Miracle-dz/UniFAR-FTUs huggingface dataset:
Rate Adaptation for Downlink Massive MIMO Networks and Underlaid D2D Links: A Learning Approach

In this paper, a novel learning-based rate adaptation mechanism is proposed for a downlink massive multiple-input-multiple-output (MIMO) network with underlaid device-to-device (D2D) links, where the link signal-to-interference-plus-noise ratio (SINR) cannot be accurately predicted before transmission, even its distribution statistics are unknown at the very beginning. Specifically, two coexistence schemes are considered: (1) the D2D links only reuse the downlink subframes; and (2) the D2D receivers also join the uplink channel estimation of the associated cells. For the second scheme, the downlink interference to the D2D receivers is suppressed at the cost of channel training overhead. The geographic distributions of the selected downlink and D2D users in each frame are modeled as two independent stochastic processes with unknown statistics. As a result, the distribution of interference power is unknown to the transmitters. In order to facilitate robust rate allocation, we first derive the asymptotic expressions of downlink and D2D signal-to-interference-plus-noise ratios (SINRs) for sufficiently large antenna number, and show that their distributions can be approximated by Gaussian or exponential random variables. Subsequently, distributive learning algorithms are proposed to evaluate the means and variances of these random variables. This enables the BSs and D2D transmitters to determine the transmission rates under a constraint on packet outage probability.
A High Speed On-Chip Soft-Start Technique With High Start-Up Stability for Current-Mode DC-DC Converter

In high efficiency and high-frequency applications, the soft-start methods for DC-DC converter with fast output voltage settling speed have attracted widespread attention. A novel soft-start circuit with controllable start-up time, high start-up speed, high stability, and no off-chip capacitor for a current mode DC-DC is proposed in this paper. The proposed soft-start circuit introduces a current-limiting feedback technique so that the inductor current during soft-start is controllable. The stability of the soft-start technique is verified theoretically in this paper. A Boost converter using this novel soft-start technique is fabricated in <inline-formula> <tex-math notation="LaTeX">$0.35~\mu \text{m}$ </tex-math></inline-formula> BCD process. The soft-start time of the Boost converter is only <inline-formula> <tex-math notation="LaTeX">$380~\mu \text{s}$ </tex-math></inline-formula> at the conditions of 45 V output voltage, <inline-formula> <tex-math notation="LaTeX">$20~\mu \text{F}$ </tex-math></inline-formula> load capacitance, and 500 mA load current.
Investigation of the Processors Evolution Using the Computer Capacity

In this article we show how the ideas of information theory help to analyze the evolution of processors. As the main tool we use the computer capacity which is close to the channel capacity considered by C.Shannon. It turned out that during the transition "from old to new" the manufacturers change the parameters that affect the computer capacity. It allows to predict the values of parameters for succeeding processors. As the main example we use the Intel processors due to their high popularity and the accessibility of detailed description of all the technical characteristics. In this article we describe the computer capacity, paying attention to its connection with the ideas and theories of information theory. Next we apply it to analysis and prediction of the processors evolution.
Error Exponents of Joint Channel Coding and Intrinsic Randomness for Memoryless Channels

This paper considers a joint channel coding and random number generation from channel outputs. Specifically, we want to transmit a message to a receiver reliably and at the same time the receiver extracts pure random bits independent of the channel input. We call this problem as the joint channel coding and intrinsic randomness problem. For stationary memoryless channels, we show exponential upper bounds on both the decoding error probability and the variational distance between the distribution of the obtained random number and the uniform distribution. We also clarify that the obtained both bounds vanish as the block length tends to infinity, whenever a pair of coding rate and random bit rate is within the achievable rate region. Further, the above performance can be obtained by a universal scheme which does not depend on the channel.
Artocarpus Trees Classification using Convolutional Neural Network

Distinguishing tree with almost similar features, from trunk, leaf, and fruit is a complex task which often resulted in inaccuracy. This study focuses on the classification of morphologically identical Artocarpus trees locally called “Rimas”(Artocarpus Altilis) and “Kamansi”(Artocarpus Camansi). Unmanned Aerial Vehicle (UAV) is used to capture Artocarpus images. A Convolutional Neural Network (CNN) is utilized to classify two Artocarpus species. The results are models that classify breadfruit and breadnut at the rate of 85.6% and 90% accuracy, respectively. Thus, the CNN classification can effectively identify almost similar trees with an acceptable result.
Overflow Probability of Codeword Cost in Variable-Length Coding Problem Allowing Non-Vanishing Error Probability

The variable-length source coding with unequal cost allowing error probability is considered for general sources. In this setting, the first- and second-order optimum mean codeword cost have already been determined. On the other hand, we focus on the overflow probability of codeword cost and determine the general formulas of the first- and second-order optimum achievable overflow threshold. We also apply our general formulas to the stationary memoryless source.
Entropy-based Graph Clustering - A Simulated Annealing Approach

We revisit a Renyi entropy based measure introduced originally for image clustering [1], and study its application to graph clustering. To effectuate Renyi entropy based graph clustering, we propose a simulated annealing algorithm. We explore our algorithm’s efficacy and limitations with the Karate club graph [2], as well as some other real world network.
Research on the Remaining Service Life Prediction of Mechanical Equipment Based on Similarity Principle

In view of problem that remaining service life of complex mechanical equipment was difficult to predict, an approach for remaining service life prediction based on similarity was presented. Feature extraction method was established based on correlation analysis, reference remaining service life and its weight can be determined by computing similarity of the relevant feature between sample data and prediction data. Finally, remaining service life can be obtained by calculating weighted sum of the reference remaining service life. Experiment results show that the proposed approach can effectively extract feature which can precisely reflect variation trend of the remaining service life of bearing, and can more effectively predict remaining service life of bearing with accuracy rate of 82.2%, and provides a scientific basis for life management of related equipment.
Renal Lymphatics: Anatomy, Physiology, and Clinical Implications

Renal lymphatics are abundant in the cortex of the normal kidney but have been largely neglected in discussions around renal diseases. They originate in the substance of the renal lobule as blind-ended initial capillaries, and can either follow the main arteries and veins toward the hilum, or penetrate the capsule to join capsular lymphatics. There are no valves present in interlobular lymphatics, which allows lymph formed in the cortex to exit the kidney in either direction. There are very few lymphatics present in the medulla. Lymph is formed from interstitial fluid in the cortex, and is largely composed of capillary filtrate, but also contains fluid reabsorbed from the tubules. The two main factors that contribute to renal lymph formation are interstitial fluid volume and intra-renal venous pressure. Renal lymphatic dysfunction, defined as a failure of renal lymphatics to adequately drain interstitial fluid, can occur by several mechanisms. Renal lymphatic inflow may be overwhelmed in the setting of raised venous pressure (e.g., cardiac failure) or increased capillary permeability (e.g., systemic inflammatory response syndrome). Similarly, renal lymphatic outflow, at the level of the terminal thoracic duct, may be impaired by raised central venous pressures. Renal lymphatic dysfunction, from any cause, results in renal interstitial edema. Beyond a certain point of edema, intra-renal collecting lymphatics may collapse, further impairing lymphatic drainage. Additionally, in an edematous, tense kidney, lymphatic vessels exiting the kidney via the capsule may become blocked at the exit point. The reciprocal negative influences between renal lymphatic dysfunction and renal interstitial edema are expected to decrease renal function due to pressure changes within the encapsulated kidney, and this mechanism may be important in several common renal conditions.
Exploring the Need for Explainable Artificial Intelligence (XAI) in Intelligent Tutoring Systems (ITS)

This work is the first step towards understanding when and if it is necessary for an Intelligent Tutoring System (ITS) to explain its underlying user modeling techniques to students. We conduct an initial pilot study to explore student attitudes towards incorporating explanations to an ITS, by asking participants for suggestions on the type of explanations, if any, that they would like to see. Our results indicate an overall positive sentiment towards wanting explanation and suggest a few design directions for incorporating explanation into an existing ITS.
Anthropogenic Factors Driving Recent Range Expansion of the Malaria Vector Anopheles stephensi

The malaria vector Anopheles stephensi is found in wide tracts of Asia and the Middle East. The discovery of its presence for the first time in the island of Sri Lanka in 2017, poses a threat of malaria resurgence in a country which had eliminated the disease in 2013. Morphological and genetic characterization showed that the efficient Indian urban vector form An. stephensi sensu stricto or type form, has recently expanded its range to Jaffna and Mannar in northern Sri Lanka that are in proximity to Tamil Nadu state in South India. Comparison of the DNA sequences of the cytochrome oxidase subunit 1 gene in An. stephensi in Jaffna and Mannar in Sri Lanka and Tamil Nadu and Puducherry states in South India showed that a haplotype that is due to a sequence change from valine to methionine in the cytochrome oxidase subunit 1 present in the Jaffna and Mannar populations has not been documented so far in Tamil Nadu/Puducherry populations. The Jaffna An. stephensi were closer to Tamil Nadu/Puducherry populations and differed significantly from the Mannar populations. The genetic findings cannot differentiate between separate arrivals of the Jaffna and Mannar An. stephensi from Tamil Nadu or a single arrival and dispersion to the two locations accompanied by micro-evolutionary changes. Anopheles stephensi was observed to undergo preimaginal development in fresh and brackish water domestic wells and over ground cement water storage tanks in the coastal urban environment of Jaffna and Mannar. Anopheles stephensi in Jaffna was resistant to the common insecticides deltamethrin, dichlorodiphenyltrichloroethane and Malathion. Its preimaginal development in wells and water tanks was susceptible to predation by the larvivorous guppy fish Poecilia reticulata. The arrival, establishment, and spread of An. stephensi in northern Sri Lanka are analyzed in relation to anthropogenic factors that favor its range expansion. The implications of the findings for global public health challenges posed by malaria and other mosquito-borne diseases are discussed.
Mash: software tools for developing interactive and transparent machine learning systems

We present Mash, a library that provides software tools for developing interactive and transparent machine learning systems. Mash provides tools for (1) back end: specifying machine learning models, and (2) front end: generating User Interfaces (UIs) that allow nontechnical end-users to interpret and interact with the models. In the back-end, Mash includes probabilistic modeling and computation graph utilities. On the front-end, UIs generated by Mash enable end users to examine the model’s predictions, manipulate model’s parameters, and observe changes in model’s predictions. Together, Mash can help developers rapidly build new models and obtain feedback from end users. We present two case studies in which Mash UIs enable end-users to: (1) assess model fairness in predicting student performance, and (2) explore movie recommendations. To support future work, we will open source Mash.
An Adaptive Force Control Method for 7-Dof Space Manipulator Repairing Malfunctioning Satellite

This paper presents an adaptive force control method for 7-Dof space manipulator repairing malfunctioning satellite. The whole control scheme contains two loops: outer force control and inner motion control. There are two parts in the force controller, position-based impedance control and direct force control. In the impedance controller, translational impedance and rotational impedance based on Euler angles are both achieved, successfully avoiding destructive contact force and adjusting the orientation of the end-effector. And the direct force controller can tracking the desired force. In the inner loop, the robustness to dynamics uncertainty is achieved by an adaptive motion control method. The global stability and the convergence of trajectory tracking error are strictly proved. During the operation, the collision between the manipulator and the chaser is easily arisen due to the wide-range motion of the second joint. An acceleration-level redundancy resolution method of the 7-Dof manipulator is proposed avoiding the ill condition. A set of numerical experiments by MSC ADAMS®/SIMULINK® co-simulation verify the validity and feasibility of the proposed approach.
Truck+ for earthmoving operations

This paper presents an automated system that integrates Global Positioning System (GPS) and Geographical Information System (GIS) in a web-based platform named Truck+. It is utilized for estimating, monitoring and forecasting productivity of hauling trucks in earthmoving operations. The developed system consists of GPS for automated site data acquisition, GIS web-based system used as graphical user interface, relational database and Discrete Event Simulation (DES) for stochastic forecasting. The paper also highlights the benefit of utilizing the actual captured data supported by DES for stochastically forecasting productivity of earthmoving operation. DES was applied to forecast productivity in a stochastic approach that makes use of the project’s captured data during the operations involved rather than data from past projects. This makes it more capable of capturing relevant factors that impact project conditions. Truck+ consists primarily of two modules: tracking/monitoring module and forecasting module. The developed system has been implemented in prototype software using object-oriented programming, ArcGIS APIs (Application Programming Interface) and deploys Microsoft Silverlight for creating and delivering rich internet web application and media. Truck+ is capable of generating graphical and tabular reports with various degrees of detail to suit the requirements of project teams. The developed system is applied to a construction project in Montreal area to demonstrate its use.
Needle modeling, insertion planning and steering for CT or MR image-guided robot-driven percutaneous procedures. (Modélisation, planification et guidage d'aiguille pour les procédures percutanées robotisées sous imagerie SCANNER ou IRM)

L’objectif de cette these est l’etude et le developpement d’un outil de planification de trajectoires adaptatives guidee par robot afin d’assister l’insertion des aiguilles biseautees semi-rigides en procedures percutanees sous imagerie scanner ou IRM. L’approche est basee sur un nouveau modele de la deflexion de l’aiguille qui permet une prediction hors ligne des deformations de l’aiguille et une amelioration des performances d’insertion en ligne. Cette amelioration permet de compenser les incertitudes sur les connaissances de l’environnement et les approximations lors de la modelisation de l’aiguille. Les resultats des tests in vitro pour differentes plateformes robotiques ont confirme la viabilite de la methode proposee.
Why these Explanations? Selecting Intelligibility Types for Explanation Goals

The increasing ubiquity of artificial intelligence (AI) has spurred the development of explainable AI (XAI) to make AI more understandable. Even as novel algorithms for explanation are being developed, researchers have called for more human interpretability. While empirical user studies can be conducted to evaluate explanation effectiveness, it remains unclear why specific explanations are helpful for understanding. We leverage a recently developed conceptual framework for user-centric reasoned XAI that draws from foundational concepts in philosophy, cognitive psychology, and AI to identify pathways for how user reasoning drives XAI needs. We identified targeted strategies for applying XAI facilities to improve understanding, trust and decision performance. We discuss how our framework can be extended and applied to other domains that need user-centric XAI. This position paper seeks to promote the design of XAI features based on human reasoning needs.
A Survey of Key Bootstrapping Protocols Based on Public Key Cryptography in the Internet of Things

The Internet of Things envisages connecting all physical objects or things to the Internet, using devices as diverse as smartphones, coffee makers, washing machines, automobiles, lamps, and wearable devices, among many others. The explosive growth of Internet-connected sensing and actuating devices has bridged the gap between the physical and the digital world, with new solutions bringing benefits to people, processes, and businesses. However, security will be a major challenge in enabling most of such applications. The lack of secure links exposes data exchanged by devices to theft and attacks, with hackers already showing a keen interest in this area. Secure communication in the IoT will require a multifaceted approach, in particular, targeting aspects as relevant as the communications’ protocols and data that need to be secured. One of the major aspects among these is how keys are bootstrapped in devices, for the purpose of supporting secure communications. In this paper, we survey the state of the art in key bootstrapping protocols based on public-key cryptography in the Internet of Things. Due to its inherent scalability, such protocols are particularly relevant for the implementation of distributed identity and trust management mechanisms on the IoT, in the context of which devices may be authenticated and trusted. The reviewed proposals are analyzed and classified on the basis of the key delivery method, the underlying cryptographic primitive, and the authentication mechanism supported. We also identify and discuss the main challenges of implementing such methods in the context of IoT applications and devices, together with the main avenues for conducting further research in the area.
A review of the efforts and roles of the public sector for BIM adoption worldwide

Building Information Modeling (BIM) adoption is spreading through the public sector (including government bodies and non-profit organizations) around the globe in the architecture, engineering and construction (AEC) industry. The public sector plays a key role in supporting and encouraging the adoption of BIM in the industry. Currently there is no comprehensive study on the efforts and roles of the public sector for BIM adoption. In this paper, different kinds of the efforts that the public sector has put for BIM adoption worldwide are reviewed to highlight the successful implementations of BIM and to identify the gaps in some countries. The countries covered in this paper are grouped into four regions – the United States, Europe, Asia, and Australasia. In each region, efforts of the public sector in different countries to BIM implementations including establishment of BIM programs and committees, organization of BIM activities and seminars, setting up of different BIM goals and promises, and preparation of BIM guidelines and standards are described and compared. This paper also identifies six major possible roles of the public sector for BIM adoption. The roles played by the public sector in each selected country are summarized and evaluated.
Descent dynamical systems and algorithms for tame optimization, and multi-objective problems. (Systèmes dynamiques de descente et algorithmes pour l'optimisation modérée, et les problèmes multi-objectif)

Dans une premiere partie, nous nous interessons aux systemes dynamiques gradients gouvernes par des fonctions non lisses, mais aussi non convexes, satisfaisant l'inegalite de Kurdyka-Lojasiewicz. Apres avoir obtenu quelques resultats preliminaires pour la dynamique de la plus grande pente continue, nous etudions un algorithme de descente general. Nous prouvons, sous une hypothese de compacite, que tout suite generee par ce schema general converge vers un point critique de la fonction. Nous obtenons aussi de nouveaux resultats sur la vitesse de convergence, tant pour les valeurs que pour les iteres. Ce schema general couvre en particulier des versions parallelisees de la methode forward-backward, autorisant une metrique variable et des erreurs relatives. Cela nous permet par exemple de proposer une version non convexe non lisse de l'algorithme Levenberg-Marquardt. Enfin, nous proposons quelques applications de ces algorithmes aux problemes de faisabilite, et aux problemes inverses. Dans une seconde partie, cette these developpe une dynamique de descente associee a des problemes d'optimisation vectoriels sous contrainte. Pour cela, nous adaptons la dynamique de la plus grande pente usuelle aux fonctions a valeurs dans un espace ordonne par un cone convexe ferme solide. Cette dynamique peut etre vue comme l'analogue continu de nombreux algorithmes developpes ces dernieres annees. Nous avons un interet particulier pour les problemes de decision multi-objectifs, pour lesquels cette dynamique de descente fait decroitre toutes les fonctions objectif au cours du temps. Nous prouvons l'existence de trajectoires pour cette dynamique continue, ainsi que leur convergence vers des points faiblement efficients. Finalement, nous explorons une nouvelle dynamique inertielle pour les problemes multi-objectif, avec l'ambition de developper des methodes rapides convergeant vers des equilibres de Pareto.