Incorporating Real-Time Rando m Time Effects in Neural Networks: A Temporal Summation Mechanism

Implementing random time effects in neural networks has been a challenge for neural network researchers. In this paper, we propose a neurophysiologically inspired temporal summation mechanism to reflect real-time random dynamic processing in neural networks. According to the physiology of neuronal firing, a presynaptic neuron sends out a burst of random spikes to a postsynaptic neuron. In the postsynaptic neuron, spikes arriving at different points in time are summed until the postsynaptic membrane potential exceeds a threshold, thus initiating postsynaptic firing. This temporal summation process can be used as a metric for deriving time predictions in neural networks. To demonstrate potential applications of temporal summation, we have employed a feedforward, two-layer network featuring a Hebbian learning rule to perform simulations using the semantic priming experimental paradigm. W e are able to successfully reproduce not only the basic patterns of observed response time data (e.g., positively skewed response time distributions and speed-accuracy trade-offs) but also the semantic priming effect and the time-course of priming as a function of stimulus-onset-asynchrony. These results suggest that the proposed temporal summation mechanism may be a promising candidate for incorporating real-time, random time effects into neural network modeling of human cognition

Smeared BTZ Black Hole from Space Noncommutativity

We study a novel phenomena of smearing of black hole horizons from the effect of space noncommutativity. We present an explicit example in $AdS_3$ space, using the Chern-Simons formulation of gravity. This produces a smeared BTZ black hole which goes beyond the classical spacetime unexpectedly and there is {\it no} reality problem in our approach with the gauge group $U(1,1) \times U(1,1)$. The horizons are smeared, due to a splitting of the Killing horizon and the apparent horizon, and there is a metric signature change to Euclidean in the smeared region. The inner boundary of the smeared region acts as a trapped surface for timelike particles but the outer as a classical barrier for ingoing particles. The lightlike signals can escape from or reach the smeared region in a {\it finite} time, which indicates that {\it the black hole is not so dark, even classically.} In addition, it is remarked that the Hawking temperature can {\it not} be defined by the regularity in the Euclidean geometry except in the non-rotating case, and the origin can be smeared by a {\it new} (apparent) horizon.Comment: Added notes about boundary conditions, NC curvature, and torsion; Accepted in JHE

On Determining Minimal Spectrally Arbitrary Patterns

In this paper we present a new family of minimal spectrally arbitrary patterns which allow for arbitrary spectrum by using the Nilpotent-Jacobian method. The novel approach here is that we use the Intermediate Value Theorem to avoid finding an explicit nilpotent realization of the new minimal spectrally arbitrary patterns.Comment: 8 page

Maximizing The Impact Of Improvement Efforts On Customer Satisfaction

When a customer satisfaction survey consists of a large number of attributes (questionnaire items), determination of critical attributes that would make the biggest impact on customers’ overall satisfaction could be important, but very tedious and time-consuming process. Even though the critical attributes are identified, the improvement efforts toward these attributes are often misdirected and wasted because of the mismatch between the improvement efforts and the critical needs of the affected customer group. This paper introduces a method with which improvement efforts can be tailored to the needs of the customer group who could bring the most impactful influence on improving customer satisfaction. For the critical attribute considered, the percentage of customers who assigned a specific satisfaction rating is obtained, and the cumulative percentages of customers are examined and the target group of customers to whom the improvement efforts would be tailored is identified. The piecewise linear approximation method is also discussed to estimate the non-linear relationship of the attribute, which also may help determine the target customer group. The overall shape of the piecewise function and the slopes at the line segments may be used in determining which attributes are satisfaction-maintaining or satisfaction-enhancing, and where and how the improvement efforts should be focused in order to maximize the effectiveness of the improvement efforts

Predict to Detect: Prediction-guided 3D Object Detection using Sequential Images

Recent camera-based 3D object detection methods have introduced sequential frames to improve the detection performance hoping that multiple frames would mitigate the large depth estimation error. Despite improved detection performance, prior works rely on naive fusion methods (e.g., concatenation) or are limited to static scenes (e.g., temporal stereo), neglecting the importance of the motion cue of objects. These approaches do not fully exploit the potential of sequential images and show limited performance improvements. To address this limitation, we propose a novel 3D object detection model, P2D (Predict to Detect), that integrates a prediction scheme into a detection framework to explicitly extract and leverage motion features. P2D predicts object information in the current frame using solely past frames to learn temporal motion features. We then introduce a novel temporal feature aggregation method that attentively exploits Bird's-Eye-View (BEV) features based on predicted object information, resulting in accurate 3D object detection. Experimental results demonstrate that P2D improves mAP and NDS by 3.0% and 3.7% compared to the sequential image-based baseline, illustrating that incorporating a prediction scheme can significantly improve detection accuracy.Comment: ICCV 202