nils-herrmann/clean_scirepeval huggingface dataset:
The front door to meckel's cave: an anteromedial corridor via expanded endoscopic endonasal approach- technical considerations and clinical series.

Tumors within Meckel's cave are challenging and often require complex approaches. In this report, an expanded endoscopic endonasal approach is reported as a substitute for or complement to other surgical options for the treatment of various tumors within this region.



A database of more than 900 patients who underwent the expanded endoscopic endonasal approach at the University of Pittsburgh Medical Center from 1998 to March of 2008 were reviewed. From these, only patients who had an endoscopic endonasal approach to Meckel's cave were considered. The technique uses the maxillary sinus and the pterygopalatine fossa as part of the working corridor. Infraorbital/V2 and the vidian neurovascular bundles are used as surgical landmarks. The quadrangular space is opened, which is bound by the internal carotid artery medially and inferiorly, V2 laterally, and the abducens nerve superiorly. This offers direct access to the anteroinferomedial segment of Meckel's cave, which can be extended through the petrous bone to reach the cerebellopontine angle.



Forty patients underwent an endoscopic endonasal approach to Meckel's cave. The most frequent abnormalities encountered were adenoid cystic carcinoma, meningioma, and schwannomas. Meckel's cave and surrounding structures were accessed adequately in all patients. Five patients developed a new facial numbness in at least 1 segment of the trigeminal nerve, but the deficit was permanent in only 2. Two patients had a transient VIth cranial nerve palsy. Nine patients (30%) showed improvement of preoperative deficits on Cranial Nerves III to VI.



In selected patients, the expanded endoscopic endonasal approach to the quadrangular space provides adequate exposure of Meckel's cave and its vicinity, with low morbidity.
Tic-Tac-Toe game with a new aspect

This study exhibits the application of the concept of matrices, probability and optimization in making an electronic Tic-Tac-Toe game using logic gates and exhibiting the concept of Boolean algebra. For a finite number of moves in every single game of Tic-Tac-Toe, the moves are recorded in a 3×3 matrix and the subsequent solution, or a winning combination, is presented from the data obtained by playing the electronic game. The solution is also displayed electronically using an LED. The circuit has been designed in a way to apply Boolean logic to analyze player's moves and thus, give a corresponding output from the electronic game and use it in matrices. The electronic Tic-Tac-Toe game is played randomly between 20 pairs of players. The impact of different opening moves is observed. Also, effect of different strategies, aggressive or defensive, on the outcome of the game is explored. The concept of Boolean algebra, logic gates, matrices and probability is applied in this game to make the foundation of the logic for this game. The most productive position for placing an `X' or `O' is found out using probabilities. Also the most effective blocking move is found out through which a player placing `O' can block `X' from winning. The skills help in understanding what strategy can be implemented to be on the winning side. The study is developed with an attempt to realistically model a tic-tac-toe game, and help in reflecting major tendencies. This knowledge helps in understanding what strategy to implement to be on the winning side.
The effect of foot reflexology massage on the level of pain during chest tube removal after open heart surgery

Pain due to the removal of the chest tube is one of the important complications after open heart surgery. In the case of inadequate pain management, sympathetic system is stimulated and can lead to irreversible complications. Studies showed the effect of reflexology massage on pain relief in other cases; so this study had been done with the aim of “determining the effect of foot reflexology on the pain of the patients under open heart surgery during chest tube removal”.  This randomized clinical study with control group was done in the hospitals covered by Baqiyatallah Medical Sciences University in 2013. Ninety samples were divided into three experimental, control and placebo-treated groups based on randomized allocation. Pain level was measured through Numerical Rating Scale (NRS) in all the three groups before intervention. In the experimental group center of the anterior one-third and in the placebotreated group, Posterior one-third of the left foot was being massaged for ten minutes before chest tube removal. There was no measurement in the control group. After removal of the chest tube, the level of the pain was measured and documented immediately. Data were analyzed by SPSS18 software by the help of descriptive and inferential statistics.  Difference in the mean of the quantitative variables including; age, height, weight and level of the body and demographic qualitative variables of the patients including; education status, occupation and marital status was not significant (p>0.05). Expected increasing of the pain due to the chest pain tube removal was not significant in the experimental group (p=0.08), while placebo-treated and control groups had significant increase of the pain (p=0.001 and p=0.000
Probabilistic traversability map generation using 3D-LIDAR and camera

Estimating the traversability of rough terrain is a critical task for an outdoor mobile robot. While classifying structured environment can be learned from large number of training data, it is an extremely difficult task to learn and estimate traversability of unstructured rough terrain. Moreover, in many cases information from a single sensor may not be sufficient for estimating traversability reliably in the absence of artificial landmarks such as lane markings or curbs. Our approach estimates traversability of the terrain and build a 2D probabilistic grid map online using 3D-LIDAR and camera. The combination of LIDAR and camera is favoured in many robotic application because they provide complementary information. Our approach assumes the data captured by these two sensors are independent and build separate traversability maps, each with information captured from one sensor. Traversability estimation with vision sensor autonomously collects training data and update classifier without human intervention as the vehicle traverse the terrain. Traversability estimation with 3D-LIDAR measures the slopes of the ground to predict the traversability. Two independently built probabilistic maps are fused using Bayes' rule to improve the detection performance. This is in contrast with other methods in which each sensor performs different tasks. We have implemented the algorithm on a UGV(Unmanned Ground Vehicle) and tested our approach on a rough terrain to evaluate the detection performance.
A 1.5V, 140µA CMOS ultra-low power common-gate LNA

This paper presents design guidelines for ultra-low power Low Noise Amplifier (LNA) design by comparing input matching, gain, and noise figure (NF) characteristics of common-source (CS) and common-gate (CG) topologies. A current-reused ultra-low power 2.2 GHz CG LNA is proposed and implemented based on 0.18 um CMOS technology. Measurement results show 13.9 dB power gain, 5.14 dB NF, and −9.3 dBm IIP3, respectively, while dissipating 140 uA from a 1.5 V supply, which shows best figure of merit (FOM) among all published ultra-low power LNAs.
A Formalization Method to Process Structured Natural Language to Logic Expressions to Detect Redundant Specification and Test Statements

Automotive systems are constantly increasing in complexity and size. Beside the increase of requirements specifications and related test specification due to new systems and higher system interaction, we observe an increase of redundant specifications. As the predominant specification language (both for requirements and test cases) is still natural text, it is not easy to detect these redundancies. In principle, to detect these redundancies, each statement has to be compared to all others. This proves to be difficult because of number and informal expression of statements. In this paper we propose a solution to the problem of detecting redundant specification and test statements described in structured natural language. We propose a formalization process for requirements specification and test statements, allowing us to detect redundant statements and thus reduce the efforts for specification and validation. Specification Pattern Systems and Linear Temporal Logic provide the base for our process. We did evaluate the method in the context of Mercedes-Benz Passenger Car Development. The results show that for the investigated sample set of test statements, we could detect about 30% of test steps as redundant. This indicates the savings potential of our approach.
Geometrical Accuracy of Bayer Pattern Images

Modern digital still cameras sample the color spectrum using a color filter array coated to the CCD array such that each pixel samples only one color channel. The result is a mosaic of color samples which is used to reconstruct the full color image by taking the information of the pixels’ neighborhood. This process is called demosaicking. While standard literature evaluates the performance of these reconstruction algorithms by comparison of a ground-truth image with a reconstructed Bayer pattern image in terms of grayscale comparison, this work gives an evaluation concept to asses the geometrical accuracy of the resulting color images. Only if no geometrical distortions are created during the demosaicking process, it is allowed to use such images for metric calculations, e.g. 3D reconstruction or arbitrary metrical photogrammetric processing.
Perceived Coping as a Mediator Between Attachment and Psychological Distress : A Structural Equation Modeling Approach

This study examined perceived coping (perceived problem-solving ability and progress in coping with problems) as a mediator between adult attachment (anxiety and avoidance) and psychological distress (depression, hopelessness, anxiety, anger, and interpersonal problems). Survey data from 515 undergraduate students were analyzed using structural equation modeling. Results indicated that perceived coping fully mediated the relationship between attachment anxiety and psychological distress and partially mediated the relationship between attachment avoidance and psychological distress. These findings suggest not only that it is important to consider attachment anxiety or avoidance in understanding distress but also that perceived coping plays an important role in these relationships. Implications for these more complex relations are discussed for both counseling interventions and further research.
Optimising Deep Belief Networks by hyper-heuristic approach

Deep Belief Networks (DBN) have been successful in classification especially image recognition tasks. However, the performance of a DBN is often highly dependent on settings in particular the combination of runtime parameter values. In this work, we propose a hyper-heuristic based framework which can optimise DBNs independent from the problem domain. It is the first time hyper-heuristic entering this domain. The framework iteratively selects suitable heuristics based on a heuristic set, apply the heuristic to tune the DBN to better fit with the current search space. Under this framework the setting of DBN learning is adaptive. Three well-known image reconstruction benchmark sets were used for evaluating the performance of this new approach. Our experimental results show this hyper-heuristic approach can achieve high accuracy under different scenarios on diverse image sets. In addition state-of-the-art meta-heuristic methods for tuning DBN were introduced for comparison. The results illustrate that our hyper-heuristic approach can obtain better performance on almost all test cases.
Revisiting Cross Modal Retrieval

This paper proposes a cross modal retrieval system that leverages on image and text encoding. Most multimodal architectures employ separate networks for each modality to capture the semantic relationship between them. However, in our work image-text encoding can achieve comparable results in terms of cross modal retrieval without having to use separate network for each modality. We show that text encodings can capture semantic relationships between multiple modalities. In our knowledge, this work is the first of its kind in terms of employing a single network and fused image-text embedding for cross modal retrieval. We evaluate our approach on two famous multimodal datasets: MS-COCO and Flickr30K.
Sensitivity Analysis of features in tolerancing based on constraint function level sets

Usually, the geometry of the manufactured product inherently varies from the nominal geometry. This may negatively affect the product functions and properties (such as quality and reliability), as well as the assemblability of the single components. In order to avoid this, the geometric variation of these component surfaces and associated geometry elements (like hole axes) are restricted by tolerances. Since tighter tolerances lead to significant higher manufacturing costs, tolerances should be specified carefully. Therefore, the impact of deviating component surfaces on functions, properties and assemblability of the product has to be analyzed. As physical experiments are expensive, methods of statistical tolerance analysis tools are widely used in engineering design. Current tolerance simulation tools lack of an appropriate indicator for the impact of deviating component surfaces. In the adoption of Sensitivity Analysis methods, there are several challenges, which arise from the specific framework in tolerancing. This paper presents an approach to adopt Sensitivity Analysis methods on current tolerance simulations with an interface module, which bases on level sets of constraint functions for parameters of the simulation model. The paper is an extension and generalization of Ziegler and Wartzack . Mathematical properties of the constraint functions (convexity, homogeneity), which are important for the computational costs of the Sensitivity Analysis, are shown. The practical use of the method is illustrated in a case study of a plain bearing. & 2014 Elsevier Ltd. All rights reserved.
Mars ascent vehicle key elements of a Mars Sample Return mission

The Mars Sample Return mission is being planned to return samples of Martian rock, soil, and atmosphere to Earth for scientific analysis. A Mars ascent vehicle (MAV) will be brought to the Martian surface within a lander; receive samples delivered by a mobile surface vehicle; and launch the samples into Mars orbit for return to Earth. The MAV is being designed as a two-stage solid-fuel vehicle with a head-end steering capability, packaged within an erectable launch tube for thermal and environmental stability during the various mission phases of cruise to Mars; entry, descent, and landing; surface and pre-launch operations; and launch. Key features of the MAV operations and design are discussed, including the thermal, environmental, and structural requirements and their conceptual design solutions during each phase; concepts for insertion of the orbiting sample into the payload bay inside the launch tube; and pre-launch operations. MAV launch to orbit details will be discussed, and key technology development challenges will be identified
A Temperature-Compensated Digitally-Controlled Crystal Pierce Oscillator for Wireless Applications

In this paper, the DCXO is based on a Pierce oscillator with two MIM capacitor arrays for tuning the anti-resonant frequency of a 19.2MHz crystal. Each array of MIM capacitors is thermometer-coded and formatted in a matrix shape to facilitate layout. Although a segmented architecture is an area-efficient method for implementing a SC array, a thermometer-coded array provides the best linearity and guarantees a monotonic frequency tuning characteristic, which is of utmost importance in an AFC system.
Divide and Conquer-Based 1D CNN Human Activity Recognition Using Test Data Sharpening †

Human Activity Recognition (HAR) aims to identify the actions performed by humans using signals collected from various sensors embedded in mobile devices. In recent years, deep learning techniques have further improved HAR performance on several benchmark datasets. In this paper, we propose one-dimensional Convolutional Neural Network (1D CNN) for HAR that employs a divide and conquer-based classifier learning coupled with test data sharpening. Our approach leverages a two-stage learning of multiple 1D CNN models; we first build a binary classifier for recognizing abstract activities, and then build two multi-class 1D CNN models for recognizing individual activities. We then introduce test data sharpening during prediction phase to further improve the activity recognition accuracy. While there have been numerous researches exploring the benefits of activity signal denoising for HAR, few researches have examined the effect of test data sharpening for HAR. We evaluate the effectiveness of our approach on two popular HAR benchmark datasets, and show that our approach outperforms both the two-stage 1D CNN-only method and other state of the art approaches.
Young children's understanding of other people's feelings and beliefs: individual differences and their antecedents.

Individual differences in young children's understanding of others' feelings and in their ability to explain human action in terms of beliefs, and the earlier correlates of these differences, were studied with 50 children observed at home with mother and sibling at 33 months, then tested at 40 months on affective-labeling, perspective-taking, and false-belief tasks. Individual differences in social understanding were marked; a third of the children offered explanations of actions in terms of false belief, though few predicted actions on the basis of beliefs. These differences were associated with participation in family discourse about feelings and causality 7 months earlier, verbal fluency of mother and child, and cooperative interaction with the sibling. Differences in understanding feelings were also associated with the discourse measures, the quality of mother-sibling interaction, SES, and gender, with girls more successful than boys. The results support the view that discourse about the social world may in part mediate the key conceptual advances reflected in the social cognition tasks; interaction between child and sibling and the relationships between other family members are also implicated in the growth of social understanding.
Superpixels and Supervoxels in an Energy Optimization Framework

Many methods for object recognition, segmentation, etc., rely on tessellation of an image into “superpixels”. A superpixel is an image patch which is better aligned with intensity edges than a rectangular patch. Superpixels can be extracted with any segmentation algorithm, however, most of them produce highly irregular superpixels, with widely varying sizes and shapes. A more regular space tessellation may be desired. We formulate the superpixel partitioning problem in an energy minimization framework, and optimize with graph cuts. Our energy function explicitly encourages regular superpixels. We explore variations of the basic energy, which allow a trade-off between a less regular tessellation but more accurate boundaries or better efficiency. Our advantage over previous work is computational efficiency, principled optimization, and applicability to 3D “supervoxel” segmentation. We achieve high boundary recall on 2D images and spatial coherence on video. We also show that compact superpixels improve accuracy on a simple application of salient object segmentation.
AES and the cryptonite crypto processor

CRYPTONITE is a programmable processor tailored to the needs of crypto algorithms. The design of CRYPTONITE was based on an in-depth application analysis in which standard crypto algorithms (AES, DES, MD5, SHA-1, etc) were distilled down to their core functionality. We describe this methodology and use AES as a central example. Starting with a functional description of AES, we give a high level account of how to implement AES efficiently in hardware, and present several novel optimizations (which are independent of CRYPTONITE).We then describe the CRYPTONITE architecture, highlighting how AES implementation issues influenced the design of the processor and its instruction set. CRYPTONITE is designed to run at high clock rates and be easy to implement in silicon while providing a significantly better performance/area/power tradeoff than general purpose processors.
A survey of 3D object selection techniques for virtual environments

Computer graphics applications controlled through natural gestures are gaining increasing popularity these days due to recent developments in low-cost tracking systems and gesture recognition technologies. Although interaction techniques through natural gestures have already demonstrated their benefits in manipulation, navigation and avatar-control tasks, effective selection with pointing gestures remains an open problem. In this paper we survey the state-of-the-art in 3D object selection techniques. We review important findings in human control models, analyze major factors influencing selection performance, and classify existing techniques according to a number of criteria. Unlike other components of the application’s user interface, pointing techniques need a close coupling with the rendering pipeline, introducing new elements to be drawn, and potentially modifying the object layout and the way the scene is rendered. Conversely, selection performance is affected by rendering issues such as visual feedback, depth perception, and occlusion management. We thus review existing literature paying special attention to those aspects in the boundary between computer graphics and human computer interaction.
Reading the legends of Roman Republican coins

Coin classification is one of the main aspects of numismatics. The introduction of an automated image-based coin classification system could assist numismatists in their everyday work and allow hobby numismatists to gain additional information on their coin collection by uploading images to a respective Web site. For Roman Republican coins, the inscription is one of the most significant features, and its recognition is an essential part in the successful research of an image-based coin recognition system. This article presents a novel way for the recognition of ancient Roman Republican coin legends. Traditional optical character recognition (OCR) strategies were designed for printed or handwritten texts and rely on binarization in the course of their recognition process. Since coin legends are simply embossed onto a piece of metal, they are of the same color as the background and binarization becomes error prone and prohibits the use of standard OCR. Therefore, the proposed method is based on state-of-the-art scene text recognition methods that are rooted in object recognition. Sift descriptors are computed for a dense grid of keypoints and are tested using support vector machines trained for each letter of the respective alphabet. Each descriptor receives a score for every letter, and the use of pictorial structures allows one to detect the optimal configuration for the lexicon words within an image; the word causing the lowest costs is recognized. Character and word recognition capabilities of the proposed method are evaluated individually; character recognition is benchmarked on three and word recognition on different datasets. Depending on the Sift configuration, lexicon, and dataset used, the word recognition rates range from 29% to 67%.