LLukas22/scidocs huggingface dataset:
Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies

We offer a systematic analysis of the use of deep learning networks for stock market analysis and prediction. Its ability to extract features from a large set of raw data without relying on prior knowledge of predictors makes deep learning potentially attractive for stock market prediction at high frequencies. Deep learning algorithms vary considerably in the choice of network structure, activation function, and other model parameters, and their performance is known to depend heavily on the method of data representation. Our study attempts to provides a comprehensive and objective assessment of both the advantages and drawbacks of deep learning algorithms for stock market analysis and prediction. Using highfrequency intraday stock returns as input data, we examine the effects of three unsupervised feature extraction methods—principal component analysis, autoencoder, and the restricted Boltzmann machine— on the network’s overall ability to predict future market behavior. Empirical results suggest that deep neural networks can extract additional information from the residuals of the autoregressive model and improve prediction performance; the same cannot be said when the autoregressive model is applied to the residuals of the network. Covariance estimation is also noticeably improved when the predictive network is applied to covariance-based market structure analysis. Our study offers practical insights and potentially useful directions for further investigation into how deep learning networks can be effectively used for stock market analysis and prediction. © 2017 Elsevier Ltd. All rights reserved.
MMPI-2 profiles 23 years after paediatric mild traumatic brain injury.

PRIMARY OBJECTIVE
Research suggest that post-concussive syndrome after mild traumatic brain injury (mTBI) is more common than chronic cognitive impairment. The aim of this study was to investigate very long-term outcome of subjective complaints after paediatric mTBI.


RESEARCH DESIGN
The study was a follow-up 23 years after a prospective head injury study at a general hospital in Norway.


METHODS AND PROCEDURES
Forty-one patients were assessed with the Minnesota Multiphasic Personality Inventory-2 (MMPI-2) 23 years after sustaining mTBI as children.


MAIN OUTCOMES AND RESULTS
A good overall outcome was found with scores close to the normative mean, average length of education and normal employment rate. However, the children that sustained complicated mTBI showed slightly more pathological scores, typical for mild post-concussive syndrome. The most important predictors of poor outcome were skull fracture and a combination of post-traumatic amnesia > 30 minutes and EEG pathology within 24 hours after TBI. No influence of pre- and post-injury risk factors on current MMPI-2 profiles was evident.


CONCLUSIONS
The results give support for the notion of potentially differential impact of uncomplicated vs complicated mTBI. The findings suggest that children and adolescents sustaining complicated mTBI may be at risk of developing subtle chronic symptoms typical of post-concussive syndrome.
ESTIMATION WITH QUADRATIC LOSS

It has long been customary to measure the adequacy of an estimator by the smallness of its mean squared error. The least squares estimators were studied by Gauss and by other authors later in the nineteenth century. A proof that the best unbiased estimator of a linear function of the means of a set of observed random variables is the least squares estimator was given by Markov [12], a modified version of whose proof is given by David and Neyman [4]. A slightly more general theorem is given by Aitken [1]. Fisher [5] indicated that for large samples the maximum likelihood estimator approximately minimizes the mean squared error when compared with other reasonable estimators. This paper will be concerned with optimum properties or failure of optimum properties of the natural estimator in certain special problems with the risk usually measured by the mean squared error or, in the case of several parameters, by a quadratic function of the estimators. We shall first mention some recent papers on this subject and then give some results, mostly unpublished, in greater detail. Pitman [13] in 1939 discussed the estimation of location and scale parameters and obtained the best estimator among those invariant under the affine transformations leaving the problem invariant. He considered various loss functions, in particular, mean squared error. Wald [18], also in 1939, in what may be considered the first paper on statistical decision theory, did the same for location parameters alone, and tried to show in his theorem 5 that the estimator obtained in this way is admissible, that is, that there is no estimator whose risk is no greater at any parameter point, and smaller at some point. However, his proof of theorem 5 is not convincing since he interchanges the order of integration in (30) without comment, and it is not clear that this integral is absolutely convergent. To our knowledge, no counterexample to this theorem is known, but in higher-dimensional cases, where the analogous argument seems at first glance only slightly less plausible, counterexamples are given in Blackwell [2] (which is discussed briefly at the end of section 3 of this paper) and in [14],
Proportionate-type Normalized Least Mean Square Algorithms

Description: The topic of this book is proportionate–type normalized least mean square (PtNLMS) adaptive filtering algorithms, which attempt to estimate an unknown impulse response by adaptively giving gains proportionate to an estimate of the impulse response and the current measured error. These algorithms offer low computational complexity and fast convergence times for sparse impulse responses in network and acoustic echo cancellation applications. New PtNLMS algorithms are developed by choosing gains that optimize user–defined criteria, such as mean square error, at all times. PtNLMS algorithms are extended for real–valued signals to complex–valued signals. The computational complexity of the presented algorithms is examined.
RGB-D multi-view object detection with object proposals and shape context

We propose a novel approach for multi-view object detection in 3D scenes reconstructed from RGB-D sensor. We utilize shape based representation using local shape context descriptors along with the voting strategy which is supported by unsupervised object proposals generated from 3D point cloud data. Our algorithm starts with a single-view object detection where object proposals generated in 3D space are combined with object specific hypotheses generated by the voting strategy. To tackle the multi-view setting, the data association between multiple views enabled view registration and 3D object proposals. The evidence from multiple views is combined in simple bayesian setting. The approach is evaluated on the Washington RGB-D scenes datasets [1], [2] containing several classes of objects in a table top setting. We evaluated our approach against the other state-of-the-art methods and demonstrated superior performance on the same dataset.
Cross-functional integration in the sustainable new product development process: The role of the environmental specialist

Abstract Companies in the twenty-first century are exposed to a variety of pressures to respond to environmental issues, and responding to these pressures affects several aspects of business such as purchasing, marketing and logistics. Managers increasingly view sustainability as a complement to their corporate agendas, or even as an opportunity. It is important to understand how firms integrate environmental issues into their businesses and how these integration strategies affect performance. The process of sustainable new product development (SNPD) is a key strategic focus to achieve economic and environmental sustainability. This paper examines the integration of environmental specialists into new product development teams that are composed of other functional specialists including marketing, manufacturing, and RD this effect is even greater for high-innovative projects.
High tidal volume and positive fluid balance are associated with worse outcome in acute lung injury.

STUDY OBJECTIVES
Recent data have suggested that ventilatory strategy could influence outcomes from acute lung injury (ALI) and ARDS. We tested the hypothesis that infection/sepsis and use of higher tidal volumes than those applied in the ARDS Network (ARDSnet) study (> 7.4 mL/kg of predicted body weight) would worsen outcome in patients with ALI/ARDS.


DESIGN
International cohort, observational study.


SETTING
One hundred ninety-eight European ICUs participating in the Sepsis Occurrence in Acutely Ill Patients study.


PATIENTS OR PARTICIPANTS
All 3,147 adult patients admitted to one of the participating ICUs between May 1, 2002, and May 15, 2002.


INTERVENTIONS
None.


MEASUREMENTS AND RESULTS
Patients were followed up until death, hospital discharge, or for 60 days. Of the 3,147 patients, 393 patients (12.5%) had ALI/ARDS. ICU and hospital mortality was higher in patients with ALI/ARDS than those without ALI/ARDS (38.9% vs 15.6% and 45.5% vs 21.0%, respectively; p < 0.001). A multivariable logistic regression analysis with ICU outcome as the dependent factor showed that the independent risks for mortality were as follows: presence of cancer, use of tidal volumes higher than those used by the ARDSnet study, degree of multiorgan dysfunction, and higher mean fluid balance. Sepsis, septic shock, and oxygenation at the onset of ALI/ARDS were not independently associated with higher mortality rates.


CONCLUSIONS
In addition to comorbidities and organ dysfunction, high tidal volumes and positive fluid balance are associated with a worse outcome from ALI/ARDS.
Hadoop eco system for big data security and privacy

Privacy and Security of Big Data is gaining high importance since recently all the technologies started to depend on Big Data. In this paper, we are going to discuss particularly Hadoop and difficulty in maintaining the privacy and security of Big Data. The main goal is to propose a Hadoop system that maintains the privacy and security of the information stored on the cloud.
Engineering resistance against tomato yellow leaf curl virus (TYLCV) using antisense RNA

One of the most severe diseases of cultivated tomato worldwide is caused by tomato yellow leaf curl virus (TYLCV), a geminivirus transmitted by the whitefly Bemisia tabaci. Here we describe the application of antisense RNAs to interfere with the disease caused by TYLCV. The target of the antisense RNA is the rare messenger RNA of the Rep protein, encoded by the C1 gene. Transgenic Nicotiana benthamiana plants expressing C1 antisense RNA were obtained and shown to resist infection by TYLCV. Some of the resistant lines are symptomless, and the replication of challenge TYLCV almost completely suppressed. The transgenes mediating resistance were shown to be effective through at least two generations of progeny.
COTS Software Selection Process

Today's need for rapid software development has generated a great interest in employing commercial-off-the-shelf (COTS) software products as a way of managing cost, developing time, and effort. With an abundance of COTS software packages to choose from, the problem now is how to systematically evaluate, rank, and select a COTS product that best meets the software project requirements and at the same time can leverage off the current corporate information technology architectural environment. This paper describes a systematic process for decision support in evaluating and ranking COTS software. Performed right after the requirement analysis phase, this process provides the evaluators with more concise, structural, and step-by-step activities for determining the best COTS software product with manageable risk. In addition, the process is presented in phases that are flexible to allow for customization or tailoring to meet various projects' requirements
Intelligent Urban Public Transportation for Accessibility Dedicated to People with Disabilities

The traditional urban public transport system generally cannot provide an effective access service for people with disabilities, especially for disabled, wheelchair and blind (DWB) passengers. In this paper, based on advanced information & communication technologies (ICT) and green technologies (GT) concepts, a dedicated public urban transportation service access system named Mobi+ has been introduced, which facilitates the mobility of DWB passengers. The Mobi+ project consists of three subsystems: a wireless communication subsystem, which provides the data exchange and network connection services between buses and stations in the complex urban environments; the bus subsystem, which provides the DWB class detection & bus arrival notification services; and the station subsystem, which implements the urban environmental surveillance & bus auxiliary access services. The Mobi+ card that supports multi-microcontroller multi-transceiver adopts the fault-tolerant component-based hardware architecture, in which the dedicated embedded system software, i.e., operating system micro-kernel and wireless protocol, has been integrated. The dedicated Mobi+ embedded system provides the fault-tolerant resource awareness communication and scheduling mechanism to ensure the reliability in data exchange and service provision. At present, the Mobi+ system has been implemented on the buses and stations of line '2' in the city of Clermont-Ferrand (France). The experiential results show that, on one hand the Mobi+ prototype system reaches the design expectations and provides an effective urban bus access service for people with disabilities; on the other hand the Mobi+ system is easily to deploy in the buses and at bus stations thanks to its low energy consumption and small form factor.
Classification of Medical Images and Illustrations in the Biomedical Literature Using Synergic Deep Learning

The Classification of medical images and illustrations in the literature aims to label a medical image according to the modality it was produced or label an illustration according to its production attributes. It is an essential and challenging research hotspot in the area of automated literature review, retrieval and mining. The significant intra-class variation and inter-class similarity caused by the diverse imaging modalities and various illustration types brings a great deal of difficulties to the problem. In this paper, we propose a synergic deep learning (SDL) model to address this issue. Specifically, a dual deep convolutional neural network with a synergic signal system is designed to mutually learn image representation. The synergic signal is used to verify whether the input image pair belongs to the same category and to give the corrective feedback if a synergic error exists. Our SDL model can be trained ’end to end’. In the test phase, the class label of an input can be predicted by averaging the likelihood probabilities obtained by two convolutional neural network components. Experimental results on the ImageCLEF2016 Subfigure Classification Challenge suggest that our proposed SDL model achieves the state-of-theart performance in this medical image classification problem and its accuracy is higher than that of the first place solution on the Challenge leader board so far. Keywords—Medical image classification; Synergic deep learning model; Dual deep convolutional neural network
Changes in Renal Function in Patients With Atrial Fibrillation: An Analysis From the RE-LY Trial.

BACKGROUND
Vitamin K-dependent factors protect against vascular and renovascular calcification, and vitamin K antagonists may be associated with a decreased glomerular filtration rate (GFR).


OBJECTIVES
This study analyzed changes in GFR during long-term treatment with warfarin or dabigatran etexilate (DE) in patients enrolled in the RE-LY (Randomized Evaluation of Long Term Anticoagulation Therapy) trial.


METHODS
Of the 18,113 patients in the RE-LY study randomized to receive DE (110 mg or 150 mg twice daily) or warfarin, 16,490 patients with atrial fibrillation had creatinine values measured at baseline and at least 1 follow-up visit. Changes in GFR for up to 30 months were evaluated.


RESULTS
GFR declined in all treatment groups. After an average of 30 months, the mean ± SE decline in GFR was significantly greater with warfarin (-3.68 ± 0.24 ml/min) compared with DE 110 mg (-2.57 ± 0.24 ml/min; p = 0.0009 vs. warfarin) and DE 150 mg (-2.46 ± 0.23 ml/min; p = 0.0002 vs. warfarin). A decrease in GFR >25% was less likely with DE 110 mg (hazard ratio: 0.81 [95% confidence interval: 0.69 to 0.96]; p = 0.017) or DE 150 mg (hazard ratio: 0.79 [95% confidence interval: 0.68 to 0.93]; p = 0.0056) than with warfarin in the observation period >18 months. Patients with poor international normalized ratio control (i.e., time in therapeutic range <65%) exhibited a faster decline in GFR. A more pronounced decline in GFR was associated with previous warfarin use and with the presence of diabetes.


CONCLUSIONS
Patients with atrial fibrillation receiving oral anticoagulation exhibited a decline in renal function that was greater in those taking warfarin versus DE, and it was amplified by diabetes and previous vitamin K antagonist use. (Randomized Evaluation of Long Term Anticoagulant Therapy [RE-LY] With Dabigatran Etexilate; NCT00262600).
The Use of Electronic Games in Therapy: a Review with Clinical Implications

Therapists and patients enjoy and benefit from interventions that use electronic games (EG) in health care and mental health settings, with a variety of diagnoses and therapeutic goals. We reviewed the use of electronic games designed specifically for a therapeutic purpose, electronic games for psychotherapy (EGP), also called serious games, and commercially produced games used as an adjunct to psychotherapy, electronic games for entertainment (EGE). Recent research on the benefits of EG in rehabilitation settings, EGP, and EGE indicates that electronic methods are often equivalent to more traditional treatments and may be more enjoyable or acceptable, at least to some consumers. Methodological concerns include the lack of randomized controlled trials (RCT) for many applications. Suggestions are offered for using EG in therapeutic practice.
Labeled Random Finite Sets and the Bayes Multi-Target Tracking Filter

An analytic solution to the multi-target Bayes recursion known as the δ-Generalized Labeled Multi-Bernoulli ( δ-GLMB) filter has been recently proposed by Vo and Vo in [“Labeled Random Finite Sets and Multi-Object Conjugate Priors,” IEEE Trans. Signal Process., vol. 61, no. 13, pp. 3460-3475, 2014]. As a sequel to that paper, the present paper details efficient implementations of the δ-GLMB multi-target tracking filter. Each iteration of this filter involves an update operation and a prediction operation, both of which result in weighted sums of multi-target exponentials with intractably large number of terms. To truncate these sums, the ranked assignment and K-th shortest path algorithms are used in the update and prediction, respectively, to determine the most significant terms without exhaustively computing all of the terms. In addition, using tools derived from the same framework, such as probability hypothesis density filtering, we present inexpensive (relative to the δ-GLMB filter) look-ahead strategies to reduce the number of computations. Characterization of the L1-error in the multi-target density arising from the truncation is presented.
Treatment cessation in noncirrhotic, e-antigen negative chronic hepatitis B is safe and effective following prolonged anti-viral suppression with nucleosides/nucleotides.

BACKGROUND
The treatment of HBeAg-negative chronic hepatitis B (CHB) is considered to be open-ended, with no guidelines for treatment cessation.


AIM
To evaluate biochemical and virological relapse requiring retreatment in noncirrhotic HBeAg-negative CHB in patients who stopped treatment following a period of prolonged viral suppression with nucleotides/nucleosides.


METHODS
We performed a single-centre retrospective chart review of patients with HBeAg-negative CHB who maintained viral suppression for 4-5 years on anti-viral treatment, and thus subsequently stopped treatment. The primary end point of composite relapse was defined by an increase in HBV DNA >2000 IU/mL, ALT elevation above 1.25 × normal or doubling of ALT from cessation, and re-initiation of anti-viral therapy.


RESULTS
We identified 33 patients with HBeAg-negative CHB who stopped treatment following viral suppression. Mean treatment duration was 5.28 ± 2.73 years. Patients were treated with lamivudine (3), adefovir (14), entecavir (4), and tenofovir (12). Eleven (33%) patients met the primary end point of composite relapse. For individual end points, 21 (63%) patients had a viral relapse, 16 (48%) had a biochemical relapse, and 16 (48%) restarted treatment, leaving 17 (52%) patients who remained treatment-free over a median 36 months of follow-up. Lower pre-treatment ALT and detectable HBV DNA within the first month after treatment discontinuation were associated with increased rates of composite relapse (HR 1.01; P = 0.022 for ALT and HR 1.01; P = 0.038 for HBV DNA).


CONCLUSION
Patients with noncirrhotic HBeAg-negative CHB can stop treatment after greater than 4-5 years of suppressive therapy with nucleosides/nucleotides with more than 50% remaining treatment-free.
A uni fi ed open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring q

Open-circuit-voltage (OCV) data is widely used for characterizing battery properties under different conditions. It contains important information that can help to identify battery state-of-charge (SOC) and state-of-health (SOH). While various OCV models have been developed for battery SOC estimation, few have been designed for SOH monitoring. In this paper, we propose a unified OCV model that can be applied for both SOC estimation and SOH monitoring. Improvements in SOC estimation using the new model compared to other existing models are demonstrated. Moreover, it is shown that the proposed OCV model can be used to perform battery SOH monitoring as it effectively captures aging information based on incremental capacity analysis (ICA). Parametric analysis and model complexity reduction are also addressed. Experimental data is used to illustrate the effectiveness of the model and its simplified version in the application context of SOC estimation and SOH monitoring. 2014 Elsevier B.V. All rights reserved.
Content Based Image Retrieval Systems using SIFT : A Survey

this paper provides the novel information about Image Retrieval and Content based image retrieval (CBIR) system since it is now a big need of society. CBIR is a method for finding similar images from large image databases. Different approaches are used for Content based image retrieval, out of which Scale invariant feature transform is very popular. In this paper, we discuss the Industrial product development using SIFT Algorithm and results obtained in several experiments proposed to evaluate the application of Scale Invariant Feature Transform (SIFT) in content based Image Retrieval (CBIR) systems. Keywords— Content Based Image Retrieval (CBIR), Scale Invariant Feature Transform (SIFT)