Miracle-dz/UniFAR-FTUs huggingface dataset:
Recurrent Neural Network Language Model Adaptation for Conversational Speech Recognition

We propose two adaptation models for recurrent neural network language models (RNNLMs) to capture topic effects and long-distance triggers for conversational automatic speech recognition (ASR). We use a fast marginal adaptation (FMA) framework to adapt a RNNLM. Our ﬁrst model is effectively a cache model – the word frequencies are estimated by counting words in a conversation (with utterance-level hold-one-out) from 1st-pass decoded word lattices, and then is interpolated with a background unigram distribution. In the second model, we train a deep neural network (DNN) on conversational transcriptions to predict word frequencies given word frequencies from 1st-pass decoded word lattices. The second model can in principle model trigger and topic effects but is harder to train. Experiments on three conversational corpora show modest WER and perplexity reductions with both adaptation models.
Real-space refinement in PHENIX for cryo-EM and crystallography

This article describes the implementation of real-space refinement in the phenix. real_space_refine program from the Phenix suite. Use of a simplified refinement target function enables fast calculation, which in turn makes it possible to identify optimal data-restraints weight as part of routine refinements with little runtime cost. Refinement of atomic models against low-resolution data benefits from the inclusion of as much additional information as is available. In addition to standard restraints on covalent geometry, phenix. real_space_refine makes use of extra information such as secondary-structure and rotamer-specific restraints, as well as restraints or constraints on internal molecular symmetry. Re-refinement of 385 cryo-EM derived models available in the PDB at resolutions of 6 Å or better shows significant improvement of models and the fit of these models to the target maps. Synopsis A description of the implementation of real-space refinement in the phenix. real_space_refine program from the Phenix suite and its application to re-refinement of cryo-EM derived models.
Coping and Its Relation to Gender, Anxiety, Depression, Fatigue, Cognitive Difficulties and Somatic Symptoms

Background: The first aim of this study was to analyse the structure of coping and to develop a measuring instrument to be used in future work. A second aim was to examine associations between coping scores and negative outcomes (anxiety, depression, fatigue, cognitive difficulties and somatic symptoms). Finally, gender differences in coping with workplace events were examined, as were the relationships between gender and subjective health outcomes. Methodology: A survey of a sample of 240 adults from the South Wales area was conducted at one-time point only. The questionnaire included a factor analysed version of the Ways of Coping Checklist (WCCL) as well as scales measuring anxiety, depression, fatigue, cognitive difficulties and somatic symptoms. Results: Results showed that negative coping styles significantly predicted negative health outcomes, and positive coping styles predicted fewer negative outcomes. No significant differences were found for health outcomes between men and women, but women were significantly more likely to use self-blame and wishful thinking coping. Conclusion: This study demonstrated that coping styles are associated with wellbeing outcomes. Further research should use more independent variables, such as workplace and individual characteristics, to explain more of the variance in health outcomes than just that explained by coping styles alone.
Thrombocytopenia After Aortic Valve Replacement: Comparison Between Sutureless Perceval S Valve and Perimount Magna Ease Bioprosthesis

Introduction The incidence of postoperative thrombocytopenia after aortic valve replacement (AVR) with the Perceval S Sutureless bioprosthesis remains unclear. The aim of this study was to report thrombocytopenia associated with the use of sutureless AVR. Methods The data was collected retrospectively for patients who had isolated AVR with sutureless Perceval S valve (Group A: 72 patients) and was compared with patients who underwent isolated sutured AVR with Perimount Magna Ease Bioprosthesis (Group B: 101 patients) in our institution between June 2014 and January 2017. Results Cardiopulmonary bypass and cross-clamp time were significantly shorter in group A. Maximum drop in platelet count was 58% mean (day 2.3) in group A versus 44% mean (day 1.7) in group B (P=0.0001). Absolute platelet count on postoperative day 1-6 in group A was significantly less than in group B (P≤0.05). Platelet count recovered to preoperative value in 44% patients in group B versus only in 26% patients in group A at discharge (P=0.018). Moderate thrombocytopenia occurs more often in group A (41% vs. 26%) (P=0.008) while severe thrombocytopenia (<50 x 109) was observed in 6% in group A but never in group B. Platelets (P=0.007) and packed red blood cells (P=0.009) transfusion was significantly higher in the group A. Conclusion The implantation of sutureless Perceval aortic valves was associated with a significant drop in platelet count postoperatively with slow recovery and higher platelets and packed red blood cells transfusion requirements. A prospective randomised trial is needed to confirm our findings.
On the Patent Claim Eligibility Prediction Using Text Mining Techniques

With the widespread of computer software in recent decades, software patent has become controversial for the patent system. Of the many patentability requirements, patentable subject matter serves as a gatekeeping function to prevent a patent from preempting future innovation. Software patents may easily fall into the gray area of abstract ideas, whose allowance may hinder future innovation. However, without a clear definition of abstract ideas, determining the patent claim subject matter eligibility is a challenging task for examiners and applicants. In this research, in order to solve the software patent eligibility issues, we propose an effective model to determine patent claim eligibility by text-mining and machine learning techniques. Drawing upon USPTO issued guidelines, we identify 66 patent cases to design domain knowledge features, including abstractness features and distinguishable word features, as well as other textual features, to develop the claim eligibility prediction model. The experiment results show our proposed model reaches the accuracy of more than 80%, and domain knowledge features play a crucial role in our prediction model.
Your Privacy Is Your Friend's Privacy: Examining Interdependent Information Disclosure on Online Social Networks

The highly interactive nature of interpersonal communication on online social networks (OSNs) impels us to think about privacy as a communal matter, with users’ private information being revealed by not only their own voluntary disclosures, but also the activities of their social ties. The current privacy literature has identified two types of information disclosures in OSNs: self-disclosure, i.e., the disclosure of an OSN user’s private information by him/herself; and co-disclosure, i.e., the disclosure of the user’s private information by other users. Although co-disclosure has been increasingly identified as a new source of privacy threat inherent to the OSN context, few systematic attempts have been made to provide a framework for understanding the commonalities and distinctions between selfvs. co-disclosure, especially pertaining to different types of private information. To address this gap, this paper presents a data-driven study that builds upon an innovative measurement for quantifying the extent to which others’ co-disclosure could lead to actual privacy harm. The results demonstrate the significant harm caused by co-disclosure and illustrate the differences between the identity elements revealed through selfand co-disclosure.
Pathwise Derivatives for Multivariate Distributions

We exploit the link between the transport equation and derivatives of expectations to construct efficient pathwise gradient estimators for multivariate distributions. We focus on two main threads. First, we use null solutions of the transport equation to construct adaptive control variates that can be used to construct gradient estimators with reduced variance. Second, we consider the case of multivariate mixture distributions. In particular we show how to compute pathwise derivatives for mixtures of multivariate Normal distributions with arbitrary means and diagonal covariances. We demonstrate in a variety of experiments in the context of variational inference that our gradient estimators can outperform other methods, especially in high dimensions.
Operator-in-the-loop simulation of a redundant manipulator under teleoperation

For an interactive simulation of 7 degree-of freedom redundant manipulator under teleoperation, this paper describes an iterative form of resolved-motion rate control in which the constraint Jacobian is constructed on-line in real time and used in the pseudoinverse method as the manipulator is teleoperated. The manipulator's tasks may involve trajectory following in free space and frequent contact with the environment through pick-and-put operation. The operator's command is interpreted as a series of increments in Cartesian space, and then the constraint Jacobian is developed between two successive increments by viewing the predecessor as the initial configuration and the successor as the target configuration. The pseudoinverse of the constraint Jacobian then generates necessary changes in joint variables and subsequently joint torque. The Jacobian constructed in this way enables us to treat both free motion and environmental contact in the same way. The simulator is built on a two-processor IRIS workstation, with one processor for graphics and the other for dynamics and control analysis. This simulator has a potential for training the teleoperator, developing operational scenarios through visualization of the simulation, and testing the design of operator-machine interface.
Estimating Software Vulnerability Counts in the Context of Cyber Risk Assessments

Stakeholders often conduct cyber risk assessments as a first step towards understanding and managing their risks due to cyber use. Many risk assessment methods in use today include some form of vulnerability analysis. Building on prior research and combining data from several sources, this paper develops and applies a metric to estimate the proportion of latent vulnerabilities to total vulnerabilities in a software system and applies the metric to five scenarios involving software on the scale of operating systems. The findings suggest caution in interpreting the results of cyber risk methodologies that depend on enumerating known software vulnerabilities because the number of unknown vulnerabilities in large-scale software tends to exceed known vulnerabilities.
Infant Emotional Outbursts Detection in Infant-parent Spoken Interactions

Detection of infant emotional outbursts, such as crying, in large corpora of recorded infant speech, is essential to the study of dyadic social process, by which infants learn to identify and regulate their own emotions. Such large corpora now exist with the advent of LENA speech monitoring systems, but are not labeled for emotional outbursts. This paper reports on our efforts to manually code child utterances as being of type ”laugh”, ”cry”, ”fuss”, ”babble” and ”hiccup”, and to develop algorithms capable of performing the same task automatically. Human labelers achieve much higher rates of inter-coder agreement for some of these categories than for others. Linear discriminant analysis (LDA) achieves better accuracy on tokens that have been coded by two human labelers than on tokens that have been coded by only one labeler, but the difference is not as much as we expected, suggesting that the acoustic and contextual features being used by human labelers are not yet available to the LDA. Convolutional neural network and hidden markov model achieve better accuracy than LDA, but worse F-score, because they over-weight the prior. Discounting the transition probability does not solve the problem.
Specialization Matters in the Firm Size-Wage Gap

This study applies the O-ring theory to explain the firm-size wage premium. It focuses on the joint role of the division of labor and employee characteristics. Including the firm heterogeneity of occupations in a standard wage regression with individual fixed effect shrinks the size coefficient by a third. Labor productivity follows a similar pattern as wages. The intuition is that individuals who work for large firms focus on a limited number of tasks become more efficient and productive, and earn higher wages. Additional predictions originating from the labor specialization hypothesis receive support from the data.
Open Domain Suggestion Mining: Problem Definition and Datasets

We propose a formal definition for the task of suggestion mining in the context of a wide range of open domain applications. Human perception of the term \emph{suggestion} is subjective and this effects the preparation of hand labeled datasets for the task of suggestion mining. Existing work either lacks a formal problem definition and annotation procedure, or provides domain and application specific definitions. Moreover, many previously used manually labeled datasets remain proprietary. We first present an annotation study, and based on our observations propose a formal task definition and annotation procedure for creating benchmark datasets for suggestion mining. With this study, we also provide publicly available labeled datasets for suggestion mining in multiple domains.
Effect of micro‐RNA on tenocytes and tendon‐related gene expression: A systematic review

The purpose of the review was to synthesize the current literature regarding the effect of miRNA on biological processes known to be involved in tendon and tenocyte development and homeostasis. Using multiple databases, a systematic review was performed with a customized search term crafted to identify any study examining micro‐RNA in relation to tendon and/or tenocytes. Results were classified based on the following categories: Gene expression, tenocyte development and differentiation, tendon tissue repair, and tenocyte senescence. A total of 3,112 potentially relevant studies were reviewed, and after exclusion criteria was applied, 15 investigations were included in the final analysis. There were 14 specific miRNA included in this review, with 11 studies reporting on tendon‐related gene expression, five reporting on tendon development and/or tenocyte differentiation, six reporting on tendon tissue repair, and five reporting on tenocyte senescence. The miR‐29 family was the most commonly reported micro‐RNA in the investigation. We also report on a number of micro‐RNA which are associated with both positive and negative effects on tendon homeostasis. © 2018 Orthopaedic Research Society. Published by Wiley Periodicals, Inc. J Orthop Res 36:2823–2829, 2018.
Knowledge based (re-)clustering

This paper presents a novel multiparadigm segmentation method based upon knowledge based clustering with reclustering. The techniques described enhance unsupervised classification and achieve pattern labeling. First domain knowledge is utilized to decide where and how a clustering algorithm is applied, then clustering is iteratively applied to focus-of-attention patterns with the knowledge of how many expected classes there are in those patterns and the prototypical patterns of a class. Examples showing clustering improvements are given from brain MRI's and satellite images.
Analysis and Control of Genetic Toggle Switches Subject to Periodic Multi-Input Stimulation

In this letter, we analyze a genetic toggle switch recently studied in the literature where the expression of two repressor proteins can be tuned by controlling two different inputs, namely the concentration of two inducer molecules in the growth medium of the cells. Specifically, we investigate the dynamics of this system when subject to pulse-width modulated inputs. We provide an analytical model that captures qualitatively the experimental observations reported in the literature and approximates its asymptotic behavior. We also discuss the effect that the system parameters have on the prediction accuracy of the model. Moreover, we propose a possible external control strategy to regulate the mean value of the fluorescence of the reporter proteins when the cells are subject to such periodic forcing.
Exploring Low Internal Reorganization Energies for Silicene Nanoclusters

High-performance materials rely on small reorganization energies to facilitate both charge separation and charge transport. Here, we performed DFT calculations to predict small reorganization energies of rectangular silicene nanoclusters with hydrogen-passivated edges denoted by H-SiNC. We observe that across all geometries, H-SiNCs feature large electron affinities and highly stabilized anionic states, indicating their potential as n-type materials. Our findings suggest that fine-tuning the size of H-SiNCs along the zigzag and armchair directions may permit the design of novel n-type electronic materials and spinctronics devices that incorporate both high electron affinities and very low internal reorganization energies.
Enhancing Information Quality as Part of the Disease Surveillance System in Malawi, Africa: Reflections on a mHealth Intervention

Public health surveillance and response to disease outbreaks is still a hurdle in many developing countries across sub-Saharan Africa. Pivotal in disease surveillance and response is the reliance on valid information, hence, the need for information which has high Information Quality (IQ) characteristics. A key issue with disease surveillance systems, stem from the diverse range of data sources with various levels of information quality that may affect the trustworthiness of the information. However, with the increasing diffusion of mobile phone technologies, there are opportunities to improve IQ. The aim of this study was to assess the information quality in data collected through a smartphone application. Based on qualitative data from interviews, workshop and system specifications, it was found that information quality improves with the use of smartphone applications but aspects such as user competence and trustworthiness, must be addressed to maximize the benefits of using mobile technologies for disease surveillance.
An automatic jigsaw puzzle solver

A computer vision system to automatically analyze and assemble an image of the pieces of a jigsaw puzzle is presented. The system, called Automatic Puzzle Solver (APS), derives a new set of features based on the shape and color characteristics of the puzzle pieces. A combination of the shape dependent features and color cues is used to match the puzzle pieces. Matching is performed using a modified iterative labeling procedure in order to reconstruct the original picture represented by the jigsaw puzzle. Algorithms for obtaining shape description and matching are explained with experimental results.
Convolutionally Coded SNR-Adaptive Transmission for Low-Latency Communications

Fifth generation new radio aims to facilitate new use cases in wireless communications. Some of these new use cases have highly demanding latency requirements; many of the powerful forward error correction codes deployed in current systems, such as the turbo and low-density parity-check codes, do not perform well when the low-latency requirement does not allow iterative decoding. As such, there is a rejuvenated interest in noniterative/one-shot decoding algorithms. Motivated by this, we propose a signal-to-noise ratio-adaptive convolutionally coded system with optimized constellations designed specifically for a particular set of convolutional code parameters. Numerical results show that significant performance improvements in terms of bit-error-rate and spectral efficiency can be obtained compared to the traditional adaptive modulation and coding systems in low-latency communications.
Does the Sex Risk Quiz Predict Mycoplasma genitalium Infection in Urban Adolescents and Young Adult Women?

Background Mycoplasma genitalium (MG) is a common sexually transmitted infection (STI), but there are limited strategies to identify individuals at risk of MG. Previously, a sex risk quiz was used to predict STIs including Chlamydia trachomatis, Neisseria gonorrhoeae, and/or Trichomonas vaginalis. The original quiz categorized individuals 25 years or younger as at risk of STIs, but the Centers for Disease Control and Prevention identifies females younger than 25 years as at risk of STIs. In this study, the quiz was changed to categorize females younger than 25 years as high risk. The objective was to determine if the age-modified risk quiz predicted MG infection. Methods A cross-sectional analysis of a prospective longitudinal study was performed including female adolescents and young adults (AYAs) evaluated in multiple outpatient clinics. Participants completed an age-modified risk quiz about sexual practices. Scores ranged from 0 to 10 and were categorized as low risk (0–3), medium risk (4–7), and high risk (8–10) based on the STI prevalence for each score. Vaginal and/or endocervical and/or urine specimens were tested for MG, T. vaginalis, C. trachomatis, and N. gonorrhoeae using the Aptima Gen-Probe nucleic amplification test. Results There were 693 participants. Most participants reported having 0 to 1 sexual partners in the last 90 days (91%) and inconsistent condom use (84%). Multivariable logistic regression analysis controlling for race, education, and symptom status demonstrated that a medium-risk score predicted MG infection among AYAs younger than 25 years (adjusted odds ratio, 2.56 [95% confidence interval, 1.06–6.18]). Conclusion A risk quiz may be useful during clinical encounters to identify AYA at risk of MG.
Supporting real-time IP multimedia services in UMTS

UMTS, the successor of GSM, is evolving toward a future wireless all-IP network. We explain how it will support real-time IP multimedia services, as these services are expected to drive the adoption of wireless all-IP networks. We not only focus on the centerpiece of IP multimedia support, the IP multimedia subsystem, but also include the GPRS transport capabilities and OSA middleware capabilities. This helps to explain how the IMS fits in with, and builds upon, other pieces of the UMTS network. We analyze the pros and cons of providing IP multimedia session control capabilities as opposed to just providing basic IP connectivity, and highlight selected features of the IMS design for discussion.