Integrated micro power generators are crucial components for micro robotic platforms to demonstrate untethered operation and to achieve autonomy. Current micro robotic electrostatic actuators typically require hundreds to thousands of voltages to output sufficient work. Pyroelectricity is one such source of high voltages that can be scaled to small form factors. This paper demonstrates a distributed pyroelectric high voltage generation mechanism to power kV actuators using alternating exposure of crystals to hot and cold water (300C to 900C water temperature). Using this fluidic temperature control, a pyroelectrically generated voltage of 2470 V was delivered to a 2 pF storage capacitor yielding a 6.10 {\mu}J stored energy. A maximum energy of 17.46 {\mu}J was delivered to a 47 pF capacitor at 861 V. The recirculating water can be used to heat a distributed array of converters to generate electricity in distant robotic actuator sections. The development of this distributed system would enable untethered micro-robot to be operated with a flexible body and free of battery recharging, which advances its applications in the real world.

A fully Bayesian treatment of complicated predictive models (such as deep neural networks) would enable rigorous uncertainty quantification and the automation of higher-level tasks including model selection. However, the intractability of sampling Bayesian posteriors over many parameters inhibits the use of Bayesian methods where they are most needed. Thermodynamic computing has emerged as a paradigm for accelerating operations used in machine learning, such as matrix inversion, and is based on the mapping of Langevin equations to the dynamics of noisy physical systems. Hence, it is natural to consider the implementation of Langevin sampling algorithms on thermodynamic devices. In this work we propose electronic analog devices that sample from Bayesian posteriors by realizing Langevin dynamics physically. Circuit designs are given for sampling the posterior of a Gaussian-Gaussian model and for Bayesian logistic regression, and are validated by simulations. It is shown, under reasonable assumptions, that the Bayesian posteriors for these models can be sampled in time scaling with $\ln(d)$, where $d$ is dimension. For the Gaussian-Gaussian model, the energy cost is shown to scale with $ d \ln(d)$. These results highlight the potential for fast, energy-efficient Bayesian inference using thermodynamic computing.

In this paper we propose a periodic solution to the problem of persistently covering a finite set of interest points with a group of autonomous mobile agents. These agents visit periodically the points and spend some time carrying out the coverage task, which we call coverage time. Since this periodic persistent coverage problem is NP-hard, we split it into three subproblems to counteract its complexity. In the first place, we plan individual closed paths for the agents to cover all the points. Second, we formulate a quadratically constrained linear program to find the optimal coverage times and actions that satisfy the coverage objective. Finally, we join together the individual plans of the agents in a periodic team plan by obtaining a schedule that guarantees collision avoidance. To this end, we solve a mixed integer linear program that minimizes the time in which two or more agents move at the same time. Eventually, we apply the proposed solution to an induction hob with mobile inductors for a domestic heating application and show its performance with experiments on a real prototype.

Retrieval-Augmented Generation (RAG), by incorporating external knowledge with parametric memory of language models, has become the state-of-the-art architecture for open-domain QA tasks. However, common knowledge bases are inherently constrained by limited coverage and noisy information, making retrieval-based approaches inadequate to answer implicit reasoning questions. In this paper, we propose an Induction-Augmented Generation (IAG) framework that utilizes inductive knowledge along with the retrieved documents for implicit reasoning. We leverage large language models (LLMs) for deriving such knowledge via a novel prompting method based on inductive reasoning patterns. On top of this, we implement two versions of IAG named IAG-GPT and IAG-Student, respectively. IAG-GPT directly utilizes the knowledge generated by GPT-3 for answer prediction, while IAG-Student gets rid of dependencies on GPT service at inference time by incorporating a student inductor model. The inductor is firstly trained via knowledge distillation and further optimized by back-propagating the generator feedback via differentiable beam scores. Experimental results show that IAG outperforms RAG baselines as well as ChatGPT on two Open-Domain QA tasks. Notably, our best models have won the first place in the official leaderboards of CSQA2.0 (since Nov 1, 2022) and StrategyQA (since Jan 8, 2023).

After a decade of research, we developed a prototype device and experimentally demonstrated that the direct phi q interaction could be memristive, as predicted by Chua in 1971. With a constant input current to avoid any parasitic inductor effect, our device meets three criteria for an ideal memristor: a single valued, nonlinear, continuously differentiable, and strictly monotonically increasing constitutive phi q curve, a pinched v i hysteresis loop, and a charge only dependent resistance. Our work represents a step forward in terms of experimentally verifying the memristive flux charge interaction but we have not reached the final because this prototype still suffers from two serious limitations: 1, a superficial but dominant inductor effect (behind which the above memristive fingerprints hide) due to its inductor-like core structure, and 2. bistability and dynamic sweep of a continuous resistance range. In this article, we also discuss how to make a fully functioning ideal memristor with multiple or an infinite number of stable states and no parasitic inductance, and give a number of suggestions, such as open structure, nanoscale size, magnetic materials with cubic anisotropy (or even isotropy), and sequential switching of the magnetic domains. Additionally, we respond to a recent challenge from arXiv.org that claims that our device is simply an inductor with memory since our device did not pass their designed capacitor-memristor circuit test. Contrary to their conjecture that an ideal memristor may not exist or may be a purely mathematical concept, we remain optimistic that researchers will discover an ideal memristor in nature or make one in the laboratory based on our current work.

The manual design of analog circuits is a tedious task of parameter tuning that requires hours of work by human experts. In this work, we make a significant step towards a fully automatic design method that is based on deep learning. The method selects the components and their configuration, as well as their numerical parameters. By contrast, the current literature methods are limited to the parameter fitting part only. A two-stage network is used, which first generates a chain of circuit components and then predicts their parameters. A hypernetwork scheme is used in which a weight generating network, which is conditioned on the circuit's power spectrum, produces the parameters of a primal RNN network that places the components. A differential simulator is used for refining the numerical values of the components. We show that our model provides an efficient design solution, and is superior to alternative solutions.