Miracle-dz/UniFAR-FTUs huggingface dataset:
Distributed Platoon Control Under Topologies With Complex Eigenvalues: Stability Analysis and Controller Synthesis

The platooning of autonomous vehicles can significantly benefit road traffic. Most previous studies on platoon control have only focused on specific communication topologies, especially those with real eigenvalues. This paper extends existing studies on distributed platoon control to more generic topologies with complex eigenvalues, including both internal stability analysis and linear controller synthesis. Linear platoon dynamics are derived using an inverse vehicle model compensation, and graph theory is employed to model the communication topology, leading to an integrated high-dimension linear model of the closed-loop platoon dynamics. Using the similarity transformation, a sufficient and necessary condition is derived for the internal stability, which is completely defined in real number field. Then, we propose a Riccati inequality based algorithm to calculate the feasible static control gain. Further, disturbance propagation is formulated as an $\text {H}_{\infty }$ performance, and the upper bound of spacing errors is explicitly derived using Lyapunov analysis. Numerical simulations with a nonlinear vehicle model validate the effectiveness of the proposed methods.
Outcome of Closed Wedge Varus Derotation Osteotomy with Trochanteric Apophysiodesis in Perthes Disease

Background Perthes disease is a condition in which a self-limiting avascular event affects the capital epiphysis of the femur with a very variable course. It has been shown frequently, however, that there is a group of patients who definitely benefit from containment, either surgical or nonsurgical, better with surgical. We studied midterm to long term outcome of closed wedge varus derotation osteotomy (VDRO) with trochanteric apophysiodesis in Perthes disease. Materials and Methods 88 children with mean age of 7.9 ± 2 years, belonging to Herring’s lateral pillar Groups B and C treated with VDRO with trochanteric apophysiodesis were included in this study. Radiological evaluation was done using Mose’s index, the epiphyseal quotient, the articulotrochanteric distance and center edge angle of Wiberg. Results Containment was achieved in all patients on postoperative radiographs. At a mean followup of 12 years, good results were obtained in 66, fair in 19, and poor in 3 patients using Catterall’s postoperative classification. Conclusions VDRO is an effective containment method of femoral head for patients belonging to the Herring’s Group B and also effective in many patients belonging to Herring’s Group C. It is a good procedure to attain containment in children <9 years of age. Trochanteric apophysiodesis reduces risk of limping.
Micro-VMS: A VMS Mobile Unit for Artisanal Fishing Vessels

It is widely accepted that Vessel Monitoring Systems (VMS) are currently a fundamental tool in the control of fishing activity. While larger vessels, because of their fishing capabilities, are the main target of this control, it is starting to be clear that by not monitoring smaller vessels, an important part of the Global Fishing effort is left uncontrolled, not because of the fishing capabilities of the single vessels themselves, but by the large numbers of vessels they represent. However, unlike larger vessels, these smaller boats have limited or no resources, and therefore installing the monitoring devices raises challenges that do not exist in their traditional VMS counterparts.Starting by identifying the main concepts that would be interesting to keep from the traditional VMS and the ones that would have to be adapted, options were taken, a mobile unit was then designed, built and tested. Proprietary HW was developed so that the unit would be well adapted to the monitoring purpose in mind at a reasonable cost. A SW application for the microcontroller was also developed and showed that it was possible to achieve the required monitoring with this device. The mobile unit developed is small enough to be carried in one hand and it’s energetically autonomous for a period of several months by using a small lithium battery and having an average power consumption of less than 50mW.
Getting to know Pepper: Effects of people's awareness of a robot's capabilities on their trust in the robot

This work investigates how human awareness about a social robot's capabilities is related to trusting this robot to handle different tasks. We present a user study that relates knowledge on different quality levels to participant's ratings of trust. Secondary school pupils were asked to rate their trust in the robot after three types of exposures: a video demonstration, a live interaction, and a programming task. The study revealed that the pupils' trust is positively affected across different domains after each session, indicating that human users trust a robot more the more awareness about the robot they have.
Sampling Based Predictive Control with Frequency Domain Input Sampling for Smooth Collision Avoidance*

This paper presents the smooth path generation method based on the sampling based model predictive control. Non-linear model predictive control framework is applied together with considering the car dynamics and non-linear constraints to achieve collision avoidance to obstacles. Since the control input must be optimized with non-linear constraints every control step in real-time, the problem is solved by sampling based approach. One of big issues on the sampling based approach is efficient sampling of smooth control input because some applications require the smoothness of control input. In proposed method, series of input are sampled in frequency domain directly in order to generate the smooth input series instead of applying the digital filtering to sampled input from specified random process. The proposed method samples the magnitudes of frequency components directly and those samples are transformed to time domain signals by Inverse Discrete Cosine Transform (IDCT.) Proposed method is implemented in simulation experiment with car dynamics simulator for collision avoidance task, and its validity is tested from the viewpoint of path tracking performance and real-time computation capability in several collision avoidance scenarios.
Resilient Distributed Control of Multi-Microgrid Systems During Failure of Communication Infrastructure

With the recognition of the benefits of Distributed Generators (DG) to the electric grid, a shift in paradigm from traditional centralized to distributed control has been observed in recent studies on power systems. In a network of multiple microgrids (MG), the collaboration among these units through a distributed control potentially increases the robustness and resilience of the power system. This paper presents a distributed control for multi-microgrid systems (MMGS) with resilience from failure of communication infrastructure. A two-level control is formulated wherein the primary control uses the estimated parameters missing from the communication process to solve the power flow problem and a secondary control ensures energy balance in spite of communication failure in the system. Different time-series analysis methods (persistent forecasting, historical averaging, Autoregressive Integrated Moving Average (ARIMA) forecasting) were integrated in the control model to determine the effect of different estimation methods in the model. Results show that the control model keeps the energy balance in the system even with the presence of communication failure in the system and a more accurate estimator gives a lesser increase in generation cost.
Distance-dependent consensus thresholds for generating group-representative structural brain networks

Large-scale structural brain networks encode white matter connectivity patterns among distributed brain areas. These connection patterns are believed to support cognitive processes and, when compromised, can lead to neurocognitive deficits and maladaptive behavior. A powerful approach for studying the organizing principles of brain networks is to construct group-representative networks from multisubject cohorts. Doing so amplifies signal to noise ratios and provides a clearer picture of brain network organization. Here, we show that current approaches for generating sparse group-representative networks overestimate the proportion of short-range connections present in a network and, as a result, fail to match subject-level networks along a wide range of network statistics. We present an alternative approach that preserves the connection-length distribution of individual subjects. We have used this method in previous papers to generate group-representative networks, though to date its performance has not been appropriately benchmarked and compared against other methods. As a result of this simple modification, the networks generated using this approach successfully recapitulate subject-level properties, outperforming similar approaches by better preserving features that promote integrative brain function rather than segregative. The method developed here holds promise for future studies investigating basic organizational principles and features of large-scale structural brain networks. Author Summary Sparse structural connectivity data from many subjects can be succinctly represented using appropriate averaging procedures. We show, however, that several popular procedures for doing so generate group-averaged networks with statistics that are dissimilar from the subject-level networks they are intended to represent. These dissimilarities, we argue, arise from the over- and underexpression of short-range and long-distance connections, respectively, in the group-averaged matrix. We present a distance-dependent thresholding procedure that preserves connection length distributions and consequently better matches subject-level networks and their statistics. These findings inform data-driven exploratory analyses of connectomes.
Performance Analysis of ITS-G5 for Smart Train Composition Coupling

In this paper we present first measurement results the use of ITS-G5 technology for Smart Train Composition Coupling. Intelligent Transport System (ITS-G5) communication link has been already use for Train-to-Train (T2T) communications. The SCOTT(Secure COnnected Trustable Things) project focuses on Vehicle to Vehicle (V2V) communications as one of the main development lines in the Smart Train Coupling Composition (STCC) use case and additionally in Vehicle to Infrastructure bidirectional (V2I/I2V) communications as one of the main development lines in the Trustable Warning System (TWS) for critical Areas use case. The V2V communications development in the SCOTT project focuses on the decentralization of the communications in order to make low latency highly reliable communications between moving train composition. These use cases will end with the demonstration of a Virtual Coupling system relying on these decentralized V2V communications. This system increases the capacity of the railway lines all over the world by allowing train compositions to drive closer to each other in a safe manner. The investigated measurement categories are channel characteristics, system performance and environmental aspects.
Analysing the Role of Last Level Caches in Controlling Chip Temperature

Dynamic Thermal Management (DTM) has become a major concern for the chip-designers, as it becomes a challenging task in recent power densed high performance Chip Multi-Processors (CMPs), due to integration of more on-chip components to meet ever increasing demand of processing power. The increased chip temperature incorporates severe circuit errors along with significant increment in leakage power consumption. Traditional DTM techniques apply DVFS or task migration to reduce core temperature, as cores are considered as the hottest on-chip components. Additionally, to commensurate high data demand of these high performance cores, large on-chip Last Level Caches (LLCs) are attached, which are the principal contributors to the on-chip leakage power consumption and occupy the largest on-chip area. As power consumption reduction plays the pivotal role in temperature reduction, hence, this work dynamically shrinks the cache size not only to reduce leakage power consumption, but also, to create on-chip thermal buffers for reducing average chip temperature by exploiting the heat transfer physics. Cache resizing decisions are taken based upon the generated cache hotspots and/or the access patterns, during process execution. Simulation results of the proposed thermal management method are compared with an existing DVFS based method (at cores) and a prior drowsy cache based technique to show its effectiveness.
Question-Question Similarity in Online Forums

In this paper, we applied deep learning framework to tackle the tasks of finding duplicate questions. We implemented some models following the siamese architecture using the popular recurrent network such as Long-Short term memory (LSTM), Bi-direction Long-Short term memory (biLSTM) to find the semantic similarity between questions. We started with a basic model and further extended the basic model into three different models. Our models provide a refined, composite representation of the questions. The addition of Convolutional Neural Network (CNN) with the recurrent networks is a new approach for the sentence representation. We also applied attention mechanism for getting better contextual meaning of the questions. We generated a representation of a question according to the context of another question for solving the task. As neural models are data driven, we trained our models extensively by making pairs, such as question-question over a large-scale real-life dataset. We used a datset consisting of 400K labeled question pairs which are published by a well known question-answer forum Quora. We evaluate our models based on metrics like accuracy, precision, recall, F1 scores. Our methods and experiments demonstrate some significant improvements over the baseline systems and the state-of-the-art systems.
Learning Montezuma's Revenge from a Single Demonstration

We propose a new method for learning from a single demonstration to solve hard exploration tasks like the Atari game Montezuma's Revenge. Instead of imitating human demonstrations, as proposed in other recent works, our approach is to maximize rewards directly. Our agent is trained using off-the-shelf reinforcement learning, but starts every episode by resetting to a state from a demonstration. By starting from such demonstration states, the agent requires much less exploration to learn a game compared to when it starts from the beginning of the game at every episode. We analyze reinforcement learning for tasks with sparse rewards in a simple toy environment, where we show that the run-time of standard RL methods scales exponentially in the number of states between rewards. Our method reduces this to quadratic scaling, opening up many tasks that were previously infeasible. We then apply our method to Montezuma's Revenge, for which we present a trained agent achieving a high-score of 74,500, better than any previously published result.
Harmonic Networks: Integrating Spectral Information into CNNs

Convolutional neural networks (CNNs) learn filters in order to capture local correlation patterns in feature space. In contrast, in this paper we propose harmonic blocks that produce features by learning optimal combinations of spectral filters defined by the Discrete Cosine Transform. The harmonic blocks are used to replace conventional convolutional layers to construct partial or fully harmonic CNNs. We extensively validate our approach and show that the introduction of harmonic blocks into state-of-the-art CNN baseline architectures results in comparable or better performance in classification tasks on small NORB, CIFAR10 and CIFAR100 datasets.
An Ontology Driven Knowledge Block Summarization Approach for Chinese Judgment Document Classification

Efficient document classification techniques are crucial to current legal applications, such as case-based reasoning, legal citations, and so on. However, Chinese judgment documents are large and highly complex, so the traditional machine leaning-based classification models are often inefficient to Chinese document classification due to the fact that they fail to incorporate the overall structure and extra domain specific knowledge. In this paper, we propose an ontology-driven knowledge block summarization approach to computing document similarity for Chinese judgment document classification. First, the extra semantic knowledge for Chinese judgment documents is adopted from the perspectives of the top-level ontology and domain-specific ontologies, where how to merge the different kinds of ontologies together in an extensible manner is further represented. Second, the core semantic knowledge residing in Chinese judgment documents can be summarized into knowledge blocks by ontology-based information extraction. Third, we use Word Mover’s Distance (WMD) is to calculate the similarity between different knowledge blocks instead of their original Chinese judgment documents. At last, the KNN-based experiments for Chinese judgment document classification were made to illustrate that our approach is more effective in achieving higher classification accuracy and has faster computation speed compared to the original WMD approach.
Arselda: An Improvement on Adaptive Random Testing by Adaptive Region Selection

Distance-aware Forgetting Fixed Size Candidate Set (DF-FSCS) is an Adaptive Random Testing (ART) technique, which lowers the computational overhead of Fixed Size Candidate Set ART (FSCS-ART), using a forgetting strategy. DF-FSCS partitions the input domain into regions, and while computing the distance of a candidate test case from executed test cases, as a vector in the input domain, it only considers test cases that are in the same region as the candidate. Although being a lightweight technique, there are two issues with DF-FSCS. First, it does not attempt to generate test cases in low-density regions, which if done, could result in a more even spread of test cases. Second, the regions it defines are smaller at the lower or upper boundaries of input domains, which declines the quality of test cases produced in these regions. We propose Arselda, an APR technique that improves DF-FSCS. By generating test cases in low-density regions that have a fewer number of test cases and enlarging regions at lower or upper boundaries of input domains, Arselda addresses the two issues mentioned above. Considering DF-FSCS as the baseline, a simulation analysis has been performed to evaluate the effectiveness of Arselda. According to the experiment results, Arselda has better failure detection effectiveness compared with the baseline for the block failure pattern. Also, Arselda has lower computational overhead than the baseline.
Switching Transigents in Gate Drive Loops of Hybrid GaN HEMTs and SiC MOSFET

Wide-Bandgap devices are believed to be promising candidates for the next-generation power electronics converter. However, the prohibitive cost and limited variety are still the main constrains before being widely used. One possible solution to mitigate these issues is hybrid switches, a combination of Si MOSFETs and WBG devices. To maximize merits of both GaN HEMT and Si MOSFET, this paper proposes a hybrid switch consisting of two GaN HEMTs and two Si MOSFETs. For such a design, a robust gate-drive loop is critical to secure the safe operation of all switches. As an instructive work, this paper presents a comprehensive analysis of the switching transient process and its impact on the gate-drive loop, practical tuning tips of gate-drive loop design are also given based on the simulation and experimental results. A 400V/ 80A full bridge prototype is developed to validate our design. Experimental result shows that, with enhanced gate-drive loop, we can continuously turn off 400V/80A@100kHz and 400V/40A@300kHz with only one GaN device paralleled to two commercial Si MOSFETs.
Secure Federated Transfer Learning

Machine learning relies on the availability of a vast amount of data for training. However, in reality, most data are scattered across different organizations and cannot be easily integrated under many legal and practical constraints. In this paper, we introduce a new technique and framework, known as federated transfer learning (FTL), to improve statistical models under a data federation. The federation allows knowledge to be shared without compromising user privacy, and enables complimentary knowledge to be transferred in the network. As a result, a target-domain party can build more flexible and powerful models by leveraging rich labels from a source-domain party. A secure transfer cross validation approach is also proposed to guard the FTL performance under the federation. The framework requires minimal modifications to the existing model structure and provides the same level of accuracy as the non-privacy-preserving approach. This framework is very flexible and can be effectively adapted to various secure multi-party machine learning tasks.
Reaching Data Confidentiality and Model Accountability on the CalTrain

Distributed collaborative learning (DCL) paradigms enable building joint machine learning models from distrusted multi-party participants. Data confidentiality is guaranteed by retaining private training data on each participant's local infrastructure. However, this approach makes today's DCL design fundamentally vulnerable to data poisoning and backdoor attacks. It limits DCL's model accountability, which is key to backtracking problematic training data instances and their responsible contributors. In this paper, we introduce CALTRAIN, a centralized collaborative learning system that simultaneously achieves data confidentiality and model accountability. CALTRAIN enforces isolated computation via secure enclaves on centrally aggregated training data to guarantee data confidentiality. To support building accountable learning models, we securely maintain the links between training instances and their contributors. Our evaluation shows that the models generated by CALTRAIN can achieve the same prediction accuracy when compared to the models trained in non-protected environments. We also demonstrate that when malicious training participants tend to implant backdoors during model training, CALTRAIN can accurately and precisely discover the poisoned or mislabeled training data that lead to the runtime mispredictions.
What Are You Hiding? Algorithmic Transparency and User Perceptions

Extensive recent media focus has been directed towards the dark side of intelligent systems, how algorithms can influence society negatively. Often, transparency is proposed as a solution or step in the right direction. Unfortunately, research is mixed on the impact of transparency on the user experience. We examine transparency in the context an interactive system that predicts positive/negative emotion from a users' written text. We unify seemingly this contradictory research under a single model. We show that transparency can negatively affect accuracy perceptions for users whose expectations were not violated by the system's prediction; however, transparency also limits the damage done when users' expectations are violated by system predictions.