Iran-linked hackers claimed responsibility for a cyberattack on Stryker Corporation, disrupting the medical technology company's Microsoft environment and wiping devices.

Plastic pollution is one of those problems everyone can see, yet few know how to tackle it effectively. I grew up walking the beaches around Tramore in County Waterford, Ireland, where plastic debris has always been part of the coastline, including bottles, fragments of fishing gear and food packaging. According to the UN, every year 19-23 million tonnes of plastic lands up in lakes, rivers and seas, and it has a huge impact on ecosystems, creating pollution and damaging animal habitats. Community groups do tremendous work cleaning these beaches, but they're essentially walking blind, guessing where plastic accumulates, missing hot spots, repeating the same stretches while problem areas may go untouched. Years later, working in marine robotics at the University of Limerick, I began developing tools to support marine clean-up and help communities find plastic pollution along our coastline.

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

In this paper, we aim to eliminate the inductive bias imposed by the embedding space geometry.

Node centralities play a pivotal role in network science, social network analysis, and recommender systems. In temporal data, static path-based centralities like closeness or betweenness can give misleading results about the true importance of nodes in a temporal graph. To address this issue, temporal generalizations of betweenness and closeness have been defined that are based on the shortest time-respecting paths between pairs of nodes.

There are many clustering algorithms which perform well towards their aim of finding cohesive groups of instances.

Step-wise explanations can explain logic puzzles and other satisfaction problems by showing how to derive decisions step by step. Each step consists of a set of constraints that derive an assignment to one or more decision variables. However, many candidate explanation steps exist, with different sets of constraints and different decisions they derive. To identify the most comprehensible one, a user-defined objective function is required to quantify the quality of each step. However, defining a good objective function is challenging. Here, interactive preference elicitation methods from the wider machine learning community can offer a way to learn user preferences from pairwise comparisons. We investigate the feasibility of this approach for step-wise explanations and address several limitations that distinguish it from elicitation for standard combinatorial problems. First, because the explanation quality is measured using multiple sub-objectives that can vary a lot in scale, we propose two dynamic normalization techniques to rescale these features and stabilize the learning process. We also observed that many generated comparisons involve similar explanations. For this reason, we introduce MACHOP (Multi-Armed CHOice Perceptron), a novel query generation strategy that integrates non-domination constraints with upper confidence bound-based diversification. We evaluate the elicitation techniques on Sudokus and Logic-Grid puzzles using artificial users, and validate them with a real-user evaluation. In both settings, MACHOP consistently produces higher-quality explanations than the standard approach.

Grasslands, constituting the world's second-largest terrestrial carbon sink, play a crucial role in biodiversity and the regulation of the carbon cycle. Currently, the Irish dairy sector, a significant economic contributor, grapples with challenges related to profitability and sustainability. Presently, grass growth forecasting relies on impractical mechanistic models. In response, we propose deep learning models tailored for univariate datasets, presenting cost-effective alternatives. Notably, a temporal convolutional network designed for forecasting Perennial Ryegrass growth in Cork exhibits high performance, leveraging historical grass height data with RMSE of 2.74 and MAE of 3.46. V alidation across a comprehensive dataset spanning 1,757 weeks over 34 years provides insights into optimal model configurations. This study enhances our understanding of model behavior, thereby improving reliability in grass growth forecasting and contributing to the advancement of sustainable dairy farming practices. Introduction Grasslands stand as the world's largest terrestrial ecosystem, serving as a pivotal source of sustenance for livestock. Tackling the escalating demand for meat and dairy products in an environmentally sustainable manner presents a formidable challenge. Encompassing 31.5% of the Earth's landmass (Latham et al., 2014), grasslands rank among the most prevalent and widespread vegetation types.