Miracle-dz/UniFAR-FTUs huggingface dataset:
Steering Top-k Influencers in Dynamic Graphs via Local Updates

We propose a generalized framework for influence maximization in large-scale, time evolving networks. Many real-life influence graphs such as social networks, telephone networks, and IP traffic data exhibit dynamic characteristics, e.g., the underlying structure and communication patterns evolve with time. Correspondingly, we develop a dynamic framework for the influence maximization problem, where we perform effective local updates to quickly adjust the top-k influencers, as the structure and communication patterns in the network change. We design a novel N-Family method (N=1, 2, 3, …) based on the maximum influence arborescence (MIA) propagation model with approximation guarantee of (1 − 1/e). We then develop heuristic algorithms by extending the N-Family approach to other information propagation models (e.g., independent cascade) and influence maximization algorithms (e.g., CELF, reverse reachable sketch). Based on a detailed empirical analysis over several real-world, dynamic, and large-scale networks, we find that our proposed solution, N-Family improves the updating time of the top-k influencers by 1 ∼ 2 orders of magnitude, compared to existing algorithms, while ensuring similar memory usage and influence spreads.
Linearity of Waveguide Integrated Modified Uni-Travelling Carrier Photodiode Arrays

We demonstrate high-speed and high-power evanescently-coupled waveguide modified uni-traveling-carrier photodiode arrays. The arrays consist of different numbers of optically parallel connected photodiodes. A responsivity of 0.46 A/W at 1550 nm, a 3-dB bandwidth of 28 GHz, and 12-dBm RF output power at 40 GHz were achieved with a 2 array chip. Furthermore, the linearity and 3rd order interception point were measured at different temperatures and frequencies in order to determine the limiting factor of the performance.
Shared-Autonomy Control for Intuitive Bimanual Tele-Manipulation

Existing dual-arm teleoperation systems function on one-to-one coupling of the human and robotic arms to fully exploit the user's dexterity during bimanual tele-manipulation. While the individual coordination of the robot end-effectors can be necessary for complex and asymmetric tasks, it may result in a cumbersome user experience during symmetric bimanual tasks (e.g. manipulating and carrying objects). In this paper we propose a novel framework that includes the one-to-one direct control and a new shared autonomy strategy. The user can autonomously choose between the two, and if the new one is selected the robots move in a coordinated way, in which desired positions are extrapolated from the movements and gestures of just one users arm. These gesture commands are interpreted and handled by the control, with the purpose of unloading the users cognitive burden. Lastly, the tele-impedance paradigm, i.e., the remote control of robot impedance and position references, is applied to both controls, to improve remote physical interaction performances. The paper reports on the overall proposed architecture, its implementation and its preliminary validation trough a multi subject experimental campaign.
Fast HEVC to SCC Transcoder by Early CU Partitioning Termination and Decision Tree-Based Flexible Mode Decision for Intra-Frame Coding

Screen content coding (SCC) was developed to enhance High Efficiency Video Coding (HEVC) for encoding screen content videos. However, HEVC has dominated the market for many years, and it leaves many legacy screen content videos encoded by HEVC. Therefore, it is desired that the legacy screen content videos are migrated from HEVC to SCC to improve the coding efficiency. This paper presents a fast transcoding algorithm by analyzing various features from four categories. They are the features from the HEVC decoder, static features, dynamic features, and spatial features. First, the coding unit (CU) depth level collected from the HEVC decoder is utilized to early terminate the CU partition in SCC. Second, a flexible encoding structure is proposed to make early mode decisions with the help of various features. On the one hand, high decision accuracy is achieved because mode decision is considered from different aspects by utilizing features from more than one category. On the other hand, high computational complexity is reduced because the flexible structure considers the decision of each mode separately. The experimental results show that the proposed algorithm provides 51.24% and 54.65% re-encoding time reduction with 1.32% and 1.25% negligible Bjøntegaard delta bitrate loss for YUV 4:2:0 and YUV 4:4:4 screen content sequences using all-intra configuration, respectively.
DPPM Reduction Methods and New Defect Oriented Test Methods Applied to Advanced FinFET Technologies

This paper presents DPPM reduction results achieved with new Defect Oriented Test (DOT) methods/patterns applied to designs manufactured in advanced FinFET technologies. Focus of this paper is on Timing-Aware Cell-Aware Test (TA-CAT) patterns targeting small-delay defects of FinFET transistors, and a new DOT method which explicitly targets chip layout dependent cell-neighborhood defects. Test results from traditional Stuck-at/Transition patterns, from traditional CAT patterns, from TA-CAT patterns, and as well from cell-neighborhood patterns, applied to FinFET technology designs, will be presented in this paper. In addition, a correlation to System-Level-Test fails will be discussed.
Classification of "Hot News" for Financial Forecast Using NLP Techniques

Complex dynamics of stock market could be attributed to various factors ranging from company’s financial ratios to investors’ sentiment and reaction to Financial news. The paper aims to classify Financial news articles as "hot" (significant) and "non-hot" (non-significant). The study is carried out using Dow Jones newswires text feed for a period of four years spanning from 2013 till 2017. Bag-of-ngrams appraoch and Term Frequency-Inverse Document Frequency (TF-IDF) were used for text representation and text weighting, respectively. Four linear classifiers, namely, Logistic Regression (LR), Support Vector Machine (SVM), k Nearest Neighbours (kNN) and multinomial Naïve Bayes (mNB) were used. Grid search was used for hyperparameter optimisation. Performance of the classifiers was evaluated using five measures, namely, success rate, precision, recall, F1 measure and area under receiver operating characteristics curve. LR and SVM outperformed other models in terms of all five performance measures for both Bag-of-ngrams model and Bag-of-ngrams model with TF-IDF approach. Use of TF-IDF improved performance of the classifiers, especially, in case of mNB. This study serves as a stepping stone in identification of important/relevant news, which could used as predictors for stock price forecasting.
Teno: An Efficient High-Throughput Computing Job Execution Framework on Tianhe-2

Large-scale and loosely-coupled applications cannot be implemented directly on high-performance computing platforms. At the same time, the deployment and maintenance of high-performance computing and high-throughput computing will result in the waste of computing resources. In order to solve the problem that existing resource management systems cannot make high-throughput computing applications execute efficiently on high-performance computers, we propose, design and implement a high-throughput computing job execution framework Teno without modifying the existing configuration environment of Slurm on Tianhe-2. It uses Slurm to implement fine-grained resource scheduling through the idea of hierarchical scheduling, and optimizes the traditional Master-Worker model, thereby speeding up the high-throughput operation and increasing the effective utilization of cluster resources. Effective fault-tolerance mechanisms such as fault recovery and error retry are also implemented. Finally we design various experiments in Tianhe-2 to test and evaluate the key factors for high-throughput calculations of Teno, Slurm and HTCondor, and analyze in detail why the performance of Teno is over that of the other two.
Air-Combat Strategy Using Deep Q-Learning

Unmanned aircraft systems (UAS) are essential components in the future air-combat. Due to high dynamics and randomness of the aircrafts, traditional methods are difficult to solve the optimal control strategy. The characteristics of reinforcement learning (RL) match the difficulty of this problem. In this paper, we build an air-combat game environment and train the agent with deep Q-learning (DQN). Despite of increasing probability of loses slightly, our method performs much better than other algorithms in the simulations. Compared with the searching based methods, like Minimax and MCTS, the policy trained by DQN can take specific tactics with a long-term view of the game. Result shows that a large number of simulations and carefully designed features and reward are the essential points of DON.
Comparison between Single, Dual and Triple Rapid Serial Visual Presentation Paradigms for P300 Speller

P300 based Speller systems are a typical application of BCIs that can be used as a communication device for users with spinal cord injury. In this paper, three different RSVP paradigms; single, dual and triple RSVP are compared in terms of character detection accuracy and ITR to find the optimal communication method for these users. Three subjects were tested to compare these methods in offline mode. The results of this comparison shows that character detection accuracy for the single, dual and triple RSVP paradigms are 0.78, 0.63 and 0.64, respectively. The mean of ITR for the single, dual and triple RSVP paradigms was also recorded to be 3.6534, 7.72 and 11.5 bit/min, respectively. Although the accuracy of the single paradigm is higher, but the ITR in the dual and triple paradigms increases significantly compared to the single paradigm. All EEGs have been recorded using an EMOTIV device which affects the quality and accuracy of the system.
Automated Pancreas Segmentation Using Recurrent Adversarial Learning

Automated pancreas segmentation based on computed tomography (CT) is one extensively useful technique of computer-aided detection for pancreas cancer diagnosis and prognosis. However, it is very challenging in practice because the pancreas, the small, soft and flexible abdominal organ, has very high anatomical variability which leads existing studies to suffer from an unsatisfactory performance. In this paper, we propose a new automated pancreas segmentation framework: Recurrent Adversarial Learning based on Generative Adversarial Network (Pancreas-GAN) to tackle this challenge. Pancreas-GAN consists of three novel modules: 1) a dilated convolutions auto-encoder module (DCAE) not only explicitly maintains global resolution of CT images but also enlarges the receptive field of filters to effectively incorporate larger context without increasing the computation complexity; 2) a local long short-term memory module (Local-LSTM) can capture the contextual segmentation correlation across neighboring image patches for boosting precise boundary segmentation; and 3) an adversarial module can constrain the spatial smoothness consistency among successive image slices based on the global distribution constraints, which further boosts the performance of Pancreas-GAN. Extensive experiments are conducted on the CT scans of 80 patients using 4-fold cross-validation and have demonstrated that Pancreas-GAN outperforms the state-of-the-art methods in terms of both the Dice similarity coefficient (DSC) of 88.72% ±3.23 and the pixel-wise accuracy of 95.34%±3.05. This reveals the effectiveness and the potential of our method in the clinical setting.
Effect of voltage stabilizers on the space charge behavior of XLPE for HVDC cable application

Space charge accumulation inside the cross-linked polyethylene (XLPE) insulation is one of the most serious problems in HVDC cable operation. In this paper, the voltage stabilizers that have excellent compatibility with XLPE are used to suppress the space charge in XLPE insulation. Three different voltage stabilizers, 4,4'-Difluorobenzophenone, 4,4'-Dihydroxybenzophenone, and 4,4'-Bis (dimethyl amino) benzyl, denoted as A, B, and C, respectively, were added into XLPE material with additive content of 0.5 wt%, and neat XLPE was used as a control. The differential scanning calorimeter (DSC) test, tensile test, DC conductivity and breakdown strength measurements were employed to characterize the thermal, mechanical and electrical properties of the samples respectively. Moreover, the space charge behaviors were investigated using the PEA method, and the trap-controlled carrier mobility was evaluated by the depolarization processes of the space charge. The mechanical and thermal analyses indicate that the presence of voltage stabilizers does not significantly change the microstructure of XLPE, as required for the preparation of HVDC cable insulation. Though the influence of voltage stabilizers on DC conductivity is not obvious, a higher breakdown strength was found in the voltage-stabilizer modified XLPE samples. In addition, XLPE modified by the voltage stabilizers B and C with higher trap-controlled carrier mobility show a good ability to suppress space charge in XLPE insulation, that is, less space charge accumulation and faster space charge dissipation than the neat XLPE. The experimental results reveal that the voltage stabilizers B and C show great promise for use in HVDC cable application.
Bootstrapping analysis of crowdsourced non-expert estimates of the number of calories in photographs of meals

A total of 1800 estimates from non-expert dietitians were collected online. This comprised of 120 subjects each providing an estimate per image (120 subjects * 15 images). The overall aim of this study was to use crowdsourcing to determine the accuracy of non-experts in estimating calories in a meal as shown in a photograph. We analyzed percentiles and used bootstrapping with replacement to determine the typical percentile rank estimate that is in proximity to the ground truth and to determine the least number of sample estimates from a crowd of non-experts that can provide a convenient and accurate estimate of the number of calories in a meal.
Versatile In-Hand Manipulation of Objects with Different Sizes and Shapes Using Neural Networks

Changing the grasping posture of objects within a robot hand is hard to achieve, especially if the objects are of various shape and size. In this paper we use a neural network to learn such manipulation with variously sized and shaped objects. The TWENDY-ONE hand possesses various properties that are effective for in-hand manipulation: a high number of actuated joints, passive degrees of freedom and soft skin, six-axis force/torque (F /T) sensors in each fingertip and distributed tactile sensors in the soft skin. The object size information is extracted from the initial grasping posture. The training data includes tactile and the object information. After training the neural network, the robot is able to manipulate objects of not only trained but also untrained size and shape. The results show the importance of size and tactile information. Importantly, the features extracted by a stacked autoencoder (trained with a larger dataset) could reduce the number of required training samples for supervised learning of in-hand manipulation.
Music Genre Classification Using Independent Recurrent Neural Network

Genre is one of the most widely mentioned music labels which have a great influence on accuracy of music recommendation. Machine learning is often used to tackle with genre classification task, but the result of the approach heavily depends on the performance of feature extraction. Deep neural network automatically learns advanced features layer by layer, which makes excellent results in many areas. Music signal is sequential and Recurrent Neural Network (RNN) is widely employed for sequential data. Among variant units of RNN, Independently Recurrent Neural Network (IndRNN) can learn long-term relationship better than popular units such as Long-Short Term Memory (LSTM) and Gated Recurrent Unit (GRU). In addition, IndRNN has better computational efficiency. Consequently, multi-layer IndRNN is used as the main part of our model to classify music genres on the GTZAN dataset. In order to keep the information loss as less as possible, scattering transform is used to preprocess the raw music data. The experimental results show that the model achieves a competitive result in music genre classification task compared with the state-of-the-art models.
Algorithms for the Multiple Variants of Registration in 3D Range Data

Des del naixement de la fotografia hi ha hagut sempre un gran interes en la possibilitat de detectar una tercera dimensio en les imatges obtingudes per una camera. Aquesta tercera dimensio permetria la diferenciacio i filtrat dels diferents objectes presents a una escena, i per tant facilitar molts del problemes actuals en la recerca de visio per computador. Per tal d'aconseguir-la, diverses tecniques s'han anat utilitzant a traves del temps, des de la classica estereoscopia fins a altres metodes mes actuals com el Structure from Motion. Durant els darrers anys l'evolucio de la tecnologia ha fet possible l'aparicio de dispositius que permeten la captura directa d'aquesta informacio 3D sense la necessitat d'una intervencio manual. L'us de diferents espectres de llum com el laser o la llum infraroja, aixi com la seva integracio en els dispositius, han possibilitat aquesta important millora, acompanyada al mateix temps d'una reduccio en el preu dels components que ha fet possible el seu us per gran part de la comunitat de recerca. Aquesta tesis esta enfocada en el principals problemes derivats de l'us de les anomenades cameres range que, a mes de la imatge RGB usual, ofereixen una imatge addicional indicant la distancia als objectes de l'escena. Gracies a l'aplicacio directa d'aquestes imatges range, on cada pixel correspon a una distancia, una recreacio 3D de l'escena capturada pot ser obtinguda facilment. Una de les seves principals avantatges es el fet de que, si la camera esta correctament calibrada, el 3D obtingut es troba expressat en unitat fisiques reals (per exemple, en metres) i no pas en pixels. Tot i aixo, l'us d'aquestes cameres range no es tan ideal com es podria suposar. Normalment diverses captures d'una escena o objecte son necessaries per tal d'aconseguir una reconstruccio completa, i alguns materials poden produir problemes que interfereixen en el correcte posicionament dels objectes. Les particularitats de la representacio 3D obtinguda fan que aquesta sigui apropiada per fer-la servir com a suport per afegir-hi altres fonts d'informacio, com ara imatges RGB o imatges infraroges. L'estructura 3D obtinguda pot ser texturitzada amb aquestes fonts d'informacio, donant un resultat integrat que pot ser molt util per solucionar problemes que no serien possibles utilitzant les imatges de forma separada. Tot i aixo, diferencies en el proces d'adquisicio entre aquest tipus d'imatges poden produir alguns problemes quan son fusionades. A mes, per tal d'obtenir una reconstruccio 3D completa d'una escena, normalment es necessari que la captura s'hagi fet des de multiples punts de vista diferents. L'alineament de totes aquestes estructures 3D obtingudes es conegut com registracio multivista, on es necessari identificar la posicio i orientacio de les cameres range en cadascuna de les presses per tal de poder alinear-les correctament. Aquest alineament s'aconsegueix normalment fen servir dos passos diferenciats: la registracio de imatges rang parell a parell, i la posterior minimitzacio de l'error considerant simultaniament totes aquestes parelles.
A Comparative Study of LSTM and DNN for Stock Market Forecasting

Prediction of stock markets is a challenging problem because of the number of potential variables as well as unpredictable noise that may contribute to the resultant prices. However, the ability to analyze stock market trends could be invaluable to investors and researchers, and thus has been of continued interest. Numerous statistical and machine learning techniques have been explored for stock analysis and prediction. We present a comparative study of two very promising artificial neural network models namely a Long Short-Term Memory (LSTM) recurrent neural network (RNN) and a deep neural network (DNN) in forecasting the daily and weekly movements of the Indian BSE Sensex index. With both networks, measures were taken to reduce overfitting. Daily predictions of the Tech Mahindra (NSE: TECHM) stock price were made to test the generalizability of the models. Both networks performed well at making daily predictions, and both generalized well to make daily predictions of the Tech Mahindra data. The LSTM RNN outperformed the DNN in terms of weekly predictions and thus, holds more promise for making longer term predictions.
A Perspective on the Performance of the CFOSAT Rotating Fan-Beam Scatterometer

The China-France Oceanography Satellite (CFOSAT) to be launched in October 2018 will carry two innovative payloads, i.e., the surface wave investigation and monitoring instrument and the rotating fan-beam scatterometer [CFOSAT scatterometer (CFOSCAT)]. Both instruments, operated in Ku-band microwave frequency, are dedicated to the measurement of sea surface wave spectra and wind vectors, respectively. This paper provides an overview of the system definition and characteristics of the CFOSCAT instrument. A prelaunch analysis is carried out to estimate the scatterometer backscatter and wind quality based on the developed CFOSCAT simulator prototype. The overall simulation includes two parts: first, a forward model is developed to simulate the ocean backscatter signals, accounting for both instrument and geophysical noise. Second, a wind inversion processor is used to retrieve wind vectors from the outputs of the forward model. The benefits and challenges of the novel observing geometries are addressed in terms of the CFOSCAT wind retrieval. The simulations show that the backscatter accuracy and the retrieved wind quality of CFOSCAT are quite promising and meet the CFOSAT mission requirements.
A Density-based Preprocessing Technique to Scale Out Clustering

Clustering big data is a challenging task, because the majority of high-quality clustering algorithms do not scale well with respect to the data set cardinality. To tackle the scalability problem, we propose a general-purpose density-based preprocessing technique, called SCOUT, implemented in the Spark framework. It allows compacting the original data by means of a set of representative points, while still preserving the original data distribution and density information. This small set of representative points may become the input to almost any clustering algorithm. Thus, also complex, high-quality in-memory algorithms can be applied. A thorough experimental evaluation shows that the proposed approach is efficient and at the same time effective.
Novel Approach for Time-Domain Modeling a Full Anechoic Chamber for NearIFar-Field Antennas Measurement

The studies presented in this paper are part of the problematic that aims the simulation and characterization of an anechoic chamber. A new approach based on the Finite Integral Method (FIT) was presented and evaluated for modeling a full anechoic chamber for antenna near/far-field measurement. The probe as well as the Antenna Under Test (AUT) are replaced by their Huygens sources calculated separately. This new technique has allowed us to gain in terms of computing resources and also to reduce the simulation time, compared to other solutions. The convergence and the stability of the proposed method are guaranteed by an autoregressive filter and an adaptive mesh. The results obtained show that the maximum error as well as the depolarization of the wave in the Quiet Zone (QZ) of the chamber are of the order of 1 dB. The calculation time is 15 h, which represents a gain of 90%.