Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploiting Structured News Information to Improve Event Detection via Dual-Level Clustering

With massive amount of news articles come to fore every seconds, helping people navigate through correlated news on a given topic of interest is a pressing need for news portals. This paper tackles Event Detection task, which refers to aggregate news articles report on the same event into tightlyfocused, topic-centered news sets. We propose a novel Dual-level Clustering Model based on News Representation with Time2vec. The advantage of our model lies in taking structured news information into consideration by constructing news vector with context vector and time vector. Besides, the effect of key entities has been taken into account by Dual-level Clustering framework. Experiments conducted on real world news corpus illustrate that our model achieves considerable success compared with several 'state-of-the-art' methods.
Automatic Generation of Review Content in Specific Domain of Social Network Based on RNN

The online social network has become a favorable site where a large number of malicious netizens spread rumors and conduct malignant competition. In this paper, we set up a method for generating review content in specific domain of social networks, which uses a recurrent neural network model to generate the social network-style review. Taking Twitter platform as an example platform, we firstly classify the review text according to the sentence pattern; secondly, aiming at different categories, we design corresponding recurrent neural network model to generate the initial review text corresponding to sentence structures; finally, we conduct automatic replacement of the generated initial text through the relevance of subject terms to achieve the effect of better adapting to hot topics. This method is not only easy to operate and economical, but also can evade the most advanced detectors. In the same environment, it is superior to the existing technology and generates more than 85.2% of the output text with correct grammar and wise contents.
Load-Independent Class E/EF Inverters and Rectifiers for MHz-Switching Applications

This paper presents a unified framework for the modeling, analysis, and design of load-independent Class E and Class EF inverters and rectifiers. These circuits are able to maintain zero-voltage switching and, hence, high efficiency for a wide load range without requiring tuning or use of a feedback loop, and to simultaneously achieve a constant amplitude ac voltage or current in inversion and a constant dc output voltage or current in rectification. As switching frequencies are gradually stepping into the megahertz (MHz) region with the use of wide-bandgap (WBG) devices such as GaN and SiC, switching loss, implementing fast control loops, and current sensing become a challenge, which load-independent operation is able to address, thus allowing exploitation of the high-frequency capability of WBG devices. The traditional Class E and EF topologies are first presented, and the conditions for load-independent operation are derived mathematically; then, a thorough analytical characterization of the circuit performance is carried out in terms of voltage and current stresses and the power-output capability. From this, design contours and tables are presented to enable the rapid implementation of these converters given particular power and load requirements. Three different design examples are used to showcase the capability of these converters in typical MHz power conversion applications using the design equations and methods presented in this paper. The design examples are chosen toward enabling efficient and high-power-density MHz converters for wireless power transfer (WPT) applications and dc/dc conversion. Specifically, a 150-W 13.56-MHz Class EF inverter for WPT, a 150-W 10-MHz miniature Class E boost converter, and a lightweight wirelessly powered drone using a 20-W 13.56-MHz Class E synchronous rectifier have been designed and are presented here.
Question Classification Based on MAC-LSTM

Question classification is very important for Question Answering (QA), and the result of question classification directly affects the quality of QA. Most of the question classification methods are based on supervised learning algorithms which require word embedding and does not consider the interrogative words features. However, question text is usually short and semantic information and word co-occurrence information are insufficient. To address the above problems, this paper proposes a multi-level Attention Convolution LSTM neural network (MAC-LSTM) for question classification. This approach uses the interrogative words attention mechanism to focus on the interrogative words features in the question contexts. At the same time, using the attention mechanism combined with the advantages of convolutional neural network and long-short memory model recurrent neural network (LSTM). MAC-LSTM is able to capture both local features of phrases as well as global and time-series features. Experiments show that, our approach achieves better performance than traditional machine learning method, ordinary convolutional neural network, and traditional LSTM on question classification tasks without the need of prior knowledge.
A Methodology for Non-Invasive 3-D Surveillance of Arteriovenous Fistulae Using Freehand Ultrasound

Objective: Surveillance techniques for arteriovenous fistulae are required to maintain functional vascular access, with two-dimensional duplex ultrasound the most widely used imaging modality. This paper presents a surveillance method for an arteriovenous fistula using a freehand three-dimensional (3-D) ultrasound system. A patient-case study highlights the applicability in a clinical environment. Methods: The freehand ultrasound system uses optical tracking to determine the vascular probe location, and as the probe is swept down a patient's arm, each B-mode slice is spatially arranged to be post-processed as a volume. The volume is segmented to obtain the 3-D vasculature for high detail analysis. Results: The results follow a patient with stenosis, undergoing surgery to have a stent placement. A surveillance scan was taken pre-surgery, postsurgery, and at a two-month follow-up. Vasculature changes are quantified using detailed analysis, and the benefits of using 3-D imaging are shown through 3-D printing and visualization. Conclusion and significance: Non-invasive 3-D surveillance of arteriovenous fistulae is possible, and a patient-specific geometry was created using ultrasound and optical tracking. Access to this non-invasive 3-D surveillance technique will enable future studies to determine patient-specific remodeling behavior, in terms of geometry and hemodynamics over time.
Introducing Automated GUI Testing and Observing Its Benefits: An Industrial Case Study in the Context of Law-Practice Management Software

Motivated by a real-world industrial need in the context of a large IT solutions company based in Turkey, the authors and their colleagues developed and introduced automated test suites for GUI testing of two large-scale law-practice management software (comprising of 414 and 105 KLOC). We report in this paper our experience in developing and introducing a set of large automated test suites (more than 50 KLOC in total), using best practices in state-of-the art and –practice, and to report its observed benefits by conducting cost-benefit analysis in the specific industrial context. The project was conducted based on the principles of case-study and "action research" in which the real industrial needs drove the research. Among the best practices that we used are the followings: (1) the page-object test pattern, (2) modularity in test code, (3) creating test-specific libraries, and (4) using systematic guidelines to decide when and what (test cases) to automate. To assess the cost-benefit and Return On Investment (ROI) of test automation, we followed a hybrid measurement approach to assess both the quantitative and qualitative (intangible) benefits of test automation. The empirical findings showed that the automated GUI testing approach has indeed benefitted the test and QA team in the company under study and automation has been highly welcome by the test engineers. By serving as a success story and experience report in development and introduction of automated test suites in an industrial setting, this paper adds to the body of evidence in this area and it aims at sharing both technical (e.g., using automated test patterns) and process aspects (e.g., test process improvement) of our project with other practitioners and researchers with the hope of encouraging more industry-academia collaborations in test automation.
A Very High-Gain-Modular Three-Phase AC/DC Soft-Switched Converter Featuring High-Gain ZCS Output Rectifier Modules Without Using Step-Up Transformers for a DC Grid in Wind Systems

This paper presents a new modular ac/dc step-up soft-switched converter with unity turns ratio high-frequency transformers for medium-voltage (MV) dc grid in wind energy systems. In the proposed converter, the three-phase boost rectifier is combined with step-up resonant circuits and high-gain output rectifier modules (called voltage quadrupler modules) to form a modular step-up power conversion unit. As a result, the proposed converter is able to achieve very high voltage gain without using any medium-to-high frequency transformers with very large turns ratio, which greatly simplifies the insulation requirement and design of the high-frequency transformer. In addition, soft-switching operations are achieved in all the semiconductor devices to maintain high efficiency. The techniques to ensure voltage balancing across the dc-link capacitors and to achieve output short-circuit protection will be discussed in this paper. To realize high-frequency MV operation, silicon carbide (SiC) devices are utilized in the converter implementation on a 1.5 MW, 690 VAC/40 kV converter system to validate the performance of the proposed circuit. Experimental results on a 3.5 kW, 145 VAC/6.2 kV, 95.4% efficiency SiC-based laboratory-scale proof-of-concept prototype are then provided to highlight the merits of this work.
Cavity-mediated collective spin-exchange interactions in a strontium superradiant laser

An atom-coupling cavity Ensembles of atoms have emerged as powerful simulators of many-body dynamics. Engineering controllable interactions between the atoms is crucial, be it direct or through a mediator. Norcia et al. developed a flexible alternative to existing atomic simulators in a system consisting of strontium atoms placed in an optical cavity. Two atomic states connected by a clock transition each served as an effective spin, with long-range spin-exchange interactions mediated by the cavity photons. With improvements, the setup is expected to be amenable to simulating nonequilibrium quantum dynamics and to have applications in metrology. Science, this issue p. 259 Engineered interactions between strontium atoms in an optical cavity lead to the emergence of a many-body energy gap. Laser-cooled and quantum degenerate atoms are being pursued as quantum simulators and form the basis of today’s most precise sensors. A key challenge toward these goals is to understand and control coherent interactions between the atoms. We observe long-range exchange interactions mediated by an optical cavity, which manifest as tunable spin-spin interactions on the pseudo spin-½ system composed of the millihertz linewidth clock transition in strontium. This leads to one-axis twisting dynamics, the emergence of a many-body energy gap, and gap protection of the optical coherence against certain sources of decoherence. Our observations will aid in the future design of versatile quantum simulators and the next generation of atomic clocks that use quantum correlations for enhanced metrology.
Decentralized Privacy Preserving Reputation System

Reputation systems are important technology for making users answerable for their behaviour in a distributed online environment. However, it has been witnessed that users in a reputation system often vacillate in providing negative criticism for fear of revenge. The solution is a privacy preserving reputation mechanism that computes reputation in such a way that private feedback of a user is not exposed. Many existing privacy preserving mechanisms rely on a centralized paradigms, hence not suitable for a decentralized environment. Systems that are decentralized are either inadequate or rely on trusted third parties that are expensive and do not protect privacy under severe adversarial models. In this paper, we describe a decentralized reputation system where we ensure that users own privacy is preserved. We implement a protocol that is built into the blockchain technology, by allowing for access control, transactions and data storage that are as indelible as the blockchain itself. Our system computation mechanism is based on a secure multiparty computation by a guaranteed verifiable secret sharing scheme. Our goal is to design a system that computes the reputation of a service provider created on the views of other entities.
User-Independent Intent Recognition for Lower Limb Prostheses Using Depth Sensing

Objective: The intent recognizers of advanced lower limb prostheses utilize mechanical sensors on the prosthesis and/or electromyographic measurements from the residual limb. Besides the delay caused by these signals, such systems require user-specific databases to train the recognizers. In this paper, our objective is the development and validation of a user-independent intent recognition framework utilizing depth sensing. Methods: We collected a depth image dataset from 12 healthy subjects engaging in a variety of routine activities. After filtering the depth images, we extracted simple features employing a recursive strategy. The feature vectors were classified using a support vector machine. For robust activity mode switching, we implemented a voting filter scheme. Results: The model selection showed that the support vector machine classifier with no dimension reduction has the highest classification accuracy. Specifically, it reached 94.1% accuracy on the testing data from four subjects. We also observed a positive trend in the accuracy of classifiers trained with data from increasing the number of subjects. Activity mode switching using a voting filter detected 732 out of 778 activity mode transitions of the four users while initiating 70 erroneous transitions during steady-state activities. Conclusion: The intent recognizer trained on multiple subjects can be used for any other subject, providing a promising solution for supervisory control of powered lower limb prostheses. Significance: A user-independent intent recognition framework has the potential to decrease or eliminate the time required for extensive data collection regiments for intent recognizer training. This could accelerate the introduction of robotic lower limb prostheses to the market.
A Metamodel Composition Driven Approach to Design New Domain Specific Modeling Languages

Designing a new domain specific modeling language is a complex and consuming task job effort. One solution is to compose existing DSMLs to form a new more complete DSML. Based on a metamodels composition we can improve the process of rapid prototyping of new DSMLs. In this paper we investigate the consequences of composing metamodels of DSMLs on their graphical syntaxes. Our motivation is to upgrade the quick development of graphical editors for a new DSML formed by the composition of multiple DSMLs metamodels. We explain how the DSMLs can be reused to rapidly form new ones with low cost. Thus, this work contributes by defining a set of rules to compose DSMLs and provide a layer for DSMLs graphical syntaxes composition. We propose three rules to compose DSMLs metamodels: reference, specialization and fusion. We have used a small use case to illustrate the approach. For each defined rule we apply it to the use case to exemplify it. We expose, throughout these examples, how graphical syntaxes can be reused. We also show on how graphical editors can be composed.
Voltage Balancing of a Modular Neutral-Point-Clamped Converter With a Carrier-Based Modulation Scheme

The five-level modular neutral-point-clamped (5L-MNPC) converter is realized by connecting two three-level neutral-point-clamped modules in series. For five-level operation, each dc-bus capacitor voltage of the modular neutral-point-clamped topology should be regulated at one-fourth of the net dc-bus voltage. In this letter, a simple voltage-balancing approach based on the carrier-based modulation scheme is proposed to achieve the above objectives. The proposed approach uses the redundancy switching states along with the current direction and the instantaneous value of the dc-bus capacitor voltage to achieve the voltage balancing in the 5L-MNPC topology. The dynamic performance of the 5L-MNPC topology with the proposed voltage-balancing approach is verified experimentally.
Shift-Enabled Graphs: Graphs Where Shift-Invariant Filters are Representable as Polynomials of Shift Operations

In digital signal processing, a shift-invariant filter can be represented as a polynomial expansion of a shift operation, that is, the $Z$-transform representation. When extended to graph signal processing (GSP), this would mean that a shift-invariant graph filter can be represented as a polynomial of the shift matrix of the graph. Prior work shows that this holds under the shift-enabled condition that the characteristic and minimum polynomials of the shift matrix are identical. While the shift-enabled condition is often ignored in the literature, this letter shows that this condition is essential for the following reasons. First, we prove that this condition is not just sufficient but also necessary for any shift-invariant filter to be representable by the shift matrix. Moreover, we provide a counterexample showing that given a filter that commutes with a non-shift-enabled graph, it is generally impossible to convert the graph into a shift-enabled graph with a shift matrix still commuting with the original filter. The result provides a deeper understanding of shift-invariant filters when applied in GSP and shows that further investigation of shift-enabled graphs is needed to make them applicable to practical scenarios.
Dynamic Analysis and Improved LVRT Performance of Multiple DG Units Equipped With Grid-Support Functions Under Unbalanced Faults and Weak Grid Conditions

Due to the increased integration of multiple distributed generation (DG) units into the distribution network, riding through short-term faults and supporting the host grid have been requested by the new grid codes in many countries. However, the literature lacks the detailed dynamic analysis and control coordination of multiple grid-connected converter-based DG units, equipped with advanced controllers in the synchronous reference frame. To fill this gap, this paper initially presents a detailed small-signal modeling framework for typical medium-voltage multi-bus power distribution systems with multiple DG units equipped with grid-support functions to operate under the unbalanced conditions. This model encompasses the positive and negative sequences of the current and voltage and is developed for three stages of the fault (i.e., before, during, and after the fault) to cover a wide range of system operating points. In addition, to precisely study the interactions among DG units, four different control modes in DG units are considered to study the system dynamics under low-voltage and unbalanced conditions and at different grid strengths. Using the proposed detailed state-space models and based on the small-signal stability analyses, different control parameters are redesigned using the eigenvalue analysis on the complete multi-DG system. As a second contribution, sensitivity analyses are performed to study the effects of different system parameters, such as line characteristics, loading levels, and unbalanced fault characteristics, on the stability of the multi-DG system under unbalanced faults. Comparative simulation and experimental results are also reported to show the accuracy and effectiveness of the theoretical analyses.
Multimodal Object Classification Using Bidirectional Gated Recurrent Unit Networks

In the field of artificial intelligence, we have observed a strong trend towards introducing external resources to aid pattern recognition tasks. In this work, we utilize external meta-data in the form of web text, i.e. natural language, to aid image classification. We adopt pre-trained convolutional neural network (CNN) to learn the mid-level representation of images, meanwhile we model the text as sequential data using bidirectional gated recurrent unit (BGRU) encoders. To address the heterogeneity of the two different representations, we employ a neural network to learn a shared multimodal representation. Experimental results show that the multimodal CNN-BGRU model achieves remarkable classification performance on the large-scale Pascal VOC-2007 and VOC-2012 datasets.
Curcumin exerts anti-inflammatory and vasoprotective effects through amelioration of NFAT-dependent endothelin-1 production in mice with acute Chagas cardiomyopathy

BACKGROUND The anti-inflammatory and cardioprotective properties of curcumin (Cur), a natural polyphenolic flavonoid isolated from the rhizomes of Curcuma longa, are increasingly considered to have beneficial effects on the progression of Chagas heart disease, caused by the protozoan parasite Trypanosoma cruzi. OBJECTIVE To evaluate the effects of oral therapy with Cur on T. cruzi-mediated cardiovasculopathy in acutely infected mice and analyse the in vitro response of parasite-infected human microvascular endothelial cells treated with this phytochemical. METHODS Inflammation of heart vessels from Cur-treated and untreated infected mice were analysed by histology, with benznidazole (Bz) as the reference compound. Parasitaemia was monitored by the direct method. Capillary permeability was visualised by Evans-blue assay. Myocardial ET-1, IL-6, and TNF-α mRNA expressions were measured by quantitative reverse transcription polymerase chain reaction (qRT-PCR). Microvascular endothelial HMEC-1 cells were infected in vitro with or without addition of Cur or Bz. Induction of the Ca2+/NFAT pathway was assessed by fluorometry, immunoblotting, and reporter assay. FINDINGS Oral Cur therapy of recently infected mice reduced inflammatory cell infiltration of myocardial arteries without lowering parasite levels. Compared to that of the phosphate-buffered saline-receiving group, hearts from Cur-treated mice showed significantly decreased vessel inflammation scores (p < 0.001), vascular permeabilities (p < 0.001), and levels of IL-6/TNF-α (p < 0.01) and ET-1 (p < 0.05) mRNA. Moreover, Cur significantly (p < 0.05 for transcript; p < 0.01 for peptide) downregulated ET-1 secretion from infected HMEC-1 cells. Remarkably, Cur addition significantly (p < 0.05 at 27.0 μM) interfered with T. cruzi-dependent activation of the Ca2+/NFATc1 signalling pathway that promotes generation of inflammatory agents in HMEC-1 cells. CONCLUSIONS Oral treatment with Cur dampens cardiovasculopathy in acute Chagas mice. Cur impairs the Ca2+/NFATc1-regulated release of ET-1 from T. cruzi-infected vascular endothelium. These findings identify new perspectives for exploring the potential of Cur-based interventions to ameliorate Chagas heart disease.