High-throughput optical neural networks based on temporal computing

An emerging generative artificial intelligence (AI) based on neural networks starts to grow in popularity with a revolutionizing capability of creating new and original content. As giant generative models with millions to billions of parameters are developed, trained and maintained, a massive and energy-efficient computing power is highly required. However, conventional digital computers are struggling to keep up with the pace of the generative model improvements. In this paper, we propose and demonstrate high-throughput optical neural networks based on temporal computing. The core weighted summation operation is realized with the use of high-speed electro-optic modulation and low-speed balanced photodetection. The input data and weight are encoded in a time sequence separately and loaded on an optical signal via two electro-optic modulators sequentially. By precisely controlling the synchronization time of the data and weight loading, the matrix multiplication is performed. Followed by a balanced photodetector, the summation is conducted, thanks to the electron accumulation of the inherent electronic integrator circuit of the low-speed photodetector. Thus, the linear weighted summation operation is implemented based on temporal computing in the optical domain. With the proposed optical linear weighted summation, a fully-connected neural network and convolutional neural network are realized. Thanks to the high-speed feature of temporal computing, a high data throughput of the optical neural network is experimentally demonstrated, and the weighting coefficients can be specified on demand, which enables a strong programmability of the optical neural network. By leveraging wavelength multiplexing technology, a scalable optical neural network could be created with a massive computing power and strong reconfigurability, which holds great potential for future giant AI applications

Ultrafast Relaxation Dynamics of Photoexcited Dirac Fermion in The Three Dimensional Dirac Semimetal Cadmium Arsenide

Three dimensional (3D) Dirac semimetals which can be seen as 3D analogues of graphene have attracted enormous interests in research recently. In order to apply these ultrahigh-mobility materials in future electronic/optoelectronic devices, it is crucial to understand the relaxation dynamics of photoexcited carriers and their coupling with lattice. In this work, we report ultrafast transient reflection measurements of the photoexcited carrier dynamics in cadmium arsenide (Cd3As2), which is one of the most stable Dirac semimetals that have been confirmed experimentally. By using low energy probe photon of 0.3 eV, we probed the dynamics of the photoexcited carriers that are Dirac-Fermi-like approaching the Dirac point. We systematically studied the transient reflection on bulk and nanoplate samples that have different doping intensities by tuning the probe wavelength, pump power and lattice temperature, and find that the dynamical evolution of carrier distributions can be retrieved qualitatively by using a two-temperature model. This result is very similar to that of graphene, but the carrier cooling through the optical phonon couplings is slower and lasts over larger electron temperature range because the optical phonon energies in Cd3As2 are much lower than those in graphene

Direct Shooting Method for Numerical Optimal Control: A Modified Transcription Approach

Direct shooting is an efficient method to solve numerical optimal control. It utilizes the Runge-Kutta scheme to discretize a continuous-time optimal control problem making the problem solvable by nonlinear programming solvers. However, conventional direct shooting raises a contradictory dynamics issue when using an augmented state to handle {high-order} systems. This paper fills the research gap by considering the direct shooting method for {high-order} systems. We derive the modified Euler and Runge-Kutta-4 methods to transcribe the system dynamics constraint directly. Additionally, we provide the global error upper bounds of our proposed methods. A set of benchmark optimal control problems shows that our methods provide more accurate solutions than existing approaches.Comment: Accepted by ECC2

Tip induced unconventional superconductivity on Weyl semimetal TaAs

Weyl fermion is a massless Dirac fermion with definite chirality, which has been long pursued since 1929. Though it has not been observed as a fundamental particle in nature, Weyl fermion can be realized as low-energy excitation around Weyl point in Weyl semimetal, which possesses Weyl fermion cones in the bulk and nontrivial Fermi arc states on the surface. As a firstly discovered Weyl semimetal, TaAs crystal possesses 12 pairs of Weyl points in the momentum space, which are topologically protected against small perturbations. Here, we report for the first time the tip induced superconductivity on TaAs crystal by point contact spectroscopy. A conductance plateau and sharp double dips are observed in the point contact spectra, indicating p-wave like unconventional superconductivity. Furthermore, the zero bias conductance peak in low temperature regime is detected, suggesting potentially the existence of Majorana zero modes. The experimentally observed tunneling spectra can be interpreted with a novel mirror-symmetry protected topological superconductor induced in TaAs, which can exhibit zero bias and double finite bias peaks, and double conductance dips in the measurements. Our work can open a broad avenue in search for new topological superconducting phases from topological Weyl materials and trigger intensive investigations for pursuing Majorana fermions

Gene Expression Profiling of Skeletal Muscle of Nursing Piglets

To gain insight into the regulation mechanism associated with the rapid gain in skeletal muscle during neonatal period, gene expression profiles of skeletal muscle of nursing pigs was investigated using Affymetrix Porcine GeneChip. A total of 1094 transcripts were detected as differential expression over time course tested (p<0.01, q<0.05). With combinative use of partitioning around medoid and hierarchical clustering, three clusters of transcripts with distinct temporal expression were defined. Gene functional categories and pathways, particularly involved in cell signaling, cell cycle, cell adhesion, ECM-receptor interaction, glycolysis, protein synthesis and degradation, and intracellular transport, were identified. Moreover, we showed 49 of the differentially expressed genes within published QTL regions or with marked deletion effects. Our study demonstrates previously uncharacterized changes in transcription accompanying early postnatal growth of skeletal muscle of pigs. It has highlighted potential cascades and important candidates for further investigation on controlling of postnatal muscle growth

Giant Anomalous Hall and Nernst Effects in a Heavy Fermion Ferromagnet

The anomalous Hall and Nernst effects describe the voltage drop perpendicular to an applied current and temperature gradient due to the magnetization of a magnetic material. These effects can be utilized to measure the Berry curvature at the Fermi energy, and have potential applications in future electronic devices and thermoelectric energy conversion. In this paper, we report giant anomalous Hall conductivity and anomalous Nernst coefficient, as high as about 1000 $\Omega^{-1}$ cm$^{-1}$ and 10 $\mu$V K$^{-1}$, respectively, in a heavy fermion ferromagnet, CeCrGe$_3$. This compound uniquely manifests strong hybridization between the 4$f$ and conduction electrons, leading to a Kondo lattice state in the presence of ferromagnetic order. Unlike conventional topological semimetals in which the electron correlation is weak, CeCrGe$_3$ manifests a strong Berry curvature field of the heavy fermion with an extremely low Fermi energy. Our findings pave the way for exploring correlation-driven topological responses in a ferromagnetic Kondo lattice environment.Comment: 22 pages, 5 figure

GMPC: Geometric Model Predictive Control for Wheeled Mobile Robot Trajectory Tracking

The configuration of most robotic systems lies in continuous transformation groups. However, in mobile robot trajectory tracking, many recent works still naively utilize optimization methods for elements in vector space without considering the manifold constraint of the robot configuration. In this letter, we propose a geometric model predictive control (MPC) framework for wheeled mobile robot trajectory tracking. We first derive the error dynamics of the wheeled mobile robot trajectory tracking by considering its manifold constraint and kinematic constraint simultaneously. After that, we utilize the relationship between the Lie group and Lie algebra to convexify the tracking control problem, which enables us to solve the problem efficiently. Thanks to the Lie group formulation, our method tracks the trajectory more smoothly than existing nonlinear MPC. Simulations and physical experiments verify the effectiveness of our proposed methods. Our pure Python-based simulation platform is publicly available to benefit further research in the community

A 36 µW 1.1 mm2 reconfigurable analog front-end for cardiovascular and respiratory signals recording

© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksThis paper presents a 1.2 V 36 µW reconfigurable analog front-end (R-AFE) as a general-purpose low-cost IC for multiple-mode biomedical signals acquisition. The R-AFE efficiently reuses a reconfigurable preamplifier, a current generator (CG), and a mixed signal processing unit, having an area of 1.1 mm2 per R-AFE while supporting five acquisition modes to record different forms of cardiovascular and respiratory signals. The R-AFE can interface with voltage-, current-, impedance-, and light-sensors and hence can measure electrocardiography (ECG), bio-impedance (BioZ), photoplethysmogram (PPG), galvanic skin response (GSR), and general-purpose analog signals. Thanks to the chopper preamplifier and the low-noise CG utilizing dynamic element matching, the R-AFE mitigates 1/f noise from both the preamplifier and the CG for improved measurement sensitivity. The IC achieves competitive performance compared to the state-of-the-art dedicated readout ICs of ECG, BioZ, GSR, and PPG, but with approximately 1.4×-5.3× smaller chip area per channel.Peer ReviewedPostprint (author's final draft