Miracle-dz/UniFAR-FTUs huggingface dataset:
A Neural Transition-based Model for Nested Mention Recognition

It is common that entity mentions can contain other mentions recursively. This paper introduces a scalable transition-based method to model the nested structure of mentions. We first map a sentence with nested mentions to a designated forest where each mention corresponds to a constituent of the forest. Our shift-reduce based system then learns to construct the forest structure in a bottom-up manner through an action sequence whose maximal length is guaranteed to be three times of the sentence length. Based on Stack-LSTM which is employed to efficiently and effectively represent the states of the system in a continuous space, our system is further incorporated with a character-based component to capture letter-level patterns. Our model gets the state-of-the-art performances in ACE datasets, showing its effectiveness in detecting nested mentions.
Acoustic modeling in Automatic Speech Recognition - A Survey

Automatic speech recognition (ASR) systems are in evolutionary process. These systems use models which work at two stages, namely the parameterization of the input noise signal followed by training and testing of the features using any classification technique. Researchers have proposed a variety of acoustic models to accomplish this complex task. In this paper, we represent overview of Hidden Markov Model (HMM), Deep Neural Networks (DNNs) and Convolutional Neural Network (CNN) based models, which are the backbone of ASR systems. CNN is advanced method which normalizes speaker variance by using local filters in convolution layer. CNN architecture has advance features like weight sharing, local filters and pooling etc. This paper will also summarize the details of improvements in CNN s using different convolution techniques.
Physical Activity Predicts Population-Level Age-Related Differences in Frontal White Matter

Physical activity has positive effects on brain health and cognitive function throughout the lifespan. Thus far, few studies have examined the effects of physical activity on white matter (WM) microstructure and psychomotor speed within the same, population-based sample (critical if conclusions are to extend to the wider population). Here, using diffusion tensor imaging and a simple reaction time task within a relatively large population-derived sample (N = 399; 18–87 years) from the Cambridge Centre for Ageing and Neuroscience (Cam-CAN), we demonstrate that physical activity mediates the effect of age on white matter integrity, measured with fractional anisotropy. Higher self-reported daily physical activity was associated with greater preservation of WM in several frontal tracts, including the genu of corpus callosum, uncinate fasciculus, external capsule and anterior limb of the internal capsule. We also show that the age-related slowing is mediated by WM integrity in the genu. Our findings contribute to a growing body of work suggesting that a physically active lifestyle may protect against age-related structural disconnection and slowing.
On Return Oriented Programming Threats in Android Runtime

Android has taken a large share of operating systems for smart devices including smartphones, and has been an attractive target to the attackers. The arms race between attackers and defenders typically occurs on two front lines — the latest attacking technology and the latest updates to the operating system (including defense mechanisms deployed). In terms of attacking technology, Return-Oriented Programming (ROP) is one of the most sophisticated attack methods on Android devices. In terms of the operating system updates, Android Runtime (ART) was the latest and biggest change to the Android family. In this paper, we investigate the extent to which Android Runtime (ART) makes Return-Oriented Programming (ROP) attacks easier or more difficulty. In particular, we show that by updating system libraries and adopting Ahead-of-Time compiling instead of Justin- Time compiling in the ART architecture, a larger number and more diverse gadgets are disclosed to ROP attackers, which serve as direct ingredients to ROP attacks. We show that between three and six times more gadgets are found on the ART adopted versions of Android due to the new ART runtime. Moreover, in constrained situations where an attacker requires specific instructions and target registers, Android running ART provides up to 30% more conditional coverage than pre-ART Android does. We additionally demonstrate a sample ROP attack on post- ART Android that would not have been possible on pre-ART Android.
Structured Multi-Label Biomedical Text Tagging via Attentive Neural Tree Decoding

We propose a model for tagging unstructured texts with an arbitrary number of terms drawn from a tree-structured vocabulary (i.e., an ontology). We treat this as a special case of sequence-to-sequence learning in which the decoder begins at the root node of an ontological tree and recursively elects to expand child nodes as a function of the input text, the current node, and the latent decoder state. We demonstrate that this method yields state-of-the-art results on the important task of assigning MeSH terms to biomedical abstracts.
Evaluation of Skin Deformation Tactile Feedback for Teleoperated Surgical Tasks

During interaction with objects using a tool, we experience force and tactile feedback. One form of tactile feedback is local fingerpad skin deformation. In this paper, we provide haptic feedback to users of a teleoperation system through a skin deformation tactile feedback device. The device is able to provide tangential and normal skin deformation in a coupled manner, and is designed so that users can grasp it with a precision grip using multiple fingerpads. By applying skin deformation feedback on multiple fingerpads, the device is able to provide multi-degree-of-freedom interaction force direction and magnitude information to the user. To evaluate the effectiveness of this approach for the performance of teleoperated manipulation tasks, we performed a study in which 20 participants used a teleoperation system to perform one of two manipulation tasks (peg transfer and tube connection) using force feedback, skin deformation feedback, and the combination of both feedback. Results showed that participants are able to use all feedback to improve task performance compared to the case without haptic feedback, although the degree of improvement depended on the nature of the task. The feedback also improved situation awareness, felt consistent with prior experience, and did not affect concentration on the task, as reported by participants.
MasterEngineer: A Game-based technique in Power Electronics and Drives Teaching

Power electronics and drives have increased their presence at industry both in renewable energies, power systems and electric traction. For this reason, future electrical engineers need to acquire advanced competences in this field. Although chalk-and-board and slides can be used at degree and master level, the inclusion of game-based activities improve the motivation and life-long learning of students. This paper presents a newly design game to deeply understand the different arrangements and performance of power converter and electric machines. The game-based approach favors the acquisition of cross competences and promotes technical discussion and exposition abilities.
Analysis of Forming Limits in Sheet Metal Forming with Pattern Recognition Methods. Part 2: Unsupervised Methodology and Application

The forming limit curve (FLC) is used in finite element analysis (FEA) for the modeling of onset of sheet metal instability during forming. The FLC is usually evaluated by achieving forming measurements with optical measurement system during Nakajima tests. Current evaluation methods such as the standard method according to DIN EN ISO 12004-2 and time-dependent methods limit the evaluation range to a fraction of the available information and show weaknesses in the context of brittle materials that do not have a pronounced constriction phase. In order to meet these challenges, a supervised pattern recognition method was proposed, whose results depend on the quality of the expert annotations. In order to alleviate this dependence on experts, this study proposes an unsupervised classification approach that does not require expert annotations and allows a probabilistic evaluation of the onset of localized necking. For this purpose, the results of the Nakajima tests are examined with an optical measuring system and evaluated using an unsupervised classification method. In order to assess the quality of the results, a comparison is made with the time-dependent method proposed by Volk and Hora, as well as expert annotations, while validated with metallographic investigations. Two evaluation methods are presented, the deterministic FLC, which provides a lower and upper limit for the onset of necking, and a probabilistic FLC, which allows definition of failure quantiles. Both methods provide a necking range that shows good correlation with the expert opinion as well as the results of the time-dependent method and metallographic examinations.
Generalized Predictive Control for Industrial Processes Based on Neuron Adaptive Splitting and Merging RBF Neural Network

An adaptive generalized predictive control (GPC) scheme for an industrial process is designed based on a neuron adaptive splitting and merging radial basis function neural network (NASM-RBFNN) in this paper. The NASM-RBFNN is developed to identify the dynamic behaviors of the industrial process. In order to provide an accurate prediction model for the GPC, a neuron adaptive splitting and merging strategy and a weighted parameters adaptive correction approach are proposed. The neuron adaptive splitting and merging strategy can automatically add or delete the hidden neurons on-line, and the weighted parameters adaptive correction approach can update the weights based on the error of the neural network. The proposed approaches can enable the NASM-RBFNN to be adapted to the time-varying production condition and stochastic disturbance. The stability analysis and convergence of neuron adaptive splitting and merging strategy and weighted parameters adaptive correction approach are given. Finally, the NASM-RBFNN-based GPC (NASM-RBFNN-GPC) is applied to control the iron removal process in the largest zinc hydrometallurgy plant in China. The industrial experiments demonstrate that the NASM-RBFNN-GPC has a satisfactory tracking and control performance.
A Novel Codeword Selection Scheme for MIMO-MAC Lower-Bound Maximization

Aiming at the limitations of the existing Limited Feedback Interference Alignment algorithms, this paper proposes a direct codeword selection scheme that maximizes the lower-bound of the user rate and reduces the sum rate loss by integrating the Bit Allocation algorithm. The target signal is decoded using the maximum signal to interference plus noise ratio (MAX-SINR) algorithm. Moreover, low complexity and global searching mechanisms are deployed to select the optimized codewords from the generated sets of codewords that approach the ideal precoder. Simulation results show that the proposed algorithm effectively improves the rate lower-bound of the system user as compared with the existing state-of-the-art algorithms.
How should we interpret estimates of individual repeatability?

Individual repeatability (R), defined as the proportion of observed variance attributable to among‐individual differences, is a widely used summary statistic in evolutionarily motivated studies of morphology, life history, physiology and, especially, behaviour. Although statistical methods to estimate R are well known and widely available, there is a growing tendency for researchers to interpret R in ways that are subtly, but importantly, different. Some view R as a property of a dataset and a statistic to be interpreted agnostically with respect to mechanism. Others wish to isolate the contributions of ‘intrinsic’ and/or ‘permanent’ individual differences, and draw a distinction between true (intrinsic) and pseudo‐repeatability arising from uncontrolled extrinsic effects. This latter view proposes a narrower, more mechanistic interpretation, than the traditional concept of repeatability, but perhaps one that allows stronger evolutionary inference as a consequence (provided analytical pitfalls are successfully avoided). Neither perspective is incorrect, but if we are to avoid confusion and fruitless debate, there is a need for researchers to recognise this dichotomy, and to ensure clarity in relation to how, and why, a particular estimate of R is appropriate in any case.
A Computational Modeling Approach Predicts Interaction of the Antifungal Protein AFP from Aspergillus giganteus with Fungal Membranes via Its γ-Core Motif

Fungal pathogens pose a serious danger to human welfare since they kill more people per year than malaria or tuberculosis and are responsible for crop losses worldwide. The treatment of fungal infections is becoming more complicated as fungi develop resistances against commonly used fungicides. Therefore, discovery and development of novel antifungal agents are of utmost importance. ABSTRACT Fungal pathogens kill more people per year globally than malaria or tuberculosis and threaten international food security through crop destruction. New sophisticated strategies to inhibit fungal growth are thus urgently needed. Among the potential candidate molecules that strongly inhibit fungal spore germination are small cationic, cysteine-stabilized proteins of the AFP family secreted by a group of filamentous Ascomycetes. Its founding member, AFP from Aspergillus giganteus, is of particular interest since it selectively inhibits the growth of filamentous fungi without affecting the viability of mammalian, plant, or bacterial cells. AFPs are also characterized by their high efficacy and stability. Thus, AFP can serve as a lead compound for the development of novel antifungals. Notably, all members of the AFP family comprise a γ-core motif which is conserved in all antimicrobial proteins from pro- and eukaryotes and known to interfere with the integrity of cytoplasmic plasma membranes. In this study, we used classical molecular dynamics simulations combined with wet laboratory experiments and nuclear magnetic resonance (NMR) spectroscopy to characterize the structure and dynamical behavior of AFP isomers in solution and their interaction with fungal model membranes. We demonstrate that the γ-core motif of structurally conserved AFP is the key for its membrane interaction, thus verifying for the first time that the conserved γ-core motif of antimicrobial proteins is directly involved in protein-membrane interactions. Furthermore, molecular dynamic simulations suggested that AFP does not destroy the fungal membrane by pore formation but covers its surface in a well-defined manner, using a multistep mechanism to destroy the membranes integrity. IMPORTANCE Fungal pathogens pose a serious danger to human welfare since they kill more people per year than malaria or tuberculosis and are responsible for crop losses worldwide. The treatment of fungal infections is becoming more complicated as fungi develop resistances against commonly used fungicides. Therefore, discovery and development of novel antifungal agents are of utmost importance.
Bug Localization Approach Using Source Code Structure with Different Structure Fields

In bug localization approach, the information of the bug was used by developer to modify the source code where the errors occurred. To fix the source code that need to be correct is a problem for developer. Numerous automatic bug localization approaches by using information retrieval have been proposed. In this paper, we propose bug localization approach by combining structure of source code with different structure fields, similarity of bugs and stack-trace. It recommends relevance bug files to fix according to their highest scores. We additionally propose bug localization to consider the source code with three difference structure fields. In recent approaches source code files are consider as a single units. It may cause many noises when the source code file is large. We implement our approach on four open source projects (AspectJ, Eclipse and SWT). We then compute our approach in term of top-N rank, mean average precision (MAP) and mean reciprocal rank (MRR) evaluation metrics. The results show that the proposed system achieves significant results.
High-Speed Maximum Power Point Tracking Module for PV Systems

Maximum power point tracking (MPPT) is a basic and indispensable requirement for photovoltaic (PV) systems under normal irradiance and under partial shading conditions (PSCs). Although the simple perturb and observe algorithm is quite effective under normal conditions, it fails to recognize the global maximum operating point (GMOP) under the PSC. This paper explores the fast determination of GMOP under PSCs using a proposed high-speed MPPT module, which operates in conjunction with a boost converter. With this high-speed MPPT module, the tracking time of the MPPT controller is considerably reduced. The tracking accuracy and efficiency are significantly better than other techniques in the literature. The concept is first implemented on a PV system simulation model and the results are further validated with a prototype implementation of a 300 W PV-fed boost converter operated under various PSCs. The enthusiastic results were finally verified in an installation of a 2.5 kW PV system. The results demonstrate that the proposed high-speed MPPT controller outperforms both duty sweep and particle swarm optimization based MPPTs.
Improved Matching Technique in Digital Image Region Duplication

Region duplication is the common type of image tampering which copies a part of an image and moved in that same image. Many techniques have been proposed to detect such tampering but it is difficult to find the true matched region in an image. Over many existing methods SIFT considered to be efficient one in matching duplicated region under different kinds of transformations, but fails when the image has reflection in it. A method with capability of detecting duplication under reflection is used in this work to make a detection system even more accurate and efficient.
Distributed transactional reads: the strong, the quick, the fresh & the impossible

This paper studies the costs and trade-offs of providing transactional consistent reads in a distributed storage system. We identify the following dimensions: read consistency, read delay (latency), and data freshness. We show that there is a three-way trade-off between them, which can be summarised as follows: (i) it is not possible to ensure at the same time order-preserving (e.g., causally-consistent) or atomic reads, Minimal Delay, and maximal freshness; thus, reading data that is the most fresh without delay is possible only in a weakly-isolated mode; (ii) to ensure atomic or order-preserving reads at Minimal Delay imposes to read data from the past (not fresh); (iii) however, order-preserving minimal-delay reads can be fresher than atomic; (iv) reading atomic or order-preserving data at maximal freshness may block reads or writes indefinitely. Our impossibility results hold independently of other features of the database, such as update semantics (totally ordered or not) or data model (structured or unstructured). Guided by these results, we modify an existing protocol to ensure minimal-delay reads (at the cost of freshness) under atomic-visibility and causally-consistent semantics. Our experimental evaluation supports the theoretical results.
Treatment With a Soluble Bone Morphogenetic Protein Type 1A Receptor (BMPR1A) Fusion Protein Increases Bone Mass and Bone Formation in Mice Subjected to Hindlimb Unloading

Previous work has shown that the soluble murine BMPR1A–fusion protein (mBMPR1A‐mFc) binds to BMP2 and BMP4 with high affinity, preventing downstream signaling. Further, treatment of intact and ovariectomized mice with mBMPR1A‐mFc leads to increased bone mass, and improved bone microarchitecture and strength, via increased bone formation and reduced resorption. In this study, we tested the effects of mBMPR1A‐mFc on disuse‐induced bone loss caused by 21 days of hindlimb unloading (HLU) via tail suspension versus cage controls (CONs). Adult female C57BL/6J mice (12 weeks old) were assigned to one of four groups (n = 10 each): CON‐VEH; CON‐mBMPR1A‐mFc; HLU‐VEH; and HLU‐mBMPR1A‐mFc. Mice were injected subcutaneously with VEH or mBMPR1A‐mFc (4.5 mg/kg, 2×/week). Leg BMD declined in the HLU‐VEH group (–5.3% ± 1.3%), whereas it was unchanged in HLU‐mBMPR1A‐mFc (–0.3% ± 0.9%, p < 0.05 versus HLU‐VEH). Leg BMD increased significantly more in CON‐mBMPR1A‐mFc than CON‐VEH (10.2% ± 0.6% versus 4.4% ± 0.8%). In the femur, trabecular, and cortical bone microarchitecture was worse in the HLU‐VEH compared to CON‐VEH mice, whereas mBMPR1A‐mFc treatment for 3 weeks led to greater Tb.BV/TV, Tb.Th, and midshaft Ct.Th in both the HLU and CON groups compared to comparable VEH‐treated counterparts (p < 0.05). HLU‐mBMPR1A‐mFc mice also had 21% greater failure load (p < 0.05) compared to their VEH‐treated counterparts. Dynamic histomorphometry indicated that treatment with mBMPR1A‐mFc led to significantly greater mineralizing surface and mineral apposition rate, resulting in a 3.5‐fold and fivefold higher bone formation rate in the mBMPR1A‐mFc‐treated CON and HLU animals versus VEH groups, respectively. mBMPR1A‐mFc‐treated mice had a similar osteoblast surface but significantly lower osteoclast surface than VEH‐treated animals in both the CON and HLU groups. Altogether, these findings suggest that treatment with the soluble BMPR1A fusion protein may be useful for maintenance of skeletal integrity in the setting of disuse‐induced bone loss. © 2017 The Authors JBMR Plus published by Wiley Periodicals, Inc. on behalf of American Society for Bone and Mineral Research.
The Effect of Gender on Investors’ Judgments and Decision-Making

We examine whether an unsophisticated investor’s own gender interacts with gender of a sell-side equity analyst to affect the investor’s judgment. Prior research shows two potential sources of gender-based discrimination that affect female investors. First, female investors’ advisors offer less risky hence lower return portfolios to female investors than to male investors with similar risk preferences as female investors are perceived as more risk adverse. Second, female equity analysts are subject to greater barriers to enter and advance in investment firms that act as if they believe clients prefer male investment advisors in a male stereotypical occupation. Using two experiments, we use the judge-advisor framework to predict and find that investor’s gender and analyst’s gender jointly influence investor’s judgment. Specifically, female-female analyst-investor pair generates the strongest reaction to analyst’s advice compared to any other analyst-investor pair, everything else equal. Further, we find that efforts to highlight equal gender performance activates gender stereotypes that reduce female investors’ receptivity to female analysts’ advice. By linking the two previously different sources of discrimination we show that they reinforce each other and find that attempts to “level the playing field” by emphasizing gender performance parity may have unexpected results.