Breakthroughs, discoveries, and DIY tips sent every weekday. A newborn star 15,000 light-years from Earth is fascinating astronomers with its dual blasts of superheated plasma jets . The rare sight captured in stunning detail by the James Webb Space Telescope (JWST) isn't only a display of cosmic forces. It's helping solve a decades' long debate about the origins of massive stellar objects. Located at the edge of the Milky Way galaxy inside a nebula known as Sharpless 2-284 (Sh2-284), the young protostar is already upwards of 10 times the mass of our sun .

Managing Type 1 Diabetes (T1D) demands constant vigilance as individuals strive to regulate their blood glucose levels to avert the dangers of dysglycemia (hyperglycemia or hypoglycemia). Despite the advent of sophisticated technologies such as automated insulin delivery (AID) systems, achieving optimal glycemic control remains a formidable task. AID systems integrate continuous subcutaneous insulin infusion (CSII) and continuous glucose monitors (CGM) data, offering promise in reducing variability and increasing glucose time-in-range. However, these systems often fail to prevent dysglycemia, partly due to limitations in prediction algorithms that lack the precision to avert abnormal glucose events. This gap highlights the need for proactive behavioral adjustments. We address this need with GLIMMER, Glucose Level Indicator Model with Modified Error Rate, a machine learning approach for forecasting blood glucose levels. GLIMMER categorizes glucose values into normal and abnormal ranges and devises a novel custom loss function to prioritize accuracy in dysglycemic events where patient safety is critical. To evaluate the potential of GLIMMER for T1D management, we both use a publicly available dataset and collect new data involving 25 patients with T1D. In predicting next-hour glucose values, GLIMMER achieved a root mean square error (RMSE) of 23.97 (+/-3.77) and a mean absolute error (MAE) of 15.83 (+/-2.09) mg/dL. These results reflect a 23% improvement in RMSE and a 31% improvement in MAE compared to the best-reported error rates.

Pre-trained language models are increasingly being used in multi-document summarization tasks. However, these models need large-scale corpora for pre-training and are domain-dependent. Other non-neural unsupervised summarization approaches mostly rely on key sentence extraction, which can lead to information loss. To address these challenges, we propose a lightweight yet effective unsupervised approach called GLIMMER: a Graph and LexIcal features based unsupervised Multi-docuMEnt summaRization approach. It first constructs a sentence graph from the source documents, then automatically identifies semantic clusters by mining low-level features from raw texts, thereby improving intra-cluster correlation and the fluency of generated sentences. Finally, it summarizes clusters into natural sentences. Experiments conducted on Multi-News, Multi-XScience and DUC-2004 demonstrate that our approach outperforms existing unsupervised approaches. Furthermore, it surpasses state-of-the-art pre-trained multi-document summarization models (e.g. PEGASUS and PRIMERA) under zero-shot settings in terms of ROUGE scores. Additionally, human evaluations indicate that summaries generated by GLIMMER achieve high readability and informativeness scores. Our code is available at https://github.com/Oswald1997/GLIMMER.

Memory-augmentation is a powerful approach for efficiently incorporating external information into language models, but leads to reduced performance relative to retrieving text. Recent work introduced LUMEN, a memory-retrieval hybrid that partially pre-computes memory and updates memory representations on the fly with a smaller live encoder. We propose GLIMMER, which improves on this approach through 1) exploiting free access to the powerful memory representations by applying a shallow reranker on top of memory to drastically improve retrieval quality at low cost, and 2) incorporating multi-task training to learn a general and higher quality memory and live encoder. GLIMMER achieves strong gains in performance at faster speeds compared to LUMEN and FiD on the KILT benchmark of knowledge-intensive tasks.

We've seen glimmers of this potential emerge in recent years. Mobile assistants get smarter all the time, from telling us when we need to leave for an appointment to encouraging us to take a walk if we've been sitting too long. Machine learning is speeding up deliveries and helping doctors diagnose diseases. Smart algorithms are stopping cybercriminals. Much of it would have been unimaginable ten years ago.

Self-aware robots with deadly intentions are a staple in sci-fi films. Now scientists have proved a robot can show a glimmer of consciousness – but luckily this android is polite. In an experiment a small humanoid solved a philosophical problem to demonstrate it could understand a question and then recognise its own voice. Scientists have proved a robot can show a glimmer of consciousness. In an experiment, Nao bots were programmed to think two of them took'dumbing pills'.