LLukas22/scidocs huggingface dataset:
Stochastic Dropout : Activation-level Dropout to Learn Better Neural Language Models

Recurrent Neural Networks are very powerful computational tools that are capable of learning many tasks across different domains. However, it is prone to overfitting and can be very difficult to regularize. Inspired by Recurrent Dropout [1] and Skip-connections [2], we describe a new and simple regularization scheme: Stochastic Dropout. It resembles the structure of recurrent dropout, but offers skip-connection over the recurrent depth. We reason the theoretical construct of such method and compare its regularization effectiveness with feedforward dropout and recurrent dropout. We demonstrate that Stochastic Dropout not only offers improvement when applied to vanilla RNN models, but also outperforms feedforward Dropout on word-level language modeling. At last, we show that the model can achieve even better result if stochastic dropout and feedforward dropout are combined.
Arterial versus capillary blood gases: A meta-analysis

A meta-analysis determined whether capillary blood gases accurately reflect arterial blood samples. A mixed effects model was used on 29 relevant studies obtained from a PubMed/Medline search. From 664 and 222 paired samples obtained from the earlobe and fingertip, respectively, earlobe compared to fingertip sampling shows that the standard deviation of the difference is about 2.5x less (or the precision is 2.5x better) in resembling arterial PO(2) over a wide range of arterial PO(2)'s (21-155 mm Hg ). The lower the arterial PO(2), the more accurate it is when predicting arterial PO(2) from any capillary sample (p<0.05). However, while earlobe sampling predicts arterial PO(2) (adjusted r(2)=0.88, mean bias=3.8 mm Hg compared to arterial), fingertip sampling does not (adjusted r(2)=0.48, mean bias=11.5 mm Hg compared to arterial). Earlobe sampling is slightly more accurate compared to fingertip sampling in resembling arterial PCO(2) (arterial versus earlobe, adjusted r(2)=0.94, mean bias=1.9 mm Hg ; arterial versus fingertip, adjusted r(2)=0.95, mean bias=2.2 mm Hg compared to arterial) but both sites can closely reflect arterial PCO(2) (880 total paired samples, range 10-114 mm Hg ). No real difference between sampling from the earlobe or fingertip were found for pH as both sites accurately reflect arterial pH over a wide range of pH (587 total paired samples, range 6.77-7.74, adjusted r(2)=0.90-0.94, mean bias=0.02). In conclusion, sampling blood from the fingertip or earlobe (preferably) accurately reflects arterial PCO(2) and pH over a wide range of values. Sampling blood, too, from earlobe (but never the fingertip) may be appropriate as a replacement for arterial PO(2), unless precision is required as the residual standard error is 6 mm Hg when predicting arterial PO(2) from an earlobe capillary sample.
STWAVE: Steady-State Spectral Wave Model User's Manual for STWAVE, Version 6.0

Estimating nearshore wave transformation and wind-wave growth is a critical component of many coastal engineering projects, e.g., predicting shoreline change, designing and maintaining coastal structures, and simulating coastal storm events. Performing physical model studies or gathering field data is not always a feasible option, and, as a result, the use of numerical models for simulating these complex coastal hydrodynamics has become a universal practice. STWAVE (STeady-state spectral WAVE), a nearshore spectral wave model, was developed by the U.S. Army Engineer Research and Development Center (ERDC), Coastal and Hydraulics Laboratory (CHL) to accurately simulate nearshore wave propagation and transformation including refraction, shoaling, breaking, and wind-wave generation. Recently, CHL has further enhanced STWAVE to include both half-plane and full-plane capabilities within a single executable; improved and streamlined file formats; and made it Earth System Modeling Framework (ESMF) compliant, which allows for easier coupling to other models. STWAVE now runs in serial mode as well as parallel in time or space on both personal computing (PC) and high-performance computing (HPC) systems. This report describes STWAVE version 6.0 capabilities, features, theory, and implementation. In depth descriptions of model parameters and examples of the new file formats are also provided. DISCLAIMER: The contents of this report are not to be used for advertising, publication, or promotional purposes. Citation of trade names does not constitute an official endorsement or approval of the use of such commercial products. All product names and trademarks cited are the property of their respective owners. The findings of this report are not to be construed as an official Department of the Army position unless so designated by other authorized documents. DESTROY THIS REPORT WHEN NO LONGER NEEDED. DO NOT RETURN IT TO THE ORIGINATOR. ERDC/CHL SR-11-1 iii
Shot Classification in Broadcast Soccer Video

In this paper, we present an effective hierarchical shot classification scheme for broadcast soccer video. We first partition a video into replay and non-replay shots with replay logo detection. Then, non-replay shots are further classified into Long, Medium, Close-up or Out-field types with color and texture features based on a decision tree. We tested the method on real broadcast FIFA soccer videos, and the experimental results demonstrate its effectiveness..
A Review on Phishing Attacks and Various Anti Phishing Techniques

Phishing is a threat that acquire sensitive information such as username, password etc through online. Phishing often takes place in email spoofing or instant messaging .Phishing email contains messages like ask the users to enter the personal information so that it is easy for hackers to hack the information. This paper presents an overview about various phishing attacks and various techniques to protect the information.
Chemical Analysis and Biological Activity of the Essential Oils of Two Valerianaceous Species from China: Nardostachys chinensis and Valeriana officinalis

In order to investigate essential oils with biological activity from local wild plants, two valerianaceous species, Nardostachys chinensis and Valeriana officinalis, were screened for their antimicrobial and antioxidant activity. The essential oils were obtained from the roots and rhizomes of the two plants by hydro-distillation, and were analyzed for their chemical composition by gas chromatography (GC) and gas chromatography-mass spectrometry (GC-MS). Calarene (25.31%), aristolone (13.35%), α-selinene (7.32%) and β-maaliene (6.70%) were the major compounds of the 23 identified components which accounted for 92.76% of the total oil of N. chinensis. Patchoulol (16.75%), α-pinene (14.81%), and β-humulene (8.19%) were the major compounds among the 20 identified components, which accounted for 88.11% of the total oil of V. officinalis. Both oils were rich in sesquiterpene hydrocarbons as well as their oxygenated derivatives. Essential oils were shown to have broad spectrum antibacterial activity with MIC values that ranged from 62.5 μg/mL to 400 μg/mL, and IC(50) values from 36.93 μg/mL to 374.72 μg/mL. The oils were also shown to have moderate antifungal activity to Candida albicans growth as well as inhibition of spore germination of Magnaporthe oryzae. Two essential oils were assessed by 1,1-diphenyl-2-picrylhydrazyl (DPPH) free radical scavenging, β-carotene bleaching and ferrozine-ferrous ions assays, respectively, to show moderate antioxidant activity. Results suggest that the isolated essential oils could be used for future development of antimicrobial and antioxidant agents.
Safety Evidence Traceability: Problem Analysis and Model

[Context and motivation] Safety evidence plays an important role in gaining confidence in the safe operation of a system in a given context. For a large system, it is necessary to provide information about thousands of artefacts that might be used as evidence and about the relationships among themselves and also with other safety assurance assets. [Question/problem] Past research has only addressed some needs of traceability in safety-critical systems and thus has not provided a complete picture of safety evidence traceability. Lack of knowledge and awareness of these needs can result in poor evidence management and lead to certification risks. [Principal ideas/results] This paper aims to provide a broad overview of safety evidence traceability needs for practice and its associated challenges. We also propose a safety evidence traceability model, which has been validated with data from real-world critical systems. [Contribution] We discuss the motivation and challenges for safety evidence traceability, and present the various traces that need to be captured and maintained. This information can help researchers to shape future research based on industry needs and can help practitioners to gain a deeper understanding and a wider knowledge of safety evidence traceability, thereby facilitating safety assurance and certification.
Apoptotic cell death in patients with sepsis, shock, and multiple organ dysfunction.

OBJECTIVES
The purpose of this study was to determine whether apoptosis is a major mechanism of cell death in patients with sepsis. The activities of caspase-3 and the antiapoptotic protein, BCL-2, were investigated also.


DESIGN
A prospective study of 20 patients who died of sepsis and multiple organ dysfunction was performed. The control group of 16 patients consisted of critically ill, nonseptic patients who were evaluated either prospectively (7) or retrospectively (9). In addition, normal colon sections from seven patients who had bowel resections were included. Apoptosis was evaluated in hematoxylin and eosin-stained specimens by deoxyuridine triphosphate nick end-labeling (TUNEL) and by DNA gel electrophoresis.


SETTING
Two academic medical centers.


PATIENTS
Critically ill patients.


MEASUREMENTS AND MAIN RESULTS
In septic patients, apoptosis was detected in diverse organs by all three methods with a predominance in lymphocytes and intestinal epithelial cells. Hematoxylin and eosin-stained specimens from septic patients demonstrated at least focal apoptosis in 56.3% of spleens, 47.1% of colons, and 27.7% of ileums. Indirect evidence of lymphocyte apoptosis in septic patients included extensive depletion of lymphocytes in white pulp and a marked lymphocytopenia in 15 of 19 patients. Hematoxylin and eosin from nonseptic patients' tissues revealed a low level of apoptosis in one patient only. The TUNEL method increased in positivity with a delay in tissue fixation and was highly positive in many tissues from both septic and nonseptic patients. Immunohistochemical staining for active caspase-3 showed a marked increase in septic vs. nonseptic patients (p < .01), with >25% to 50% of cells being positive focally in the splenic white pulp of six septic but in no nonseptic patients.


CONCLUSIONS
We conclude that caspase-3-mediated apoptosis causes extensive lymphocyte apoptosis in sepsis and may contribute to the impaired immune response that characterizes the disorder.
Roads as a threat to the Serengeti ecosystem.

R. FYUMAGWA,∗ E. GERETA,∗ S. HASSAN,† J. R. KIDEGHESHO,† E. M. KOHI,∗ J. KEYYU,∗ F. MAGIGE,‡ I. M. MFUNDA,§ A. MWAKATOBE,∗ J. NTALWILA,∗ J. W. NYAHONGO,∗∗ V. RUNYORO,†† AND E. RØSKAFT‡‡ §§ ∗Tanzania Wildlife Research Institute (TAWIRI), Box 661, Arusha, Tanzania †Sokoine University of Agriculture (SUA), Department of Wildlife Management, P.O. Box 3073, Morogoro, Tanzania ‡University of Dar es Salaam (UDSM), Department of Zoology and Wildlife Conservation, P.O. Box 35064, Dar es Salaam, Tanzania §Ministry of Natural Resources and Tourism (MNRT), Division of Policy & Planning, P.O. Box 9372, Dar es Salaam, Tanzania ∗∗University of Dodoma (UDOM), School of Biological Sciences, College of Natural and Mathematical Sciences, P.O. Box 259, Dodoma, Tanzania ††Ngorongoro Conservation Area Authority, (NCAA), P.O. Box 1, Ngorongoro Crater, Arusha, Tanzania ‡‡Norwegian University of Science and Technology (NTNU), Department of Biology, NO 7491, Trondheim, Norway
Porokeratotic eccrine ostial and dermal duct nevus: A noteworthy presentation

We herein present a case of PEODDN that had unique clinical and histopathological findings. Our patient was a 30-year-old healthy lady who presented with multiple, asymptomatic, elevated skin lesions over her left upper limb. Small lesions appeared three years ago over her left index finger and gradually extended to the forearm. There was no seasonal variation. Cutaneous examination demonstrated multiple well-circumscribed, hyperpigmented, verrucous papules on the dorsum of the left index finger extending up to the extensor aspect of mid forearm. Some of the lesions were discrete while others coalesced to form plaques in a linear configuration. Of note, many of these papules showed a central pit [Figure 1]. Hair, nails, and mucosae were within normal limits; however, the nail of the index finger showed multiple shallow pits and a single longitudinal groove. Family history and systemic examination were not contributory. Linear porokeratosis, linear lichen planus, linear verrucous epidermal nevus, and linear Darier’s disease were considered in the list of clinical differentials. A complete blood cell count with differential analysis and a comprehensive biochemistry panel did not reveal any abnormality. Histopathology of a representative skin lesion from the forearm showed hyperkeratosis, acanthosis, and papillomatosis. Cornoid lamella overlying a dilated eccrine duct ostium was visualized. An interesting finding was the presence of focal lichenoid infiltrate in the upper dermis [Figure 2a and b]. Based on the clinical and histopathological findings, our case was diagnosed as PEODDN along with a unique histological finding of a lichenoid infiltrate.
Combining multiple sources of knowledge in deep CNNs for action recognition

Although deep convolutional neural networks (CNNs) have shown remarkable results for feature learning and prediction tasks, many recent studies have demonstrated improved performance by incorporating additional handcrafted features or by fusing predictions from multiple CNNs. Usually, these combinations are implemented via feature concatenation or by averaging output prediction scores from several CNNs. In this paper, we present new approaches for combining different sources of knowledge in deep learning. First, we propose feature amplification, where we use an auxiliary, hand-crafted, feature (e.g. optical flow) to perform spatially varying soft-gating on intermediate CNN feature maps. Second, we present a spatially varying multiplicative fusion method for combining multiple CNNs trained on different sources that results in robust prediction by amplifying or suppressing the feature activations based on their agreement. We test these methods in the context of action recognition where information from spatial and temporal cues is useful, obtaining results that are comparable with state-of-the-art methods and outperform methods using only CNNs and optical flow features.
The tilt illusion: Phenomenology and functional implications

The perceived orientation of a line or grating is affected by the orientation structure of the surrounding image: the tilt illusion. Here, I offer a selective review of the literature on the tilt illusion, focusing on functional aspects. The review explores the merits of mechanistic accounts of the tilt illusion based upon sensory gain control in which neuronal responses are normalized by the pooled activity of other units. The role of inhibition between orientation-selective neurons is discussed, and it is argued that their associated disinhibition must also be taken into account in order to model the full angular dependence of the tilt illusion on surround orientation. Parallels are drawn with adaptation as modulation by the temporal rather than spatial context within which an image fragment is processed. The chromatic selectivity of the tilt illusion and the extent of its dependence on the visibility of the surround are used to infer characteristics of the neuronal normalization pools and the loci in the cortical processing hierarchy at which gain control operates. Finally, recent evidence is discussed as to the possible clinical relevance of the tilt illusion as a biomarker for schizophrenia.
Emotional speech: Towards a new generation of databases

8 Research on speech and emotion is moving from a period of exploratory research into one where there is a prospect 9 of substantial applications, notably in human–computer interaction. Progress in the area relies heavily on the devel10 opment of appropriate databases. This paper addresses four main issues that need to be considered in developing 11 databases of emotional speech: scope, naturalness, context and descriptors. The state of the art is reviewed. A good deal 12 has been done to address the key issues, but there is still a long way to go. The paper shows how the challenge of 13 developing appropriate databases is being addressed in three major recent projects––the Reading–Leeds project, the 14 Belfast project and the CREST–ESP project. From these and other studies the paper draws together the tools and 15 methods that have been developed, addresses the problems that arise and indicates the future directions for the de16 velopment of emotional speech databases. 2002 Published by Elsevier Science B.V.
Assessment of Heavy Metal Enrichment and Degree of Contamination around the Copper-Nickel Mine in the Selebi Phikwe Region, Eastern Botswana

This paper focuses on heavy metal (Co, Cu, Fe, Pb, Mn, Ni and Zn) enrichment and degree of contamination in the soils windward of the Selebi Phikwe copper and nickel mine (eastern Botswana). Pollution by these metals was examined using several calculation methods: enrichment factors, modified degree of contamination, pollution load index and geoaccumulation index. Enrichment factors were further assessed by comparing present day soil metal contents obtained using actual Fe content in standard earth material obtained from literature with that measured in soils sampled from areas adjacent to the site of concern to represent “uncontaminated” background concentrations. The latter method was found to be the most relevant and the normalised enrichment values demonstrate moderate enrichment of Cu and Ni. The proposed modified calculation method of degree of contamination, which involves determination of arithmetic mean of contamination factors by individual studied metals, revealed moderate contamination by metals. The PLI, which basically is a measure of site quality, indicated deterioration due to metal contamination. These results were further supported by values obtained from indices of geoaccumulation method of pollution assessment. Principal component analysis at windward site of the mine extracted two major components: 1. high positive loading of Pb, Ni, Cu and Zn associated with anthropogenic influence by smelter emissions and 2.high positive loadings of Co, Mn and Fe are associated with contributions from natural processes. This evaluation would serve to create consistent calculation methods as a contribution towards building a consensus on methodologies applicable to the study of polluted soils.
Sparseness Meets Deepness: 3D Human Pose Estimation from Monocular Video

This paper addresses the challenge of 3D full-body human pose estimation from a monocular image sequence. Here, two cases are considered: (i) the image locations of the human joints are provided and (ii) the image locations of joints are unknown. In the former case, a novel approach is introduced that integrates a sparsity-driven 3D geometric prior and temporal smoothness. In the latter case, the former case is extended by treating the image locations of the joints as latent variables to take into account considerable uncertainties in 2D joint locations. A deep fully convolutional network is trained to predict the uncertainty maps of the 2D joint locations. The 3D pose estimates are realized via an Expectation-Maximization algorithm over the entire sequence, where it is shown that the 2D joint location uncertainties can be conveniently marginalized out during inference. Empirical evaluation on the Human3.6M dataset shows that the proposed approaches achieve greater 3D pose estimation accuracy over state-of-the-art baselines. Further, the proposed approach outperforms a publicly available 2D pose estimation baseline on the challenging PennAction dataset.
Does Knowledge Management really matter? Linking knowledge management practices, competitiveness and economic performance

Purpose – While nowadays an extensive literature promoting knowledge management (KM) exists, there is a worrying shortage of empirical studies demonstrating an actual connection between KM activities and organizational outcomes. To bridge this gap, this paper aims to examine the link between KM practices, firm competitiveness and economic performance. Design/methodology/approach – This paper proposes a framework of KM practices consisting of human resource management (HRM) and information communication technology (ICT). These both are hypothesized to impact competitiveness and economic performance of the firm. Hypotheses are then tested with structural equation modeling by using a survey dataset of 234 companies. Findings – The results show that HRM and ICT practices for managing knowledge are quite strongly correlated and have a statistically significant influence on both financial performance and competitiveness of the firm. The findings also indicate that ICT practices improve financial performance only when they are coupled with HRM practices. Research limitations/implications – The data are limited to companies from Finland, Russia and China. Practical implications – The paper contributes to managerial practice by pointing out the importance of utilizing a combination of both social and technical means for KM and illustrating that they do matter for the company bottom line. Originality/value – This paper contributes to the literature on knowledge-based organizing by empirically analyzing the performance impact of various areas of KM. It thereby tests the proposition put forth in many previous theoretical and case-based studies that KM promotes high organizational performance. It also addresses the interaction of social and technical KM practices in producing organizational outcomes.
Unsupervised feature extraction with autoencoder trees

The autoencoder is a popular neural network model that learns hidden representations of unlabeled data. Typically, singleor multilayer perceptrons are used in constructing an autoencoder, but we use soft decision trees (i.e., hierarchical mixture of experts) instead. Such trees have internal nodes that implement soft multivariate splits through a gating function and all leaves are weighted by the gating values on their path to get the output. The encoder tree converts the input to a lower dimensional representation in its leaves, which it passes to the decoder tree that reconstructs the original input. Because the splits are soft, the encoder and decoder trees can be trained back to back with stochastic gradient-descent to minimize reconstruction error. In our experiments on handwritten digits, newsgroup posts, and images, we observe that the autoencoder trees yield as small and sometimes smaller reconstruction error when compared with autoencoder perceptrons. One advantage of the tree is that it learns a hierarchical representation at different resolutions at its different levels and the leaves specialize at different local regions in the input space. An extension with locally linear mappings in the leaves allows a more flexible model. We also show that the autoencoder tree can be used with multimodal data where a mapping from one modality (i.e., image) to another (i.e., topics) can be learned. © 2017 Elsevier B.V. All rights reserved.