nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of concussive symptoms, cognitive performance, and psychological symptoms between acute blast-versus nonblast-induced mild traumatic brain injury.

Blast-related head injuries are one of the most prevalent injuries among military personnel deployed in service of Operation Iraqi Freedom. Although several studies have evaluated symptoms after blast injury in military personnel, few studies compared them to nonblast injuries or measured symptoms within the acute stage after traumatic brain injury (TBI). Knowledge of acute symptoms will help deployed clinicians make important decisions regarding recommendations for treatment and return to duty. Furthermore, differences more apparent during the acute stage might suggest important predictors of the long-term trajectory of recovery. This study evaluated concussive, psychological, and cognitive symptoms in military personnel and civilian contractors (N = 82) diagnosed with mild TBI (mTBI) at a combat support hospital in Iraq. Participants completed a clinical interview, the Automated Neuropsychological Assessment Metric (ANAM), PTSD Checklist-Military Version (PCL-M), Behavioral Health Measure (BHM), and Insomnia Severity Index (ISI) within 72 hr of injury. Results suggest that there are few differences in concussive symptoms, psychological symptoms, and neurocognitive performance between blast and nonblast mTBIs, although clinically significant impairment in cognitive reaction time for both blast and nonblast groups is observed. Reductions in ANAM accuracy were related to duration of loss of consciousness, not injury mechanism.
Aeroelastic Tailoring of the NASA Common Research Model via Novel Material and Structural Configurations

This work explores the use of tow steered composite laminates, functionally graded metals (FGM), thickness distributions, and curvilinear rib/spar/stringer topologies for aeroelastic tailoring. Parameterized models of the Common Research Model (CRM) wing box have been developed for passive aeroelastic tailoring trade studies. Metrics of interest include the wing weight, the onset of dynamic flutter, and the static aeroelastic stresses. Compared to a baseline structure, the lowest aggregate static wing stresses could be obtained with tow steered skins (47% improvement), and many of these designs could reduce weight as well (up to 14%). For these structures, the trade-off between flutter speed and weight is generally strong, although one case showed both a 100% flutter improvement and a 3.5% weight reduction. Material grading showed no benefit in the skins, but moderate flutter speed improvements (with no weight or stress increase) could be obtained by grading the spars (4.8%) or ribs (3.2%), where the best flutter results were obtained by grading both thickness and material. For the topology work, large weight reductions were obtained by removing an inner spar, and performance was maintained by shifting stringers forward and/or using curvilinear ribs: 5.6% weight reduction, a 13.9% improvement in flutter speed, but a 3.0% increase in stress levels. Flutter resistance was also maintained using straightrotated ribs although the design had a 4.2% lower flutter speed than the curved ribs of similar weight and stress levels were higher. These results will guide the development of a future design optimization scheme established to exploit and combine the individual attributes of these technologies.
CrescendoNet: A Simple Deep Convolutional Neural Network with Ensemble Behavior

We introduce a new deep convolutional neural network, CrescendoNet, by stacking simple building blocks without residual connections. Each Crescendo block contains independent convolution paths with increased depths. The numbers of convolution layers and parameters are only increased linearly in Crescendo blocks. In experiments, CrescendoNet with only 15 layers outperforms almost all networks without residual connections on benchmark datasets, CIFAR10, CIFAR100, and SVHN. Given sufficient amount of data as in SVHN dataset, CrescendoNet with 15 layers and 4.1M parameters can match the performance of DenseNet-BC with 250 layers and 15.3M parameters. CrescendoNet provides a new way to construct high performance deep convolutional neural networks without residual connections. Moreover, through investigating the behavior and performance of subnetworks in CrescendoNet, we note that the high performance of CrescendoNet may come from its implicit ensemble behavior, which differs from the FractalNet that is also a deep convolutional neural network without residual connections. Furthermore, the independence between paths in CrescendoNet allows us to introduce a new path-wise training procedure, which can reduce the memory needed for training.
A Case of Attempted Bilateral Self-Enucleation in a Patient with Bipolar Disorder

Attempted and completed self-enucleation, or removal of one's own eyes, is a rare but devastating form of self-mutilation behavior. It is often associated with psychiatric disorders, particularly schizophrenia, substance induced psychosis, and bipolar disorder. We report a case of a patient with a history of bipolar disorder who gouged his eyes bilaterally as an attempt to self-enucleate himself. On presentation, the patient was manic with both psychotic features of hyperreligous delusions and command auditory hallucinations of God telling him to take his eyes out. On presentation, the patient had no light perception vision in both eyes and his exam displayed severe proptosis, extensive conjunctival lacerations, and visibly avulsed extraocular muscles on the right side. An emergency computed tomography scan of the orbits revealed small and irregular globes, air within the orbits, and intraocular hemorrhage. He was taken to the operating room for surgical repair of his injuries. Attempted and completed self-enucleation is most commonly associated with schizophrenia and substance induced psychosis, but can also present in patients with bipolar disorder. Other less commonly associated disorders include obsessive-compulsive disorder, depression, mental retardation, neurosyphilis, Lesch-Nyhan syndrome, and structural brain lesions.
Non-Negative Networks Against Adversarial Attacks

Adversarial attacks against neural networks are a problem of considerable importance, for which effective defenses are not yet readily available. We make progress toward this problem by showing that non-negative weight constraints can be used to improve resistance in specific scenarios. In particular, we show that they can provide an effective defense for binary classification problems with asymmetric cost, such as malware or spam detection. We also show the potential for non-negativity to be helpful to non-binary problems by applying it to image
Attachment Aware Schools : Working with Families to Enhance Parental Engagement and Home-School Relationships

June 31, 2017  August 14, 2017 Accepted: September 12, 2017  Application of attachment theory in school contexts lacks empirical evidence. The Attachment Aware Schools pilot project was commissioned by two Local Authorities in England to improve the educational outcomes of Looked After Children, and to build an evidence base. Informed by attachment research, the Attachment Aware Schools program provides a coherent and integrated theoretical framework, discourse, and practice for all practitioners working with children and young people.
Nonnegative Matrix and Tensor Factorizations : An algorithmic perspective

A common thread in various approaches for model reduction, clustering, feature extraction, classification, and blind source separation (BSS) is to represent the original data by a lower-dimensional approximation obtained via matrix or tensor (multiway array) factorizations or decompositions. The notion of matrix/tensor factorizations arises in a wide range of important applications and each matrix/tensor factorization makes different assumptions regarding component (factor) matrices and their underlying structures. So choosing the appropriate one is critical in each application domain. Approximate low-rank matrix and tensor factorizations play fundamental roles in enhancing the data and extracting latent (hidden) components.
Towards embedded control for resonant scanning MEMS micromirror

This paper describes the design and realization of an electrostatic actuated MEMS mirror operating at a resonance frequency of 23.5 KHz with a PLL feedback loop. The design is based upon a thorough understanding of the (non-linear) dynamical behavior of the mirror. Using an external position sensitive device (PSD) the proper working of the PLL is demonstrated. Next we study the possibility to replace the PSD sensor with an embedded capacitive phase-angle sensor. We show measurements of capacitance changes with large parasitic influences while actuating the mirror in a feed forward mode. This demonstrates the feasibility of a fully embedded control for a resonant scanning MEMS mirror.
Restoring large-scale brain networks in PTSD and related disorders: a proposal for neuroscientifically-informed treatment interventions

Three intrinsic connectivity networks in the brain, namely the central executive, salience, and default mode networks, have been identified as crucial to the understanding of higher cognitive functioning, and the functioning of these networks has been suggested to be impaired in psychopathology, including posttraumatic stress disorder (PTSD).



1) To describe three main large-scale networks of the human brain; 2) to discuss the functioning of these neural networks in PTSD and related symptoms; and 3) to offer hypotheses for neuroscientifically-informed interventions based on treating the abnormalities observed in these neural networks in PTSD and related disorders.



Literature relevant to this commentary was reviewed.



Increasing evidence for altered functioning of the central executive, salience, and default mode networks in PTSD has been demonstrated. We suggest that each network is associated with specific clinical symptoms observed in PTSD, including cognitive dysfunction (central executive network), increased and decreased arousal/interoception (salience network), and an altered sense of self (default mode network). Specific testable neuroscientifically-informed treatments aimed to restore each of these neural networks and related clinical dysfunction are proposed.



Neuroscientifically-informed treatment interventions will be essential to future research agendas aimed at targeting specific PTSD and related symptoms.
A Shape-Based Approach to Robust Image Segmentation

We propose a novel segmentation approach for introducing shape priors in the geometric active contour framework. Following the work of Leventon, we propose to revisit the use of linear principal component analysis (PCA) to introduce prior knowledge about shapes in a more robust manner. Our contribution in this paper is twofold. First, we demonstrate that building a space of familiar shapes by applying PCA on binary images (instead of signed distance functions) enables one to constrain the contour evolution in a way that is more faithful to the elements of a training set. Secondly, we present a novel region-based segmentation framework, able to separate regions of different intensities in an image. Shape knowledge and image information are encoded into two energy functionals entirely described in terms of shapes. This consistent description allows for the simultaneous encoding of multiple types of shapes and leads to promising segmentation results. In particular, our shape-driven segmentation technique offers a convincing level of robustness with respect to noise, clutter, partial occlusions, and blurring.
An algorithm for suffix stripping

Removing suffixes by automatic means is an operation which is especially useful in the field of information retrieval. In a typical IR environment, one has a collection of documents, each described by the words in the document title and possibly by words in the document abstract. Ignoring the issue of precisely where the words originate, we can say that a document is represented by a vetor of words, or terms. Terms with a common stem will usually have similar meanings, for example:
Predicting taxi demand at high spatial resolution: Approaching the limit of predictability

In big cities, taxi service is imbalanced. In some areas, passengers wait too long for a taxi, while in others, many taxis roam without passengers. Knowledge of where a taxi will become available can help us solve the taxi demand imbalance problem. In this paper, we employ a holistic approach to predict taxi demand at high spatial resolution. We showcase our techniques using two real-world data sets, yellow cabs and Uber trips in New York City, and perform an evaluation over 9,940 building blocks in Manhattan. Our approach consists of two key steps. First, we use entropy and the temporal correlation of human mobility to measure the demand uncertainty at the building block level. Second, to identify which predictive algorithm can approach the theoretical maximum predictability, we implement and compare three predictors: the Markov predictor (a probability-based predictive algorithm), the Lempel-Ziv-Welch predictor (a sequence-based predictive algorithm), and the Neural Network predictor (a predictive algorithm that uses machine learning). The results show that predictability varies by building block and, on average, the theoretical maximum predictability can be as high as 83%. The performance of the predictors also vary: the Neural Network predictor provides better accuracy for blocks with low predictability, and the Markov predictor provides better accuracy for blocks with high predictability. In blocks with high maximum predictability, the Markov predictor is able to predict the taxi demand with an 89% accuracy, 11% better than the Neural Network predictor, while requiring only 0.03% computation time. These findings indicate that the maximum predictability can be a good metric for selecting prediction algorithms.
Experimental performance comparison between TCP vs UDP tunnel using OpenVPN

The comparison between TCP and UDP tunnels have not been sufficiently reported in the scientific literature. In this work, we use OpenVPN as a platform to demonstrate the performance between TCP/UDP. The de facto belief has been that TCP tunnel provides a permanent tunnel and therefore ensures a reliable transfer of data between two end points. However the effects of transmitting TCP within a UDP tunnel has been explored and could provide a valuable attempt. The results provided in this paper demonstrates that indeed TCP in UDP tunnel provides better latency. Throughout this paper, a series of tests have been performed, UDP traffic was sent inside UDP tunnel and TCP tunnel successively. The same tests was performed using TCP traffic.
Segmentation and pre-recognition of Arabic handwriting

We propose a novel algorithm for the segmentation and prerecognition of offline handwritten Arabic text. Our character segmentation method over-segments each word, and then removes extra breakpoints using knowledge of letter shapes. On a test set of 200 images, 92.3% of the segmentation points were detected correctly, with 5.1% instances of over-segmentation. The prerecognition component annotates each detected letter with shape information, to be used for recognition in future work.
A Comparison of Event Representations in DEFT

This paper will discuss and compare event representations across a variety of types of event annotation: Rich Entities, Relations, and Events (Rich ERE), Light Entities, Relations, and Events (Light ERE), Event Nugget (EN), Event Argument Extraction (EAE), Richer Event Descriptions (RED), and Event-Event Relations (EER). Comparisons of event representations are presented, along with a comparison of data annotated according to each event representation. An event annotation experiment is also discussed, including annotation for all of these representations on the same set of sample data, with the purpose of being able to compare actual annotation across all of these approaches as directly as possible. We walk through a brief example to illustrate the various annotation approaches, and to show the intersections among the various annotated data sets.
Cyber-bullying taxonomies: Definition, forms, consequences and mitigation strategies

Having a black e-day seems to be experienced by every individual. With the advent of social networks and electronic tools, the online antisocial behaviors have increased, introduced a prevalent problem known as Cyber-Bullying. However, attention to the development of taxonomies with clear definition and boundaries of this problem, and to identifying preventive practices against cyber-bullying is very minimal. This paper aims to develop taxonomies of cyber-bullying definition, forms and consequences, and then crucial mitigation strategies to prevent and avoid such problem. Cyber-bullying definition can be identified in terms of child cyber-bullying, cyber-grooming, adult cyber-stalking and workplace cyber-bullying. The paper found that cyber-bullying forms taxonomy can be identified in terms of Internet-bullying and mobile-bullying. Cyber-bullying consequences taxonomy can be identified in terms of social and cultural, psychological and health, financial and unemployment, and technical consequences. Finally, cyber-bullying mitigation strategies taxonomy identified technology-, psychology-, and administrative-oriented approaches. The paper also found that no studies have been cited by the literature on cyber-bullying in most of Middle-east countries. This paper contributes by introducing those taxonomies to further understand cyber-bullying, to improve scientific awareness of this problem, and to highlight potential solution.
Patterns of cascading behavior in WeChat moments

WeChat is the largest acquaintance social networking platform in China, which has about 938 million monthly active user accounts. WeChat Moments, known as Friends Circle, serves social networking functions in which users can view information shared by friends. This paper addresses the problem of analyzing the patterns of cascading behavior in WeChat Moments. We obtain 229021 information cascades from WeChat Moments, in which more than 5 million users are involved during 45 days. We analyze these cascades from four aspects to understand the patterns of cascading behavior in WeChat Moments, including the patterns of diffusion structure, temporal dynamic, spatial dynamic and user behavior. In addition, the correlations between these patterns are examined. Our findings contribute to promoting products, predicting and even regulating public opinion.
Feature-Based Matrix Factorization

Recommender system has been more and more popular and widely used in many applications recently. The increasing information available, not only in quantities but also in types, leads to a big challenge for recommender system that how to leverage these rich information to get a better performance. Most traditional approaches try to design a specific model for each scenario, which demands great efforts in developing and modifying models. In this technical report, we describe our implementation of feature-based matrix factorization. This model is an abstract of many variants of matrix factorization models, and new types of information can be utilized by simply defining new features, without modifying any lines of code. Using the toolkit, we built the best single model reported on track 1 of KDDCup’11.
Beyond OCR: Multi-faceted understanding of handwritten document characteristics

In the previous chapters, we proposed several features for writer identification, historical manuscript dating and localization separately. In this chapter, we present a summarization of the proposed features for different applications by proposing a joint feature distribution (JFD) principle to design novel discriminative features which could be the joint distribution of features on adjacent positions or the joint distribution of different features on the same location. Following the proposed JFD principle, we introduce seventeen features, including twelve textural-based and five grapheme-based features. We evaluate these features for different applications from four different perspectives to understand handwritten documents beyond OCR, by writer identification, script recognition, historical manuscript dating and localization.
DAG-Based Distributed Ledgers

In 2008 Bitcoin [Nak09] was introduced as the first decentralized digital currency. Its core underlying technology is the blockchain which is essentially a distributed append-only database. In particular, blockchain solves the key issue in decentralized digital currencies – the double spending problem – which asks: “if there is no central authority, what stops a malicious party from spending the same unit of currency multiple times”. Blockchain solves this problem by keeping track of each transaction that has been ever made while being robust against adversarial modifications.
Positive-tone photodefinable polyimide for low temperature cure

Photodefinable Polyimides (PI) and polybenz-oxazoles (PBO) which have been widely used for various electronic applications such as buffer coating, interlayer dielectric and protection layer usually need high temperature cure condition over 300 °C to complete the cyclization and achieve good film properties. In addition, PI and PBO are also utilized recently for re-distribution layer of wafer level package. In this application, lower temperature curability is strongly required in order to prevent the thermal damage of the semi-conductor device and the other packaging material. Then, to meet this requirement, we focused on pre-cyclized polyimide with phenolic hydroxyl groups since this polymer showed the good solubility to aqueous TMAH and there was no need to apply high temperature cure condition. As a result of our study, the positive-tone photodefinable material could be obtained by using DNQ and combination of epoxy cross-linker enabled to enhance the chemical and PCT resistance of the cured film made even at 170 °C. Furthermore, the adhesion to copper was improved probably due to secondary hydroxyl groups which were generated from reacted epoxide groups. In this report, we introduce our concept of novel photodefinable positive-tone polyimide for low temperature cure.
Federated Byzantine Agreement to Ensure Trustworthiness of Digital Manufacturing Platforms

In this paper, we explore the use of the Stellar Consensus Protocol (SCP) and its Federated Byzantine Agreement (FBA) algorithm for ensuring trust and reputation between federated, cloud-based platform instances (nodes) and their participants. Our approach is grounded on federated consensus mechanisms, which promise data quality managed through computational trust and data replication, without a centralized authority. We perform our experimentation on the ground of the NIMBLE cloud manufacturing platform, which is designed to support growth of B2B digital manufacturing communities and their businesses through federated platform services, managed by peer-to-peer networks. We discuss the message exchange flow between the NIMBLE application logic and Stellar consensus logic.