Search

In-memory computing (IMC) architectures mitigate the von Neumann bottleneck encountered in traditional deep learning accelerators. Its energy efficiency can realize deep learning-based edge applications. However, because IMC is implemented using analog circuits, inherent non-idealities in the hardware pose significant challenges. This paper presents physical neural networks (PNNs) for constructing physical models of IMC. PNNs can address the synaptic current's dependence on membrane potential, a challenge in charge-domain IMC systems. The proposed model is mathematically equivalent to spiking neural networks with reversal potentials. With a novel technique called differentiable spike-time discretization, the PNNs are efficiently trained. We show that hardware non-idealities traditionally viewed as detrimental can enhance the model's learning performance. This bottom-up methodology was validated by designing an IMC circuit with non-ideal characteristics using the sky130 process. When employing this bottom-up approach, the modeling error reduced by an order of magnitude compared to conventional top-down methods in post-layout simulations., Comment: 53 pages, 20 figures

Controlling hands in the high-dimensional action space has been a longstanding challenge, yet humans naturally perform dexterous tasks with ease. In this paper, we draw inspiration from the human embodied cognition and reconsider dexterous hands as learnable systems. Specifically, we introduce MoDex, a framework which employs a neural hand model to capture the dynamical characteristics of hand movements. Based on the model, a bidirectional planning method is developed, which demonstrates efficiency in both training and inference. The method is further integrated with a large language model to generate various gestures such as ``Scissorshand" and ``Rock\&Roll." Moreover, we show that decomposing the system dynamics into a pretrained hand model and an external model improves data efficiency, as supported by both theoretical analysis and empirical experiments. Additional visualization results are available at https://tongwu19.github.io/MoDex., Comment: 7 pages

This study examines the impact of the COVID-19 pandemic on information-seeking behaviors among international students, with a focus on the r/f1visa subreddit. Our study indicates a considerable rise in the number of users posting more than one question during the pandemic. Those asking recurring questions demonstrate more active involvement in communication, suggesting a continuous pursuit of knowledge. Furthermore, the thematic focus has shifted from questions about jobs before COVID-19 to concerns about finances, school preparations, and taxes during COVID-19. These findings carry implications for support policymaking, highlighting the importance of delivering timely and relevant information to meet the evolving needs of international students. To enhance international students' understanding and navigation of this dynamic environment, future research in this field is necessary., Comment: 10th International Conference on Computational Social Science IC2S2, July 17-20, 2024, Philadelphia, USA

This paper studies an optimization problem for joint radial distribution system network reconfiguration and power dispatch for distributed generation (DG) maximization. We provide counterexamples to show that for DG maximization, standard techniques such as interior point method (as in Matpower), linear approximation and second order cone relaxation (e.g., by Jabr et.~al.~and by Farivar and Low) do not deliver the desired control. Instead, we propose a control decision model based on exact DistFlow equations bypassing relaxation and a solution approach based on spatial branch-and-bound algorithm. We justify our work with comparative studies and numerical demonstrations with benchmarks and a 533-bus real example, performing reconfiguration and power dispatch in a time scale relevant for control center applications.

The inflationary universe creates particle pairs, which are entangled in their momenta due to momentum conservation. Operators involving the momenta of the fluctuations can be rewritten into pseudo-spin operators, such as the Gour-Khanna-Mann-Revzen (GKMR) pseudo-spin. Making use of these pseudo-spin operators, cosmological Bell inequalities can be formulated. The violation of these Bell inequalities indicates the quantum nature of primordial fluctuations. In this work, we focus on primordial curvature perturbations. Since curvature perturbations arise from gravity, their action includes the Gibbons-Hawking-York boundary term. We clarify the role of the boundary term in selecting suitable initial conditions for linear perturbations. After that, we proceed to the interactions of cosmological perturbations, including the bulk and boundary interaction terms, which introduce decoherence effects. These decoherence effects change the expectation value of the Bell operator, and gradually restore the Bell inequality. We describe this process by a ``Bell test curve'', which offers a window around 5 e-folds for testing the quantum origin of cosmological perturbations. We also explore the possibility of extracting the information of the decoherence rate and the structure of primordial interactions from the Bell test curve., Comment: v3: 44 pages, 6 figures; an extra appendix and figures are added; typos are corrected. Match the JCAP version

Manganese telluride (MnTe) has garnered strong interest recently for its antiferromagnetic semiconductor properties, which are promising for applications in spintronics, data storage, and quantum computing. In this study, we discovered that the deposition of FeTe at 300oC onto zinc-blende MnTe (ZB-MnTe) via molecular beam epitaxy (MBE) results in a phase transition from ZB-MnTe to a layered MnTe (l-MnTe) phase with van der Waals (vdW) gaps, which is a previously unreported phase of MnTe. The l-MnTe phase was characterized using cross-sectional high-angle annular dark-field (HAADF) imaging, energy-dispersive X-ray spectroscopy (EDS) mapping, and X-ray photoelectron spectroscopy (XPS). The Fe/Te flux ratio during FeTe deposition was found to be critical to the phase transition, an increased Fe/Te flux ratio used for the FeTe growth leads to localized formation of layered Mn4Te3 (l-Mn4Te3), while a decreased Fe/Te flux ratio only generates a single monolayer of l-MnTe at the interface and the rest turns into a distorted ZB-MnTe (dZB-MnTe) phase. It was also found that FT-MT heterostructures grown at a lower substrate temperature of 250oC, as the Fe/Te flux ratio decreases, the ZB-MnTe layer was first transformed to dZB-MnTe and then to wurtzite MnTe (WZ-MnTe). The FeTe/l-MnTe heterostructure exhibits high-quality superconducting properties with a three-dimensional nature as demonstrated by its magneto-transport properties and there is evidence that l-MnTe seems to play a key role in inducing the observed superconductivity. Most importantly, this study reports the realization of layered structures of MnTe by an in-situ approach via chemical interactions, which might be further applied to generating unprecedented phases of materials under certain conditions., Comment: 25 pages, 10 figures

Most vision-based tactile sensors use elastomer deformation to infer tactile information, which can not sense some modalities, like temperature. As an important part of human tactile perception, temperature sensing can help robots better interact with the environment. In this work, we propose a novel multimodal vision-based tactile sensor, SATac, which can simultaneously perceive information of temperature, pressure, and shear. SATac utilizes thermoluminescence of strontium aluminate (SA) to sense a wide range of temperatures with exceptional resolution. Additionally, the pressure and shear can also be perceived by analyzing Voronoi diagram. A series of experiments are conducted to verify the performance of our proposed sensor. We also discuss the possible application scenarios and demonstrate how SATac could benefit robot perception capabilities.

The Bi-Te binary system, characterized by the homologous series of the (Bi2)m(Bi2Te3)n, has always attracted research interest for its layered structures and potential in advanced materials applications. Despite Bi2Te3 has been extensively studied, exploration of other compounds has been constrained by synthesis challenges. This study reports the molecular beam epitaxy (MBE) growth of FeTe on Bi2Te3, demonstrating that varying growth conditions can turn the Bi2Te3 layer into different Bi-Te phases and form corresponding FeTe/Bi-Te heterostructures. Our combined analysis using reflection high-energy electron diffraction (RHEED), high-resolution X-ray diffraction (HRXRD), and high-resolution scanning transmission electron microscopy (HR-STEM), indicates that specific growth conditions used for the growth of the FeTe layer can facilitate the extraction of Te from Bi2Te3, leading to the formation of Bi4Te3 and Bi6Te3. Additionally, by lowering the FeTe growth temperature to 230 oC, Te extraction from the Bi2Te3 layer could be avoided, preserving the Bi2Te3 structure. Notably, all the three FeTe/Bi-Te structures exhibit superconductivity with the FeTe/Bi2Te3 heterostructure enjoying the highest superconductivity quality. These findings introduce a novel method for realizing Bi4Te3 and Bi6Te3 through Te extraction by growing FeTe on Bi2Te3, driven by the high reactivity between Fe and Te. This approach holds promise for synthesizing other members of the Bi-Te series, expanding the functional potential of these materials., Comment: 25 pages, 10 figures

Quasi-Monte Carlo (QMC) methods have developed over several decades. With the explosion in computational science, there is a need for great software that implements QMC algorithms. We summarize the QMC software that has been developed to date, propose some criteria for developing great QMC software, and suggest some steps toward achieving great software. We illustrate these criteria and steps with the Quasi-Monte Carlo Python library (QMCPy), an open-source community software framework, extensible by design with common programming interfaces to an increasing number of existing or emerging QMC libraries developed by the greater community of QMC researchers.

This paper studies a distributed online convex optimization problem, where agents in an unbalanced network cooperatively minimize the sum of their time-varying local cost functions subject to a coupled inequality constraint. To solve this problem, we propose a distributed dual subgradient tracking algorithm, called DUST, which attempts to optimize a dual objective by means of tracking the primal constraint violations and integrating dual subgradient and push sum techniques. Different from most existing works, we allow the underlying network to be unbalanced with a column stochastic mixing matrix. We show that DUST achieves sublinear dynamic regret and constraint violations, provided that the accumulated variation of the optimal sequence grows sublinearly. If the standard Slater's condition is additionally imposed, DUST acquires a smaller constraint violation bound than the alternative existing methods applicable to unbalanced networks. Simulations on a plug-in electric vehicle charging problem demonstrate the superior convergence of DUST.

Modern Visual-Based Tactile Sensors (VBTSs) use cost-effective cameras to track elastomer deformation, but struggle with ambient light interference. Solutions typically involve using internal LEDs and blocking external light, thus adding complexity. Creating a VBTS resistant to ambient light with just a camera and an elastomer remains a challenge. In this work, we introduce WStac, a self-illuminating VBTS comprising a mechanoluminescence (ML) whisker elastomer, camera, and 3D printed parts. The ML whisker elastomer, inspired by the touch sensitivity of vibrissae, offers both light isolation and high ML intensity under stress, thereby removing the necessity for additional LED modules. With the incorporation of machine learning, the sensor effectively utilizes the dynamic contact variations of 25 whiskers to successfully perform tasks like speed regression, directional identification, and texture classification. Videos are available at: https://sites.google.com/view/wstac/.

In response to newly found security vulnerabilities, or as part of a moving target defense, a fast and safe control software update scheme for networked control systems is highly desirable. We here develop such a scheme for intelligent electronic devices (IEDs) in power distribution systems, which is a solution to the so-called software update rollout problem. This problem seeks to minimize the makespan of the software rollout, while guaranteeing safety in voltage and current at all buses and lines despite possible worst-case update failure where malfunctioning IEDs may inject harmful amounts of power into the system. Based on the nonlinear DistFlow equations, we derive linear relations relating software update decisions to the worst-case voltages and currents, leading to a decision model both tractable and more accurate than previous models based on the popular linearized DistFlow equations. Under reasonable protection assumptions, the rollout problem can be formulated as a vector bin packing problem and instances can be built and solved using scalable computations. Using realistic benchmarks including one with 10,476 buses, we demonstrate that the proposed method can generate safe and effective rollout schedules in real-time.

It is well-known that the primordial scalar curvature and tensor perturbations, $\zeta$ and $\gamma_{ij}$, are conserved on super-horizon scales in minimal inflation models. However, their wave functional has a rapidly oscillating phase which is slow-roll unsuppressed, as can be seen either from boundary (total-derivative) terms of cosmological perturbations, or the WKB approximation of the Wheeler-DeWitt equation. Such an oscillatory phase involves gravitational non-linearity between scalar and tensor perturbations. By tracing out unobserved modes, the oscillatory phase causes faster decoherence of primordial gravitons compared to those by bulk interactions. Our results put a stronger lower bound of decoherence effect to the recent proposals probing squeezed primordial gravitons., Comment: 42 pages, 3 figures, 1 table, references added, typos corrected

Reservoir computing (RC) can efficiently process time-series data by transferring the input signal to randomly connected recurrent neural networks (RNNs), which are referred to as a reservoir. The high-dimensional representation of time-series data in the reservoir significantly simplifies subsequent learning tasks. Although this simple architecture allows fast learning and facile physical implementation, the learning performance is inferior to that of other state-of-the-art RNN models. In this paper, to improve the learning ability of RC, we propose self-modulated RC (SM-RC), which extends RC by adding a self-modulation mechanism. The self-modulation mechanism is realized with two gating variables: an input gate and a reservoir gate. The input gate modulates the input signal, and the reservoir gate modulates the dynamical properties of the reservoir. We demonstrated that SM-RC can perform attention tasks where input information is retained or discarded depending on the input signal. We also found that a chaotic state emerged as a result of learning in SM-RC. This indicates that self-modulation mechanisms provide RC with qualitatively different information-processing capabilities. Furthermore, SM-RC outperformed RC in NARMA and Lorentz model tasks. In particular, SM-RC achieved a higher prediction accuracy than RC with a reservoir 10 times larger in the Lorentz model tasks. Because the SM-RC architecture only requires two additional gates, it is physically implementable as RC, providing a new direction for realizing edge AI.