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Introduction Securing wireless communications in internet-of-things (IoT) requires both generation and synchronization of random numbers in real-time. However, resource constraints on an IoT device limit the use of computationally intensive random number generators and the use of global positioning systems (GPS) for synchronization. In this paper, we propose a synchronized pseudo-random number generator (SPRNG) that uses a combination of a fast, low-complexity linear-feedback-shift-register (LFSR) based PRNG and a slow but secure, synchronized seed generator based on self-powered timers. Methods A prototype synchronized self-powered timer (SSPT) array was fabricated in a standard silicon process and was used to generate dynamic random seeds for the LFSR. The SSPTs use quantum-mechanical tunneling of electrons to operate without any external power and are practically secure against tampering, snooping, and side-channel attacks (both power and electromagnetic). Results In this work, we explore protocols to periodically and securely generate random bits using the self-powered timers for seeding the LFSR. We also show that the time-varying random seeds extend and break the LFSR periodic cycles, thus making it difficult for an attacker to predict the random output or the random seed. Using the National Institute of Standards and Technology (NIST) test suite we verify the randomness of the measured seeds from the fabricated ensemble of SSPTs together with the random bit sequences generated by a software-seeded LFSR. Discussions In this modality, the proposed SPRNG could be used as a trusted platform module (TPM) on IoTs and used for verifying and authenticating secure transactions (e.g., software upgrades). Since the SPRNG system does not require access to GPS for synchronization, therefore it could be used in many resource-constrained and adversarial environments. 

Introduction For artificial synapses whose strengths are assumed to be bounded and can only be updated with finite precision, achieving optimal memory consolidation using primitives from classical physics leads to synaptic models that are too complex to be scaled in-silico . Here we report that a relatively simple differential device that operates using the physics of Fowler-Nordheim (FN) quantum-mechanical tunneling can achieve tunable memory consolidation characteristics with different plasticity-stability trade-offs. Methods A prototype FN-synapse array was fabricated in a standard silicon process and was used to verify the optimal memory consolidation characteristics and used for estimating the parameters of an FN-synapse analytical model. The analytical model was then used for large-scale memory consolidation and continual learning experiments. Results We show that compared to other physical implementations of synapses for memory consolidation, the operation of the FN-synapse is near-optimal in terms of the synaptic lifetime and the consolidation properties. We also demonstrate that a network comprising FN-synapses outperforms a comparable elastic weight consolidation (EWC) network for some benchmark continual learning tasks. Discussions With an energy footprint of femtojoules per synaptic update, we believe that the proposed FN-synapse provides an ultra-energy-efficient approach for implementing both synaptic memory consolidation and continual learning on a physical device. 

In 2022, one of its worst cholera outbreaks began in Bangladesh, and the Dhaka hospital treated more than 1300 patients and ca. 42,000 diarrheal cases from March-1 to April-10, 2022. Here, we present genomic attributes of V. cholerae O1 responsible for the 2022 Dhaka outbreak and 960 7th pandemic El Tor (7PET) strains from 88 countries. Results show strains isolated during the Dhaka outbreak cluster with 7PET wave-3 global clade strains, but comprise subclade BD-1.2, for which the most recent common ancestor appears to be that responsible for recent endemic cholera in India. BD-1.2 strains are present in Bangladesh since 2016, but not establishing dominance over BD-2 lineage strains until 2018 and predominantly associated with endemic cholera. In conclusion, the recent shift in lineage and genetic attributes, including serotype switching of BD-1.2 from Ogawa to Inaba, may explain the increasing number of cholera cases in Bangladesh. 

Abstract In this paper we present an adaptive synaptic array that can be used to improve the energy-efficiency of training machine learning (ML) systems. The synaptic array comprises of an ensemble of analog memory elements, each of which is a micro-scale dynamical system in its own right, storing information in its temporal state trajectory. The state trajectories are then modulated by a system level learning algorithm such that the ensemble trajectory is guided towards the optimal solution. We show that the extrinsic energy required for state trajectory modulation can be matched to the dynamics of neural network learning which leads to a significant reduction in energy-dissipated for memory updates during ML training. Thus, the proposed synapse array could have significant implications in addressing the energy-efficiency imbalance between the training and the inference phases observed in artificial intelligence (AI) systems.