nils-herrmann/clean_scirepeval huggingface dataset:
The recognition of multi-finger prehensile postures using LDA

The prehensile hand gestures play an important role in daily living for seizing or holding subjects stably. In order to realize the accurate recognition of eight prehensile hand gestures with a minimal number of electrodes, an off-line myoelectric control system with only two electrodes is developed. We choose Mean Absolute Value, Variance, the fourth-order Autoregressive Coefficient, Zero Crossings, Mean Frequency and Middle Frequency as original electromyography feature set and utilize the linear discriminant analysis to reduce the dimension and complete classification. The extent of dimension reduction is inveslectromyography yoelectric control rehensile gestures DA verage method tigated and on the premise of it, the average accuracy can achieve 97.46% in the recognition of six hand gestures. The optimal feature set based on the original feature set is determined to be Mean Absolute Value, Variance, and the fourth-order Autoregressive Coefficient, which yields an average accuracy of 95.94% in the recognition of eight hand gestures. An average method is proposed to improve the accuracy further, resulting in the average accuracy in eight gestures being 98.12% and the best individual accuracy of some hand gestures being 100%.
A roadmap for enterprise system implementation

Enterprise systems are complex and expensive and create dramatic organizational change. Implementing an enterprise system can be the "corporate equivalent of a root canal," a meaningful analogy given that an ES with its single database replaces myriad special-purpose legacy systems that once operated in isolation. An ES, or enterprise resource planning system, has the Herculean task of seamlessly supporting and integrating a full range of business processes, uniting functional islands and making their data visible across the organization in real time. The authors offer guidelines based on five years of observing ES implementations that can help managers circumvent obstacles and control the tensions during and after the project.
The hybrid OmniClimber robot : Wheel based climbing , arm based plane transition , and switchable magnet adhesion

Climbing robots that integrate an articulated arm as their main climbing mechanism can eventually take advantage of their arm for plane transition and thus to operate on 3D structures rather than only climbing planar surfaces. However, they are usually slower than wheel based climbing robots. Within this research we address this problem by integration of a light-weight arm and adhesion mechanism into an omnidirectional wheel based climbing robot, thus forming a hybrid mechanism that is agile in climbing and still able to perform plane transition. A 2DOF (Degree of Freedom) plannar mechanism with 2 linear actuators was designed as a light-weight manipulator for the transition mechanism. Furthermore, we customized and developed actuated switchable magnets both for the robot chassis and also as the adhesion unit of the arm. These units allow us to control the amount of magnetic adhesion force, resulting in better adaptation to different surface characteristics. The adhesion units are safe for climbing applications with a very small power consumption. The conceptual and the detailed design of the mechanisms are presented. The robots were developed and successfully tested on a ferromagnetic structure.
Bit-width reduction and customized register for low cost convolutional neural network accelerator

This paper presents a low area and energy efficient hardware accelerator for the deep convolutional neural networks (CNNs). Based on the multiply-accumulate (MAC) based architecture, three design techniques are proposed to reduce the hardware cost of the convolutional computations. First, to reduce the computational bit-width of convolutions, an adaptive bit-width reduction scheme is proposed based on differential input method. The bit-width reduction approach can reduce the 37 % of operation bit-width with almost ignorable CNN accuracy degradation. Second, it has been found that adapting bi-directional filtering window in CNN accelerator can considerably reduce the energy for data movement with much smaller number of memory accesses. To expedite the bi-directional filtering operations, we also propose a bidirectional first-input-first-output (bi-FIFO). With SRAM bit-cell layout manner, the proposed bi-FIFO facilitates fast data re-distribution with area and energy efficiency. To verify the effectiveness of the proposed techniques, the AlexNet accelerator has been designed. The numerical results show that the proposed adaptive bit-width reduction scheme achieves 25.9% and 47.3% of area and energy savings, respectively. The bi-FIFO based accelerator also achieves 33 % improved processing time.
Neurofeedback: One of today's techniques in psychiatry?

Neurofeedback is a technique that aims to teach a subject to regulate a brain parameter measured by a technical interface to modulate his/her related brain and cognitive activities. However, the use of neurofeedback as a therapeutic tool for psychiatric disorders remains controversial. The aim of this review is to summarize and to comment the level of evidence of electroencephalogram (EEG) neurofeedback and real-time functional magnetic resonance imaging (fMRI) neurofeedback for therapeutic application in psychiatry.



Literature on neurofeedback and mental disorders but also on brain computer interfaces (BCI) used in the field of neurocognitive science has been considered by the group of expert of the Neurofeedback evaluation & training (NExT) section of the French Association of biological psychiatry and neuropsychopharmacology (AFPBN).



Results show a potential efficacy of EEG-neurofeedback in the treatment of attentional-deficit/hyperactivity disorder (ADHD) in children, even if this is still debated. For other mental disorders, there is too limited research to warrant the use of EEG-neurofeedback in clinical practice. Regarding fMRI neurofeedback, the level of evidence remains too weak, for now, to justify clinical use. The literature review highlights various unclear points, such as indications (psychiatric disorders, pathophysiologic rationale), protocols (brain signals targeted, learning characteristics) and techniques (EEG, fMRI, signal processing).



The field of neurofeedback involves psychiatrists, neurophysiologists and researchers in the field of brain computer interfaces. Future studies should determine the criteria for optimizing neurofeedback sessions. A better understanding of the learning processes underpinning neurofeedback could be a key element to develop the use of this technique in clinical practice.
Towards Real-Time Unsupervised Monocular Depth Estimation on CPU

Unsupervised depth estimation from a single image is a very attractive technique with several implications in robotic, autonomous navigation, augmented reality and so on. This topic represents a very challenging task and the advent of deep learning enabled to tackle this problem with excellent results. However, these architectures are extremely deep and complex. Thus, real-time performance can be achieved only by leveraging power-hungry GPUs that do not allow to infer depth maps in application fields characterized by low-power constraints. To tackle this issue, in this paper we propose a novel architecture capable to quickly infer an accurate depth map on a CPU, even of an embedded system, using a pyramid of features extracted from a single input image. Similarly to state-of-the-art, we train our network in an unsupervised manner casting depth estimation as an image reconstruction problem. Extensive experimental results on the KITTI dataset show that compared to the top performing approach our network has similar accuracy but a much lower complexity (about 6% of parameters) enabling to infer a depth map for a KITTI image in about 1.7 s on the Raspberry Pi 3 and at more than 8 Hz on a standard CPU. Moreover, by trading accuracy for efficiency, our network allows to infer maps at about 2 Hz and 40 Hz respectively, still being more accurate than most state-of-the-art slower methods. To the best of our knowledge, it is the first method enabling such performance on CPUs paving the way for effective deployment of unsupervised monocular depth estimation even on embedded systems.
Benzoate, a D-Amino Acid Oxidase Inhibitor, for the Treatment of Early-Phase Alzheimer Disease: A Randomized, Double-Blind, Placebo-Controlled Trial

N-methyl-D-aspartate receptor (NMDAR)-mediated neurotransmission is vital for learning and memory. Hypofunction of NMDAR has been reported to play a role in the pathophysiology of Alzheimer disease (AD), particularly in the early phase. Enhancing NMDAR activation might be a novel treatment approach. One of the methods to enhance NMDAR activity is to raise the levels of NMDA coagonists by blocking their metabolism. This study examined the efficacy and safety of sodium benzoate, a D-amino acid oxidase inhibitor, for the treatment of amnestic mild cognitive impairment and mild AD.



We conducted a randomized, double-blind, placebo-controlled trial in four major medical centers in Taiwan. Sixty patients with amnestic mild cognitive impairment or mild AD were treated with 250-750 mg/day of sodium benzoate or placebo for 24 weeks. Alzheimer's Disease Assessment Scale-cognitive subscale (the primary outcome) and global function (assessed by Clinician Interview Based Impression of Change plus Caregiver Input) were measured every 8 weeks. Additional cognition composite was measured at baseline and endpoint.



Sodium benzoate produced a better improvement than placebo in Alzheimer's Disease Assessment Scale-cognitive subscale (p = .0021, .0116, and .0031 at week 16, week 24, and endpoint, respectively), additional cognition composite (p = .007 at endpoint) and Clinician Interview Based Impression of Change plus Caregiver Input (p = .015, .016, and .012 at week 16, week 24, and endpoint, respectively). Sodium benzoate was well-tolerated without evident side-effects.



Sodium benzoate substantially improved cognitive and overall functions in patients with early-phase AD. The preliminary results show promise for D-amino acid oxidase inhibition as a novel approach for early dementing processes.
Double flap phalloplasty in transgender men: Surgical technique and outcome of pedicled anterolateral thigh flap phalloplasty combined with radial forearm free flap urethral reconstruction

Radial forearm free flap (RFFF) tube-in-tube phalloplasty is the most performed phalloplasty technique worldwide. The conspicuous donor-site scar is a drawback for some transgender men. In search for techniques with less conspicuous donor-sites, we performed a series of one-stage pedicled anterolateral thigh flap (ALT) phalloplasties combined with RFFF urethral reconstruction. In this study, we aim to describe this technique and assess its surgical outcome in a series of transgender men.



Between January 2008 and December 2015, nineteen transgender men (median age 37, range 21-57) underwent pedicled ALT phalloplasty combined with RFFF urethral reconstruction in one stage. The surgical procedure was described. Patient demographics, surgical characteristics, intra- and postoperative complications, hospitalization length, and reoperations were recorded.



The size of the ALT flaps ranged from 12 × 12 to 15 × 13 cm, the size of the RFFFs from 14 × 3 to 17 × 3 cm. Median clinical follow-up was 35 months (range 3-95). Total RFFF failure occurred in two patients, total ALT flap failure in one patient, and partial necrosis of the ALT flap in one patient. Long-term urinary complications occurred in 10 (53%) patients, of which 9 concerned urethral strictures.



In experienced hands, one-stage pedicled ALT phalloplasty combined with RFFF urethral reconstruction is a feasible alternative surgical option in eligible transgender men, who desire a less conspicuous forearm scar. Possible drawbacks comprise flap-related complications, difficult inner flap monitoring and urethral complications.
Evidence review to investigate the support for subtypes of children with difficulty processing and integrating sensory information.

We investigated the evidence for subtypes in children with difficulty processing and integrating sensory information. Fifty-seven articles were incorporated into a systematic literature review; only 4 articles provided direct evidence for subtypes. These studies did not provide a comprehensive assessment of all sensory functions and sensory-based motor functions (i.e., praxis) and included different diagnostic groups. Therefore, generalized conclusions about subtypes could not be drawn. The other 53 studies reviewed provided meaningful information about strengths and challenges that children with difficulty processing and integrating sensory information demonstrate, but these studies were limited in scope. A principal theme was the importance of conducting comprehensive assessments of sensory-based functions, including multiple measures of sensory integrative functions such as praxis, sensory modulation, and sensory discrimination in children and adolescents with various clinical disorders. In addition, more consistency in the use of specific assessment tools will allow for synthesis of data across studies.
NIVR: Neuro imaging in virtual reality

Visualization is a critical component of neuroimaging, and how to best view data that is naturally three dimensional is a long standing question in neuroscience. Many approaches, programs, and techniques have been developed specifically for neuroimaging. However, exploration of 3D information through a 2D screen is inherently limited. Many neuroscientific researchers hope that with the recent commercialization and popularization of VR, it can offer the next-step in data visualization and exploration. Neuro Imaging in Virtual Reality (NIVR), is a visualization suite that employs various immersive visualizations to represent neuroimaging information in VR. Some established techniques, such as raymarching volume visualization, are paired with newer techniques, such as near-field rendering, to provide a broad basis of how we can leverage VR to improve visualization and navigation of neuroimaging data. Several of the neuroscientific visualization approaches presented are, to our knowledge, the first of their kind. NIVR offers not only an exploration of neuroscientific data visualization, but also a tool to expose and educate the public regarding recent advancements in the field of neuroimaging. By providing an engaging experience to explore new techniques and discoveries in neuroimaging, we hope to spark scientific interest through a broad audience. Furthermore, neuroimaging offers deep and expansive datasets; a single scan can involve several gigabytes of information. Visualization and exploration of this type of information can be challenging, and real-time exploration of this information in VR even more so. NIVR explores pathways which make this possible, and offers preliminary stereo visualizations of these types of massive data.
Compact Wideband Circularly Polarized Patch Antenna Array Using Metasurface

This letter proposes a compact wideband circularly polarized (CP) antenna array, which is a set of 2 × 2 metasurface-based CP patch antennas fed by a sequential-phase (SP) network. The single element is composed of a truncated corner square patch sandwiched between the ground plane and the metasurface of a lattice of 4 × 4 periodic metal plates. These metasurface-based antennas are incorporated with the SP network of a sequentially rotated series-parallel feed to achieve wideband operation. The radiation pattern and operational bandwidth in the high-frequency region were improved by reducing the spacing between the driven patches while maintaining the structure and overall size of the metasurface. The final design, with an overall size of 64 × 64 × 2.34 mm<sup>3 </sup> (approximately 1.26λ<sub>o</sub> × 1.26λ<sub>o</sub> × 0.046λ<sub>o</sub> at 5.9 GHz), was fabricated and measured. The antenna array has a measured |<inline-formula> <tex-math notation="LaTeX">$S_{11}$</tex-math></inline-formula>| < −10-dB bandwidth of 4.40–8.00 GHz (58.06%), a 3-dB axial ratio bandwidth of 4.75–7.25 GHz (41.67%), a 3-dB gain bandwidth of 4.8–7.0 GHz (37.3%), and a peak gain of 12.08 dBic at 6.0 GHz. In addition, the antenna array yielded a broadside, left-hand CP radiation with a symmetrical profile, low sidelobe level, and high radiation efficiency.
Blood glucose level prediction based on support vector regression using mobile platforms

The correct treatment of diabetes is vital to a patient's health: Staying within defined blood glucose levels prevents dangerous short- and long-term effects on the body. Mobile devices informing patients about their future blood glucose levels could enable them to take counter-measures to prevent hypo or hyper periods. Previous work addressed this challenge by predicting the blood glucose levels using regression models. However, these approaches required a physiological model, representing the human body's response to insulin and glucose intake, or are not directly applicable to mobile platforms (smart phones, tablets). In this paper, we propose an algorithm for mobile platforms to predict blood glucose levels without the need for a physiological model. Using an online software simulator program, we trained a Support Vector Regression (SVR) model and exported the parameter settings to our mobile platform. The prediction accuracy of our mobile platform was evaluated with pre-recorded data of a type 1 diabetes patient. The blood glucose level was predicted with an error of 19 % compared to the true value. Considering the permitted error of commercially used devices of 15 %, our algorithm is the basis for further development of mobile prediction algorithms.
A document-based neural relevance model for effective clinical decision support

Clinical Decision Support (CDS) can be regarded as an information retrieval (IR) task, where medical records are used to retrieve the full-text biomedical articles to satisfy the information needs from physicians, aiming at better medical solutions. Recent attempts have introduced the advances of deep learning by employing neural IR methods for CDS, where, however, only the document-query relationship is modeled, resulting in non-optimal results in that a medial record can barely reflect the information included in a relevant biomedical article which is usually much longer. Therefore, in addition to the document-query relationship, we propose a document-based neural relevance model (DNRM), addressing the mismatch by utilizing the content of relevant articles to complement the medical records. Specifically, our DNRM model evaluates a document relative to a query and to several pseudo relevant documents for the query at the same time, capturing the interactions from both parts with a feed forward network. Experimental results on the standard Text REtrieval Conference (TREC) CDS track dataset confirm the superior performance of the proposed DNRM model.
Predicting future learning from baseline network architecture

Human behavior and cognition result from a complex pattern of interactions between brain regions. The flexible reconfiguration of these patterns enables behavioral adaptation, such as the acquisition of a new motor skill. Yet, the degree to which these reconfigurations depend on the brain's baseline sensorimotor integration is far from understood. Here, we asked whether spontaneous fluctuations in sensorimotor networks at baseline were predictive of individual differences in future learning. We analyzed functional MRI data from 19 participants prior to six weeks of training on a new motor skill. We found that visual-motor connectivity was inversely related to learning rate: sensorimotor autonomy at baseline corresponded to faster learning in the future. Using three additional scans, we found that visual-motor connectivity at baseline is a relatively stable individual trait. These results suggest that individual differences in motor skill learning can be predicted from sensorimotor autonomy at baseline prior to task execution.
Implementation of Block Adaptive Quantizer as a peripheral module for the FPGA-based SAR system

Due to large quantity of data needed for image synthesis in SAR applications, methods of raw signal compression were developed alongside actual imaging systems. Although performance of modern processing units allows on-platform, online image synthesis, data compressor still can be a valuable addition. Since it is no longer necessary part of SAR system, it should be delivered in a flexible, easy to use and low cost form - like low-resources demanding Intellectual Property core. In this paper chosen properties of raw SAR signal and some of compression methods are presented followed by compressor IP core implementation results.
Orthodontics in 3 millennia. Chapter 11: the golden age of orthodontics.

For orthodontists, the post-World War II era was characterized by the introduction of fluoridation, sit-down dentistry, and an upswing in extractions. Postwar prosperity, the baby boom, and increased enlightenment of parents contributed to what was later called the "golden age of orthodontics." The subsequent clamor for more orthodontists led to a proliferation of graduate departments and inauguration of the AAO Preceptorship Program. There was also an increase in mixed-dentition treatment, requiring improved methods of analyzing arch lengths.
StatPal II pH and Blood Gas Analysis System evaluated.

We evaluated a new portable instrument, the PPG StatPal II pH and Blood Gas Analysis System, designed for "point-of-care" measurements of blood gases and pH. Inaccuracy (% of target value) and imprecision (CV%) were assessed by blood tonometry and comparison with a Corning 178. Within-day results for PCO2 inaccuracy and imprecision ranged from 98.2% to 102.9% and 3.3% to 3.9%, respectively; for PO2, these were 95.5% to 102.3% and 2.3% to 3.0%, respectively. Between-day results for PCO2 inaccuracy and imprecision ranged from 99.2% to 99.3% and from 2.9% to 3.2%, respectively; for PO2, the ranges were 96.2% to 98.2% and 2.6% to 3.0%, respectively. Two PCO2 outliers (in 645 samples = 0.3%) were observed. In general, tonometry recovery, measurement stability, and pH bias results for the StatPal II and Corning 178 were comparable. We conclude that the StatPal II performs within acceptable ranges of inaccuracy and imprecision.
Observational pain scales in critically ill adults.

Pain is a common and distressing symptom in critically ill patients. Uncontrolled pain places patients at risk for numerous adverse psychological and physiological consequences, some of which may be life-threatening. A systematic assessment of pain is difficult in intensive care units because of the high percentage of patients who are noncommunicative and unable to self-report pain. Several tools have been developed to identify objective measures of pain, but the best tool has yet to be identified. A comprehensive search on the reliability and validity of observational pain scales indicated that although the Critical-Care Pain Observation Tool was superior to other tools in reliably detecting pain, pain assessment in individuals incapable of spontaneous neuromuscular movements or in patients with concurrent conditions, such as chronic pain or delirium, remains an enigma.
Metabolomics and Metabolic Diseases: Where Do We Stand?

Metabolomics, or the comprehensive profiling of small molecule metabolites in cells, tissues, or whole organisms, has undergone a rapid technological evolution in the past two decades. These advances have led to the application of metabolomics to defining predictive biomarkers for incident cardiometabolic diseases and, increasingly, as a blueprint for understanding those diseases' pathophysiologic mechanisms. Progress in this area and challenges for the future are reviewed here.