From POS tagging to dependency parsing for biomedical event extraction

Background: Given the importance of relation or event extraction from biomedical research publications to support knowledge capture and synthesis, and the strong dependency of approaches to this information extraction task on syntactic information, it is valuable to understand which approaches to syntactic processing of biomedical text have the highest performance. Results: We perform an empirical study comparing state-of-the-art traditional feature-based and neural network-based models for two core natural language processing tasks of part-of-speech (POS) tagging and dependency parsing on two benchmark biomedical corpora, GENIA and CRAFT. To the best of our knowledge, there is no recent work making such comparisons in the biomedical context; specifically no detailed analysis of neural models on this data is available. Experimental results show that in general, the neural models outperform the feature-based models on two benchmark biomedical corpora GENIA and CRAFT. We also perform a task-oriented evaluation to investigate the influences of these models in a downstream application on biomedical event extraction, and show that better intrinsic parsing performance does not always imply better extrinsic event extraction performance. Conclusion: We have presented a detailed empirical study comparing traditional feature-based and neural network-based models for POS tagging and dependency parsing in the biomedical context, and also investigated the influence of parser selection for a biomedical event extraction downstream task. Availability of data and material: We make the retrained models available at https://github.com/datquocnguyen/BioPosDepComment: Accepted for publication in BMC Bioinformatic

Teaching Language to Students with Autism

This meta-synthesis of the literature on methods of instruction to students with ASD examines the various methods of teaching language to students with ASD. While each student learns language at his or her own pace, the author has found that certain methods yield results quicker, and these methods need to be examined critically for any literature on their reliability, efficacy, and scientific research. If a student with autism can be taught language quickly, therefore mitigating any further delays in academic development relative to peers, then this methodology should be made accessible to all teachers of such students

Desegregating HRM: A Review and Synthesis of Micro and Macro Human Resource Management Research

Since the early 1980’s the field of HRM has seen the independent evolution of two independent subfields (strategic and functional), which we believe is dysfunctional to the field as a whole. We propose a typology of HRM research based on two dimensions: Level of analysis (individual/ group or organization) and number of practices (single or multiple). We use this framework to review the recent research in each of the four sub-areas. We argue that while significant progress has been made within each area, the potential for greater gains exists by looking across each area. Toward this end we suggest some future research directions based on a more integrative view of HRM. We believe that both areas can contribute significantly to each other resulting in a more profound impact on the field of HRM than each can contribute independently

Empirical Health Law Scholarship: The State of the Field

The last three decades have seen the blossoming of the fields of health law and empirical legal studies and their intersection--empirical scholarship in health law and policy. Researchers in legal academia and other settings have conducted hundreds of studies using data to estimate the effects of health law on accident rates, health outcomes, health care utilization, and costs, as well as other outcome variables. Yet the emerging field of empirical health law faces significant challenges--practical, methodological, and political. The purpose of this Article is to survey the current state of the field by describing commonly used methods, analyzing enabling and inhibiting factors in the production and uptake of this type of research by policymakers, and suggesting ways to increase the production and impact of empirical health law studies. In some areas of inquiry, high-quality research has been conducted, and the findings have been successfully imported into policy debates and used to inform evidence-based lawmaking. In other areas, the level of rigor has been uneven, and the best evidence has not translated effectively into sound policy. Despite challenges and historical shortcomings, empirical health law studies can and should have a substantial impact on regulations designed to improve public safety, increase both access to and quality of health care, and foster technological innovation

Secure Cascade Channel Synthesis

We consider the problem of generating correlated random variables in a distributed fashion, where communication is constrained to a cascade network. The first node in the cascade observes an i.i.d. sequence $X^n$ locally before initiating communication along the cascade. All nodes share bits of common randomness that are independent of $X^n$. We consider secure synthesis - random variables produced by the system appear to be appropriately correlated and i.i.d. even to an eavesdropper who is cognizant of the communication transmissions. We characterize the optimal tradeoff between the amount of common randomness used and the required rates of communication. We find that not only does common randomness help, its usage exceeds the communication rate requirements. The most efficient scheme is based on a superposition codebook, with the first node selecting messages for all downstream nodes. We also provide a fleeting view of related problems, demonstrating how the optimal rate region may shrink or expand.Comment: Submitted to IEEE Transactions on Information Theor

Research Synthesis for the California Zero Traffic Fatalities Task Force

This research synthesis consists of a set of white papers that jointly provide a review of research on the current practicefor setting speed limits and future opportunities to improve roadway safety. This synthesis was developed to inform thework of the Zero Traffic Fatalities Task Force, which was formed in 2019 by the California State Transportation Agencyin response to California Assembly Bill 2363 (Friedman). The statutory goal of the Task Force is to develop a structured,coordinated process for early engagement of all parties to develop policies to reduce traffic fatalities to zero. Thisreport addresses the following critical issues related to the work of the Task Force: (i) the relationship between trafficspeed and safety; (ii) lack of empirical justification for continuing to use the 85th percentile rule; (iii) why we need toreconsider current speed limit setting practices; (iv) promising alternatives to current methods of setting speed limits;and (v) improving road designs to increase road user safety

A Critical Analysis of Synthesizer User Interfaces for Timbre

In this paper, we review and analyse categories of user interface used in hardware and software electronic music synthesizers. Problems with the user specification and modification of timbre are discussed. Three principal types of user interface for controlling timbre are distinguished. A problem common to all three categories is identified: that the core language of each category has no well-defined mapping onto the task languages of subjective timbre categories as used by musicians

Expediting TTS Synthesis with Adversarial Vocoding

Recent approaches in text-to-speech (TTS) synthesis employ neural network strategies to vocode perceptually-informed spectrogram representations directly into listenable waveforms. Such vocoding procedures create a computational bottleneck in modern TTS pipelines. We propose an alternative approach which utilizes generative adversarial networks (GANs) to learn mappings from perceptually-informed spectrograms to simple magnitude spectrograms which can be heuristically vocoded. Through a user study, we show that our approach significantly outperforms na\"ive vocoding strategies while being hundreds of times faster than neural network vocoders used in state-of-the-art TTS systems. We also show that our method can be used to achieve state-of-the-art results in unsupervised synthesis of individual words of speech.Comment: Published as a conference paper at INTERSPEECH 201

A Study of User's Performance and Satisfaction on the Web Based Photo Annotation with Speech Interaction

This paper reports on empirical evaluation study of users' performance and satisfaction with prototype of Web Based speech photo annotation with speech interaction. Participants involved consist of Johor Bahru citizens from various background. They have completed two parts of annotation task; part A involving PhotoASys; photo annotation system with proposed speech interaction and part B involving Microsoft Microsoft Vista Speech Interaction style. They have completed eight tasks for each part including system login and selection of album and photos. Users' performance was recorded using computer screen recording software. Data were captured on the task completion time and subjective satisfaction. Participants need to complete a questionnaire on the subjective satisfaction when the task was completed. The performance data show the comparison between proposed speech interaction and Microsoft Vista Speech interaction applied in photo annotation system, PhotoASys. On average, the reduction in annotation performance time due to using proposed speech interaction style was 64.72% rather than using speech interaction Microsoft Vista style. Data analysis were showed in different statistical significant in annotation performance and subjective satisfaction for both styles of interaction. These results could be used for the next design in related software which involves personal belonging management.Comment: IEEE Publication Format, https://sites.google.com/site/journalofcomputing