Miracle-dz/UniFAR-FTUs huggingface dataset:
A Hybrid System for Chinese Grammatical Error Diagnosis and Correction

This paper introduces the DM_NLP team’s system for NLPTEA 2018 shared task of Chinese Grammatical Error Diagnosis (CGED), which can be used to detect and correct grammatical errors in texts written by Chinese as a Foreign Language (CFL) learners. This task aims at not only detecting four types of grammatical errors including redundant words (R), missing words (M), bad word selection (S) and disordered words (W), but also recommending corrections for errors of M and S types. We proposed a hybrid system including four models for this task with two stages: the detection stage and the correction stage. In the detection stage, we first used a BiLSTM-CRF model to tag potential errors by sequence labeling, along with some handcraft features. Then we designed three Grammatical Error Correction (GEC) models to generate corrections, which could help to tune the detection result. In the correction stage, candidates were generated by the three GEC models and then merged to output the final corrections for M and S types. Our system reached the highest precision in the correction subtask, which was the most challenging part of this shared task, and got top 3 on F1 scores for position detection of errors.
To Attend or not to Attend: A Case Study on Syntactic Structures for Semantic Relatedness

With the recent success of Recurrent Neural Networks (RNNs) in Machine Translation (MT), attention mechanisms have become increasingly popular. The purpose of this paper is two-fold; firstly, we propose a novel attention model on Tree Long Short-Term Memory Networks (Tree-LSTMs), a tree-structured generalization of standard LSTM. Secondly, we study the interaction between attention and syntactic structures, by experimenting with three LSTM variants: bidirectional-LSTMs, Constituency Tree-LSTMs, and Dependency Tree-LSTMs. Our models are evaluated on two semantic relatedness tasks: semantic relatedness scoring for sentence pairs (SemEval 2012, Task 6 and SemEval 2014, Task 1) and paraphrase detection for question pairs (Quora, 2017).
Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems

Background: Despite the recent advancements in the modeling of glycemic dynamics for type 1 diabetes mellitus, automatically considering unannounced meals and exercise without manual user inputs remains challenging. Method: An adaptive model identification technique that incorporates exercise information and estimates of the effects of unannounced meals obtained automatically without user input is proposed in this work. The effects of the unknown consumed carbohydrates are estimated using an individualized unscented Kalman filtering algorithm employing an augmented glucose-insulin dynamic model, and exercise information is acquired from noninvasive physiological measurements. The additional information on meals and exercise is incorporated with personalized estimates of plasma insulin concentration and glucose measurement data in an adaptive model identification algorithm. Results: The efficacy of the proposed personalized and adaptive modeling algorithm is demonstrated using clinical data involving closed-loop experiments of the artificial pancreas system, and the results demonstrate accurate glycemic modeling with the average root-mean-square error (mean absolute error) of 25.50 mg/dL (18.18 mg/dL) for six-step (30 minutes ahead) predictions. Conclusions: The approach presented is able to identify reliable time-varying individualized glucose-insulin models.
Modeling discourse cohesion for discourse parsing via memory network

Identifying long-span dependencies between discourse units is crucial to improve discourse parsing performance. Most existing approaches design sophisticated features or exploit various off-the-shelf tools, but achieve little success. In this paper, we propose a new transition-based discourse parser that makes use of memory networks to take discourse cohesion into account. The automatically captured discourse cohesion benefits discourse parsing, especially for long span scenarios. Experiments on the RST discourse treebank show that our method outperforms traditional featured based methods, and the memory based discourse cohesion can improve the overall parsing performance significantly.
Building a HOG Descriptor Model of Pedestrian Images Using GA and GP Learning

For detecting a pedestrian by using features of images, it is generally needed to establish a reference model that is used to match with input images. The support vector machine (SVM) or AdaBoost Cascade method have been generally used to train the reference pedestrian model in the approaches using the histogram of oriented gradients (HOG) as features of the pedestrian model. In this paper, we propose a new approach to match HOG features of input images with reference model and to learn the structure and parameters of the reference model. The Gaussian scoring method proposed in this paper evaluates the degree of feature coincidence with HOG maps divided with angle of the HOG vector. We also propose two approaches for leaning of the reference model: genetic algorithm (GA) based learning and genetic programming (GP) based learning. The GA and GP are used to search the best parameters of the gene and nonlinear function representing feature map of pedestrian model, respectively. We performed experiments to verify the performance of proposed method in terms of accuracy and processing time with INRIA person dataset.
A Review of 5G Power Amplifier Design at cm-Wave and mm-Wave Frequencies

The 5G wireless revolution presents some dramatic challenges to the design of handsets and communication infrastructures, as 5G targets higher than 10 Gbps download speed using millimeter-wave (mm-Wave) spectrum with multiple-input multiple-output (MIMO) antennas, connecting densely deployed wireless devices for Internet-of-Everything (IoE), and very small latency time for ultrareliable machine type communication, etc. The broadband modulation bandwidth for 5G RF transmitters (i.e., maximum possibly even above 1 GHz) demands high-power efficiency and stringent linearity from its power amplifier (PA). Additionally, the phased-array MIMO antennas with numerous RF front-ends (RFFEs) will require unprecedented high integration level with low cost, making the design of 5G PA one of the most challenging tasks. As the centimeter-wave (cm-Wave) 5G systems will probably be deployed on the market earlier than their mm-Wave counterparts, we will review in this paper the latest development on 15 GHz and 28 GHz 5G cm-Wave PAs extensively, while also covering some key mm-Wave PAs in the literature. Our review will focus on the available options of device technologies, novel circuit and system architectures, and efficiency enhancement techniques at power back-off for 5G PA design.
Osterix functions downstream of anti-Müllerian hormone signaling to regulate Müllerian duct regression

Significance In mammals, each embryo forms both male and female reproductive tract progenitor tissues. Anti-Müllerian hormone (AMH) secreted by fetal testes acts on mesenchyme cells adjacent to Müllerian duct (MD) epithelium, the progenitor tissue of female reproductive tract, to induce MD epithelial regression. While AMH and early AMH signaling components are elucidated, downstream gene networks directing this process are largely unknown. A global nonbiased approach using whole-transcriptome sequencing of fetal MD mesenchymal cells identified 82 factors as potential target genes of AMH including Osterix (Osx). Our findings provide in vivo evidence that Osx is an AMH-induced gene that regulates MD regression. Identification of Osx may provide key insights into gene-regulatory networks underlying MD regression, male sex differentiation, and mesenchyme–epithelial interactions. In mammals, the developing reproductive tract primordium of male and female fetuses consists of the Wolffian duct and the Müllerian duct (MD), two epithelial tube pairs surrounded by mesenchyme. During male development, mesenchyme–epithelia interactions mediate MD regression to prevent its development into a uterus, oviduct, and upper vagina. It is well established that transforming growth factor-β family member anti-Müllerian hormone (AMH) secreted from the fetal testis and its type 1 and 2 receptors expressed in MD mesenchyme regulate MD regression. However, little is known about the molecular network regulating downstream actions of AMH signaling. To identify potential AMH-induced genes and regulatory networks controlling MD regression in a global nonbiased manner, we examined transcriptome differences in MD mesenchyme between males (AMH signaling on) and females (AMH signaling off) by RNA-seq analysis of purified fetal MD mesenchymal cells. This analysis found 82 genes up-regulated in males during MD regression and identified Osterix (Osx)/Sp7, a key transcriptional regulator of osteoblast differentiation and bone formation, as a downstream effector of AMH signaling during MD regression. Osx/OSX was expressed in a male-specific pattern in MD mesenchyme during MD regression. OSX expression was lost in mutant males without AMH signaling. In addition, transgenic mice ectopically expressing human AMH in females induced a male pattern of Osx expression. Together, these results indicate that AMH signaling is necessary and sufficient for Osx expression in the MD mesenchyme. In addition, MD regression was delayed in Osx-null males, identifying Osx as a factor that regulates MD regression.
WiParkFind: Finding Empty Parking Slots Using WiFi

With ever increasing number of vehicles, shortage of parking space is becoming a serious problem. Going to shopping, school, and workplace can be a headache as finding an available parking spot is getting harder causing wasted time and gas. In this paper, we present WiParkFind: a low-cost, non-intrusive, and real- time parking occupancy monitoring system based on WiFi signals. The channel state information (CSI) of received WiFi signals is analyzed by using a machine learning technique to capture distinctive characteristics of CSI data that are strongly correlated with the number of empty parking slots in order to detect whether there is an empty slot, and how many empty slots are available. Compared with contemporary approaches based on magnetic sensors deployed on individual parking slots, WiParkFind utilizes low-cost off- the-shelf WiFi devices, dramatically reducing the cost for purchasing, installing, and maintaining a large number of sensors, and backend server systems. A proof-of-concept system of WiParkFind was developed and deployed in a department parking lot. The results demonstrate that the average classification accuracy of WiParkFind over a week of data collection is 78.2%, and the accuracy is improved to 90.8% with a tolerance of one empty slot.
Determining Multi-Layer Factors That Drive the Carbon Capability of Urban Residents in Response to Climate Change: An Exploratory Qualitative Study in China

The active promotion of carbon abatement to mitigate global climate change and protect the environment and public health has become the international consensus. The carbon capability is a key index for measuring the potential reduction of the carbon emissions by urban residents, and thus encouraging residents to exhibit normal and autonomous low-carbon behavior has become an important issue. In this study, based on grounded theory, data from in-depth interviews were encoded at three levels to identify the multi-layer factors that drive the carbon capability of urban residents, and we constructed a theoretical model for policy intervention. The results showed that individual factors, organizational factors, social factors, and social demographic variables were the main variables that affected the carbon capability, and utility experience perception was the main intermediary variable that affected the carbon capability. There was an obvious gap between utility experience perception and carbon capability. Low carbon selection cost was an internal situational variable that regulated the relationship between these factors, and the policy situation and technical situation were external situational variables. There were two-way effects on the carbon capability and utility experience perception. Thus, we explored these driving factors and the role of the carbon capability model. The results of this study may facilitate targeted policy thinking and the development of an implementation path for government in order to formulate effective guiding policies to enhance the carbon capability of urban residents.
RECap: Run-Escape Capsule for On-demand Managed Service Delivery in the Cloud

Application runtimes are undergoing a fundamental transformation in the cloud, from general-purpose operating systems (OSes) in virtual machines (VMs) to lightweight, minimal OSes in microcontainers. On one hand, such transformation is helping reduce application footprint in the cloud to increase agility, density and to minimize attack surface. On the other hand it makes it challenging to implement system and application management tasks. Inspired from the on-demand Function as a Service (FaaS) model in serverless computing, in RECap we are designing a cloud-native solution to deliver systems and application management tasks through specially-managed Capsule containers. Capsule containers are dynamically attached to the running containers for the duration of their implemented function and are safely removed from application context afterwards. More generally, RECap framework allows us to design disaggregated on-demand managed service delivery for containers in the cloud. In this paper, we describe the motivation and the emerging opportunity for RECap in the cloud. We discuss its core design principles, performance, security and manageability trade-offs. We present current design of RECap for the Kubernetes platform.
Priority-Based Caching Policy at a Hybrid IoT Proxy

We describe a priority based caching policy for a hybrid IoT proxy which is updated using POST methods from IoT nodes. However, if the user request finds that cached record is outdated, the proxy will send a validation GET request to the IoT node. We model the performance of the multi- staged IoT proxy/gateway, assuming two classes of user requests with different arrival rates. We investigate the effects of caching in maintaining the freshness of cache records while maintaining low delay and low energy communications. A network of queues is used to model the hybrid proxy IoT gateway over a single hop 6LowPAN/IEEE 802.15. 4 cluster. The evaluation results show that the proposed model can reduce the probability of outdated records while providing energy saving for both classes of the requests.
Potential Roles of the Retinoblastoma Protein in Regulating Genome Editing

Genome editing is an important tool for modifying genomic DNA through introducing DNA insertion or deletion at specific locations of a genome. Recently CRISPR/Cas9 has been widely employed to improve the efficiency of genome editing. The Cas9 nuclease creates site-specific double strand breaks (DSBs) at targeted loci in the genome. Subsequently, the DSBs are repaired by two pathways: Homologous Recombination (HR) and Non-Homologous End-Joining (NHEJ). HR has been considered as “error-free” because it repairs DSBs by copying DNA sequences from a homologous DNA template, while NHEJ is “error-prone” as there are base deletions or insertions at the breakage site. Recently, RB1, a gene that is commonly mutated in retinoblastoma, has been reported to affect the repair efficiencies of HR and NHEJ. This review focuses on the roles of RB1 in repairing DNA DSBs, which have impacts on the precision and consequences of the genome editing, both at the targeted loci and the overall genome.
Transliteration and Byte Pair Encoding to Improve Tamil to Sinhala Neural Machine Translation

Neural Machine Translation (NMT) is the current state-of-the-art machine translation technique. However, applicability of NMT for language pairs that have high morphological variations is still debatable. Lack of language resources, especially a sufficiently large parallel corpus causes additional issues, which leads to very poor translation performance, when NMT is applied to languages with high morphological variations. In this paper, we present three techniques to improve domain-specific NMT performance of the under-resourced language pair Sinhala and Tamil that have high morphological variations. Out of these three techniques, transliteration is a novel approach to improve domain-specific NMT performance for language pairs such as Sinhala and Tamil that share a common grammatical structure and have moderate lexical similarity. We built the first transliteration system for Sinhala to English and Tamil to English, which provided an accuracy of 99.6%, when tested with the parallel corpus we used for NMT training. The other technique we employed is Byte Pair Encoding (BPE), which is a technique that has been used to achieve open vocabulary translation with a fixed vocabulary of subword symbols. Our experiments show that while the translation based on independent BPE models and pure transliteration perform moderately, integrating transliteration to build a joint BPE model for the aforementioned language pair increases the translation quality by 1.68 BLEU score.
Detoxification- and Immune-Related Transcriptomic Analysis of Gills from Bay Scallops (Argopecten irradians) in Response to Algal Toxin Okadaic Acid

To reveal the molecular mechanisms triggered by okadaic acid (OA)-exposure in the detoxification and immune system of bay scallops, we studied differentially-expressed genes (DEGs) and the transcriptomic profile in bay scallop gill tissue after 48 h exposure to 500 nM of OA using the Illumina HiSeq 4000 deep-sequencing platform. De novo assembly of paired-end reads yielded 55,876 unigenes, of which 3204 and 2620 genes were found to be significantly up- or down-regulated, respectively. Gene ontology classification and enrichment analysis of the DEGs detected in bay scallops exposed to OA revealed four ontologies with particularly high functional enrichment, which were ‘cellular process’ (cellular component), ‘metabolic process’ (biological process), ‘immune system process’ (biological process), and ‘catalytic process’ (molecular function). The DEGs revealed that cyclic AMP-responsive element-binding proteins, acid phosphatase, toll-like receptors, nuclear erythroid 2-related factor, and the NADPH2 quinone reductase-related gene were upregulated. In contrast, the expression of some genes related to glutathione S-transferase 1, C-type lectin, complement C1q tumor necrosis factor-related protein, Superoxide dismutase 2 and fibrinogen C domain-containing protein, decreased. The outcomes of this study will be a valuable resource for the study of gene expression induced by marine toxins, and will help understanding of the molecular mechanisms underlying the scallops’ response to OA exposure.
Data Reduction Techniques for Simulation, Visualization and Data Analysis

Data reduction is increasingly being applied to scientific data for numerical simulations, scientific visualizations and data analyses. It is most often used to lower I/O and storage costs, and sometimes to lower in‐memory data size as well. With this paper, we consider five categories of data reduction techniques based on their information loss: (1) truly lossless, (2) near lossless, (3) lossy, (4) mesh reduction and (5) derived representations. We then survey available techniques in each of these categories, summarize their properties from a practical point of view and discuss relative merits within a category. We believe, in total, this work will enable simulation scientists and visualization/data analysis scientists to decide which data reduction techniques will be most helpful for their needs.
The Role of the Nuclear Factor κB Pathway in the Cellular Response to Low and High Linear Energy Transfer Radiation

Astronauts are exposed to considerable doses of space radiation during long-term space missions. As complete shielding of the highly energetic particles is impracticable, the cellular response to space-relevant radiation qualities has to be understood in order to develop countermeasures and to reduce radiation risk uncertainties. The transcription factor Nuclear Factor κB (NF-κB) plays a fundamental role in the immune response and in the pathogenesis of many diseases. We have previously shown that heavy ions with a linear energy transfer (LET) of 100–300 keV/µm have a nine times higher potential to activate NF-κB compared to low-LET X-rays. Here, chemical inhibitor studies using human embryonic kidney cells (HEK) showed that the DNA damage sensor Ataxia telangiectasia mutated (ATM) and the proteasome were essential for NF-κB activation in response to X-rays and heavy ions. NF-κB’s role in cellular radiation response was determined by stable knock-down of the NF-κB subunit RelA. Transfection of a RelA short-hairpin RNA plasmid resulted in higher sensitivity towards X-rays, but not towards heavy ions. Reverse Transcriptase real-time quantitative PCR (RT-qPCR) showed that after exposure to X-rays and heavy ions, NF-κB predominantly upregulates genes involved in intercellular communication processes. This process is strictly NF-κB dependent as the response is completely absent in RelA knock-down cells. NF-κB’s role in the cellular radiation response depends on the radiation quality.
Efficient Voxel Marking for Hierarchical Volumetric Fusion

When fusing depth images into a 3D volumetric model, a crucial task is to mark macro-cells as empty or as intersected by the noisy surface represented by the depth image. This paper proposes a simple marking algorithm for the GPU implementation of hierarchical volumetric fusion. The method is based on multi-level DDA ray-casting. The GPU implementation is of scattering type, but we also show a solution to avoid atomic writes, which improves performance.
The D.A.V.I.D.E. big-data-powered fine-grain power and performance monitoring support

On the race toward exascale supercomputing systems are facing important challenges which limit the efficiency of the system. Among all, power and energy consumption fueled by the end of Dennard's scaling start to show their impact on limiting supercomputers peak performance and cost effectiveness. In this paper we present and describe a new methodology based on a set of HW and SW extensions for fine-grain monitoring of power and aggregation of them for fast analysis and visualization. We propose a turn-key system which uses MQTT communication layer, NoSQL database, fine grain monitoring and in future AI technology to measure and control power and performance. This methodology is shown as an integrated feature of the D.A. V.I. D.E. supercomputing machine.
Overcoming 5G PRACH Capacity Shortfall by Combining Zadoff-Chu and M-Sequences

5G physical random access channel (PRACH) will use several new preamble formats in order to enable new use cases. These new formats, however, results in a PRACH capacity shortfall. In this paper, we propose a solution based on an extension of the LTE PRACH sequence set. Cell-specific sequences are derived from single-root Zadoff-Chu (ZC) sequences with a zero-correlation zone, and if necessary, different m-sequence covers instead of multiple roots as in LTE. While this construction is shown to preserve a similar low-correlation zone as with the LTE design, it does not consume the other ZC roots which can then be used to support more cells. The proposed PRACH design is evaluated by 5G link-level simulations to verify the expected good detection performance and low PAPR.