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A Multi-Modal Non-Invasive Deep Learning Framework for Progressive Prediction of Seizures
No full textThis paper introduces an innovative framework designed for progressive (granular in time to onset) prediction of seizures through the utilization of a Deep Learning (DL) methodology based on non-invasive multi-modal sensor networks. Epilepsy, a debilitating neurological condition, affects an estimated 65 million individuals globally, with a substantial proportion facing drug-resistant epilepsy despite pharmacological interventions. To address this challenge, we advocate for predictive systems that provide timely alerts to individuals at risk, enabling them to take precautionary actions. Our framework employs advanced DL techniques and uses personalized data from a network of non-invasive electroencephalogram (EEG) and electrocardiogram (ECG) sensors, thereby enhancing prediction accuracy. The algorithms are optimized for real-time processing on edge devices, mitigating privacy concerns and minimizing data transmission overhead inherent in cloud-based solutions, ultimately preserving battery energy. Additionally, our system predicts the countdown time to seizures (with 15-minute intervals up to an hour prior to the onset), offering critical lead time for preventive actions. Our multi-modal model achieves 95% sensitivity, 98% specificity, and 97% accuracy, averaged among 29 patients.4 pages, 5 figures, Proceedings of the IEEE 20th International Conference on Body Sensor Networks (BSN), October 202
Economic Diversification and Social Progress in the GCC Countries: A Study on the Transition from Oil-Dependency to Knowledge-Based Economies
No full textThe Gulf Cooperation Council countries -- Oman, Bahrain, Kuwait, UAE, Qatar, and Saudi Arabia -- holds strategic significance due to its large oil reserves. However, these nations face considerable challenges in shifting from oil-dependent economies to more diversified, knowledge-based systems. This study examines the progress of Gulf Cooperation Council (GCC) countries in achieving economic diversification and social development, focusing on the Social Progress Index (SPI), which provides a broader measure of societal well-being beyond just economic growth. Using data from the World Bank, covering 2010 to 2023, the study employs the XGBoost machine learning model to forecast SPI values for the period of 2024 to 2026. Key components of the methodology include data preprocessing, feature selection, and the simulation of independent variables through ARIMA modeling. The results highlight significant improvements in education, healthcare, and women\u27s rights, contributing to enhanced SPI performance across the GCC countries. However, notable challenges persist in areas like personal rights and inclusivity. The study further indicates that despite economic setbacks caused by global disruptions, including the COVID-19 pandemic and oil price volatility, GCC nations are expected to see steady improvements in their SPI scores through 2027. These findings underscore the critical importance of economic diversification, investment in human capital, and ongoing social reforms to reduce dependence on hydrocarbons and build knowledge-driven economies. This research offers valuable insights for policymakers aiming to strengthen both social and economic resilience in the region while advancing long-term sustainable development goals
Automorphism-Assisted Quantum Approximate Optimization Algorithm for efficient graph optimization
No full textIn this article we report on the application of the Quantum Approximate Optimization Algorithm (QAOA) to solve the unweighted MaxCut problem on tree-structured graphs. Specifically, we utilize the Nauty (No Automorphisms, Yes?) package to identify graph automorphisms, focusing on determining edge equivalence classes. These equivalence classes also correspond to symmetries in the terms of the associated Ising Hamiltonian. By exploiting these symmetries, we achieve a significant reduction in the complexity of the Hamiltonian, thereby facilitating more efficient quantum simulations. We conduct benchmark experiments on graphs with up to 34 nodes on memory and CPU intensive TPU provided by google Colab, applying QAOA with a single layer (). The approximation ratios obtained from both the full and symmetry-reduced Hamiltonians are systematically compared. Our results show that using automorphism-based symmetries to reduce the Pauli terms in the Hamiltonian can significantly decrease computational overhead without compromising the quality of the solutions obtained.13 pages, 2 figure
Topic-Conversation Relevance (TCR) Dataset and Benchmarks
No full textWorkplace meetings are vital to organizational collaboration, yet a large percentage of meetings are rated as ineffective. To help improve meeting effectiveness by understanding if the conversation is on topic, we create a comprehensive Topic-Conversation Relevance (TCR) dataset that covers a variety of domains and meeting styles. The TCR dataset includes 1,500 unique meetings, 22 million words in transcripts, and over 15,000 meeting topics, sourced from both newly collected Speech Interruption Meeting (SIM) data and existing public datasets. Along with the text data, we also open source scripts to generate synthetic meetings or create augmented meetings from the TCR dataset to enhance data diversity. For each data source, benchmarks are created using GPT-4 to evaluate the model accuracy in understanding transcription-topic relevance.To be published in 38th Conference on Neural Information Processing Systems (NeurIPS 2024) Track on Datasets and Benchmark
HOUND: High-Order Universal Numerical Differentiator for a Parameter-free Polynomial Online Approximation
No full textThis paper introduces a scalar numerical differentiator, represented as a system of nonlinear differential equations of any high order. We derive the explicit solution for this system and demonstrate that, with a suitable choice of differentiator order, the error converges to zero for polynomial signals with additive white noise. In more general cases, the error remains bounded, provided that the highest estimated derivative is also bounded. A notable advantage of this numerical differentiation method is that it does not require tuning parameters based on the specific characteristics of the signal being differentiated. We propose a discretization method for the equations that implements a cumulative smoothing algorithm for time series. This algorithm operates online, without the need for data accumulation, and it solves both interpolation and extrapolation problems without fitting any coefficients to the data.to be publishe
The Nikiforov-Uvarov method
No full textWe review the so-called Nikiforov-Uvarov method along with some basic results about classical orthogonal polynomials and hypergeometric functions related to the hypergeometric differential equation. The method is employed to address certain eigenvalue problems that appear in quantum mechanics, namely, time-independent Schrödinger equation, paying special attention to properly and completely characterising its spectrum. Finally, some potentials are discussed and solved
Material Girl Launch Report
No full textMaterial Girl was a two-stage sounding rocket designed and flown during the 2022-2023 academic year. Weighing a total of 197 lbs and measuring 176 inches in length, the vehicle incorporated many improvements from the team\u27s previous rocket, Mr. Blue Sky.
The rocket used a Student Researched and Developed (SRAD) P-class motor on each stage, a mechanical staging system, and recovery systems for the booster, sustainer, and nose cone. Flight events were controlled by a commercial off-the-shelf (COTS) avionics flight computer, with a custom flight computer carried as a payload. The motor grain geometry and fin dimensions were optimized using an in-house software developed from RASAero and RocketPy
Linear super-commuting maps and super-biderivations on Hom-lie superalgebras
No full textThis paper investigates the fundamental connections between linear super-commuting maps, super-biderivations, and centroids in Hom-Lie superalgebras under certain conditions. Our work generalizes the results of Bresar and Zhao on Lie algebras.13 page
Venire: A Machine Learning-Guided Panel Review System for Community Content Moderation
Get PDFResearch into community content moderation often assumes that moderation teams govern with a single, unified voice. However, recent work has found that moderators disagree with one another at modest, but concerning rates. The problem is not the root disagreements themselves. Subjectivity in moderation is unavoidable, and there are clear benefits to including diverse perspectives within a moderation team. Instead, the crux of the issue is that, due to resource constraints, moderation decisions end up being made by individual decision-makers. The result is decision-making that is inconsistent, which is frustrating for community members. To address this, we develop Venire, an ML-backed system for panel review on Reddit. Venire uses a machine learning model trained on log data to identify the cases where moderators are most likely to disagree. Venire fast-tracks these cases for multi-person review. Ideally, Venire allows moderators to surface and resolve disagreements that would have otherwise gone unnoticed. We conduct three studies through which we design and evaluate Venire: a set of formative interviews with moderators, technical evaluations on two datasets, and a think-aloud study in which moderators used Venire to make decisions on real moderation cases. Quantitatively, we demonstrate that Venire is able to improve decision consistency and surface latent disagreements. Qualitatively, we find that Venire helps moderators resolve difficult moderation cases more confidently. Venire represents a novel paradigm for human-AI content moderation, and shifts the conversation from replacing human decision-making to supporting it
DAHAs of Type and Character Varieties
Get PDFThis paper studies the spherical subalgebra of the double affine Hecke algebra of type and relates it, at the classical level , to a certain character variety of the four-punctured Riemann sphere. This establishes a conjecture from math.QA/0504089. As a by-product, we find a completed phase space for the trigonometric van Diejen system, explicitly integrate its dynamics and explain how it can be obtained via Hamiltonian reduction.39 pages; preliminary version (comments are welcome!