nils-herrmann/clean_scirepeval huggingface dataset:
Arterial vascularization of the pineal gland

The arterial vascularization of the pineal gland (PG) remains a debatable subject. This study aims to provide detailed information about the arterial vascularization of the PG. Thirty adult human brains were obtained from routine autopsies. Cerebral arteries were separately cannulated and injected with colored latex. The dissections were carried out using a surgical microscope. The diameters of the branches supplying the PG at their origin and vascularization areas of the branches of the arteries were investigated. The main artery of the PG was the lateral pineal artery, and it originated from the posterior circulation. The other arteries included the medial pineal artery from the posterior circulation and the rostral pineal artery mainly from the anterior circulation. Posteromedial choroidal artery was an important artery that branched to the PG. The arterial supply to the PG was studied comprehensively considering the debate and inadequacy of previously published studies on this issue available in the literature. This anatomical knowledge may be helpful for surgical treatment of pathologies of the PG, especially in children who develop more pathology in this region than adults.
Overlapping layered learning

Layered learning is a hierarchical machine learning paradigm that enables learning of complex behaviors by incrementally learning a series of sub-behaviors. A key feature of layered learning is that higher layers directly depend on the learned lower layers. In its original formulation, lower layers were frozen prior to learning higher layers. This article considers a major extension to the paradigm that allows learning certain behaviors independently, and then later stitching them together by learning at the “seams” where their influences overlap. The UT Austin Villa 2014 RoboCup 3D simulation team, using such overlapping layered learning, learned a total of 19 layered behaviors for a simulated soccer-playing robot, organized both in series and in parallel. To the best of our knowledge this is more than three times the number of layered behaviors in any prior layered learning system. Furthermore, the complete learning process is repeated on four additional robot body types, showcasing its generality as a paradigm for efficient behavior learning. The resulting team won the  2014 championship with an undefeated record, scoring 52 goals and conceding none. This article includes a detailed experimental analysis of the team’s performance and the overlapping layered learning approach that led to its success.
Toxicity and repellency of compounds from clove (Syzygium aromaticum) to red imported fire ants Solenopsis invicta (Hymenoptera: Formicidae).

The toxicity and repellency of the bioactive chemicals of clove (Syzygium aromaticum) powder, eugenol, eugenol acetate, and beta-caryophyllene were evaluated against workers of the red imported fire ant, Solenopsis invicta Buren. Clove powder applied at 3 and 12 mg/cm2 provided 100% ant mortality within 6 h, and repelled 99% within 3 h. Eugenol was the fastest acting compound against red imported fire ant compared with eugenol acetate, beta-caryophyllene, and clove oil. The LT50 values inclined exponentially with the increase in the application rate of the chemical compounds tested. However, repellency did not increase with the increase in the application rate of the chemical compounds tested, but did with the increase in exposure time. Eugenol, eugenol acetate, as well as beta-caryophyllene and clove oil may provide another tool for red imported fire ant integrated pest management, particularly in situations where conventional insecticides are inappropriate.
The relationship between organizational culture and the deployment of agile methods

Systems development normally takes place in a specific organizational context, including organizational culture. Previous research has identified organizational culture as a factor that potentially affects the deployment systems development methods.  The purpose is to analyze the relationship between organizational culture and the postadoption deployment of agile methods.  This study is a theory development exercise. Based on the Competing Values Model of organizational culture, the paper proposes a number of hypotheses about the relationship between organizational culture and the deployment of agile methods.  Inspired by the agile methods thirteen new hypotheses are introduced and discussed. They have interesting implications, when contrasted with ad hoc development and with traditional systems devel-
An Improved Equation for TBS and ADD: Establishing a Reliable Postmortem Interval Framework for Casework and Experimental Studies.

Megyesi et al.'s ( 2005, 50, 618) paper was important to forensic anthropology as it introduced a quantitative framework for estimating time since death in human cadavers, based upon physical appearance by way of scoring on a novel scale. However, errors concerning rounding, temperature scale, and incorrect use of a statistical regression model render their predictive formula unusable. Based upon only their more reliable data, a more appropriate regression model to predict accumulated degree days (ADD) from total body score (TBS) is presented. The new model is also a superior fit (r(2)  = 0.91) and produces markedly narrower confidence intervals than the original, which also allowed impossible, negative ADD values. Explanations of the shortcomings in the original analysis and calculations are presented, which it is hoped will help forensic scientists avoid making similar mistakes.
Context-Aware Energy Saving System With Multiple Comfort-Constrained Optimization in M2M-Based Home Environment

Most previous work in household energy conservation has focused on rule-based home automation to achieve energy savings, with relatively few researchers focusing on context-aware technologies. As a result, user comfort is often disregarded and few solutions handle decision conflicts caused by multiple activities undertaken by multiple users. The main contribution of this work is twofold. First, a comprehensive human-centric and context-aware comfort index is proposed to evaluate how users feel under particular environmental conditions with regard to thermal, illumination, and appliance-usage preferences. Second, the energy savings is formulated into an optimization problem to minimize the total energy consumption, even under multiple user comfort constraints. Short-term evaluation in our simulated home environment resulted in energy savings of at least 28.98%. Long-term evaluation using a home simulator resulted in energy savings of 33.7%. Most importantly, the energy savings in both situations was achieved under multiple user comfort constraints, representing a truly human-centric living environment.
Hierarchical Category Detector for Clothing Recognition from Visual Data

Clothing detection is an important step for retrieving similar clothing items, organizing fashion photos, artificial intelligence powered shopping assistants and automatic labeling of large catalogues. Training a deep learning based clothing detector requires pre-defined categories (dress, pants etc) and a high volume of annotated image data for each category. However, fashion evolves and new categories are constantly introduced in the marketplace. For example, consider the case of jeggings which is a combination of jeans and leggings. Detection of this new category will require adding annotated data specific to jegging class and subsequently relearning the weights for the deep network. In this paper, we propose a novel object detection method that can handle newer categories without the need of obtaining new labeled data and retraining the network. Our approach learns the visual similarities between various clothing categories and predicts a tree of categories. The resulting framework significantly improves the generalization capabilities of the detector to novel clothing products.
Emotional Responses to Art : From Collation and Arousal to Cognition and Emotion

Emotions and art are intimately related (Tan, 2000). From ancient to modern times, theories of aesthetics have emphasized the role of art in evoking, shaping, and modifying human feelings. The experimental study of preferences, evaluations, and feelings related to art has a long history in psychology. Aesthetics is one of the oldest areas of psychological research, dating to Fechner's (1876) landmark work. Psychology has had a steady interest in aesthetic problems since then, but art has never received as much attention as one would expect (see Berlyne, 1971a; Tan, 2000; Valentine, 1962). The study of art and the study of emotions, as areas of scientific inquiry, both languished during much of the last century. It is not surprising that the behavioral emphasis on observable action over inner experience would lead to a neglect of research on aesthetics. In an interesting coincidence, both art and emotion resurfaced in psychology at about the same time. As emotion psychologists began developing theories of basic emotions (Ekman & Friesen, 1971; Izard, 1971; Tomkins, 1962), experimental psychologists began tackling hedonic qualities of art (Berlyne, 1971a, 1972, 1974). Since then, the psychology of emotion and the psychology of art have had little contact (see Silvia, in press-b; Tan, 2000).
A dynamic code coverage approach to maximize fault localization efficiency

Spectrum-based fault localization is amongst the most effective techniques for automatic fault localization. However, abstractions of program execution traces, one of the required inputs for this technique, require instrumentation of the software under test at a statement level of granularity in order to compute a list of potential faulty statements. This introduces a considerable overhead in the fault localization process, which can even become prohibitive in, e.g., resource constrained environments. To counter this problem, we propose a new approach, coined Dynamic Code Coverage (DCC), aimed at reducing this instrumentation overhead. This technique, by means of using coarser instrumentation, starts by analyzing coverage traces for large components of the system under test. It then progressively increases the instrumentation detail for faulty components, until the statement level of detail is reached. To assess the validity of our proposed approach, an empirical evaluation was performed, injecting faults in six real-world software projects. The empirical evaluation demonstrates that the dynamic code coverage approach reduces the execution overhead that exists in spectrum-based fault localization, and even presents a more concise potential fault ranking to the user. We have observed execution time reductions of 27% on average and diagnostic report size reductions of 77% on average.
Functional Brain Networks Develop from a “Local to Distributed” Organization

The mature human brain is organized into a collection of specialized functional networks that flexibly interact to support various cognitive functions. Studies of development often attempt to identify the organizing principles that guide the maturation of these functional networks. In this report, we combine resting state functional connectivity MRI (rs-fcMRI), graph analysis, community detection, and spring-embedding visualization techniques to analyze four separate networks defined in earlier studies. As we have previously reported, we find, across development, a trend toward 'segregation' (a general decrease in correlation strength) between regions close in anatomical space and 'integration' (an increased correlation strength) between selected regions distant in space. The generalization of these earlier trends across multiple networks suggests that this is a general developmental principle for changes in functional connectivity that would extend to large-scale graph theoretic analyses of large-scale brain networks. Communities in children are predominantly arranged by anatomical proximity, while communities in adults predominantly reflect functional relationships, as defined from adult fMRI studies. In sum, over development, the organization of multiple functional networks shifts from a local anatomical emphasis in children to a more "distributed" architecture in young adults. We argue that this "local to distributed" developmental characterization has important implications for understanding the development of neural systems underlying cognition. Further, graph metrics (e.g., clustering coefficients and average path lengths) are similar in child and adult graphs, with both showing "small-world"-like properties, while community detection by modularity optimization reveals stable communities within the graphs that are clearly different between young children and young adults. These observations suggest that early school age children and adults both have relatively efficient systems that may solve similar information processing problems in divergent ways.
Brain regions involved in the retrieval of spatial and episodic details associated with a familiar environment: An fMRI study

Functional magnetic resonance imaging (fMRI) was used to compare brain activity during the retrieval of coarse- and fine-grained spatial details and episodic details associated with a familiar environment. Long-time Toronto residents compared pairs of landmarks based on their absolute geographic locations (requiring either coarse or fine discriminations) or based on previous visits to those landmarks (requiring episodic details). An ROI analysis of the hippocampus showed that all three conditions activated the hippocampus bilaterally. Fine-grained spatial judgments recruited an additional region of the right posterior hippocampus, while episodic judgments recruited an additional region of the right anterior hippocampus, and a more extensive region along the length of the left hippocampus. To examine whole-brain patterns of activity, Partial Least Squares (PLS) analysis was used to identify sets of brain regions whose activity covaried with the three conditions. All three comparison judgments recruited the default mode network including the posterior cingulate/retrosplenial cortex, middle frontal gyrus, hippocampus, and precuneus. Fine-grained spatial judgments also recruited additional regions of the precuneus, parahippocampal cortex and the supramarginal gyrus. Episodic judgments recruited the posterior cingulate and medial frontal lobes as well as the angular gyrus. These results are discussed in terms of their implications for theories of hippocampal function and spatial and episodic memory.
Content-Adaptive 360-Degree Video Coding Using Hybrid Cubemap Projection

In this paper, a novel hybrid cubemap projection (HCP) is proposed to improve the 360-degree video coding efficiency. HCP allows adaptive sampling adjustments in the horizontal and vertical directions within each cube face. HCP parameters of each cube face can be adjusted based on the input 360-degree video content characteristics for a better sampling efficiency. The HCP parameters can be updated periodically to adapt to temporal content variation. An efficient HCP parameter estimation algorithm is proposed to reduce the computational complexity of parameter estimation. Experimental results demonstrate that HCP format achieves on average luma (Y) BD-rate reduction of 11.51%, 8.0%, and 0.54% compared to equirectangular projection format, cubemap projection format, and adjusted cubemap projection format, respectively, in terms of end-to-end WS-PSNR.
Multistability in a neuron model with extracellular potassium dynamics.

Experiments show a primary role of extracellular potassium concentrations in neuronal hyperexcitability and in the generation of epileptiform bursting and depolarization blocks without synaptic mechanisms. We adopt a physiologically relevant hippocampal CA1 neuron model in a zero-calcium condition to better understand the function of extracellular potassium in neuronal seizurelike activities. The model neuron is surrounded by interstitial space in which potassium ions are able to accumulate. Potassium currents, Na{+}-K{+} pumps, glial buffering, and ion diffusion are regulatory mechanisms of extracellular potassium. We also consider a reduced model with a fixed potassium concentration. The bifurcation structure and spiking frequency of the two models are studied. We show that, besides hyperexcitability and bursting pattern modulation, the potassium dynamics can induce not only bistability but also tristability of different firing patterns. Our results reveal the emergence of the complex behavior of multistability due to the dynamical [K{+}]{o} modulation on neuronal activities.
Production-level facial performance capture using deep convolutional neural networks

We present a real-time deep learning framework for video-based facial performance capture---the dense 3D tracking of an actor's face given a monocular video. Our pipeline begins with accurately capturing a subject using a high-end production facial capture pipeline based on multi-view stereo tracking and artist-enhanced animations. With 5--10 minutes of captured footage, we train a convolutional neural network to produce high-quality output, including self-occluded regions, from a monocular video sequence of that subject. Since this 3D facial performance capture is fully automated, our system can drastically reduce the amount of labor involved in the development of modern narrative-driven video games or films involving realistic digital doubles of actors and potentially hours of animated dialogue per character. We compare our results with several state-of-the-art monocular real-time facial capture techniques and demonstrate compelling animation inference in challenging areas such as eyes and lips.
Blount disease.

Two clinically distinct forms of Blount disease (early-onset and late-onset), based on whether the lower-limb deformity develops before or after the age of four years, have been described. Although the etiology of Blount disease may be multifactorial, the strong association with childhood obesity suggests a mechanical basis. A comprehensive analysis of multiplanar deformities in the lower extremity reveals tibial varus, procurvatum, and internal torsion along with limb shortening. Additionally, distal femoral varus is commonly noted in the late-onset form. When a patient has early-onset disease, a realignment tibial osteotomy before the age of four years decreases the risk of recurrent deformity. Gradual correction with distraction osteogenesis is an effective means of achieving an accurate multiplanar correction, especially in patients with late-onset disease.
The reliability, minimal detectable change and concurrent validity of a gravity-based bubble inclinometer and iphone application for measuring standing lumbar lordosis.

To investigate the reliability, minimal detectable change (MDC90) and concurrent validity of a gravity-based bubble inclinometer (inclinometer) and iPhone® application for measuring standing lumbar lordosis.



Two investigators used both an inclinometer and an iPhone® with an inclinometer application to measure lumbar lordosis of 30 asymptomatic participants.



ICC models 3,k and 2,k were used for the intrarater and interrater analysis, respectively. Good interrater and intrarater reliability was present for the inclinometer with Intraclass Correlation Coefficients (ICC) of 0.90 and 0.85, respectively and the iPhone® application with ICC values of 0.96 and 0.81. The minimal detectable change (MDC90) indicates that a change greater than or equal to 7° and 6° is needed to exceed the threshold of error using the iPhone® and inclinometer, respectively. The concurrent validity between the two instruments was good with a Pearson product-moment coefficient of correlation (r) of 0.86 for both raters. Ninety-five percent limits of agreement identified differences ranging from 9° greater in regards to the iPhone® to 8° less regarding the inclinometer.



Both the inclinometer and iPhone® application possess good interrater reliability, intrarater reliability and concurrent validity for measuring standing lumbar lordosis. This investigation provides preliminary evidence to suggest that smart phone applications may offer clinical utility comparable to inclinometry for quantifying standing lumbar lordosis. Clinicians should recognize potential individual differences when using these devices interchangeably.
Hessian Matrix vs. Gauss-Newton Hessian Matrix

In this paper, we investigate how the Gauss–Newton Hessian matrix affects the basin of convergence in Newton-type methods. Although the Newton algorithm is theoretically superior to the Gauss–Newton algorithm and the Levenberg–Marquardt (LM) method as far as their asymptotic convergence rate is concerned, the LM method is often preferred in nonlinear least squares problems in practice. This paper presents a theoretical analysis of the advantage of the Gauss–Newton Hessian matrix. It is proved that the Gauss–Newton approximation function is the only nonnegative convex quadratic approximation that retains a critical property of the original objective function: taking the minimal value of zero on an (n − 1)-dimensional manifold (or affine subspace). Due to this property, the Gauss–Newton approximation does not change the zero-on-(n − 1)-D “structure” of the original problem, explaining the reason why the Gauss–Newton Hessian matrix is preferred for nonlinear least squares problems, especially when the initial point is far from the solution.
Intriguingly Simple and Fast Transit Routing

This paper studies the problem of computing optimal journeys in dynamic public transit networks. We introduce a novel algorithmic framework, called Connection Scan Algorithm (CSA), to compute journeys. It organizes data as a single array of connections, which it scans once per query. Despite its simplicity, our algorithm is very versatile. We use it to solve earliest arrival and multi-criteria profile queries. Moreover, we extend it to handle the minimum expected arrival time (MEAT) problem, which incorporates stochastic delays on the vehicles and asks for a set of (alternative) journeys that in its entirety minimizes the user’s expected arrival time at the destination. Our experiments on the dense metropolitan network of London show that CSA computes MEAT queries, our most complex scenario, in 272ms on average.
Manufacturing of Full Automatic Carwash Using with Intelligent Control Algorithms

In this paper the intelligent control of full automatic car wash using a programmable logic controller (PLC) has been investigated and designed to do all steps of carwashing. The Intelligent control of full automatic carwash has the ability to identify and profile the geometrical dimensions of the vehicle chassis. Vehicle dimension identification is an important point in this control system to adjust the washing brushes position and time duration. The study also tries to design a control set for simulating and building the automatic carwash. The main purpose of the simulation is to develop criteria for designing and building this type of carwash in actual size to overcome challenges of automation. The results of this research indicate that the proposed method in process control not only increases productivity, speed, accuracy and safety but also reduce the time and cost of washing based on dynamic model of the vehicle. A laboratory prototype based on an advanced intelligent control has been built to study the validity of the design and simulation which it’s appropriate performance confirms the validity of this study. Automatic Carwash, Dimension, PLC.