Miracle-dz/UniFAR-FTUs huggingface dataset:
Colorizing Color Images

This paper describes a method of improving the quality of the color in color images by colorizing them. In particular, color quality may suffer from improper white balance and other factors such as inadequate camera characterization. Colorization generally refers to the problem of turning a luminance image into a realistic looking color image and impressive results have been reported in the computer vision literature. Based on the assumption that if colorization can successfully predict colors from luminance data alone then it should certainly be able to predict colors from color data, the proposed method employs colorization to ‘color’ color images. Tests show that the proposed method quite effectively removes color casts—including spatially varying color casts—created by changes in the illumination. The colorization method itself is based on training a deep neural network to learn the connection between the colors in an improperly balanced image and those in a properly balanced one. Unlike many traditional white-balance methods, the proposed method is image-in-image-out and does not explicitly estimate the chromaticity of the illumination nor apply a von-Kries-type adaptation step. The colorization method is also spatially varying and so handles spatially varying illumination conditions without further modiﬁcation.
Can adult mental health be predicted by childhood future-self narratives? Insights from the CLPsych 2018 Shared Task

The CLPsych 2018 Shared Task B explores how childhood essays can predict psychological distress throughout the author’s life. Our main aim was to build tools to help our psychologists understand the data, propose features and interpret predictions. We submitted two linear regression models: ModelA uses simple demographic and word-count features, while ModelB uses linguistic, entity, typographic, expert-gazetteer, and readability features. Our models perform best at younger prediction ages, with our best unofficial score at 23 of 0.426 disattenuated Pearson correlation. This task is challenging and although predictive performance is limited, we propose that tight integration of expertise across computational linguistics and clinical psychology is a productive direction.
Generalized Pose Estimation from Line Correspondences with Known Vertical Direction

We propose a novel method to compute the absolute pose of a generalized camera based on straight lines, which are common in urban environment. The only assumption about the imaging model is that 3D straight lines are projected via projection planes determined by the line and camera projection directions, i.e. correspondences are given as a 3D world line and its projection plane. Since modern cameras are frequently equipped with various location and orientation sensors, we assume that the vertical direction (e.g. a gravity vector) is available. Therefore we formulate the problem in terms of 4 unknowns using 3D line - projection plane correspondences which yields a closed form solution. The solution can be used as a minimal solver as well as a least squares solver without reformulation. The proposed algorithm have been evaluated on various synthetic datasets as well as on real data. Experimental results confirm state of the art performance both in terms of quality and computing time.
Statistical Learning for Anomaly Detection in Cloud Server Systems: A Multi-Order Markov Chain Framework

As a major strategy to ensure the safety of IT infrastructure, anomaly detection plays a more important role in cloud computing platform which hosts the entire applications and data. On top of the classic Markov chain model, we proposed in this paper a feasible multi-order Markov chain based framework for anomaly detection. In this approach, both the high-order Markov chain and multivariate time series are adopted to compose a scheme described in algorithms along with the training procedure in the form of statistical learning framework. To curb time and space complexity, the algorithms are designed and implemented with non-zero value table and logarithm values in initial and transition matrices. For validation, the series of system calls and the corresponding return values are extracted from classic Defense Advanced Research Projects Agency (DARPA) intrusion detection evaluation data set to form a two-dimensional test input set. The testing results show that the multi-order approach is able to produce more effective indicators: in addition to the absolute values given by an individual single-order model, the changes in ranking positions of outputs from different-order ones also correlate closely with abnormal behaviours.
Handling implicit and explicit constraints in manipulation planning

This paper deals with manipulation planning. The problem consists in automatically computing paths for a system composed of one or several robots, with one or several grippers and one or several objects that can be grasped and moved by the robots. The problem gives rise to constraints that can be either explicit – an object is in a gripper – or implicit – an object is hold by two different grippers. This paper proposes an algorithm that handles such sets of constraints and solves them in an explicit way as much as possible. When all constraints cannot be made explicit, substitution is operated between variables to make the resulting implicit constraint with as few variables as possible. The manipulation planning problem is modelled as a constraint graph that stores all the constraints of the problem.
Modified (2/1-k) output gain Ćuk DC-to-DC converter circuit for renewable power applications

Renewable energy applications have become a focal point of most researches in recent years to achieve an eco-friendly atmosphere and mitigate the dangers posed by global warming and climate changes. A modified high gain DC-to-DC Ćuk converter using voltage lift technique is presented in this paper. The converter generates an amplified version of the input voltage through the inclusion of a switched capacitor topology. In perspective view, the resultant modified Ćuk circuit generates a voltage conversion ratio of twenty at duty ratio of 0.9 by using 3 inductors, 5 capacitors and 4 diodes. A detailed analytical analysis is validated by simulations conducted in Matlab/Simulink.
Stingray-HPC: A Scalable Parallel Seismic Raytracing System

The Stingray raytracer was developed for marine seismology to compute minimum travel time from all sources in an earth model to determine the 3D geophysical structure below the ocean floor. The original sequential implementation of Stingray used Dijkstra's single-source, shortest-path (SSSP) algorithm. A data parallel version of Stingray was developed based on the Bellman-Ford-Moore iterative SSSP algorithm. Single node experiments demonstrated performance improvements from parallelization with multicore (using OpenMP) and manycore processors (using CUDA). Calculating seismic ray paths for larger earth models requires distributed, multi-node algorithms utilizing domain decomposition methods. Preliminary 2D decomposition strategies show promising scaling results. However, a general 3D decomposition methodology is needed to handle any seismic raytracing problem on any HPC computing platform. In this paper, we present Stingray-HPC, a framework for scalable seismic raytracing which can automatically decompose a 3D earth model across nodes in a distributed environment, allocate ghost cell regions for iterative updates, coordinate ghost cell communications, and test for global convergence. Stingray-HPC is implemented with MPI and either OpenMP or CUDA for node- level calculations. Our results validate Stingray-HPC's ability to handle large models (over a billion points) and to solve these models efficiently at scale up to 512 GPU nodes.
Closed-loop active thermal control via power routing of parallel DC-DC converters

In the recent years, the energy scenario has been experiencing a huge revolution in terms of new technologies, converter topologies and control techniques where the smart-grid concept is the main actor. Everyday new requirements appear in terms of performance, efficiency, robustness and reliability. The modular converter feature helps to solve some of them however the reliability is hard-depending of many factors such us thermal stress or aging which cause the main device failures. Proper thermal management is the key of power converter maintenance in order to reduce the operation and maintenance cost. This paper proposes a combined control strategy for dc-dc converters taking into account the thermal stress in order to balance the aging of the devices using an intelligent strategy to manage the power losses.
AEDR: An Adaptive Mechanism to Achieve Online Learning Rate Dynamically

The distribution of connection strength between neural network units contains all the information of neural network. However, learning algorithms, as the key to the weight correction of neural network, retain more sensitive hyper-parameters which require endless ways of configuring. In this paper, we present a novel adaptive mechanism called Adaptive Exponential Decay Rate(AEDR). AEDR allows to adaptively calculate exponential decay rate using moving average of gradients and squared gradients over time, otherwise it need tune exponential decay rate. The mechanism reduces the ways of configuring hyper-parameters. Moreover, it enables varying learning rates for different parameters. The mechanism is then applied to Adadelta by Long Short-Term Memory(LSTM) to demonstrate how learning rate adapt dynamically under AEDR. Our mechanism improves the results significantly and achieve state-of-the-art performance on two real data sets, the IMDB(movie reviews) and SemEval-2016(sentiment analysis in twitter).
What makes software engineers go that extra mile?

Current research suggests that motivation factors in software engineering are evolving and that our current understanding of motivation in software engineering is out of date. In this paper an analysis of semi-structured interviews with 13 professional software engineers is presented. The data suggests that „the work‟ makes software engineers go that extra mile and that „people‟ are important, but that „obstacles‟ really sap the energy of software engineers.
Accelerating Consensus in High-Order Leader-Follower Networks

We consider the problem of accelerating the convergence to consensus of a network of homogeneous high-order agents, through the injection of an additional control input performed by a leader. After a brief set-up description, we derive the characteristic polynomial of the resulting system under the leader’s control. We introduce the concept of leader controlled distributed consensus, by imposing that the leader’s action improves the convergence speed but does not affect the consensus value, except possibly for a scaling factor. Finally, we prove that, under certain assumptions, consensus can be achieved with arbitrary speed.
Genome Sequence of Methylotrophic Azospirillum sp. Strain B2, Isolated from a Raised Sphagnum Bog

ABSTRACT Azospirillum sp. strain B2 is a soil bacterium which was originally isolated from the Sosvyatskoe raised Sphagnum bog in Russia. Here, we present the approximately 8-Mb draft genome sequence of Azospirillum sp. B2, with the aim of providing insight into the genomic basis of its ecological success in peatland settings.
Quest for the information: using intelligent search for finding telemedical sites

We present an intelligent search tool, which we developed in order to automate search and evaluation of Web sites. We used TFIDF heuristics to determine term frequency and decision trees to evaluate the quality of sites. The training set for the decision tree contained manually evaluated Web sites. Each Web site was described by the combination of various attributes, complexity metrics and the evaluation. The intelligent search tool is equipped with a user friendly interface, which enables people to exploit the tool to its limits with minimum effort, in their quest for information. For testing purposes, we looked for sites with telemedical content. The set of sites, which was the result of using intelligent search tool was evaluated by a group of students.
The Efficacy and Safety of Azelaic Acid 15% Foam in the Treatment of Facial Acne Vulgaris.

BACKGROUND Azelaic acid demonstrates anti-inflammatory, anti-oxidative, anti-comedogenic, and anti-microbial effects. Azelaic acid 20% cream is currently approved for the treatment of acne vulgaris, and azelaic acid 15% foam has recently been approved for rosacea. Given the favorable tolerability profile of foam preparations, it is reasonable to assume that azelaic acid 15% foam could serve as a viable treatment option for facial acne. OBJECTIVE To examine the efficacy and safety of azelaic acid 15% foam in the treatment of moderate-to-severe facial acne Methods: Twenty subjects with moderate-to-severe facial acne vulgaris were enrolled in this two-center, open-label pilot study. All study subjects were treated with azelaic acid 15% foam for 16 weeks. Efficacy analyses were based on the change in facial investigator global assessment (FIGA) and changes in total, inflammatory, non-inflammatory lesion counts between baseline and week 16. RESULTS There was a significant reduction in FIGA scores from baseline to week 16 (p = .0004), with 84% of subjects experiencing at least a 1 grade improvement, and 63% of subjects achieving a final grade of Clear or Almost Clear. All subjects experienced reductions in inflammatory and total lesion counts by week 16, and 89% of subjects experienced reductions in non-inflammatory lesions. Azelaic acid 15% foam was well tolerated, with almost all instances of erythema, dryness, peeling, oiliness, pruritus, and burning being of mild or trace degree, and most adverse effects resolving by the end of the study. CONCLUSION Azelaic acid 15% foam is effective and safe in the treatment of facial acne vulgaris. Given the convenience of foam vehicles, azelaic acid 15% foam should be considered as a viable treatment option for this condition. J Drugs Dermatol. 2018;17(6):641-645.
A fast and compact data structure of storing multi-attribute relations among words

Word relation is primitive knowledge and it is very useful for natural language processing systems. In the traditional systems, although each knowledge dictionary is constructed separately, recent natural language applications become more complex by integrating the above multi-attribute relationships. This paper presents an efficient data structure by introducing a trie that can define the linkage among leaves. The linkage enables us to share the basic words required for multi-attribute relations. Theoretical observations show that the worst-case time complexity of retrieving multi-attribute relations is a constant. From the simulation results, it is shown that the presented method is about 1/3 smaller than that of the competitive methods.
Consensus seeking of delayed high-order multi-agent systems with predictor-based algorithm

This paper investigates the high-order consensus problem for the multi-agent systems with agent’s dynamics described by high-order integrator, and adopts a general consensus algorithm composed of the states’ coordination control. Under communication delay, consensus algorithm in usual asynchronously-coupled form just can make the agents achieve a stationary consensus, and suﬃcient consensus condition is obtained based on frequency-domain analysis. Besides, a predictor-based consensus algorithm is constructed via multiplying the delayed neighboring agents’ states by a delay-related compensation part. In our proposed algorithm, a compensating delay is introduced to match the communication delay. Specially, the original high-order consensus is regained when the compensating delay equals to the communication delay, but cannot be achieved if the compensating delay is not equivalent to the communication delay. Moreover, suﬃcient consensus convergence conditions are also obtained for the agents under our predictor-based algorithm with diﬀerent compensating delay. Numerical studies for multiple quadrotors illustrate the correctness of our results.
On the Context-Aware, Dynamic Spectrum Access for Robust Intraplatoon Communications

Vehicle platooning is a promising technology that allows to improve the traffic efficiency and passengers safety. Platoons that use cooperative adaptive cruise control, however, require a reliable radio link between platoon members to ensure a required distance between the cars within the platoon, thus maintaining platoon safety. Nowadays, the communication can be realized with the use of 802.11p or cellular vehicle-to-vehicle (C-V2V), but none of this technology is able to provide a reliable link especially in the presence of high traffic or urban scenarios. Therefore, in this paper, we propose a dynamic spectrum management mechanism in V2V communications for platooning purposes. A management system architecture is proposed that comprises the use of context-aware databases, sensing nodes, and spectrum allocation entity. The proposed robust system design aims to keep only the minimum necessary information transmitted over the conventional intelligent transportation system (ITS) channel, while moving the remaining data (nonsafety, service-aided, or infotainment) to an alternative channel that is selected from the available pool of spectrum white spaces. The initial analysis indicates that the proposed system may significantly improve the performance of wireless communications for the purpose of vehicle platooning.
Dual Inhibitors as a New Challenge for Cancer Multidrug Resistance Treatment.

BACKGROUND Dual-targeting in cancer treatment by a single drug is an unconventional approach in relation to drug combinations. The rationale for the development of dual-targeting agents is to overcome incomplete efficacy and drug resistance frequently present when applying individual targeting agents. Consequently, -a more favorable outcome of cancer treatment is expected with dual-targeting strategies. METHODS We reviewed the literature, concentrating on the association between clinically relevant and/or novel dual inhibitors with potential to modulate multidrug resistant phenotype of cancer cells, particularly the activity of P-glycoprotein. The balanced analysis of content was performed to emphasize the most important findings and optimize the structure of this review. RESULTS Two-hundred and forty five papers were included in the review. The introductory part was interpreted by 9 papers. Tyrosine kinase inhibitors' role in the inhibition of P-glycoprotein and chemosensitization was illustrated by 87 papers. The contribution of natural-based compounds in overcoming multidrug resistance was reviewed using 92 papers, while specific dual inhibitors acting against microtubule assembling and/or topoisomerases were described with 55 papers. Eleven papers gave an insight into a novel and less explored approach with hybrid drugs. Their influence on P-glycoprotein and multidrug resistance was also evaluated. CONCLUSION These findings bring into focus rational anticancer strategies with dual-targeting agents. Most evaluated synthetic and natural drugs showed a great potential in chemosensitization. Further steps in this direction are needed for the optimization of anticancer treatment.
Torque Cancelation of Integrated Battery Charger Based on Six-Phase Permanent Magnet Synchronous Motor Drives for Electric Vehicles

This paper proposes a torque cancelation strategy for a nonisolated three-phase integrated battery charger topology for light and medium duty electric vehicle drives based on six-phase permanent magnet (PM) synchronous machines. The charger requires a three-phase grid interface and utilizes the machine windings as the input filter inductances after minor reconfiguration. The drive power electronics are used to control the currents drawn from the three-phase grid. The current flowing through the machine windings induces a torque on the PM machine rotor which is eliminated by the proposed torque cancelation strategy in battery charging mode. The torque cancelation strategy is general and works with both symmetric and asymmetrically wound six-phase PM machines. The strategy is also capable of eliminating the torque when the rotor is displaced from the stator $d$ -axis. The linear quadratic regulator with the integral action control scheme is used to accommodate for the asymmetry caused by the dependence of winding inductance on the rotor position. Co-simulation results with Finite-Element Analysis and hardware experiments show the topology based on six-phase PM machines can be used to charge or discharge the battery for grid support functions while the torque on the machine shaft remains canceled.
Flexible Expert Finding on the Web via Semantic Hypergraph Learning and Affinity Propagation Model

Expert finding (EF) task has received widespread attention as an important task of information retrieval.One key category of EF is expert finding on the web, which seeks to rank influential public figures from diverse webpage sources with respect to given query.Previous web expert finding approach relies on casting webpages to hypergraph structure and run heat diffusion process to find the top ranking person vertices according to their heat of popularity.Such approach suffer from two major drawbacks:First, previous web expert finding approach (CoDiffusion) suffers from unflexibility of selecting queries.This means that all corresponding queries must be stored as vertices in hypergraph index beforehand, otherwise CoDiffusion cannot run the expert finding process.Such defect make it ungeneric and incapable of handling the newly invented technical terms or phrases in real world scenarios.Second, the performance of previous approach is less satisfying.We incorporate semantic relatedness information with Hypergraph Learning Framework and Affinity Propagation ({HLFAP}) to handle the above drawbacks.In order to overcome the first disadvantage, we distribute initial heat to the related vertices according to their semantic similarity on given query. In order to solve the second disadvantage, we propose semantic labeled hypergraph learning framework and person influence affinity propagation model to make high quality candidates can receive more heat transition. Experimental results shows that our generic methodology achieves more satisfying results than the non-semantics state-of-the-art baseline method.