Miracle-dz/UniFAR-FTUs huggingface dataset:
Thermal and Efficiency Analysis of Five-Level Multilevel-Clamped Multilevel Converter Considering Grid Codes

In this paper, a five-level multilevel-clamped multilevel converter (5L- MLC2) topology is analyzed based on a typical grid integration application for renewable energy system. The loss and thermal distributions of the power devices in different switching legs are investigated and illustrated under rated condition. Afterward, a loss and efficiency evaluation method is proposed and applied which takes into account various injected reactive power ranges regulated by the grid codes. It is concluded that the loss and thermal distributions of the 5L- MLC2 multilevel topology are unequal between the clamping switching legs and the main switching legs; it also shows a good and consistent efficiency characteristic under different output power ranges when complying with the grid codes.
Graph Regularized Auto-Encoders for Image Representation

Image representation has been intensively explored in the domain of computer vision for its significant influence on the relative tasks such as image clustering and classification. It is valuable to learn a low-dimensional representation of an image which preserves its inherent information from the original image space. At the perspective of manifold learning, this is implemented with the local invariant idea to capture the intrinsic low-dimensional manifold embedded in the high-dimensional input space. Inspired by the recent successes of deep architectures, we propose a local invariant deep nonlinear mapping algorithm, called graph regularized auto-encoder (GAE). With the graph regularization, the proposed method preserves the local connectivity from the original image space to the representation space, while the stacked auto-encoders provide explicit encoding model for fast inference and powerful expressive capacity for complex modeling. Theoretical analysis shows that the graph regularizer penalizes the weighted Frobenius norm of the Jacobian matrix of the encoder mapping, where the weight matrix captures the local property in the input space. Furthermore, the underlying effects on the hidden representation space are revealed, providing insightful explanation to the advantage of the proposed method. Finally, the experimental results on both clustering and classification tasks demonstrate the effectiveness of our GAE as well as the correctness of the proposed theoretical analysis, and it also suggests that GAE is a superior solution to the current deep representation learning techniques comparing with variant auto-encoders and existing local invariant methods.
German Cockroach Extract Induces Activation of Human Eosinophils to Release Cytotoxic Inflammatory Mediators

Background: Eosinophils play an important role in the pathogenesis of allergic diseases. Sensitization and exposure to cockroach allergen have been demonstrated to be one of the major risk factors for the development of bronchial asthma. However, little is known regarding the functional capacity of cockroach extract antigen to activate human eosinophils. Objective: We investigated whether German cockroach extract can activate human eosinophils to release cytotoxic inflammatory mediators. Methods: Purified eosinophils from the peripheral blood were incubated with various concentrations (0–200 µg/ml) of German cockroach extract antigen. Effector functions of eosinophils were checked by degranulation and superoxide anion production. In addition, we examined surface expression of CD11b and CD69, and intracellular activation of p38 mitogen-activated protein kinase (p38 MAP kinase) in cockroach-stimulated eosinophils. Results: German cockroach extract induced degranulation and superoxide production from human eosinophils. In addition, incubation of eosinophils for 3 h with the cockroach extract resulted in an increased level of the surface expression of CD11b and CD69. Furthermore, cockroach-induced superoxide production from eosinophils was significantly inhibited by the pretreatment of cells with a p38 MAP kinase inhibitor SB202190. Indeed, a large amount of phosphorylated forms of p38 MAP kinase was detected in cockroach-stimulated eosinophils. Conclusions: Our results suggest that German cockroach extract induces activation of human eosinophils to release cytotoxic inflammatory mediators such as superoxide and granular proteins.
Product-code vector quantization of cepstral parameters for speech recognition over the WWW

We follow the paradigm that we have previously introduced for the encoding of the recognizer parameters in a client-server model used for recognition over wireless networks and the WWW, trying to maximize recognition performance instead of perceptual reproduction. We present a new encoding scheme for the mel frequency-warped cepstral parameters (MFCCs) that uses product-code vector quantization, and we find that the required bit rate to achieve the recognition performance of highquality unquantized speech is just 2000 bits per second. We also investigate the effect of additive noise on the recognition performance when quantized features are used, and we find that a small increase in the bit rate can provide the necessary robustness.
Lightweight self-protecting JavaScript

This paper introduces a method to control JavaScript execution. The aim is to prevent or modify inappropriate behaviour caused by e.g. malicious injected scripts or poorly designed third-party code. The approach is based on modifying the code so as to make it self-protecting: the protection mechanism (security policy) is embedded into the code itself and intercepts security relevant API calls. The challenges come from the nature of the JavaScript language: any variables in the scope of the program can be redefined, and code can be created and run on-the-fly. This creates potential problems, respectively, for tamper-proofing the protection mechanism, and for ensuring that no security relevant events bypass the protection. Unlike previous approaches to instrument and monitor JavaScript to enforce or adjust behaviour, the solution we propose is lightweight in that (i) it does not require a modified browser, and (ii) it does not require any run-time parsing and transformation of code (including dynamically generated code). As a result, the method has low run-time overhead compared to other methods satisfying (i), and the lack of need for browser modifications means that the policy can even be applied on the server to mitigate some effects of cross-site scripting bugs.
The Radicalization Puzzle: A Theoretical Synthesis of Empirical Approaches to Homegrown Extremism

Why and how do individuals residing in relatively peaceful and affluent Western societies come to embrace extremist ideologies that emanate from distant places? We summarize the most recent empirical literature on the causes and dynamics of radicalization, and evaluate the state of the art in the study of Islamist homegrown extremism in the West. We propose a theoretical synthesis based on four factors that come together to produce violent radicalization: personal and collective grievances, networks and interpersonal ties, political and religious ideologies, and enabling environments and support structures. We propose adopting a “puzzle” metaphor that represents a multifactor and contextualized approach to understanding how ordinary individuals transform into violent extremists. We concluded with three recommendations to strengthen the empirical foundations of radicalization studies.
Locating POS Terminals from Credit Card Transactions

Credit card is a popular payment method and the transaction data keeps track of purchasing activities in people's daily lives. Extracting location of people's activities is an important task in many data mining problems because it may greatly help improve user experience and the service provided to people. Locating people from credit card transactions is equivalent to determining the location of every POS terminal where a payment takes place. This is however not an easy task because the locations of terminals are not usually provided to the credit card issuing companies and only a few terminals can be unambiguously located through map service by providing the merchants' names. In this paper, we propose a system to infer the locations of POS terminals using transaction data and map service. We first construct a transaction graph where the nodes are POS terminals. We then propose a two phase algorithm to find out uncertain and unknown locations of the terminals. In the first phase, we try to eliminate the uncertainty of POS terminals with multiple candidate locations. We show this problem is NP-hard and then give an effective heuristic algorithm to solve it. In the second phase, we compute the locations of unknown POS terminals by propagating the locations of known ones with spatial-temporal constraints. The algorithm is evaluated using a real-world credit card transaction data set and the result is promising for business applications.
The Epicardium and the Development of the Atrioventricular Junction in the Murine Heart

Insight into the role of the epicardium in cardiac development and regeneration has significantly improved over the past ten years. This is mainly due to the increasing availability of new mouse models for the study of the epicardial lineage. Here we focus on the growing understanding of the significance of the epicardium and epicardially-derived cells in the formation of the atrioventricular (AV) junction. First, through the process of epicardial epithelial-to-mesenchymal transformation (epiEMT), the subepicardial AV mesenchyme is formed. Subsequently, the AV-epicardium and epicardially-derived cells (EPDCs) form the annulus fibrosus, a structure important for the electrical separation of atrial and ventricular myocardium. Finally, the AV-EPDCs preferentially migrate into the parietal AV valve leaflets, largely replacing the endocardially-derived cell population. In this review, we provide an overview of what is currently known about the regulation of the events involved in this process.
Human Brain Biochemistry

The human brain that serves as a center of the nervous system is structurally unique. It is extraordinarily complex and highly specialized in its distinct heterogeneous anatomical regions as its function remains a great challenge. The neuron is the functional unit that depends on special anatomical and chemical connections with other units of the system. The essential biochemical connections of the nerve cell have special morphological features: synaptic contact that is mediated by chemical molecules ensures sequential propagation of neurotransmission of electrical pulses through units of the system. The chemical energy expended in maintaining the distribution gradients of cations across cellular membranes, and the chemical neurotransmission causes an alteration in cation distribution. The energy utilization mechanisms that underlie cations re-distribution are not peculiar to the nervous system, but they are of particular importance to neural function because the mechanisms of chemical transmission are peculiar to the nervous system. Human nerve cells have the ability to generate electrical impulses that can travel through the body without a significant loss of impulse strength. Such unique features are based on semi-permeable excitable membranes that alter permeation to small chemical molecules and to cations. The biochemical function of the brain is demonstrated in the efficient production of energy required to accomplish the processes mentioned above, and it is essentially ATP that is stored and produced from glucose oxidation to carbon dioxide and water. The brain has virtually no reserves of chemical energy (glucose 1-2 µmoles/g and ATP 3 µmoles/g) to function for minutes only, considering that this organ is 2% of total adult weight that consumes 20% of the whole body glucose through a constant blood supply. Yet, the various factors that regulate glucose uptake and its utilization in the central nervous system are not well understood. This review is an attempt to update the rapidly expanding information on human brain neurotransmission biochemistry, though the adaptive processes of learning; cognitive performance and memory in the brain have subtle relationships.
Learning to Rank Using Privileged Information

Many computer vision problems have an asymmetric distribution of information between training and test time. In this work, we study the case where we are given additional information about the training data, which however will not be available at test time. This situation is called learning using privileged information (LUPI). We introduce two maximum-margin techniques that are able to make use of this additional source of information, and we show that the framework is applicable to several scenarios that have been studied in computer vision before. Experiments with attributes, bounding boxes, image tags and rationales as additional information in object classification show promising results.
Developmental exposure to diuron causes splenotoxicity in male Sprague-Dawley rat pups

This study investigated whether perinatal exposure to diuron [3-(3,4-dichlorophenyl)-1-1-dimethylurea] might exert adverse effects on rat lymphoid organs. Pregnant Sprague-Dawley (SD) rats were exposed to diuron at 500, 750 or 1250 ppm in the diet from gestational days (GD) 12–21 and during lactation. At postnatal day (PND) 42, male pups were euthanized and thymus, spleen, mesenteric lymph node and femur were collected for histopathological analysis. Food consumption and body weight gain were significantly reduced in dams exposed to 1250 ppm during gestation period. Also, Diuron at 750 and 1250 ppm produced: (1) increased relative spleen weight associated histologically with severe congestion in red pulp, (2) enhanced extramedullary hematopoiesis and hemosiderosis as well as (3) depletion of lymphoid follicles in white pulp. Flow cytometric analysis revealed a significant reduction in B lymphocytes (CD45RA+) in male pups but T lymphocytes (CD4+, CD8+ and CD4+/CD8+) were not markedly affected. Thus, data suggest that Diuron-induced maternal toxicity in dams exposed to high dose and perinatal exposure to this herbicide produced spleen toxicity as evidenced by a reduction in B lymphocyte number in male SD pups.
Cross-language bug localization

Bug localization refers to the process of identifying source code files that contain defects from textual descriptions in bug reports. Existing bug localization techniques work on the assumption that bug reports, and identifiers and comments in source code files, are written in the same language (i.e., English). However, software users from non-English speaking countries (e.g., China) often use their native languages (e.g., Chinese) to write bug reports. For this setting, existing studies on bug localization would not work as the terms that appear in the bug reports do not appear in the source code. We refer to this problem as cross-language bug localization. In this paper, we propose a cross-language bug localization algorithm named CrosLocator, which is based on language translation. Since different online translators (e.g., Google and Microsoft translators) have different translation accuracies for various texts, CrosLocator uses multiple translators to convert a non-English textual description of a bug report into English -- each bug report would then have multiple translated versions. For each translated version, CrosLocator applies a bug localization technique to rank source code files. Finally, CrosLocator combines the multiple ranked lists of source code files. Our preliminary experiment on Ruby-China shows that CrosLocator could achieve mean reciprocal rank (mrr) and mean average precision (map) scores of up to 0.146 and 0.116, which outperforms a baseline approach by an average of 10% and 12% respectively.
Selecting syntactic attributes for authorship attribution

In this work we present a methodology to select syntactic attributes for authorship attribution. The approach takes into account a multi-objective genetic algorithm and a Support Vector Machine classifier and it operates in a wrapper mode. Through a series of comprehensive experiments on a database composed of 3000 short articles written in Portuguese we show that the proposed methodology is able to provide a concise subset of attributes, which increases the recognition rate in about 15 percentage points.
Current Harmonics From Single-Phase Grid-Connected Inverters—Examination and Suppression

Environmental conditions and operational modes may significantly impact the distortion level of the injected current from single-phase grid-connected inverter systems, such as photovoltaic (PV) inverters, which may operate in cloudy days with a maximum power point tracking, in a nonunity power factor, or in the low-voltage ride-through mode with reactive current injection. In this paper, the mechanism of the harmonic current injection from grid-connected single-phase inverter systems is thus explored, and the analysis is conducted on single-phase PV systems. In particular, the analysis is focused on the impacts of the power factor and the feed-in grid current level on the quality of the feed-in grid current from single-phase inverters. As a consequence, an internal model principle-based high-performance current control solution is tailor-made and developed for single-phase grid-connected systems to produce high-quality currents in different operation conditions, where a design procedure is also provided. The developed current controller in this paper can achieve a minimum steady-state error while maintaining a relatively fast transient response, and also being feasible in other single-phase applications as a promising harmonic mitigation solution. Experiments on single-phase grid-connected systems have verified the correctness of the relevant analysis and also the effectiveness of the tailor-made control solution in terms of good harmonic mitigation.
Haptic Data Transmission Based on the Prediction and Compression

In this paper, we present a floating-point compression method using position prediction for haptic data transmission. The main objective of our work is to reduce the size of bandwidth used for haptic data transmission in networked haptic virtual environments, such as remote-teaching or tele-operation, by compressing the size of packets composed of floating-point numbers. In order to compress floating-point numbers, we first analyze the characteristics of floating-point arithmetic and haptic data transmission. Then, we introduce a simple position prediction method for the movement of a haptic cursor (also known as a haptic pointer or haptic probe). Finally, we propose a compression method on the basis of those characteristics and prediction method. We also build an experimental test bed for the evaluation of the proposed method and conclude that our method is effective enough for improving haptic data transmission.
A Hybrid User and Item-Based Collaborative Filtering with Smoothing on Sparse Data

Collaborative filtering, the most successful recommender system technology to date, helps people make choices based on the opinions of other people. Existing collaborative filtering methods, mainly user-based and item-based methods, predict new ratings by aggregating rating information from either similar users or items. However, a large amount of ratings of similar items or similar users may be unavailable because of the sparse characteristic inherent to the rating data. For this reason, we present a Hybrid Predictive Algorithm with Smoothing (HSPA). HSPA uses item-based methods to provide the basis for data smoothing and builds the predictive model based on both users' aspects and items' aspects in order to ensure robust to data sparsity and predictive accuracy. Moreover, HSPA utilizes the user clusters to achieve high scalability. Experimental results from real datasets show that HSPA effectively contributes to the improvement of prediction on sparse data
A Proposition-Based Abstractive Summariser

Abstractive summarisation is not yet common amongst today’s deployed and research systems. Most existing systems either extract sentences or compress individual sentences. In this paper, we present a summariser that works by a different paradigm. It is a further development of an existing summariser that has an incremental, proposition-based content selection process but lacks a natural language (NL) generator for the final output. Using an NL generator, we can now produce the summary text to directly reflect the selected propositions. Our evaluation compares textual quality of our system to the earlier preliminary output method, and also uses ROUGE to compare to various summarisers that use the traditional method of sentence extraction, followed by compression. Our results suggest that cutting out the middle-man of sentence extraction can lead to better abstractive summaries.
Outlier detection as the primary step for promotion planning in retail

Forecasting the increase of customer demand during discount promotions is a fundamental business assignment in retail, which can nowadays be performed by sophisticated data mining algorithms. The calculations are based on data gained during previous promotions. Apart from choosing the right mining algorithm, the quality of prediction models strongly depends on the quality of the training data. Outliers are points of data that do not conform to a defined notion of normal behavior and are therefore either excluded from the training set, or their impact on the model is weighted in a manner different from other data. In this paper we propose a new approach to outlier analysis, with the aim of distinguishing between the outliers associated with an outlier-generating store or product and the outliers that can be classified as noisy data. Outlier analysis is performed with a multidimensional perception of the dataset typical for data warehousing and OLAP. We also introduce measures that estimate the probability of store or product being an outlier generator and conduct experiments to determine their critical threshold values.
Use of diversity techniques for weak GNSS signal tracking in fading environments

This research focuses on improving the GNSS signal measurement performance in harsh multipath environments. The nature of GNSS signal propagation in typical signal impeding wireless channels such as indoors and urban environments is studied. The signal power distribution, delay spread, and Doppler spread characteristics of real GNSS signals in such environments are investigated. Based on the observations obtained from this characterization, a closed-loop tracking architecture based on spatial diversity reception is proposed. The proposed algorithm is tested on real data collected using two antennas in indoor static and urban kinematic scenarios. The carrier loop tracking, code loop tracking, and pseudorange errors are compared between single antenna reception and dual antenna spatial diversity reception for different scenarios. Results show that the proposed tracking method improves the Doppler measurement by more than 50% and pseudorange measurements by 2-5 m compared to a single antenna receiver.