LLukas22/scidocs huggingface dataset:
DUAL-BAND PATCH ANTENNA FOR WLAN APPLICATIONS

A compact dual-band patch antenna is proposed and measured in this paper. The proposed antenna employs a U-shaped slot and two mitered corners to achieve two operating frequency bands, 2.30 ∼ 2.50GHz and 4.50 ∼ 6.36GHz, which meet the specifications of IEEE 802.11b/g/a standard for WLAN applications. Full wave analysis is performed to simulate the characteristics of the proposed antenna using CST microwave studio. Moreover, a fabricated prototype which has compact dimensions of 20.0mm× 25 mm× 1mm exhibits agreement between measured and simulated parameters and radiation patterns.
HeteClass: A Meta-path based framework for transductive classification of objects in heterogeneous information networks

Transductive classification using labeled and unlabeled objects in a heterogeneous information network for knowledge extraction is an interesting and challenging problem. Most of the real-world networks are heterogeneous in their natural setting and traditional methods of classification for homogeneous networks are not suitable for heterogeneous networks. In a heterogeneous network, various meta-paths connecting objects of the target type, on which classification is to be performed, make the classification task more challenging. The semantic of each meta-path would lead to the different accuracy of classification. Therefore, weight learning of meta-paths is required to leverage their semantics simultaneously by a weighted combination. In this work, we propose a novel meta-path based framework, HeteClass, for transductive classification of target type objects. HeteClass explores the network schema of the given network and can also incorporate the knowledge of the domain expert to generate a set of meta-paths. The regularization based weight learning method proposed in HeteClass is effective to compute the weights of symmetric as well as asymmetric meta-paths in the network, and the weights generated are consistent with the real-world understanding. Using the learned weights, a homogeneous information network is formed on target type objects by the weighted combination, and transductive classification is performed. The proposed framework HeteClass is flexible to utilize any suitable classification algorithm for transductive classification and can be applied on heterogeneous information networks with arbitrary network schema. Experimental results show the effectiveness of the HeteClass for classification of unlabeled objects in heterogeneous information networks using real-world data sets. © 2016 Elsevier Ltd. All rights reserved.
IDS : Intrusion Detection System the Survey of Information Security

With the increased use of computerized / online transactions it is very much of the importance to secure the information from intruders. Intrusion detection is the process of monitoring the activities or events occurring in the computer system or network and analyzing them to find out suspicious events intruding the system or network. Such events will be reported to the administrator of Intrusion Detection System(IDS) who will decide the further action. This Paper surveys different types of IDS and lists preventive methods.An intrusion detection system (IDS) is software that automates the intrusion detection process. An intrusion prevention system (IPS) is software that has all the capabilities of an intrusion detection system and can also attempt to stop possible incidents. Keywords—Intruder, Intrusion, anomaly, IDS, NIDS, HIDS
Poisson Noise Reduction with Higher-order Natural Image Prior Model

Poisson denoising is an essential issue for various imaging applications, such as night vision, medical imaging and microscopy. State-of-the-art approaches are clearly dominated by patch-based non-local methods in recent years. In this paper, we aim to propose a local Poisson denoising model with both structure simplicity and good performance. To this end, we consider a variational modeling to integrate the so-called Fields of Experts (FoE) image prior, that has proven an effective higher-order Markov Random Fields (MRF) model for many classic image restoration problems. We exploit several feasible variational variants for this task. We start with a direct modeling in the original image domain by taking into account the Poisson noise statistics, which performs generally well for the cases of high SNR. However, this strategy encounters problem in cases of low SNR. Then we turn to an alternative modeling strategy by using the Anscombe transform and Gaussian statistics derived data term. We retrain the FoE prior model directly in the transform domain. With the newly trained FoE model, we end up with a local variational model providing strongly competitive results against state-of-the-art non-local approaches, meanwhile bearing the property of simple structure. Furthermore, our proposed model comes along with an additional advantage, that the inference is very efficient as it is well-suited for parallel computation on GPUs. For images of size 512× 512, our GPU implementation takes less ∗Corresponding author. Email address: chenyunjin_nudt@hotmail.com (Yunjin Chen) Preprint submitted to SIAM Journal on Imaging Sciences September 20, 2016 ar X iv :1 60 9. 05 72 2v 1 [ cs .C V ] 1 9 Se p 20 16 than 1 second to produce state-of-the-art Poisson denoising performance.
Behavior Based Manipulation : Theory and Prosecution Evidence

If investors are not fully rational, what can smart money do? This paper provides an example in which smart money can strategically take advantage of investors’ behavioral biases and manipulate the price process to make profit. The paper considers three types of traders, behavior-driven investors who are less willing to sell losers than to sell winners (dispositional effect), arbitrageurs, and a manipulator who can influence asset prices. We show that, due to the investors’ behavioral biases and the limit of arbitrage, the manipulator can profit from a “pump-and-dump” trading strategy by accumulating the speculative asset while pushing the asset price up, and then selling the asset at high prices. Since nobody has private information, manipulation here is completely trade-based. The paper also endogenously derives several asset-pricing anomalies, including excess volatility, momentum and reversal. As an empirical test, the paper presents some empirical evidence from the U.S. SEC prosecution of “pump-and-dump” manipulation cases that are consistent with our model. JEL: G12, G18
Automatic model construction with Gaussian processes

This thesis develops a method for automatically constructing, visualizing and describing a large class of models, useful for forecasting and finding structure in domains such as time series, geological formations, and physical dynamics. These models, based on Gaussian processes, can capture many types of statistical structure, such as periodicity, changepoints, additivity, and symmetries. Such structure can be encoded through kernels, which have historically been hand-chosen by experts. We show how to automate this task, creating a system that explores an open-ended space of models and reports the structures discovered. To automatically construct Gaussian process models, we search over sums and products of kernels, maximizing the approximate marginal likelihood. We show how any model in this class can be automatically decomposed into qualitatively different parts, and how each component can be visualized and described through text. We combine these results into a procedure that, given a dataset, automatically constructs a model along with a detailed report containing plots and generated text that illustrate the structure discovered in the data. The introductory chapters contain a tutorial showing how to express many types of structure through kernels, and how adding and multiplying different kernels combines their properties. Examples also show how symmetric kernels can produce priors over topological manifolds such as cylinders, toruses, and Möbius strips, as well as their higher-dimensional generalizations. This thesis also explores several extensions to Gaussian process models. First, building on existing work that relates Gaussian processes and neural nets, we analyze natural extensions of these models to deep kernels and deep Gaussian processes. Second, we examine additive Gaussian processes, showing their relation to the regularization method of dropout. Third, we combine Gaussian processes with the Dirichlet process to produce the warped mixture model: a Bayesian clustering model having nonparametric cluster shapes, and a corresponding latent space in which each cluster has an interpretable parametric form.
IT Security Investments Through the Lens of the Resource-Based View: A new Theoretical Model and Literature Review

IT security has become a major issue for organizations as they need to protect their assets, including IT resources, intellectual property and business processes, against security attacks. Disruptions of ITbased business activities can easily lead to economic damage, such as loss of productivity, revenue and
Bird collisions with power lines: failing to see the way ahead?

Visual fields were determined in three bird species representative of families known to be subject to high levels of mortality associated with power lines; kori bustards Ardeotis kori, Otididae, blue cranes Anthropoides paradisea, Gruidae and white storks Ciconia ciconia, Ciconiidae. In all species the frontal visual fields showed narrow and vertically long binocular fields typical of birds that take food items directly in the bill under visual guidance. However, these species differed markedly in the vertical extent of their binocular fields and in the extent of the blind areas which project above and below the binocular fields in the forward facing hemisphere. The importance of these blind areas is that when in flight, head movements in the vertical plane (pitching the head to look downwards) will render the bird blind in the direction of travel. Such movements may frequently occur when birds are scanning below them (for foraging or roost sites, or for conspecifics). In bustards and cranes pitch movements of only 25 and 35 respectively are sufficient to render the birds blind in the direction of travel; in storks head movements of 55 are necessary. That flying birds can render themselves blind in the direction of travel has not been previously recognised and has important implications for the effective mitigation of collisions with human artefacts including wind turbines and power lines. These findings have applicability to species outside of these families especially raptors (Accipitridae) which are known to have small binocular fields and large blind areas similar to those of bustards and cranes, and are also known to be vulnerable to power line collisions. 2010 Elsevier Ltd. All rights reserved.
Towards a new age of virtual ADME/TOX and multidimensional drug discovery

With the continual pressure to ensure follow-up molecules to billiondollar blockbuster drugs, there is a hurdle in profitability and growth forpharmaceutical companies in the next decades. With each success and failurewe increasingly appreciate that a key to the success of synthesized moleculesthrough the research and development process is the possession of drug-likeproperties. These properties include an adequate bioactivity as well as adequatesolubility, an ability to cross critical membranes (intestinal and sometimesblood-brain barrier), reasonable metabolic stability and of course safety inhumans. Dependent on the therapeutic area being investigated it might also bedesirable to avoid certain enzymes or transporters to circumvent potentialdrug-drug interactions. It may also be important to limit the induction ofthese same proteins that can result in further toxicities. We have clearlymoved the assessment of in vitro absorption, distribution, metabolism, excretionand toxicity (ADME/TOX) parameters much earlier in the discovery organizationthan a decade ago with the inclusion of higher throughput systems. We are alsonow faced with huge amounts of ADME/TOX data for each molecule that needinterpretation and also provide a valuable resource for generating predictivecomputational models for future drug discovery. The present review aims toshow what tools exist today for visualizing and modeling ADME/TOX data, what toolsneed to be developed, and how both the present and future tools are valuable for virtualfiltering using ADME/TOX and bioactivity properties in parallel as a viable addition topresent practices.
Association of Kawasaki disease with tropospheric wind patterns

The causal agent of Kawasaki disease (KD) remains unknown after more than 40 years of intensive research. The number of cases continues to rise in many parts of the world and KD is the most common cause of acquired heart disease in childhood in developed countries. Analyses of the three major KD epidemics in Japan, major non-epidemic interannual fluctuations of KD cases in Japan and San Diego, and the seasonal variation of KD in Japan, Hawaii, and San Diego, reveals a consistent pattern wherein KD cases are often linked to large-scale wind currents originating in central Asia and traversing the north Pacific. Results suggest that the environmental trigger for KD could be wind-borne. Efforts to isolate the causative agent of KD should focus on the microbiology of aerosols.
An optimal delay management algorithm from passengers' viewpoints considering the whole railway network

We propose an algorithm for an optimal delay management. We set passengers’ dissatisfaction of all passengers in the whole railway network as a criterion and developed an algorithm which seeks for a delay management plan which makes passengers’ dissatisfaction minimum. The algorithm is a combination of simulation and optimization. Simulation part consists of a train traffic simulator and a passenger flow simulator which work in parallel. The train traffic simulator forecasts future train diagram considering the dynamic interaction between trains and passengers. The passenger flow simulator traces behaviour of all the passengers one by one and calculates how many passengers get on/off at each station. This information is given to the train traffic simulator and necessary dwell times are calculated. Passengers’ dissatisfaction is also estimated from the results of the passenger flow simulation. In the optimization part, we use the tabu search algorithm. We will show the details of our algorithm together with numerical results using real world data.
EFNS guidelines on diagnosis and management of neuromyelitis optica.

BACKGROUND AND PURPOSE
Neuromyelitis optica (NMO) or Devic's disease is a rare inflammatory and demyelinating autoimmune disorder of the central nervous system (CNS) characterized by recurrent attacks of optic neuritis (ON) and longitudinally extensive transverse myelitis (LETM), which is distinct from multiple sclerosis (MS). The guidelines are designed to provide guidance for best clinical practice based on the current state of clinical and scientific knowledge.


SEARCH STRATEGY
Evidence for this guideline was collected by searches for original articles, case reports and meta-analyses in the MEDLINE and Cochrane databases. In addition, clinical practice guidelines of professional neurological and rheumatological organizations were studied.


RESULTS
Different diagnostic criteria for NMO diagnosis [Wingerchuk et al. Revised NMO criteria, 2006 and Miller et al. National Multiple Sclerosis Society (NMSS) task force criteria, 2008] and features potentially indicative of NMO facilitate the diagnosis. In addition, guidance for the work-up and diagnosis of spatially limited NMO spectrum disorders is provided by the task force. Due to lack of studies fulfilling requirement for the highest levels of evidence, the task force suggests concepts for treatment of acute exacerbations and attack prevention based on expert opinion.


CONCLUSIONS
Studies on diagnosis and management of NMO fulfilling requirements for the highest levels of evidence (class I-III rating) are limited, and diagnostic and therapeutic concepts based on expert opinion and consensus of the task force members were assembled for this guideline.
Compressive properties of commercially available polyurethane foams as mechanical models for osteoporotic human cancellous bone

BACKGROUND
Polyurethane (PU) foam is widely used as a model for cancellous bone. The higher density foams are used as standard biomechanical test materials, but none of the low density PU foams are universally accepted as models for osteoporotic (OP) bone. The aim of this study was to determine whether low density PU foam might be suitable for mimicking human OP cancellous bone.


METHODS
Quasi-static compression tests were performed on PU foam cylinders of different lengths (3.9 and 7.7 mm) and of different densities (0.09, 0.16 and 0.32 g cm(-3)), to determine the Young's modulus, yield strength and energy absorbed to yield.


RESULTS
Young's modulus values were 0.08-0.93 MPa for the 0.09 g cm(-3) foam and from 15.1-151.4 MPa for the 0.16 and 0.32 g cm(-3) foam. Yield strength values were 0.01-0.07 MPa for the 0.09 g cm(-3) foam and from 0.9-4.5 MPa for the 0.16 and 0.32 g cm(-3) foam. The energy absorbed to yield was found to be negligible for all foam cylinders.


CONCLUSION
Based on these results, it is concluded that 0.16 g cm(-3) PU foam may prove to be suitable as an OP cancellous bone model when fracture stress, but not energy dissipation, is of concern.
Safety, pharmacokinetics, pharmacodynamics, and antitumor activity of dalantercept, an activin receptor-like kinase-1 ligand trap, in patients with advanced cancer.

PURPOSE
The angiogenesis inhibitor dalantercept (formerly ACE-041) is a soluble form of activin receptor-like kinase-1 (ALK1) that prevents activation of endogenous ALK1 by bone morphogenetic protein-9 (BMP9) and BMP10 and exhibits antitumor activity in preclinical models. This first-in-human study of dalantercept evaluated its safety, tolerability, pharmacokinetics, pharmacodynamics, and antitumor activity in adults with advanced solid tumors.


EXPERIMENTAL DESIGN
Patients in dose-escalating cohorts received dalantercept subcutaneously at one of seven dose levels (0.1-4.8 mg/kg) every 3 weeks until disease progression. Patients in an expansion cohort received dalantercept at 0.8 or 1.6 mg/kg every 3 weeks until disease progression.


RESULTS
In 37 patients receiving dalantercept, the most common treatment-related adverse events were peripheral edema, fatigue, and anemia. Edema and fluid retention were dose-limiting toxicities and responded to diuretic therapy. No clinically significant, treatment-related hypertension, proteinuria, gross hemorrhage, or gastrointestinal perforations were observed. One patient with refractory squamous cell cancer of the head and neck had a partial response, and 13 patients had stable disease according to RECISTv1.1, eight of whom had prolonged periods (≥12 weeks) of stable disease. Correlative pharmacodynamic markers included tumor metabolic activity and tumor blood flow, which decreased from baseline in 63% and 82% of evaluable patients, respectively, and telangiectasia in eight patients.


CONCLUSION
Dalantercept was well-tolerated at doses up to 1.6 mg/kg, with a safety profile distinct from inhibitors of the VEGF pathway. Dalantercept displayed promising antitumor activity in patients with advanced refractory cancer, and multiple phase II studies are underway.
Fuzzy supervised classification of remote sensing images

Abstruct-In the conventional remote sensing supervised classification, training information and classification results are represented in a one-pixel-one-class method. Class mixture cannot be taken into consideration in training a classifier and in determining pixels’ membership. The expressive limitation has reduced the classification accuracy level and led to the poor extraction of information. This paper describes a fuzzy supervised classification method in which geographical information is represented as fuzzy sets. The algorithm consists of two major steps: The estimate of fuzzy parameters from fuzzy training data, and afuzzy partition of spectral space. Partial membership of pixels allows component cover classes of mixed pixels to be identified and more accurate statistical parameters to be generated, and, in turn, a higher classification accuracy to be achieved. Results of classifying a Landsat MSS image are presented and their accuracy is analyzed. Keywords-Remote sensing, fuzzy set theory, image classification, information representation.
The Effect of Traumatic Experiences and Psychiatric Symptoms on the Life Satisfaction of North Korean Refugees.

BACKGROUND
Successful adaptation of refugees to a new society can be hindered by traumatic experiences and psychiatric symptoms. This study aims to examine the relationship between trauma, psychiatric symptoms and life satisfaction of North Korean refugees resettled in South Korea.


METHODS
A total of 211 North Korean refugees living in South Korea completed a series of questionnaires on the history of their previous traumatic experiences, life satisfaction in South Korea, depression, anxiety, somatization and post-traumatic stress disorder (PTSD) symptoms.


RESULTS
North Korean refugees who had experienced more traumatic events were less satisfied with their economic status in South Korea. Severe depression, anxiety, somatization or PTSD symptoms negatively correlated with their overall satisfaction in South Korea. In the stepwise regression model including all psychiatric symptoms and the number of traumatic experiences as dependent variables, only anxiety, but not trauma, predicted lower life satisfaction in South Korea.


CONCLUSIONS
Traumatic experiences of North Korean refugees negatively affected the life satisfaction, especially the economic satisfaction, in South Korea. Since the negative effect of trauma was mainly mediated by psychiatric symptoms, the strategy of relieving psychiatric symptoms of traumatized refugees may help the adaptation of refugees.
Extraction of brain tumor based on morphological operations

This paper describes the efficient framework for the extraction of brain tumor from the MR images. Before the segmentaion process, median filter is used to filter the image, Then, morpholigical gardient is computed and added with the filtered image for the intensity enhancement. After the enhancement process, the thresholding value is calculated using the mean and standard deviation of the image. This threholding value is used to binarize the the image followed by the morphological opreations. Moreover, the combination of the morphological operations allows to compute local thresholding image supproted by flood-fill algorithm and pixel replacement process finally to extract the tumor from the brain. Thus, it provides a new source of evidence in the field of segmentation that the specialist can aggregate with the segmenation results to soften it is own decision.
An artificial neural network model for river flow forecasting a comparison between ANN and ARIMA

Careful modeling and precise river flow forecast is fundamental and is the basis for a wide range of problems related to the management of river hydrologic systems. Artificial Neural Networks (ANNs) are being used increasingly to predict and forecast water resources variables specially MLP network. The Feed forward Multi-layer perceptron (MLP) with back propagation algorithm has applied in this research. The selected ANN model were used to train and forecast the daily flows into the Silveh Dam on the Lavin River in IRAN, between years 2002–2012. In order to compare the performance of MLP, Box-Jenkins model was used. The results indicated that, in general, ANN approaches yielded reasonably good forecasts for 7-day lead times in comparison with the Auto Regressive Integrated Moving Average (ARIMA) model. Keywords; River flow forecasting; ARIMA; ANN; Feed forward multi-layer perceptron (MLP)