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Monkeypox virus (MPXV) outbreaks have been reported in various countries worldwide; however, there is no specific vaccine against MPXV. In this study, therefore, we employed computational approaches to design a multi-epitope vaccine against MPXV. Initially, cytotoxic T lymphocyte (CTL), helper T lymphocyte (HTL), linear B lymphocytes (LBL) epitopes were predicted from the cell surface-binding protein and envelope protein A28 homolog, both of which play essential roles in MPXV pathogenesis. All of the predicted epitopes were evaluated using key parameters. A total of 7 CTL, 4 HTL, and 5 LBL epitopes were chosen and combined with appropriate linkers and adjuvant to construct a multi-epitope vaccine. The CTL and HTL epitopes of the vaccine construct cover 95.57% of the worldwide population. The designed vaccine construct was found to be highly antigenic, non-allergenic, soluble, and to have acceptable physicochemical properties. The 3D structure of the vaccine and its potential interaction with Toll-Like receptor-4 (TLR4) were predicted. Molecular dynamics (MD) simulation confirmed the vaccine's high stability in complex with TLR4. Finally, codon adaptation and in silico cloning confirmed the high expression rate of the vaccine constructs in strain K12 of Escherichia coli (E. coli). These findings are very encouraging; however, in vitro and animal studies are needed to ensure the potency and safety of this vaccine candidate. [ABSTRACT FROM AUTHOR]

Quantum computers and algorithms can offer exponential performance improvement over some NP-complete programs which cannot be run efficiently through a Von Neumann computing approach. In this paper, we present BayeSyn, which utilizes an enhanced stochastic program synthesis and Bayesian optimization to automatically generate quantum programs from high-level languages subject to certain constraints. We find that stochastic synthesis can comparatively and efficiently generate a program with a lower cost from the high dimensional program space. We also realize that hyperparameters used in stochastic synthesis play a significant role in determining the optimal program. Therefore, BayeSyn utilizes Bayesian optimization to fine-tune such parameters to generate a suitable quantum program. [ABSTRACT FROM AUTHOR]

With the increasing demand for distributed big data analytics and data-intensive programs which contribute to large volumes of packets among processing elements (PEs) and memory banks, we witness a pressing need for new mathematical models and algorithms that can engineer a brain-inspired plasticity into the computing platforms by mining the topological complexity of high-level programs (HLPs) and exploiting their self-similar and fractal characteristics for designing reconfigurable domain-specific computing architectures. In this article, we present Plasticity-on-Chip (PoC) by engineering plasticity into ”artificial brains” to mine and exploit the self-similarity of HLPs. First, we present a communication modeling of HLPs (e.g., C/C++ implementations of various applications) that relies on static and dynamic compiler analysis of programs with varying input seeds, performing comprehensive program analysis of all traces, and representing the HLPs as weighted directed acyclic graphs while capturing the intrinsic timing constraints and data/control flow requirements. Second, we propose a rigorous mathematical framework for determining the optimal parallel degree of executing a set of interacting HLPs (by partitioning them into clusters of densely interconnected supernodes - tasks) which helps us decide the number of available heterogeneous PEs, the amount of required memory and the structure of the synthesized deadlock-free irregular NoC topology that offers an efficient communication medium. These clusters serve as abstract models of computation for the synthesized PEs within the parallel execution model. Finally, exploiting the fractal and complex networks concepts, we extract in-depth features from graphs that serve as inputs for distributed reinforcement learning. Our experimental results on synthesized PEs and NoCs show performance improvements as high as 7.61x when compared to the traditional NoC and 2.6x compared to gem5-Aladdin. [ABSTRACT FROM AUTHOR]

Logic cell modelling is an important component in the analysis and design of CMOS integrated circuits, mostly due to nonlinear behaviour of CMOS cells with respect to the voltage signal at their input and output pins. A current-based model for CMOS logic cells is presented, which can be used for effective crosstalk noise and delta delay analysis in CMOS VLSI circuits. Existing current source models are expensive and need a new set of Spice-based characterisation, which is not compatible with typical EDA tools. In this article we present Imodel, a simple nonlinear logic cell model that can be derived from the typical cell libraries such as NLDM, with accuracy much higher than NLDM-based cell delay models. In fact, our experiments show an average error of 3% compared to Spice. This level of accuracy comes with a maximum runtime penalty of 19% compared to NLDM-based cell delay models on medium-sized industrial designs. [ABSTRACT FROM AUTHOR]

This article presents a detailed analysis of the crosstalk-affected delay of coupled interconnects considering process variations. We utilise a distributed RC-π model of the interconnections to accurately model process variations. In particular, we perform a detailed investigation of various crosstalk scenarios and study the impact of different parameters on crosstalk delay. Although accounting for the effect of correlations among parameters of the neighbouring wire segments, statistical properties of the crosstalk-affected propagation delays are characterised and discussed. Monte Carlo-based simulations using Spice demonstrate the effectiveness of the proposed approach in accurately modeling the correlation-aware process variations and their impact on interconnect delay in the presence of crosstalk. [ABSTRACT FROM AUTHOR]

Social media have emerged as increasingly popular means and environments for information gathering and propagation. This vigorous growth of social media contributed not only to a pandemic (fast-spreading and far-reaching) of rumors and misinformation, but also to an urgent need for text-based rumor detection strategies. To speed up the detection of misinformation, traditional rumor detection methods based on hand-crafted feature selection need to be replaced by automatic artificial intelligence (AI) approaches. AI decision making systems require to provide explanations in order to assure users of their trustworthiness. Inspired by the thriving development of generative adversarial networks (GANs) on text applications, we propose a GAN-based layered model for rumor detection with explanations. To demonstrate the universality of the proposed approach, we demonstrate its benefits on a gene classification with mutation detection case study. Similarly to the rumor detection, the gene classification can also be formulated as a text-based classification problem. Unlike fake news detection that needs a previously collected verified news database, our model provides explanations in rumor detection based on tweet-level texts only without referring to a verified news database. The layered structure of both generative and discriminative models contributes to the outstanding performance. The layered generators produce rumors by intelligently inserting controversial information in non-rumors, and force the layered discriminators to detect detailed glitches and deduce exactly which parts in the sentence are problematic. On average, in the rumor detection task, our proposed model outperforms state-of-the-art baselines on PHEME dataset by 26.85 % in terms of macro-f1. The excellent performance of our model for textural sequences is also demonstrated by the gene mutation case study on which it achieves 72.69 % macro-f1 score. [ABSTRACT FROM AUTHOR]

The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the social, financial and political entities around the world. Without an existing effective medical therapy, vaccines are urgently needed to avoid the spread of this disease. In this study, we propose an in silico deep learning approach for prediction and design of a multi-epitope vaccine (DeepVacPred). By combining the in silico immunoinformatics and deep neural network strategies, the DeepVacPred computational framework directly predicts 26 potential vaccine subunits from the available SARS-CoV-2 spike protein sequence. We further use in silico methods to investigate the linear B-cell epitopes, Cytotoxic T Lymphocytes (CTL) epitopes, Helper T Lymphocytes (HTL) epitopes in the 26 subunit candidates and identify the best 11 of them to construct a multi-epitope vaccine for SARS-CoV-2 virus. The human population coverage, antigenicity, allergenicity, toxicity, physicochemical properties and secondary structure of the designed vaccine are evaluated via state-of-the-art bioinformatic approaches, showing good quality of the designed vaccine. The 3D structure of the designed vaccine is predicted, refined and validated by in silico tools. Finally, we optimize and insert the codon sequence into a plasmid to ensure the cloning and expression efficiency. In conclusion, this proposed artificial intelligence (AI) based vaccine discovery framework accelerates the vaccine design process and constructs a 694aa multi-epitope vaccine containing 16 B-cell epitopes, 82 CTL epitopes and 89 HTL epitopes, which is promising to fight the SARS-CoV-2 viral infection and can be further evaluated in clinical studies. Moreover, we trace the RNA mutations of the SARS-CoV-2 and ensure that the designed vaccine can tackle the recent RNA mutations of the virus. [ABSTRACT FROM AUTHOR]

Dynamic energy pricing provides a promising solution for the utility companies to incentivize energy users to perform demand side management in order to minimize their electric bills. Moreover, the emerging decentralized smart grid, which is a likely infrastructure scenariofor future electrical power networks, allows energy consumers to select their energy provider from among multiple utility companies in any billing period. This paper thus starts by considering an oligopolistic energy market with multiple non-cooperative (competitive) utility companies, and addresses the problem of determining dynamic energy prices for every utility company in this market based on a modified Bertrand Competition Model of user behaviors. Two methods of dynamic energy pricing are proposed for a utility company to maximize its total profit. The first method finds the greatest lower bound on the total profit that can be achieved by the utility company, whereas the second method finds the best response of a utility company to dynamic pricing policies that the other companies have adopted in previous billing periods. To exploit the advantages of each method while compensating their shortcomings, an adaptive dynamic pricing policy is proposed based on a machine learning technique, which finds a good balance between invocations of the two aforesaid methods. Experimental results show that the adaptive policy results in consistently high profit for the utility company no matter what policies are employed by the other companies. [ABSTRACT FROM AUTHOR]

Traditional logical equivalence checking (LEC), which plays a major role in the entire chip design process, faces challenges of meeting the requirements demanded by the many emerging technologies that are based on logic models different from the standard complementary metal oxide semiconductor (CMOS). In this article, we propose an LEC framework to be employed in the verification process of superconducting electronics (SCE). Our LEC framework is compatible with the existing CMOS technologies and can also check features and capabilities that are unique to SCE. For instance, the performance of nonresistively biased single-flux quantum (SFQ) circuits benefits from ultradeep pipelining, and verification of such circuits requires new models and algorithms. We, therefore, present the multicycle input dependency circuit model which is a novel model representation of design to explicitly capture the dependency of primary outputs of the circuit on sequences of internal signals and inputs. Embedding the proposed circuit model and several structural checking modules, the process of verification can be independent of the underlying technology and signaling. We benchmark the proposed framework on postsynthesis SFQ and 4-phase adiabatic quantum flux parametron netlists. Results show a comparative verification time of SFQ circuit benchmark, including 16-bit integer divider and ISCAS’85 circuits with respect to the ABC tool for similar CMOS circuits. [ABSTRACT FROM AUTHOR]

Cloud computing has attracted both end-users and cloud service providers (CSPs) in recent years. Improving resource utilization rate (RUtR), such as CPU and memory usages on servers, while maintaining quality of service (QoS) is one key challenge faced by CSPs with warehouse-scale datacenters. Prior works proposed various algorithms to reduce energy cost or to improve RUtR, which either lack the fine-grained task scheduling capabilities, or fail to take a comprehensive system model into consideration. This article presents H2O-Cloud, a Hierarchical and Hybrid Online task scheduling framework for warehouse-scale Cloud service providers, to improve resource usage effectiveness while maintaining QoS. H2O-Cloud is highly scalable and considers comprehensive information, such as various workload scenarios, cloud platform configurations, user request information, and dynamic pricing model. The hierarchy and hybridity of the framework, combined with its deep reinforcement learning (DRL) engines, enable H2O-Cloud to efficiently start on-the-go scheduling and learning in an unpredictable environment without pretraining. Our experiments confirm the high efficiency of the proposed H2O-Cloud when compared to baseline approaches, in terms of energy and cost while maintaining QoS. Compared with a state-of-the-art DRL-based algorithm, H2O-Cloud achieves up to 201.17% energy cost efficiency improvement, 47.88% energy efficiency improvement, and 551.76% reward rate improvement. [ABSTRACT FROM AUTHOR]

Recently, Deep Convolutional Neural Network (DCNN) has been recognized as the most effective model for pattern recognition and classification tasks. With the fast growing Internet of Things (IoTs) and wearable devices, it becomes attractive to implement DCNNs in embedded and portable systems. However, novel computing paradigms are urgently required to deploy DCNNs that have huge power consumptions and complex topologies in systems with limited area and power supply. Recent works have demonstrated that Stochastic Computing (SC) can radically simplify the hardware implementation of arithmetic units and has the potential to bring the success of DCNNs to embedded systems. This paper introduces normalization and dropout, which are essential techniques for the state-of-the-art DCNNs, to the existing SC-based DCNN frameworks. In this work, the feature extraction block of DCNNs is implemented using an approximate parallel counter, a near-max pooling block and an SC-based rectified linear activation unit. A novel SC-based normalization design is proposed, which includes a square and summation unit, an activation unit and a division unit. The dropout technique is integrated into the training phase and the learned weights are adjusted during the hardware implementation. Experimental results on AlexNet with the ImageNet dataset show that the SC-based DCNN with the proposed normalization and dropout techniques achieves 3.26% top-1 accuracy improvement and 3.05% top-5 accuracy improvement compared with the SC-based DCNN without these two essential techniques, confirming the effectiveness of our normalization and dropout designs. • A novel and efficient stochastic computing based normalization design is proposed, which is comprised of summation and square, activation, and division units. • Dropout technique is integrated to the stochastic computing based deep convolutional neural network frameworks. • Experimental results on AlexNet with the ImageNet dataset show that the proposed normalization and dropout techniques achieves 3.26% top-1 accuracy improvement and 3.05% top-5 accuracy improvement, confirming the effectiveness of our normalization and dropout designs. [ABSTRACT FROM AUTHOR]

The Heating, Ventilation and Air Conditioning (HVAC) system accounts for nearly half of the energy consumption of a typical building. Additionally, the need for HVAC changes over hours and days as does the electric energy price. Level of comfort of the building occupants is, however, a primary concern, which tends to overwrite pricing. Dynamic HVAC control under a dynamic energy pricing model while meeting an acceptable level of occupants' comfort is thus critical to achieve energy efficiency in buildings in a sustainable manner. Finally, there is the possibility that the building is equipped with some renewable sources of power such as solar panels mounted on the rooftop. The presence of Hybrid Electrical Energy Storage (HEES) system in a target building would enable peak power shaving by adopting a suitable charging and discharging schedule for each Electrical Energy Storage (EES) element, while simultaneously meeting building energy efficiency and user comfort requirements. Achieving this goal requires detailed information (or predictions) about the amount of local power generation from the renewable source plus the power consumption load of the building. This paper addresses the co-scheduling problem of HVAC control and HEES system management to achieve energy-efficient smart buildings, while also accounting for the degradation of the battery state-of-health during charging and discharging operations (which in turn determines the amortized cost of owning and utilizing a battery storage system). A time-of-use dynamic pricing scenario is assumed and various energy loss components are considered, including power dissipation in the power conversion circuitry, the rate capacity effect in the batteries, and the self-discharge in the super-capacitor. A global optimization framework targeting the entire billing cycle is presented and an adaptive co-scheduling algorithm is provided to dynamically update the optimal HVAC air flow control and the HEES system management in each time slot during the billing cycle to mitigate the prediction error of unknown parameters. Experimental results show that the proposed algorithm achieves up to 10% in the total electric utility cost compared with some baseline methods. [ABSTRACT FROM AUTHOR]