nils-herrmann/clean_scirepeval huggingface dataset:
Posture cognitive dual tasking: A relevant marker of depression related psychomotor retardation. An illustration of the positive impact of repetitive transcranial magnetic stimulation in patients with major depressive disorder.
Venue: Journal of psychiatric research

This study examined whether postural control variables, particularly the center of pressure (COP) velocity based parameters, could be a relevant hallmark of depression related psychomotor retardation (PMR) We first aimed at investigating the interplay between the PMR scores and the COP performance in patients with major depressive disorder (MDD) as compared to age matched healthy controls; secondly, we focused on the impact of a repetitive transcranial magnetic stimulation (rTMS) treatment on depression, PMR scores and postural performance. 16 MDD patients, and a control group of 16 healthy adults, were asked to maintain quiet standing balance during two trials with or without vision, and while backward counting (dual task) All the position and velocity based COP variables were computed. Before and after the rTMS session (n eligible MDD 10) we assessed the depression level with the Montgomery Asberg Depression Rating Scale (MADRS) the PMR scores with the French Retardation Rating Scale for Depression (ERD) and postural performance. Before the treatment, significant positive partial correlations were found between the pre ERD scores and the velocity based COP variables, especially in the dual task conditions (p 0.05) In contrast, there was no significant correlation between the post ERD scores and any postural parameter after the treatment. The MADRS and ERD scores showed a significant decrease between before and after the rTMS intervention. For the first time, the findings clearly validated the view that the assessment of postural performance easy to envisage in clinical settings constitutes a reliable and objective marker of PMR in MDD patients.
Advances in Computing and Data Sciences
Venue: Communications in Computer and Information Science

ing Communication Methodology in IoT Sensors to Eliminate Redundancy and Cycles 541 Sharad Saxena Analysis of Application Layer Protocols in Internet of Things 550 S. Sasirekha, S. Swamynathan, S. Chandini, and K. Keerthana Classification of Emotions from Images Using Localized Subsection Information 562 Abhishek Singh Kilak and Namita Mittal Comparative Study of Classification Techniques for Weather Data 572 Shweta Panjwani, S. Naresh Kumar, and Laxmi Ahuja Content Based Component Retrieval Based on Neural Network (NN) Classification Method 577 Rupali Garg and Jagpuneet Kaur Bajwa Data Mining Classification Models for Industrial Planning 585 Ricardo Braganca, Filipe Portela, A. Vale, Tiago Guimaraes, and Manuel Santos Designing a Smart Contract Application Layer for Transacting Decentralized Autonomous Organizations. 595 Alex Norta Digitization of Ancient Manuscripts and Inscriptions A Review 605 N. Jayanthi, S. Indu, Snigdhaa Hasija, and Prateek Tripathi
Swept Source Optical Coherence Tomography Angio(tm) (Topcon Corp, Japan) Technology Review.
Venue: Developments in ophthalmology

Optical coherence tomography (OCT) angiography (OCTA) is a novel, noninvasive, three dimensional imaging technique that allows for the visualization of intravascular flow in the microvasculature. Swept source OCT technology utilizes longer wavelength infrared light than conventional spectral domain OCT. This enables improved penetration into tissue and imaging through optical opacities and is invisible to the subject. Topcon has recently developed an innovative OCTA algorithm, OCTARA (OCTA Ratio Analysis) which benefits from being paired with swept source OCT. OCTARA aims to provide improved detection sensitivity of low blood flow and reduced motion artifacts without compromising axial resolution. In this chapter, we describe the implementation of OCTARA with swept source OCT technology, the technical specifications of acquisition (e.g. the number of scans, area of examination field, etc. along with the algorithm's function and principles for analysis of B scan data to achieve angiographic visualization. Examples of OCTA scans performed using the OCTARA algorithm and a comparison of these scans with images obtained using other technologies are also presented.
Interatomic Coulomb interaction and electron nematic bond order in FeSe

Despite having the simplest atomic structure, bulk FeSe has an observed electronic structure with the largest deviation from the band theory predictions among all Fe based superconductors and exhibits a low temperature nematic electronic state without intervening magnetic order. We show that the Fe Fe interatomic Coulomb repulsion $V$ offers a natural explanation for the puzzling electron correlation effects in FeSe superconductors. It produces a strongly renormalized low energy band structure where the van Hove singularity sits remarkably close to Fermi level in the high temperature electron liquid phase as observed experimentally. This proximity enables the quantum fluctuations in $V$ to induce a rotational symmetry breaking electronic bond order in the $d$ wave channel. We argue that this emergent low temperature $d$ wave bond nematic state, different from the commonly discussed ferro orbital order and spin nematicity, has been observed recently by several angle resolved photoemission experiments detecting the lifting of the band degeneracies at high symmetry points in the Brillouin zone. We present a symmetry analysis of the space group and identify the hidden antiunitary $T$ symmetry that protects the band degeneracy and the electronic order/interaction that can break the symmetry and lift the degeneracy. We show that the $d$ wave nematic bond order, together with the spin orbit coupling, provide a unique explanation of the temperature dependence, momentum space anisotropy, and domain effects observed experimentally. We discuss the implications of our findings on the structural transition, the absence of magnetic order, and the intricate competition between nematicity and superconductivity in FeSe superconductors.
Resilience: International Policies, Practices and Discourses

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JCL: A High Performance Computing Java Middleware
Venue: ICEIS

Java Ca&La or just JCL is a distributed shared memory reflective lightweight middleware for Java developers whose main goals are: i) provide a simple deployment strategy, where automatic code registration occurs, ii) support a collaborative multi developer cluster environment where applications can interact without explicit dependencies, iii) execute existing sequential Java code over both multi core machines and cluster of multi core machines without refactorings, enabling the separation of business logic from distribution issues in the development process, iv) provide a multi core/multi computer portable code. This paper describes JCLas features and architecture; compares and contrasts JCL to other Java based middleware systems, and reports performance measurements of JCL applications.
A general purpose service oriented middleware model for WSN
Venue: 2016 Eighth International Conference on Ubiquitous and Future Networks (ICUFN)

Wireless sensor networks (WSN) play an important role in many applications like healthcare, traffic monitoring, security, environmental and infrastructure monitoring. When designing these applications for different domains like healthcare, traffic management or military intelligence, people face a lot of challenges. Service oriented architecture (SOA) is an emerging area in distributed computing that uses services as a software component to complete a task. Service oriented middleware (SOM) is based on SOA and is very beneficial in helping to overcome WSN challenges. SOM provides different services to applications and the underlying network to reduce the complexity of the system. SOM helps in achieving interoperability and heterogeneity. In this paper we discuss some available SOM and highlight different services provided by these approaches. Also, a general purpose SOM is proposed that will fit with different types of applications from different domains.
Distributed Software Infrastructure for General Purpose Services in Smart Grid
Venue: IEEE Transactions on Smart Grid

In this paper, the design of an event driven middleware for general purpose services in smart grid (SG) is presented. The main purpose is to provide a peer to peer distributed software infrastructure to allow the access of new multiple and authorized actors to SGs information in order to provide new services. To achieve this, the proposed middleware has been designed to be: 1) event based; 2) reliable; 3) secure from malicious information and communication technology attacks; and 4) to enable hardware independent interoperability between heterogeneous technologies. To demonstrate practical deployment, a numerical case study applied to the whole U.K. distribution network is presented, and the capabilities of the proposed infrastructure are discussed.
Integration of data fusion and reinforcement learning techniques for the rank aggregation problem
Venue: Int. J. Mach. Learn. Cybern.

Rank aggregation or combining multiple ranked lists is the heart of meta search engines in web information retrieval. In this paper, a novel rank aggregation method is proposed, which utilizes both data fusion operators and reinforcement learning algorithms. Such integration enables us to use the compactness property of data fusion methods as well as the exploration and exploitation capabilities of reinforcement learning techniques. The proposed algorithm is a two steps process. In the first step, ranked lists of local rankers are combined based on their mean average precisions with a variety of data fusion operators such as optimistic and pessimistic ordered weighted averaging (OWA) operators. This aggregation provides a compact representation of the utilized benchmark dataset. In the second step, a Markov decision process (MDP) model is defined for the aggregated data. This MDP enables us to apply reinforcement learning techniques such as Q learning and SARSA for learning the best ranking. Experimentations on the LETOR4.0 benchmark dataset demonstrates that the proposed method outperforms baseline rank aggregation methods such as Borda Count and the family of coset permutation distance based stage wise (CPS) rank aggregation methods on P@n and NDCG@n evaluation criteria. The achieved improvement is especially more noticeable in the higher ranks in the final ranked list, which is usually more attractive to Web users.
Domain specific query generation from natural language text
Venue: 2016 Sixth International Conference on Innovative Computing Technology (INTECH)

This paper presents an approach to automate the generation of Structure Query Language from Natural Language Text. Software requirements specifications are most important part of Natural Language Processing, as little mistake in this phase results in absurd software design. Software Specifications are used in software industry. When we automatic translate these Natural Language Text into Structured Query Text we find Many issues because Software Specification is not an independent sentence they have many module related to each other. so when we translate these English text we have found many issues such as discourse, semantic and negation problem. The evaluation method for Natural Language texts is to test against a list of sentences, each of which is paired with yes or no. For this case I have study Natural Language text and their problems in my MS thesis.  Texts are and what issues are found when we automatic translate these Texts into SQL. We used Stanford dependency parser for text translation.
Interacting with Financial Data using Natural Language
Venue: SIGIR

Financial and economic data are typically available in the form of tables and comprise mostly of monetary amounts, numeric and other domain specific fields. They can be very hard to search and they are often made available out of context, or in forms which cannot be integrated with systems where text is required, such as voice enabled devices. This work presents a novel system that enables both experts in the finance domain and non expert users to search financial data with both keyword and natural language queries. Our system answers the queries with an automatically generated textual description using Natural Language Generation (NLG) The answers are further enriched with derived information, not explicitly asked in the user query, to provide the context of the answer. The system is designed to be flexible in order to accommodate new use cases without significant development effort, thus allowing fast integration of new datasets.
When is the Time Ripe for Natural Language Processing for Patent Passage Retrieval?
Venue: CIKM

Patent text is a mixture of legal terms and domain specific terms. In technical English text, a multi word unit method is often deployed as a word formation strategy in order to expand the working vocabulary, i.e. introducing a new concept without the invention of an entirely new word. In this paper we explore query generation using natural language processing technologies in order to capture domain specific concepts represented as multi word units. In this paper we examine a range of query generation methods using both linguistic and statistical information. We also propose a new method to identify domain specific terms from other more general phrases. We apply a machine learning approach using domain knowledge and corpus linguistic information in order to learn domain specific terms in relation to phrases' Termhood values. The experiments are conducted on the English part of the CLEF IP 2013 test collection. The outcome of the experiments shows that the favoured method in terms of PRES and recall is when a language model is used and search terms are extracted with a part of speech tagger and a noun phrase chunker. With our proposed methods we improve each evaluation metric significantly compared to the existing state of the art for the CLEP IP 2013 test collection: for PRES@100 by 26% (0.544 from 0.433) for recall@100 by 17% (0.631 from 0.540) and on document MAP by 57% (0.300 from 0.191)
On the tribological behavior of retrieved hip femoral heads affected by metallic debris. A comparative investigation by stylus and optical profilometer for a new roughness measurement protocol

In this study, 35 retrieved ceramics femoral heads with evidence of metallic dark lines on the surface were analyzed. The surface roughness of these femoral heads was acquired using both a stylus contact profiler and an optical non contact profilometer. The metallic deposition on hip ball surface, also known as Metal Transfer (MT) appeared as dark metallic and can occur during the surgery or during the reduction of a dislocated prosthesis. In this study, we validate a new protocol to measure surface roughness on retrieved femoral heads, by using the aforementioned two different acquisition techniques and also to investigate the hypothesis that such metal transfer retrieved ceramic femoral head is associated with increased surface roughness. All femoral components of this investigation showed remarkable differences in roughness values between MT affected and MT unaffected areas. The two acquisition procedures conventional stylus and 3D confocal profilometer confirmed a satisfying agreement, even considering the obvious resolution difference.
Sepsis: pathophysiology and clinical management
Venue: British Medical Journal

Sepsis, severe sepsis, and septic shock represent increasingly severe systemic inflammatory responses to infection. Sepsis is common in the aging population, and it disproportionately affects patients with cancer and underlying immunosuppression. In its most severe form, sepsis causes multiple organ dysfunction that can produce a state of chronic critical illness characterized by severe immune dysfunction and catabolism. Much has been learnt about the pathogenesis of sepsis at the molecular, cell, and intact organ level. Despite uncertainties in hemodynamic management and several treatments that have failed in clinical trials, investigational therapies increasingly target sepsis induced organ and immune dysfunction. Outcomes in sepsis have greatly improved overall, probably because of an enhanced focus on early diagnosis and fluid resuscitation, the rapid delivery of effective antibiotics, and other improvements in supportive care for critically ill patients. These improvements include lung protective ventilation, more judicious use of blood products, and strategies to reduce nosocomial infections.
Increased Wnt5a in squamous cell lung carcinoma inhibits endothelial cell motility
Venue: BMC Cancer

BackgroundAngiogenesis is important both in normal tissue function and disease and represents a key target in lung cancer (LC) therapy. Unfortunately, the two main subtypes of non small cell lung cancers (NSCLC) namely, adenocarcinoma (AC) and squamous cell carcinoma (SCC) respond differently to anti angiogenic e.g. anti vascular endothelial growth factor (VEGF) A treatment with life threatening side effects, often pulmonary hemorrhage in SCC. The mechanisms behind such adverse reactions are still largely unknown, although peroxisome proliferator activator receptor (PPAR) gamma as well as Wnt s have been named as molecular regulators of the process. As the Wnt microenvironments in NSCLC subtypes are drastically different, we hypothesized that the particularly high levels of non canonical Wnt5a in SCC might be responsible for alterations in blood vessel growth and result in serious adverse reactions.MethodsPPARgamma, VEGF A, Wnt5a, miR 27b and miR 200b levels were determined in resected adenocarcinoma and squamous cell carcinoma samples by qRT PCR and TaqMan microRNA assay. The role of PPARgamma in VEGF A expression, and the role of Wnts in overall regulation was investigated using PPARgamma knock out mice, cancer cell lines and fully human, in vitro 3 dimensional (3D) distal lung tissue aggregates. PPARgamma mRNA and protein levels were tested by qRT PCR and immunohistochemistry, respectively. PPARgamma activity was measured by a PPRE reporter system. The tissue engineered lung tissues expressing basal level and lentivirally delivered VEGF A were treated with recombinant Wnts, chemical Wnt pathway modifiers, and were subjected to PPARgamma agonist and antagonist treatment.ResultsPPARgamma down regulation and VEGF A up regulation are characteristic to both AC and SCC. Increased VEGF A levels are under direct control of PPARgamma. PPARgamma levels and activity, however, are under Wnt control. Imbalance of both canonical (in AC) and non canonical (in SCC) Wnts leads to PPARgamma down regulation. While canonical Wnts down regulate PPARgamma directly, non canonical Wnt5a increases miR27b that is known regulator of PPARgamma.ConclusionDuring carcinogenesis the Wnt microenvironment alters, which can downregulate PPARgamma leading to increased VEGF A expression. Differences in the Wnt microenvironment in AC and SCC of NSCLC lead to PPARgamma decrease via mechanisms that differentially alter endothelial cell motility and branching which in turn can influence therapeutic response.
QUANTITATIVE PRECIPITATION FORECAST USING NUMERICAL WEATHER PREDICTION AND METEOROLOGICAL SATELLITE FOR KELANTAN AND KLANG RIVER BASINS

The unusual heavy rainfall episodes over Kelantan River Basin in 2014 had caused massive destruction and several deaths. The unprecedented storm events at the north eastern Peninsular Malaysia and many other places indicate the need for enhanced storm forecasting to improve disaster preparedness among the civilian. Quantitative precipitation forecast (QPF) from atmospheric model combined with geostationary meteorological satellite information as input to hydrodynamic model for flood forecasting system can potentially provide improved lead time for warning. In this study, a QPF model is developed using the multilayer neural network with data inputs from the numerical weather prediction (NWP) model products combined with the geostationary meteorological satellite infrared and visible image features to forecast precipitation for a flood prone area in a tropical region. The results indicate that the model can satisfactorily produce 1 hour rainfall forecast with improved accuracy for larger forecast area. The R 2 for areal average rainfall for Kelantan river basin is 0.674 and for Klang river basin is 0.893 whereas the R 2 for point rainfall is 0.392 for Kelantan river basin and 0.495 for Klang river basin.
Effect of sieve particle size on functional, thermal, rheological and pasting properties of Indian and Turkish lentil flour

The influence of particle size (74 210 mm) on compositional, functional, rheological, thermal and pasting properties of two commercial lentil flour samples (Indian and Turkish) was investigated. Reduction of particle size significantly affected the composition of the flour. Turkish lentil flour fractions had higher bulk density (480 600 kg/m3) than Indian one. Particle size did not influence the water holding capacity of flour, however, the process temperature increased those parameters significantly. Instrumental color parameters a and b reduced abruptly and the lightness value, L increased while the particle size was reduced from 210 to 74 mm. Laser diffraction analysis showed bimodal particle size distributions of lentil flour. Two distinct thermal transitions exhibited by lentil samples and the glass transition temperature varied among samples. Non isothermal heating of lentil particle dispersions demonstrated a gradual decrease in the peak complex viscosity with decreasing particle size. Pasting parameters and micrographs of lentil fractions showed a significant difference between two samples. The obtained results could be useful for the food industry to manufacture lentil based food products with a defined particle size to obtain desirable functional and rheological characteristics.