nils-herrmann/clean_scirepeval huggingface dataset:
Training word embeddings for deep learning in biomedical text mining tasks

Most word embedding methods are proposed with general purpose which take a word as a basic unit and learn embeddings according to words' external contexts. However, in biomedical text mining, there are many biomedical entities and syntactic chunks which contain rich domain information, and the semantic meaning of a word is also strongly related to those information. Hence, we present a biomedical domain-specific word embedding model by incorporating stem, chunk and entity to train word embeddings. We also present two deep learning architectures respectively for two biomedical text mining tasks, by which we evaluate our word embeddings and compare them with other models. Experimental results show that our biomedical domain-specific word embeddings overall outperform other general-purpose word embeddings in these deep learning methods for biomedical text mining tasks.
The treatment gap in mental health care.

Mental disorders are highly prevalent and cause considerable suffering and disease burden. To compound this public health problem, many individuals with psychiatric disorders remain untreated although effective treatments exist. We examine the extent of this treatment gap. We reviewed community-based psychiatric epidemiology studies that used standardized diagnostic instruments and included data on the percentage of individuals receiving care for schizophrenia and other non-affective psychotic disorders, major depression, dysthymia, bipolar disorder, generalized anxiety disorder (GAD), panic disorder, obsessive-compulsive disorder (OCD), and alcohol abuse or dependence. The median rates of untreated cases of these disorders were calculated across the studies. Examples of the estimation of the treatment gap for WHO regions are also presented. Thirty-seven studies had information on service utilization. The median treatment gap for schizophrenia, including other non-affective psychosis, was 32.2%. For other disorders the gap was: depression, 56.3%; dysthymia, 56.0%; bipolar disorder, 50.2%; panic disorder, 55.9%; GAD, 57.5%; and OCD, 57.3%. Alcohol abuse and dependence had the widest treatment gap at 78.1%. The treatment gap for mental disorders is universally large, though it varies across regions. It is likely that the gap reported here is an underestimate due to the unavailability of community-based data from developing countries where services are scarcer. To address this major public health challenge, WHO has adopted in 2002 a global action programme that has been endorsed by the Member States.
Gradient Descent Learns Linear Dynamical Systems

We prove that stochastic gradient descent efficiently converges to the global optimizer of the maximum likelihood objective of an unknown linear time-invariant dynamical system from a sequence of noisy observations generated by the system. Even though the objective function is non-convex, we provide polynomial running time and sample complexity bounds under strong but natural assumptions. Linear systems identification has been studied for many decades, yet, to the best of our knowledge, these are the first polynomial guarantees for the problem we consider.  bounds, time series, over-parameterization
Conjoined twins: implications for blastogenesis.

It is difficult to draw sweeping general conclusions about the blastogenesis of CT, principally because so few thoroughly studied cases are reported. It is to be hoped that methods such as painstaking gross or electronic dissection will increase the number of well-documented cases. Nevertheless, the following conclusions can be proposed: 1. Most CT can be classified into a few main anatomic types (or paradigms), and there are also rare transitional types that show gradation between the main types. 2. Most CT have two full notochordal axes ; the ventral organs induced along these axes may be severely disorientated, malformed, or aplastic in the process of being arranged within one body. Reported anatomic types of CT represent those notochordal arrangements that are compatible with reasonably complete embryogenesis. New ventro-lateral axes are formed in many types of CT because of space constriction in the ventral zones. The new structures represent areas of "mutual recognition and organization" rather than "fusion" . 3. Orientations of the pairs of axes in the embryonic disc can be deduced from the resulting anatomy. Except for dicephalus, the axes are not side by side. Notochords are usually "end-on" or ventro-ventral in orientation . 4. A single gastrulation event or only partial duplicated gastrulation event seems to occur in dicephalics, despite a full double notochord. 5. The anatomy of diprosopus requires further clarification, particularly in cases with complete crania rather than anencephaly-equivalent. Diprosopus CT offer the best opportunity to study the effects of true forking of the notochord, if this actually occurs. 6. In cephalothoracopagus, thoracopagus, and ischiopagus, remarkably complete new body forms are constructed at right angles to the notochordal axes. The extent of expression of viscera in these types depends on the degree of noncongruity of their ventro-ventral axes (Figs. 4, 11, 15b). 7. Some organs and tissues fail to develop (interaction aplasia) because of conflicting migrational pathways or abnormal concentrations of morphogens in and around the neoaxes. 8. Where the cardiovascular system is discordantly expressed in dicephalus and thoracopagus twins, the right heart is more severely malformed, depending on the degree of interaction of the two embryonic septa transversa. 9. The septum transversum provides mesenchymal components to the heawrt and liver; the epithelial components (derived fro the foregut[s]) may vary in number from the number of mesenchymal septa transversa contributing to the liver of the CT embryo.(ABSTRACT TRUNCATED AT 400
Automatic detection and recognition of road sign for driver assistance system

Automatic road-signs recognition is becoming a part of Driver Assisting Systems which role is to increase safety and driving comfort. This paper presents an efficient approach for detecting and recognizing road sign in traffic scene images acquired from a moving vehicle. The developed road sign recognition system is divided into two stages: detection stage to localize signs from a whole image, and classification stage that classifies the detected sign into one of the reference signs. The detection module segments the input image in the YCBCR colour space, and then detects road signs using a shape filtering method. The classification module determines the type of detected road signs using a Multi-layer Perceptron neural networks. An extensive experimentation has shown that the proposed approach is robust enough to detect and recognize road signs under varying translation, rotation and lighting conditions.
A prototype of PC based remote control of irrigation

In recent years, more efficient and positive use of current water resources together with global warming becomes important. New technologies and ideas have been developed for many years to optimal use of water resources especially in agricultural field. Growers irrigate their own areas uniformly. However demand of water, fertilizer and agricultural chemicals are different for each trees or crops depending on plant ages and chemical content of soil. Determination of water demand for crops and trees is important to protect fresh water resources. In this study, a prototype of solar powered, low cost, remote controlled real time monitoring irrigation system was designed to control drip irrigation. Software (ValCon, developed by authors with C# language in Visual Studio.Net 2008 editor) was developed to control irrigation valve and monitor water content of soil. Control method of irrigation (automatic or manual) can be selected by users. Only water content of soil was monitored. Nevertheless by using sensors which measure other features of water or air, it is also possible to extend the designed system. Remote controlled site-specific irrigation scheme prevents moisture stress of trees and salification besides providing the efficient use of fresh water resources. Also, this irrigation method removes labour that is needed for flooding irrigation
Environmental context and human memory

Five experiments examined the effects of environmental context on recall and recognition. In Experiment 1, variability of input environments produced higher free recall performance than unchanged input environments. Experiment 2 showed improvements in cued recall when storage and test contexts matched, using a paradigm that unconfounded the variables of context mismatching and context change. In Experiment 3, recall of categories and recall of words within a category were better for same-context than different-context recall. In Experiment 4, subjects given identical input conditions showed strong effects of environmental context when given a free recall test, yet showed no main effects of context on a recognition test. The absence of an environmental context effect on recognition was replicated in Experiment 5, using a cued recognition task to control the semantic encodings of test words. In the discussion of these experiments, environmental context is compared with other types of context, and an attempt is made to identify the memory processes influenced by environmental context.
Eigenvalues of the Hessian in Deep Learning: Singularity and Beyond

We look at the eigenvalues of the Hessian of a loss function before and after training. The eigenvalue distribution is seen to be composed of two parts, the bulk which is concentrated around zero, and the edges which are scattered away from zero. We present empirical evidence for the bulk indicating how over-parametrized the system is, and for the edges that depend on the input data.
Validation, Verification, and Testing of Computer Software

Software quahty is achieved through the apphcatlon of development techniques and the use of verification procedures throughout the development process Careful consideratmn of specific quality attmbutes and validation reqmrements leads to the selection of a balanced collection of review, analysis, and testing techmques for use throughout the life cycle. This paper surveys current verification, validation, and testing approaches and discusses their strengths, weaknesses, and life-cycle usage. In conjunction with these, the paper describes automated tools used to nnplement vahdation, verification, and testmg. In the discussion of new research thrusts, emphasis is gwen to the continued need to develop a stronger theoretical basis for testing and the need to employ combinations of tools and techniques that may vary over each apphcation.
Design And Implementation Of 2 Bit Ternary ALU Slice

This paper describes the architecture, design & implementation of 2 bit ternary ALU (T-ALU) slice. The proposed ALU is designed for two-bit operation & can be used for n bit operations by cascading n/2 ALU slices. This ALU is implemented using C-MOS ternary logic gates (T-Gates) for ternary arithmetic & logic circuits. Ternary gates are implemented using enhancement / depletion MOSFET technology, thus proposed ALU is suitable for LSI / VLSI implementation. The designed technique used here requires only two stages i.e . decoder & T-gates, as against three stages i.e. decoder, binary gates & encoder require in conventional ternary logic implementation.  Ternary, Unary function, T-Gates, Literal.
Recurrent Convolutional Neural Networks for Discourse Compositionality

The compositionality of meaning extends beyond the single sentence. Just as words combine to form the meaning of sentences, so do sentences combine to form the meaning of paragraphs, dialogues and general discourse. We introduce both a sentence model and a discourse model corresponding to the two levels of compositionality. The sentence model adopts convolution as the central operation for composing semantic vectors and is based on a novel hierarchical convolutional neural network. The discourse model extends the sentence model and is based on a recurrent neural network that is conditioned in a novel way both on the current sentence and on the current speaker. The discourse model is able to capture both the sequentiality of sentences and the interaction between different speakers. Without feature engineering or pretraining and with simple greedy decoding, the discourse model coupled to the sentence model obtains state of the art performance on a dialogue act classification experiment.
A Context-Enriched Neural Network Method for Recognizing Lexical Entailment

Recognizing lexical entailment (RLE) always plays an important role in inference of natural language, i.e., identifying whether one word entails another, for example, fox entails animal. In the literature, automatically recognizing lexical entailment for word pairs deeply relies on words’ contextual representations. However, as a “prototype” vector, a single representation cannot reveal multifaceted aspects of the words due to their homonymy and polysemy. In this paper, we propose a supervised Context-Enriched Neural Network (CENN) method for recognizing lexical entailment. To be specific, we first utilize multiple embedding vectors from different contexts to represent the input word pairs. Then, through different combination methods and attention mechanism, we integrate different embedding vectors and optimize their weights to predict whether there are entailment relations in word pairs. Moreover, our proposed framework is flexible and open to handle different word contexts and entailment perspectives in the text corpus. Extensive experiments on five datasets show that our approach significantly improves the performance of automatic RLE in comparison with several state-of-the-art methods.
Domain-Adversarial Neural Networks

We introduce a new representation learning algorithm suited to the context of domain adaptation, in which data at training and test time come from similar but different distributions. Our algorithm is directly inspired by theory on domain adaptation suggesting that, for effective domain transfer to be achieved, predictions must be made based on a data representation that cannot discriminate between the training (source) and test (target) domains. We propose a training objective that implements this idea in the context of a neural network, whose hidden layer is trained to be predictive of the classification task, but uninformative as to the domain of the input. Our experiments on a sentiment analysis classification benchmark, where the target domain data available at training time is unlabeled, show that our neural network for domain adaption algorithm has better performance than either a standard neural network or an SVM, even if trained on input features extracted with the state-of-theart marginalized stacked denoising autoencoders of Chen et al. (2012).
Continuous authentication for mouse dynamics: A pattern-growth approach

Mouse dynamics is the process of identifying individual users based on their mouse operating characteristics. Although previous work has reported some promising results, mouse dynamics is still a newly emerging technique and has not reached an acceptable level of performance. One of the major reasons is intrinsic behavioral variability. This study presents a novel approach by using pattern-growth-based mining method to extract frequent-behavior segments in obtaining stable mouse characteristics, employing one-class classification algorithms to perform the task of continuous user authentication. Experimental results show that mouse characteristics extracted from frequent-behavior segments are much more stable than those from holistic behavior, and the approach achieves a practically useful level of performance with FAR of 0.37% and FRR of 1.12%. These findings suggest that mouse dynamics suffice to be a significant enhancement for a traditional authentication system. Our dataset is publicly available to facilitate future research.
Experiences of Time Loss among Videogame Players: An Empirical Study

Playing videogames is now a major leisure pursuit, yet research in the area is comparatively sparse. Previous correlational evidence suggests that subjective time loss occurs during playing videogames. This study examined experiences of time loss among a relatively large group of gamers (n = 280). Quantitative and qualitative data were collected through an online survey. Results showed that time loss occurred irrespective of gender, age, or frequency of play, but was associated with particular structural characteristics of games such as their complexity, the presence of multi-levels, missions and/or high scores, multiplayer interactions, and plot. Results also demonstrated that time loss could have both positive and negative outcomes for players. Positive aspects of time loss included helping players to relax and temporarily escape from reality. Negative aspects included the sacrificing of other things in their lives, guilty feelings about wasted time, and social conflict. It is concluded that for many gamers, losing track of time is a positive experience and is one of the main reasons for playing videogames.
Graph Topic Scan Statistic for Spatial Event Detection

Spatial event detection is an important and challenging problem. Unlike traditional event detection that focuses on the timing of global urgent event, the task of spatial event detection is to detect the spatial regions (e.g. clusters of neighboring cities) where urgent events occur. In this paper, we focus on the problem of spatial event detection using textual information in social media. We observe that, when a spatial event occurs, the topics relevant to the event are often discussed more coherently in cities near the event location than those far away. In order to capture this pattern, we propose a new method called Graph Topic Scan Statistic (Graph-TSS) that corresponds to a generalized log-likelihood ratio test based on topic modeling. We first demonstrate that the detection of spatial event regions under Graph-TSS is NP-hard due to a reduction from classical node-weighted prize-collecting Steiner tree problem (NW-PCST). We then design an efficient algorithm that approximately maximizes the graph topic scan statistic over spatial regions of arbitrary form. As a case study, we consider three applications using Twitter data, including Argentina civil unrest event detection, Chile earthquake detection, and United States influenza disease outbreak detection. Empirical evidence demonstrates that the proposed Graph-TSS performs superior over state-of-the-art methods on both running time and accuracy.
E-Government: Evolving relationship of citizens and government, domestic, and international development

The purpose of this study is to explore the emerging applications of E-Government, both in the United States and in the International Community. Information technology allows governments to service citizens in a more timely, effective, and cost-efficient method. E-Government implementation may meet initial citizen resistance, require cultural sensitivity, and change the way citizens and governments relate to each other. There is a focus on the domestic and international state of EGovernment and the importance of citizen acceptance of E-Government. D 2005 Elsevier Inc. All rights reserved.
Nutch: A Flexible and Scalable Open-Source Web Search Engine

Nutch is an open-source Web search engine that can be used at global, local, and even personal scale. Its initial design goal was to enable a transparent alternative for global Web search in the public interest — one of its signature features is the ability to “explain” its result rankings. Recent work has emphasized how it can also be used for intranets; by local communities with richer data models, such as the Creative Commons metadata-enabled search for licensed content; on a personal scale to index a user's files, email, and web-surfing history; and we also report on several other research projects built on Nutch. In this paper, we present how the architecture of the Nutch system enables it to be more flexible and scalable than other comparable systems today.
A 24GS/s 6b ADC in 90nm CMOS

This paper presents a 24 GS/s 6 b ADC in 90 nm CMOS with the highest ENOB up to 12 GHz input frequency and lowest power consumption of 1.2 W compared to ADCs with similar performance. It uses an interleaved architecture of SAR type self-calibrating converters operating from 1 V supply combined with an array of 2.5 V T/Hs with delay, gain and offset-calibration capability.
DiFacto: Distributed Factorization Machines

Factorization Machines offer good performance and useful embeddings of data. However, they are costly to scale to large amounts of data and large numbers of features. In this paper we describe DiFacto, which uses a refined Factorization Machine model with sparse memory adaptive constraints and frequency adaptive regularization. We show how to distribute DiFacto over multiple machines using the Parameter Server framework by computing distributed subgradients on minibatches asynchronously. We analyze its convergence and demonstrate its efficiency in computational advertising datasets with billions examples and features.
DeepMutation: Mutation Testing of Deep Learning Systems

Deep learning (DL) defines a new data-driven programming paradigm where the internal system logic is largely shaped by the training data. The standard way of evaluating DL models is to examine their performance on a test dataset. The quality of the test dataset is of great importance to gain confidence of the trained models. Using an inadequate test dataset, DL models that have achieved high test accuracy may still lack generality and robustness. In traditional software testing, mutation testing is a well-established technique for quality evaluation of test suites, which analyzes to what extent a test suite detects the injected faults. However, due to the fundamental difference between traditional software and deep learning-based software, traditional mutation testing techniques cannot be directly applied to DL systems. In this paper, we propose a mutation testing framework specialized for DL systems to measure the quality of test data. To do this, by sharing the same spirit of mutation testing in traditional software, we first define a set of source-level mutation operators to inject faults to the source of DL (i.e., training data and training programs). Then we design a set of model-level mutation operators that directly inject faults into DL models without a training process. Eventually, the quality of test data could be evaluated from the analysis on to what extent the injected faults could be detected. The usefulness of the proposed mutation testing techniques is demonstrated on two public datasets, namely MNIST and CIFAR-10, with three DL models.
The Foundamentions and Assumption of Technology Enhanced Srtudent Centered Learning Environments

Direct instruction approaches, as well as the design processes that support them, have been criticized for failing to reflect contemporary research and theory in teaching, learning, and technology. Learning systems are needed that encourage divergent reasoning, problem solving, and critical thinking. Student-centered learning environments have been touted as a means to support such processes. With the emergence of technology, many barriers to implementing innovative alternatives may be overcome. The purposes of this paper are to review and critically analyze research and theory related to technology-enhanced studentcentered learning environments and to identify their foundations and assumptions.