nils-herrmann/clean_scirepeval huggingface dataset:
Relationship of the zygomatic facial nerve to the retaining ligaments of the face: the Sub-SMAS danger zone.

The transition zone between cheek superficial musculoaponeurotic system (SMAS) and malar SMAS is difficult to raise because of proximity of zygomatic nerve branches. The authors attempted to clarify the three-dimensional anatomy of the retaining ligaments in relation to nerve branches in this area.



Facial dissection was performed on 22 cadaver hemifaces. The zygomatic and masseteric retaining ligaments and the zygomatic and buccal facial branches in the area of dissection were identified. Ninety-five percent confidence regions for the locations of the zygomaticus major origin and the main zygomatic retaining ligament and upper masseteric retaining ligament were created.



The distribution, density, and size of the retaining ligaments varied. The main zygomatic and upper masseteric retaining ligaments were located at a mean distance of 44.91 ± 9.72 mm and 46.35 ± 8.34 mm from the tragus. An upper zygomatic branch passed between the main zygomatic and the upper masseteric retaining ligaments and was always located deep (4.07 ± 1.29 mm) in the sub-SMAS plane of dissection and passed deep under the upper third of the zygomaticus major muscle. An inferior zygomatic branch passed inferior to the upper masseteric retaining ligament or penetrated its inferior margin (54 percent of cases) and was located more superficially (1.41 ± 0.95 mm), becoming visible just distal to the ligament.



Despite anatomical variation, the main zygomatic and upper masseteric retaining ligaments create a safe passage in between, through which a zygomatic facial branch passes deep. The area of danger is immediately inferomedial to the upper masseteric retaining ligament, where a zygomatic branch becomes superficial and vulnerable.
From Physics to Number theory via Noncommutative Geometry , II

We give here a comprehensive treatment of the mathematical theory of per-turbative renormalization (in the minimal subtraction scheme with dimensional regularization), in the framework of the Riemann–Hilbert correspondence and motivic Galois theory. We give a detailed overview of the work of Connes– Kreimer , [32]. We also cover some background material on affine group schemes, Tannakian categories, the Riemann–Hilbert problem in the regular singular and irregular case, and a brief introduction to motives and motivic Ga-lois theory. We then give a complete account of our results on renormalization and motivic Galois theory announced in [35]. Our main goal is to show how the divergences of quantum field theory, which may at first appear as the undesired effect of a mathematically ill-formulated theory, in fact reveal the presence of a very rich deeper mathematical structure, which manifests itself through the action of a hidden " cosmic Galois group " 1 , which is of an arithmetic nature, related to motivic Galois theory. Historically, perturbative renormalization has always appeared as one of the most elaborate recipes created by modern physics, capable of producing numerical quantities of great physical relevance out of a priori meaningless mathematical expressions. In this respect, it is fascinating for mathematicians and physicists alike. The depth of its origin in quantum field theory and the precision with which it is confirmed by experiments undoubtedly make it into one of the jewels of modern theoretical physics. For a mathematician in quest of " meaning " rather than heavy formalism, the attempts to cast the perturbative renormalization technique in a conceptual framework were so far falling short of accounting for the main computational aspects, used for instance in QED. These have to do with the subtleties involved in the subtraction of infinities in the evaluation of Feynman graphs and do not fall under the range of " asymptotically free theories " for which constructive quantum field theory can provide a mathematically satisfactory formulation., where the conceptual meaning of the detailed computational devices used in perturbative renormalization is analysed. Their work shows that the recursive procedure used by physicists is in fact identical to a mathematical method of extraction of finite values known as the Birkhoff decomposition, applied to a loop γ(z) with values in a complex pro-unipotent Lie group G.
The bio-energetic theory of carcinogenesis.

The altered energy metabolism of tumor cells provides a viable target for a non toxic chemotherapeutic approach. An increased glucose consumption rate has been observed in malignant cells. Warburg (Nobel Laureate in medicine) postulated that the respiratory process of malignant cells was impaired and that the transformation of a normal cell to malignant was due to defects in the aerobic respiratory pathways. Szent-Györgyi (Nobel Laureate in medicine) also viewed cancer as originating from insufficient availability of oxygen. Oxygen by itself has an inhibitory action on malignant cell proliferation by interfering with anaerobic respiration (fermentation and lactic acid production). Interestingly, during cell differentiation (where cell energy level is high) there is an increased cellular production of oxidants that appear to provide one type of physiological stimulation for changes in gene expression that may lead to a terminal differentiated state. The failure to maintain high ATP production (high cell energy levels) may be a consequence of inactivation of key enzymes, especially those related to the Krebs cycle and the electron transport system. A distorted mitochondrial function (transmembrane potential) may result. This aspect could be suggestive of an important mitochondrial involvement in the carcinogenic process in addition to presenting it as a possible therapeutic target for cancer. Intermediate metabolic correction of the mitochondria is postulated as a possible non-toxic therapeutic approach for cancer.
Understanding source code evolution using abstract syntax tree matching

Mining software repositories at the source code level can provide a greater understanding of how software evolves. We present a tool for quickly comparing the source code of different versions of a C program. The approach is based on partial abstract syntax tree matching, and can track simple changes to global variables, types and functions. These changes can characterize aspects of software evolution useful for answering higher level questions. In particular, we consider how they could be used to inform the design of a dynamic software updating system. We report results based on measurements of various versions of popular open source programs. including BIND, OpenSSH, Apache, Vsftpd and the Linux kernel.
Learning to Control a Low-Cost Manipulator using Data-Efficient Reinforcement Learning

Over the last years, there has been substantial progress in robust manipulation in unstructured environments. The long-term goal of our work is to get away from precise, but very expensive robotic systems and to develop affordable, potentially imprecise, self-adaptive manipulator systems that can interactively perform tasks such as playing with children. In this paper, we demonstrate how a low-cost off-the-shelf robotic system can learn closed-loop policies for a stacking task in only a handful of trials—from scratch. Our manipulator is inaccurate and provides no pose feedback. For learning a controller in the work space of a Kinect-style depth camera, we use a model-based reinforcement learning technique. Our learning method is data efficient, reduces model bias, and deals with several noise sources in a principled way during long-term planning. We present a way of incorporating state-space constraints into the learning process and analyze the learning gain by exploiting the sequential structure of the stacking task.
Learning Video Object Segmentation from Static Images

Inspired by recent advances of deep learning in instance segmentation and object tracking, we introduce the concept of convnet-based guidance applied to video object segmentation. Our model proceeds on a per-frame basis, guided by the output of the previous frame towards the object of interest in the next frame. We demonstrate that highly accurate object segmentation in videos can be enabled by using a convolutional neural network (convnet) trained with static images only. The key component of our approach is a combination of offline and online learning strategies, where the former produces a refined mask from the previous frame estimate and the latter allows to capture the appearance of the specific object instance. Our method can handle different types of input annotations such as bounding boxes and segments while leveraging an arbitrary amount of annotated frames. Therefore our system is suitable for diverse applications with different requirements in terms of accuracy and efficiency. In our extensive evaluation, we obtain competitive results on three different datasets, independently from the type of input annotation.
An LSTM Attention-based Network for Reading Comprehension

A significant goal of natural language processing (NLP) is to devise a system capable of machine understanding of text. A typical system can be tested on its ability to answer questions based on a given context document. One appropriate dataset for such a system is the Stanford Question Answering Dataset (SQuAD), a crowdsourced dataset of over 100k (question, context, answer) triplets. In this work, we focused on creating such a question answering system through a neural net architecture modeled after the attentive reader and sequence attention mix models.
Variations in pattern of pubertal changes in girls.

In Great Britain some girls begin to menstruate in their 10th or 11th years, while others who are equally healthy may not experience menarche until they 'are 14, 15, or even older. Menarche is, however, only a single event in the combination of physical changes which constitute puberty. The adolescent growth spurt, the development of the breasts, and the growth of the pubic hair occur more or less concurrently, and take, on the average, about 3 years from beginning to completion, with menarche occurring usually in the latter half of this period (Tanner, 1962). At present we lack detailed information about the rate at which girls progress through the stages of puberty and about the relation of one event to another. Only longitudinal studies (i.e. studies in which the same individuals are examined repeatedly over a period of time) can provide this information, which would be helpful both to the clinician in distinguishing the normal from the abnormal, and to the neuro-endocrinologist in constructing hypotheses about the mechanisms by which puberty is controlled. Present knowledge is based on studies carried out on small numbers of children in the United States a generation ago, together with some German studies of a similar period (for literature see Tanner, 1962). The only recent European study in which the events of puberty have been followed longitudinally is the Harpenden Growth Study, which began 19 years ago in England and is still in progress. Anthropometric measurements have been taken, and the development of the breasts and pubic hair have been recorded photographically at 3monthly intervals throughout puberty. Though these data may be subject to certain biases, discussed below, they provide information available from no other source. This paper describes the extent of normal individual variation observed in the events of puberty among the girls of the Harpenden Growth Study. We discuss: (a) variation in the chrono-
Joint Face Alignment and 3D Face Reconstruction with Application to Face Recognition

Face alignment and 3D face reconstruction are traditionally accomplished as separated tasks. By exploring the strong correlation between 2D landmarks and 3D shapes, in contrast, we propose a joint face alignment and 3D face reconstruction method to simultaneously solve these two problems for 2D face images of arbitrary poses and expressions. This method, based on a summation model of 3D face shapes and cascaded regression in 2D and 3D face shape spaces, iteratively and alternately applies two cascaded regressors, one for updating 2D landmarks and the other for 3D face shape. The 3D face shape and the landmarks are correlated via a 3D-to-2D mapping matrix. Unlike existing methods, the proposed method can fully automatically generate both pose-and-expression-normalized (PEN) and expressive 3D face shapes and localize both visible and invisible 2D landmarks. Based on the PEN 3D face shapes, we devise a method to enhance face recognition accuracy across poses and expressions. Both linear and nonlinear implementations of the proposed method are presented and evaluated in this paper. Extensive experiments show that the proposed method can achieve the state-of-the-art accuracy in both face alignment and 3D face reconstruction, and benefit face recognition owing to its reconstructed PEN 3D face shapes.
The Impact of Commercial Banks ’ Credit to Agriculture on Agricultural Development in Nigeria : An Econometric Analysis

This study aimed at evaluating the impact of commercial banks' credit to agricultural sector under the Agricultural Credit Guarantee Scheme Fund in Nigeria. Until the mid-seventies, agriculture was the primary foreign exchange earner for Nigeria. Now it has lost its prime position to the mineral sector. Of these factors, inadequate capital is considered as the single most important factor affecting the performance of the sector. It therefore empirically examined the impact of Agricultural Credit Guarantee Scheme Fund, agricultural product prices, government fund allocation and commercial banks’ credit to agricultural sector on agricultural productivity. The result revealed that Agricultural Credit Guarantee Scheme Fund and Government fund allocation to agriculture produced a significant positive effect on agricultural productivity, while the other variables produced a significant negative effect. It is recommended that farmers should be encouraged to be applying for loans from the participating banks to enhance their agricultural activities and productivity.
Semantic image segmentation using fully convolutional neural networks with multi-scale images and multi-scale dilated convolutions

In this work, we investigate the effects of the cascade architecture of dilated convolutions and the deep network architecture of multi-resolution input images on the accuracy of semantic segmentation. We show that a cascade of dilated convolutions is not only able to efficiently capture larger context without increasing computational costs, but can also improve the localization performance. In addition, the deep network architecture for multi-resolution input images increases the accuracy of semantic segmentation by aggregating multi-scale contextual information. Furthermore, our fully convolutional neural network is coupled with a model of fully connected conditional random fields to further remove isolated false positives and improve the prediction along object boundaries. We present several experiments on two challenging image segmentation datasets, showing substantial improvements over strong baselines.
Sudden Deaths : Taking Stock of Geographic Ties

Analysis of a worldwide sample of sudden deaths of politicians reveals a market-adjusted 1.7% decline in the value of companies headquartered in the politician’s hometown. The decline in value is followed by a drop in the rate of growth in sales and access to credit. Our results are particularly pronounced for family firms, firms with high growth prospects, firms in industries over which the politician has jurisdiction, and firms headquartered in highly corrupt countries.
A continuous and objective evaluation of emotional experience with interactive play environments

Researchers are using emerging technologies to develop novel play environments, while established computer and console game markets continue to grow rapidly. Even so, evaluating the success of interactive play environments is still an open research challenge. Both subjective and objective techniques fall short due to limited evaluative bandwidth; there remains no corollary in play environments to task performance with productivity systems. This paper presents a method of modeling user emotional state, based on a user's physiology, for users interacting with play technologies. Modeled emotions are powerful because they capture usability and playability through metrics relevant to ludic experience; account for user emotion; are quantitative and objective; and are represented continuously over a session. Furthermore, our modeled emotions show the same trends as reported emotions for fun, boredom, and excitement; however, the modeled emotions revealed differences between three play conditions, while the differences between the subjective reports failed to reach significance.
Feature-Guided Black-Box Safety Testing of Deep Neural Networks

Despite the improved accuracy of deep neural networks, the discovery of adversarial examples has raised serious safety concerns. Most existing approaches for crafting adversarial examples necessitate some knowledge (architecture, parameters, etc) of the network at hand. In this paper, we focus on image classifiers and propose a feature-guided black-box approach to test the safety of deep neural networks that requires no such knowledge. Our algorithm employs object detection techniques such as SIFT (Scale Invariant Feature Transform) to extract features from an image. These features are converted into a mutable saliency distribution, where high probability is assigned to pixels that affect the composition of the image with respect to the human visual system. We formulate the crafting of adversarial examples as a two-player turn-based stochastic game, where the first player’s objective is to minimise the distance to an adversarial example by manipulating the features, and the second player can be cooperative, adversarial, or random. We show that, theoretically, the two-player game can converge to the optimal strategy, and that the optimal strategy represents a globally minimal adversarial image. For Lipschitz networks, we also identify conditions that provide safety guarantees that no adversarial examples exist. Using Monte Carlo tree search we gradually explore the game state space to search for adversarial examples. Our experiments show that, despite the black-box setting, manipulations guided by a perception-based saliency distribution are competitive with state-of-the-art methods that rely on white-box saliency matrices or sophisticated optimization procedures. Finally, we show how our method can be used to evaluate robustness of neural networks in safety-critical applications such as traffic sign recognition in self-driving cars.
Differential Cryptanalysis of Salsa and ChaCha - An Evaluation with a Hybrid Model

While Salsa and ChaCha are well known software oriented stream ciphers, since the work of Aumasson et al in FSE 2008 there aren’t many significant results against them. The basic model of their attack was to introduce differences in the IV bits, obtain biases after a few forward rounds, as well as to look at the Probabilistic Neutral Bits (PNBs) while reverting back. In this paper we first consider the biases in the forward rounds, and estimate an upper bound on the number of rounds till such biases can be observed. For this, we propose a hybrid model (under certain assumptions), where initially the nonlinear rounds as proposed by the designer are considered, and then we employ their linearized counterpart. The effect of reverting the rounds with the idea of PNBs is also considered. Based on the assumptions and analysis, we conclude that 12 rounds of Salsa and ChaCha should be considered sufficient for 256-bit keys under the current best known attack models.
A Novel Feature Extraction for Robust EMG Pattern Recognition

Varieties of noises are major problem in recognition of Electromyography (EMG) signal. Hence, methods to remove noise become most significant in EMG signal analysis. White Gaussian noise (WGN) is used to represent interference in this paper. Generally, WGN is difficult to be removed using typical filtering and solutions to remove WGN are limited. In addition, noise removal is an important step before performing feature extraction, which is used in EMG-based recognition. This research is aimed to present a novel feature that tolerate with WGN. As a result, noise removal algorithm is not needed. Two novel mean and median frequencies (MMNF and MMDF) are presented for robust feature extraction. Sixteen existing features and two novelties are evaluated in a noisy environment. WGN with various signal-to-noise ratios (SNRs), i.e. 20-0 dB, was added to the original EMG signal. The results showed that MMNF performed very well especially in weak EMG signal compared with others. The error of MMNF in weak EMG signal with very high noise, 0 dB SNR, is about 5-10% and closed by MMDF and Histogram, whereas the error of other features is more than 20%. While in strong EMG signal, the error of MMNF is better than those from other features. Moreover, the combination of MMNF, Histrogram of EMG and Willison amplitude is used as feature vector in classification task. The experimental result shows the better recognition result in noisy environment than other success feature candidates. From the above results demonstrate that MMNF can be used for new robust feature extraction.
The Indian Buffet Process: An Introduction and Review

The Indian buffet process is a stochastic process defining a probability distribution over equivalence classes of sparse binary matrices with a finite number of rows and an unbounded number of columns. This distribution is suitable for use as a prior in probabilistic models that represent objects using a potentially infinite array of features, or that involve bipartite graphs in which the size of at least one class of nodes is unknown. We give a detailed derivation of this distribution, and illustrate its use as a prior in an infinite latent feature model. We then review recent applications of the Indian buffet process in machine learning, discuss its extensions, and summarize its connections to other stochastic processes.
RRTX: Real-Time Motion Planning/Replanning for Environments with Unpredictable Obstacles

We present RRT, the first asymptotically optimal samplingbased motion planning algorithm for real-time navigation in dynamic environments (containing obstacles that unpredictably appear, disappear, and move). Whenever obstacle changes are observed, e.g., by onboard sensors, a graph rewiring cascade quickly updates the search-graph and repairs its shortest-path-to-goal subtree. Both graph and tree are built directly in the robot’s state space, respect the kinematics of the robot, and continue to improve during navigation. RRT is also competitive in static environments—where it has the same amortized per iteration runtime as RRT and RRT* Θ (logn) and is faster than RRT ω ( log n ) . In order to achieve O (logn) iteration time, each node maintains a set of O (logn) expected neighbors, and the search graph maintains -consistency for a predefined .