nils-herrmann/clean_scirepeval huggingface dataset:
Associations of Cognitive Function Scores with Carbon Dioxide, Ventilation, and Volatile Organic Compound Exposures in Office Workers: A Controlled Exposure Study of Green and Conventional Office Environments

The indoor built environment plays a critical role in our overall well-being because of both the amount of time we spend indoors (~90%) and the ability of buildings to positively or negatively influence our health. The advent of sustainable design or green building strategies reinvigorated questions regarding the specific factors in buildings that lead to optimized conditions for health and productivity.



We simulated indoor environmental quality (IEQ) conditions in "Green" and "Conventional" buildings and evaluated the impacts on an objective measure of human performance: higher-order cognitive function.



Twenty-four participants spent 6 full work days (0900-1700 hours) in an environmentally controlled office space, blinded to test conditions. On different days, they were exposed to IEQ conditions representative of Conventional [high concentrations of volatile organic compounds (VOCs)] and Green (low concentrations of VOCs) office buildings in the United States. Additional conditions simulated a Green building with a high outdoor air ventilation rate (labeled Green+) and artificially elevated carbon dioxide (CO2) levels independent of ventilation.



On average, cognitive scores were 61% higher on the Green building day and 101% higher on the two Green+ building days than on the Conventional building day (p < 0.0001). VOCs and CO2 were independently associated with cognitive scores.



Cognitive function scores were significantly better under Green+ building conditions than in the Conventional building conditions for all nine functional domains. These findings have wide-ranging implications because this study was designed to reflect conditions that are commonly encountered every day in many indoor environments.


 Associations of cognitive function scores with carbon dioxide, ventilation, and volatile organic compound exposures in office workers: a controlled exposure study of green and conventional office environments. Environ Health Perspect 124:805-812; http://dx.doi.org/10.1289/ehp.1510037.
Low-cost 3-axis soft tactile sensors for the human-friendly robot Vizzy

In this paper we present a low-cost and easy to fabricate 3-axis tactile sensor based on magnetic technology. The sensor consists in a small magnet immersed in a silicone body with an Hall-effect sensor placed below to detect changes in the magnetic field caused by displacements of the magnet, generated by an external force applied to the silicone body. The use of a 3-axis Hall-effect sensor allows to detect the three components of the force vector, and the proposed design assures high sensitivity, low hysteresis and good repeatability of the measurement: notably, the minimum sensed force is about 0.007N. All components are cheap and easy to retrieve and to assemble; the fabrication process is described in detail and it can be easily replicated by other researchers. Sensors with different geometries have been fabricated, calibrated and successfully integrated in the hand of the human-friendly robot Vizzy. In addition to the sensor characterization and validation, real world experiments of object manipulation are reported, showing proper detection of both normal and shear forces.
Severe Darier's disease in a psychiatric patient*

Darier's disease is characterized by dense keratotic lesions in the seborrheic areas of the body such as scalp, forehead, nasolabial folds, trunk and inguinal region. It is a rare genodermatosis, an autosomal dominant inherited disease that may be associated with neuropsichiatric disorders. It is caused by ATPA2 gene mutation, presenting cutaneous and dermatologic expressions. Psychiatric symptoms are depression, suicidal attempts, and bipolar affective disorder. We report a case of Darier's disease in a 48-year-old female patient presenting severe cutaneous and psychiatric manifestations.
Gut inflammation and microbiome in spondyloarthritis

Spondyloarthritis (SpA) is chronic inflammatory disease involving joints and the spine. Bowel inflammation is common in SpA, which may be classified as acute or chronic. Chronic gut inflammation is most common in SpA patients with axial involvement as compared to those presenting with peripheral involvement alone. The pathogenesis of gut inflammation in SpA could be explained by two factors—over-activation of immunological cells and altered gut microbiome. This is exemplified by SpA animal models, namely HLA-B27-expressing transgenic animals and SKG mice models. Immunological mechanisms include homing of activated T cells from gut into synovium, excess pro-inflammatory cytokines secretion by immune cells such as IL-23 and genetic variations in immunological genes. The evidence for role of gut microbiome in SpA is gradually emerging. Recently, metagenomic study of gut microbiome by sequencing of microbial nucleic acids has enabled identification of new microbial taxa and their functions in gut of patients with SpA. In SpA, the gut microbiome could emerge as diagnostic and prognostic marker of disease. Modulation of gut microbiome is slated to have therapeutic potential as well.
Publicly Verifiable Boolean Query over Outsourced Encrypted Data

Outsourcing storage and computation to the cloud has become a common practice for businesses and individuals. As the cloud is semi-trusted or susceptible to attacks, many researches suggest that the outsourced data should be encrypted and then retrieved by using searchable symmetric encryption (SSE) schemes. Since the cloud is not fully trusted, we doubt whether it would always process queries correctly or not. Therefore, there is a need for users to verify their query results. Motivated by this, in this paper, we propose a publicly verifiable dynamic searchable symmetric encryption scheme based on the accumulation tree. We first construct an accumulation tree based on encrypted data and then outsource both of them to the cloud. Next, during the search operation, the cloud generates the corresponding proof according to the query result by mapping Boolean query operations to set operations while keeping privacy-preservation and achieving the verification requirements: authenticity, freshness, and completeness. The security analysis and performance evaluation show that the proposed scheme is privacy-preserving and practical.
Regular Linear Temporal Logic

We present regular linear temporal logic (RLTL), a logic that generalizes linear temporal logic with the ability to use regular expressions arbitrarily as sub-expressions. Every LTL operator can be defined as a context in regular linear temporal logic. This implies that there is a (linear) translation from LTL to RLTL. Unlike LTL, regular linear temporal logic can define all ω-regular languages, while still keeping the satisfiability problem in PSPACE. Unlike the extended temporal logics ETL∗, RLTL is defined with an algebraic signature. In contrast to the linear time μ-calculus, RLTL does not depend on fix-points in its syntax.
From Lifestyle Vlogs to Everyday Interactions

A major stumbling block to progress in understanding basic human interactions, such as getting out of bed or opening a refrigerator, is lack of good training data. Most past efforts have gathered this data explicitly: starting with a laundry list of action labels, and then querying search engines for videos tagged with each label. In this work, we do the reverse and search implicitly: we start with a large collection of interaction-rich video data and then annotate and analyze it. We use Internet Lifestyle Vlogs as the source of surprisingly large and diverse interaction data. We show that by collecting the data first, we are able to achieve greater scale and far greater diversity in terms of actions and actors. Additionally, our data exposes biases built into common explicitly gathered data. We make sense of our data by analyzing the central component of interaction - hands. We benchmark two tasks: identifying semantic object contact at the video level and non-semantic contact state at the frame level. We additionally demonstrate future prediction of hands.
Phyto-Nutrient Diversity in Morinda Citrifolia L . Genotypes of Andaman Islands , India

The Indian mulberry or Noni (Morinda citrifolia L.) is one of the emerging sources of natural antioxidants for herbal and pharmaceutical industry. The genus Morinda has more than 150 species in which M. citrifolia is identified as most important for health and economic point of view. Present study revealed significant (p < 0.05) diversity in 33 genotypes of M. citrifolia from Andaman and Nicobar Islands (India) for phyto-constituents. The promising genotypes viz. FRG-14, JGH-5, TRA-1, TRA-2 and HD-6 were identified for commercial uses. Correlation analysis in M. citrifolia germplasm showed strong correlation between carotenoids and ascorbic acid (r2 =0.973; p<0.05), tannin (r2 =0.598; p<0.05), flavonoids (r2 =0.691; p<0.05) and phenol (r2 =0.598; p<0.05). The genotypes showed wide range for antioxidant capacity which showed positive correlation with carotenoids (r2 =0.335; p<0.05), flavonoids (r2 =0.249; p<0.05) and Cu (r2 =0.953; p<0.05), Mn (r2 =0.953; p<0.05) and Mg (r2 =0.582; p<0.05). The diversity analysis is useful for designing breeding strategies for phyto-nutrient rich genotypes for better recovery in health products.
Nearest neighbor selection for iteratively kNN imputation

Existing kNN imputation methods for dealing with missing data are designed according to Minkowski distance or its variants, and have been shown to be generally efficient for numerical variables (features, or attributes). To deal with heterogeneous (i.e., mixed-attributes) data, we propose a novel kNN (k nearest neighbor) imputation method to iteratively imputing missing data, named GkNN (gray kNN) imputation. GkNN selects k nearest neighbors for each missing datum via calculating the gray distance between the missing datum and all the training data rather than traditional distance metric methods, such as Euclidean distance. Such a distance metric can deal with both numerical and categorical attributes. For achieving nearest neighbors NN imputation the better effectiveness, GkNN regards all the imputed instances (i.e., the missing data been imputed) as observed data, which with complete instances (instances without missing values) together to iteratively impute other missing data. We experimentally evaluate the proposed approach, and demonstrate that the gray distance is much better than the Minkowski distance at both capturing the proximity relationship (or nearness) of two instances and dealing with mixed attributes. Moreover, experimental results also rithm show that the GkNN algo
Virtual anthropology: useful radiological tools for age assessment in clinical forensic medicine and thanatology

Virtual anthropology consists of the introduction of modern slice imaging to biological and forensic anthropology. Thanks to this non-invasive scientific revolution, some classifications and staging systems, first based on dry bone analysis, can be applied to cadavers with no need for specific preparation, as well as to living persons. Estimation of bone and dental age is one of the possibilities offered by radiology. Biological age can be estimated in clinical forensic medicine as well as in living persons. Virtual anthropology may also help the forensic pathologist to estimate a deceased person’s age at death, which together with sex, geographical origin and stature, is one of the important features determining a biological profile used in reconstructive identification. For this forensic purpose, the radiological tools used are multislice computed tomography and, more recently, X-ray free imaging techniques such as magnetic resonance imaging and ultrasound investigations. We present and discuss the value of these investigations for age estimation in anthropology.
Are Autonomous Mobile Robots Able to Take Over Construction ? A Review

May 17, 2015. Received in revised form: October 15, 2015.  October 25, 2015. Although construction has been known as a highly complex application field for autonomous robotic systems, recent advances in this field offer great hope for using robotic capabilities to develop automated construction. Today, space research agencies seek to build infrastructures without human intervention, and construction companies look to robots with the potential to improve construction quality, efficiency, and safety, not to mention flexibility in architectural design. However, unlike production robots used, for instance, in automotive industries, autonomous robots should be designed with special consideration for challenges such as the complexity of the cluttered and dynamic working space, human-robot interactions and inaccuracy in positioning due to the nature of mobile systems and the lack of affordable and precise self-positioning solutions. This paper briefly reviews state-ofthe-art research into automated construction by autonomous mobile robots. We address and classify the relevant studies in terms of applications, materials, and robotic systems. We also identify ongoing challenges and discuss about future robotic requirements for automated construction.
Modeling complex systems using fuzzy cognitive maps

This research deals with the soft computing methodology of fuzzy cognitive map (FCM). Here a mathematical description of FCM is presented and a new methodology based on fuzzy logic techniques for developing the FCM is examined. The capability and usefulness of FCM in modeling complex systems and the application of FCM to modeling and describing the behavior of a heat exchanger system is presented. The applicability of FCM to model the supervisor of complex systems is discussed and the FCM-supervisor for evaluating the performance of a system is constructed; simulation results are presented and discussed.
Structure validation by Calpha geometry: phi,psi and Cbeta deviation.

Geometrical validation around the Calpha is described, with a new Cbeta measure and updated Ramachandran plot. Deviation of the observed Cbeta atom from ideal position provides a single measure encapsulating the major structure-validation information contained in bond angle distortions. Cbeta deviation is sensitive to incompatibilities between sidechain and backbone caused by misfit conformations or inappropriate refinement restraints. A new phi,psi plot using density-dependent smoothing for 81,234 non-Gly, non-Pro, and non-prePro residues with B < 30 from 500 high-resolution proteins shows sharp boundaries at critical edges and clear delineation between large empty areas and regions that are allowed but disfavored. One such region is the gamma-turn conformation near +75 degrees,-60 degrees, counted as forbidden by common structure-validation programs; however, it occurs in well-ordered parts of good structures, it is overrepresented near functional sites, and strain is partly compensated by the gamma-turn H-bond. Favored and allowed phi,psi regions are also defined for Pro, pre-Pro, and Gly (important because Gly phi,psi angles are more permissive but less accurately determined). Details of these accurate empirical distributions are poorly predicted by previous theoretical calculations, including a region left of alpha-helix, which rates as favorable in energy yet rarely occurs. A proposed factor explaining this discrepancy is that crowding of the two-peptide NHs permits donating only a single H-bond. New calculations by Hu et al.  for Ala and Gly dipeptides, using mixed quantum mechanics and molecular mechanics, fit our nonrepetitive data in excellent detail. To run our geometrical evaluations on a user-uploaded file, see MOLPROBITY (http://kinemage.biochem.duke.edu) or RAMPAGE (http://www-cryst.bioc.cam.ac.uk/rampage).
Fast and scalable reachability queries on graphs by pruned labeling with landmarks and paths

Answering reachability queries on directed graphs is ubiquitous in many applications involved with graph-shaped data as one of the most fundamental and important operations. However, it is still highly challenging to efficiently process them on large-scale graphs. Transitive-closure-based methods consume prohibitively large index space, and online-search-based methods answer queries too slowly. Labeling-based methods attain both small index size and query time, but previous indexing algorithms are not scalable at all for processing large graphs of the day. In this paper, we propose new labeling-based methods for reachability queries, referred to as pruned landmark labeling and pruned path labeling. They follow the frameworks of 2-hop cover and 3-hop cover, but their indexing algorithms are based on the recent notion of pruned labeling and improve the indexing time by several orders of magnitude, resulting in applicability to large graphs with tens of millions of vertices and edges. Our experimental results show that they attain remarkable trade-offs between fast query time, small index size and scalability, which previous methods have never been able to achieve. Furthermore, we also discuss the ingredients of the efficiency of our methods by a novel theoretical analysis based on the graph minor theory.
Finding community structure in mega-scale social networks: [extended abstract]

Community analysis algorithm proposed by Clauset, Newman, and Moore (CNM algorithm) finds community structure in social networks. Unfortunately, CNM algorithm does not scale well and its use is practically limited to networks whose sizes are up to 500,000 nodes. We show that this inefficiency is caused from merging communities in unbalanced manner and that a simple heuristics that attempts to merge community structures in a balanced manner can dramatically improve community structure analysis. The proposed techniques are tested using data sets obtained from existing social networking service that hosts 5.5 million users. We have tested three three variations of the heuristics. The fastest method processes a SNS friendship network with 1 million users in 5 minutes (70 times faster than CNM) and another friendship network with 4 million users in 35 minutes, respectively. Another one processes a network with 500,000 nodes in 50 minutes (7 times faster than CNM), finds community structures that has improved modularity, and scales to a network with 5.5 million.
Pathways for emotions and memory II . Afferent input to the anterior thalamic nuclei from prefrontal , temporal , hypothalamic areas and the basal ganglia in the rhesus monkey

The anterior thalamic nuclei are a key link in pathways associated with emotions and memory. In the preceding study we found that one of the anterior nuclei, the anterior medial (AM), had particularly robust connections with specific medial prefrontal and orbitofrontal cortices and moderate connections with frontal polar cortices. The goal of this study was to use a direct approach to determine the sources of projections to the AM nucleus from all prefrontal cortices, as well as from temporal structures and the hypothalamic mammillary body, known for their role in distinct aspects of memory and emotion. We addressed this issue with targeted injections of retrograde fluorescent tracers in the AM nucleus to determine its sources of input. Projection neurons directed to the AM nucleus were found in the deep layers of most prefrontal cortices (layers V and VI), and were most densely distributed in medial areas 24, 32 and 25, orbitofrontal areas 13 and 25, and lateral areas 10 and 46. Most projection neurons were found in layer VI, though in medial prefrontal cortices and dorsal area 9 about a third were found in layer V, a significantly higher proportion than in lateral and orbitofrontal cortices. In the temporal lobe, projection neurons originated mostly from the hippocampal formation (ammonic field CA3 and subicular complex), and the amygdala (basolateral, lateral, and basomedial nuclei). In the hypothalamus, a significant number of neurons in the ipsilateral medial mammillary body projected to the AM nucleus, some of which were positive for calbindin (CB) or parvalbumin (PV), markers expressed, respectively, in “diffuse” and “specific” pathways in the thalamus [ (1998a) 49]. As recipient of diverse signals, the AM nucleus is in a key position to link pathways associated with emotions, and may be an important interface for systems associated with retrieval of information from long-term memory in the process of solving problems within working memory. Finally, the internal segment of the globus pallidus (GPi) issued projections to AM, suggesting direct linkage with executive systems through the basal ganglia. The diverse connections of the AM nucleus may help explain the varied deficits in memory and emotions seen in neurodegenerative and psychiatric diseases affecting the anterior thalamic nuclei.
ACTIVE APPEARANCE MODEL AND PCA BASED FACE RECOGNITION SYSTEM

At present face recognition has wide area of applications such as security, law enforcement. Imaging conditions, Orientation, Pose and presence of occlusion are huge problems associated with face recognition. The performance of face recognition systems decreases due to these problems. Discriminant Analysis (LDA) or Principal Components Analysis (PCA) is used to get better recognition results. Human face contains relevant information that can extracted from face model developed by PCA technique. Principal Components Analysis method uses eigenface approach to describe face image variation. A face recognition technique that is robust to all situations is not available. Some techniques are better in case of illumination, some for pose problem and some for occlusion problem. This paper presents some algorithms for face recognition. We discuss how greater accuracy can be achieved by extracting features from the boundaries of the faces by using Active Shape Models and, the skin textures, using Active Appearance Models, originally proposed by Cootes and Talyor. Index Terms Eigenfaces, face recognition, Active shape models, PCA. J.Savitha et al, International Journal of Computer Science and Mobile Computing, Vol.4 Issue.4, April2015, pg. 722-731