Search

The negative herding effect tends to cause the spread of negative emotions among netizens, resulting in significant public opinion crises. Therefore, linking the negative herding effect and online public opinion, we aim to exploring the connection between netizens' emotions, opinions, and the negative herding effect. To better distinguish the emotions in texts, a new BiGADG (Bidirectional Gated recurrent unit based on Attention with Distance‐based dependency syntax Graph convolutional network) model is proposed in this article. This model includes primarily the proposed dependency syntax based on dependency distance, Bidirectional Gated Recurrent Unit (BiGRU), multi‐head self‐attention and Graph Convolutional Network (GCN). Particularly, the dependency syntax based on dependency distances incorporates word‐to‐word dependency distances into the traditional dependency syntax, which enriches the syntactic structural features of the text. After the experiments, the accuracy of BiGADG model is 86.53%, which is 3.11% higher compared to BERT model. It is evident that our model for sentiment classification has been improved. Based on the results of sentiment classification, we investigate the existence of negative herding behaviour and its underlying motivation in online public opinion. We have found that the negative herding effect, if not effectively managed, may trigger large‐scale and uncontrollable public opinion crises. [ABSTRACT FROM AUTHOR]

Divisive normalization is a model of canonical computation of brain circuits. We demonstrate that two cascaded divisive normalization processors (DNPs), carrying out intensity/contrast gain control and elementary motion detection, respectively, can model the robust motion detection realized by the early visual system of the fruit fly. We first introduce a model of elementary motion detection and rewrite its underlying phase-based motion detection algorithm as a feedforward divisive normalization processor. We then cascade the DNP modeling the photoreceptor/amacrine cell layer with the motion detection DNP. We extensively evaluate the DNP for motion detection in dynamic environments where light intensity varies by orders of magnitude. The results are compared to other bio-inspired motion detectors as well as state-of-the-art optic flow algorithms under natural conditions. Our results demonstrate the potential of DNPs as canonical building blocks modeling the analog processing of early visual systems. The model highlights analog processing for accurately detecting visual motion, in both vertebrates and invertebrates. The results presented here shed new light on employing DNP-based algorithms in computer vision. [ABSTRACT FROM AUTHOR]

Direct band gap GeSn alloys have recently emerged as promising lasing source materials for monolithic integration on Si substrate. In this work, optically pumped mid-infrared GeSn lasers were studied with the observation of dual-wavelength lasing at 2187 nm and 2460 nm. Two simultaneous lasing regions include a GeSn buffer layer (bulk) and a SiGeSn/GeSn multiple quantum well structure that were grown seamlessly using a chemical vapor deposition reactor. The onset of dual lasing occurs at 420 kW/cm2. The wider bandgap SiGeSn partitioning barrier enables the independent operation of two gain regions. While the better performance device in terms of lower threshold may be obtained by using two MQW regions design, the preliminary results and discussions in this work paves a way towards all-group-IV dual wavelength lasers monolithically integrated on Si substrate. [ABSTRACT FROM AUTHOR]

Recent progress on (Si)GeSn optoelectronic devices holds great promise for photonic integration on Si substrates. In parallel to the development of bulk devices, (Si)GeSn-based quantum wells (QWs) have been investigated, aiming to improve device performance. While multiple QW structures are preferred for the device applications, a single quantum well (SQW) is more suitable for optical property studies. In this work, a comprehensive study of an SiGeSn/GeSn SQW was conducted. The calculated band diagram provided band alignment and energies of possible transitions. This SQW features a direct bandgap well with L–Γ valley energy separation of 50 meV, and barrier heights for both electron and hole are greater than 80 meV. Using two continuous-wave and two pulsed pumping lasers, the analysis of PL spectra allows for identifying different transitions and a better understanding of the SQW optical properties. This study could provide guidance for advancing the future QW design toward device applications. [ABSTRACT FROM AUTHOR]

SiGeSn material is of great interest for the development of all-group-IV lasers on a Si substrate. While GeSn-based lasers have been reported worldwide, probing the fundamental limit to lase is highly desirable to reveal the material capability as a gain medium. In this work, three GeSn-based multiple quantum well lasers, with four wells, six wells, and ten wells, were characterized. The four-well device cannot achieve lasing due to the thin active region (thickness of 81 nm), resulting in insufficient optical confinement factor. With thicker active region, both six-well (92 nm) and ten-well (136 nm) devices show clear lasing output. The ten-well sample exhibits a higher lasing temperature of 90 K. The finding of this work reveals the fundamental limit of the required optical confinement factor to achieve lasing for a Fabry–Pérot cavity edge emitting laser devices. [ABSTRACT FROM AUTHOR]

The Drosophila brain has only a fraction of the number of neurons of higher organisms such as mice and humans. Yet the sheer complexity of its neural circuits recently revealed by large connectomics datasets suggests that computationally modeling the function of fruit fly brain circuits at this scale poses significant challenges. To address these challenges, we present here a programmable ontology that expands the scope of the current Drosophila brain anatomy ontologies to encompass the functional logic of the fly brain. The programmable ontology provides a language not only for modeling circuit motifs but also for programmatically exploring their functional logic. To achieve this goal, we tightly integrated the programmable ontology with the workflow of the interactive FlyBrainLab computing platform. As part of the programmable ontology, we developed NeuroNLP++, a web application that supports free-form English queries for constructing functional brain circuits fully anchored on the available connectome/synaptome datasets, and the published worldwide literature. In addition, we present a methodology for including a model of the space of odorants into the programmable ontology, and for modeling olfactory sensory circuits of the antenna of the fruit fly brain that detect odorant sources. Furthermore, we describe a methodology for modeling the functional logic of the antennal lobe circuit consisting of a massive number of local feedback loops, a characteristic feature observed across Drosophila brain regions. Finally, using a circuit library, we demonstrate the power of our methodology for interactively exploring the functional logic of the massive number of feedback loops in the antennal lobe. [ABSTRACT FROM AUTHOR]

The fruit fly's natural visual environment is often characterized by light intensities ranging across several orders of magnitude and by rapidly varying contrast across space and time. Fruit fly photoreceptors robustly transduce and, in conjunction with amacrine cells, process visual scenes and provide the resulting signal to downstream targets. Here, we model the first step of visual processing in the photoreceptor-amacrine cell layer. We propose a novel divisive normalization processor (DNP) for modeling the computation taking place in the photoreceptor-amacrine cell layer. The DNP explicitly models the photoreceptor feedforward and temporal feedback processing paths and the spatio-temporal feedback path of the amacrine cells. We then formally characterize the contrast gain control of the DNP and provide sparse identification algorithms that can efficiently identify each the feedforward and feedback DNP components. The algorithms presented here are the first demonstration of tractable and robust identification of the components of a divisive normalization processor. The sparse identification algorithms can be readily employed in experimental settings, and their effectiveness is demonstrated with several examples. [ABSTRACT FROM AUTHOR]

This paper presents an ultra-low-power voice activity detector (VAD). It uses analog signal processing for acoustic feature extraction (AFE) directly on the microphone output, approximate event-driven analog-to-digital conversion (ED-ADC), and digital deep neural network (DNN) for speech/non-speech classification. New circuits, including the low-noise amplifier, bandpass filter, and full-wave rectifier contribute to the more than 9 $\times $ normalized power/channel reduction in the feature extraction front-end compared to the best prior art. The digital DNN is a three-hidden-layer binarized multilayer perceptron (MLP) with a 2-neuron output layer and a 48-neuron input layer that receives parallel event streams from the ED-ADCs. To obtain the DNN weights via off-line training, a customized front-end model written in python is constructed to accelerate feature generation in software emulation, and the model parameters are extracted from Spectre simulations. The chip, fabricated in 0.18- $\mu \text{m}$ CMOS, has a core area of 1.66 $\times $ 1.52 mm2 and consumes 1 $\mu \text{W}$. The classification measurements using the 1-hour 10-dB signal-to-noise ratio audio with restaurant background noise show a mean speech/non-speech hit rate of 84.4%/85.4% with a 1.88%/4.65% 1- $\sigma $ variation across ten dies that are all loaded with the same weights. [ABSTRACT FROM AUTHOR]

In this work, all group-IV band-to-band lasers based on SiGeSn/GeSn/SiGeSn multi-quantum-well structures were demonstrated. Lasing performance was investigated via two 4-well samples. The thinner GeSn well sample exhibits a maximum lasing temperature of 20 K and a threshold of 55 kW/cm2 at 10 K, while the thicker well sample features a higher maximum operating temperature of 90 K and lower lasing thresholds of 25 and 62 kW/cm2 at 10 and 77 K, respectively. The distinct results were tentatively interpreted mainly by the difference of gain volume. This result provides guidance for the future GeSn quantum well laser optimization for higher performance. [ABSTRACT FROM AUTHOR]

We investigate the sparse functional identification of complex cells and the decoding of spatio-temporal visual stimuli encoded by an ensemble of complex cells. The reconstruction algorithm is formulated as a rank minimization problem that significantly reduces the number of sampling measurements (spikes) required for decoding. We also establish the duality between sparse decoding and functional identification and provide algorithms for identification of low-rank dendritic stimulus processors. The duality enables us to efficiently evaluate our functional identification algorithms by reconstructing novel stimuli in the input space. Finally, we demonstrate that our identification algorithms substantially outperform the generalized quadratic model, the nonlinear input model, and the widely used spike-triggered covariance algorithm. [ABSTRACT FROM AUTHOR]

This paper reports the study of GeSn/GeSn/GeSn single quantum well (SQW) and double quantum wells (DQWs). The quantum well (QW) structures were grown on Ge buffered Si substrates using an industrial standard reduced-pressure chemical vapor deposition system. Pseudomorphically grown structures were observed using x-ray diffraction measurements. Defect-free interfaces between each layer were revealed using cross-sectional transmission electron microscopy. Atomic-scale high-resolution transmission electron microscopy and Fourier transform patterns exhibited the high crystalline quality of QWs. Temperature-dependent photoluminescence (PL) was performed, and the emission peaks attributed to the QW region were identified. The dominant optical transition changed from direct bandgap transition at 300 K to indirect bandgap transition at 10 K. Theoretical calculation showed the type-I band alignment for the QWs. Moreover, the Γ and L valley electron distributions and non-radiative lifetimes were evaluated, which further explained the PL characteristics of the QW samples. [ABSTRACT FROM AUTHOR]

Previous research demonstrated that global phase alone can be used to faithfully represent visual scenes. Here we provide a reconstruction algorithm by using only local phase information. We also demonstrate that local phase alone can be effectively used to detect local motion. The local phase-based motion detector is akin to models employed to detect motion in biological vision, for example, the Reichardt detector. The local phase-based motion detection algorithm introduced here consists of two building blocks. The first building block measures/evaluates the temporal change of the local phase. The temporal derivative of the local phase is shown to exhibit the structure of a second order Volterra kernel with two normalized inputs. We provide an efficient, FFT-based algorithm for implementing the change of the local phase. The second processing building block implements the detector; it compares the maximum of the Radon transform of the local phase derivative with a chosen threshold. We demonstrate examples of applying the local phase-based motion detection algorithm on several video sequences. We also show how the locally detected motion can be used for segmenting moving objects in video scenes and compare our local phase-based algorithm to segmentation achieved with a widely used optic flow algorithm. [ABSTRACT FROM AUTHOR]

In this paper, we investigate neural circuit architectures encoding natural visual scenes with neuron models consisting of dendritic stimulus processors (DSPs) in cascade with biophysical spike generators (BSGs). DSPs serve as functional models of processing of stimuli up to and including the neuron's active dendritic tree. BSGs model spike generation at the axon hillock level where neurons respond to aggregated synaptic currents. The highly nonlinear behavior of BSGs calls for novel methods of input/output (I/O) analysis of neural encoding circuits and novel decoding algorithms for signal recovery. On the encoding side we characterize the BSG I/O with a phase response curve (PRC) manifold and interpret neural encoding as generalized sampling. We provide a decoding algorithm that recovers visual stimuli encoded by a neural circuit with intrinsic noise sources. In the absence of noise, we give conditions on perfect reconstruction of natural visual scenes. We extend the architecture to encompass neuron models with on–off BSGs with self- and cross-feedback. With the help of the PRC manifold, decoding is shown to be tractable even for a wide signal dynamic range. Consequently, bias currents that were essential in the encoding process can largely be reduced or eliminated. Finally, we present examples of massively parallel encoding and decoding of natural visual scenes on a cluster of graphical processing units (GPUs). We evaluate the signal reconstruction under different noise conditions and investigate the performance of signal recovery in the Nyquist region and for different temporal bandwidths. [ABSTRACT FROM PUBLISHER]

We consider a class of neural circuit models with internal noise sources arising in sensory systems. The basic neuron model in these circuits consists of a dendritic stimulus processor (DSP) cascaded with a biophysical spike generator (BSG). The dendritic stimulus processor is modeled as a set of nonlinear operators that are assumed to have a Volterra series representation. Biophysical point neuron models, such as the Hodgkin-Huxley neuron, are used to model the spike generator. We address the question of how intrinsic noise sources affect the precision in encoding and decoding of sensory stimuli and the functional identification of its sensory circuits. We investigate two intrinsic noise sources arising (i) in the active dendritic trees underlying the DSPs, and (ii) in the ion channels of the BSGs. Noise in dendritic stimulus processing arises from a combined effect of variability in synaptic transmission and dendritic interactions. Channel noise arises in the BSGs due to the fluctuation of the number of the active ion channels. Using a stochastic differential equations formalism we show that encoding with a neuron model consisting of a nonlinear DSP cascaded with a BSG with intrinsic noise sources can be treated as generalized sampling with noisy measurements. For single-input multi-output neural circuit models with feedforward, feedback and cross-feedback DSPs cascaded with BSGs we theoretically analyze the effect of noise sources on stimulus decoding. Building on a key duality property, the effect of noise parameters on the precision of the functional identification of the complete neural circuit with DSP/BSG neuron models is given. We demonstrate through extensive simulations the effects of noise on encoding stimuli with circuits that include neuron models that are akin to those commonly seen in sensory systems, e.g., complex cells in V1. [ABSTRACT FROM AUTHOR]

In this work we study the nature of the band gap in GeSn alloys for use in silicon-based lasers. Special attention is paid to Sn-induced band mixing effects. We demonstrate from both experiment and ab-initio theory that the (direct) Γ-character of the GeSn band gap changes continuously with alloy composition and has significant Γ-character even at low (6%) Sn concentrations. The evolution of the Γ-character is due to Sn-induced conduction band mixing effects, in contrast to the sharp indirect-to-direct band gap transition obtained in conventional alloys such as Al1−xGaxAs. Understanding the band mixing effects is critical not only from a fundamental and basic properties viewpoint but also for designing photonic devices with enhanced capabilities utilizing GeSn and related material systems. [ABSTRACT FROM AUTHOR]

This paper reports the comprehensive characterization of a Ge0.92Sn0.08/Ge0.86Sn0.14/Ge0.92Sn0.08 single quantum well. By using a strain relaxed Ge0.92Sn0.08 buffer, the direct bandgap Ge0.86Sn0.14 QW was achieved, which is unattainable by using only a Ge buffer. Band structure calculations and optical transition analysis revealed that the quantum well features type-I band alignment. The photoluminescence spectra showed dramatically increased quantum well peak intensity at lower temperature, confirming that the Ge0.86Sn0.14 quantum well is a direct bandgap material. [ABSTRACT FROM AUTHOR]