nils-herrmann/clean_scirepeval huggingface dataset:
Chapter 2 Machine Learning : An Indispensable Tool in Bioinformatics

The increase in the number and complexity of biological databases has raised the need for modern and powerful data analysis tools and techniques. In order to fulfill these requirements, the machine learning discipline has become an everyday tool in bio-laboratories. The use of machine learning techniques has been extended to a wide spectrum of bioinformatics applications. It is broadly used to investigate the underlying mechanisms and interactions between biological molecules in many diseases, and it is an essential tool in any biomarker discovery process. In this chapter, we provide a basic taxonomy of machine learning algorithms, and the characteristics of main data preprocessing, supervised classification, and clustering techniques are shown. Feature selection, classifier evaluation, and two supervised classification topics that have a deep impact on current bioinformatics are presented. We make the interested reader aware of a set of popular web resources, open source software tools, and benchmarking data repositories that are frequently used by the machine
Automatic number plate recognition

The main focus in this research paper is to experiment deeply with, and find alternative solutions to the image segmentation and character recognition problems within the License Plate Recognition framework. Three main stages are identified in such applications. First, it is necessary to locate and extract the license plate region from a larger scene image. Second, having a license plate region to work with, the alphanumeric characters in the plate need to be extracted from the background. Third, deliver them to an character system (BOX APPROACH)for recognition. In order to identify a vehicle by reading its license plate successfully, it is obviously necessary to locate the plate in the scene image provided by some acquisition system (e.g. video or still camera). Locating the region of interest helps in dramatically reducing both the computational expense and algorithm complexity. For example, a currently common1024x768 resolution image contains a total of 786,432pixels, while the region of interest (in this case a license plate) may account for only 10% of the image area. Also, the input to the following segmentation and recognition stages is simplified, resulting in easier algorithm design and shorter computation times. The paper mainly work with the standard license plates but the techniques, algorithms and parameters that is be used can be adjusted easily for any similar number plates even with other alpha-numeric set.
Principled Uncertainty Estimation for Deep Neural Networks

When the cost of misclassifying a sample is high, it is useful to have an accurate estimate of uncertainty in the prediction for that sample. There are also multiple types of uncertainty which are best estimated in different ways, for example, uncertainty that is intrinsic to the training set may be well-handled by a Bayesian approach, while uncertainty introduced by shifts between training and query distributions may be better-addressed by density/support estimation. In this paper, we examine three types of uncertainty: model capacity uncertainty, intrinsic data uncertainty, and open set uncertainty, and review techniques that have been derived to address each one. We then introduce a unified hierarchical model, which combines methods from Bayesian inference, invertible latent density inference, and discriminative classification in a single end-to-end deep neural network topology to yield efficient per-sample uncertainty estimation. Our approach addresses all three uncertainty types and readily accommodates prior/base rates for binary detection.
Enterprise Applications: a Conceptual Look at Erp, Crm, and Scm

The telecommunications industry, like many others, is experiencing a watershed. No longer can customers pursue technological advances just for technology's sake. Technology must support real, measurable, and innovative goals of the enterprise. The technologies and terms that comprise every major provider's portfolio are starting to look and sound alike. New product offerings appear almost identical to existing products in the same market. The terms VPN, MPLS, convergence, the ubiquitous " IP, " service level agreements (SLA), single points of contact, managed network services, and global footprints are important in the telecommunications market, but we have heard them all before. The competitive differentiation that service providers desperately seek will not occur on this homogenous slate of technology and service offerings. Only when service providers truly understand what is happening from the customer's perspective will real competitive differentiation take place. Providers must realize that they do not drive the networking and telecom environment; the customers' strategic and tactical objectives drive it. If service providers wish to position at higher levels in the corporation, they must change the way they communicate. Such communication should not only show an understanding of the enterprise applications themselves but also an understanding of how the applications relate to the service providers' product set. This paper will outline three (of the many) enterprise applications and business drivers service providers can use to differentiate themselves. We will examine the concepts of data warehousing and data mining for the purpose of effective enterprise resource planning (ERP), customer relationship management (CRM), and supply chain management (SCM). We will define the major aspects of each, examine the drivers and impacts of each, and consider how each relates to the service providers' product sets.
KMPI: measuring knowledge management performance

This paper provides a new metric, knowledge management performance index (KMPI), for assessing the performance of a firm in its knowledge management (KM) at a point in time. Firms are assumed to have always been oriented toward accumulating and applying knowledge to create economic value and competitive advantage. We therefore suggest the need for a KMPI which we have defined as a logistic function having five components that can be used to determine the knowledge circulation process (KCP): knowledge creation, knowledge accumulation, knowledge sharing, knowledge utilization, and knowledge internalization. When KCP efficiency increases, KMPI will also expand, enabling firms to become knowledgeintensive. To prove KMPI’s contribution, a questionnaire survey was conducted on 101 firms listed in the KOSDAQ market in Korea. We associated KMPI with three financial measures: stock price, price earnings ratio (PER), and R&D expenditure. Statistical results show that the proposed KMPI can represent KCP efficiency, while the three financial performance measures are also useful.
Sentence Encoders on STILTs: Supplementary Training on Intermediate Labeled-data Tasks

Pretraining with language modeling and related unsupervised tasks has recently been shown to be a very effective enabling technology for the development of neural network models for language understanding tasks. In this work, we show that although language model-style pretraining is extremely effective at teaching models about language, it does not yield an ideal starting point for efficient transfer learning. By supplementing language model-style pretraining with further training on data-rich supervised tasks, we are able to achieve substantial additional performance improvements across the nine target tasks in the GLUE benchmark. We obtain an overall score of 76.9 on GLUE—a 2.3 point improvement over our baseline system adapted from Radford et al. (2018) and a 4.1 point improvement over Radford et al.’s reported score. We further use training data downsampling to show that the benefits of this supplementary training are even more pronounced in data-constrained regimes.
Distributed Time-Varying Graph Filtering

One of the cornerstones of the field of signal processing on graphs are graph filters, direct analogues of classical filters, but intended for signals defined on graphs. This work brings forth new insights on the distributed graph filtering problem. We design a family of autoregressive moving average (ARMA) recursions, which (i) are able to approximate any desired graph frequency response, and (ii) give exact solutions for tasks such as graph signal denoising and interpolation. The design philosophy, which allows us to design the ARMA coefficients independently from the underlying graph, renders the ARMA graph filters suitable in static and, particularly, time-varying settings. The latter occur when the graph signal and/or graph are changing over time. We show that in case of a time-varying graph signal our approach extends naturally to a two-dimensional filter, operating concurrently in the graph and regular time domains. We also derive sufficient conditions for filter stability when the graph and signal are time-varying. The analytical and numerical results presented in this paper illustrate that ARMA graph filters are practically appealing for static and time-varying settings, accompanied by strong theoretical guarantees.  distributed graph filtering, signal processing on graphs, time-varying graph signals, time-varying graphs
1 Chitosan Derivatives : Introducing New 2 Functionalities with a Controlled Molecular 3 Architecture for Innovative Materials † 4

The functionalization of polymeric substances is of great interest for the development of 15 innovative materials for advanced applications. For many decades, the functionalization of 16 chitosan has been a convenient way to improve its properties with the aim to prepare new 17 materials with specialized characteristics. In the present article, we summarize the latest methods 18 for the modification and derivatization of chitin and chitosan, trying to introduce specific 19 functional groups under experimental conditions, which allow a control over the macromolecular 20 architecture. This is motivated because an understanding of the interdependence between chemical 21 structure and properties is an important condition for proposing innovative materials. New 22 advances in methods and strategies of functionalization such as click chemistry approach, grafting 23 onto copolymerization, coupling with cyclodextrins and reactions in ionic liquids are discussed. 24
Deep Air Learning: Interpolation, Prediction, and Feature Analysis of Fine-Grained Air Quality

The interpolation, prediction, and feature analysis of fine-gained air quality are three important topics in the area of urban air computing. The solutions to these topics can provide extremely useful information to support air pollution control, and consequently generate great societal and technical impacts. Most of the existing work solves the three problems separately by different models. In this paper, we propose a general and effective approach to solve the three problems in one model called the Deep Air Learning (DAL). The main idea of DAL lies in embedding feature selection and semi-supervised learning in different layers of the deep learning network. The proposed approach utilizes the information pertaining to the unlabeled spatio-temporal data to improve the performance of the interpolation and the prediction, and performs feature selection and association analysis to reveal the main relevant features to the variation of the air quality. We evaluate our approach with extensive experiments based on real data sources obtained in Beijing, China. Experiments show that DAL is superior to the peer models from the recent literature when solving the topics of interpolation, prediction, and feature analysis of fine-gained air quality.
Principles and Safety Measures of Electrosurgery in Laparoscopy

Electrosurgical units are the most common type of electrical equipment in the operating room. A basic understanding of electricity is needed to safely apply electrosurgical technology for patient care.



We reviewed the literature concerning the essential biophysics, the incidence of electrosurgical injuries, and the possible mechanisms for injury. Various safety guidelines pertaining to avoidance of injuries were also reviewed.



Electrothermal injury may result from direct application, insulation failure, direct coupling, capacitive coupling, and so forth.



A thorough knowledge of the fundamentals of electrosurgery by the entire team in the operating room is essential for patient safety and for recognizing potential complications. Newer hemostatic technologies can be used to decrease the incidence of complications.
Design and electro-thermal analysis of a platinum micro heater for gas sensors

Micro Hotplate (MHP) is the key component in micro-sensors particularly gas sensors. In this paper, we have presented the design and simulation results of a meander micro heater based on platinum material. A comparative study by simulating two different heater thicknesses has also been presented in this paper. The membrane size is 1.4mm × 1.6mm and a thickness of 1.4μm. Above the membrane, a platinum film was deposed with a size of 1.1 × 1.1 mm and a various thickness of 0.1 μm and 0.15 μm. Power consumption and temperature distribution were determined in the Platinum micro heater's structure over a supply voltage of 5, 6 and 7 V.
Incorporating visual features into word embeddings: A bimodal autoencoder-based approach

Multimodal semantic representation is an evolving area of research in natural language processing as well as computer vision. Combining or integrating perceptual information, such as visual features, with linguistic features is recently being actively studied. This paper presents a novel bimodal autoencoder model for multimodal representation learning: the autoencoder learns in order to enhance linguistic feature vectors by incorporating the corresponding visual features. During the runtime, owing to the trained neural network, visually enhanced multimodal representations can be achieved even for words for which direct visual-linguistic correspondences are not learned. The empirical results obtained with standard semantic relatedness tasks demonstrate that our approach is generally promising. We further investigate the potential efficacy of the enhanced word embeddings in discriminating antonyms and synonyms from vaguely related words.
An improved density peaks-based clustering method for social circle discovery in social networks

With the development of Internet, social networks have become important platforms which allow users to follow streams of posts generated by their friends and acquaintances. Through mining a collection of nodes with similarities, community detection can make us understand the characteristics of complex network deeply. Therefore, community detection has attracted increasing attention in recent years. Since targeted at on-line social networks, we investigate how to exploit user's profile and topological structure information in social circle discovery. Firstly, according to directionality of linkages, we put forward inlink Salton metric and out-link Salton metric to measure user's topological structure. Then we propose an improved density peaks-based clustering method and deploy it to discover social circles with overlap on account of user's profileand topological structure-based features. Experiments on real-world dataset demonstrate the effectiveness of the proposed framework. Further experiments are conducted to understand the importance of different parameters and different features in social circle discovery. & 2015 Elsevier B.V. All rights reserved.
Deep Learning Stock Volatility with Google Domestic Trends

We have applied a Long Short-Term Memory neural network to model S&P 500 volatility, incorporating Google domestic trends as indicators of the public mood and macroeconomic factors. In a held-out test set, our Long Short-Term Memory model gives a mean absolute percentage error of 24.2%, outperforming linear Ridge/Lasso and autoregressive GARCH benchmarks by at least 31%. This evaluation is based on an optimal observation and normalization scheme which maximizes the mutual information between domestic trends and daily volatility in the training set. Our preliminary investigation shows strong promise for better predicting stock behavior via deep learning and neural network models.
The importance of gesture in children's spatial reasoning.

On average, men outperform women on mental rotation tasks. Even boys as young as 4 1/2 perform better than girls on simplified spatial transformation tasks. The goal of our study was to explore ways of improving 5-year-olds' performance on a spatial transformation task and to examine the strategies children use to solve this task. We found that boys performed better than girls before training and that both boys and girls improved with training, whether they were given explicit instruction or just practice. Regardless of training condition, the more children gestured about moving the pieces when asked to explain how they solved the spatial transformation task, the better they performed on the task, with boys gesturing about movement significantly more (and performing better) than girls. Gesture thus provides useful information about children's spatial strategies, raising the possibility that gesture training may be particularly effective in improving children's mental rotation skills.
Review on thermal energy storage with phase change materials and applications

The use of a latent heat storage system using phase change materials (PCMs) is an effective way of storing thermal energy and has the advantages of high-energy storage density and the isothermal nature of the storage process. PCMs have been widely used in latent heat thermalstorage systems for heat pumps, solar engineering, and spacecraft thermal control applications. The uses of PCMs for heating and cooling applications for buildings have been investigated within the past decade. There are large numbers of PCMs that melt and solidify at a wide range of temperatures, making them attractive in a number of applications. This paper also summarizes the investigation and analysis of the available thermal energy storage systems incorporating PCMs for use in different applications.
Mental Health in Sexual Minority and Transgender Women.

Although sexual minority women (SMW) and transgender women have become increasingly visible in recent years and have made progress in achieving civil rights, they continue to face significant levels of discrimination, stigma, and physical violence. As a result, each group faces a wide variety of health disparities, including mental illness and substance use disorders. Overall, both SMW and transgender women experience higher rates of mood and anxiety disorders, suicidality, and substance use disorders than their heterosexual and cisgender counterparts. This article is a general introduction to these issues and concludes with recommendations for working with sexual minority and transgender women.
Interpretable Deep Models for ICU Outcome Prediction

Exponential surge in health care data, such as longitudinal data from electronic health records (EHR), sensor data from intensive care unit (ICU), etc., is providing new opportunities to discover meaningful data-driven characteristics and patterns ofdiseases. Recently, deep learning models have been employedfor many computational phenotyping and healthcare prediction tasks to achieve state-of-the-art performance. However, deep models lack interpretability which is crucial for wide adoption in medical research and clinical decision-making. In this paper, we introduce a simple yet powerful knowledge-distillation approach called interpretable mimic learning, which uses gradient boosting trees to learn interpretable models and at the same time achieves strong prediction performance as deep learning models. Experiment results on Pediatric ICU dataset for acute lung injury (ALI) show that our proposed method not only outperforms state-of-the-art approaches for morality and ventilator free days prediction tasks but can also provide interpretable models to clinicians.
Overview of Deep Learning Architectures for Classifying Brain Signals

One of the challenges in modeling cognitive events from electroencephalogram (EEG) data is finding representations that are invariant to interand intra-subject differences, as well as the inherent noise associated with EEG data collection. Herein, we explore the capabilities of the recent deep neural architectures for modeling cognitive events from EEG data. In this paper, we present recent achievements applying deep learning for EEG signal classification. We investigate the use of feed forward, convolutional, recurrent neural nets, as well as deep belief networks, echo-state networks, reservoir computing, and denoising auto encoder models. We present the application of these architectures for classifying user intent generated through different motor imagery; BCI to control wheelchair and robotic arm; mental load classification; discriminating emotional state; feature dimensionality reduction for EEG data. Many of the models prove to be more accurate and more efficient than current state-of-the-art models.
The aspectual makeup of Perfect participles and the interpretations of the Perfect *

1. The types of perfect Interpretation-wise, several types of perfect expressions have been recognized in the literature (e. To illustrate, a present perfect can have one of at least three interpretations: (1) a. Since 2000, Alexandra has lived in LA.  Alexandra has been in LA (before). EXPERIENTIAL c. Alexandra has (just) arrived in LA.  The three types of perfect make different claims about the temporal location of the underlying eventuality, i.e., of live in LA in (1a), be in LA in (1b), arrive in LA in (1c), with respect to a reference time. The UNIVERSAL perfect, as in (1a), asserts that the underlying eventuality holds throughout an interval, delimited by the time of utterance and a certain time in the past (in this case, the year 2000). The EXPERIENTIAL perfect, as in (1b), asserts that the underlying eventuality holds at a proper subset of an interval, extending back from the utterance time. The RESULTATIVE perfect makes the same assertion as the Experiential perfect, with the added meaning that the result of the underlying eventuality (be in LA is the result of arrive in LA) holds at the utterance time. The distinction between the Experiential and the Resultative perfects is rather subtle. The two are commonly grouped together as the EXISTENTIAL perfect (McCawley 1971, Mittwoch 1988) and this terminology is adopted here as well. 1 Two related questions arise: (i) Is the distinction between the three types of perfect grammatically based? (ii) If indeed so, then is it still possible to posit a common representation for the perfect – a uniform structure with a single meaning – which, in combination with certain other syntactic components , each with a specialized meaning, results in the three different readings? This paper suggests that the answer to both questions is yes. To start addressing these questions, let us look at some of the known factors behind the various interpretations of the perfect. It has to be noted that the different perfect readings are not a peculiarity of the present perfect despite the fact that they are primarily discussed in relation to that form. The same interpretations are available to the past, future and nonfinite per
Trick Me If You Can: Adversarial Writing of Trivia Challenge Questions

Modern question answering systems have been touted as approaching human performance. However, existing question answering datasets are imperfect tests. Questions are written with humans in mind, not computers, and often do not properly expose model limitations. To address this, we develop an adversarial writing setting, where humans interact with trained models and try to break them. This annotation process yields a challenge set, which despite being easy for trivia players to answer, systematically stumps automated question answering systems. Diagnosing model errors on the evaluation data provides actionable insights to explore in developing robust and generalizable question answering systems.