nils-herrmann/clean_scirepeval huggingface dataset:
Sliding mode and shaped input vibration control of flexible systems

In this paper, the vibration reduction problem is investigated for a flexible spacecraft during attitude maneuvering. A new control strategy is proposed, which integrates both the command input shaping and the sliding mode output feedback control (SMOFC) techniques. Specifically, the input shaper is designed for the reference model and implemented outside of the feedback loop in order to achieve the exact elimination of the residual vibration by modifying the existing command. The feedback controller, on the other hand, is designed based on the SMOFC such that the closed-loop system behaves like the reference model with input shaper, where the residual vibrations are eliminated in the presence of parametric uncertainties and external disturbances. An attractive feature of this SMOFC algorithm is that the parametric uncertainties or external disturbances of the system do not need to satisfy the so-called matching conditions or invariance conditions provided that certain bounds are known. In addition, a smoothed hyperbolic tangent function is introduced to eliminate the chattering phenomenon. Compared with the conventional methods, the proposed scheme guarantees not only the stability of the closed-loop system, but also the good performance as well as the robustness. Simulation results for the spacecraft model show that the precise attitudes control and vibration suppression are successfully achieved.
Recommendations with Negative Feedback via Pairwise Deep Reinforcement Learning

Recommender systems play a crucial role in mitigating the problem of information overload by suggesting users' personalized items or services. The vast majority of traditional recommender systems consider the recommendation procedure as a static process and make recommendations following a fixed strategy. In this paper, we propose a novel recommender system with the capability of continuously improving its strategies during the interactions with users. We model the sequential interactions between users and a recommender system as a Markov Decision Process (MDP) and leverage Reinforcement Learning (RL) to automatically learn the optimal strategies via recommending trial-and-error items and receiving reinforcements of these items from users' feedback. Users' feedback can be positive and negative and both types of feedback have great potentials to boost recommendations. However, the number of negative feedback is much larger than that of positive one; thus incorporating them simultaneously is challenging since positive feedback could be buried by negative one. In this paper, we develop a novel approach to incorporate them into the proposed deep recommender system (DEERS) framework. The experimental results based on real-world e-commerce data demonstrate the effectiveness of the proposed framework. Further experiments have been conducted to understand the importance of both positive and negative feedback in recommendations.
SSID Oracle Attack on Undisclosed Wi-Fi Preferred Network Lists

User’s location privacy concerns have been further raised by today’s Wi-Fi technology omnipresence. Preferred Network Lists (PNLs) are a particularly interesting source of private location information, as devices are storing a list of previously used hotspots. Privacy implications of a disclosed PNL have been covered by numerous papers, mostly focusing on passive monitoring attacks. Nowadays, however, more and more devices no longer transmit their PNL in clear, thus mitigating passive attacks. Hidden PNLs are still vulnerable against active attacks whereby an attacker mounts a fake SSID hotspot set to one likely contained within targeted PNL. If the targeted device has this SSID in the corresponding PNL, it will automatically initiate a connection with the fake hotspot thus disclosing this information to the attacker. By iterating through different SSIDs (from a predefined dictionary) the attacker can eventually reveal a big part of the hidden PNL. Considering user mobility, executing active attacks usually has to be done within a short opportunity window, while targeting nontrivial SSIDs from user’s PNL. The existing work on active attacks against hidden PNLs often neglects both of these challenges. In this paper we propose a simple mathematical model for analyzing active SSID dictionary attacks, allowing us to optimize the effectiveness of the attack under the above constraints (limited window of opportunity and targeting nontrivial SSIDs). Additionally, we showcase an examplemethod for building an effective SSIDdictionary using top-N recommender algorithm and validate our model through simulations and extensive real-life tests.
Iii. Sleep assessment methods.

Sleep is a complex phenomenon that could be understood and assessed at many levels. Sleep could be described at the behavioral level (relative lack of movements and awareness and responsiveness) and at the brain level (based on EEG activity). Sleep could be characterized by its duration, by its distribution during the 24-hr day period, and by its quality (e.g., consolidated versus fragmented). Different methods have been developed to assess various aspects of sleep. This chapter covers the most established and common methods used to assess sleep in infants and children. These methods include polysomnography, videosomnography, actigraphy, direct observations, sleep diaries, and questionnaires. The advantages and disadvantages of each method are highlighted.
Scalable Kernel Density Classification via Threshold-Based Pruning

Density estimation forms a critical component of many analytics tasks including outlier detection, visualization, and statistical testing. These tasks often seek to classify data into high and low-density regions of a probability distribution. Kernel Density Estimation (KDE) is a powerful technique for computing these densities, offering excellent statistical accuracy but quadratic total runtime. In this paper, we introduce a simple technique for improving the performance of using a KDE to classify points by their density (density classification). Our technique, thresholded kernel density classification (tKDC), applies threshold-based pruning to spatial index traversal to achieve asymptotic speedups over naïve KDE, while maintaining accuracy guarantees. Instead of exactly computing each point's exact density for use in classification, tKDC iteratively computes density bounds and short-circuits density computation as soon as bounds are either higher or lower than the target classification threshold. On a wide range of dataset sizes and dimensions, tKDC demonstrates empirical speedups of up to 1000x over alternatives.
THE SMART CAR PROJECT: A CASE STUDY IN COMPUTER-MEDIATED CONTROL

Currently successful execution of many human-in-the-loop manipulation tasks directly depends on the operator’s skill or a programmer’s knowledge of the presumed environment in which the task will be performed. Computer mediation of human inputs can augment this process to: (i) permit easy and rapid incorporation of local sensory information to augment performance, (ii) provide variable performance (precisionand power-) assist for output motions/forces, and (iii) hierarchical distribution of control and graceful degradation. Such mediated control has enormous potential to both reduce operator error and permit incorporation of greater autonomy into human/robot interaction. We chose to examine two sets of tasks to help enhance a remote operator’s performance: one involving precision in setting the forward velocity in the presence of variable loads/disturbances and the other improves safety by assisting the operator to avoid obstacles by carefully mediating the operator’s joystick inputs. The overall goal is to endow a set of local reflexes that use local sensory information to override the user’s input in order to enhance security, safety, and performance. In particular, we implement and evaluate the paradigm of mediated control for remotely driving a mobile robot system (a 1/10 scale remote control truck equipped with Basic Stamp 2 microprocessors) that will serve as our scaled inexpensive testbed. We discuss various aspects of the design and implementation of the Smart Car mobile platform. Beginning with the process of selection of the mechanical platform, we will discuss our motivation and reasoning for making several of the design choices necessary to create the testbed in the subsequent sections. In addition, we discuss the issues pertaining to implementation of two controllers. One for obstacle detection and the other for Wheel RPM PID control. We present the motivation and detail of the actual implementation. We use commercial off the shelf (COTS) hardware and integrated the whole system. Validation and calibration is a very critical step in the whole process. While an ad-hoc solution approach would also suffice for general demonstration purposes, we are interested in using the Smart Car as a scaled testbed. In particular, we are also interested in obtaining quantitative data and hence we spent considerable time validating our system. We first calibrate the independent subsystems, which include the transmitter/receiver, sensors, and then consider the calibration of the controller’s interaction with the entire system. A test setup with an external reference tachometer was created to validate open and closed loop response of the various controllers. Such mediated control has considerable significance and application in a wide range of applications ranging from “Smart Highway Systems” to semiautonomous exploratory rovers.
Effectiveness of treatment with a brace in girls who have adolescent idiopathic scoliosis. A prospective, controlled study based on data from the Brace Study of the Scoliosis Research Society.

In a prospective study by the Scoliosis Research Society, 286 girls who had adolescent idiopathic scoliosis, a thoracic or thoracolumbar curve of 25 to 35 degrees, and a mean age of twelve years and seven months (range, ten to fifteen years) were followed to determine the effect of treatment with observation only (129 patients), an underarm plastic brace (111 patients), and nighttime surface electrical stimulation (forty-six patients). Thirty-nine patients were lost to follow-up, leaving 247 (86 per cent) who were followed until maturity or who were dropped from the study because of failure of the assigned treatment. The end point of failure of treatment was defined as an increase in the curve of at least 6 degrees, from the time of the first roentgenogram, on two consecutive roentgenograms. As determined with use of this end point, treatment with a brace failed in seventeen of the 111 patients; observation only, in fifty-eight of the 129 patients; and electrical stimulation, in twenty-two of the forty-six patients. According to survivorship analysis, treatment with a brace was associated with a success rate of 74 per cent (95 per cent confidence interval, 52 to 84) at four years; observation only, with a success rate of 34 per cent (95 per cent confidence interval, 16 to 49); and electrical stimulation, with a success rate of 33 per cent (95 per cent confidence interval, 12 to 60).(ABSTRACT TRUNCATED AT 250 WORDS)
Natural Resources and Economic Growth : What Is the Connection ?

The structure of recent models of the relationship between natural resource abundance or intensity and economic growth is nearly always the same. An abundance of or heavy dependence on natural resources is taken to influence some variable or mechanism “X” which impedes growth. An important challenge for economic growth theorists and empirical workers in the field is to identify and map these intermediate variables and mechanisms. To date, four main channels of transmission from natural resource abundance or intensity to slow economic growth have been suggested in the literature. As we shall see, these channels can be described as crowding out: natural capital, it will be argued, tends to crowd out other types of capital and thereby inhibit economic growth.
Separation of mAbs molecular variants by analytical hydrophobic interaction chromatography HPLC: overview and applications

Hydrophobic interaction chromatography-high performance liquid chromatography (HIC-HPLC) is a powerful analytical method used for the separation of molecular variants of therapeutic proteins. The method has been employed for monitoring various post-translational modifications, including proteolytic fragments and domain misfolding in etanercept (Enbrel®); tryptophan oxidation, aspartic acid isomerization, the formation of cyclic imide, and α amidated carboxy terminus in recombinant therapeutic monoclonal antibodies; and carboxy terminal heterogeneity and serine fucosylation in Fc and Fab fragments. HIC-HPLC is also a powerful analytical technique for the analysis of antibody-drug conjugates. Most current analytical columns, methods, and applications are described, and critical method parameters and suitability for operation in regulated environment are discussed, in this review.
CANCER IMMUNOLOGY AT THE CROSSROADS : COMPLEMENTARY THERAPEUTICMODALITIES 210 TemperatureMatters ! AndWhy It Should Matter to Tumor Immunologists

Using B cells from patients with chronic lymphocytic leukemia (CLL), Nakahara and colleagues have produced a lamprey monoclonal antibody with CLL idiotope specificity that can be used for early detection of leukemia recurrence. Lamprey antibodies can be generated rapidly and offer a complementary approach to the use of classical Ig-based anti-idiotope antibodies in the monitoring and management of patients with CLL.
The microbiome and disorders of the central nervous system

Alterations of the gut microbiota have been associated with stress-related disorders including depression and anxiety and irritable bowel syndrome (IBS). More recently, researchers have started investigating the implication of perturbation of the microbiota composition in neurodevelopmental disorders including autism spectrum disorders and Attention-Deficit Hypersensitivity Disorder (ADHD). In this review we will discuss how the microbiota is established and its functions in maintaining health. We also summarize both pre and post-natal factors that shape the developing neonatal microbiota and how they may impact on health outcomes with relevance to disorders of the central nervous system. Finally, we discuss potential therapeutic approaches based on the manipulation of the gut bacterial composition.
Effects of sensory-enhanced yoga on symptoms of combat stress in deployed military personnel.

We examined the effects of sensory-enhanced hatha yoga on symptoms of combat stress in deployed military personnel, compared their anxiety and sensory processing with that of stateside civilians, and identified any correlations between the State-Trait Anxiety Inventory scales and the Adolescent/Adult Sensory Profile quadrants.



Seventy military personnel who were deployed to Iraq participated in a randomized controlled trial. Thirty-five received 3 wk (≥9 sessions) of sensory-enhanced hatha yoga, and 35 did not receive any form of yoga.



Sensory-enhanced hatha yoga was effective in reducing state and trait anxiety, despite normal pretest scores. Treatment participants showed significantly greater improvement than control participants on 16 of 18 mental health and quality-of-life factors. We found positive correlations between all test measures except sensory seeking. Sensory seeking was negatively correlated with all measures except low registration, which was insignificant.



The results support using sensory-enhanced hatha yoga for proactive combat stress management.
A Topological Descriptor of Acoustic Images for Navigation and Mapping

The use of robots in underwater exploration is increasing in the last years. The automation of the monitoring, inspection and underwater maintenance tasks require a good mapping and localization system. One of the key issues of these systems is how to summarize the sensory information in order to recognize an area that has already been visited. This paper proposes a description method of acoustic images acquired by a forward looking sonar (FLS) using a graph of Gaussian probability density function. This structure represents both shape and topological relation. Furthermore, we also presented a method to match the descriptors in a efficient way. We evaluated the method in a real dataset acquired by a underwater vehicle performing autonomous navigation and mapping tasks in a marine environment.
LBANN: livermore big artificial neural network HPC toolkit

Recent successes of deep learning have been largely driven by the ability to train large models on vast amounts of data. We believe that High Performance Computing (HPC) will play an increasingly important role in helping deep learning achieve the next level of innovation fueled by neural network models that are orders of magnitude larger and trained on commensurately more training data. We are targeting the unique capabilities of both current and upcoming HPC systems to train massive neural networks and are developing the Livermore Big Artificial Neural Network (LBANN) toolkit to exploit both model and data parallelism optimized for large scale HPC resources. This paper presents our preliminary results in scaling the size of model that can be trained with the LBANN toolkit.
Between a Block and a Typeface: Designing and Evaluating Hybrid Programming Environments

The last ten years have seen a proliferation of introductory programming environments designed for learners across the K-12 spectrum. These environments include visual block-based tools, text-based languages designed for novices, and, increasingly, hybrid environments that blend features of block-based and text-based programming. This paper presents results from a quasi-experimental study investigating the affordances of a hybrid block/text programming environment relative to comparable block-based and textual versions in an introductory high school computer science class. The analysis reveals the hybrid environment demonstrates characteristics of both ancestors while outperforming the block-based and text-based versions in certain dimensions. This paper contributes to our understanding of the design of introductory programming environments and the design challenge of creating and evaluating novel representations for learning.
Free-space detection using online disparity-supervised color modeling

This work contributes to vision processing for intelligent vehicle applications with an emphasis on Advanced Driver Assistance Systems (ADAS). A key issue for ADAS is the robust and efficient detection of free drivable space in front of the vehicle. To this end, we propose a stixel-based probabilistic color-segmentation algorithm to distinguish the ground surface from obstacles in traffic scenes. Our system learns color appearance models for free-space and obstacle classes in an online and self-supervised fashion. To this end, it applies a disparity-based segmentation, which can run in the background of the critical system path and at a lower frame rate than the color-based algorithm. This strategy enables an algorithm without a real-time disparity estimate. As a consequence, the current road scene can be analyzed without the extra latency of disparity estimation. This feature translates into a reduced response time from data acquisition to data analysis, which is a critical property for high-speed ADAS. Our evaluation over different color modeling strategies on publicly available data shows that the color-based analysis can achieve similar (77.6% vs. 77.3% correct) or even better results (4.3% less missed obstacle-area) in difficult imaging conditions, compared to a state-of-the-art disparity-only method.
SAR Image Classification via Deep Recurrent Encoding Neural Networks

Synthetic aperture radar (SAR) image classification is a fundamental process for SAR image understanding and interpretation. With the advancement of imaging techniques, it permits to produce higher resolution SAR data and extend data amount. Therefore, intelligent algorithms for high-resolution SAR image classification are demanded. Inspired by deep learning technology, an end-to-end classification model from the original SAR image to final classification map is developed to automatically extract features and conduct classification, which is named deep recurrent encoding neural networks (DRENNs). In our proposed framework, a spatial feature learning network based on long–short-term memory (LSTM) is developed to extract contextual dependencies of SAR images, where 2-D image patches are transformed into 1-D sequences and imported into LSTM to learn the latent spatial correlations. After LSTM, nonnegative and Fisher constrained autoencoders (NFCAEs) are proposed to improve the discrimination of features and conduct final classification, where nonnegative constraint and Fisher constraint are developed in each autoencoder to restrict the training of the network. The whole DRENN not only combines the spatial feature learning power of LSTM but also utilizes the discriminative representation ability of our NFCAE to improve the classification performance. The experimental results tested on three SAR images demonstrate that the proposed DRENN is able to learn effective feature representations from SAR images and produce competitive classification accuracies to other related approaches.
EFNS guidelines on diagnosis and management of neuromyelitis optica.

Neuromyelitis optica (NMO) or Devic's disease is a rare inflammatory and demyelinating autoimmune disorder of the central nervous system (CNS) characterized by recurrent attacks of optic neuritis (ON) and longitudinally extensive transverse myelitis (LETM), which is distinct from multiple sclerosis (MS). The guidelines are designed to provide guidance for best clinical practice based on the current state of clinical and scientific knowledge.


SEARCH STRATEGY
Evidence for this guideline was collected by searches for original articles, case reports and meta-analyses in the MEDLINE and Cochrane databases. In addition, clinical practice guidelines of professional neurological and rheumatological organizations were studied.



Different diagnostic criteria for NMO diagnosis [Wingerchuk et al. Revised NMO criteria, 2006 and Miller et al. National Multiple Sclerosis Society (NMSS) task force criteria, 2008] and features potentially indicative of NMO facilitate the diagnosis. In addition, guidance for the work-up and diagnosis of spatially limited NMO spectrum disorders is provided by the task force. Due to lack of studies fulfilling requirement for the highest levels of evidence, the task force suggests concepts for treatment of acute exacerbations and attack prevention based on expert opinion.



Studies on diagnosis and management of NMO fulfilling requirements for the highest levels of evidence (class I-III rating) are limited, and diagnostic and therapeutic concepts based on expert opinion and consensus of the task force members were assembled for this guideline.