Hasil untuk "Literature (General)"

Renqian Luo, Liai Sun, Yingce Xia et al.

Pre-trained language models have attracted increasing attention in the biomedical domain, inspired by their great success in the general natural language domain. Among the two main branches of pre-trained language models in the general language domain, i.e. BERT (and its variants) and GPT (and its variants), the first one has been extensively studied in the biomedical domain, such as BioBERT and PubMedBERT. While they have achieved great success on a variety of discriminative downstream biomedical tasks, the lack of generation ability constrains their application scope. In this paper, we propose BioGPT, a domain-specific generative Transformer language model pre-trained on large-scale biomedical literature. We evaluate BioGPT on six biomedical natural language processing tasks and demonstrate that our model outperforms previous models on most tasks. Especially, we get 44.98%, 38.42% and 40.76% F1 score on BC5CDR, KD-DTI and DDI end-to-end relation extraction tasks, respectively, and 78.2% accuracy on PubMedQA, creating a new record. Our case study on text generation further demonstrates the advantage of BioGPT on biomedical literature to generate fluent descriptions for biomedical terms.

Katherine L. Silversides

The computational neural-network structures described in the literature are often based on the concept of linear neural units (LNUs). The biological neuron is a complex computing element, which performs more computations than just linear summation. The computational efficiency of the neural network depends on its structure and the training methods employed. Higher-order combinations of inputs and weights will yield higher neural performance. In this paper, a quadratic-neural unit (QNU) has been developed using a novel general matrix form of the quadratic operation. We have used the QNU for realizing different logic circuits.

Chandan Singh, J. S. Khamba

The chapters demonstrate two SEM programs with distinct user interfaces and capabilities (Amos and Mplus) with enough specificity that readers can conduct their own analyses without consulting additional resources. Examples from social work literature highlight best practices for the specification, estimation, interpretation, and modification of structural equation models. Oftentimes, confirmatory factor analysis and general structure modeling are the most flexible, powerful, and appropriate choices for social work data.

J. Gilmer, S. Schoenholz, Patrick F. Riley et al.

Supervised learning on molecules has incredible potential to be useful in chemistry, drug discovery, and materials science. Luckily, several promising and closely related neural network models invariant to molecular symmetries have already been described in the literature. These models learn a message passing algorithm and aggregation procedure to compute a function of their entire input graph. At this point, the next step is to find a particularly effective variant of this general approach and apply it to chemical prediction benchmarks until we either solve them or reach the limits of the approach. In this paper, we reformulate existing models into a single common framework we call Message Passing Neural Networks (MPNNs) and explore additional novel variations within this framework. Using MPNNs we demonstrate state of the art results on an important molecular property prediction benchmark; these results are strong enough that we believe future work should focus on datasets with larger molecules or more accurate ground truth labels.

M. Peters, C. Godfrey, H. Khalil et al.

Daniel E. Olivares, A. Mehrizi‐Sani, A. Etemadi et al.

The increasing interest in integrating intermittent renewable energy sources into microgrids presents major challenges from the viewpoints of reliable operation and control. In this paper, the major issues and challenges in microgrid control are discussed, and a review of state-of-the-art control strategies and trends is presented; a general overview of the main control principles (e.g., droop control, model predictive control, multi-agent systems) is also included. The paper classifies microgrid control strategies into three levels: primary, secondary, and tertiary, where primary and secondary levels are associated with the operation of the microgrid itself, and tertiary level pertains to the coordinated operation of the microgrid and the host grid. Each control level is discussed in detail in view of the relevant existing technical literature.

F. Boons, Florian Lüdeke‐Freund

Oscar Bartra, Joseph T. McGuire, J. Kable

A. Moorhead, Diane E Hazlett, L. Harrison et al.

Background There is currently a lack of information about the uses, benefits, and limitations of social media for health communication among the general public, patients, and health professionals from primary research. Objective To review the current published literature to identify the uses, benefits, and limitations of social media for health communication among the general public, patients, and health professionals, and identify current gaps in the literature to provide recommendations for future health communication research. Methods This paper is a review using a systematic approach. A systematic search of the literature was conducted using nine electronic databases and manual searches to locate peer-reviewed studies published between January 2002 and February 2012. Results The search identified 98 original research studies that included the uses, benefits, and/or limitations of social media for health communication among the general public, patients, and health professionals. The methodological quality of the studies assessed using the Downs and Black instrument was low; this was mainly due to the fact that the vast majority of the studies in this review included limited methodologies and was mainly exploratory and descriptive in nature. Seven main uses of social media for health communication were identified, including focusing on increasing interactions with others, and facilitating, sharing, and obtaining health messages. The six key overarching benefits were identified as (1) increased interactions with others, (2) more available, shared, and tailored information, (3) increased accessibility and widening access to health information, (4) peer/social/emotional support, (5) public health surveillance, and (6) potential to influence health policy. Twelve limitations were identified, primarily consisting of quality concerns and lack of reliability, confidentiality, and privacy. Conclusions Social media brings a new dimension to health care as it offers a medium to be used by the public, patients, and health professionals to communicate about health issues with the possibility of potentially improving health outcomes. Social media is a powerful tool, which offers collaboration between users and is a social interaction mechanism for a range of individuals. Although there are several benefits to the use of social media for health communication, the information exchanged needs to be monitored for quality and reliability, and the users’ confidentiality and privacy need to be maintained. Eight gaps in the literature and key recommendations for future health communication research were provided. Examples of these recommendations include the need to determine the relative effectiveness of different types of social media for health communication using randomized control trials and to explore potential mechanisms for monitoring and enhancing the quality and reliability of health communication using social media. Further robust and comprehensive evaluation and review, using a range of methodologies, are required to establish whether social media improves health communication practice both in the short and long terms.

D. Foreman-Mackey, D. Hogg, D. Lang et al.

We introduce a stable, well tested Python implementation of the affine-invariant ensemble sampler for Markov chain Monte Carlo (MCMC) proposed by Goodman & Weare (). The code is open source and has already been used in several published projects in the astrophysics literature. The algorithm behind emcee has several advantages over traditional MCMC sampling methods and it has excellent performance as measured by the autocorrelation time (or function calls per independent sample). One major advantage of the algorithm is that it requires hand-tuning of only 1 or 2 parameters compared to ∼N2 for a traditional algorithm in an N-dimensional parameter space. In this document, we describe the algorithm and the details of our implementation. Exploiting the parallelism of the ensemble method, emcee permits any user to take advantage of multiple CPU cores without extra effort. The code is available online at http://dan.iel.fm/emcee under the GNU General Public License v2.

H. Cooper

C. Graf, Dirk Vossen, A. Imhof et al.

J. Landis, G. Koch

S. Nix, Michelle D. Smith, B. Vicenzino

BackgroundHallux valgus (HV) is a foot deformity commonly seen in medical practice, often accompanied by significant functional disability and foot pain. Despite frequent mention in a diverse body of literature, a precise estimate of the prevalence of HV is difficult to ascertain. The purpose of this systematic review was to investigate prevalence of HV in the overall population and evaluate the influence of age and gender.MethodsElectronic databases (Medline, Embase, and CINAHL) and reference lists of included papers were searched to June 2009 for papers on HV prevalence without language restriction. MeSH terms and keywords were used relating to HV or bunions, prevalence and various synonyms. Included studies were surveys reporting original data for prevalence of HV or bunions in healthy populations of any age group. Surveys reporting prevalence data grouped with other foot deformities and in specific disease groups (e.g. rheumatoid arthritis, diabetes) were excluded. Two independent investigators quality rated all included papers on the Epidemiological Appraisal Instrument. Data on raw prevalence, population studied and methodology were extracted. Prevalence proportions and the standard error were calculated, and meta-analysis was performed using a random effects model.ResultsA total of 78 papers reporting results of 76 surveys (total 496,957 participants) were included and grouped by study population for meta-analysis. Pooled prevalence estimates for HV were 23% in adults aged 18-65 years (CI: 16.3 to 29.6) and 35.7% in elderly people aged over 65 years (CI: 29.5 to 42.0). Prevalence increased with age and was higher in females [30% (CI: 22 to 38)] compared to males [13% (CI: 9 to 17)]. Potential sources of bias were sampling method, study quality and method of HV diagnosis.ConclusionsNotwithstanding the wide variation in estimates, it is evident that HV is prevalent; more so in females and with increasing age. Methodological quality issues need to be addressed in interpreting reports in the literature and in future research.

M. Lîebert, B.M.B. LJ. HIGGINSON, Ph. G.J.A. SEN-GUPTA

Daniel W Piepers, Rachel A Robbins

It is widely agreed that the human face is processed differently from other objects. However there is a lack of consensus on what is meant by a wide array of terms used to describe this “special” face processing (e.g., holistic and configural) and the perceptually relevant information within a face (e.g., relational properties and configuration). This paper will review existing models of holistic/configural processing, discuss how they differ from one another conceptually, and review the wide variety of measures used to tap into these concepts. In general we favor a model where holistic processing of a face includes some or all of the interrelations between features and has separate coding for features. However, some aspects of the model remain unclear. We propose the use of moving faces as a way of clarifying what types of information are included in the holistic representation of a face.

Quang-Cuong Bui, Emanuele Iannone, Maria Camporese et al.

The exploit or the Proof of Concept of the vulnerability plays an important role in developing superior vulnerability repair techniques, as it can be used as an oracle to verify the correctness of the patches generated by the tools. However, the vulnerability exploits are often unavailable and require time and expert knowledge to craft. Obtaining them from the exploit generation techniques is another potential solution. The goal of this survey is to aid the researchers and practitioners in understanding the existing techniques for exploit generation through the analysis of their characteristics and their usability in practice. We identify a list of exploit generation techniques from literature and group them into four categories: automated exploit generation, security testing, fuzzing, and other techniques. Most of the techniques focus on the memory-based vulnerabilities in C/C++ programs and web-based injection vulnerabilities in PHP and Java applications. We found only a few studies that publicly provided usable tools associated with their techniques.

Levent Gülüm, Süheyla Esin Köksal, Emrah Güler et al.

The objective of this study was to evaluate the physicochemical, bioactive, and technological properties of pasta made from durum wheat semolina that was partially replaced with Acorn flour at levels of 10%, 20%, and 30%. The incorporation of Acorn flour had a substantial impact on the nutritional composition of the pasta, resulting in increases in total phenolic content (TPC), total flavonoid content (TFC), and antioxidant capacity in comparison with the control sample. The highest values for TPC and TFC were found in the samples containing 20% and 30% Acorn flour (p<0.05), demonstrating the functional potential of this formulation. However, an increase in the quantity of Acorn flour used in the pasta production process resulted in a noticeable darkening of the pasta's colour. This observation is consistent with the findings of previous research conducted on the use of non-traditional flours. While the increased amounts of Acorn flour resulted in enhanced nutritional and antioxidant profiles, the darker appearance and alterations in texture may have implications for sensory and visual acceptability. The present findings are corroborated by extant literature, which demonstrates that functional flours such as buckwheat, chickpea, lentil, chia, and sorghum have exhibited analogous trends in enhancing bioactive compounds and altering technological properties. Incorporation of Acorn flour at levels ranging from 10% to 20% optimises the health benefits of pasta while maintaining its desirable sensory and structural characteristics. Presented research contributes to the valorization of non-wood forest product (NWFP) resources and the development of innovative functional pasta products using sustainable ingredients.

Chengbo Yuan, Chuan Wen, Tong Zhang et al.

We address the challenge of acquiring real-world manipulation skills with a scalable framework. We hold the belief that identifying an appropriate prediction target capable of leveraging large-scale datasets is crucial for achieving efficient and universal learning. Therefore, we propose to utilize 3D flow, which represents the future trajectories of 3D points on objects of interest, as an ideal prediction target. To exploit scalable data resources, we turn our attention to human videos. We develop, for the first time, a language-conditioned 3D flow prediction model directly from large-scale RGBD human video datasets. Our predicted flow offers actionable guidance, thus facilitating zero-shot skill transfer in real-world scenarios. We deploy our method with a policy based on closed-loop flow prediction. Remarkably, without any in-domain finetuning, our method achieves an impressive 81\% success rate in zero-shot human-to-robot skill transfer, covering 18 tasks in 6 scenes. Our framework features the following benefits: (1) scalability: leveraging cross-embodiment data resources; (2) wide application: multiple object categories, including rigid, articulated, and soft bodies; (3) stable skill transfer: providing actionable guidance with a small inference domain-gap. Code, data, and supplementary materials are available https://general-flow.github.io

Dmytro Valiaiev

Since language models produce fake text quickly and easily, there is an oversupply of such content in the public domain. The degree of sophistication and writing style has reached a point where differentiating between human authored and machine-generated content is nearly impossible. As a result, works generated by language models rather than human authors have gained significant media attention and stirred controversy.Concerns regarding the possible influence of advanced language models on society have also arisen, needing a fuller knowledge of these processes. Natural language generation (NLG) and generative pre-trained transformer (GPT) models have revolutionized a variety of sectors: the scope not only permeated throughout journalism and customer service but also reached academia. To mitigate the hazardous implications that may arise from the use of these models, preventative measures must be implemented, such as providing human agents with the capacity to distinguish between artificially made and human composed texts utilizing automated systems and possibly reverse-engineered language models. Furthermore, to ensure a balanced and responsible approach, it is critical to have a full grasp of the socio-technological ramifications of these breakthroughs. This literature survey aims to compile and synthesize accomplishments and developments in the aforementioned work, while also identifying future prospects. It also gives an overview of machine-generated text trends and explores the larger societal implications. Ultimately, this survey intends to contribute to the development of robust and effective approaches for resolving the issues connected with the usage and detection of machine-generated text by exploring the interplay between the capabilities of language models and their possible implications.