ANALYSIS AND MODULATION OF MOLECULAR QUANTUM-DOT CELLULAR AUTOMATA (QCA) DEVICES

Field-Coupled nanocomputing (FCN) paradigms offer fundamentally new approaches for digital computing without involving current transistors. Such paradigms perform computations using local field interactions between nanoscale building blocks which are organized with purposes. Among several FCN paradigms currently under active investigation, the Molecular Quantum-dot Cellular Automata (MQCA) is found to be the most promising and its unique features make it attractive as a candidate for post-CMOS nanocomputing. MQCA is based on electrostatic interactions among quantum cells with nanometer scale eliminating the need of charge transportation, hence its energy consumption is significantly decreased. Meanwhile it also possesses the potential of high throughput if efficient pipelining of information propagation is introduced. This could be realized adopting external clock signals which precisely control the adiabatic switching and direction of data flow in MQCA circuits. In this work, in order to model MQCA as electronic devices and analyze its information propagation with clock taken into account, an effective algorithm based on ab-initio simulations and modelling of molecular interactions has been applied in presence of a proposed clock mechanism for MQCA, including the binary wire, the wire bus and the majority voter. The quantitative results generated depict compelling clocked information propagation phenomena of MQCA devices and most importantly, provide crucial feedback for future MQCA experimental implementation

Machine learning enabled millimeter wave cellular system and beyond

Millimeter-wave (mmWave) communication with advantages of abundant bandwidth and immunity to interference has been deemed a promising technology for the next generation network and beyond. With the help of mmWave, the requirements envisioned of the future mobile network could be met, such as addressing the massive growth required in coverage, capacity as well as traffic, providing a better quality of service and experience to users, supporting ultra-high data rates and reliability, and ensuring ultra-low latency. However, due to the characteristics of mmWave, such as short transmission distance, high sensitivity to the blockage, and large propagation path loss, there are some challenges for mmWave cellular network design. In this context, to enjoy the benefits from the mmWave networks, the architecture of next generation cellular network will be more complex. With a more complex network, it comes more complex problems. The plethora of possibilities makes planning and managing a complex network system more difficult. Specifically, to provide better Quality of Service and Quality of Experience for users in the such network, how to provide efficient and effective handover for mobile users is important. The probability of handover trigger will significantly increase in the next generation network, due to the dense small cell deployment. Since the resources in the base station (BS) is limited, the handover management will be a great challenge. Further, to generate the maximum transmission rate for the users, Line-of-sight (LOS) channel would be the main transmission channel. However, due to the characteristics of mmWave and the complexity of the environment, LOS channel is not feasible always. Non-line-of-sight channel should be explored and used as the backup link to serve the users. With all the problems trending to be complex and nonlinear, and the data traffic dramatically increasing, the conventional method is not effective and efficiency any more. In this case, how to solve the problems in the most efficient manner becomes important. Therefore, some new concepts, as well as novel technologies, require to be explored. Among them, one promising solution is the utilization of machine learning (ML) in the mmWave cellular network. On the one hand, with the aid of ML approaches, the network could learn from the mobile data and it allows the system to use adaptable strategies while avoiding unnecessary human intervention. On the other hand, when ML is integrated in the network, the complexity and workload could be reduced, meanwhile, the huge number of devices and data could be efficiently managed. Therefore, in this thesis, different ML techniques that assist in optimizing different areas in the mmWave cellular network are explored, in terms of non-line-of-sight (NLOS) beam tracking, handover management, and beam management. To be specific, first of all, a procedure to predict the angle of arrival (AOA) and angle of departure (AOD) both in azimuth and elevation in non-line-of-sight mmWave communications based on a deep neural network is proposed. Moreover, along with the AOA and AOD prediction, a trajectory prediction is employed based on the dynamic window approach (DWA). The simulation scenario is built with ray tracing technology and generate data. Based on the generated data, there are two deep neural networks (DNNs) to predict AOA/AOD in the azimuth (AAOA/AAOD) and AOA/AOD in the elevation (EAOA/EAOD). Furthermore, under an assumption that the UE mobility and the precise location is unknown, UE trajectory is predicted and input into the trained DNNs as a parameter to predict the AAOA/AAOD and EAOA/EAOD to show the performance under a realistic assumption. The robustness of both procedures is evaluated in the presence of errors and conclude that DNN is a promising tool to predict AOA and AOD in a NLOS scenario. Second, a novel handover scheme is designed aiming to optimize the overall system throughput and the total system delay while guaranteeing the quality of service (QoS) of each user equipment (UE). Specifically, the proposed handover scheme called O-MAPPO integrates the reinforcement learning (RL) algorithm and optimization theory. An RL algorithm known as multi-agent proximal policy optimization (MAPPO) plays a role in determining handover trigger conditions. Further, an optimization problem is proposed in conjunction with MAPPO to select the target base station and determine beam selection. It aims to evaluate and optimize the system performance of total throughput and delay while guaranteeing the QoS of each UE after the handover decision is made. Third, a multi-agent RL-based beam management scheme is proposed, where multiagent deep deterministic policy gradient (MADDPG) is applied on each small-cell base station (SCBS) to maximize the system throughput while guaranteeing the quality of service. With MADDPG, smart beam management methods can serve the UEs more efficiently and accurately. Specifically, the mobility of UEs causes the dynamic changes of the network environment, the MADDPG algorithm learns the experience of these changes. Based on that, the beam management in the SCBS is optimized according the reward or penalty when severing different UEs. The approach could improve the overall system throughput and delay performance compared with traditional beam management methods. The works presented in this thesis demonstrate the potentiality of ML when addressing the problem from the mmWave cellular network. Moreover, it provides specific solutions for optimizing NLOS beam tracking, handover management and beam management. For NLOS beam tracking part, simulation results show that the prediction errors of the AOA and AOD can be maintained within an acceptable range of ±2. Further, when it comes to the handover optimization part, the numerical results show the system throughput and delay are improved by 10% and 25%, respectively, when compared with two typical RL algorithms, Deep Deterministic Policy Gradient (DDPG) and Deep Q-learning (DQL). Lastly, when it considers the intelligent beam management part, numerical results reveal the convergence performance of the MADDPG and the superiority in improving the system throughput compared with other typical RL algorithms and the traditional beam management method

Is the local ion density sufficient to drive NaCl nucleation in vacuum and in water?

Even though nucleation is ubiquitous in different science and engineering problems, investigating nucleation is extremely difficult due to the complicated ranges of time and length scales involved. In this work, we simulate NaCl nucleation in both molten and aqueous environments using enhanced sampling all-atom molecular dynamics with deep learning-based estimation of reaction coordinates. By incorporating various structural order parameters and learning the reaction coordinate as a function thereof, we achieve significantly improved sampling relative to traditional ad hoc descriptions of what drives nucleation, particularly in the aqueous medium. Our results reveal a one-step nucleation mechanism in both environments, with reaction coordinate analysis highlighting the importance of local ion density in distinguishing solid and liquid states. However, while fluctuations in the local ion density are necessary to drive nucleation, they are not sufficient. Our analysis shows that near the transition states, descriptors such as enthalpy and local structure become crucial. Our protocol proposed here enables robust nucleation analysis and phase sampling, and could offer insights into nucleation mechanisms for generic small molecules in different environments

Boosting Adversarial Attacks on Neural Networks with Better Optimizer

Convolutional neural networks have outperformed humans in image recognition tasks, but they remain vulnerable to attacks from adversarial examples. Since these data are crafted by adding imperceptible noise to normal images, their existence poses potential security threats to deep learning systems. Sophisticated adversarial examples with strong attack performance can also be used as a tool to evaluate the robustness of a model. However, the success rate of adversarial attacks can be further improved in black-box environments. Therefore, this study combines a modified Adam gradient descent algorithm with the iterative gradient-based attack method. The proposed Adam Iterative Fast Gradient Method is then used to improve the transferability of adversarial examples. Extensive experiments on ImageNet showed that the proposed method offers a higher attack success rate than existing iterative methods. By extending our method, we achieved a state-of-the-art attack success rate of 95.0% on defense models

SCERPA: a Self-Consistent Algorithm for the Evaluation of the Information Propagation in Molecular Field-Coupled Nanocomputing

Among the emerging technologies that are intended to outperform the current CMOS technology, the field-coupled nanocomputing (FCN) paradigm is one of the most promising. The molecular quantum-dot cellular automata (MQCA) has been proposed as possible FCN implementation for the expected very high device density and possible room temperature operations. The digital computation is performed via electrostatic interactions among nearby molecular cells, without the need for charge transport, extremely reducing the power dissipation. Due to the lack of mature analysis and design methods, especially from an electronics standpoint, few attempts have been made to study the behavior of logic circuits based on real molecules, and this reduces the design capability. In this article, we propose a novel algorithm, named self-consistent electrostatic potential algorithm (SCERPA), dedicated to the analysis of molecular FCN circuits. The algorithm evaluates the interaction among all molecules in the system using an iterative procedure. It exploits two optimizations modes named Interaction Radius and Active Region which reduce the computational cost of the evaluation, enabling SCERPA to support the simulation of complex molecular FCN circuits and to characterize consequentially the technology potentials. The proposed algorithm fulfills the need for modeling the molecular structures as electronic devices and provides important quantitative results to analyze the information propagation, motivating and supporting further research regarding molecular FCN circuits and eventual prototype fabrication

Process Variability and Electrostatic Analysis of Molecular QCA

Molecular quantum-dot cellular automata (mQCA) is an emerging paradigm for nanoscale computation. Its revolutionary features are the expected operating frequencies (THz), the high device densities, the noncryogenic working temperature, and, above all, the limited power densities. The main drawback of this technology is a consequence of one of its very main advantages, that is, the extremely small size of a single molecule. Device prototyping and the fabrication of a simple circuit are limited by lack of control in the technological process [Pulimeno et al. 2013a]. Moreover, high defectivity might strongly impact the correct behavior of mQCA devices. Another challenging point is the lack of a solid method for analyzing and simulating mQCA behavior and performance, either in ideal or defective conditions. Our contribution in this article is threefold: (i) We identify a methodology based on both ab-initio simulations and post-processing of data for analyzing an mQCA system adopting an electronic point of view (we baptized this method as "MoSQuiTo"); (ii) we assess the performance of an mQCA device (in this case, a bis- ferrocene molecule) working in nonideal conditions, using as a reference the information on fabrication-critical issues and on the possible defects that we are obtaining while conducting our own ongoing experiments on mQCA: (iii) we determine and assess the electrostatic energy stored in a bis-ferrocene molecule both in an oxidized and reduced form. Results presented here consist of quantitative information for an mQCA device working in manifold driving conditions and subjected to defects. This information is given in terms of: (a) output voltage; (b) safe operating area (SOA); (c) electrostatic energy; and (d) relation between SOA and energy, that is, possible energy reduction subject to reliability and functionality constraints. The whole analysis is a first fundamental step toward the study of a complex mQCA circuit. It gives important suggestions on possible improvements of the technological processes. Moreover, it starts an interesting assessment on the energy of an mQCA, one of the most promising features of this technolog

Greenhouse Gas Emissions from Asphalt Pavement Construction: A Case Study in China.

In China, the construction of asphalt pavement has a significant impact on the environment, and energy use and greenhouse gas (GHG) emissions from asphalt pavement construction have been receiving increasing attention in recent years. At present, there is no universal criterion for the evaluation of GHG emissions in asphalt pavement construction. This paper proposes to define the system boundaries for GHG emissions from asphalt pavement by using a process-based life cycle assessment method. A method for evaluating GHG emissions from asphalt pavement construction is suggested. The paper reports a case study of GHG emissions from a typical asphalt pavement construction project in China. The results show that the greenhouse gas emissions from the mixture mixing phase are the highest, and account for about 54% of the total amount. The second highest GHG emission phase is the production of raw materials. For GHG emissions of cement stabilized base/subbase, the production of raw materials emits the most, about 98%. The GHG emission for cement production alone is about 92%. The results indicate that any measures to reduce GHG emissions from asphalt pavement construction should be focused on the raw materials manufacturing stage. If the raw materials production phase is excluded, the measures to reduce GHG emissions should be aimed at the mixture mixing phase

Inhibiting Aspergillus flavus growth and degrading aflatoxin B1 by combined beneficial microbes

Aflatoxin B1 (AFB1) is a type of toxin produced by Aspergillus flavus, which has a negative effect on animal production and economic profits. In order to inhibit A. flavus growth and degrade aflatoxin, the optimal  proportion of beneficial microbes such as Lactobacillus casei, Bacillus subtilis and Pichia anomala were selected. The results show that AFB1 production and mycelium weight of A. flavus was decreased by more than 34 folds (161.05 vs. 4.69 µ/L) and 7.7 folds (6.98 vs. 0.90 mg/ml) with the free-cell supernatants of L. casei and B. subtilis (P<0.05), respectively. The optimal proportion of L. casei, B. subtilis and P. anomala was 2:1:2 for inhibiting A. flavus growth determined by 3x3 orthogonal design. Based on the optimal proportion of three microbial species, the maximum AFB1 degradation was during 24 to 48 h incubation (P<0.05). When three species of beneficial microbes were mixed with yeast cell wall and oligosaccharide, both of them could not help the microbes in AFB1 degradation. The combined microbial incubation showed that AFB1 contents in the supernatant and cells were 10.25 (P<0.05) and 3.34 µg/L, lower than the control group (68.55 µg/L), indicating that most of the AFB1 were degraded by the microbes and only a little of them were absorbed and deposited in microbial cells.Key words: Aspergillus flavus, aflatoxin B1 detoxification, beneficial microbes, yeast cell wall, oligosaccharide

Region Refinement Network for Salient Object Detection

Albeit intensively studied, false prediction and unclear boundaries are still major issues of salient object detection. In this paper, we propose a Region Refinement Network (RRN), which recurrently filters redundant information and explicitly models boundary information for saliency detection. Different from existing refinement methods, we propose a Region Refinement Module (RRM) that optimizes salient region prediction by incorporating supervised attention masks in the intermediate refinement stages. The module only brings a minor increase in model size and yet significantly reduces false predictions from the background. To further refine boundary areas, we propose a Boundary Refinement Loss (BRL) that adds extra supervision for better distinguishing foreground from background. BRL is parameter free and easy to train. We further observe that BRL helps retain the integrity in prediction by refining the boundary. Extensive experiments on saliency detection datasets show that our refinement module and loss bring significant improvement to the baseline and can be easily applied to different frameworks. We also demonstrate that our proposed model generalizes well to portrait segmentation and shadow detection tasks

Experimental study on the response relationship between environmental DNA concentration and biomass of Schizothorax prenanti in still water

The superiority of the environmental DNA (eDNA) method for estimating the biomass of aquatic species has been demonstrated. However, the relationship between eDNA concentration and biomass is difficult to clarify under the influence of complex water flow and habitat conditions. It seriously restricts the popularization and application of the eDNA method in estimating aquatic biomass. In this paper, a typical fish species of rivers in southwest China, Schizothorax prenanti, was selected as the target species. Under standardized laboratory hydrostatic conditions, two environmental factors, water pH and water temperature were firstly determined through pre-experiments. Then we investigated the correlation between eDNA concentration and biomass under different body sizes and different body size compositions. The experimental results showed that water pH and the water temperature had a great influence on eDNA concentration. Therefore, the effects of these environmental factors need to be considered simultaneously when using eDNA concentration to estimate biomass. Under the premise of consistent environmental conditions, the biomass of Schizothorax prenanti was positively correlated with the eDNA concentration when the individual body size was the same. For each 1% increase in biomass of the fish, the eDNA concentration of adult (larger size) fish increased by 0.98%, while the eDNA concentration of juvenile (smaller size) fish increased by 1.38%. The smaller the size of individual fish, the greater the increase of eDNA concentration with biomass, and the increase of juvenile fish was about 1.4 times that the adult fish. When the biomass was the same but the body size composition was different, the higher the proportion of small body size individuals in the population, the higher the eDNA concentration. Special attention needs to be paid to the body size composition of the population to avoid the biomass estimation being lower than the actual value when the smaller size fish are dominant. The experimental results provide a strong basis for a more accurate estimation of aquatic biomass in reservoirs, lakes, and other still water areas by using the eDNA method