Miracle-dz/UniFAR-FTUs huggingface dataset:
Family of Integrated Multi-Input Multi-Output DC-DC Power Converters

This paper explores a family of integrated multiport converters using three-switch which can provide single-input dual-output (SIDO) or dual-input single-output (DISO) with bidirectional power flow between any two ports. The concept can be extended to the n-switch converters to achieve more inputs and/or outputs. The proposed converters can be applied to interfacing sources, loads and storage elements having different voltage levels in applications such as dc nanogrids, electric vehicle, multiport power supplies, distributed generation systems. Various topological configurations of the integrated multiport n-switch converter are investigated. The operating principles and PWM control strategy of these converters are analyzed in detail. A universalized hardware prototype is built, experimental results are provided for verification.
Multi-Domain Neural Machine Translation with Word-Level Domain Context Discrimination

With great practical value, the study of Multi-domain Neural Machine Translation (NMT) mainly focuses on using mixed-domain parallel sentences to construct a unified model that allows translation to switch between different domains. Intuitively, words in a sentence are related to its domain to varying degrees, so that they will exert disparate impacts on the multi-domain NMT modeling. Based on this intuition, in this paper, we devote to distinguishing and exploiting word-level domain contexts for multi-domain NMT. To this end, we jointly model NMT with monolingual attention-based domain classification tasks and improve NMT as follows: 1) Based on the sentence representations produced by a domain classifier and an adversarial domain classifier, we generate two gating vectors and use them to construct domain-specific and domain-shared annotations, for later translation predictions via different attention models; 2) We utilize the attention weights derived from target-side domain classifier to adjust the weights of target words in the training objective, enabling domain-related words to have greater impacts during model training. Experimental results on Chinese-English and English-French multi-domain translation tasks demonstrate the effectiveness of the proposed model. Source codes of this paper are available on Github https://github.com/DeepLearnXMU/WDCNMT.
Performance Prediction of a Dual Inverter Open Winding IPMSM Drive Considering Machine's Saturation and Losses

In this paper, the effects of core saturation and machine's losses are considered to predict the performance of a conventional single inverter and a dual inverter open winding Interior Permanent Magnet Synchronous Motor (IPMSM) drive in Maximum Torque per Ampere (MTPA) and Field Weakening (FW) regions using Finite Element Analysis (FEA). It is shown how core saturation, which is a direct function of machine's loading, affects the d- and q-axis inductances and saliency ratio, and the losses including core, solid (due to skin and proximity effects), and mechanical losses significantly change in various operating points and may affect the operating capability of the machine in different drive configurations.
Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging

Fact-checking of textual sources needs to effectively extract relevant information from large knowledge bases. In this paper, we extend an existing pipeline approach to better tackle this problem. We propose a neural ranker using a decomposable attention model that dynamically selects sentences to achieve promising improvement in evidence retrieval F1 by 38.80%, with (x65) speedup compared to a TF-IDF method. Moreover, we incorporate lexical tagging methods into our pipeline framework to simplify the tasks and render the model more generalizable. As a result, our framework achieves promising performance on a large-scale fact extraction and verification dataset with speedup.
A Study on Reduction Techniques of a Wideband Common-Mode Voltage Produced by a PWM Inverter

Switching speed of next-generation power devices such as silicon carbide is about ten times faster than those of silicon devices. This aspect may increase the frequency ranges of common-mode (CM) voltage accompanied by switching operations. Meanwhile, operating frequency ranges of noise filters typically limited by parasitic effects and frequency dependencies of magnetic components. Thus, it is difficult to suppress a wideband noise by installing only conventional passive noise filter. This paper studies about the combination of a conventional low-frequency passive filter and a high-frequency passive filter (HF-PF) which consists of a high-frequency magnetic material such as nickel-zinc ferrite. Furthermore, the proposed active CM voltage feedback circuit is applied for improving the performance of the HF-PF. An insertion effect against CM voltage produced by a pulse-width-modulated inverter is evaluated experimentally. Experimental results show that the constructed system effectively reduce CM voltage in the frequency range from 100 kHz to 100 MHz.
Three Strategies to Improve One-to-Many Multilingual Translation

Due to the benefits of model compactness, multilingual translation (including many-to-one, many-to-many and one-to-many) based on a universal encoder-decoder architecture attracts more and more attention. However, previous studies show that one-to-many translation based on this framework cannot perform on par with the individually trained models. In this work, we introduce three strategies to improve one-to-many multilingual translation by balancing the shared and unique features. Within the architecture of one decoder for all target languages, we first exploit the use of unique initial states for different target languages. Then, we employ language-dependent positional embeddings. Finally and especially, we propose to divide the hidden cells of the decoder into shared and language-dependent ones. The extensive experiments demonstrate that our proposed methods can obtain remarkable improvements over the strong baselines. Moreover, our strategies can achieve comparable or even better performance than the individually trained translation models.
Influence of Single or Multiple Faults Short-circuit, Broken Rotor Bar and Eccentricity on the Torque and Rotor Force in Induction Motors

Based on finite element analysis in time domain of a squirrel cage induction motor, this paper studies the influence of the three faults - stator short-circuit, broken rotor bar and static eccentricity, on the mean values and on the harmonics of motor torque and unbalanced rotor force. Results for healthy motor operation and for seven faulty cases, with single, double and triple faults, are evaluated and compared. The influence of the broken bar fault on the motor torque, single or in combination with the short-circuit fault or/and with the eccentricity, is much more important than the influence of the other two faults. Related to the rotor unbalanced force, all faulty cases are characterized by important or very important increase of this force compared with the healthy case.
Winding Design Independent Calculation Method for Short Circuit Currents in Permanent Magnet Synchronous Machines

When designing permanent magnet (PM) synchronous machines the demagnetizing effect of armature short circuit currents on the PMs needs to be considered. In some cases there can be a need to estimate the demagnetizing field from the winding without knowing the winding scheme. To do this, a lumped parameter model of the dynamics of the magnetic field and armature current distribution is proposed. Validation of the model using two different machines shows acceptable agreement. The proposed model is found to be useful for its particular purpose of determining the approximate short circuit current distribution in the armature without knowing the winding design.
Dual-Probe Linearly Configured BOCDA System With Enlarged Modulation Amplitude

We introduce a linearly configured Brillouin optical correlation-domain analysis (LC-BOCDA) sensor that uses a dual-probe wave and control of the correlation order to enlarge the frequency modulation amplitude of the deployed optical signals. The system is able to compensate the signal distortions and noise that impaired previous single-probe LC-BOCDA setups for frequency modulations larger than half the Brillouin frequency. These impairments are found to have originated in the Brillouin loss of the pump and in the beating of pump and probe spectral components at the receiver, both of which are avoided in this new sensor configuration. Applying our technique, we experimentally demonstrate an LC-BOCDA sensor, in which the frequency modulation amplitude can be increased to 9 GHz, leading to nearly twofold enhancement of the number of spatially resolved measurement points.
Hybrid Cloud Service Selection Strategies: A Qualitative Meta-Analysis

Hybrid cloud computing enjoys the benefits from the two worlds of private cloud and public cloud. The combination of the two unique clouds introduces new challenges that must necessarily be addressed before adoption. Much work has been done tackling these challenges from various perspectives that have contributed to a diffusion of the main issues confronting the hybrid cloud. A harmonization of the various research themes was the motivation for this study. The goal was to identify, make a high-level categorization and describe thematic areas that address the unique challenges arising from combining two or more distinctive cloud deployment models. This paper, through a qualitative systematic literature review contributes to the synthesis of themes arising from proposed interventions to hybrid cloud challenges. A total of 34 studies were synthesized resulting in three thematic areas of data security, cost optimization and performance enhancement. The study showed majority of research on hybrid cloud challenges belonged to one or more of these thematic areas that can aid enterprise leaders formulate strategies for hybrid cloud adoption.
Efficient Clue-Based Route Search on Road Networks (Extended Abstract)

With the advances in geo-positioning technologies and location-based services, it is nowadays quite common for road networks to have textual contents on the vertices. Previous work on identifying an optimal route that covers a sequence of query keywords has been studied in recent years. However, in many practical scenarios, an optimal route might not always be desirable. Therefore, in this paper, we investigate the problem of clue-based route search (CRS), which allows a user to provide clues on keywords and spatial relationships. First, we propose a greedy algorithm and a dynamic programming algorithm as baselines. To improve efficiency, we develop a branch-and-bound algorithm that prunes unnecessary vertices in query processing. In order to quickly locate candidate, we propose an AB-tree that stores both the distance and keyword information in tree structure. To further reduce the index size, we construct a PB-tree by utilizing the virtue of 2-hop label index to pinpoint the candidate. Extensive experiments are conducted and verify the superiority of our algorithms and index structures.
Adaptive Adaptive Indexing

In nature, many species became extinct as they could not adapt quickly enough to their environment. They were simply not fit enough to adapt to more and more challenging circumstances. Similar things happen when algorithms are too static to cope with particular challenges of their "environment", be it the workload, the machine, or the user requirements. In this regard, in this paper we explore the well-researched and fascinating family of adaptive indexing algorithms. Classical adaptive indexes solely adapt the indexedness of the data to the workload. However, we will learn that so far we have overlooked a second higher level of adaptivity, namely the one of the indexing algorithm itself. We will coin this second level of adaptivity meta-adaptivity. Based on a careful experimental analysis, we will develop an adaptive index, which realizes meta-adaptivity by (1) generalizing the way reorganization is performed, (2) reacting to the evolving indexedness and varying reorganization effort, and (3) defusing skewed distributions in the input data. As we will demonstrate, this allows us to emulate the characteristics of a large set of specialized adaptive indexing algorithms. In an extensive experimental study we will show that our meta-adaptive index is extremely fit in a variety of environments and outperforms a large amount of specialized adaptive indexes under various query access patterns and key distributions.
Cross-lingual Lexical Sememe Prediction

Sememes are defined as the minimum semantic units of human languages. As important knowledge sources, sememe-based linguistic knowledge bases have been widely used in many NLP tasks. However, most languages still do not have sememe-based linguistic knowledge bases. Thus we present a task of cross-lingual lexical sememe prediction, aiming to automatically predict sememes for words in other languages. We propose a novel framework to model correlations between sememes and multi-lingual words in low-dimensional semantic space for sememe prediction. Experimental results on real-world datasets show that our proposed model achieves consistent and significant improvements as compared to baseline methods in cross-lingual sememe prediction. The codes and data of this paper are available at https://github.com/thunlp/CL-SP.
Effects of Fast-Acting Hearing-Aid Compression on Audibility, Forward Masking and Speech Perception

Dynamic range compression (DRC) is a widely-used compensation strategy in hearing aids. However, the choice of the compression parameters, such as time constants, is still the subject of an ongoing debate. This contribution evaluates the efficacy of fast-acting DRC as a hearing-loss compensation strategy in a range of experimental conditions. First, fast-acting DRC was investigated considering temporal masking of narrowband stimuli. The results of a model-driven evaluation showed that the measures of temporal resolution can be improved with fast-acting compression with a very short release time (10 ms). Second, the effects of compression on speech audibility and noise-induced forward masking were evaluated in a highly-controlled scenario. The application of very short compression time constants was shown to improve HI listeners' consonant recognition performance. Finally, despite the benefits of fast-acting compression apparent in controlled conditions, it may introduce distortion in realistic scenarios, such as a reduction in the signal-to-noise ratio (SNR). A novel signal-to-noise-ratio-aware compensation strategy is discussed, which switches between fast- and slow-acting compression depending on the presence of the target signal and therefore preserves the natural relationship between the target and the background. An objective evaluation of the algorithm is presented and its potential applications are discussed.
Talk the talk and walk the walk! Association between Weekly Exercise Load and
 Knowledge about Recommendations for Fighting Innactivity

Mailing Address: Diogo Thadeu Meira Casa de Saúde São José R. Macedo Sobrinho, 2. Postal Code: 22271-080, Humaitá, Rio de Janeiro, RJ Brazil. E-mail: diogothadeumeira@gmail.com Talk the talk and walk the walk! Association between Weekly Exercise Load and Knowledge about Recommendations for Fighting Innactivity Diogo Thadeu Meira,1 Leonardo Giglio Gonçalves,1 Luiz Henrique Araújo,1 Matheus Gasparini,1 Bernardo Souza Cunha,1 Amanda Monteiro,2 Michele Heringer,1 Christiane Prado,2 Andre Volschan,3 Fabrício Braga da Silva1,2 Casa de Saúde São José,1 Rio de Janeiro, RJ Brazil Laboratório de Performance Humana,2 Rio de Janeiro, RJ Brazil Hospital Pró-Cardíaco,3 Rio de Janeiro, RJ Brazil
Efficient Transfer Learning for Neural Network Language Models

We apply transfer learning techniques to create topically and/or stylistically biased natural language models from small data samples, given generic long short-term memory (LSTM) language models trained on larger data sets. Although LSTM language models are powerful tools with wide-ranging applications, they require enormous amounts of data and time to train. Thus, we build general purpose language models that take advantage of large standing corpora and computational resources proactively, allowing us to build more specialized analytical tools from smaller data sets on demand. We show that it is possible to construct a language model from a small, focused corpus by first training an LSTM language model on a large corpus (e.g., the text from English Wikipedia) and then retraining only the internal transition model parameters on the smaller corpus. We also show that a single general language model can be reused through transfer learning to create many distinct special purpose language models quickly with modest amounts of data.
Rejecting the Null Hypothesis of Apathetic Retweeting of US Politicians and SPLC-defined Hate Groups in the 2016 US Presidential Election

We characterize the Twitter networks of both major presidential candidates, Donald Trump and Hillary Clinton, with various American hate groups defined by the US Southern Poverty Law Center (SPLC). We further examined the Twitter networks for Bernie Sanders, Ted Cruz, and Paul Ryan, for 9 weeks around the 2016 election (4 weeks prior to the election and 4 weeks post-election). By carefully accounting for the observed heterogeneity in the Twitter activity levels across individuals under the null hypothesis of apathetic retweeting that is formalized as a random network model based on the directed, multi-edged, self-looped, configuration model, our data revealed via a generalized Fisher's exact test that there were significantly many Twitter accounts linked to SPLC-defined hate groups belonging to seven ideologies (Anti-Government, Anti-Immigrant, Anti-LGBT, Anti-Muslim, Alt-Right, Neo-Nazi, and White-Nationalist) and also to @realDonaldTrump relative to the accounts of the other four politicians. The exact hypothesis test uses Apache Spark's distributed sort and join algorithms to produce independent samples in a fully scalable way from the null model.
Experimental Benchmark for Magnetic Noise and Vibrations Analysis in Electrical Machines

This article presents an experimental setup dedicated to the analysis of magnetic noise and vibrations in radial flux electrical machines. Both electromagnetic excitation and structural response of the electrical machine are simplified to provide the first benchmark of the phenomenon of electromagnetically -excited noise and vibrations. The test bench properties as well as the magnetic, vibration and acoustic upcoming extensive measurements are shared to the scientific community, providing a clear reference case to compare different modeling and simulation approaches.
Listening to Lions: Animal-Borne Acoustic Sensors Improve Bio-logger Calibration and Behaviour Classification Performance

Efforts to better understand patterns of animal behaviour have often been restricted by several environmental, human and experimental limitations associated with the collection of animal behavioural data. The introduction of new bio-logging technology has offered an alternative means of recording animal behaviour continuously and is being used in an increasing number of studies. Accurately calibrating these bio-loggers, however, still remains a challenge in many cases. Using lions as an example species, we test how audio recordings from animal-borne acoustic sensors can improve calibration and behaviour classification. Through a collaborative effort between computer scientists, engineers and zoologists, custom designed acoustic bio-loggers were fitted to eight lions and recorded audio simultaneously with accelerometer and magnetometer data. Audio recordings were then used as the source of ground truth to train random forest classification models as well as to provide additional predictor variables for behaviour classification. We demonstrated near-perfect classification performance for five lion behaviour classes when all component variables were combined, with an average per-class precision of 98.5%. Using accelerometer features only, the audio-trained classifier predicted behaviours with an average per-class precision of 94.3%. On-animal audio recordings are therefore able to provide a valuable source of ground-truth for calibrating bio-loggers while also offering additional predictive features for increasing the accuracy of behaviour classification. This technological innovation has wide ranging application and provides a useful tool for behavioural ecologists wishing to collect fine scale behavioural data for animal research and conservation.
Influence of Magnetic Saturation and Rotor Eccentricity on Back- EMF of Novel Hybrid-excited Stator Slot Opening Permanent Magnet Machine.

This paper introduces a novel hybrid-excited stator slot opening permanent magnet (PM) machine (HSSPMM). The operation principle of the machine is investigated and the effects of magnetic saturation due to PMs and rotor eccentricity are analyzed by finite element method (FEM). It shows that different from the conventional PM machines, the open-circuit back-EMF in this novel HSSPMM should be zero but may become non-zero if significant magnetic saturation exists, and further, the rotor eccentricity has a detrimental effect on the waveforms, amplitudes and symmetries of 3-phases back-EMFs, as confirmed by FEA and tests.
General Analytical Model for Inductive Power Transfer System with EMF Canceling Coils

This paper provides an analytical model and design criteria of additional windings for reducing electromagnetic field (EMF) generated from inductive power transfer (IPT) systems. In particular, the canceling coils are connected to the main transmission coils with common-mode connection or differential-mode connection. Otherwise, the canceling coils are short-circuited. Parameter variation, which may degrade the system efficiency, occurs in the IPT system due to the unwanted coupling between the main transmission coils and the canceling coils. Therefore, theoretical analysis of the IPT system with the canceling coils is conducted in order to evaluate the effects of the EMF shielding methods on the system parameter variation. The calculation results of the system parameters agree to the measurement results in the prototype of the four-winding transformer with the error of lower than 5%.
Dara: hybrid model checking of distributed systems

Building correct implementations of distributed systems continues to elude us. Solutions consist of abstract modeling languages such as TLA+, PLusCal, which specify models of systems and tools like Coq, and SPIN which verify correctness of models but require considerable amount of effort, or transparent model checkers like MODIST, CMC and CHESS which suffer from state space explosion, rendering them impractical to use as they are too slow. We propose Dara, a novel hybrid technique that combines the speed of abstract model checkers with the correctness and ease-of-use of transparent model checkers. Dara utilizes tests as well as a transparent model checker to generate logs from real executions of the system. The generated logs are analyzed to infer a model of the system which is model-checked by SPIN to verify user-provided invariants. Invariant violations are reported as likely bug traces. These traces are then passed to a replay engine which tries to replay the traces as real executions of the system to remove false positives. We are currently evaluating Dara's efficiency and usability.