Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal-aware Flow Field Optimization for Energy Saving of Data Centers

Improving the energy efficiency of data centers has become an essential problem. Flow field optimization has emerged in recent years as a critical part of thermal management. A novel temperature prediction model based on artificial neural network for various flow field is presented in this paper. The model approximates the solution given by Computational Fluid Dynamics methods, and it is used to formulate a constrained nonlinear optimization problem with the objective of minimizing the energy consumption of the cooling system. The particle swarm optimization algorithm is employed to optimize the flow field in the data center level. The optimizer tunes the flow rates of individual racks based on the predictions of the temperatures of the racks proactively. Simulation results demonstrate that over-provisioning of cooling air is eliminated by the flow field optimization. The efficiency of the cooling system is improved by 17% compared to the traditional temperature-based feedback controller.
Time-Optimal Freeform S-Curve Profile Under Positioning Error and Robustness Constraints

The problems on designing a time-optimal motion profile in the motion system with elasticity are briefly reviewed. Related positioning error and robustness constraints are constructed based on the analytic envelope of the residual vibration response, which is formulated by the proposed energy-based approach. The time-optimal model under such constraints is applied in the proposed freeform S-curve profile, which is the superset of symmetric S-curve and asymmetric S-curve profiles, to minimize the positioning time, while keeping the positioning accuracy under a neighborhood range of modeling frequency. The optimized motion profile shows that the proposed freeform S-curve profile allows the time-optimal model to simultaneously optimize the magnitude and phase terms in the residual vibration response in order to obtain an optimal tradeoff between shortening the positioning time and keeping the positioning accuracy. The experimental results show that the proposed optimization model can significantly reduce the positioning time compared with the traditional S-curve under the same physical limits. The universality of the optimization model is verified by experiments with short-distance and long-distance cases. The robustness of the proposed optimization model is also validated by these experiments. Moreover, the analysis on the optimized freeform S-curve profile reveals that perfect waveform cancellation is not always necessary to limit the residual vibration.
Modeling and Control of a Bifilar Crane Payload

This paper presents a modeling technique and a controller for an underactuated crane payload. The crane payload is modeled as a bifilar pendulum. The payload is attached to a sheave block, such that a cable can freely run to either side. This configuration is often used in different types of cranes, including offshore cranes. To achieve asymptotic stability in the absence of damping, we propose a controller based on an energy approach and the passivity properties of the system. We prove stability of the system with the proposed controller using LaSalle's invariance principle. The control performance is studied in the numerical simulations. The simulation results show that all the states of the closed-loop system with coupled sway and skew dynamics converge to the origin.
Distributed Randomized Algorithms for PageRank Based on a Novel Interpretation

PageRank is a well-known centrality measure for the web used in search engines, representing the importance of each web page. In this paper, we develop a new class of distributed algorithms for the computation of PageRank. Each page computes its own PageRank value by interacting with pages connected over hyperlinks. Our approach is based on a simple but novel interpretation of PageRank. Gossip-type randomization is employed in the update schemes, and it is shown that the page selection need not be limited to the uniform distribution. In comparison with other existing techniques, significant advantages can be seen in their convergence performance as demonstrated via numerical examples.
A New Robust Adaptive Control for Uncertain Nonlinear Systems

A novel adaptive control with $\sigma$-modification on the basis of immersion and invariance (I&I) theory is proposed for a class of uncertain nonlinear systems in the paper. The idea is inspired by two difficulties of conventional adaptive control encountered in engineering applications. One is the difficulty of adaptive law design coming from the construction of Lyapunov function. The other is the unpredictable unstable phenomena of adaption under non-parametric uncertainties. The proposed method can be applied to deal with these problems, which is a beneficial attempt to facilitate the practical implication of adaptive control. It turns out to be a structured design method without requiring a Lyapunov function and robust to non-parametric uncertainties. Moreover, constrained command filter backstepping is adopted to meet the amplitude and rate constraints on the states and actuators. The effectiveness of the proposed control method is illustrated by a numerical simulation.
5G-TRANSFORMER: Slicing and Orchestrating Transport Networks for Industry Verticals

This article dives into the design of the next generation mobile transport networks to simultaneously support the needs of various vertical industries with diverse range of networking and computing requirements. Network slicing has emerged as the most promising approach to address this challenge by enabling per-slice management of virtualized resources. We aim to bring the network slicing paradigm into mobile transport networks by provisioning and managing slices tailored to the needs of different vertical industries. Our technical approach is twofold: (i) enabling vertical industries to meet their service requirements within customized slices; and (ii) aggregating and federating transport networking and computing fabric, from the edge up to the core and cloud, to create and manage slices throughout a federated virtualized infrastructure. The main focus of the article is on major technical highlights of vertical-oriented slicing mechanisms for 5G mobile networks.
Analysis and automatic classification of web search queries for diversification requirements

Search result diversification enables the modern day search engines to construct a result list that consists of documents that are relevant to the user query and at the same time, diverse enough to meet the expectations of a diverse user population. However, all the queries received by a search engine may not benefit from diversification. Further, different types of queries may benefit from different diversification mechanisms. In this paper we present an analysis of logs of a commercial web search engine and study the web search queries for their diversification requirements. We analyze queries based on their click entropy and popularity and propose a query taxonomy based on their diversification requirements. We then carry out the task of automatically classifying web search queries into one of the classes of our proposed taxonomy. We utilize various query-based, click-based and reformulation-based features for the query classification task and achieve strong classification results.
Transfer Learning and Organic Computing for Autonomous Vehicles

Autonomous Vehicles(AV) are one of the brightest promises of the future which would help cut down fatalities and improve travel time while working in harmony. Autonomous vehicles will face with challenging situations and experiences not seen before. These experiences should be converted to knowledge and help the vehicle prepare better in the future. Online Transfer Learning will help transferring prior knowledge to a new task and also keep the knowledge updated as the task evolves. This paper presents the different methods of transfer learning, online transfer learning and organic computing that could be adapted to the domain of autonomous vehicles.
Vibration-Based Bearing Fault Diagnosis Using Reflection Coefficients of the Autoregressive Model

Bearing faults are one of the main reasons of rotary machines failure. Monitoring vibration signal is an effective method for diagnosing faulty bearings and preventing thus catastrophic failures. However, existing algorithms neither offer satisfactory accuracy nor are efficient for real-time implementation due to complexity in feature extraction part. In this paper, we propose an accurate method for bearing diagnosis customized for real-time implementation. The proposed system estimates power spectral density of vibration signal using an autoregressive model for feature extraction. This is a novel use of autoregressive model for fault diagnosis which reduces the dimensionality of vibration signal and captures its frequency contents simultaneously. The proposed system can diagnose different bearing faults under variable load conditions with above 99 % accuracy.
The effectiveness of cognitive behavioural therapy for pain in childhood and adolescence: a meta-analytic review

Objectives A variety of chronic painful conditions are present in the paediatric population. Patients with chronic pain often experience considerable scepticism and avoidance by health care providers. This meta-analytic review aimed to utilise well-designed studies, in examining the effectiveness of cognitive behavioural therapy (CBT) in the treatment of chronic pain in children and adolescents. Methods Nine randomized controlled trial studies examining CBT for chronic pain were reviewed. Outcome measures were child reported pain intensity, pain duration and functional disability. Results CBT had a large effect on pain intensity for recurrent abdominal pain (RAP), a small effect on headaches, and a medium effect on fibromyalgia. CBT had a medium effect on pain duration across pain types. CBT had a large effect on functional disability for RAP, a small effect on fibromyalgia and a moderate effect on headaches. Findings are limited by the small number of studies and varied control conditions. Conclusions CBT may be effective in reducing child reported pain symptomology. Future studies using a larger sample and examining the differential impact of varied control conditions are needed.
Developing a Stress-Testing Platform for Cyber-Physical Water Infrastructure

Water supply and sanitation infrastructures are essential for our welfare, but vulnerable to several attacks, typically of physical and cyber types. Cyber-physical attacks on critical infrastructures include chemical and/or biological contamination, physical or communications disruption between the network elements and the supervisory SCADA. Due to the ever-changing landscape of the digital world and the rising concerns about security, there is an emerging need for conceptualizing critical infrastructure as cyber-physical systems and develop a holistic risk management framework for its physical and cyber protection. The framework aims to strengthen the capacities of water utilities to systematically protect their systems, determine gaps in security technologies and improve risk management approaches. Our work envisions the development of a stress testing modelling platform, able to simulate the water system as a complete cyber-physical infrastructure and investigate attack scenarios and possible mitigation measures.
Reducing the Power Consumption of a Shape Memory Alloy Wire Actuator Drive by Numerical Analysis and Experiment

The use of shape memory alloy (SMA) wire actuators for a moving body has some advantages over other conventional actuators because of the high specific power, small size, and simplicity of these actuators. However, the large power consumption of SMA wire actuators prevents its practical use. This paper shows how to optimize the input voltage of SMA wire actuators. First, a numerical actuator model is created from the measured data. Next, input voltage waves are optimized for three evaluation functions using multiobjective optimization: maximum input voltage, time-averaged actuators power consumption, and time-averaged bending moment (the output of actuator). The obtained numerical results show that a high maximum input voltage achieves efficient drive of SMA wire actuators. We also verified the tradeoff relations of the consumed power, maximum input voltage, and output from the SMA wire actuator under one condition of the multiobjective optimization. Experimental data also partially supported the tradeoff relations obtained by the multiobjective optimization and the numerical simulation.
Equilibrium Distributions of Populations of Biological Species on Networks of Social Sites

ABSTRACT We investigate the problem of how a population of biological species would distribute over a given network of social sites so that their social contacts through the connected sites can be maximized (or minimized). This problem has applications in modelling the behaviours of social (or solitary) species such as the development of social groups in human society and the spread of solitary animals in distant habitats. We show that this problem can be formulated as an evolutionary game, with the equilibrium state of the game corresponding to a strategy for choosing the residing sites, each with a certain probability, or equivalently, to a distribution of the population on these sites. The game has a symmetric payoff matrix, and can therefore be analyzed via the solution of a corresponding quadratic programme: An equilibrium strategy of the game is a KKT point of the quadratic programme, which may be a local maximizer, local minimizer, or saddle point, but it is evolutionarily stable if and only if it is a strict local maximizer. In general, with a goal to maximize the social contacts, the species tend to spread on network sites where there are dense connections such as a complete subnetwork or in other words, a network clique. We show that at equilibrium, the population may or may not distribute on a network clique, but the stability of the equilibrium state does depend on the structure of the selected subnetwork. In particular, we show that the distribution of the population on a maximal network clique is evolutionarily stable unless the clique is ‘attached’ to another clique of the same or larger size, when the population may be able to switch or expand to the neighbouring clique to increase or at least maintain its total amount of contacts. However, the distribution of the population on a non-clique subnetwork is always evolutionarily unstable or weakly evolutionarily stable at the very best, for the population can always move away from its current distribution without decreasing its total amount of contacts. We conclude that the strategies to spread on maximal network cliques are not only equilibrium strategies but also evolutionarily more stable than those on non-clique subnetworks, thus theoretically reaffirming the evolutionary advantages of joining social cliques in social networks for social species.
Lipids and polyunsaturated fatty acid levels in deliberate self-harm: a 10-year follow-up study

Objectives To evaluate if n-3 polyunsaturated fatty acids (PUFAs) and lipid levels are associated with episodes of self-harm or depression over a 10-year period. Methods We included 40 individuals who self-harmed and 40 controls. Episodes of self-harm and depression were ascertained and levels of depression, impulsivity, suicidal ideation and plasma lipid levels measured at baseline and at 10-year follow-up. Results Further episode(s) of self-harm occurred in 26% of cases. Omega-3 PUFAs or lipids were not predictive of depressive or self-harm episodes. Baseline eicosapentaenoic acid levels were modestly correlated with suicidal ideation at follow-up and dihomo-γ-linolenic acid and arachidonic acid were modestly correlated with motor impulsivity at follow-up in cases. Conclusions Despite significant negative correlations at baseline between plasma lipids, n-3 PUFAs and psychopathology, these levels were not predictive of clinical outcome over a 10-year period. Further research however is required due to the relatively low sample size and the risk of selection bias due to loss to follow-up in this study.
Comparison of Early Outcomes for Normothermic and Hypothermic Cardiopulmonary Bypass in Children Undergoing Congenital Heart Surgery

Objective: Comparison of early outcomes of normothermic cardiopulmonary bypass (N-CPB, ≥35°C) with hypothermic cardiopulmonary bypass (H-CPB, 28–34°C) for congenital heart defects. Methods: Data from 99 patients <2 years operated with N-CPB (n = 48) or H-CPB (n = 51) were retrospectively reviewed: aortic X-clamping and CPB duration, vasoactive inotropic score (VIS), arterial lactate, pH and base excess, urine output, extubation, PICU stay, transfusion requirements, chest drain losses, costs of transfusions, and costs of PICU stay. Results: The two groups were homogeneous for diagnosis, risk factors, surgery and demographic variables: N-CPB age 7.7 ± 6.1 months, weight 6.2 ± 2.4 kg, and H-CPB age 6.6 ± 6.5 months, weight 6.1 ± 2.4 kg. There were no hospital deaths in either group. VIS in N-CPB was lower than H-CPB on PICU arrival (9.7 ± 5.9 vs. 13.4 ± 7.9, P < 0.005), after 4 h (7.0 ± 5.2 vs. 11.1 ± 7.3, P < 0.001) and 24 h (2.8 ± 3.6 vs. 5.6 ± 5.6, P < 0.003); arterial pH was better at PICU arrival (7.33 ± 0.09 vs. 7.30 ± 0.09, P = 0.046) after 4 h (7.35 ± 0.07 vs. 7.32 ± 0.07, P = 0.022) and after 24 h (7.37 ± 0.05 vs. 7.35 ± 0.05, P = 0.01). Extubation was earlier in N-CPB than in H-CPB (22 ± 27 vs. 48 ± 57 h, P = 0.003) as PICU discharge (61 ± 46 h vs. 87 ± 69 h, P = 0.021). Transfusion requirements in operating room were lower in N-CPB vs. H-CPB for RBC, FFP, cryoprecipitate, and platelets, while during the first 24 h in PICU were lower only for cryoprecipitate and platelets. Chest drain losses (mL/kg) on PICU arrival, after 4 and 24 h were lower with N-CPB vs. H-CPB (respectively 1.5 ± 1.4 vs. 2.5 ± 2.7, P = 0.013, 7.8 ± 6.0 vs. 10.9 ± 8.7, P = 0.025, and 23.0 ± 12.0 vs. 27.9 ± 15.2, P = 0.043). Tranexamic acid infusion was required in 7/48 (14.6%) patients with N-CPB vs. 18/51(= 35.3%) in H-CPB (P = 0.009). The average total costs/patient of blood and blood products (RBC, FFP, cryoprecipitate, platelets) were lower in N-CPB vs. H-CPB for both the first 24 h after surgery (£204 ± 169 vs. £306 ± 254, P = 0.011) as well as during the total duration of PICU stay (£239 ± 193 vs. £427 ± 337, P = 0.001). The average cost/patient/day of stay in PICU was lower in N-CPB than in H-CPB (£4,067 ± 3,067 vs. £5,800 ± 4,600, P = 0.021). Conclusions: N-CPB may reduce inotropic and respiratory support, shorten PICU stay, and decrease peri-operative transfusion requirements, with subsequent costs reduction, compared to H-CPB. Future studies are needed to validate and support wider use of N-CPB.
Simulation and Comparative Analysis of Packet Delivery in Flying Ad Hoc Network (FANET) Using AODV

Node connectivity is examined in the article as one of the most important characteristics of any information and telecommunication network quality. The article is devoted to determining the FANET parameters PDR (Packet Delivery Ratio) more than 90% at the specified territory. All results were obtained in the course of the experimental study. Simulation modeling was as a research method and it was based on ns-2 network simulator. Characteristics of the network developed according to the simulation model using the AODV routing protocol have been specified. The connectivity dependence on such network parameters as the number of nodes and the transmission range has been examined. Analysis results of the routes lengths depending on the parameters are presented.
On the Decision Boundary of Deep Neural Networks

While deep learning models and techniques have achieved great empirical success, our understanding of the source of success in many aspects remains very limited. In an attempt to bridge the gap, we investigate the decision boundary of a production deep learning architecture with weak assumptions on both the training data and the model. We demonstrate, both theoretically and empirically, that the last weight layer of a neural network converges to a linear SVM trained on the output of the last hidden layer, for both the binary case and the multi-class case with the commonly used cross-entropy loss. Furthermore, we show empirically that training a neural network as a whole, instead of only fine-tuning the last weight layer, may result in better bias constant for the last weight layer, which is important for generalization. In addition to facilitating the understanding of deep learning, our result can be helpful for solving a broad range of practical problems of deep learning, such as catastrophic forgetting and adversarial attacking. The experiment codes are available at this https URL
Mere Exposure Effects on Implicit Stimulus Evaluation: The Moderating Role of Evaluation Task, Number of Stimulus Presentations, and Memory for Presentation Frequency

The mere exposure (ME) effect refers to the well-established finding that people evaluate a stimulus more positively after repeated exposure to it. So far, the vast majority of studies on ME effects have examined changes in explicit stimulus evaluation. We describe the results of three large-scale studies (combined N = 3,623) that examined ME effects on implicit stimulus evaluation. We looked at three moderators of these effects: the implicit evaluation measure, the number of stimulus presentations, and memory for presentation frequency. We observed ME effects on implicit stimulus evaluations as measured with an Implicit Association Test (IAT) and Affect Misattribution Procedure (AMP), but not an Evaluative Priming Task (EPT). ME effects were more robust when there were relatively few stimulus presentations and when participants had accurate memory for the presentation frequencies. We discuss how these findings relate to ME effects on explicit evaluations as well as theoretical and practical implications.
A Bio-inspired Motivational Decision Making System for Social Robots Based on the Perception of the User

Nowadays, many robotic applications require robots making their own decisions and adapting to different conditions and users. This work presents a biologically inspired decision making system, based on drives, motivations, wellbeing, and self-learning, that governs the behavior of the robot considering both internal and external circumstances. In this paper we state the biological foundations that drove the design of the system, as well as how it has been implemented in a real robot. Following a homeostatic approach, the ultimate goal of the robot is to keep its wellbeing as high as possible. In order to achieve this goal, our decision making system uses learning mechanisms to assess the best action to execute at any moment. Considering that the proposed system has been implemented in a real social robot, human-robot interaction is of paramount importance and the learned behaviors of the robot are oriented to foster the interactions with the user. The operation of the system is shown in a scenario where the robot Mini plays games with a user. In this context, we have included a robust user detection mechanism tailored for short distance interactions. After the learning phase, the robot has learned how to lead the user to interact with it in a natural way.