In this paper, we propose a recurrent neural network (RNN)-based framework for estimating the parameters of the fractional Poisson process (FPP), which models event arrivals with memory and long-range dependence. The Long Short-Term Memory (LSTM) network estimates the key parameters $μ>0$ and $β\in(0,1)$ from sequences of inter-arrival times, effectively modeling their temporal dependencies. Our experiments on synthetic data show that the proposed approach reduces the mean squared error (MSE) by about 55.3\% compared to the traditional method of moments (MOM) and performs reliably across different training conditions. We tested the method on two real-world high-frequency datasets: emergency call records from Montgomery County, PA, and AAPL stock trading data. The results show that the LSTM can effectively track daily patterns and parameter changes, indicating its effectiveness on real-world data with complex time dependencies.

Neural Information Processing Systems http://nips.cc/

Radio remains a pervasive medium for mass information dissemination, with AM/FM stations reaching more Americans than either smartphone-based social networking or live television. Increasingly, radio broadcasts are also streamed online and accessed over the Internet. We present WavePulse, a framework that records, documents, and analyzes radio content in real-time. While our framework is generally applicable, we showcase the efficacy of WavePulse in a collaborative project with a team of political scientists focusing on the 2024 Presidential Elections. We use WavePulse to monitor livestreams of 396 news radio stations over a period of three months, processing close to 500,000 hours of audio streams. These streams were converted into time-stamped, diarized transcripts and analyzed to track answer key political science questions at both the national and state levels. Our analysis revealed how local issues interacted with national trends, providing insights into information flow. Our results demonstrate WavePulse's efficacy in capturing and analyzing content from radio livestreams sourced from the Web. Code and dataset can be accessed at \url{https://wave-pulse.io}.

Computerized Adaptive Testing (CAT) provides an efficient and tailored method for assessing the proficiency of examinees, by dynamically adjusting test questions based on their performance. Widely adopted across diverse fields like education, healthcare, sports, and sociology, CAT has revolutionized testing practices. While traditional methods rely on psychometrics and statistics, the increasing complexity of large-scale testing has spurred the integration of machine learning techniques. This paper aims to provide a machine learning-focused survey on CAT, presenting a fresh perspective on this adaptive testing method. By examining the test question selection algorithm at the heart of CAT's adaptivity, we shed light on its functionality. Furthermore, we delve into cognitive diagnosis models, question bank construction, and test control within CAT, exploring how machine learning can optimize these components. Through an analysis of current methods, strengths, limitations, and challenges, we strive to develop robust, fair, and efficient CAT systems. By bridging psychometric-driven CAT research with machine learning, this survey advocates for a more inclusive and interdisciplinary approach to the future of adaptive testing.

In numerous applications, binary reactions or event counts are observed and stored within high-order tensors. Tensor decompositions (TDs) serve as a powerful tool to handle such high-dimensional and sparse data. However, many traditional TDs are explicitly or implicitly designed based on the Gaussian distribution, which is unsuitable for discrete data. Moreover, most TDs rely on predefined multi-linear structures, such as CP and Tucker formats. Therefore, they may not be effective enough to handle complex real-world datasets. To address these issues, we propose ENTED, an \underline{E}fficient \underline{N}onparametric \underline{TE}nsor \underline{D}ecomposition for binary and count tensors. Specifically, we first employ a nonparametric Gaussian process (GP) to replace traditional multi-linear structures. Next, we utilize the \pg augmentation which provides a unified framework to establish conjugate models for binary and count distributions. Finally, to address the computational issue of GPs, we enhance the model by incorporating sparse orthogonal variational inference of inducing points, which offers a more effective covariance approximation within GPs and stochastic natural gradient updates for nonparametric models. We evaluate our model on several real-world tensor completion tasks, considering binary and count datasets. The results manifest both better performance and computational advantages of the proposed model.

A modified commercial drone may have been responsible for an attempted attack on a Pennsylvania power substation last year, the first reported case of a drone assault on the U.S.'s energy infrastructure. Authorities believe a DJI Mavic 2 drone with a thick copper wire tethered to it was found in June 2020 was likely intended to disrupt operations'by creating a short circuit to cause damage to transformers or distribution lines,' according to a joint intelligence bulletin from the FBI, Department of Homeland Security, and the National Counterterrorism Center released October 28. If the wire had come into contact with any of the power plant's high-voltage equipment it could have resulted in a short circuit, power failure or even a fire, according to New Scientist. The Drive reported the drone was recovered by authorities from a substation near Hershey, Pennsylvania, about 100 miles from Philadelphia. No groups has claimed responsibility: The device's camera and internal memory card had been removed and identifying labels were removed, in a likely attempt to obscure its origins.

Columbia University professor and robotics engineer Hod Lipson knows the importance of artificial intelligence (AI) on a global level. "It permeates everything we do, from the stock market, from predicting the weather to what product you're going to buy," he said Wednesday during the second day of the virtual Ai4 2020 conference. AI falls into the category of an exponential technology, meaning it accelerates with time. Both biopharma and med-tech companies are increasingly pulling the technology into their business operations, working on programs that can assist in everything from drug discovery and clinical trial recruitment to precision diagnostics and patient compliance efforts. Computing power has doubled every 20 months or so for the past 120 years, Lipson said, moving from mechanical instruments to graphics processing units (GPUs) today.

With the release of Qlik Sense Business on Tuesday, Qlik extended the reach of its cloud-first capabilities. The offering replaces Qlik Sense Cloud Business, which the analytics and business intelligence vendor, based in King of Prussia, Pa., debuted in 2015. In addition, Qlik rolled out Qlik Sense September 2019, the latest update of its central BI product. Qlik Sense Business is a SaaS offering built on third-generation BI capabilities -- augmented intelligence and machine learning. It differs from Qlik Sense Cloud Business by removing limits on the number of users, connecting more seamlessly to Qlik Sense Enterprise and providing expanded AI and machine learning capabilities.

Tensor decompositions are fundamental tools for multiway data analysis. Existing approaches, however, ignore the valuable temporal information along with data, or simply discretize them into time steps so that important temporal patterns are easily missed. Moreover, most methods are limited to multilinear decomposition forms, and hence are unable to capture intricate, nonlinear relationships in data. To address these issues, we formulate event-tensors, to preserve the complete temporal information for multiway data, and propose a novel Bayesian nonparametric decomposition model. Our model can (1) fully exploit the time stamps to capture the critical, causal/triggering effects between the interaction events, (2) flexibly estimate the complex relationships between the entities in tensor modes, and (3) uncover hidden structures from their temporal interactions. For scalable inference, we develop a doubly stochastic variational Expectation-Maximization algorithm to conduct an online decomposition. Evaluations on both synthetic and real-world datasets show that our model not only improves upon the predictive performance of existing methods, but also discovers interesting clusters underlying the data.

Welcome to my first Machine Learning project! This post will be focused on solving the real problem. We will try to predict location and daily quantity of 911 calls in US Montgomery County (PA). Such project could enable help & rescue teams, such as police, fire department and emergency medical services to prepare for upcoming events and better plan their work. The intro story is totally made up.