The average life expectancy is increasing globally due to advancements in medical technology, preventive health care, and a growing emphasis on gerontological health. Therefore, developing technologies that detect and...
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Improper lane-changing behaviors may result in breakdown of traffic flow and the occurrence of various types of collisions. This study investigates lane-changing behaviors of multiple vehicles and the stimulative effe...
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Signatures which are believed to be written biometrics of the human being carries lot of information which helps to individualize and identify. To recognize the signatures in Forensic domain scientific principle are u...
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Signatures which are believed to be written biometrics of the human being carries lot of information which helps to individualize and identify. To recognize the signatures in Forensic domain scientific principle are used. To enrich the scientific examination this study aims to introduce the concept of trigonometrical *** handwritten signature is denouement of neuromuscular activity, voluntary movement of muscles is involved. Handwritten signature is learned, practiced, repeated and reproduced by an Individual for several years. Certainly, it possesses fluency of repetitive movement to draw particular set of alphabets with writing instruments over the *** of trigonometrical real functions like trigonometric or circular functions, angle functions and goniometric functions help us to assess such abstract drawings. Based on consideration of trigonometric functions with to reason the covariance between base length and height of first character with respect to aspect angle at first character of *** function mostly represents periodic phenomena or harmonic repetitions and handwritten signature being learned skill acquired by repetitive exhibition of same triggered us to study about covariance shown by metric parameters in handwritten *** work in handwritten signatures is been done emphasizing on pen pressure, stroke sequencing, baseline elevation or by considering entire signature as a complex neural networking problem. The natural variations within signatures makes machine learning algorithms even more complex. In contemporary signature comparison and verification studies comprise of parameters like formation of particular alphabet, embellishments, baseline shifts, beginning and ending strokes, loops or other significant shape formation and other peculiar distinguishing factors marked by experts on original signature *** this paper, a new parameter of aspect angle that is angle encompassed by baselength of the signatu
Many modern high-performing machine learning models such as GPT-3 primarily rely on scaling up models, e.g., transformer networks. Simultaneously, a parallel line of work aims to improve the model performance by augme...
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The new kind of ferroelectret electroactive polymers for flexible film sensors represent an attractive opportunity for self-powered wearable devices, which could offer sustainable and convenient health care services f...
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Measures of similarity (or dissimilarity) are a key ingredient to many machine learning algorithms. We introduce DID, a pairwise dissimilarity measure applicable to a wide range of data spaces, which leverages the dat...
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Predicting metro passenger flow precisely is of great importance for dynamic traffic planning. Deep learning algorithms have been widely applied due to their robust performance in modelling non-linear systems. However...
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While many classical notions of learnability (e.g., PAC learnability) are distribution-free, utilizing the specific structures of an input distribution may improve learning performance. For example, a product distribu...
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Spiking neural networks (SNN) have started to deliver energy-efficient, massively parallel, and low-latency solutions to AI problems, facilitated by the emerging neuromorphic hardware. To harness these computational b...
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Spiking neural networks (SNN) have started to deliver energy-efficient, massively parallel, and low-latency solutions to AI problems, facilitated by the emerging neuromorphic hardware. To harness these computational benefits, SNN need to be trained by learning algorithms that adhere to brain-inspired neuromorphic principles, namely event-based, local, and online computations. However, the state-of-the-art SNN training algorithms are based on backpropagation that does not follow the above neuromorphic computational principles. Due to its limited biological plausibility, the application of backprop to SNN requires non-local feedback pathways for transmitting continuous-valued errors, and relies on gradients from future timesteps. The recent introduction of biologically plausible modifications to backprop has helped overcome several of its limitations, but limits the degree to which backprop is approximated, which hinders its performance. Here, we propose a biologically plausible gradient-based learning algorithm for SNN that is functionally equivalent to backprop, while adhering to all three neuromorphic computational principles. We introduced multi-compartment spiking neurons with local eligibility traces to compute the gradients required for learning, and a periodic"sleep" phase to further improve the approximation to backprop during which a local Hebbian rule aligns the feedback and feedforward weights. Our method achieved the same level of performance as backprop with multi-layer fully connected SNN on MNIST (98.13%) and the event-based N-MNIST (97.59%) datasets. We then deployed our learning algorithm on Intel’s Loihi neuromorphic processor to train a 1-hidden-layer network for MNIST, and obtained 93.32% test accuracy while consuming 400 times less energy per training sample than BioGrad on GPU. Our work demonstrates that optimal learning is feasible in neuromorphic computing, and further pursuing its biological plausibility can better capture the computational b
Overparameterization is known to permit strong generalization performance in neural networks. In this work, we provide an initial theoretical analysis of its effect on catastrophic forgetting in a continual learning s...
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