Miracle-dz/UniFAR-FTUs huggingface dataset:
Opinion Target Extraction in Chinese Microblog Posts Based on Candidate Classification

Opinion target extraction aims to find the object to which an opinion is expressed. It is helpful in getting a comprehensive understanding about public opinions on hot spot social events. In Chinese microblog posts analysis, due to the absence of words with emotional tendency (opinion words), traditional approaches relying on opinion words are not suitable. In this paper, we propose an unsupervised method based on candidate classification. Opinion target candidates are first classified according to syntactic and semantic features as well as the similarity of their head words. Then, based on similarity between sentences, an iterative algorithm is explored to extract opinion targets. Experimental results show that this approach achieves higher accuracy than state-of-the-art systems.
Long-Term Follow-up of Self-Modeling as an Intervention for Stuttering

Abstract This brief article reports a long-term follow-up of the effects of self-modeling on the reduction of stuttering in two groups of students who initially evidenced different mean percentages of stuttered words. The 2-and 4-year follow-up is based on 7 students who were involved in two separate studies published in 1996 and 1998. The 3 students involved in the 1996 study who initially exhibited substantially higher mean percentages of stuttered words relative to the students involved in the 1998 study, maintained their treatment effects after 4 years. Specifically, 2 students exhibited normal fluency by evidencing at or below 4% stuttered words; the third student exhibited near fluency with 5% stuttered words. Similarly, Students 2 and 4 involved in the 1998 study maintained their treatment effects and exhibited normal fluency by evidencing at or below 4% stuttered words. The remaining 2 students (1 and 3), who were fluent at the end of the study, evidenced an increase to 7% stuttered words, which is indicative of disfluency.
Comprehensive framework for accurate diffusion MRI parameter estimation

During the last decade, many approaches have been proposed for improving the estimation of diffusion measures. These techniques have already shown an increase in accuracy based on theoretical considerations, such as incorporating prior knowledge of the data distribution. The increased accuracy of diffusion metric estimators is typically observed in well‐defined simulations, where the assumptions regarding properties of the data distribution are known to be valid. In practice, however, correcting for subject motion and geometric eddy current deformations alters the data distribution tremendously such that it can no longer be expressed in a closed form. The image processing steps that precede the model fitting will render several assumptions on the data distribution invalid, potentially nullifying the benefit of applying more advanced diffusion estimators. In this work, we present a generic diffusion model fitting framework that considers some statistics of diffusion MRI data. A central role in the framework is played by the conditional least squares estimator. We demonstrate that the accuracy of that particular estimator can generally be preserved, regardless the applied preprocessing steps, if the noise parameter is known a priori. To fulfill that condition, we also propose an approach for the estimation of spatially varying noise levels. Magn Reson Med, 70:972–984, 2013. © 2012 Wiley Periodicals, Inc.
Question Answering via Integer Programming over Semi-Structured Knowledge

Answering science questions posed in natural language is an important AI challenge. Answering such questions often requires non-trivial inference and knowledge that goes beyond factoid retrieval. Yet, most systems for this task are based on relatively shallow Information Retrieval (IR) and statistical correlation techniques operating on large unstructured corpora. We propose a structured inference system for this task, formulated as an Integer Linear Program (ILP), that answers natural language questions using a semi-structured knowledge base derived from text, including questions requiring multi-step inference and a combination of multiple facts. On a dataset of real, unseen science questions, our system significantly outperforms (+14%) the best previous attempt at structured reasoning for this task, which used Markov Logic Networks (MLNs). It also improves upon a previous ILP formulation by 17.7%. When combined with unstructured inference methods, the ILP system significantly boosts overall performance (+10%). Finally, we show our approach is substantially more robust to a simple answer perturbation compared to statistical correlation methods.
Medicago truncatula CYP716A12 Is a Multifunctional Oxidase Involved in the Biosynthesis of Hemolytic Saponins[W]

Triterpenic saponins participate in plant defense mechanisms and possess a broad spectrum of biological and pharmacological properties. This article identifies the P450 cytochrome CYP716A12 as a key gene in the synthesis of hemolytic saponins in Medicago truncatula by a combination of genetic and biochemical analyses. Saponins, a group of glycosidic compounds present in several plant species, have aglycone moieties that are formed using triterpenoid or steroidal skeletons. In spite of their importance as antimicrobial compounds and their possible benefits for human health, knowledge of the genetic control of saponin biosynthesis is still poorly understood. In the Medicago genus, the hemolytic activity of saponins is related to the nature of their aglycone moieties. We have identified a cytochrome P450 gene (CYP716A12) involved in saponin synthesis in Medicago truncatula using a combined genetic and biochemical approach. Genetic loss-of-function analysis and complementation studies showed that CYP716A12 is responsible for an early step in the saponin biosynthetic pathway. Mutants in CYP716A12 were unable to produce hemolytic saponins and only synthetized soyasaponins, and were thus named lacking hemolytic activity (lha). In vitro enzymatic activity assays indicate that CYP716A12 catalyzes the oxidation of β-amyrin and erythrodiol at the C-28 position, yielding oleanolic acid. Transcriptome changes in the lha mutant showed a modulation in the main steps of triterpenic saponin biosynthetic pathway: squalene cyclization, β-amyrin oxidation, and glycosylation. The analysis of CYP716A12 expression in planta is reported together with the sapogenin content in different tissues and stages. This article provides evidence for CYP716A12 being a key gene in hemolytic saponin biosynthesis.
Oligomerization of p62 allows for selection of ubiquitinated cargo and isolation membrane during selective autophagy

Autophagy is a major pathway for the clearance of harmful material from the cytoplasm. During autophagy, cytoplasmic material is delivered into the lysosomal system by organelles called autophagosomes. Autophagosomes form in a de novo manner and, in the course of their formation, isolate cargo material from the rest of the cytoplasm. Cargo specificity is conferred by autophagic cargo receptors that selectively link the cargo to the autophagosomal membrane decorated with ATG8 family proteins such as LC3B. Here we show that the human cargo receptor p62/SQSTM-1 employs oligomerization to stabilize its interaction with LC3B and linear ubiquitin when they are clustered on surfaces. Thus, oligomerization enables p62 to simultaneously select for the isolation membrane and the ubiquitinated cargo. We further show in a fully reconstituted system that the interaction of p62 with ubiquitin and LC3B is sufficient to bend the membrane around the cargo. DOI: http://dx.doi. org/10.7554/eLife.08941. 001
Abnormal thalamic function in patients with vestibular migraine

Objective: To investigate the functional response of neural pathways associated with vestibular stimulation in patients with vestibular migraine (VM). Methods: Twelve patients with VM underwent whole-brain blood oxygen level–dependent (BOLD) fMRI during ear irrigation with cold water. The functional response of neural pathways to this stimulation in patients with VM was compared with age- and sex-matched patients with migraine without aura and healthy controls. Secondary analyses explored associations between BOLD signal change and clinical features of migraine in patients. Results: We observed a robust cortical and subcortical pattern of BOLD signal change in response to ear irrigation across all participants. Patients with VM showed a significantly increased thalamic activation in comparison with both patients with migraine without aura and healthy controls. The magnitude of thalamic activation was positively correlated with the frequency of migraine attacks in patients with VM. Conclusions: We provide novel evidence for abnormal thalamic functional response to vestibular stimulation in patients with VM. These functional abnormalities in central vestibular processing may contribute to VM pathophysiology.
Strict detailed balance is unnecessary in Monte Carlo simulation

Detailed balance is an overly strict condition to ensure a valid Monte Carlo simulation. We show that, under fairly general assumptions, a Monte Carlo simulation need satisfy only the weaker balance condition. Not only does our proof show that sequential updating schemes are correct, but also it establishes the correctness of a whole class of new methods that simply leave the Boltzmann distribution invariant.
Geophysical Monitoring of a Field‐Scale Biostimulation Pilot Project

The USGS conducted a geophysical investigation in support of a U.S. Naval Facilities Engineering Command, Southern Division field‐scale biostimulation pilot project at Anoka County Riverfront Park (ACP), downgradient of the Naval Industrial Reserve Ordnance Plant, Fridley, Minnesota. The goal of the pilot project is to evaluate subsurface injection of vegetable oil emulsion (VOE) to stimulate microbial degradation of chlorinated hydrocarbons. To monitor the emplacement and movement of the VOE and changes in water chemistry resulting from VOE dissolution and/or enhanced biological activity, the USGS acquired cross‐hole radar zero‐offset profiles, traveltime tomograms, and borehole geophysical logs during five site visits over 1.5 years. Analysis of pre‐ and postinjection data sets using petrophysical models developed to estimate VOE saturation and changes in total dissolved solids provides insights into the spatial and temporal distribution of VOE and ground water with altered chemistry. Radar slowness‐difference tomograms and zero‐offset slowness profiles indicate that the VOE remained close to the injection wells, whereas radar attenuation profiles and electromagnetic induction logs indicate that bulk electrical conductivity increased downgradient of the injection zone, diagnostic of changing water chemistry. Geophysical logs indicate that some screened intervals were located above or below zones of elevated dissolved solids; hence, the geophysical data provide a broader context for interpretation of water samples and evaluation of the biostimulation effort. Our results include (1) demonstration of field and data analysis methods for geophysical monitoring of VOE biostimulation and (2) site‐specific insights into the spatial and temporal distributions of VOE at the ACP.
Instance-Level Constraints in Density-Based Clustering

Clustering data into meaningful groups is one of most important tasks of both artificial intelligence and data mining. In general, clustering methods are considered unsupervised. However, in recent years, so-named constraints become more popular as means of incorporating additional knowledge into clustering algorithms. Over the last years, a number of clustering algorithms employing different types of constraints have been proposed. In this paper we focus on instance level constraints such as must-link and cannot-link and present theoretical considerations on employing them in a well know density-based clustering algorithm – DBSCAN. Additionally we present a modified version of the algorithm using the discussed type of constraints.
Compiler Construction for Lockstep Execution of Multithreaded Processors

Embedded applications intrinsically have high degree of parallelism, but it is difficult to exploit the parallelism due to resource constraint of embedded platforms. In order to overcome the problem, we introduced a promising processor solution to support parallel thread execution with pretty good performance while consuming small hardware resources. We call this processor as Multithread Lockstep Execution Processor (MLEP). Since each iteration of parallel loops performs the same sequence of instructions at most time while manipulating different data, we only need to partially duplicate a pipeline resource to support the multithreading. This architecture makes it possible that parallel threads execute synchronously in a lockstep manner. However, because of providing a totally different kind of thread execution, it sometime makes programmers confused when parallelizing code for the processor. In this paper, we introduce a compiler infrastructure for our architecture in detail with discussion of how to support OpenMP APIs and how to integrate the Omni OpenMP compiler with our backend code generator. Also, for verification of our compiler system, we show that our code generation scheme delivers the same performance as handed codes.
Humanoid Walking Robot: Modeling, Inverse Dynamics, and Gain Scheduling Control

This article presents reference-model-based control design for a 10 degree-of-freedom bipedal walking robot, using nonlinear gain scheduling. The main goal is to show concentrated mass models can be used for prediction of the required joint torques for a bipedal walking robot. Relatively complicated architecture, high DOF, and balancing requirements make the control task of these robots difficult. Although linear control techniques can be used to control bipedal robots, nonlinear control is necessary for better performance. The emphasis of this work is to show that the reference model can be a bipedal walking model with concentrated mass at the center of gravity, which removes the problems related to design of a pseudo-inverse system. Another significance of this approach is the reduced calculation requirements due to the simplified procedure of nominal joint torques calculation. Kinematic and dynamic analysis is discussed including results for joint torques and ground force necessary to implement a prescribed walking motion. This analysis is accompanied by a comparison with experimental data. An inverse plant and a tracking error linearization-based controller design approach is described. We propose a novel combination of a nonlinear gain scheduling with a concentrated mass model for the MIMO bipedal robot system.
Optimal priority MDC video streaming for networks with path diversity

This paper proposes a robust video streaming scheme for priority networks with path diversity, based on a combined approach of multiple description coding (MDC) with optimal picture classification into two priorities. A binary classification algorithm is proposed to define high (HP) and low (LP) priority network abstraction layer units (NALU), which in turn define the packet priorities. An optimisation algorithm is used to find HP pictures, based on dynamic programming and relying on minimisation of the packet loss concealment distortion. The paper shows that the proposed algorithm is able to effectively improve the decoded video without increasing the MDC stream redundancy. The overall performance evaluation, carried out by simulating MDC video streaming over lossy networks with path diversity, demonstrates that the proposed algorithm yields higher video quality for a wide range of packet loss rates (PLR). Comparing with no-priority MDC video streaming schemes, the simulation results show that the proposed algorithm can improve the average PSNR results in 0.7-3.2dB for packet loss rates between 3% and 15%.
On Decentralized In-network Aggregation in Real-World Scenarios with Crowd Mobility

Recently proposed applications for monitoring the behavior of real-world crowds with wireless sensor nodes rely on decentralized in-network aggregation. Although some of the aggregation algorithms for wireless sensor networks seem appealing for such applications, we are not aware of any deployments of these algorithms in real-world scenarios with crowd mobility. As a step toward filling this gap, we thus discuss our experiences with decentralized in-network aggregation from a few such deployments involving up to 177 nodes. We compare two main classes of algorithms for basic aggregates. We show that algorithms based on probabilistic, order- and duplicate-insensitive sketches outperform algorithms based on gradual variance reduction. To this end, however, they have to be adapted considerably to minimize the traffic, latency, and errors of the aggregation process, and to account for some real-world issues. In short, while the algorithms do have a potential for the envisioned crowd-monitoring applications, deploying them is not trivial.
High Performance Scalable and Expressive Modeling Environment to Study Mobile Malware in Large Dynamic Networks

Large scale realistic simulations of malware on mobile wireless networks have recently become an increasingly important application of high-performance computing. We propose EpiCure - an individual-based, scalable high performance computing oriented modeling environment to study malware propagation over realistic mobile networks. It is designed specifically to work on commodity cluster architectures. EpiCure runs extremely fast for realistic instances that involve: (i) large time-varying networks consisting of millions of heterogeneous individuals with time varying interaction neighborhoods, (ii) dynamic interactions between disease propagation, device behavior, and the exogenous interventions, and (iii) large number of replicated runs necessary for statistically sound estimates about the stochastic epidemic evolution. We find that EpiCure runs several orders of magnitude faster than another comparable simulation tool while delivering similar results. Beyond simple compute speed, EpiCure has been designed so that analysts can easily represent a range of interventions leading to improved human productivity and ease of use. This is an increasingly important metric in high performance computing. We illustrate EpiCure using three case studies that bring out the novel features of EpiCure.
Amplitude and frequency dependence of spike timing: implications for dynamic regulation.

The spike-time reliability of motoneurons in the Aplysia buccal motor ganglion was studied as a function of the frequency content and the relative amplitude of the fluctuations in the neuronal input, calculated as the coefficient of variation (CV). Measurements of spike-time reliability to sinusoidal and aperiodic inputs, as well as simulations of a noisy leaky integrate-and-fire neuron stimulated by spike trains drawn from a periodically modulated process, demonstrate that there are three qualitatively different CV-dependent mechanisms that determine reliability: noise-dominated (CV < 0.05 for Aplysia motoneurons) where spike timing is unreliable regardless of frequency content; resonance-dominated (CV approximately 0.05-0.25) where reliability is reduced by removal of input frequencies equal to motoneuron firing rate; and amplitude-dominated (CV >0.35) where reliability depends on input frequencies greater than motoneuron firing rate. In the resonance-dominated regime, changes in the activity of the presynaptic inhibitory interneuron B4/5 alter motoneuron spike-time reliability. The increases or decreases in reliability occur coincident with small changes in motoneuron spiking rate due to changes in interneuron activity. Injection of a hyperpolarizing current into the motoneuron reproduces the interneuron-induced changes in reliability. The rate-dependent changes in reliability can be understood from the phase-locking properties of regularly spiking motoneurons to periodic inputs. Our observations demonstrate that the ability of a neuron to support a spike-time code can be actively controlled by varying the properties of the neuron and its input.
The Effect of Completing a Surrogacy Information and Decision-Making Tool upon Admission to an Intensive Care Unit on Length of Stay and Charges

Background and Purpose Many critically ill patients in intensive care units (ICUs) are unable to communicate their wishes about goals of care, particularly about the use of life-sustaining treatments. Surrogates and clinicians struggle with medical decisions because of a lack of clarity regarding patients’ preferences, leading to prolonged hospitalizations and increased costs. This project focused on the development and implementation of a tool to facilitate a better communication process by (1) assuring the early identification of a surrogate if indicated on admission and (2) clarifying the decision-making standards that the surrogate was to use when participating in decision making. Before introducing the tool into the admissions routine, the staff were educated about its use and value to the decision-making process. Project and Methods The study was to determine if early use of a simple method of identifying a patient’s surrogate and treatment preferences might impact length of stay (LOS) and total hospital charges. A pre- and post-intervention study design was used. Nurses completed the surrogacy information tool for all patients upon admission to the neuroscience ICU. Subjects (total N=203) were critically ill patients who had been on a mechanical ventilator for 96 hours or longer, or in the ICU for seven days or longer. The project included staff education on biomedical ethics, critical communication skills, early identification of families and staff in crisis, and use of a simple tool to document patients’ surrogates and previously expressed care wishes. Data on hospital LOS and hospital charges were collected through a retrospective review of medical records for similar four-month time frames pre- and post-implementation of the assessment tool. Results Significant differences were found between pre- and post-groups in terms of hospital LOS (F=6.39, p=.01) and total hospital charges (F=7.03, p=.009). Conclusions Project findings indicate that the use of a simple admission assessment tool, supported by staff education about its completion, use, and available resources, can decrease LOS and lower total hospital charges. The reasons for the difference between the pre- and post-intervention groups remain unclear. Further research is needed to evaluate if the quality of communications between patients, their legally authorized representatives, and clinicians—as suggested in the literature—may have played a role in decreasing LOS and total hospital charges.
FLAP: Flash-aware Prefetching for Improving SSD-based Disk Cache

In modern enterprise storage systems, there is a trend that using NAND flash based solid state disks (SSDs) as a second-level disk cache to reduce the slow access to hard disk drives (HDDs) by caching the hot data of HDDs with SSDs. However, using SSDs for both caching and prefetching has rarely been discussed due to the performance penalty caused by unsuccessful prefetching, including garbage collection cost and disk bandwidth wasting. In this paper, a FLash-Aware Prefetching (FLAP) scheme has been presented to provide aggressive prefetching for improving the performance of sequential disk accesses. FLAP has been evaluated using well known real-world workloads. Experiment results show that FLAP can offer performance improvement under sequential workloads compared with pure caching, while reducing the internal garbage collection of SSDs