Tootfinder

Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification
Tarik Crnovrsanin, Calvin Yu, Dane Hankamer, Cody Dunne
https://arxiv.org/abs/2403.00565

Predicting UAV Type: An Exploration of Sampling and Data Augmentation for Time Series Classification
Unmanned aerial vehicles are becoming common and have many productive uses. However, their increased prevalence raises safety concerns -- how can we protect restricted airspace? Knowing the type of unmanned aerial vehicle can go a long way in determining any potential risks it carries. For instance, fixed-wing craft can carry more weight over longer distances, thus potentially posing a more significant threat. This paper presents a machine learning model for classifying unmanned aerial vehicles…

An All-Optical General-Purpose CPU and Optical Computer Architecture
Michael Kissner, Leonardo Del Bino, Felix P\"asler, Peter Caruana, George Ghalanos
https://arxiv.org/abs/2403.00045

An All-Optical General-Purpose CPU and Optical Computer Architecture
Energy efficiency of electronic digital processors is primarily limited by the energy consumption of electronic communication and interconnects. The industry is almost unanimously pushing towards replacing both long-haul, as well as local chip interconnects, using optics to drastically increase efficiency. In this paper, we explore what comes after the successful migration to optical interconnects, as with this inefficiency solved, the main source of energy consumption will be electronic digita…

"People come people go / While we here / The object is to grow I don't know how long I'll go on / All I know I'm gonna share my soul" #SareemPoems #Newselph 🎧

Dynamic Deterministic Constant-Approximate Distance Oracles with $n^{\epsilon}$ Worst-Case Update Time
Bernhard Haeupler, Yaowei Long, Thatchaphol Saranurak
https://arxiv.org/abs/2402.18541

Dynamic Deterministic Constant-Approximate Distance Oracles with $n^ε$ Worst-Case Update Time
We present a new distance oracle in the fully dynamic setting: given a weighted undirected graph $G=(V,E)$ with $n$ vertices undergoing both edge insertions and deletions, and an arbitrary parameter $ε$ where $1/\log^{c} n<ε<1$ and $c>0$ is a small constant, we can deterministically maintain a data structure with $n^ε$ worst-case update time that, given any pair of vertices $(u,v)$, returns a $2^{{\rm poly}(1/ε)}$-approximate distance between $u$ and $v$ in ${\rm poly}(1/ε)\log\log n$ quer…

Betting on what is neither verifiable nor falsifiable
Abhimanyu Pallavi Sudhir, Long Tran-Thanh
https://arxiv.org/abs/2402.14021 https://

Betting on what is neither verifiable nor falsifiable
Prediction markets are useful for estimating probabilities of claims whose truth will be revealed at some fixed time -- this includes questions about the values of real-world events (i.e. statistical uncertainty), and questions about the values of primitive recursive functions (i.e. logical or algorithmic uncertainty). However, they cannot be directly applied to questions without a fixed resolution criterion, and real-world applications of prediction markets to such questions often amount to pr…

Numerical Schemes for 3-Wave Kinetic Equations: A Complete Treatment of the Collision Operator
Steven Walton, Minh-Binh Tran
https://arxiv.org/abs/2402.17481

Numerical Schemes for 3-Wave Kinetic Equations: A Complete Treatment of the Collision Operator
In our previous work, numerical schemes for a simplified version of 3-wave kinetic equations, in which only the simple forward-cascade terms of the collision operators are kept, have been successfully designed, especially to capture the long time dynamics of the equation given the multiple blow-up time phenomenon. In this second work in the series, we propose numerical treatments for the complete 3-wave kinetic equations, in which the complete, much more complicated collision operators are full…

Large Language Model Interaction Simulator for Cold-Start Item Recommendation
Feiran Huang, Zhenghang Yang, Junyi Jiang, Yuanchen Bei, Yijie Zhang, Hao Chen
https://arxiv.org/abs/2402.09176

Large Language Model Interaction Simulator for Cold-Start Item Recommendation
Recommending cold items is a long-standing challenge for collaborative filtering models because these cold items lack historical user interactions to model their collaborative features. The gap between the content of cold items and their behavior patterns makes it difficult to generate accurate behavioral embeddings for cold items. Existing cold-start models use mapping functions to generate fake behavioral embeddings based on the content feature of cold items. However, these generated embeddin…

Predicting the Slowing of Stellar Differential Rotation by Instability-Driven Turbulence
B. Tripathi, A. J. Barker, A. E. Fraser, P. W. Terry, E. G. Zweibel
https://arxiv.org/abs/2403.07395

Predicting the Slowing of Stellar Differential Rotation by Instability-Driven Turbulence
Differentially rotating stars and planets transport angular momentum internally due to turbulence at rates that have long been a challenge to predict reliably. We develop a self-consistent saturation theory, using a statistical closure approximation, for hydrodynamic turbulence driven by the axisymmetric Goldreich--Schubert--Fricke (GSF) instability at the stellar equator with radial differential rotation. This instability arises when fast thermal diffusion eliminates the stabilizing effects of…

Fractional regularity, global persistence, and asymptotic properties of the Boussinesq equations on bounded domains
Mustafa Sencer Ayd{\i}n, Pranava Jayanti
https://arxiv.org/abs/2403.12509

Fractional regularity, global persistence, and asymptotic properties of the Boussinesq equations on bounded domains
We address the long-time behavior of the 2D Boussinesq system, which consists of the incompressible Navier-Stokes equations driven by a non-diffusive density. We construct globally persistent solutions on a smooth bounded domain, when the initial data belongs to $(H^k\cap V)\times H^k$ for $k\in\mathbb{N}$ and $H^s\times H^s$ for $0

Quantum geometric tensor and the topological characterization of the extended Su-Schrieffer-Heeger model
Xiang-Long Zeng, Wen-Xi Lai, Yi-Wen Wei, Yu-Quan Ma
https://arxiv.org/abs/2404.08222

Quantum geometric tensor and the topological characterization of the extended Su-Schrieffer-Heeger model
We investigate the quantum metric and topological Euler number in a cyclically modulated Su-Schrieffer-Heeger (SSH) model with long-range hopping terms. By computing the quantum geometry tensor, we derive exactly expressions for the quantum metric and Berry curvature of the energy band electrons, and we obtain the phase diagram of the model marked by the first Chern number. Furthermore, we also obtain the topological Euler number of the energy band based on the Gauss-Bonnet theorem on the topol…

Femtosecond dynamics on the nanoscale of intense laser-induced grating plasma
Ankit Dulat, Sagar Dam, Sk Rakeeb, Amit D. Lad, Yash M. Ved, G. Ravindra Kumar
https://arxiv.org/abs/2403.10908

Femtosecond dynamics on the nanoscale of intense laser-induced grating plasma
The complex interaction dynamics of intense femtosecond (fs) pulses and their picosecond (ps)-long leading edge with nanostructured solids occur at both the nanometer and the femtosecond scales, making them extremely difficult to measure directly. Here, we present pump-probe-based measurements that capture the ultrafast evolution of relativistically intense laser-driven grating plasma on fs time and nanometer spatial scales. We measure the transient reflectivity and spectrum of the scattered or…

Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting
Chu Myaet Thwal, Ye Lin Tun, Kitae Kim, Seong-Bae Park, Choong Seon Hong
https://arxiv.org/abs/2402.06638

Transformers with Attentive Federated Aggregation for Time Series Stock Forecasting
Recent innovations in transformers have shown their superior performance in natural language processing (NLP) and computer vision (CV). The ability to capture long-range dependencies and interactions in sequential data has also triggered a great interest in time series modeling, leading to the widespread use of transformers in many time series applications. However, being the most common and crucial application, the adaptation of transformers to time series forecasting has remained limited, wit…

Predicting the Slowing of Stellar Differential Rotation by Instability-Driven Turbulence
B. Tripathi, A. J. Barker, A. E. Fraser, P. W. Terry, E. G. Zweibel
https://arxiv.org/abs/2403.07395

Predicting the Slowing of Stellar Differential Rotation by Instability-Driven Turbulence
Differentially rotating stars and planets transport angular momentum internally due to turbulence at rates that have long been a challenge to predict reliably. We develop a self-consistent saturation theory, using a statistical closure approximation, for hydrodynamic turbulence driven by the axisymmetric Goldreich--Schubert--Fricke (GSF) instability at the stellar equator with radial differential rotation. This instability arises when fast thermal diffusion eliminates the stabilizing effects of…

Biological computations: limitations of attractor-based formalisms and the need for transients
Daniel Koch, Akhilesh Nandan, Gayathri Ramesan, Aneta Koseska
https://arxiv.org/abs/2404.10369

Biological computations: limitations of attractor-based formalisms and the need for transients
Living systems, from single cells to higher vertebrates, receive a continuous stream of non-stationary inputs that they sense, e.g., via cell surface receptors or sensory organs. Integrating these time-varying, multi-sensory, and often noisy information with memory using complex molecular or neuronal networks, they generate a variety of responses beyond simple stimulus-response association, including avoidance behavior, life-long-learning or social interactions. In a broad sense, these processe…

Biological computations: limitations of attractor-based formalisms and the need for transients
Daniel Koch, Akhilesh Nandan, Gayathri Ramesan, Aneta Koseska
https://arxiv.org/abs/2404.10369

Biological computations: limitations of attractor-based formalisms and the need for transients
Living systems, from single cells to higher vertebrates, receive a continuous stream of non-stationary inputs that they sense, e.g., via cell surface receptors or sensory organs. Integrating these time-varying, multi-sensory, and often noisy information with memory using complex molecular or neuronal networks, they generate a variety of responses beyond simple stimulus-response association, including avoidance behavior, life-long-learning or social interactions. In a broad sense, these processe…