Efficient scaling of large language models with mixture of experts and 3D analog in-memory computing.

Large language models (LLMs), with their remarkable generative capacities, have greatly impacted a range of fields, but they face scalability challenges due to their large parameter counts, which result in high costs for training and inference. The trend of increasing model sizes is exacerbating these challenges, particularly in terms of memory footprint, latency and energy consumption. Here we explore the deployment of 'mixture of experts' (MoEs) networks-networks that use conditional computing to keep computational demands low despite having many parameters-on three-dimensional (3D) non-volatile memory (NVM)-based analog in-memory computing (AIMC) hardware.

Demonstration of high-reconfigurability and low-power strong physical unclonable function empowered by FeFET cycle-to-cycle variation and charge-domain computing.

Physical unclonable functions (PUFs) are of immense potential in authentication scenarios for Internet of Things (IoT) devices. For creditable and lightweight PUF applications, key attributes, including low power, high reconfigurability and large challenge-response pair (CRP) space, are desirable. Here, we report a ferroelectric field-effect transistor (FeFET)-based strong PUF with high reconfigurability and low power, which leverages the FeFET cycle-to-cycle variation throughout the workflow and introduces charge-domain in-memory computing.

Duchenne Muscular Dystrophy in Two Half-Brothers Due to Inherited 306 Kb Inverted Insertion of 10p15.1 into Intron 44 of the Dp427m Transcript of the Gene.

Duchenne muscular dystrophy (DMD) is a rare genetic disorder caused by the absence of a fully functional dystrophin protein in myocytes. In skeletal muscle, the lack of dystrophin ultimately results in muscle wasting and the replacement of myocytes with fatty or fibrous tissues. In the heart, cardiomyocytes eventually fail and cause fatal cardiomyopathy.

Morphometric Analysis of the Infraorbital Canal, Groove, and Foramen in the Indian Population: A Retrospective Analytical Study.

Aim: The aim of this research is to analyse and assess the anatomical variability of the infraorbital canal, groove, and foramen using high-resolution CT images of the Indian population using mimics software.

Materials And Methods: A total of 100 high-resolution CT (HRCT) data (200 hemi-faces) with a range of 18-65 years in Digital Imaging and Communication in Medicine (DICOM) format was incorporated in mimics software. Infraorbital foramen (IOF), infraorbital groove (IOG), and infraorbital canal (IOC) were evaluated retrospectively using seven parameters.

Feasibility of snapshot testing using wearable sensors to detect cardiorespiratory illness (COVID infection in India).

The COVID-19 pandemic has challenged the current paradigm of clinical and community-based disease detection. We present a multimodal wearable sensor system paired with a two-minute, movement-based activity sequence that successfully captures a snapshot of physiological data (including cardiac, respiratory, temperature, and percent oxygen saturation). We conducted a large, multi-site trial of this technology across India from June 2021 to April 2022 amidst the COVID-19 pandemic (Clinical trial registry name: International Validation of Wearable Sensor to Monitor COVID-19 Like Signs and Symptoms; NCT05334680; initial release: 04/15/2022).