Environment Animals Wildlife Birds A giant-footed bird showed up in a Massachusetts backyard. The purple gallinule found its way north through unusual winds. Breakthroughs, discoveries, and DIY tips sent every weekday. A winter storm blew an unexpected visitor from the south into a backyard in New Bedford, Massachusetts--a purple gallinule (). These gorgeously colored birds with shockingly large feet, live in marshes from the southeastern United States through South America.

The deployment of GCNs in the cloud raises privacy concerns due to potential adversarial attacks on client data. To address security concerns, Privacy-Preserving Machine Learning (PPML) using Homo-morphic Encryption (HE) secures sensitive client data.

We propose S$^2$GPT-PINN, a sparse and small model for solving parametric partial differential equations (PDEs). Similar to Small Language Models (SLMs), S$^2$GPT-PINN is tailored to domain-specific (families of) PDEs and characterized by its compact architecture and minimal computational power. Leveraging a small amount of extremely high quality data via a mathematically rigorous greedy algorithm that is enabled by the large full-order models, S$^2$GPT-PINN relies on orders of magnitude less parameters than PINNs to achieve extremely high efficiency via two levels of customizations. The first is knowledge distillation via task-specific activation functions that are transferred from Pre-Trained PINNs. The second is a judicious down-sampling when calculating the physics-informed loss of the network compressing the number of data sites by orders of magnitude to the size of the small model.

Artificial neural network pruning is a method in which artificial neural network sizes can be reduced while attempting to preserve the predicting capabilities of the network. This is done to make the model smaller or faster during inference time. In this work we analyze the ability of a selection of artificial neural network pruning methods to generalize to a new cybersecurity dataset utilizing a simpler network type than was designed for. We analyze each method using a variety of pruning degrees to best understand how each algorithm responds to the new environment. This has allowed us to determine the most well fit pruning method of those we searched for the task. Unexpectedly, we have found that many of them do not generalize to the problem well, leaving only a few algorithms working to an acceptable degree.

Traditional fine-tuning methods, while effective, often require substantial computational resources and large, static datasets, making them impractical for real-time applications. Moreover, these models notoriously suffer from catastrophic forgetting, rapid performance degradation on previously learned tasks when presented with new data (Luo et al., 2023). Recent literature addresses catastrophic forgetting via techniques such as replay buffers, which periodically reintroduce previously learned data, and Low-Rank Adaptation (LoRA), a parameter-efficient fine-tuning approach designed to reduce computational overhead (Smith & Jones, 2024; Hu et al., 2021). Although these methods individually show promise, there remains a notable gap in understanding their efficacy and interaction within real-time, streaming learning environments. In this work, we bridge this gap by integrating LoRA with a lightweight replay mechanism under stringent streaming constraints, simulating real-world conditions where models must continually adapt using limited computational resources and data batches. We focus specifically on three distinct domains,medical, genetic, and legal,to evaluate the generalizability and robustness of our approach.

Beamforming techniques are considered as essential parts to compensate severe path losses in millimeter-wave (mmWave) communications. In particular, these techniques adopt large antenna arrays and formulate narrow beams to obtain satisfactory received powers. However, performing accurate beam alignment over narrow beams for efficient link configuration by traditional standard defined beam selection approaches, which mainly rely on channel state information and beam sweeping through exhaustive searching, imposes computational and communications overheads. And, such resulting overheads limit their potential use in vehicle-to-infrastructure (V2I) and vehicle-to-vehicle (V2V) communications involving highly dynamic scenarios. In comparison, utilizing out-of-band contextual information, such as sensing data obtained from sensor devices, provides a better alternative to reduce overheads. This paper presents a deep learning-based solution for utilizing the multi-modality sensing data for predicting the optimal beams having sufficient mmWave received powers so that the best V2I and V2V line-of-sight links can be ensured proactively. The proposed solution has been tested on real-world measured mmWave sensing and communication data, and the results show that it can achieve up to 98.19% accuracies while predicting top-13 beams. Correspondingly, when compared to existing been sweeping approach, the beam sweeping searching space and time overheads are greatly shortened roughly by 79.67% and 91.89%, respectively which confirm a promising solution for beamforming in mmWave enabled communications.

Dynamic Treatment Regimes (DTRs) provide a systematic approach for making sequential treatment decisions that adapt to individual patient characteristics, particularly in clinical contexts where survival outcomes are of interest. Censoring-Aware Tree-Based Reinforcement Learning (CA-TRL) is a novel framework to address the complexities associated with censored data when estimating optimal DTRs. We explore ways to learn effective DTRs, from observational data. By enhancing traditional tree-based reinforcement learning methods with augmented inverse probability weighting (AIPW) and censoring-aware modifications, CA-TRL delivers robust and interpretable treatment strategies. We demonstrate its effectiveness through extensive simulations and real-world applications using the SANAD epilepsy dataset, where it outperformed the recently proposed ASCL method in key metrics such as restricted mean survival time (RMST) and decision-making accuracy. This work represents a step forward in advancing personalized and data-driven treatment strategies across diverse healthcare settings.

Anomaly detection in time series data is a well-studied problem due to its importance in detecting faults, intrusions, and unusual events in critical systems [1, 3]. Extensive surveys have reviewed methods for general anomaly detection [1], outlier analysis [3], and specifically for temporal data [4]. Despite this progress, accurately identifying anomalies in time series remains challenging [14]. A key difficulty is that anomalies are often sparse--comprising only a tiny fraction of observations [2]. This extreme class imbalance makes it hard for models to recognize anomalies without producing many false alarms [6]. One strategy to detect anomalies is to forecast future behavior and flag deviations between predictions and actual values [15, 16]. Classical forecasting models, such as ARIMA [12] and exponential smoothing, as well as decomposition-based methods like Prophet [13], have been applied to model normal time series patterns and identify outliers when residuals exceed a threshold. Numerous other approaches leverage deep generative models (e.g., variational autoencoders [17], GANs [18]) or attention mechanisms [19] to improve multivariate time series anomaly detection. However, most prior methods treat multivariate time series as an unstructured collection of variables, not accounting for known relationships among them.

Former Bristol County, Mass. Sheriff Thomas Hodgson joins'Fox & Friends' to discuss Boston city officials refusing to cooperate with ICE deportations. Flyers showing the names, pictures, and phone numbers of Immigration and Customs Enforcement (ICE) agents have surfaced in a Southern California neighborhood. Multiple federal law enforcement sources confirmed to Fox News national correspondent Bill Melugin that anti-ICE activists, who have been interfering with ICE operations in the Los Angeles area in recent days, have now started putting up posters featuring the personal information of ICE and Homeland Security Investigations (HSI) officers working in the Los Angeles and Southern California area. The posters, which were written in Spanish, translate roughly to read "CAREFUL WITH THESE FACES." "These armed agents work in Southern California. ICE and HSI racially terrorize and criminalize entire communities with their policies. They kidnap people from their homes and from the streets, separating families and fracturing communities. Many people have died while locked up in jails, prisons, and detention centers," the posters continued.