nils-herrmann/clean_scirepeval huggingface dataset:
An Empirical Study of Client-Side JavaScript Bugs

Client-side JavaScript is widely used in web applications to improve user-interactivity and minimize client-server communications. Unfortunately, web applications are prone to JavaScript faults. While prior studies have demonstrated the prevalence of these faults, no attempts have been made to determine their root causes and consequences.  The goal of our study is to understand the root causes and impact of JavaScript faults and how the results can impact JavaScript programmers, testers and tool developers.  We perform an empirical study of 317 bug reports from 12 bug repositories. The bug reports are thoroughly examined to classify and extract information about the fault's cause (the error) and consequence (the failure and impact).  The majority (65%) of JavaScript faults are DOM-related, meaning they are caused by faulty interactions of the JavaScript code with the Document Object Model (DOM). Further, 80% of the highest impact JavaScript faults are DOM-related. Finally, most JavaScript faults originate from programmer mistakes committed in the JavaScript code itself, as opposed to other web application components such as the server-side or HTML code.  Given the prevalence of DOM-related faults, JavaScript programmers need development tools that can help them reason about the DOM. Also, testers should prioritize detection of DOM-related faults as most high impact faults belong to this category. Finally, developers can use the error patterns we found to design more powerful static analysis tools for JavaScript.
Neuroelectrical Hyperscanning Measures Simultaneous Brain Activity in Humans

In this study we illustrate a methodology able to follow and study concurrent and simultaneous brain processes during cooperation between individuals, with non invasive EEG methodologies. We collected data from fourteen pairs of subjects while they were playing a card game with EEG. Data collection was made simultaneously on all the subjects during the card game. An extension of the Granger-causality approach allows us to estimate the functional connection between signals estimated from different Regions of Interest (ROIs) in different brains during the analyzed task. Finally, with the use of graph theory, we contrast the functional connectivity patterns of the two players belonging to the same team. Statistically significant functional connectivities were obtained from signals estimated in the ROIs modeling the anterior cingulate cortex (ACC) and the prefrontal areas described by the Brodmann areas 8 with the signals estimated in all the other modelled cortical areas. Results presented suggested the existence of Granger-sense causal relations between the EEG activity estimated in the prefrontal areas 8 and 9/46 of one player with the EEG activity estimated in the ACC of their companion. We illustrated the feasibility of functional connectivity methodology on the EEG hyperscannings performed on a group of subjects. These functional connectivity estimated from the couple of brains could suggest, in statistical and mathematical terms, the modelled cortical areas that are correlated in Granger-sense during the solution of a particular task. EEG hyperscannings could be used to investigate experimental paradigms where the knowledge of the simultaneous interactions between the subjects have a value.
Lysis of the orbicularis retaining ligament and orbicularis oculi insertion: a powerful modality for lower eyelid and cheek rejuvenation.

The techniques of lower blepharoplasty are evolving to reflect the concept that the lower eyelid contour does not stop at the inferior orbital rim, and that the lid-cheek junction must often be modified to restore the midface to a youthful configuration. Multiple procedures have been proposed to smooth the lid-cheek junction and tear trough. The author proposes a technique of carbon dioxide laser lysis of the orbicularis retaining ligament and of the orbicularis oculi insertion onto the maxilla to release the tethering of the lower lid and cheek and allow recontouring of the lid-cheek junction in an extended transcutaneous lower blepharoplasty.



Retrospective review of 80 extended lower blepharoplasty procedures with carbon dioxide laser lysis of the orbicularis retaining ligament and of the orbicularis oculi insertion performed in the past 3 years was undertaken. Follow-up ranged from 4 to 26 months, with an average of 7.2 months. The efficacy, risks, and complications of this procedure were assessed.



The complication rate for this procedure is not significantly higher than that for standard transcutaneous blepharoplasty, and the procedure allows significant improvement of the lid-cheek junction and rejuvenation of the upper midface.



Lysis of the orbicularis retaining ligament and lower orbicularis oculi insertion is a safe and effective adjunct to lower blepharoplasty. It is a powerful modality that allows significant rejuvenation of the lid-cheek complex and upper cheek.



Therapeutic, IV.
Detecting stable clusters using principal component analysis.

Clustering is one of the most commonly used tools in the analysis of gene expression data(1, 2). The usage in grouping genes is based on the premise that co-expression is a result of co-regulation. It is thus a preliminary step in extracting gene networks and inference of gene function (3, 4). Clustering of experiments can be used to discover novel phenotypic aspects of cells and tissues (3, 5, 6), including sensitivity to drugs(7), and can also detect artifacts of experimental conditions (8). Clustering and its applications in biology are presented in greater detail in the chapter by Zhao and Karypis (see also (9)). While we focus on gene expression data in this chapter, the methodology presented here is applicable for other types of data as well. Clustering is a form of unsupervised learning, i.e. no information on the class variable is assumed, and the objective is to find the “natural” groups in the data. However, most clustering algorithms generate a clustering even if the data has no inherent cluster structure, so external validation tools are required. Given a set of partitions of the data into an increasing number of clusters (e.g. by a hierarchical clustering algorithm, or k-means), such a validation tool will tell the user the number of clusters in the data (if any). Many methods have been proposed in the literature to address this problem (10–15). Recent studies have shown the advantages of sampling-based methods (12, 14). These methods are based on the idea that when a partition has captured the structure in the data, this partition should be stable with respect to perturbation of the data. Bittner et al. (16) used a similar approach to validate clusters representing gene expression of melanoma patients. The emergence of cluster structure depends on several choices: data representation and normalization, the choice of a similarity measure and clustering algorithm. In this chapter we extend the stability-based validation of cluster structure, and propose stability as a figure of merit that is useful for comparing clustering solutions, thus helping in making these choices. We use this framework to demonstrate the ability of Principal Component Analysis (PCA) to extract features relevant to the cluster structure. We use stability as a tool for simultaneously choosing the number of principal components and the number of clusters; we compare the performance of different similarity measures and normalization schemes. The approach is demonstrated through a case study of yeast gene expression data from Eisenet al. (1). For yeast, a functional classification of a large number of genes is known, and we use this classification for validating the results produced by clustering. A method for comparing clustering solutions specifically applicable to gene expression data was introduced in(17). However, it cannot be used to choose the number of clusters, and is not directly applicable in choosing the number of principal components. The results of clustering are easily corrupted by the addition of noise: even a few
Stadtpilot: First fully autonomous test drives in urban traffic

The Stadtpilot project aims at autonomous driving on Braunschweig's inner city ring road. For this purpose, an autonomous vehicle called “Leonie” has been developed. In October 2010, after two years of research, “Leonie's” abilities were presented in a public demonstration. This vehicle is one of the first worldwide to show the ability of driving autonomously in real urban traffic scenarios. This paper describes the legal issues and the homologation process for driving autonomously in public traffic in Braunschweig, Germany. It also dwells on the Safety Concept, the system architecture and current research activities.
Brain Activation during Face Perception: Evidence of a Developmental Change

Behavioral studies suggest that children under age 10 process faces using a piecemeal strategy based on individual distinctive facial features, whereas older children use a configural strategy based on the spatial relations among the face's features. The purpose of this study was to determine whether activation of the fusiform gyrus, which is involved in face processing in adults, is greater during face processing in older children (1214 years) than in younger children (8 10 years). Functional MRI scans were obtained while children viewed faces and houses. A developmental change was observed: Older children, but not younger children, showed significantly more activation in bilateral fusiform gyri for faces than for houses. Activation in the fusiform gyrus correlated significantly with age and with a behavioral measure of configural face processing. Regions believed to be involved in processing basic facial features were activated in both younger and older children. Some evidence was also observed for greater activation for houses versus faces for the older children than for the younger children, suggesting that processing of these two stimulus types becomes more differentiated as children age. The current results provide biological insight into changes in visual processing of faces that occur with normal development.
Self-supervised Learning of Motion Capture

Current state-of-the-art solutions for motion capture from a single camera are optimization driven: they optimize the parameters of a 3D human model so that its re-projection matches measurements in the video (e.g. person segmentation, optical flow, keypoint detections etc.). Optimization models are susceptible to local minima. This has been the bottleneck that forced using clean green-screen like backgrounds at capture time, manual initialization, or switching to multiple cameras as input resource. In this work, we propose a learning based motion capture model for single camera input. Instead of optimizing mesh and skeleton parameters directly, our model optimizes neural network weights that predict 3D shape and skeleton configurations given a monocular RGB video. Our model is trained using a combination of strong supervision from synthetic data, and self-supervision from differentiable rendering of (a) skeletal keypoints, (b) dense 3D mesh motion, and (c) human-background segmentation, in an end-to-end framework. Empirically we show our model combines the best of both worlds of supervised learning and test-time optimization: supervised learning initializes the model parameters in the right regime, ensuring good pose and surface initialization at test time, without manual effort. Self-supervision by back-propagating through differentiable rendering allows (unsupervised) adaptation of the model to the test data, and offers much tighter fit than a pretrained fixed model. We show that the proposed model improves with experience and converges to low-error solutions where previous optimization methods fail.
FPGA implementation of a large-number multiplier for fully homomorphic encryption

The first plausible scheme of fully homomorphic encryption (FHE), introduced by Gentry in 2009, was considered a major breakthrough in the field of information security. FHE allows the evaluation of arbitrary functions directly on encrypted data on untrusted servers. However, previous implementations of FHE on general-purpose processors had very long latency, which makes it impractical for cloud computing. The most computationally intensive components in the Gentry-Halevi FHE primitives are the large-number modular multiplications and additions. In this paper, we attempt to use customized circuits to speedup the large number multiplication. Strassen's algorithm is employed in the design of an efficient, high-speed large-number multiplier. In particular, we propose an architecture design of an 768K-bit multiplier. As a key compoment, an 64K-point finite-field fast Fourier transform (FFT) processor is designed and prototyped on the Stratix-V FPGA. At 100 MHz, the FPGA implementation is about twice as fast as the same FFT algorithm executed on the NVIDA C2050 GPU which has 448 cores running at 1.15 GHz but at much lower power consumption.
Brain tumor segmentation: A performance analysis using K-Means, Fuzzy C-Means and Region growing algorithm

Medical imaging is a technique that is extensively used to create images of human body for medical and research purposes. Magnetic Resonance Imaging (MRI) is a powerful visualization tool that permits to acquire images of internal anatomy of human body in a secure and non-invasive manner. Automatic brain tumor detection from MRI images has become one of the major areas of medical research. The important task in the diagnosis of brain tumor is to determine the exact location, orientation and area of the abnormal tissues. This paper discuss the performance analysis of image segmentation techniques, viz., K-Means Clustering, Fuzzy C-Means Clustering and Region Growing for detection of brain tumor from sample MRI images of brain. The performance evaluation of the above mentioned techniques is done on the basis of error percentage as compared to ground truth. The real time database is taken from Rajiv Gandhi Cancer Institute & Research Centre, Delhi, India (RGCI&RC).
Low-Rank Approximations for Conditional Feedforward Computation in Deep Neural Networks

Scalability properties of deep neural networks raise key re search questions, particularly as the problems considered become larger and more challenging. This paper expands on the idea of conditional computation introd uce in , where the nodes of a deep network are augmented by a set of gating uni ts that determine when a node should be calculated. By factorizing the wei ght matrix into a low-rank approximation, an estimation of the sign of the pr -nonlinearity activation can be efficiently obtained. For networks using rec tifi d-linear hidden units, this implies that the computation of a hidden unit wit h an estimated negative pre-nonlinearity can be omitted altogether, as its val ue will become zero when nonlinearity is applied. For sparse neural networks, this c an result in considerable speed gains. Experimental results using the MNIST and SVHN d ata sets with a fully-connected deep neural network demonstrate the perf ormance robustness of the proposed scheme with respect to the error introduced b y the conditional computation process.
Recursive Bayesian Control of Multichannel Acoustic Echo Cancellation

We present a novel recursive Bayesian method in the DFT-domain to address the multichannel acoustic echo cancellation problem. We model the echo paths between the loudspeakers and the near-end microphone as a multichannel random variable with a first-order Markov property. The incorporation of the near-end observation noise, in conjunction with the multichannel Markov model, leads to a multichannel state-space model. We derive a recursive Bayesian solution to the multichannel state-space model, which turns out to be well suited for input signals that are not only auto-correlated but also cross-correlated. We show that the resulting multichannel state-space frequency-domain adaptive filter (MCSSFDAF) can be efficiently implemented due to the submatrix-diagonality of the state-error covariance. The filter offers optimal tracking and robust adaptation in the presence of near-end noise and echo path variability.
Deep Neural Networks with Massive Learned Knowledge

Regulating deep neural networks (DNNs) with human structured knowledge has shown to be of great benefit for improved accuracy and interpretability. We develop a general framework that enables learning knowledge and its confidence jointly with the DNNs, so that the vast amount of fuzzy knowledge can be incorporated and automatically optimized with little manual efforts. We apply the framework to sentence sentiment analysis, augmenting a DNN with massive linguistic constraints on discourse and polarity structures. Our model substantially enhances the performance using less training data, and shows improved interpretability. The principled framework can also be applied to posterior regularization for regulating other statistical models.
Logistic Model Tree Extraction From Artificial Neural Networks

Artificial neural networks (ANNs) are a powerful and widely used pattern recognition technique. However, they remain "black boxes" giving no explanation for the decisions they make. This paper presents a new algorithm for extracting a logistic model tree (LMT) from a neural network, which gives a symbolic representation of the knowledge hidden within the ANN. Landwehr's LMTs are based on standard decision trees, but the terminal nodes are replaced with logistic regression functions. This paper reports the results of an empirical evaluation that compares the new decision tree extraction algorithm with Quinlan's C4.5 and ExTree. The evaluation used 12 standard benchmark datasets from the university of California, Irvine machine-learning repository. The results of this evaluation demonstrate that the new algorithm produces decision trees that have higher accuracy and higher fidelity than decision trees created by both C4.5 and ExTree.
Dynamic magnetic resonance imaging of human brain activity during primary sensory stimulation.

Neuronal activity causes local changes in cerebral blood flow, blood volume, and blood oxygenation. Magnetic resonance imaging (MRI) techniques sensitive to changes in cerebral blood flow and blood oxygenation were developed by high-speed echo planar imaging. These techniques were used to obtain completely noninvasive tomographic maps of human brain activity, by using visual and motor stimulus paradigms. Changes in blood oxygenation were detected by using a gradient echo (GE) imaging sequence sensitive to the paramagnetic state of deoxygenated hemoglobin. Blood flow changes were evaluated by a spin-echo inversion recovery (IR), tissue relaxation parameter T1-sensitive pulse sequence. A series of images were acquired continuously with the same imaging pulse sequence (either GE or IR) during task activation. Cine display of subtraction images (activated minus baseline) directly demonstrates activity-induced changes in brain MR signal observed at a temporal resolution of seconds. During 8-Hz patterned-flash photic stimulation, a significant increase in signal intensity (paired t test; P less than 0.001) of 1.8% +/- 0.8% (GE) and 1.8% +/- 0.9% (IR) was observed in the primary visual cortex (V1) of seven normal volunteers. The mean rise-time constant of the signal change was 4.4 +/- 2.2 s for the GE images and 8.9 +/- 2.8 s for the IR images. The stimulation frequency dependence of visual activation agrees with previous positron emission tomography observations, with the largest MR signal response occurring at 8 Hz. Similar signal changes were observed within the human primary motor cortex (M1) during a hand squeezing task and in animal models of increased blood flow by hypercapnia. By using intrinsic blood-tissue contrast, functional MRI opens a spatial-temporal window onto individual brain physiology.
AI assisted ethics

The growing number of ‘smart’ instruments, those equipped with AI, has raised concerns because these instruments make autonomous decisions; that is, they act beyond the guidelines provided them by programmers. Hence, the question the makers and users of smart instrument (e.g., driver-less cars) face is how to ensure that these instruments will not engage in unethical conduct (not to be conflated with illegal conduct). The article suggests that to proceed we need a new kind of AI program—oversight programs—that will monitor, audit, and hold operational AI programs accountable.
A neural network that finds a naturalistic solution for the production of muscle activity

It remains an open question how neural responses in motor cortex relate to movement. We explored the hypothesis that motor cortex reflects dynamics appropriate for generating temporally patterned outgoing commands. To formalize this hypothesis, we trained recurrent neural networks to reproduce the muscle activity of reaching monkeys. Models had to infer dynamics that could transform simple inputs into temporally and spatially complex patterns of muscle activity. Analysis of trained models revealed that the natural dynamical solution was a low-dimensional oscillator that generated the necessary multiphasic commands. This solution closely resembled, at both the single-neuron and population levels, what was observed in neural recordings from the same monkeys. Notably, data and simulations agreed only when models were optimized to find simple solutions. An appealing interpretation is that the empirically observed dynamics of motor cortex may reflect a simple solution to the problem of generating temporally patterned descending commands.
Transcranial magnetic stimulation of left prefrontal cortex impairs working memory

Several lines of evidence suggest that the prefrontal cortex is involved in working memory. Our goal was to determine whether transient functional disruption of the dorsolateral prefrontal cortex (DLPFC) would impair performance in a sequential-letter working memory task.



Subjects were shown sequences of letters and asked to state whether the letter just displayed was the same as the one presented 3-back. Single-pulse transcranial magnetic stimulation (TMS) was applied over the DLPFC between letter presentations.



TMS applied over the left DLPFC resulted in increased errors relative to no TMS controls. TMS over the right DLPFC did not alter working memory performance.



Our results indicate that the left prefrontal cortex has a crucial role in at least one type of working memory.