Miracle-dz/UniFAR-FTUs huggingface dataset:
MemoReader: Large-Scale Reading Comprehension through Neural Memory Controller

Machine reading comprehension helps machines learn to utilize most of the human knowledge written in the form of text. Existing approaches made a significant progress comparable to human-level performance, but they are still limited in understanding, up to a few paragraphs, failing to properly comprehend lengthy document. In this paper, we propose a novel deep neural network architecture to handle a long-range dependency in RC tasks. In detail, our method has two novel aspects: (1) an advanced memory-augmented architecture and (2) an expanded gated recurrent unit with dense connections that mitigate potential information distortion occurring in the memory. Our proposed architecture is widely applicable to other models. We have performed extensive experiments with well-known benchmark datasets such as TriviaQA, QUASAR-T, and SQuAD. The experimental results demonstrate that the proposed method outperforms existing methods, especially for lengthy documents.
Parallelizing Multiple Pipelines of One Query in a Main Memory Database Cluster

To fully use the advanced resources of a main memory database cluster, we take independent parallelism into account to parallelize multiple pipelines of one query. However, scheduling resources to multiple pipelines is an intractable problem. Traditional static approaches to this problem may lead to a serious waste of resources and suboptimal execution order of pipelines, because it is hard to predict the actual data distribution and fluctuating workloads at compile time. In response, we propose a dynamic scheduling algorithm, List with Filling and Preemption (LFPS), based on two techniques. (1) Adaptive filling improves resource utilization by issuing more extra pipelines to adaptively fill idle resource "holes" during execution. (2) Cost-based preemption strictly guarantees scheduling the pipelines on a critical path first at run time. We implement LFPS in our prototype database system. Under the workloads of TPC-H, experiments show our work improves the finish time of parallelizable pipelines from one query up to 2.3X than a static approach and 1.7X than a serialized execution.
De-identifying Free Text of Japanese Dummy Electronic Health Records

A new law was established in Japan to promote utilization of EHRs for research and developments, while de-identification is required to use EHRs. However, studies of automatic de-identification in the healthcare domain is not active for Japanese language, no de-identification tool available in practical performance for Japanese medical domains, as far as we know. Previous work shows that rule-based methods are still effective, while deep learning methods are reported to be better recently. In order to implement and evaluate a de-identification tool in a practical level, we implemented three methods, rule-based, CRF, and LSTM. We prepared three datasets of pseudo EHRs with de-identification tags manually annotated. These datasets are derived from shared task data to compare with previous work, and our new data to increase training data. Our result shows that our LSTM-based method is better and robust, which leads to our future work that plans to apply our system to actual de-identification tasks in hospitals.
Device Modeling of MgO-Barrier Tunneling Magnetoresistors for Hybrid Spintronic-CMOS

Spintronic sensors, which are based on the tunneling-magnetoresistive (TMR) effect, have been utilized in detecting low-magnetic fields. However, still no computer-based model of these devices is available to integrated circuit designer to implement them in a hybrid spintronic-CMOS system. We developed a finite-element method (FEM)-based model of a MgO-barrier TMR device in COMSOL Multiphysics. The parameters of this model were extracted from the state-of-the-art fabrication and experimental data. Results were compared with respect to the model geometry and the used material. The proposed TMR sensor model offers a linear response with a high TMR ratio of 233% at 10-mV power supply. The model was exported to Cadence Spectre to create a compact model using Verilog-A language. The developed sensor model was simulated with its analog front end in the same environment. This model provided a reliable benchmark for modeling of the future hybrid spintronic-CMOS developments.
MemFix: static analysis-based repair of memory deallocation errors for C

We present MemFix, an automated technique for fixing memory deallocation errors in C programs. MemFix aims to fix memory-leak, double-free, and use-after-free errors, which occur when developers fail to properly deallocate memory objects. MemFix attempts to fix these errors by finding a set of free-statements that correctly deallocate all allocated objects without causing double-frees and use-after-frees. The key insight behind MemFix is that finding such a set of deallocation statements corresponds to solving an exact cover problem derived from a variant of typestate static analysis. We formally present the technique and experimentally show that MemFix is able to fix real errors found in open-source programs. Because MemFix is based on a sound static analysis, the generated patches guarantee to fix the original errors without introducing new errors.
Very Delayed Remote Ischemic Post-conditioning Induces Sustained Neurological Recovery by Mechanisms Involving Enhanced Angioneurogenesis and Peripheral Immunosuppression Reversal

Ischemic conditioning is defined as a transient and subcritical period of ischemia integrated in an experimental paradigm that involves a stimulus of injurious ischemia, activating endogenous tissue repair mechanisms that lead to cellular protection under pathological conditions like stroke. Whereas ischemic pre-conditioning is irrelevant for stroke treatment, ischemic post-conditioning, and especially non-invasive remote ischemic post-conditioning (rPostC) is an innovative and potential strategy for stroke treatment. Although rPostC has been shown to induce neuroprotection in stroke models before, resulting in some clinical trials on the way, fundamental questions with regard to its therapeutic time frame and its underlying mechanisms remain elusive. Hence, we herein used a model of non-invasive rPostC of hind limbs after cerebral ischemia in male C57BL6 mice, studying the optimal timing for the application of rPostC and its underlying mechanisms for up to 3 months. Mice undergoing rPostC underwent three different paradigms, starting with the first cycle of rPostC 12 h, 24 h, or 5 days after stroke induction, which is a very delayed time point of rPostC that has not been studied elsewhere. rPostC as applied within 24 h post-stroke induces reduction of infarct volume on day three. On the contrary, very delayed rPostC does not yield reduction of infarct volume on day seven when first applied on day five, albeit long-term brain injury is significantly reduced. Likewise, very delayed rPostC yields sustained neurological recovery, whereas early rPostC (i.e., <24 h) results in transient neuroprotection only. The latter is mediated via heat shock protein 70 that is a well-known signaling protein involved in the pathophysiological cellular cascade of cerebral ischemia, leading to decreased proteasomal activity and decreased post-stroke inflammation. Very delayed rPostC on day five, however, induces a pleiotropic effect, among which a stimulation of angioneurogenesis, a modulation of the ischemic extracellular milieu, and a reversal of the stroke-induced immunosuppression occur. As such, very delayed rPostC appears to be an attractive tool for future adjuvant stroke treatment that deserves further preclinical attention before large clinical trials are in order, which so far have predominantly focused on early rPostC only.
Hybrid and Generalized Variable Step Size Adaptive Filtering

In this paper, we propose novel way to generalize the concept of adaptive filtering techniques with hybrid solution and generalization of variable step size normalized least-me an-square adaptive filter (VSS NLMS). Simulations experiments in the context estimation of sparse desired impulse response have shown that introduced filters outperform the classical reference solution.
SHC: Distributed Query Processing for Non-Relational Data Store

We introduce a simple data model to process non-relational data for relational operations, and SHC (Apache Spark - Apache HBase Connector), an implementation of this model in the cluster computing framework, Spark. SHC leverages optimization techniques of relational data processing over the distributed and column-oriented key-value store (i.e., HBase). Compared to existing systems, SHC makes two major contributions. At first, SHC offers a much tighter integration between optimizations of relational data processing and non-relational data store, through a plug-in implementation that integrates with Spark SQL, a distributed in-memory computing engine for relational data. The design makes the system maintenance relatively easy, and enables users to perform complex data analytics on top of key-value store. Second, SHC leverages the Spark SQL Catalyst engine for high performance query optimizations and processing, e.g., data partitions pruning, columns pruning, predicates pushdown and data locality. SHC has been deployed and used in multiple production environments with hundreds of nodes, and provides OLAP query processing on petabytes of data efficiently.
A Study of Reddit-User's Response to Rape

The growth of social media has created an open web where people freely share their opinion and even discuss sensitive subjects in online forums. Forums such as Reddit help support seekers by serving as a portal for open discussions for various stigmatized subjects such as rape. This paper investigates the potential roles of online forums and if such forums provide intended resources to the people who seek support. Specifically, the open nature of forums allows us to study how online users respond to seeker's queries or needs; through their response, we attempt to assess the range of topics covered by responders in regards to the issues, concerns and, obstacles faced by the victims of rape and sexual abuse, using rape-related posts from Reddit. We employ natural language processing techniques to extract topics of responses, examine how diverse these topics are to answer research questions such as whether responses are limited to emotional support; if not, what other topics are; what the diversity of topics manifests; and how online response differs from traditional response found in a physical world.
Consistent Snapshot Algorithms for In-Memory Database Systems: Experiments and Analysis

In-memory databases (IMDBs) are gaining increasing popularity in big data applications, where clients commit updates intensively. Consistent snapshot is a key step in backup and recovery of IMDBs, thus an important factor for system performance of IMDBs. Formally, the in-memory consistent snapshot problem refers to taking an in-memory consistent time-in-point snapshot with the constraints that 1) clients can read the latest data items, and 2) any data item in the snapshot should not be overwritten. Various snapshot algorithms have been proposed in the academia to trade off throughput and latency, yet industrial IMDBs such as Redis still stick to the simple fork algorithm. As an understanding of this phenomenon, we conduct comprehensive performance evaluations on mainstream snapshot algorithms. Surprisingly, we observe that the simple fork algorithm indeed outperforms the state-of-the-arts in update-intensive workload scenarios. On this basis, we identify the drawbacks of existing research and propose two lightweight improvements. Extensive evaluations on synthetic data and Redis show that our lightweight improvements yield better performance than fork, the current industrial standard, and the representative snapshot algorithms from the academia. Finally, we have opensourced the implementation of all the above snapshot algorithms to facilitate practitioners to benchmark the performance of each algorithm and select proper methods for different application scenarios.
Estimation of the Battery Health by Monitoring the Electric Motor Drive Performance in a Building Application

This paper proposes a novel method for the online estimation of the battery (BT) health fade by monitoring the electric motor drive performance. The proposed technique is based on the evaluation of the energy capacity fade and the internal resistance increase of the BT by using the measurements of the instantaneous speed and torque of the motor drive and the BT voltage. This is implemented by utilizing a model based neural-logic system that online evaluates the behavior of the motor drive and the BT performance. Specifically, the estimated current energy capacity and internal resistance of the BT are compared with the initial operation data and through an evaluation algorithm, it is online estimated the BT health condition. In this paper, the developed technique is focused on Li-ion BTs; however, it can also be used with other BT types by properly changing the implementation scheme. The proposed system is intended to be utilized by electric motion applications in residential buildings such as elevators, household tools, etc. that are either powered by BTs or the BTs are used for energy saving. The effectiveness and the practicality of the suggested BT health estimation technique have been validated by several simulation results that are extensively discussed throughout the paper.
Unified Overview of Matrix-Monotonic Optimization for MIMO Transceivers

Matrix-monotonic optimization exploits the monotonic nature of positive semi-definite matrices to derive optimal diagonalizable structures for the matrix variables of matrix-variate optimization problems. Based on the optimal structures derived, the associated optimization problems can be substantially simplified and underlying physical insights can also be revealed. In this paper, a comprehensive overview of the applications of matrix-monotonic optimization to multiple-input multiple-output (MIMO) transceiver design is provided under various power constraints, and matrix-monotonic optimization is investigated for various types of channel state information (CSI) condition. Specifically, three cases are investigated: 1)~both the transmitter and receiver have imperfect CSI; 2)~ perfect CSI is available at the receiver but the transmitter has no CSI; 3)~perfect CSI is available at the receiver but the channel estimation error at the transmitter is norm-bounded. In all three cases, the matrix-monotonic optimization framework can be used for deriving the optimal structures of the optimal matrix variables. Furthermore, based on the proposed framework, three specific applications are given under three types of power constraints. The first is transceiver optimization for the multi-user MIMO uplink, the second is signal compression in distributed sensor networks, and the third is robust transceiver optimization of multi-hop amplify-and-forward cooperative networks.
Hamming-Distance-Based Binary Representation of Numbers

Numbers are represented as binary arrays in computer storage. We propose a length-n binary representation of numbers from 0 to <tex>$2^{n}-1$</tex> based on Hamming distances such that for any <tex>$i\in\{0,\ \ldots,\ 2^{n}-1\}$</tex>, if a constant number of bits (out of <tex>$n)$</tex> are flipped, the normalized <tex>$L_{1}$</tex> distance from the distorted number <tex>$i_{\text{error}}$</tex> to <tex>$i$</tex> is vanishing when <tex>$n$</tex> tends to infinity. More precisely, <tex>$\max_{i}\frac{\vert i_{\text{error}}-i\vert}{2^{n}}=o(\frac{1}{\sqrt{n}})$</tex>. A pair of encoder and decoder with <tex>$O(n)$</tex> time complexity is presented to establish the Hamming-distance-based bijection between <tex>$\{0,1\}^{n}$</tex> and <tex>$\{0,1,\ldots,2^{n}-1\}$</tex>.
Enable Co-Simulation for Industrial Automation by an FMU Exporter for IEC 61499 Models

Different causes have contributed to the shift of paradigm in the automation field from centralized to modular and distributable. Software and hardware of Programmable Logic Controllers (PLCs) have evolved to be synchronized with the demands nowadays and new standards are trying to follow this trend. One of them is the IEC 61499 standard, which is intended for modelling distributed industrial solutions with a vendor-independent format. This shifting is also bringing new challenges and this paper focuses on the virtual commissioning of plants, which can become a harder task when the system is distributed and involves different interconnected modules. A major problem when doing a virtual commissioning is the coupling between the different physical systems and controlling tools since many vendors still offer closed integrated solutions. Functional Mockup Interface (FMI) is a standard for a cosimulation interface which intends to fill the gaps between different modelling tools, by packaging the models into Functional Mockup Unit (FMU) and allowing a co-simulation master algorithm to have access to the inside model through the proposed interface. This paper presents a mapping between IEC 61499 models and the FMI standard and an implementation of a tool that can export IEC 61499 models into FMUs, which would allow the co-simulation of physical plants and the PLCs software that controls it. An experiment is shown where a simple Proportional-Integral-Derivative (PID) controller of a tank system is exported as an FMU and co-simulated together with a tank system modelled using OpenModelica.
Three-Phase to High-Frequency Single-Phase Matrix Converter : A Frequency Control Suitable for Soft Switching

Insulated AC/DC converters using matrix converters (MCs) have higher efficiency, smaller size, and longer lifespan compared with conventional ones. For downsizing of high-frequency (HF) transformers in these converters, developments of HF output MC control methods are active. It is expected to improve efficiency by applying soft switching techniques for these converters. However, to achieve soft switching, phase difference between an output voltage and a current is required. As a result, output power factor decreases. In this paper, a relationship between soft switching conditions and output power factor is revealed. Considering this relationship, the authors propose a new MC control method to achieve soft switching and maximum output power factor. Additionally, this method enables to suppress the distortion of input currents and reduce ripple of the output current. Also, the MC can control the output current and instantaneous reactive power. The authors confirm that experimental results show validity of the method.
Semi-Automatic Training Set Construction for Supervised Sentiment Analysis in Political Contexts

Standard sentiment analysis techniques usually rely either on sets of rules based on semantic and affective information or in machine learning approaches whose quality heavily depend on the size and significance of a training set of pre-labeled text samples. In many situations, this labeling needs to be performed by hand, potentially limiting the size of the training set. In order to address this issue, in this work we propose a methodology to retrieve text samples from Twitter and automatically label them. Additionally, we also tackle the situation in which the base rates of positive and negative sentiment samples in the training and test sets are biased with respect to the system in which the classifier is intended to be applied.
Green tea could improve the performance of cognitive tasks: A pilot study with wearable brain imaging device

Recently, the consumption of green tea is on the rise due to its potential positive health effects. The main origin for these effects have been attributed to the presence of two compounds found in tea, caffeine and L-theanine with caffine being related to attention, energy, motivation, self-confidence, alertness and concentration and L-theanine related to relaxation. In this study, a wearable functional Near-Infrared Spectroscopy (fNIRS) device was used to monitor neural activity. The device operating at a wavelength of 810 nm could detect absorption changes in total oxyhemoglobin (HbO) due to neural activity and the current study focused on addressing changes in attention under the consumption of green tea and other beverages. In this pilot study, a total of 6 Japanese undergraduate students (3 males and 3 females) participated and the cognitive tasks included multiplication (M) and word generation (W) game in English. It was found that during both the multiplication and word tasks, the relative decrease in total HbO was smaller for green tea in comparison to both control or no drink and other beverages for almost all the subjects. In conclusion, we suggest for the existence of a correlation between drink type and task performance and this could have implications not only in classrooms and but also form the basis of studying the cognitive effects of different drinks available in the market.
Mining and Modeling Complex Leadership Dynamics of Movement data

Leadership is an essential part of collective decision and organization in social animals, including humans. In nature, leadership is dynamic and varies with context or temporal factors. Understanding dynamics of leadership, such as how leaders change, emerge, or converge, allows scientists to gain more insight into group decision-making and collective behavior in general. However, given only data of individual activities, it is challenging to infer these dynamic leadership events. In this paper, we focus on mining and modeling frequent patterns of leadership dynamics. We formalize a new computational problem, Mining Patterns Of Leadership Dynamics, as well as propose a framework as a solution of this problem. Our framework can be used to address several questions regarding leadership dynamics of group movement. We use the leadership inference framework, mFLICA, to infer the time series of leaders from movement datasets, then propose the approach to mine and model frequent patterns of leadership dynamics. We evaluate our framework performance by using several simulated datasets, as well as using the real-world dataset of baboon movement to demonstrate the application of our framework. There are no existing methods to address this problem, thus, we modify and extend the existing leadership inference framework to provide a non-trivial baseline. Our framework performs better than this baseline in all datasets. Moreover, we also propose a method to perform statistical significance tests, comparing inferred frequent patterns of leadership dynamics with our proposed null hypotheses. Our framework opens the opportunities for scientists to generate scientific hypotheses that can be tested statistically regarding dynamics of leadership in movement data.
ALGeoSPF: A Hierarchical Factorization Model for POI Recommendation

The task of points-of-interest (POI) recommendations has become an essential feature in location-based social networks (LBSNs) with the significant growth of shared data on LBSNs. However it remains a challenging problem, because the decision process of a user choosing to visit a POI depends on numerous factors. The high level of sparsity of the data in LBSNs makes the POI recommendation problem even more challenging, especially for large geographical areas and worldwide datasets. Moreover, in this context the mobility behavior of the users is very heterogeneous, ranging from urban to worldwide mobility. In this paper, we explore the impact of spatial clustering on the recommendation quality. The proposed approach combines spatial clustering with users' influences. It is based on a Poisson factorization model built on an implicit social network, inferred from the geographical mobility patterns. We conduct a comprehensive performance evaluation of our approach on the YFCC dataset (a very large-scale real-world dataset). The experiments show that our approach achieves a significantly superior recommendation quality compared to other state-of-the-art recommendation techniques.