(\mathbb{R},+)$. Our central result states that, at the level of Grothendieck groups, the classification of $\Gamma$-rational polyhedra in $\mathbb{R}^n$ up to affine transformations in $\Gamma^n\rtimes \mathrm{GL}_n(\mathbb{Z})$ is equivalent to the classification up to affine transformations in $\Gamma^n\rtimes \mat

Ege Küçükkömürcü, Berk Nezir Gün, Emre Yüce

Sound reconstruction via arbitrary objects has been a popular method in recent years, based on the recording of scattered light from the target object with a high-speed detector. In this work, we demonstrate the use of multi-mode fiber as a medium that enables reconstruction at a much further distance. By placing a speaker near the fiber and using a high-spe

Deven Panchal

While the 5G technology of cellular communications promises great capacity and coverage to access information anywhere and anytime, it is feared to have huge power consumption. Significant research been has been directed towards solving this problem which exists both on the subscribers side as well as the operators side. There have been efforts like predicti

Ekaterina Amerik, Andrey Soldatenkov, Misha Verbitsky

The ample cone of a compact Kahler $n$-manifold $M$ is the intersection of its Kahler cone and the real subspace generated by integer (1,1)-classes. Its isotropic boundary is the set of all points $\eta$ on its boundary such that $\int_M \eta^n=0$. We are interested in the relation between the shape of the isotropic boundary of the ample cone of a hyperkahle

Toukaiddine Petit

We compute the index of a Lie Borel Lie Algbra of a simple Lie algebra.

Deven Panchal

This paper gives an overview of Software Defined Optical Networks or SDONs and how they can be implemented. It traces the evolution of Optical networks upto GMPLS and traces the idea of SDN and builds upto OpenFlow. The paper explores the need for SDONs and explains what a SDON solution could look like, including the hardware. It also seeks to explain how Op

Sadi Shahriar, Kavya Somayajula, Conner Winkeljohn, Jeremy Mason

Nanoporous gold (np-Au) has found use in applications ranging from catalysis to biosensing where pore morphology plays a critical role in performance. While morphology evolution of bulk np-Au has been widely studied, knowledge about its thin film form is limited. This work hypothesizes that mechanical compliance of the thin film substrate can play a critical

John A. Rhodes, Hector Banos, Jingcheng Xu, Cécile Ané

Interest in the inference of evolutionary networks relating species or populations has grown with the increasing recognition of the importance of hybridization, gene flow and admixture, and the availability of large-scale genomic data. However, what network features may be validly inferred from various data types under different models remains poorly underst

Nurlan Abiev

Sets related to positively curved invariant Riemannian metrics on generalized Wallach spaces are considered. The problem arises in studying of the evolution of such metrics under the normalized Ricci flow equation. For Riemannian metrics of the Wallach spaces $\operatorname{SU}(3)/T_{\max}$, $\operatorname{Sp(3)}/ \left(\operatorname{Sp(1)}\right)^3$ and $F_

Léopold Van Brandt, Denis Flandre, Jean-Charles Delvenne

SRAM bitcells in retention mode behave as autonomous stochastic nonlinear dynamical systems. From observation of variability-aware transient noise simulations, we provide an unidimensional model, fully characterizable by conventional deterministic SPICE simulations, insightfully explaining the mechanism of intrinsic noise-induced bit flips. The proposed mode

Zhiyang Xu, Chao Feng, Rulin Shao, Trevor Ashby

Despite vision-language models' (VLMs) remarkable capabilities as versatile visual assistants, two substantial challenges persist within the existing VLM frameworks: (1) lacking task diversity in pretraining and visual instruction tuning, and (2) annotation error and bias in GPT-4 synthesized instruction tuning data. Both challenges lead to issues such as po

Jia Li, Jianqiang Zhao

In this paper we consider a family of multiple Hurwitz zeta values with bi-indices parameterized by $\mu$ with $\Ree(\mu)>0$. These values are equipped with both the $\mu$-stuffle product from their series definition and the shuffle product from their integral expressions. We will give a detailed analysis of the two different products and discuss their regul

Leah Bigwood, Anna-Christina Eilers, Robert A. Simcoe

Observations of high-redshift quasars hosting billion solar mass black holes at $z\gtrsim6$ challenge our understanding of early supermassive black hole (SMBH) growth. In this work, we conduct a near-infrared spectroscopic study of $19$ quasars at $6.2\lesssim z\lesssim 7.5$, using the Folded-port InfraRed Echellette (FIRE) instrument on the $6.5$-meter Mage

Satwik Kundu, Debarshi Kundu, Swaroop Ghosh

Cloud hosting of quantum machine learning (QML) models exposes them to a range of vulnerabilities, the most significant of which is the model stealing attack. In this study, we assess the efficacy of such attacks in the realm of quantum computing. We conducted comprehensive experiments on various datasets with multiple QML model architectures. Our findings r

Zirou Qiu, Abhijin Adiga, Madhav V. Marathe, S. S. Ravi

Discrete dynamical systems are commonly used to model the spread of contagions on real-world networks. Under the PAC framework, existing research has studied the problem of learning the behavior of a system, assuming that the underlying network is known. In this work, we focus on a more challenging setting: to learn both the behavior and the underlying topol

Léopold Van Brandt, Jean-Charles Delvenne, Denis Flandre

Stability of ultra-low-voltage SRAM bitcells in retention mode is threatened by two types of uncertainty: process variability and intrinsic noise. While variability dominates the failure probability, noise-induced bit flips in weakened bitcells lead to dynamic instability. We study both effects jointly in a unified SPICE simulation framework. Starting from a

Guiming Hardy Chen, Shunian Chen, Ruifei Zhang, Junying Chen

Large vision-language models (LVLMs) have shown premise in a broad range of vision-language tasks with their strong reasoning and generalization capabilities. However, they require considerable computational resources for training and deployment. This study aims to bridge the performance gap between traditional-scale LVLMs and resource-friendly lite versions

Tejpalsingh Siledar, Swaroop Nath, Sankara Sri Raghava Ravindra Muddu, Rupasai Rangaraju

Evaluation of opinion summaries using conventional reference-based metrics rarely provides a holistic evaluation and has been shown to have a relatively low correlation with human judgments. Recent studies suggest using Large Language Models (LLMs) as reference-free metrics for NLG evaluation, however, they remain unexplored for opinion summary evaluation. M

Abhra Chaudhuri, Serban Georgescu, Anjan Dutta

Invariance learning algorithms that conditionally filter out domain-specific random variables as distractors, do so based only on the data semantics, and not the target domain under evaluation. We show that a provably optimal and sample-efficient way of learning conditional invariances is by relaxing the invariance criterion to be non-commutatively directed

Jérôme Michaud, Anna Jon-and

Recent advances in large language models using deep learning techniques have renewed interest on how languages can be learned from data. However, it is unclear whether or how these models represent grammatical information from the learned languages. In addition, the models must be pre-trained on large corpora before they can be used. In this work, we propose

Till Beemelmanns, Yuchen Tao, Bastian Lampe, Lennart Reiher

Storing and transmitting LiDAR point cloud data is essential for many AV applications, such as training data collection, remote control, cloud services or SLAM. However, due to the sparsity and unordered structure of the data, it is difficult to compress point cloud data to a low volume. Transforming the raw point cloud data into a dense 2D matrix structure

Dmytro Shchyrba, Izabela Paniczek

Selection of perefect parameters for low-pass filters can sometimes be an expensive problem with no analytical solution or differentiability of cost function. In this paper, we introduce a new PSO-inspired algorithm, that incorporates the positive experiences of the swarm to learn the geometry of the search space,thus obtaining the ability to consistently re

Wei Wang, Peng Wang, Hong Guo, Xi Kang

For decades, the boundary of cosmic filaments have been a subject of debate. In this work, we determine the physically-motivated radii of filaments by constructing stacked galaxy number density profiles around the filament spines. We find that the slope of the profile changes with distance to the filament spine, reaching its minimum at approximately 1 Mpc at

Till Beemelmanns, Quan Zhang, Christian Geller, Lutz Eckstein

Multi-modal 3D object detection models for automated driving have demonstrated exceptional performance on computer vision benchmarks like nuScenes. However, their reliance on densely sampled LiDAR point clouds and meticulously calibrated sensor arrays poses challenges for real-world applications. Issues such as sensor misalignment, miscalibration, and dispar

Jaylen Jones, Lingbo Mo, Eric Fosler-Lussier, Huan Sun

Counter narratives - informed responses to hate speech contexts designed to refute hateful claims and de-escalate encounters - have emerged as an effective hate speech intervention strategy. While previous work has proposed automatic counter narrative generation methods to aid manual interventions, the evaluation of these approaches remains underdeveloped. P

Siddhant Vernekar, Jolly Xavier

Weak coherent source (WCS) and spontaneous parametric down converted heralded single photon pairs have found applications in quantum key distribution (QKD) and quantum imaging (QI) experiments. Decoy state methods have also been used to enhance the security for QKD and QI. We study quantum secured imaging with the decoy state heralded single photon source (H

Benjamin Scellier

Analog electrical networks have long been investigated as energy-efficient computing platforms for machine learning, leveraging analog physics during inference. More recently, resistor networks have sparked particular interest due to their ability to learn using local rules (such as equilibrium propagation), enabling potentially important energy efficiency g

Esteban Rojas-Gatjens, Quinten A. Akkerman, Liberato Manna, Ajay Ram Srimath Kandada

The use of semiconductor nanocrystals in scalable quantum technologies requires characterization of the exciton coherence dynamics in an \emph{ensemble} of electronically isolated crystals in which system-bath interactions are nevertheless strong. In this communication, we identify signatures of Fano-like interference between excitons and photocarriers in th

Tien-Cuong Dinh, Subhroshekhar Ghosh, Hao Wu

Let $X$ be a compact Riemann surface and $\mathcal L$ be a positive line bundle on it. We study the conditional zero expectation of all the holomorphic sections of $\mathcal L^n$ which do not vanish on $D$ for some fixed open subset $D$ of $X$. We prove that as $n$ tends to infinity, the zeros of these sections are equidistributed outside $D$ with respect to

Yasuko Kawahata

This research note is organized with respect to a novel approach to solving problems related to the spread of fake news and effective fact-checking. Focusing on the least-cost routing problem, the discussion is organized with respect to the use of Metzler functions and Metzler matrices to model the dynamics of information propagation among news providers. Wi

Agnes Luhtaru, Martin Vainikko, Krista Liin, Kais Allkivi-Metsoja

The project was funded in 2021-2023 by the National Programme of Estonian Language Technology. Its main aim was to develop spelling and grammar correction tools for the Estonian language. The main challenge was the very small amount of available error correction data needed for such development. To mitigate this, (1) we annotated more correction data for mod

Lars Nieradzik, Henrike Stephani, Jördis Sieburg-Rockel, Stephanie Helmling

In this study, we explore the explainability of neural networks in agriculture and forestry, specifically in fertilizer treatment classification and wood identification. The opaque nature of these models, often considered 'black boxes', is addressed through an extensive evaluation of state-of-the-art Attribution Maps (AMs), also known as class activation map

Yunxiang Song, Yaowen Hu, Marko Lončar, Kiyoul Yang

Optical frequency combs are indispensable links between the optical and microwave domains, enabling a wide range of applications including precision spectroscopy, ultrastable frequency generation, and timekeeping. Chip-scale integration miniaturizes bulk implementations onto photonic chips, offering highly compact, stable, and power-efficient frequency comb

M. D. Borrás, J. C. Bravo, J. C. Montaño

This paper presents an effective approach to identify power quality events based on IEEE Std 1159-2009 caused by intermittent power sources like those of renewable energy. An efficient characterization of these disturbances is granted by the use of two useful wavelet based indices. For this purpose, a wavelet-based Global Disturbance Ratio index (GDR), defin

Davide Castaldo, Marta Rosa, Stefano Corni

We show that optimal control of the electron dynamics is able to prepare molecular ground states, within chemical accuracy, with evolution times approaching the bounds imposed by quantum mechanics. We propose a specific parameterization of the molecular evolution only in terms of interaction already present in the molecular Hamiltonian. Thus, the proposed me

Inigo Incer, Noel Csomay-Shanklin, Aaron Ames, Richard M. Murray

We consider the problem of reasoning about networked and layered control systems using assume-guarantee specifications. As these systems are formed by the interconnection of components that operate under various clocks, we introduce a new logic, Multiclock Logic (MCL), to be able to express the requirements of components form the point of view of their local

Weizhe Liu, Sylvain Veilleux, Gabriela Canalizo, Todd M. Tripp

While stellar processes are believed to be the main source of feedback in dwarf galaxies, the accumulating discoveries of AGN in dwarf galaxies over recent years arouse the interest to also consider AGN feedback in them. Fast, AGN-driven outflows, a major mechanism of AGN feedback, have indeed been discovered in dwarf galaxies and may be powerful enough to p

Yuqi Jiang, Yan Li, Yize Chen

Rapid progress in machine learning and deep learning has enabled a wide range of applications in the electricity load forecasting of power systems, for instance, univariate and multivariate short-term load forecasting. Though the strong capabilities of learning the non-linearity of the load patterns and the high prediction accuracy have been achieved, the in

Dimitris Moustos, Charis Anastopoulos

A small quantum system within the gravitational field of a massive body will be entangled with the quantum degrees of freedom of the latter. Hence, the massive body acts as an environment, and it induces non-unitary dynamics, noise, and decoherence to the quantum system. It is impossible to shield systems on Earth from this gravity-mediated decoherence, whic

Matthew Yedutenko, Federico Paredes-Valles, Lyes Khacef, Guido C. H. E. De Croon

Motion detection is a primary task required for robotic systems to perceive and navigate in their environment. Proposed in the literature bioinspired neuromorphic Time-Difference Encoder (TDE-2) combines event-based sensors and processors with spiking neural networks to provide real-time and energy-efficient motion detection through extracting temporal corre

Lorenzo Lorenzetti

The use of statistical methods to model gravitational systems is crucial to physics practice, but the extent to which thermodynamics and statistical mechanics genuinely apply to these systems is a contentious issue. This paper provides new conceptual foundations for gravitational thermodynamics by reconsidering the nature of key concepts like equilibrium and

Marco Fraccaroli, Olli Saari, Christoph Thiele

We prove bounds in the strict local $L^{2}(\mathbb{R}^{d})$ range for trilinear Fourier multiplier forms with a $d$-dimensional singular subspace. Given a fixed parameter $K \ge 1$, we treat multipliers with non-degenerate singularity that are push-forwards by $K$-quasiconformal matrices of suitable symbols. As particular applications, our result recovers th

V. G. Bardakov, V. A. Bovdi

In the present article we define and investigate relative Rota--Baxter operators and relative averaging operators on racks and rack algebras. Also, if B is a Rota--Baxter or averaging operator on a rack X, then we can extend B by linearity to the rack algebra k[X]. On the other side, we have definitions of Rota--Baxter and averaging operators on arbitrary al

Pablo Geraldo Bastías

Causal inference with observational data critically relies on untestable and extra-statistical assumptions that have (sometimes) testable implications. Well-known sets of assumptions that are sufficient to justify the causal interpretation of certain estimators are called identification strategies. These templates for causal analysis, however, do not perfect

Matteo Priorelli, Ivilin Peev Stoianov

By dynamic planning, we refer to the ability of the human brain to infer and impose motor trajectories related to cognitive decisions. A recent paradigm, active inference, brings fundamental insights into the adaptation of biological organisms, constantly striving to minimize prediction errors to restrict themselves to life-compatible states. Over the past y

Marius Brusselmans, Luiz Max Carvalho, Samuel L. Hong, Jiansi Gao

Modern phylogenetics research is often performed within a Bayesian framework, using sampling algorithms such as Markov chain Monte Carlo (MCMC) to approximate the posterior distribution. These algorithms require careful evaluation of the quality of the generated samples. Within the field of phylogenetics, one frequently adopted diagnostic approach is to eval

Ju-Hyung Lee, Dong-Ho Lee, Joohan Lee, Jay Pujara

The burgeoning field of on-device AI communication, where devices exchange information directly through embedded foundation models, such as language models (LMs), requires robust, efficient, and generalizable communication frameworks. However, integrating these frameworks with existing wireless systems and effectively managing noise and bit errors pose signi

Francesco Ortu, Zhijing Jin, Diego Doimo, Mrinmaya Sachan

Interpretability research aims to bridge the gap between empirical success and our scientific understanding of the inner workings of large language models (LLMs). However, most existing research focuses on analyzing a single mechanism, such as how models copy or recall factual knowledge. In this work, we propose a formulation of competition of mechanisms, wh

Darioush Keivan, Xingang Guo, Peter Seiler, Geir Dullerud

In this paper, we revisit model-free policy search on an important robust control benchmark, namely $\mu$-synthesis. In the general output-feedback setting, there do not exist convex formulations for this problem, and hence global optimality guarantees are not expected. Apkarian (2011) presented a nonconvex nonsmooth policy optimization approach for this pro

Tesfay Zemuy Gebrekidan, Sebastian Stein, Timothy J. Norman

Recently, there has been an explosion of mobile applications that perform computationally intensive tasks such as video streaming, data mining, virtual reality, augmented reality, image processing, video processing, face recognition, and online gaming. However, user devices (UDs), such as tablets and smartphones, have a limited ability to perform the computa

Alberto Abadie, Anish Agarwal, Raaz Dwivedi, Abhin Shah

This article introduces a new estimator of average treatment effects under unobserved confounding in modern data-rich environments featuring large numbers of units and outcomes. The proposed estimator is doubly robust, combining outcome imputation, inverse probability weighting, and a novel cross-fitting procedure for matrix completion. We derive finite-samp

Renxi Wang, Haonan Li, Xudong Han, Yixuan Zhang

Large language models (LLMs) have achieved success in acting as agents, which interact with environments through tools such as search engines. However, LLMs are optimized for language generation instead of tool use during training or alignment, limiting their effectiveness as agents. To resolve this problem, previous work has first collected interaction traj

Guruprerana Shabadi, Nathanaël Fijalkow, Théo Matricon

The field of reinforcement learning (RL) is concerned with algorithms for learning optimal policies in unknown stochastic environments. Programmatic RL studies representations of policies as programs, meaning involving higher order constructs such as control loops. Despite attracting a lot of attention at the intersection of the machine learning and formal m

Ranit Dutta, Ayan Ghosh, Shinjan Mandal, K. Watanabe

Superconductivity (SC) in twisted bilayer graphene (tBLG) has been explored by varying carrier concentrations, twist angles, and screening strength, with the aim of uncovering its origin and possible connections to strong electronic correlations in narrow bands and various resulting broken symmetries. However, the link between the tBLG band structure and the

Katsuya Shigematsu, Hikaru Hoshino, Eiko Furutani

This paper discusses discretization methods for implementing nonlinear model predictive controllers using Iterative Linear Quadratic Regulator (ILQR). Finite-difference approximations are mostly used to derive a discrete-time state equation from the original continuous-time model. However, the timestep of the discretization is sometimes restricted to be smal

Charilaos Efthymiou

We present novel results for fast mixing of Glauber dynamics using the newly introduced and powerful Spectral Independence method from [Anari, Liu, Oveis-Gharan: FOCS 2020]. We mainly focus on the Hard-core model and the Ising model. We obtain bounds for fast mixing with the parameters expressed in terms of the spectral radius of the adjacency matrix, improv

Vishal Choudhury, Chinmay Khandekar, Ashwin K. Boddeti, Ali Jishi

Characterizing the optical response of magneto-optic and magnetic materials usually relies on semi-classical models (e.g. Lorentz oscillator model) involving few parameters or models based on a detailed quantum mechanical description of the underlying response. These models typically involve a few parameters that are estimated via fitting the experimental da

Yifan Zhou, Yan Shing Liang

Quantum Image Processing (QIP) is a field that aims to utilize the benefits of quantum computing for manipulating and analyzing images. However, QIP faces two challenges: the limitation of qubits and the presence of noise in a quantum machine. In this research, we propose a novel approach to address the issue of noise in QIP. By training and employing a mach

Ilia Pirashvili

Grothendieck's theory of fibred categories establishes an equivalence between fibred categories and pseudo functors. It plays a major role in algebraic geometry and categorical logic. This paper aims to show that fibrations are also very important in monoid theory. Among other things, we generalise Grothendieck's result slightly and show that there exists an

Jinghao Zhang, Yuting Liu, Qiang Liu, Shu Wu

Recently, the powerful large language models (LLMs) have been instrumental in propelling the progress of recommender systems (RS). However, while these systems have flourished, their susceptibility to security threats has been largely overlooked. In this work, we reveal that the introduction of LLMs into recommendation models presents new security vulnerabil

András Bátkai, Ingrid Gessner

Stereotype Vorstellungen von Mathematik und Mathematiker*innen beeinflussen das Interesse von Jugendlichen an MINT-F\"achern. Daher pl\"adiert dieser Beitrag daf\"ur, popul\"are Filme und erfolgreiche Serien nicht nur im Fremdsprachenunterricht, sondern auch im Mathematikunterricht einzusetzen. Durch die Analyse audiovisueller Medien im Unterricht k\"onnen v

Lanning Wei, Jun Gao, Huan Zhao, Quanming Yao

Graph-structured data are the commonly used and have wide application scenarios in the real world. For these diverse applications, the vast variety of learning tasks, graph domains, and complex graph learning procedures present challenges for human experts when designing versatile graph learning approaches. Facing these challenges, large language models (LLM

Matias D. Cattaneo, Rocio Titiunik

In his 2022 IMS Medallion Lecture delivered at the Joint Statistical Meetings, Prof. Dylan S. Small eloquently advocated for the use of protocols in observational studies. We discuss his proposal and, inspired by his ideas, we develop a protocol for the regression discontinuity design.

Liam Collins, Advait Parulekar, Aryan Mokhtari, Sujay Sanghavi

A striking property of transformers is their ability to perform in-context learning (ICL), a machine learning framework in which the learner is presented with a novel context during inference implicitly through some data, and tasked with making a prediction in that context. As such, that learner must adapt to the context without additional training. We explo

Yichen Wang, Shangbin Feng, Abe Bohan Hou, Xiao Pu

The widespread use of large language models (LLMs) is increasing the demand for methods that detect machine-generated text to prevent misuse. The goal of our study is to stress test the detectors' robustness to malicious attacks under realistic scenarios. We comprehensively study the robustness of popular machine-generated text detectors under attacks from d

Ming Yin, Yichang Xu, Minghong Fang, Neil Zhenqiang Gong

Federated recommendation is a prominent use case within federated learning, yet it remains susceptible to various attacks, from user to server-side vulnerabilities. Poisoning attacks are particularly notable among user-side attacks, as participants upload malicious model updates to deceive the global model, often intending to promote or demote specific targe

I. I. Denysiuk, I. A. Skurativska, I. V. Bielinskyi, O. M. Sizonenko

The research deals with the studies of the velocity fields of non-equilibrium fluid filtration in a layer under harmonic action on it and assessment of the influence of relaxation effects on the attenuation of the amplitude of the initial disturbance. A mathematical model of non-equilibrium plane-radial filtration with a generalized dynamic Darcy law in the

J. C. Greer

Accurate solution of the many-electron problem including correlations remains intractable except for few-electron systems. Describing interacting electrons as a superposition of independent electron configurations results in an apparent combinatorial scaling to achieve an accurate solution. Many approximate approaches for large systems have been introduced,

Yaroslav Zharov, Yury Khudyakov, Evgeniia Fedotova, Evgeny Grigorenko

Modern-day Integrated Development Environments (IDEs) have come a long way from the early text editing utilities to the complex programs encompassing thousands of functions to help developers. However, with the increasing number of efficiency-enhancing tools incorporated, IDEs gradually became sophisticated software with a steep learning curve. The rise of t

Vladimir A. Stoica, Tiannan Yang, Sujit Das, Yue Cao

Ultrafast stimuli can stabilize metastable states of matter inaccessible by equilibrium means. Establishing the spatiotemporal link between ultrafast excitation and metastability is crucial to understanding these phenomena. Here, we use single-shot optical-pump, X-ray-probe measurements to provide snapshots of the emergence of a persistent polar vortex super

Arian Askari, Roxana Petcu, Chuan Meng, Mohammad Aliannejadi

Identifying user intents in information-seeking dialogs is crucial for a system to meet user's information needs. Intent prediction (IP) is challenging and demands sufficient dialogs with human-labeled intents for training. However, manually annotating intents is resource-intensive. While large language models (LLMs) have been shown to be effective in genera

Xun Wang, Xin Xie, Cunqing Hua, Jianan Hong

Decision-directed channel estimation (DDCE) is one kind of blind channel estimation method that tracks the channel blindly by an iterative algorithm without relying on the pilots, which can increase the utilization of wireless resource. However, one major problem of DDCE is the performance degradation caused by error accumulation during the tracking process.

Federico Becattini, Lorenzo Berlincioni, Luca Cultrera, Alberto Del Bimbo

Neuromorphic sensors, also known as event cameras, are a class of imaging devices mimicking the function of biological visual systems. Unlike traditional frame-based cameras, which capture fixed images at discrete intervals, neuromorphic sensors continuously generate events that represent changes in light intensity or motion in the visual field with high tem

Emilio Calvanese Strinati, George C. Alexandropoulos, Navid Amani, Maurizio Crozzoli

This paper introduces the distributed and intelligent integrated sensing and communications (DISAC) concept, a transformative approach for 6G wireless networks that extends the emerging concept of integrated sensing and communications (ISAC). DISAC addresses the limitations of the existing ISAC models and, to overcome them, it introduces two novel foundation

Jie Jian, Jun Liao, Heguo Liu

Let $p$ be an odd prime and let $\mathcal{F}$ be a fusion system over a finite $p$-group $P$. A fusion system $\mathcal{F}$ is said to be nilpotent if $\mathcal{F}=\mathcal{F}_{P}(P)$. In this paper we provide new criteria for saturated fusion systems $\mathcal{F}$ to be nilpotent, which can be viewed as extension of the $p$-nilpotency theorem of Glauberman

Gleb Rodionov, Liudmila Prokhorenkova

Neural algorithmic reasoning aims to capture computations with neural networks by training models to imitate the execution of classical algorithms. While common architectures are expressive enough to contain the correct model in the weight space, current neural reasoners struggle to generalize well on out-of-distribution data. On the other hand, classical co

Federico Becattini, Xiaolin Chen, Andrea Puccia, Haokun Wen

Recommending fashion items often leverages rich user profiles and makes targeted suggestions based on past history and previous purchases. In this paper, we work under the assumption that no prior knowledge is given about a user. We propose to build a user profile on the fly by integrating user reactions as we recommend complementary items to compose an outf

Hankz Hankui Zhuo, Xin Chen, Rong Pan

Plan synthesis aims to generate a course of actions or policies to transit given initial states to goal states, provided domain models that could be designed by experts or learnt from training data or interactions with the world. Intrigued by the claims of emergent planning capabilities in large language models (LLMs), works have been proposed to investigate

Stefano Carnà, Serena Ferracci, Francesco Quaglia, Alessandro Pellegrini

We present a kernel-level infrastructure that allows system-wide detection of malicious applications attempting to exploit cache-based side-channel attacks to break the process confinement enforced by standard operating systems. This infrastructure relies on hardware performance counters to collect information at runtime from all applications running on the

Yujia Zhou, Zheng Liu, Jiajie Jin, Jian-Yun Nie

Retrieval-augmented generation have become central in natural language processing due to their efficacy in generating factual content. While traditional methods employ single-time retrieval, more recent approaches have shifted towards multi-time retrieval for multi-hop reasoning tasks. However, these strategies are bound by predefined reasoning steps, potent

Koren Lazar, Matan Vetzler, Guy Uziel, David Boaz

In the digital era, the widespread use of APIs is evident. However, scalable utilization of APIs poses a challenge due to structure divergence observed in online API documentation. This underscores the need for automatic tools to facilitate API consumption. A viable approach involves the conversion of documentation into an API Specification format. While pre

Tomer Shushi

In this short paper, we propose a new quantum effect that naturally emerges from describing the quantum particle as a classical fluid. Following the hydrodynamical formulation of quantum mechanics for a particle in a finite convex region, we show how the maximum values of the wavefunction's amplitude lie along the boundaries of the region when imposing a van

Zhixuan Rao, Jiawei Yang, Changkun Song, Mujie Rao

Combining resonant excitation with Purcell-enhanced single quantum dots (QDs) stands out as a prominent strategy for realizing high performance solid-state single photon sources. However, optimizing photon efficiency requires addressing challenges associated with effectively separating the excitation laser from QDs' emission. Traditionally, this involves pol

Junfei Wu, Qiang Liu, Ding Wang, Jinghao Zhang

Object hallucination has been an Achilles' heel which hinders the broader applications of large vision-language models (LVLMs). Object hallucination refers to the phenomenon that the LVLMs claim non-existent objects in the image. To mitigate the object hallucinations, instruction tuning and external model-based detection methods have been proposed, which eit

Zhong-Xue Zhang, James Jing Yu Zhao

Briggs conjectured that if a polynomial $a_0+a_1x+\cdots+a_nx^n$ with real coefficients has only negative zeros, then $$a^2_k(a^2_k - a_{k-1}a_{k+1}) > a^2_{k-1}(a^2_{k+1} - a_ka_{k+2})$$ for any $1\leq k\leq n-1$. The Boros-Moll sequence $\{d_i(m)\}_{i=0}^m$ arises in the study of evaluation of certain quartic integral, and a lot of interesting inequalities

Hyoyoon Lee, Junguk Lee

We study relativized Lascar groups, which are formed by relativizing Lascar groups to the solution set of a partial type $\Sigma$. We introduce the notion of a Lascar tuple for $\Sigma$ and by considering the space of types over a Lascar tuple for $\Sigma$, the topology for a relativized Lascar group is (re-)defined and some fundamental facts about the Galoi

Dao Thanh Hai, Isaac Woungang

In accommodating the continued explosive growth in Internet traffic, optical core networks have been evolving accordingly thanks to numerous technological and architectural innovations. From an architectural perspective, the adoption of optical-bypass networking in the last two decades has resulted in substantial cost savings, owning to the elimination of ma