Large Language Model (LLM) powered agents have emerged as effective planners for Automated Machine Learning (AutoML) systems. While most existing AutoML approaches focus on automating feature engineering and model architecture search, recent studies in time series forecasting suggest that lightweight models can often achieve state-of-the-art performance. This observation led us to explore improving data quality, rather than model architecture, as a potentially fruitful direction for AutoML on time series data. We propose DCATS, a Data-Centric Agent for Time Series. DCATS leverages metadata accompanying time series to clean data while optimizing forecasting performance. We evaluated DCATS using four time series forecasting models on a large-scale traffic volume forecasting dataset. Results demonstrate that DCATS achieves an average 6% error reduction across all tested models and time horizons, highlighting the potential of data-centric approaches in AutoML for time series forecasting.

Autonomic Dysreflexia (AD) is a potentially life-threatening condition characterized by sudden, severe blood pressure (BP) spikes in individuals with spinal cord injury (SCI). Early, accurate detection is essential to prevent cardiovascular complications, yet current monitoring methods are either invasive or rely on subjective symptom reporting, limiting applicability in daily file. This study presents a non-invasive, explainable machine learning framework for detecting AD using multimodal wearable sensors. Data were collected from 27 individuals with chronic SCI during urodynamic studies, including electrocardiography (ECG), photoplethysmography (PPG), bioimpedance (BioZ), temperature, respiratory rate (RR), and heart rate (HR), across three commercial devices. Objective AD labels were derived from synchronized cuff-based BP measurements. Following signal preprocessing and feature extraction, BorutaSHAP was used for robust feature selection, and SHAP values for explainability. We trained modality- and device-specific weak learners and aggregated them using a stacked ensemble meta-model. Cross-validation was stratified by participants to ensure generalizability. HR- and ECG-derived features were identified as the most informative, particularly those capturing rhythm morphology and variability. The Nearest Centroid ensemble yielded the highest performance (Macro F1 = 0.77+/-0.03), significantly outperforming baseline models. Among modalities, HR achieved the highest area under the curve (AUC = 0.93), followed by ECG (0.88) and PPG (0.86). RR and temperature features contributed less to overall accuracy, consistent with missing data and low specificity. The model proved robust to sensor dropout and aligned well with clinical AD events. These results represent an important step toward personalized, real-time monitoring for individuals with SCI.

All over the world, political parties, politicians, and campaigns explore how Artificial Intelligence (AI) can help them win elections. However, the effects of these activities are unknown. We propose a framework for assessing AI's impact on elections by considering its application in various campaigning tasks. The electoral uses of AI vary widely, carrying different levels of concern and need for regulatory oversight. To account for this diversity, we group AI-enabled campaigning uses into three categories - campaign operations, voter outreach, and deception. Using this framework, we provide the first systematic evidence from a preregistered representative survey and two preregistered experiments (n=7,635) on how Americans think about AI in elections and the effects of specific campaigning choices. We provide three significant findings. There is a misalignment of incentives for deceptive practices and their externalities. We cannot count on public opinion to provide strong enough incentives for parties to forgo tactical advantages from AI-enabled deception. There is a need for regulatory oversight and systematic outside monitoring of electoral uses of AI. Still, regulators should account for the diversity of AI uses and not completely disincentivize their electoral use. Elections are times of high public attention on campaigns and their tools of communication. A representative survey of Americans shows that people dislike all kinds of AI uses in campaigns but are more critical of deceptive uses than those improving campaign operations or voter outreach (Study 1, n = 1,199). A survey experiment shows that when learning about specific AI uses in campaigns, American respondents reacted much more negatively to deceptive uses (Study 2, n = 1,985). Our study identifies a misalignment of incentives for deceptive practices and their externalities. We cannot count on public opinion to provide strong enough incentives for parties to forgo tactical advantages from AI-enabled deception.

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In the fall of 2018, corporate finance advisory firm Hampleton published a report titled, "The healthtech sector is currently one of the most dynamic in technology M&A." As a summary of the report notes, "aging populations, increasing patient demands and the rise of lifestyle diseases, coupled with pressure on the costs for delivering care are forcing healthcare providers to innovate to improve the quality of their services and lower their prices." Those innovations are made possible by technologies that range from blockchain and artificial intelligence to big data analysis and advanced sensors. IoT connectivity plays a key role, too. And data is central, but not on its own.

NoC interconnect IP to be dataflow backbone for image signal processors providing enhanced sensitivity, high-resolution HD imaging through low current, low power in a single-chip solution for the security/surveillance market. Eyenix's imaging solution provides a step-function advance over their previous product, replacing a 3rd-party artificial intelligence (AI) function with a superior capability developed in-house for super-resolution imaging. This is provided in a tightly integrated system design, including functions for image stabilization for mobile usage and image dewarping for wide-angle camera correction. The first application is destined for surveillance camera applications. Eyenix chose Arteris IP on-chip interconnect technology as a part of Eyenix's proprietary image processing chip because it enables Eyenix to design and integrate a complete and superior Eyenix imaging solution without dependency on external IP blocks for the AI function.

The combination of WekaFS and NVIDIA enables customers to accelerate AI/ML initiatives that dramatically accelerate their time-to-value and time-to-market CAMPBELL, Calif., June 29, 2021 – WekaIO (Weka), one of the fastest-growing data platforms for artificial intelligence/machine learning (AI/ML), life sciences research,

CAMPBELL, Calif., March 26, 2020 – WekaIO (Weka) announced that The Business Intelligence Group has named Weka a winner in its Artificial Intelligence Excellence Awards program. The Weka File System (WekaFS), Weka's flagship product that is uniquely built to solve big problems, delivers the industry's best performance at any scale. WekaFS has a clean sheet design that handles the demands of new emerging and converging workloads, including artificial intelligence (AI) and machine/deep learning (ML/DL), high-performance data analytics (HPDA), and high-performance computing (HPC). The file system can deliver 80 GB/sec of bandwidth to a single GPU server, scale to Exabytes in a single namespace, and support an entire pipeline for edge-to-core-to-cloud workflows. The system also delivers operational agility with versioning, explainability, and reproducibility along with governance and compliance with in-line encryption and data protection.

WIRE)--WekaIO (Weka), the innovation leader in high-performance, scalable file storage for data-intensive applications, today announced that Innoviz, a leading manufacturer of high-performance, solid-state Light Detection and Ranging (LiDAR) sensors and Perception Software that enables the mass-production of autonomous vehicles, has selected the Weka File System (WekaFS) to accelerate its Artificial Intelligence (AI) and deep learning workflows. WekaFS has been chosen by Innoviz to improve application performance at scale and deliver high bandwidth I/O to its GPU cluster. Innoviz's solid-state LiDAR sensors are key to the future of autonomous cars. The sensors and Perception Software, which identifies, classifies, segments, and tracks objects to give autonomous vehicles a better understanding of the 3D driving scene, rely heavily on AI. Having recently closed its Series C funding round with $170M secured, Innoviz is choosing and developing the right technologies to empower it to realize its expansion plans and enhance its manufacturing capabilities.

Manjrekar comes to WekaIO from SwiftStack, a leading provider of data storage and management solutions for data-driven customers. During his time at SwiftStack, he was Head of AI, responsible for Product, Solutions, and Corporate Development. Prior to SwiftStack, he had roles as Senior Director at Vexata, EMC, NetApp, Force10 Networks and previously held Senior Eng. Manjrekar is a seasoned IT professional who has extensive experience in building and managing emerging businesses across the USA, Asia, and EMEA. He brings an established background in providing effective product management, solutions marketing, and strategic alliances, along with a strong sense of business development vision and leadership to his new position.