Miracle-dz/UniFAR-FTUs huggingface dataset:
Transcatheter Decellularized Tissue-Engineered Heart Valve (dTEHV) Grown on Polyglycolic Acid (PGA) Scaffold Coated with P4HB Shows Improved Functionality over 52 Weeks due to Polyether-Ether-Ketone (PEEK) Insert

Many congenital heart defects and degenerative valve diseases require replacement of heart valves in children and young adults. Transcatheter xenografts degenerate over time. Tissue engineering might help to overcome this limitation by providing valves with ability for self-repair. A transcatheter decellularized tissue-engineered heart valve (dTEHV) was developed using a polyglycolic acid (PGA) scaffold. A first prototype showed progressive regurgitation after 6 months in-vivo due to a suboptimal design and misguided remodeling process. A new geometry was developed accordingly with computational fluid dynamics (CFD) simulations and implemented by adding a polyether-ether-ketone (PEEK) insert to the bioreactor during cultivation. This lead to more belly-shaped leaflets with higher coaptation areas for this second generation dTEHV. Valve functionality assessed via angiography, intracardiac echocardiography, and MRI proved to be much better when compared the first generation dTEHV, with preserved functionality up to 52 weeks after implantation. Macroscopic findings showed no thrombi or signs of acute inflammation. For the second generation dTEHV, belly-shaped leaflets with soft and agile tissue-formation were seen after explantation. No excessive leaflet shortening occurred in the second generation dTEHV. Histological analysis showed complete engraftment of the dTEHV, with endothelialization of the leaflets and the graft wall. Leaflets consisted of collagenous tissue and some elastic fibers. Adaptive leaflet remodeling was visible in all implanted second generation dTEHV, and most importantly no fusion between leaflet and wall was found. Very few remnants of the PGA scaffold were detected even 52 weeks after implantation, with no influence on functionality. By adding a polyether-ether-ketone (PEEK) insert to the bioreactor construct, a new geometry of PGA-scaffold based dTEHV could be implemented. This resulted in very good valve function of the implanted dTEHV over a period of 52 weeks.
Optimizing Automatic Evaluation of Machine Translation with the ListMLE Approach

Automatic evaluation of machine translation is critical for the evaluation and development of machine translation systems. In this study, we propose a new model for automatic evaluation of machine translation. The proposed model combines standard n-gram precision features and sentence semantic mapping features with neural features, including neural language model probabilities and the embedding distances between translation outputs and their reference translations. We optimize the model with a representative list-wise learning to rank approach, ListMLE, in terms of human ranking assessments. The experimental results on WMT’2015 Metrics task indicated that the proposed approach yields significantly better correlations with human assessments than several state-of-the-art baseline approaches. In particular, the results confirmed that the proposed list-wise learning to rank approach is useful and powerful for optimizing automatic evaluation metrics in terms of human ranking assessments. Deep analysis also demonstrated that optimizing automatic metrics with the ListMLE approach is a reasonable method and adding the neural features can gain considerable improvements compared with the traditional features.
Multiscale event detection using convolutional quadtrees and adaptive geogrids

Increasing popularity of social networks made them a viable data source for many data mining applications and event detection is no exception. Researchers aim not only to find events that happen in networks but more importantly to identify and locate events occurring in the real world. In this paper, we propose an enhanced version of quadtree - convolutional quadtree (ConvTree) - and demonstrate its advantage compared to the standard quadtree. We also introduce the algorithm for searching events of different scales using geospatial data obtained from social networks. The algorithm is based on statistical analysis of historical data, generation of ConvTrees representing the normal state of the city and anomalies evaluation for events detection. Experimental study conducted on the dataset of 60 million geotagged Instagram posts in the New York City area demonstrates that the proposed approach is able to find a wide range of events from very local (indie band concert or wedding party) to city (baseball game or holiday march) and even country scale (political protest or Christmas) events. This opens up a perspective of building a simple and fast yet powerful system for real-time multiscale events monitoring.
Deep Learning in the Wavelet Domain

This paper examines the possibility of, and the possible advantages to learning the filters of convolutional neural networks (CNNs) for image analysis in the wavelet domain. We are stimulated by both Mallat's scattering transform and the idea of filtering in the Fourier domain. It is important to explore new spaces in which to learn, as these may provide inherent advantages that are not available in the pixel space. However, the scattering transform is limited by its inability to learn in between scattering orders, and any Fourier domain filtering is limited by the large number of filter parameters needed to get localized filters. Instead we consider filtering in the wavelet domain with learnable filters. The wavelet space allows us to have local, smooth filters with far fewer parameters, and learnability can give us flexibility. We present a novel layer which takes CNN activations into the wavelet space, learns parameters and returns to the pixel space. This allows it to be easily dropped in to any neural network without affecting the structure. As part of this work, we show how to pass gradients through a multirate system and give preliminary results.
Computer Generated Image Alterations Database

Modern user interfaces (UI) use animations and special effects to appear more interesting and appealing to consumers, such is the case for modern Set-Top-Boxes (STB). In order to provide their consumers with functional and reliable products, manufacturers are required to perform functionality verification (FV) of each product. Automated FV systems are becoming a prevailing method of FV due to manual FV becoming exceedingly time consuming as the devices become more complex. Generally, full reference (FR) image comparison between the device output image and the referent image is used in order to perform FV of device under test (DUT). Common methods for image comparison proved to be ineffective when comparing images captured from UIs containing animated objects or dynamic background. In order to provide manufacturers with high-end FV systems, it is necessary to develop new methods for image comparison capable of comparing images despite alterations caused by UI animations. Development of such algorithms requires large image databases which effectively simulate such image alterations. In this paper we present new Computer Generated Image Alterations Database (CGIAD) which contains 10000 test images with alterations caused by object transformations commonly used in UI animations (rotation, scaling, occlusion, and translation). CGIAD is freely available to the research community at http://www.rt-rk.com/other/CGIAD.html.
The Effect of Inaccuracy of MLC Leaf Position against Dose Distribution on VMAT Delivery Technique using EGSnrc Monte Carlo Simulation

A Monte Carlo simulation on VMAT delivery technique has implemented to investigate the effect of inaccuracy of MLC leaf position between the plan and the actual of MLC leaf position. The study aim to compare the dose distributions between these two parameters. In general, these studies divided into two stages, commissioning of head Linac and simulation of the VMAT delivery technique. At the commissioning stage, Monte Carlo simulation on PDD and dose profile for field size 6 × 6 cm2, 10 × 10 cm2 and 20 × 20 cm2 was validating by experimental data. Commissioning results showed a good agreement between the simulation and experimental data. The 6.4 MeV initial incident electrons were used with an average deviation less than 5% for all field size. In the VMAT delivery technique simulation stage, an analysis of DynaLog file using homemade MATLAB program is used and generated the input files for DOSXYZnrc. One dimension of dose profile analyzed at some point sampling to determine differences in the dose distribution in the cylinder acrylic phantom. DynaLog analysis on the data file showed that the leaf error position is less than 1 mm is 97.1% of the total leaf position and no leaf error position is more than 2 mm. The doses distribution error was discovered less than 2% at isocenter. This study showed there is no significant error on doses distribution due to <1% inaccuracy of MLC leaf position. The Monte Carlo simulation was done using 24.7 billion particles of the phase space history file with a standard deviation of Monte Carlo calculations by 31.66%.
An Ensemble Model Based on Siamese Neural Networks for the Question Pairs Matching Task

The problem of question pairs matching aims to seek whether the underlying semantics of two questions are equivalent. For WeBank Chinese question pairs which are collected from real-world intelligent customer service questions, the goal is to identify question pairs that have the same intent. In this paper, we propose an ensemble model which based on both word and character level neural networks such as the convolutional neural network (CNN), and the long short-term memory network (LSTM) for modeling semantic similarity. And we adopt an enhanced deep semantic model (R-ESIM) which is proved to be more effective for sentence modeling. Our model takes 10-fold cross-validation into account to improve the generalization ability. In the evaluation of the CCKS 2018 shared task three, our model achieves the F1 score of 0.85085 for the opening test data which ranks the second.
DelFin: A Deep Learning Based CSI Fingerprinting Indoor Localization in IoT Context

Many applications in Internet of Things (IoT) require an ubiquitous localization to provide their services. Whereas the global navigation satellite systems are mainly used in outdoor environment, multiple solutions based on mobile sensors or wireless communication infrastructures exist for indoor localization. One of them is the fingerprinting approach which consists in collecting the signals at known locations in a studied area and estimating the locations of new incoming signals thanks to the collected database. This approach interests many researches due to its connection with machine learning concepts. In this paper we propose to implement a deep learning architecture for a fingerprinting localization based on Wi-Fi channel frequency responses in IoT context. Our solution, DelFin reduces the median and 90-th percentile localization errors up to 50% and 47% respectively compared to other fingerprinting methods. DelFin has been tested with different spatial distributions of training locations in the studied area and still performed the best results.
Multiple Angle Analysis of 30-MeV Silicon Ion Beam Radiation Effects on InGaN/GaN Multiple Quantum Wells Blue Light-Emitting Diodes

High-resolution X-ray diffraction, temperature-dependent photoluminescence (PL), time-resolved PL, and positron annihilation spectroscopy are employed to investigate the degradation mechanism of InGaN/GaN multiple quantum wells (MQWs) light-emitting diodes (LEDs) under silicon ion irradiation. Reduction of the quantum-confined Stark effect due to crystalline strain relaxation, enhancement of indium localization due to thermal spike, generation of nonradiative recombination centers (NRCs), and carrier removal effect due to atom displacement are revealed to be critical factors in LEDs postirradiation performance. New NRCs are proven to be the main reason for the degradation of the internal quantum efficiency of MQWs. The increase of the threshold voltage and leakage current in LEDs are caused by the carrier removal effect and new defects in bandgap induced by radiation. In addition, new NRCs are found to appear earlier than indium localization and carrier removal effect with increasing silicon ion fluence. Atom displacement defects are revealed to be located mainly in p-type GaN and MQWs layers. Radiation-induced nitrogen vacancies are considered compensation donors in p-type GaN, whereas all other nitrogen and gallium-related defects are NRCs in MQWs.
Unsupervised Deep Feature Learning to Reduce the Collection of Fingerprints for Indoor Localization Using Deep Belief Networks

One of the most practical localization techniques is WLAN-based fingerprinting for location-based services because of the availability of WLAN Access Points (APs). This technique measures the Received Signal Strength (RSS) from APs at each indicated location to construct fingerprints. However, the collection of fingerprints is notoriously laborious and needs to be repeatedly updated due to the changes of environments. To reduce the workload of fingerprinting, we apply Deep Belief Networks to unlabeled RSS measurements to extract hidden features of the fingerprints, and thereby minimize the collection of fingerprints. These features are used as inputs for conventional regression techniques such as Support Vector Machine and K-Nearest Neighbors. The experiment results show that our feature representations learned from unlabeled fingerprints provide better performance for indoor localization than baseline approaches with a small fraction of labeled fingerprints traditionally used. In the experiment, our approach already improves the localization accuracy by 1.9 m when using only 10% of labeled fingerprints, compared to the closest baseline approach which used 100% of labeled fingerprints.
A Survey on Data Imputation Techniques: Water Distribution System as a Use Case

The presence of missing data is problematic in most quantitative research studies. Water distribution systems (WDSs) are not immune to this problem. In fact, missing data is an inherent feature of a WDS. There are various techniques and methods to address missing data ranging from simply deleting the data to using complex algorithms to impute missing data. This paper reviews the different imputation options available from traditional methods (such as deletion and single imputation) to more modern and advanced methods (such as multiple imputation, model-based procedures, and machine learning techniques). The concept, application, and qualitative advantages and disadvantages of these methods are discussed. In addition, a novel approach for selecting an applicable technique is presented. The approach is a “top-down bottom-up” two-prong approach for the selection of a data analysis and missing data technique. The bottom-up approach facilitates the top-down selection of a suitable technique by analyzing the data and narrowing down the selection options. As a use case, this paper also reviews techniques that are used to impute missing data in WDSs.
Vision-Based Indoor Positioning of a Robotic Vehicle with a Floorplan

This paper presents a vision-based indoor positioning system of a small robotic vehicle utilizing knowledge of the building floorplan. Using images taken by a monocular camera rigidly mounted onto the deck of the vehicle, the localization system obtains initial geometry of the environment and camera motion by running Structure from Motion. The localization system resolves the scale ambiguity present in the data by associating planar structures in the 3D point cloud with walls of the building. In order to extract the planes, we developed a Scale Invariant Planar RANSAC (SIPR) algorithm which handles situations of scale ambiguity in the point cloud data. Our Wall Plane Fusion algorithm forms correspondences between walls and computed planes, and the best such correspondence is used as an external constraint to the Bundle Adjustment algorithm which is run on the Structure from Motion data. A necessary condition for providing a global positioning solution is that one wall be in view. This paper provides results in both simulated and real-world scenarios.
A Differential Low Power Wake-Up Circuit Based on Systematic Offset for RFID Applications

This paper presents a wake-up circuit for battery-assisted passive RFID applications. The circuit is composed by a differential envelope detector and a comparator with a systematic offset. The differential RF signal envelope detector and the comparator are designed to increase the wake-up circuit sensitivity and to have low power consumption, improving battery lifetime. The wake-up circuit was designed and implemented in standard CMOS 0.18 um process as part of an UHF RFID battery-assisted passive system, achieving a sensitivity of −31 dBm and consuming only 150 nA.
Examining the Critical Success Factors of Startup in Thailand Using Structural Equation Model

Startup is a fast-growing business model by funding from investors, however, it also has a high failure rate and risk. This research aims to examine critical success factors of and their relations in Thai startups. Structural equation model is applied for studying causal relationships between factors by using factor analysis and multivariable regression analysis. Based on sixteen observed variables, the results show that there are four critical success factors of Thai startups, namely support partner, business model, market opportunity, and customer perspective. Especially, the factors of the business model and support partner have direct effects on potential and success in startups. This research is a knowledge framework which assists, not only young startups to succeed their business, but also investors to evaluate investments in startups. Moreover, the paper also provides a literature review which is useful for researchers in the area of a startup.
Achieving Differential Privacy using Methods from Calculus

We introduce derivative sensitivity, an analogue to local sensitivity for continuous functions. We use this notion in an analysis that determines the amount of noise to be added to the result of a database query in order to obtain a certain level of differential privacy, and demonstrate that derivative sensitivity allows us to employ powerful mechanisms from calculus to perform the analysis for a variety of queries. We have implemented the analyzer and evaluated its efficiency and precision. We also show the flexibility of derivative sensitivity in specifying the quantitative privacy notion of the database, as desired by the data owner. Instead of only using the `number of changed rows' metric, our metrics can depend on the locations and amounts of changes in a much more nuanced manner. This will help to make sure that the distance is not larger than the data owner desires (which would undermine privacy), thereby encouraging the adoption of differentially private data analysis mechanisms.
New Control Technique to Improve the Grid Power by Using PV-STATCOM

This paper introduces novel control method to utilize the solar plant inverter as static synchronous compensator (STATCOM) which is also called as PVSTATCOM to improve the power transmission capacity of the grid. The solar farm is ideal for the nighttime and it produces less power in morning period and late afternoon period. Therefore these are the expensive assets which are remaining unutilized during this period of 24hrs. To overcome these problems solar plant along with maximum power point tracking [MPPT] produces active power during daytime and full capacity of solar plant inverter which is not used in night period is used with help of point of common coupling [PCC] voltage controller and damping controller to improve the power transmission capacity of line and also power factor of the grid line. The inverter capacity which is left after the real power generation is used to perform the given objective. This new control technique is used to increase the grid transfer capacity for which other alternatives are used like FACT devices.
Real-Time Data Mining for Event Streams

Information systems produce different types of event logs; in many situations, it may be desirable to look for trends inside these logs. We show how trends of various kinds can be computed over such logs in real time, using a generic framework called the trend distance workflow. Many common computations on event streams turn out to be special cases of this workflow, depending on how a handful of workflow parameters are defined. This process has been implemented and tested in a real-world event stream processing tool, called BeepBeep. Experimental results show that deviations from a reference trend can be detected in realtime for streams producing up to thousands of events per second.
Some challenges regarding cloaking and earthquake protection

Building upon analogies with cloaking of elastic waves in plates, a large scale experiment has demonstrated unprecedented control of surface seismic waves in structured soils. Here, we would like to review recent research advances and remaining challenges in the theory and applications of seismic metamaterials for cloaking and earthquake protection. We recall some results on transformation elastodynamics and introduce mathematical theory of near cloaking for elastic equations. The former is a natural framework for scattering problems in unbounded domains, while the latter addresses boundary measurements in bounded domains. These two fields of investigation bring complementary information on cloaking efficiency. Intimate links between cloaking and wave protection will be also discussed.
CVTE SLU: A Hybrid System for Command Understanding Task Oriented to the Music Field

The dialogue system lies at the core of many natural language processing applications, and thus has received much attention. The 2018 China conference on knowledge graph and semantic computing (CCKS) challenge sets up a evaluation competition for command understanding task oriented to the music field. In this task, we mainly focus on two sub-tasks: (1) Intent identification in music field task, and (2) Slot filling in field task. We proposed a hybrid system based on rule, Multi-fastText and CRF (conditional random field) methods for this task. The experiment results show that, the F1 score of intent identification is 0.867, the F1 score of slot filling is 0.780 and the accuracy of the no-slot discourse is 0.977. The overall score is 1.312, which proves the effectiveness of our system.
Sentiment Analysis for Ad Hoc Discussions Using Multilingual Knowledge-Based Approach

Over the past few years the sentiment analysis task of users' posts in social networks has become very popular among researchers. In this paper, authors present and describe the developed multi-lingual knowledge-based approach of sentiment analysis in major conflict ad hoc discussions of the social network Twitter. An experiment is made in which the quality of the proposed method is evaluated with different parameters on two real ad hoc discussions: Ferguson unrest (USA) and Biryuliovo bashings (Russia). The results of the experiment show a good quality of the sentiment analysis of the discussions. In particular, the average value of the accuracy of Russian and English is 0.65, and the f-measure is 0.7.