Speculative decoding has emerged as a potential solution for speeding up inferences using large language models (LLMs). However, training-based speculative methods require the creation of draft models that typically lack generalizability, whereas training-free speculative frameworks offer only limited speed-up gains. SPECTRA, a novel speculative framework developed by researchers from JAIST, accelerates LLM inferences without additional training and surpasses existing speculative decoding methods across diverse functionalities, making it a practical tool for real-world LLM applications.

Kyoto, Japan -- Northern fur seals are long-distance migrants, moving seasonally between breeding colonies and wintering grounds. After leaving breeding colonies in autumn, they travel south for winter foraging, returning in early spring. This allows them to avoid sea ice and harsh conditions in northern waters while relocating to ice-free areas.

The southward migration of these seals has been relatively well-documented. One of their primary wintering areas is the Sea of Japan, where they can find abundant prey. They have also been reported to damage fishing gear and remove catch directly from nets, potentially competing with local fisheries.

The seals' northbound migration from wintering grounds back to breeding colonies in spring remains poorly understood, however. Factors such as tag loss, limited battery life, and the difficulty of capturing animals at sea pose particular challenges.

Many animals, including humans, can flexibly modulate their responsiveness to sounds according to different situations. This ability allows them to optimize the use of their limited brain resources by prioritizing the processing of critical information at any given moment. In line with this idea, a research team at Nagoya University in Japan has demonstrated that when female fruit flies are eager to mate, dopaminergic signals can influence their sensitivity to sounds, including courtship sounds from males, which are an essential source of information for species reproduction.

We have proposed a new therapeutic effect of CAR-T-cell therapy through a lymphoma case treated with bispecific antibody (BsAb). Memory-formed T cells had been developed in the body after CAR-T-cell therapy. Interestingly, bystander CAR-negative CD8⁺ T cells in the CAR-T-cell product successfully expanded in periphery and lymph node, and mostly propagated after treatment with BsAb, which resulted in complete remission. Therefore, such sequential approach using CAR-T/BsAb might have a potential to further enhance antitumor effects.

A group at Niigata University (Japan) has investigated the pathogenic mechanisms of nephrotic syndrome, one of the most prevalent kidney diseases, which presents severe proteinuria, and revealed that elevated activity of cdc42, a small GTPase, caused by anti-nephrin antibody is a critical initiation event leading to proteinuria. The major parts of nephrotic syndrome are reported to be induced by autoantibody against nephrin. Suppression of cdc42 activity could be a promising therapy for nephrotic syndrome.

What happens when things combine? This question lies at the heart of the Borell-Brascamp-Lieb inequality (BBL), a mathematical relation widely applied across many fields of mathematics, science and beyond. Now, researchers from the Okinawa Institute of Science and Technology (OIST), University of Tokyo and University of Florence have provided a new proof for this powerful inequality, taking an unconventional approach based on heat and diffusion equations.

Inspired by biology, researchers have achieved the highest performing underwater adhesive hydrogel technology to date through a data mining and machine learning approach.

Researchers from the National Institute of Physiological Sciences found that ‘denoising’, a method used to clean diffusion-weighted MRI (dMRI) data, alters the appearance of dMRI images and the estimated signal-to-noise ratio of the data, but had limited impacts on the ability to detect optic tract abnormalities in glaucoma.

Drug delivery systems (DDSs) like nanoparticles, hydrogels, micelles, or liposomes improve  drug availability while minimizing off-target distribution and side effects. Nanoparticle-based DDSs further enhance functionality owing to their larger surface area, leading to better stability, targeted delivery, and controlled drug release. But their low drug-loading capacity hinders desired therapeutic effect. Now, however, Professor Motoichi Kurisawa from Japan Advanced Institute of Science and Technology addresses this challenge with a green tea nanoparticle-based DDSs.

Professor Shogo Okada leads the Social Signal and Multimodal Interaction Laboratory at Japan Advanced Institute of Science and Technology (JAIST). His lab focuses on developing computational models of multimodal social signals – speech, voice tone, body language – to understand their impact on social skills. His lab develops emotionally sensitive AI systems by combining multimodal sensing technology, AI, and cognitive science. Prof. Okada aims to build a harmonious future where technological advances and humane abilities go hand-in-hand.