Infrared focal plane arrays (FPAs) are image sensors, widely used in thermal imaging systems across various fields. Future advancements in FPAs are expected to require high data rates, which can cause heat leakage from electrical interconnects into FPAs, increasing noise and raising cooling requirements. In a new study, researchers demonstrated cryogenic vertical-cavity surface-emitting laser (VCSEL)-based optical interconnects that can achieve high-speed communication while reducing thermal leakage and power consumption.

SJTU researchers propose a dynamic MCPV model and CZone mechanism, slashing memory footprint by 90%.

Waveguide-based electro-optic (E-O) frequency combs are prized for their flexible pump wavelength, tunable repetition rate and near-100% conversion efficiency, but their spectral span is usually limited to a few nanometers. Researchers now report a broadband waveguide E-O comb based on "mode circulation": a single beam is recirculated through the same phase-modulation region eight times as four distinct waveguide modes (TE0–TE3). Enabled by a Z-propagation mode multiplexer that suppresses mode hybridization in anisotropic lithium-niobate-on-insulator waveguides, the device boosts the modulation index by ~7.5×, generating 128 comb lines across 25.6 nm at 25 GHz with an effective half-wave voltage of just 1.24 V and only 7 dB on-chip loss.

A new study published in the Chinese Journal of Aeronautics （https://doi.org/10.1016/j.cja.2025.103943） tries to break this deadlock. Researchers from Northwestern Polytechnical University and Tsinghua University have developed a novel modeling framework called differential equation model long-short term memory neural network (DEM-LSTM). This hybrid approach integrates the theoretical rigor of differential equations with the nonlinear mapping capabilities of LSTM neural network, offering a solution that is both accurate and physically interpretable.

The high annotation costs in medical detection tasks make semi-supervised object detection (SSOD) methods particularly promising. However, current research has paid little attention to medical image SSOD, and conventional SSOD approaches struggle with the unique challenges of medical imaging—scarce annotations and difficult-to-learn features.

Sequential recommendation is an important research task in the field of recommendation systems. The goal of the sequential recommendation task can be defined as follows: given a user’s interaction sequence and the associated timestamp sequence, predict the next item that the user is likely to interact with at the next time step. The existing methods have limitations in capturing the diversity of long-term interests, the dynamics of short-term user interest, and the hierarchical relationship between them.