Versatile stochastic dot product circuits based on nonvolatile memories for high performance neurocomputing and neurooptimization.

The key operation in stochastic neural networks, which have become the state-of-the-art approach for solving problems in machine learning, information theory, and statistics, is a stochastic dot-product. While there have been many demonstrations of dot-product circuits and, separately, of stochastic neurons, the efficient hardware implementation combining both functionalities is still missing. Here we report compact, fast, energy-efficient, and scalable stochastic dot-product circuits based on either passively integrated metal-oxide memristors or embedded floating-gate memories. The circuit's high performance is due to mixed-signal implementation, while the efficient stochastic operation is achieved by utilizing circuit's noise, intrinsic and/or extrinsic to the memory cell array. The dynamic scaling of weights, enabled by analog memory devices, allows for efficient realization of different annealing approaches to improve functionality. The proposed approach is experimentally verified for two representative applications, namely by implementing neural network for solving a four-node graph-partitioning problem, and a Boltzmann machine with 10-input and 8-hidden neurons

New opportunities for secure communication networks using shaped femtosecond laser pulses inducing filamentation processes in the atmosphere

The current study discusses new opportunities for secure ground to satellite communications using shaped femtosecond pulses that induce spatial hole burning in the atmosphere for efficient communications with data encoded within super-continua generated by femtosecond pulses. Refractive index variation across the different layers in the atmosphere may be modelled using assumptions that the upper strata of the atmosphere and troposphere behaving as layered composite amorphous dielectric networks composed of resistors and capacitors with different time constants across each layer. Input-output expressions of the dynamics of the networks in the frequency domain provide the transmission characteristics of the propagation medium. Femtosecond pulse shaping may be used to optimize the pulse phase-front and spectral composition across the different layers in the atmosphere. A generic procedure based on evolutionary algorithms to perform the pulse shaping is proposed. In contrast to alternative procedures that would require ab initio modelling and calculations of the propagation constant for the pulse through the atmosphere, the proposed approach is adaptive, compensating for refractive index variations along the column of air between the transmitter and receiver

New opportunities for secure communication networks using shaped femtosecond laser pulses inducing filamentation processes in the atmosphere

The current study discusses new opportunities for secure ground to satellite communications using shaped femtosecond pulses that induce spatial hole burning in the atmosphere for efficient communications with data encoded within super-continua generated by femtosecond pulses. Refractive index variation across the different layers in the atmosphere may be modelled using assumptions that the upper strata of the atmosphere and troposphere behaving as layered composite amorphous dielectric networks composed of resistors and capacitors with different time constants across each layer. Input-output expressions of the dynamics of the networks in the frequency domain provide the transmission characteristics of the propagation medium. Femtosecond pulse shaping may be used to optimize the pulse phase-front and spectral composition across the different layers in the atmosphere. A generic procedure based on evolutionary algorithms to perform the pulse shaping is proposed. In contrast to alternative procedures that would require ab initio modelling and calculations of the propagation constant for the pulse through the atmosphere, the proposed approach is adaptive, compensating for refractive index variations along the column of air between the transmitter and receiver

Movie Reviews Sentiment Analysis Using BERT

Sentiment analysis (SA) or opinion mining is analysis of emotions and opinions from texts. It is one of the active research areas in Natural Language Processing (NLP). Various approaches have been deployed in the literature to address the problem. These techniques devise complex and sophisticated frameworks in order to attain optimal accuracy with their focus on polarity classification or binary classification. In this paper, we aim to fine-tune BERT in a simple but robust approach for movie reviews sentiment analysis to provide better accuracy than state-of-the-art (SOTA) methods. We start by conducting sentiment classification for every review, followed by computing overall sentiment polarity for all the reviews. Both polarity classification and fine-grained classification or multi-scale sentiment distribution are implemented and tested on benchmark datasets in our work. To optimally adapt BERT for sentiment classification, we concatenate it with a Bidirectional LSTM (BiLSTM) layer. We also implemented and evaluated some accuracy improvement techniques including Synthetic Minority Over-sampling TEchnique (SMOTE) and NLP Augmenter (NLPAUG) to improve the model for prediction of multi-scale sentiment distribution. We found that including NLPAUG improved accuracy, however SMOTE did not work well. Lastly, a heuristic algorithm is applied to compute overall polarity of predicted reviews from the model output vector. We call our model BERT+BiLSTM-SA, where SA stands for Sentiment Analysis. Our best-performing approach comprises BERT and BiLSTM on binary, three-class, and four-class sentiment classifications, and SMOTE augmentation, in addition to BERT and BiLSTM, on five-class sentiment classification. Our approach performs at par with SOTA techniques on both classifications. For example, on binary classification, we obtain 97.67% accuracy, while the best performing SOTA model, NB-weighted-BON+dvcosine,has 97.40% accuracy on the popular IMDb dataset. The baseline, Entailment as Few-Shot Learners (EFL), is outperformed on this task by 1.30%. On the other hand, for five-class classification on SST-5, the best SOTA model, RoBERTa+large+Self-explaining, has 55.5% accuracy, while we obtain 59.48% accuracy. We outperform the baseline on this task, BERT-large, by 3.6%

Movie Reviews Sentiment Analysis Using BERT

Sentiment analysis (SA) or opinion mining is analysis of emotions and opinions from texts. It is one of the active research areas in Natural Language Processing (NLP). Various approaches have been deployed in the literature to address the problem. These techniques devise complex and sophisticated frameworks in order to attain optimal accuracy with their focus on polarity classification or binary classification. In this paper, we aim to fine-tune BERT in a simple but robust approach for movie reviews sentiment analysis to provide better accuracy than state-of-the-art (SOTA) methods. We start by conducting sentiment classification for every review, followed by computing overall sentiment polarity for all the reviews. Both polarity classification and fine-grained classification or multi-scale sentiment distribution are implemented and tested on benchmark datasets in our work. To optimally adapt BERT for sentiment classification, we concatenate it with a Bidirectional LSTM (BiLSTM) layer. We also implemented and evaluated some accuracy improvement techniques including Synthetic Minority Over-sampling TEchnique (SMOTE) and NLP Augmenter (NLPAUG) to improve the model for prediction of multi-scale sentiment distribution. We found that including NLPAUG improved accuracy, however SMOTE did not work well. Lastly, a heuristic algorithm is applied to compute overall polarity of predicted reviews from the model output vector. We call our model BERT+BiLSTM-SA, where SA stands for Sentiment Analysis. Our best-performing approach comprises BERT and BiLSTM on binary, three-class, and four-class sentiment classifications, and SMOTE augmentation, in addition to BERT and BiLSTM, on five-class sentiment classification. Our approach performs at par with SOTA techniques on both classifications. For example, on binary classification, we obtain 97.67% accuracy, while the best performing SOTA model, NB-weighted-BON+dvcosine,has 97.40% accuracy on the popular IMDb dataset. The baseline, Entailment as Few-Shot Learners (EFL), is outperformed on this task by 1.30%. On the other hand, for five-class classification on SST-5, the best SOTA model, RoBERTa+large+Self-explaining, has 55.5% accuracy, while we obtain 59.48% accuracy. We outperform the baseline on this task, BERT-large, by 3.6%

Robust Energy Consumption Prediction with a Missing Value-Resilient Metaheuristic-based Neural Network in Mobile App Development

Energy consumption is a fundamental concern in mobile application development, bearing substantial significance for both developers and end-users. Moreover, it is a critical determinant in the consumer's decision-making process when considering a smartphone purchase. From the sustainability perspective, it becomes imperative to explore approaches aimed at mitigating the energy consumption of mobile devices, given the significant global consequences arising from the extensive utilisation of billions of smartphones, which imparts a profound environmental impact. Despite the existence of various energy-efficient programming practices within the Android platform, the dominant mobile ecosystem, there remains a need for documented machine learning-based energy prediction algorithms tailored explicitly for mobile app development. Hence, the main objective of this research is to propose a novel neural network-based framework, enhanced by a metaheuristic approach, to achieve robust energy prediction in the context of mobile app development. The metaheuristic approach here plays a crucial role in not only identifying suitable learning algorithms and their corresponding parameters but also determining the optimal number of layers and neurons within each layer. To the best of our knowledge, prior studies have yet to employ any metaheuristic algorithm to address all these hyperparameters simultaneously. Moreover, due to limitations in accessing certain aspects of a mobile phone, there might be missing data in the data set, and the proposed framework can handle this. In addition, we conducted an optimal algorithm selection strategy, employing 13 metaheuristic algorithms, to identify the best algorithm based on accuracy and resistance to missing values. The comprehensive experiments demonstrate that our proposed approach yields significant outcomes for energy consumption prediction.Comment: The paper is submitted to a related journa

Smart Cage Active Contours and their application to brain image segmentation

In this work we present a new segmentation method named Smart Cage Active Contours (SCAC) that combines a parametrized active contour framework named Cage Active Contours (CAC), based on a ne trans- formations, with Active Shape Models (ASM). Our method e ectively restricts the shapes the evolving contours can take without the need of the training images to be manually landmarked. We apply our method to segment the caudate nuclei subcortical structure of a set of 40 subjects in magnetic resonance brain images, with promising results

Meta-Prior: Meta learning for Adaptive Inverse Problem Solvers

Deep neural networks have become a foundational tool for addressing imaging inverse problems. They are typically trained for a specific task, with a supervised loss to learn a mapping from the observations to the image to recover. However, real-world imaging challenges often lack ground truth data, rendering traditional supervised approaches ineffective. Moreover, for each new imaging task, a new model needs to be trained from scratch, wasting time and resources. To overcome these limitations, we introduce a novel approach based on meta-learning. Our method trains a meta-model on a diverse set of imaging tasks that allows the model to be efficiently fine-tuned for specific tasks with few fine-tuning steps. We show that the proposed method extends to the unsupervised setting, where no ground truth data is available. In its bilevel formulation, the outer level uses a supervised loss, that evaluates how well the fine-tuned model performs, while the inner loss can be either supervised or unsupervised, relying only on the measurement operator. This allows the meta-model to leverage a few ground truth samples for each task while being able to generalize to new imaging tasks. We show that in simple settings, this approach recovers the Bayes optimal estimator, illustrating the soundness of our approach. We also demonstrate our method's effectiveness on various tasks, including image processing and magnetic resonance imaging