Stretchy computers on threads that can be stitched into clothes could be used to record whole-body data that most medical sensors can't pick up. Wearable technologies, such as smartwatches, monitor signals from the body like heart rate or temperature, but typically only from a single spot. This can give an incomplete picture of how the body is functioning. Now, Yoel Fink at the Massachusetts Institute of Technology and his colleagues have developed a computer that can be stitched into clothes, made from chips that are connected in a thread of copper and elastic fibre. The thread has 256 kilobytes of on-board memory, around that of a simple calculator, as well as sensors that can detect temperature, heart rate and body movements.

From the very first scene of The Wild Robot, the new animated movie from director Chris Sanders (How to Train Your Dragon), adapted from the first in a trilogy of children's novels by Peter Brown, the viewer is plunged along with the protagonist into a new and alien world. A robot washes up on the shore of a lushly forested island, surrounded by the flotsam of some sort of wrecked vehicle--a plane? a spacecraft?--and immediately begins scanning the area for someone she can help. Rozzum Unit 7134, voiced by Lupita Nyong'o and soon to be known as "Roz," has been designed to, as she puts it, offer "integrated, multifaceted task accomplishment" to whatever human requests it of her. The problem is, the island where she's washed up has no human inhabitants, and the animals witnessing the arrival of this hulking metal biped regard Roz as nothing but a menacing predator to be either fought or fled. A witty time-lapse montage shows the robot powering down for a bit so her software can learn to decode the animal sounds around her, enabling her to communicate with all the island's denizens.

In recent weeks, as so-called cheapfake video clips suggesting President Joe Biden is unfit for office have gone viral on social media, a Kremlin-affiliated disinformation network has been promoting a parody music video featuring Biden wearing a diaper and being pushed around in a wheelchair. The video is called "Bye, Bye Biden" and has been viewed more than 5 million times on X since it was first promoted in the middle of May. It depicts Biden as senile, wearing a hearing aid, and taking a lot of medication. It also shows him giving money to a character who seems to represent illegal migrants while denying money to US citizens until they change their costume to mimic the Ukrainian flag. Another scene shows Biden opening the front door of a family home that features a Confederate flag on the wall and allowing migrants to come in and take over. Finally, the video contains references to stolen election conspiracies pushed by former president Donald Trump.

Fault detection is crucial in industrial systems to prevent failures and optimize performance by distinguishing abnormal from normal operating conditions. Data-driven methods have been gaining popularity for fault detection tasks as the amount of condition monitoring data from complex industrial systems increases. Despite these advances, early fault detection remains a challenge under real-world scenarios. The high variability of operating conditions and environments makes it difficult to collect comprehensive training datasets that can represent all possible operating conditions, especially in the early stages of system operation. Furthermore, these variations often evolve over time, potentially leading to entirely new data distributions in the future that were previously unseen. These challenges prevent direct knowledge transfer across different units and over time, leading to the distribution gap between training and testing data and inducing performance degradation of those methods in real-world scenarios. To overcome this, our work introduces a novel approach for continuous test-time domain adaptation. This enables early-stage robust anomaly detection by addressing domain shifts and limited data representativeness issues. We propose a Test-time domain Adaptation Anomaly Detection (TAAD) framework that separates input variables into system parameters and measurements, employing two domain adaptation modules to independently adapt to each input category. This method allows for effective adaptation to evolving operating conditions and is particularly beneficial in systems with scarce data. Our approach, tested on a real-world pump monitoring dataset, shows significant improvements over existing domain adaptation methods in fault detection, demonstrating enhanced accuracy and reliability.

A new golden age in astronomy is upon us, dominated by data. Large astronomical surveys are broadcasting unprecedented rates of information, demanding machine learning as a critical component in modern scientific pipelines to handle the deluge of data. The upcoming Legacy Survey of Space and Time (LSST) of the Vera C. Rubin Observatory will raise the big-data bar for time-domain astronomy, with an expected 10 million alerts per-night, and generating many petabytes of data over the lifetime of the survey. Fast and efficient classification algorithms that can operate in real-time, yet robustly and accurately, are needed for time-critical events where additional resources can be sought for follow-up analyses. In order to handle such data, state-of-the-art deep learning architectures coupled with tools that leverage modern hardware accelerators are essential. We showcase how the use of modern deep compression methods can achieve a $18\times$ reduction in model size, whilst preserving classification performance. We also show that in addition to the deep compression techniques, careful choice of file formats can improve inference latency, and thereby throughput of alerts, on the order of $8\times$ for local processing, and $5\times$ in a live production setting. To test this in a live setting, we deploy this optimised version of the original time-series transformer, t2, into the community alert broking system of FINK on real Zwicky Transient Facility (ZTF) alert data, and compare throughput performance with other science modules that exist in FINK. The results shown herein emphasise the time-series transformer's suitability for real-time classification at LSST scale, and beyond, and introduce deep model compression as a fundamental tool for improving deploy-ability and scalable inference of deep learning models for transient classification.

Fink and team have published a paper in Advances in Space Research that details a "communication network that would link rovers, lake landers, and even submersible vehicles through a so-called mesh topology network, allowing the machines to work together as a team, independently from human input," according to a press release. The scientists named their patent-pending concept the "Breadcrumb-Style Dynamically Deployed Communication Network" paradigm or DDCN, based on the fairy tale'Hansel and Gretel'. According to Fink, DDCN could help resolve one of NASA's Space Technology Grand Challenges by helping overcome the limited ability of current technology to safely traverse environments on comets, asteroids, moons, and planetary bodies. "If you remember the book, you know how Hansel and Gretel dropped breadcrumbs to make sure they'd find their way back. In our scenario, the'breadcrumbs' are miniaturized sensors that piggyback on the rovers, which deploy the sensors as they traverse a cave or other subsurface environment," explained Fink.

We present the Active Galactic Nuclei (AGN) classifier as currently implemented within the Fink broker. Features were built upon summary statistics of available photometric points, as well as color estimation enabled by symbolic regression. The learning stage includes an active learning loop, used to build an optimized training sample from labels reported in astronomical catalogs. Using this method to classify real alerts from the Zwicky Transient Facility (ZTF), we achieved 98.0% accuracy, 93.8% precision and 88.5% recall. We also describe the modifications necessary to enable processing data from the upcoming Vera C. Rubin Observatory Large Survey of Space and Time (LSST), and apply them to the training sample of the Extended LSST Astronomical Time-series Classification Challenge (ELAsTiCC). Results show that our designed feature space enables high performances of traditional machine learning algorithms in this binary classification task.

We consider the problem of reasoning from inconsistent hybrid theories, i.e., combinations of a structural part given by a classical first order theory (e.g., an ontology) and a rules part as a set of declarative logic program rules (under answer-set semantics). Paraconsistent reasoning is achieved by defining an appropriate semantics, so-called paraconsistent semi-equilibrium model semantics for such hybrid theories. Appropriateness of the semantics is established with respect to desirable properties attesting design objectives, such us to generalize the underlying semantics in case of consistency, as well as to generalize existing paraconsistent semantics for the individual parts. A complexity analysis of corresponding reasoning tasks complements these results.

We consider human activity recognition (HAR) from wearable sensor data in manual-work processes, like warehouse order-picking. Such structured domains can often be partitioned into distinct process steps, e.g., packaging or transporting. Each process step can have a different prior distribution over activity classes, e.g., standing or walking, and different system dynamics. Here, we show how such context information can be integrated systematically into a deep neural network-based HAR system. Specifically, we propose a hybrid architecture that combines a deep neural network-that estimates high-level movement descriptors, attributes, from the raw-sensor data-and a shallow classifier, which predicts activity classes from the estimated attributes and (optional) context information, like the currently executed process step. We empirically show that our proposed architecture increases HAR performance, compared to state-of-the-art methods. Additionally, we show that HAR performance can be further increased when information about process steps is incorporated, even when that information is only partially correct.

MIT researchers have created the first fiber with digital capabilities, able to sense, store, analyze, and infer activity after being sewn into a shirt. Yoel Fink, who is a professor of material sciences and electrical engineering, a Research Laboratory of Electronics principal investigator, and the senior author on the study, says digital fibers expand the possibilities for fabrics to uncover the context of hidden patterns in the human body that could be used for physical performance monitoring, medical inference, and early disease detection. Or, you might someday store your wedding music in the gown you wore on the big day -- more on that later. Fink and his colleagues describe the features of the digital fiber in Nature Communications. Until now, electronic fibers have been analog -- carrying a continuous electrical signal -- rather than digital, where discrete bits of information can be encoded and processed in 0s and 1s.