February 2024 arXiv papers — page 84

Shaojie Zhang, Yinghui Wang, Bin Nan, Wei Li

To address the issue of increased triangulation uncertainty caused by selecting views with small camera baselines in Structure from Motion (SFM) view selection, this paper proposes a robust error-resistant view selection method. The method utilizes a triangulation-based computation to obtain an error-resistant model, which is then used to construct an error-

Jun Gao, Huan Zhao, Wei Wang, Changlong Yu

In this study, we present EventRL, a reinforcement learning approach developed to enhance event extraction for large language models (LLMs). EventRL utilizes outcome supervision with specific reward functions to tackle prevalent challenges in LLMs, such as instruction following and hallucination, manifested as the mismatch of event structure and the generati

Jing Huang, Xiangyu Chu, Xin Ma, Kwok Wai Samuel Au

In robotic deformable object manipulation (DOM) applications, constraints arise commonly from environments and task-specific requirements. Enabling DOM with constraints is therefore crucial for its deployment in practice. However, dealing with constraints turns out to be challenging due to many inherent factors such as inaccessible deformation models of defo

J. T. Xie, J. B. Wang, N. Wang, R. Manchester

The Parkes 20 cm Multibeam pulsar surveys have discovered nearly half of the known pulsars and revealed many distant pulsars with high dispersion measures. Using a sample of 1,301 pulsars from these surveys, we have explored the spatial distribution and birth rate of normal pulsars. The pulsar distances used to calculate the pulsar surface density are estima

Yao Shu, Jiongfeng Fang, Ying Tiffany He, Fei Richard Yu

First-order optimization (FOO) algorithms are pivotal in numerous computational domains such as machine learning and signal denoising. However, their application to complex tasks like neural network training often entails significant inefficiencies due to the need for many sequential iterations for convergence. In response, we introduce first-order optimizat

Lin Chen, Jiayi Lian, Yuchen Mao, Guochuan Zhang

We propose an $\widetilde{O}(n + 1/\eps)$-time FPTAS (Fully Polynomial-Time Approximation Scheme) for the classical Partition problem. This is the best possible (up to a polylogarithmic factor) assuming SETH (Strong Exponential Time Hypothesis) [Abboud, Bringmann, Hermelin, and Shabtay'22]. Prior to our work, the best known FPTAS for Partition runs in $\wide

Ruicheng Ao, Hongyu Chen, David Simchi-Levi, Feng Zhu

We consider the problem of online resource allocation with average budget constraints. At each time point the decision maker makes an irrevocable decision of whether to accept or reject a request before the next request arrives with the goal to maximize the cumulative rewards. In contrast to existing literature requiring the total resource consumption is bel

Yijie Wang, Mingjian Hong, Luwen Huangfu, Sheng Huang

In the realm of Zero-Shot Learning (ZSL), we address biases in Generalized Zero-Shot Learning (GZSL) models, which favor seen data. To counter this, we introduce an end-to-end generative GZSL framework called D$^3$GZSL. This framework respects seen and synthesized unseen data as in-distribution and out-of-distribution data, respectively, for a more balanced

Zihao Zhan, Yirui Yang, Haoqi Shan, Hanqiu Wang

Wireless charging is becoming an increasingly popular charging solution in portable electronic products for a more convenient and safer charging experience than conventional wired charging. However, our research identified new vulnerabilities in wireless charging systems, making them susceptible to intentional electromagnetic interference. These vulnerabilit

Peng Xing, Yinghui Li, Shirong Ma, Xinnian Liang

Chinese Spelling Correction (CSC) aims to detect and correct spelling errors in given sentences. Recently, multi-domain CSC has gradually attracted the attention of researchers because it is more practicable. In this paper, we focus on the key flaw of the CSC model when adapting to multi-domain scenarios: the tendency to forget previously acquired knowledge

Ming Xuan Chua, Yoshiro Okubo, Shuhua Peng, Thanh Nho Do

A common challenge in Bicep Curls rehabilitation is muscle compensation, where patients adopt alternative movement patterns when the primary muscle group cannot act due to injury or fatigue, significantly decreasing the effectiveness of rehabilitation efforts. The problem is exacerbated by the growing trend toward transitioning from in-clinic to home-based r

Yinghui Li, Shang Qin, Haojing Huang, Yangning Li

Recently, Large Language Models (LLMs) have been widely studied by researchers for their roles in various downstream NLP tasks. As a fundamental task in the NLP field, Chinese Grammatical Error Correction (CGEC) aims to correct all potential grammatical errors in the input sentences. Previous studies have shown that LLMs' performance as correctors on CGEC re

Xiaohu Liu, Kang Ma, Jian Liu, Wei Zhao

Magnetic-array-type current sensors have garnered increasing popularity owing to their notable advantages, including broadband functionality, a large dynamic range, cost-effectiveness, and compact dimensions. However, the susceptibility of the measurement error of one or more magnetic measurement units (MMUs) within the current sensor to drift significantly

Karol Kowalski, Nicholas P. Bauman, Guang Hao Low, Martin Roetteler

Theoretical descriptions of excited states of molecular systems in high-energy regimes are crucial for supporting and driving many experimental efforts at light source facilities. However, capturing their complicated correlation effects requires formalisms that provide a hierarchical infrastructure of approximations. These approximations lead to an increased

Yifan Yang, Jiajun Zhou, Ngai Wong, Zheng Zhang

Various parameter-efficient fine-tuning (PEFT) techniques have been proposed to enable computationally efficient fine-tuning while maintaining model performance. However, existing PEFT methods are still limited by the growing number of trainable parameters with the rapid deployment of Large Language Models (LLMs). To address this challenge, we present LoRETT

Gonzalo Contreras, José Antônio G. Miranda, Luiz Gustavo Perona

We study the topological entropy of the Lagrangian flow restricted to an energy level $E_{L}^{-1}(c) \subset TM$ for $ c >e_0(L)$. We prove that if the flow of the Tonelli Lagrangian $ L: M \to \mathbb{R}$, on a closed manifold of dimension $ n+1$, has a non-hyperbolic closed orbit or an infinite number of closed orbits with energy $ c>e_0(L)$ and satisfies

Haochen Wu, Xinting Zhu, Shuchang Li, Ying Zhou

Strategic Traffic Management Initiatives (TMIs) such as Ground Delay Programs (GDPs) play a crucial role in mitigating operational costs associated with demand-capacity imbalances. However, GDPs can only be planned (e.g., duration, delay assignments) with confidence if the future capacities at constrained resources (i.e., airports) are predictable. In realit

Yue Zhang, Jingxuan Zuo, Ke Su, Liqiang Jing

Multimodal summarization aims to generate a concise summary based on the input text and image. However, the existing methods potentially suffer from unfactual output. To evaluate the factuality of multimodal summarization models, we propose two fine-grained and explainable evaluation frameworks (FALLACIOUS) for different application scenarios, i.e. reference

James E. Gallagher, Aryav Gogia, Edward J. Oughton

Segment Anything Model (SAM) is drastically accelerating the speed and accuracy of automatically segmenting and labeling large Red-Green-Blue (RGB) imagery datasets. However, SAM is unable to segment and label images outside of the visible light spectrum, for example, for multispectral or hyperspectral imagery. Therefore, this paper outlines a method we call

S. Wucherer, R. McMurray, K. Y. Ng, F. Kerber

During the execution of handling processes in manufacturing, it is difficult to measure the process forces with state-of-the-art gripper systems since they usually lack integrated sensors. Thus, the exact state of the gripped object and the actuating process forces during manipulation and handling are unknown. This paper proposes a deep learning regression m

Yiyang Zhou, Chenhang Cui, Rafael Rafailov, Chelsea Finn

Instruction-following Vision Large Language Models (VLLMs) have achieved significant progress recently on a variety of tasks. These approaches merge strong pre-trained vision models and large language models (LLMs). Since these components are trained separately, the learned representations need to be aligned with joint training on additional image-language p

Eshwar Ram Arunachaleswaran, Natalie Collina, Aaron Roth, Mirah Shi

Blasiok et al. [2023] proposed distance to calibration as a natural measure of calibration error that unlike expected calibration error (ECE) is continuous. Recently, Qiao and Zheng [2024] gave a non-constructive argument establishing the existence of an online predictor that can obtain $O(\sqrt{T})$ distance to calibration in the adversarial setting, which

Zhichao Xu, Jiepu Jiang

Empathy is critical for effective and satisfactory conversational communication. Prior efforts to measure conversational empathy mostly focus on expressed communicative intents -- that is, the way empathy is expressed. Yet, these works ignore the fact that conversation is also a collaboration involving both speakers and listeners. In contrast, we propose a m

Haoyun Huang, Waseem Hussain, S. A. Myers, L. N. Pfeiffer

The composite fermion theory opened a new chapter in understanding many-body correlations through the formation of emergent particles. The formation of two-flux and four-flux composite fermions is well established. While there are limited data linked to the formation of six-flux composite fermions, topological protection associated with them is conspicuously

Yiqiang Li

We establish an embedding from the Hecke algebra associated with the edge contraction of a Coxeter system along an edge to the Hecke algebra associated with the original Coxeter system.

Martin Mellado, Eduardo Vendrell, Filomena Ferrucci, Andrea Abate

This paper presents the design, objectives, experiences, and results of an international cooperation project funded by the European Commission in the context of the Erasmus Intensive Programme (IP, for short) designed to improve students' curricula. An IP is a short programme of study (minimum 2 weeks) that brings together university students and staff from

Min Zhang, Jianfeng He, Taoran Ji, Chang-Tien Lu

The fairness and trustworthiness of Large Language Models (LLMs) are receiving increasing attention. Implicit hate speech, which employs indirect language to convey hateful intentions, occupies a significant portion of practice. However, the extent to which LLMs effectively address this issue remains insufficiently examined. This paper delves into the capabi

Jan Philipp Gabriel, Robin Horstmann, Martin Tress

The supra-molecular structure of a liquid is strongly connected to its dynamics which in turn controls macroscopic properties such as viscosity. Consequently, detailed knowledge about how this structure changes with temperature is essential to understand the thermal evolution of the dynamics ranging from the liquid to the glass. Here we combine infrared spec

Anna Naszodi

Measuring changes in overall inequality between different educational groups is often performed by quantifying variations in educational marital homophily across consecutive generations. However, this task becomes challenging when the education level of marriageable individuals is generation-specific. To address this challenge, various indicators have been p

Ademide O. Mabadeje, Michael J. Pyrcz

High-dimensional datasets present substantial challenges in statistical modeling across various disciplines, necessitating effective dimensionality reduction methods. Deep learning approaches, notable for their capacity to distill essential features from complex data, facilitate modeling, visualization, and compression through reduced dimensionality latent f

Liying Han, Mani B. Srivastava

Robots and autonomous systems require an understanding of complex events (CEs) from sensor data to interact with their environments and humans effectively. Traditional end-to-end neural architectures, despite processing sensor data efficiently, struggle with long-duration events due to limited context sizes and reasoning capabilities. Recent advances in neur

Daniel Han-Kwan, Toan T. Nguyen, Frédéric Rousset

In this work, we consider the relativistic Vlasov-Maxwell system, linearized around a spatially homogeneous equilibrium, set in the whole space $\mathbb{R}^3 \times \mathbb{R}^3$. The equilibrium is assumed to belong to a class of radial, smooth, rapidly decaying functions. Under appropriate conditions on the initial data, we prove algebraic decay (of disper

Ayan Banerjee, Sanket Biswas, Josep Lladós, Umapada Pal

Object detection in documents is a key step to automate the structural elements identification process in a digital or scanned document through understanding the hierarchical structure and relationships between different elements. Large and complex models, while achieving high accuracy, can be computationally expensive and memory-intensive, making them impra

Niyousha Hosseinichimeh, Aritra Majumdar, Ross Williams, Navid Ghaffarzadegan

We introduce and test the System Dynamics Bot, a computer program leveraging a large language model to automate the creation of causal loop diagrams from textual data. To evaluate its performance, we ensembled two distinct databases. The first dataset includes 20 causal loop diagrams and associated texts sourced from the system dynamics literature. The secon

Abe Bohan Hou, Jingyu Zhang, Yichen Wang, Daniel Khashabi

Recent watermarked generation algorithms inject detectable signatures during language generation to facilitate post-hoc detection. While token-level watermarks are vulnerable to paraphrase attacks, SemStamp (Hou et al., 2023) applies watermark on the semantic representation of sentences and demonstrates promising robustness. SemStamp employs locality-sensiti

Shaochen Xu, Zihao Wu, Huaqin Zhao, Peng Shu

In this study, we leverage LLM to enhance the semantic analysis and develop similarity metrics for texts, addressing the limitations of traditional unsupervised NLP metrics like ROUGE and BLEU. We develop a framework where LLMs such as GPT-4 are employed for zero-shot text identification and label generation for radiology reports, where the labels are then u

Gianluca Fabiani

We investigate the concept of Best Approximation for Feedforward Neural Networks (FNN) and explore their convergence properties through the lens of Random Projection (RPNNs). RPNNs have predetermined and fixed, once and for all, internal weights and biases, offering computational efficiency. We demonstrate that there exists a choice of external weights, for

Jiayi Liu, Tinghan Yang, Jennifer Neville

Large language models (LLMs) have become pivotal in recent research. However, during the inference process, LLMs still require substantial resources. In this paper, we propose CliqueParcel, a method designed to improve the efficiency of LLMs via prompt batching. Existing strategies to optimize inference efficiency often compromise on output quality, leading

Thomas E. Portegys

This is an examination of some methods that learn causations in event sequences. A causation is defined as a conjunction of one or more cause events occurring in an arbitrary order, with possible intervening non-causal events, that lead to an effect. The methods include recurrent and non-recurrent artificial neural networks (ANNs), as well as a histogram-bas

Pietro Caputo, Cyril Labbé, Hubert Lacoin

We investigate a quadratic dynamical system known as nonlinear recombinations. This system models the evolution of a probability measure over the Boolean cube, converging to the stationary state obtained as the product of the initial marginals. Our main result reveals a cutoff phenomenon for the total variation distance in both discrete and continuous time.

Robert A. McCutcheon, Stefan Ostermann, Susanne F. Yelin

We present a model describing the transmission of light through atomic media with a vanishing index of refraction. Zero index materials are of particular interest as the infinite phase velocity of light within the material offers the potential to manipulate electromagnetic waves to mediate dipole-dipole interactions over extended distances. We focus on the p

Demian Pouzo

This paper provides a bound for the supremum of sample averages over a class of functions for a general class of mixing stochastic processes with arbitrary mixing rates. Regardless of the speed of mixing, the bound is comprised of a concentration rate and a novel measure of complexity. The speed of mixing, however, affects the former quantity implying a phas

Anand V Lalwani, Abel John, Satish Shetty, Miriam Giparakis

This follow-on work investigates the effect of temperature on the frequency limit of 2-dimensional electron gas (2DEG) Hall-effect sensors.

Ashley P. Saunders, Victoria Chen, Jierong Wang, Amalya C. Johnson

Confinement of monolayers into quasi-one-dimensional atomically-thin nanoribbons could lead to novel quantum phenomena beyond those achieved in their bulk and monolayer counterparts. However, current experimental availability of nanoribbon species beyond graphene has been limited to bottom-up synthesis or top-down patterning. In this study, we introduce a ve

Pedro M. M. da Silveira, José F. Fontanari

Synchronization is one of the most striking instances of collective behavior, occurring in many natural phenomena. For example, in some ant species, ants are inactive within the nest most of the time, but their bursts of activity are highly synchronized and involve the entire nest population. Here we revisit a simulation model that generates this synchronize

Jinho Choi

In this paper, we propose an over-the-air (OTA)-based approach for distributed matrix-vector multiplications in the context of distributed machine learning (DML). Thanks to OTA computation, the column-wise partitioning of a large matrix enables efficient workload distribution among workers (i.e., local computing nodes) based on their computing capabilities.

Haochen Wu, Kevin R. Sun, Jackson A. Miller, Oliver Jia-Richards

The burgeoning commercial space transportation industry necessitates an expansion of launch infrastructure to meet rising demands. However, future operations from these large-scale infrastructures can result in new impacts, particularly to air traffic operations. To rigorously reason about where such future spaceports might be located and what their impacts

Friedrich Martin Schneider, Sławomir Solecki

We give new examples of topological groups that do not have non-trivial continuous unitary representations, the so-called exotic groups. We prove that all groups of the form $L^0(\phi, G)$, where $\phi$ is a pathological submeasure and $G$ is a topological group, are exotic. This result extends, with a different proof, a theorem of Herer and Christensen on e

Calum Buchanan, Puck Rombach

The saturation number $\operatorname{sat}(n, H)$ of a graph $H$ and positive integer $n$ is the minimum size of a graph of order $n$ which does not contain a subgraph isomorphic to $H$ but to which the addition of any edge creates such a subgraph. Erd\H{o}s, Hajnal, and Moon first studied saturation numbers of complete graphs, and Cameron and Puleo introduce

C. S. Kim, M. V. N. Murthy, Dibyakrupa Sahoo

In a recent article arXiv:2402.05172 [hep-ph], the authors discuss the question "whether quantum statistics can help distinguish between Dirac and Majorana neutrinos." The paper contains, among other things, an unsubstantiated critique of the results derived in our papers arXiv:2106.11785 [hep-ph] and arXiv:2307.05654 [hep-ph]. One of the criticisms is relat

M. Maneyro, E. G. S. Luna, M. Peláez

We study the high-energy behavior of the elastic scattering amplitude using two distinct unitarization schemes: the eikonal and the $U$-matrix. Our analysis begins with a formalism involving solely Pomerons, incorporating pion-loop insertions in the Pomeron trajectory representing the nearest singularity generated by $t$-channel unitarity. Subsequently, we e

Daniel Soler, Oscar Mariño, David Huergo, Martín de Frutos

We propose a reinforcement learning strategy to control wind turbine energy generation by actively changing the rotor speed, the rotor yaw angle and the blade pitch angle. A double deep Q-learning with a prioritized experience replay agent is coupled with a blade element momentum model and is trained to allow control for changing winds. The agent is trained

Huiju Lee, Yi Xia

Phonons, as quantized vibrational modes in crystalline materials, play a crucial role in determining a wide range of physical properties, such as thermal and electrical conductivity, making their study a cornerstone in materials science. In this study, we present a simple yet effective strategy for deep learning harmonic phonons in crystalline solids by leve

Atefeh Mohseni Ejiyeh

This paper confronts the pressing challenges of sixth-generation (6G) wireless communication networks by harnessing the unique capabilities of Unmanned Aerial Vehicles (UAVs). With the ambitious promises of 6G, including ultra-reliable 1 Tbps data delivery and ultra-low latency, the demand for innovative solutions becomes imperative. Traditional terrestrial

Anna Coleman, Gabrielle Fischberg, Charles Gong, Joshua Harrington

A paired $k$-to-$k$ disjoint path cover of a graph $G$ is a collection of pairwise disjoint path subgraphs $P_1,P_2,\dotsc,P_k$ such that each $P_i$ has prescribed vertices $s_i$ and $t_i$ as endpoints and the union of $P_1,P_2,\dotsc,P_k$ contains all vertices of $G$. In this paper, we introduce bipartite transposition-like graphs, which are inductively con

Sachin D. Dias, Simon Vaughan, Mehdy Lefkir, Graham Wynn

We analyse the X-ray spectrum of the black hole X-ray binary MAXI J1820+070 using observations from XMM-Newton and NuSTAR during 'hard' states of its 2018-2019 outburst. We take a fully Bayesian approach, and this is one of the first papers to present a fully Bayesian workflow for the analysis of an X-ray binary X-ray spectrum. This allows us to leverage the

Man Chon Iao, Yatheesan J. Selvakumar

We propose an indirect inference strategy for estimating heterogeneous-agent business cycle models with micro data. At its heart is a first-order vector autoregression that is grounded in linear filtering theory as the cross-section grows large. The result is a fast, simple and robust algorithm for computing an approximate likelihood that can be easily paire

Mads Greisen Højlund, Ove Christiansen

We propose a new formulation of time-dependent coupled cluster with adaptive basis functions and division of the one-particle space into active and secondary subspaces. The formalism is fully bivariational in the sense of a real-valued time-dependent bivariational principle and converges to the complete-active-space solution, a property that is obtained by t

Massimiliano Berti, Roberto Feola, Michela Procesi, Shulamit Terracina

We prove that all the solutions of a quasi-periodically forced linear Klein-Gordon equation $\psi_{tt}-\psi_{xx}+\mathtt{m}\psi+Q(\omega t)\psi=0 $ where $ Q(\omega t) := a^{(2)}(\omega t, x) \partial_{xx} + a^{(1)}(\omega t, x)\partial_x + a^{(0)}(\omega t, x) $ is a differential operator of order $ 2 $, parity preserving and reversible, are almost periodic

J. François, L. Ravera

We make a case for the unique relevance of Cartan geometry for gauge theories of gravity and supergravity. We introduce our discussion by recapitulating historical threads, providing motivations. In a first part we review the geometry of classical gauge theory, as a background for understanding gauge theories of gravity in terms of Cartan geometry. The secon

Nagaraj Nandihalli

The unprecedented demand for sophisticated, self-powered, compact, ultrafast, cost-effective, and broadband light sensors for a myriad of applications has spurred a lot of research, precipitating in a slew of studies over the last decade. Apart from the photosensing ability of an active element in the light sensor, the device architecture is crucial in terms

Igor R. S. Brito, Alessandro V. M. Oliveira

In this study, we will address some topics related to the privatization of airports in the scientific literature, in an attempt to provide an answer to the following question: does the privatization of airports bring positive results? Firstly, we turn our attention to the motivations leading to privatization, considering the two main parties involved, the go

Bruno F. Oliveira, Alessandro V. M. Oliveira

This work focuses on trying to understand how the construction of an airline's network is made. For this purpose, the case of Azul was studied, investigating which and how factors affect the decision of this airline to enter domestic routes, in addition to analyzing how the merger of Azul with the regional airline Trip affected the company's network planning

Carolina B. Resende, Alessandro V. M. Oliveira

In recent years, the topic of global warming and greenhouse gas emissions has been increasingly in the media. This theme has raised various flags, showing concern for the future and seeking alternatives for a more sustainable life over the years. When studying greenhouse gas emissions, one of the main emitters of such gases is the burning of fuels, in which

Murillo Massaretto, Alessandro V. M. Oliveira

The exploration of existing commercial opportunities has been increasing the share of commercial (non-aeronautical) revenues in the total revenues of airports worldwide. These revenues, also called commercial, non-aeronautical, or non-tariff revenues, come from rentals, duty-free shops, food and beverage sales, parking, advertising, etc. In other words, ever

Rodolfo R. Narcizo, Alessandro V. M. Oliveira

This study addresses the growing standardization of airline fleets, highlighting that frequent passengers are more likely to fly on the same aircraft model more often. The objective is to analyze the fleet management of airlines and the impact of a reduced variety of models on company operations. The benefits of standardization, such as operational efficienc

Ana B. R. Eufrásio, Alessandro V. M. Oliveira

This study explores the approaches used by airlines in setting flight times. It highlights the need to balance operational and strategic factors, such as optimizing the use of resources - including aircraft, crew, and fuel - and managing the risks related to delays and congestion. The work details a national analysis focused on domestic flights, investigatin

João B. T. Szenczuk, Alessandro V. M. Oliveira

This work presents an analysis of recent literature examining factors that influence flight times in Brazil, with special attention to the impact of new technology implementations, specifically Performance-Based Navigation (PBN). PBN procedures began to be implemented in Brazilian airspace in 2009 and represent a new concept of air navigation, using satellit

William E. Bendinelli, Alessandro V. M. Oliveira

Flight delays are a reality in the modern air industry worldwide. However, studies in the literature have investigated the competitive determinants of delays arising from factors originating at the airport and along the route separately. This work aims to present a national study that used a unifying approach from the literature, considering the local and gl

Bruno F. Oliveira, Alessandro V. M. Oliveira

This study aims to discuss the impacts of a low-cost airline on the air transport market and, especially, to present the most recent findings from specialized literature in the field. To this end, various works on this topic, published since 2015, were selected and analyzed. From this analysis, it was possible to categorize the main topics discussed in the p

Renan P. de Oliveira, Alessandro V. M. Oliveira

This study discusses the literature on the convergence of business models of airlines in Brazilian air transport, focusing on the formation of flight networks. Initially, it analyzes the determinants of the network formation patterns of the "fundamental" business models (archetypes) of airlines in the first years after the sector's deregulation. Then, it dis

Shinpei Nakamura-Sakai, Laura Forastiere, Brian Macdonald

In the realm of competitive sports, understanding the performance dynamics of athletes, represented by the age curve (showing progression, peak, and decline), is vital. Our research introduces a novel framework for quantifying age-specific treatment effects, enhancing the granularity of performance trajectory analysis. Firstly, we propose a methodology for e

Sara Fish, Yannai A. Gonczarowski, Sergiu Hart

We study a matching problem between agents and public goods, in settings without monetary transfers. Since goods are public, they have no capacity constraints. There is no exogenously defined budget of goods to be provided. Rather, each provided good must justify its cost by being utilized by sufficiently many agents, leading to strong complementarities in t

Noureddine Snanou

Let $G_{2}$ be a group which acts trivially on an abelian group $G_{1}$. As is well known, each perturbed direct product of $G_{1}$ and $G_{2}$ under a 2-cocycle $\varepsilon\in Z^{2}(G_{2},G_{1})$ determines a central extension of $G_{1}$ by $G_{2}$. The purpose of this paper is to study perturbed direct products of groups and to decide in some cases how th

Yaniv Cohen, Tomer Gafni, Ronen Greenberg, Kobi Cohen

We consider the problem of dynamic channel allocation (DCA) in cognitive communication networks with the goal of maximizing a global signal-to-interference-plus-noise ratio (SINR) measure under a specified target quality of service (QoS)-SINR for each network. The shared bandwidth is partitioned into K channels with frequency separation. In contrast to the m

Anne Dranowski, Meng Guo, Aaron Lauda, Andrew Manion

Leveraging skew Howe duality, we show that Lawson-Lipshitz-Sarkar's spectrification of Khovanov's arc algebra gives rise to 2-representations of categorified quantum groups over $\mathbb{F}_2$ that we call spectral 2-representations. These spectral 2-representations take values in the homotopy category of spectral bimodules over spectral categories. We view

Neta Glazer, Aviv Navon, Aviv Shamsian, Ethan Fetaya

One of the challenges in applying reinforcement learning in a complex real-world environment lies in providing the agent with a sufficiently detailed reward function. Any misalignment between the reward and the desired behavior can result in unwanted outcomes. This may lead to issues like "reward hacking" where the agent maximizes rewards by unintended behav

Avy Soffer, Gavin Stewart

We study the large-time behavior of global energy class ($H^1$) solutions of the one-dimensional nonlinear Schr\"odinger equation with a general localized potential term and a defocusing nonlinear term. By using a new type of interaction Morawetz estimate localized to an exterior region, we prove that these solutions decompose into a free wave and a weakly l

Mile Mitrovic

The thesis focuses on developing a data-driven algorithm, based on machine learning, to solve the stochastic alternating current (AC) chance-constrained (CC) Optimal Power Flow (OPF) problem. Although the AC CC-OPF problem has been successful in academic circles, it is highly nonlinear and computationally demanding, which limits its practical impact. The pro

Auste Simkute, Lev Tankelevitch, Viktor Kewenig, Ava Elizabeth Scott

Generative AI (GenAI) systems offer opportunities to increase user productivity in many tasks, such as programming and writing. However, while they boost productivity in some studies, many others show that users are working ineffectively with GenAI systems and losing productivity. Despite the apparent novelty of these usability challenges, these 'ironies of

Shiva Ebrahimi, Xuan Guo

Tandem mass spectrometry (MS/MS) stands as the predominant high-throughput technique for comprehensively analyzing protein content within biological samples. This methodology is a cornerstone driving the advancement of proteomics. In recent years, substantial strides have been made in Data-Independent Acquisition (DIA) strategies, facilitating impartial and

Mihaela Cătălina Stoian, Eleonora Giunchiglia, Thomas Lukasiewicz

Deep learning has been at the core of the autonomous driving field development, due to the neural networks' success in finding patterns in raw data and turning them into accurate predictions. Moreover, recent neuro-symbolic works have shown that incorporating the available background knowledge about the problem at hand in the loss function via t-norms can fu

Vahid Badrkhani, Marco F. P. ten Eikelder, Rene R. Hiemstra, Dominik Schillinger

The macro-element variant of the hybridized discontinuous Galerkin (HDG) method combines advantages of continuous and discontinuous finite element discretization. In this paper, we investigate the performance of the macro-element HDG method for the analysis of compressible flow problems at moderate Reynolds numbers. To efficiently handle the corresponding la

C. Aparajit, Anandam Choudhary, Ankit Dulat, Mickael Grech

Extreme intensity contrast is considered essential for ultraintense, femtosecond laser excitation of solid targets, in particular for studies with structured or ultra-thin targets. Second-harmonic generation has been used to maximize the contrast in the nanosecond and picosecond timescales but the resulting pulses can have intense broad femtosecond structure

Shaokun Zhang, Jieyu Zhang, Jiale Liu, Linxin Song

Researchers and practitioners have recently reframed powerful Large Language Models (LLMs) as agents, enabling them to automate complex tasks largely via the use of specialized functions. To facilitate the development of LLM agents, we present a novel paradigm of training LLM agents without modifying the LLM weights, which is particularly useful when the LLM

Subhajit Paul, Abhishek Dhar, Debasish Chaudhuri

We explore the dynamics of a tracer in an active particle harmonic chain, investigating the influence of interactions. Our analysis involves calculating mean-squared displacements (MSD) and space-time correlations through Green's function techniques and numerical simulations. Depending on chain characteristics, i.e., different time scales determined by inter

Anna-Christina Samaha, Jacques Doumani, T. Elijah Kritzell, Hongjing Xu

Graphene-based terahertz (THz) devices have emerged as promising platforms for a variety of applications, leveraging graphene's unique optoelectronic properties. This review explores recent advancements in utilizing graphene in THz technology, focusing on two main aspects: THz molecular sensing and THz wave modulation. In molecular sensing, the environment-s

Tobias Kallehauge, Anders E. Kalør, Fengchun Zhang, Petar Popovski

This paper presents an experimental validation for prediction of rare fading events using channel distribution information (CDI) maps that predict channel statistics from measurements acquired at surrounding locations using spatial interpolation. Using experimental channel measurements from 127 locations, we demonstrate the use case of providing statistical

Matan Avitan, Ryan Cotterell, Yoav Goldberg, Shauli Ravfogel

Interventions targeting the representation space of language models (LMs) have emerged as an effective means to influence model behavior. Such methods are employed, for example, to eliminate or alter the encoding of demographic information such as gender within the model's representations and, in so doing, create a counterfactual representation. However, bec

Kejing Lu, Chuan Xiao, Yoshiharu Ishikawa

Approximate nearest neighbor search (ANNS) in high-dimensional spaces is a pivotal challenge in the field of machine learning. In recent years, graph-based methods have emerged as the superior approach to ANNS, establishing a new state of the art. Although various optimizations for graph-based ANNS have been introduced, they predominantly rely on heuristic m

Eunkyung Jo, Yuin Jeong, SoHyun Park, Daniel A. Epstein

Recent large language models (LLMs) offer the potential to support public health monitoring by facilitating health disclosure through open-ended conversations but rarely preserve the knowledge gained about individuals across repeated interactions. Augmenting LLMs with long-term memory (LTM) presents an opportunity to improve engagement and self-disclosure, b

Md. Alamin Talukder, Selina Sharmin, Md Ashraf Uddin, Md Manowarul Islam

Wireless Sensor Networks (WSNs) play a pivotal role as infrastructures, encompassing both stationary and mobile sensors. These sensors self-organize and establish multi-hop connections for communication, collectively sensing, gathering, processing, and transmitting data about their surroundings. Despite their significance, WSNs face rapid and detrimental att

Himani Verma, Kamal Singh, Ranjan K. Mallik

Terrestrial free-space optical (FSO) communication systems, while designed to operate on large unlicensed optical bandwidths, are fundamentally power-constrained due to stringent eye-safety regulations. Moreover, channel fluctuations inherent to terrestrial FSO links further reduce the received optical power. Consequently, the achievable signal-to-noiseratio

Matthew R. DeVerna, Francesco Pierri, Yong-Yeol Ahn, Santo Fortunato

Understanding how misinformation affects the spread of disease is crucial for public health, especially given recent research indicating that misinformation can increase vaccine hesitancy and discourage vaccine uptake. However, it is difficult to investigate the interaction between misinformation and epidemic outcomes due to the dearth of data-informed holis

MohammadJavad Kazemi, Ghadir Jafari

Relaxing the postulates of an axiomatic theory is a natural way to find more general theories, and historically, the discovery of non-Euclidean geometry is a famous example of this procedure. Here, we use this way to extend quantum mechanics by ignoring the heart of Heisenberg's quantum mechanics -- We do not assume the existence of a position operator that

Bruce W. Lee, JaeHyuk Lim

We argue that language-only models don't learn the physical manifestation of language. We present an empirical investigation of visual-auditory properties of language through a series of tasks, termed H-Test. These tasks highlight a fundamental gap between human linguistic understanding and the sensory-deprived linguistic understanding of LLMs. In support of

Ayan Banerjee, Sudan Hansraj, Anirudh Pradhan, Abdelghani Errehymy

In the standard approach to studying wormhole geometry, the presence of dark energy is unavoidable to ensure traversability. The dark energy provides the negative gravity effect to keep the throat open. The question we analyse is whether the same can be achieved without dark energy. It turns out that if we couple the trace of energy-momentum with the standar

Wendi Cui, Zhuohang Li, Hao Sun, Damien Lopez

Designing optimal prompts for Large Language Models (LLMs) is a complicated and resource-intensive task, often requiring substantial human expertise and effort. Existing approaches typically separate the optimization of prompt instructions and in-context learning examples, leading to incohesive prompts that are defined and represented by suboptimal task perf

Fatih E. Bilgen, Ahmet B. Kilic, Ozgur B. Akan

Molecular communication (MC) has promising potential and a wide range of applications. However, odor-based communication which is common in nature, has not been sufficiently examined within the context of MC, yet. In this paper, we introduce a novel approach for implementing odor-based MC systems. We propose a new modulation scheme called Odor Perceptual Shi

Nuojin Cheng, Stephen Becker

Bayesian optimization is a widely used technique for optimizing black-box functions, with Expected Improvement (EI) being the most commonly utilized acquisition function in this domain. While EI is often viewed as distinct from other information-theoretic acquisition functions, such as entropy search (ES) and max-value entropy search (MES), our work reveals