"People are scared, says one federal worker. "Is WIRED hiring?" jokes another. Federal workers have grown accustomed to a specific kind of dread over the past year . As of July, more than 150,000 federal workers had resigned from their roles since president Donald Trump took office for the second time, according to . Tens of thousands were also fired. For the past few months, it seemed like this bloodletting was over--but that all changed on Friday. Thousands of employees at eight government agencies were subjected to RIFs, or reductions in force--the government's formal process of laying off federal workers. According to a court filing from the Office of Management and Budget (OMB) on Friday, this latest round of firings has affected more than 4,000 federal employees. The court filing also claimed that the administration targeted the Treasury and the Department of Health and Human Services the hardest, hacking away at a combined 2,500 jobs across the two agencies and the entire Washington, DC, office of the Centers for Disease Control and Prevention . The Department of Education culled nearly its entire team handling special education, CNN reported on Tuesday . At the Environmental Protection Agency, the Department of Energy, and the Department of Housing and Urban Development, cuts ranged from a few dozen to several hundred jobs, according to the same filing. Who says their goal is to traumatize people?" says one IRS worker, referencing private speeches given by Russell Vought, the head of OMB and a key architect of the Heritage Foundation's Project 2025 who has been the public face of the job-cutting.

How does the training data affect a model's behavior? This is the question we seek to answer with data attribution. The leading practical approaches to data attribution are based on influence functions (IF). IFs utilize a first-order Taylor approximation to efficiently predict the effect of removing a set of samples from the training set without retraining the model, and are used in a wide variety of machine learning applications. However, especially in the high-dimensional regime (# params $\geq Ω($# samples$)$), they are often imprecise and tend to underestimate the effect of sample removals, even for simple models such as logistic regression. We present rescaled influence functions (RIF), a new tool for data attribution which can be used as a drop-in replacement for influence functions, with little computational overhead but significant improvement in accuracy. We compare IF and RIF on a range of real-world datasets, showing that RIFs offer significantly better predictions in practice, and present a theoretical analysis explaining this improvement. Finally, we present a simple class of data poisoning attacks that would fool IF-based detections but would be detected by RIF.

The Isolation Forest (iForest), proposed by Liu, Ting, and Zhou at TKDE 2012, has become a prominent tool for unsupervised anomaly detection. However, recent research by Hariri, Kind, and Brunner, published in TKDE 2021, has revealed issues with iForest. They identified the presence of axis-aligned ghost clusters that can be misidentified as normal clusters, leading to biased anomaly scores and inaccurate predictions. In response, they developed the Extended Isolation Forest (EIF), which effectively solves these issues by eliminating the ghost clusters introduced by iForest. This enhancement results in improved consistency of anomaly scores and superior performance. We reveal a previously overlooked problem in the Extended Isolation Forest (EIF), showing that it is vulnerable to ghost inter-clusters between normal clusters of data points. In this paper, we introduce the Rotated Isolation Forest (RIF) algorithm which effectively addresses both the axis-aligned ghost clusters observed in iForest and the ghost inter-clusters seen in EIF. RIF accomplishes this by randomly rotating the dataset (using random rotation matrices and QR decomposition) before feeding it into the iForest construction, thereby increasing dataset variation and eliminating ghost clusters. Our experiments conclusively demonstrate that the RIF algorithm outperforms iForest and EIF, as evidenced by the results obtained from both synthetic datasets and real-world datasets.

Precision medicine is becoming a focus in medical research recently, as its implementation brings values to all stakeholders in the healthcare system. Various statistical methodologies have been developed tackling problems in different aspects of this field, e.g., assessing treatment heterogeneity, identifying patient subgroups, or building treatment decision models. However, there is a lack of new tools devoted to selecting and prioritizing predictive biomarkers. We propose a novel tree-based ensemble method, random interaction forest (RIF), to generate predictive importance scores and prioritize candidate biomarkers for constructing refined treatment decision models. RIF was evaluated by comparing with the conventional random forest and univariable regression methods and showed favorable properties under various simulation scenarios. We applied the proposed RIF method to a biomarker dataset from two phase III clinical trials of bezlotoxumab on $\textit{Clostridium difficile}$ infection recurrence and obtained biologically meaningful results.

Rough inclusion functions (RIFs) are known by many other names in formal approaches to vagueness, belief, and uncertainty. Their use is often poorly grounded in factual knowledge or involve wild statistical assumptions. The concept of contamination introduced and studied by the present author across a number of her papers, concerns mixing up of information across semantic domains (or domains of discourse). RIFs play a key role in contaminating algorithms and some solutions that seek to replace or avoid them have been proposed and investigated by the present author in some of her earlier papers. The proposals break many algorithms of rough sets in a serious way. In this research, algorithm-friendly granular generalizations of such functions that reduce contamination are proposed and investigated from a mathematically sound perspective. Interesting representation results are proved and a core algebraic strategy for generalizing Skowron-Polkowski style of rough mereology is formulated.

From 24 – 28 April, four ECHORD experiments will present their work at Hannover Messe, the world's biggest trade show. You can see the latest advances in EU-funded robotics technology development in Hall 17, stand C70, and get the chance to develop your own ideas with their on-site competition. CoCoMaps, Collaborative Cognitive Maps, uses an expanded version of the existing Cognitive Map Architecture implemented on Honda's ASIMO robot in an environment with more complex tasks than already attempted. This will allow the robot to interact in more complex ways, in particular, to simultaneously interact with another robot and more than one person at a time. Thus, the project aims for a group of 2 robots and 2 humans enabling social interactions that can coexist with the robots' attention to – and completion of – practical tasks in the workplace.

Robotics Innovation Facilities, or RIFs, are robotic labs, financed via the ECHORD project by the European Commission. They offer access to high-tech robotic equipment and expertise at zero risk; using the RIF is not only free of charge, they also safeguard their users' intellectual property. Companies, institutions, and organisations can apply to use RIFs. Currently, there are three RIFs within the ECHORD project located in Italy, France, and the UK. Two 360 Lab Tours are now available, the last video will be released late March.