nils-herrmann/clean_scirepeval huggingface dataset:
Comparison of peri-articular liposomal bupivacaine and standard bupivacaine for postsurgical analgesia in total knee arthroplasty: A systematic review and meta-analysis.

This meta-analysis aimed to compare the efficacy and safety of intraoperative peri-articular liposomal bupivacaine and standard bupivacaine in patients undergoing total knee arthroplasty.



A systematic search was performed in Medline (1966-2016.9), PubMed (1966-2016.9), Embase (1980-2016.9), ScienceDirect (1985-2016.9) and the Cochrane Library. Only high-quality studies were selected. Meta-analysis was performed using Stata 11.0 software.



Three randomized controlled trials (RCTs) and two non-randomized controlled trials (Non-RCTs), including 1214 patients met the inclusion criteria. The present meta-analysis indicated that there were significant differences between groups in terms of visual analogue scale (VAS) score at 24 h (SMD = -0.241, 95% CI: -0.374 to -0.108, P = 0.000), VAS score at 48 h (SMD = -0.124, 95% CI: -0.256 to 0.009, P = 0.0068), morphine equivalent consumption on POD 1 (SMD = -0.275, 95% CI: -0.398 to -0.153, P = 0.000) and incidence of nausea (RD = 0.038, 95% CI: 0.001 to 0.074, P = 0.042) and vomiting (RD = 0.38, 95% CI: 0.003 to 0.072, P = 0.032).



Compared to standard bupivacaine, intraoperative peri-articular liposomal bupivacaine infiltration promotes superior pain relief and less morphine consumption after total knee arthroplasty. In addition, there were fewer side effects associated with liposomal bupivacaine infiltration.
Towards a List of Heuristics to Evaluate Smartphone Apps Targeted at Older Adults: A Study with Apps that Aim at Promoting Health and Well-Being

This paper presents a list of heuristics to evaluate smartphone apps for older adults. It further verifies the usefulness of the proposed list through a study performed with two groups of five expert evaluators, who inspected two popular health and fitness smartphone apps -- Nike+ and Run keeper -- through heuristic evaluation. Additionally, the evaluators completed a post-evaluation survey to provide feedback to the researchers about the usefulness, strengths and gaps of the heuristics list. The results of the heuristic evaluations and post questionnaires demonstrate both a comprehensive application of the heuristics as well as an overall positive assessment of their quality and potential to identify usability issues of smartphone apps targeted at older adults.
A Survey on Temporal Databases and Data mining

Temporal database is a database which captures and maintains past, present and future data. Conventional databases are not suitable for handling such time varying data. In this context temporal database has gained a significant importance in the field of databases and data mining. The major objective of this research is to perform a detailed survey on temporal databases and the various temporal data mining techniques and explore the various research issues in temporal data mining. We also throw light on the temporal association rules and temporal clustering works carried in literature.
A millimeter wave wideband differential line to waveguide transition using short ended slot line

This paper presents a millimeter wave wideband differential line to waveguide transition using a short ended slot line. The slot line connected in parallel to the rectangular waveguide can effectively compensate the frequency dependence of the susceptance in the waveguide. Thus it is suitable to achieve a wideband characteristic together with a simpler structure. It is experimentally demonstrated that the proposed transitions have the relative bandwidth of 20.2 % with respect to -10 dB reflection, which is a significant wideband characteristic compared with the conventional transition's bandwidth of 11%.
Feature Denoising for Improving Adversarial Robustness

Adversarial attacks to image classification systems present challenges to convolutional networks and opportunities for understanding them. This study suggests that adversarial perturbations on images lead to noise in the features constructed by these networks. Motivated by this observation, we develop new network architectures that increase adversarial robustness by performing feature denoising. Specifically, our networks contain blocks that denoise the features using non-local means or other filters; the entire networks are trained end-to-end. When combined with adversarial training, our feature denoising networks substantially improve the state-of-the-art in adversarial robustness in both white-box and black-box attack settings. On ImageNet, under 10-iteration PGD white-box attacks where prior art has 27.9% accuracy, our method achieves 55.7%; even under extreme 2000-iteration PGD white-box attacks, our method secures 42.6% accuracy. A network based on our method was ranked first in Competition on Adversarial Attacks and Defenses (CAAD) 2018 — it achieved 50.6% classification accuracy on a secret, ImageNet-like test dataset against 48 unknown attackers, surpassing the runner-up approach by ∼10%. Code and models will be made publicly available.
Are Existing Knowledge Transfer Techniques Effective for Deep Learning with Edge Devices?

With the emergence of edge computing paradigm, many applications such as image recognition and augmented reality require to perform machine learning (ML) and artificial intelligence (AI) tasks on edge devices. Most AI and ML models are large and computational-heavy, whereas edge devices are usually equipped with limited computational and storage resources. Such models can be compressed and reduced for deployment on edge devices, but they may loose their capability and not perform well. Recent works used knowledge transfer techniques to transfer information from a large network (termed teacher) to a small one (termed student) in order to improve the performance of the latter. This approach seems to be promising for learning on edge devices, but a thorough investigation on its effectiveness is lacking. This paper provides an extensive study on the performance (in both accuracy and convergence speed) of knowledge transfer, considering different student architectures and different techniques for transferring knowledge from teacher to student. The results show that the performance of KT does vary by architectures and transfer techniques. A good performance improvement is obtained by transferring knowledge from both the intermediate layers and last layer of the teacher to a shallower student. But other architectures and transfer techniques do not fare so well and some of them even lead to negative performance impact.
Combined EEG-Gyroscope-tDCS Brain Machine Interface System for Early Management of Driver Drowsiness

In this paper, we present the design and implementation of a wireless, wearable brain machine interface (BMI) system dedicated to signal sensing and processing for driver drowsiness detection (DDD). Owing to the importance of driver drowsiness and the possibility for brainwaves-based DDD, many electroencephalogram (EEG)-based approaches have been proposed. However, few studies focus on the early detection of driver drowsiness and on the early management of driver drowsiness using a closed-loop algorithm. The reported wireless and wearable BMI system is used for 1) simultaneous EEG and gyroscope-based head movement measurement for the early detection of driver drowsiness and 2) simultaneous EEG and transcranial direct current stimulation (tDCS) for the early management of driver drowsiness. To achieve the purposes of easy-to-use and distraction-free driving, a Bluetooth low-energy module is embedded in this BMI system and used to communicate with a fully wearable consumer device, a smartwatch, which coordinates the work of drowsiness monitoring and brain stimulation with its embedded closed-loop algorithm. The proposed system offers a 128 Hz sampling rate per channel, 12-bit and 16-bit resolution for a single-channel EEG and a three-channel gyroscope, and a maximum 2 mA current for the tDCS. The current consumption of the whole headset system is 56 mA. The battery life of the smartwatch is 9 h. The DDD experimental results show that the proposed system obtained a 93.67% five-level overall accuracy, a 96.15% two-level (alert versus slightly drowsy) accuracy, and maximum 16- to 23-min wakefulness maintenance.
AI and Security Informatics

Since the tragic events of September 11, security research has become critically important for the entire world. Academics in fields such as computational science, information systems, social sciences, engineering, and medicine have been called on to help enhance our ability to fight violence, terrorism, and other crimes. The US 2002 National Strategy for Homeland Security report identified science and technology as the keys to winning this international security war.1 It is widely believed that information technology will play an indispensable role in making the world safer2 by supporting intelligence and knowledge discovery through collecting, processing, analyzing, and utilizing terrorismand crime-related data.
Collective Program Analysis

Popularity of data-driven software engineering has led to an increasing demand on the infrastructures to support efficient execution of tasks that require deeper source code analysis. While task optimization and parallelization are the adopted solutions, other research directions are less explored. We present collective program analysis (CPA), a technique for scaling large scale source code analyses, especially those that make use of control and data flow analysis, by leveraging analysis specific similarity. Analysis specific similarity is about, whether two or more programs can be considered similar for a given analysis. The key idea of collective program analysis is to cluster programs based on analysis specific similarity, such that running the analysis on one candidate in each cluster is sufficient to produce the result for others. For determining analysis specific similarity and clustering analysis-equivalent programs, we use a sparse representation and a canonical labeling scheme. Our evaluation shows that for a variety of source code analyses on a large dataset of programs, substantial reduction in the analysis time can be achieved; on average a 69% reduction when compared to a baseline and on average a 36% reduction when compared to a prior technique. We also found that a large amount of analysis-equivalent programs exists in large datasets.
Septal batten graft to correct cartilaginous deformities in endonasal septoplasty.

To evaluate the usefulness of septal batten grafts to correct cartilaginous septal deformities in endonasal septoplasty.



Retrospective study.



University medical center.



Of 430 patients who underwent endonasal septoplasties from January 2006 to January 2011, 30 received septal batten grafts and were enrolled in the study. Twenty-eight patients were male and 2 were female.



Thirty consecutive patients received septal batten grafts and were followed up for more than 6 months. Patterns of septal deformity, materials used for batten graft, surgical results, symptom improvement, findings of acoustic rhinometry, and surgical complications were investigated.



Among the 30 patients, 5 were revision cases. Most of the deformities were characterized as moderate to severe degrees of curved or angulated deviations of the cartilaginous septum. The batten graft was performed with either septal cartilage (n = 21) or bony septum (n = 9). A straight septum was achieved in 90% of all procedures. Subjective symptoms of nasal obstruction were improved in all patients, as evaluated by the Nasal Obstruction Symptom Evaluation scale. Acoustic rhinometry revealed that after surgery the mean minimal cross-sectional area changed from 0.33 cm² to 0.42 cm² (P = .02) and the nasal volume from 4.71 mL to 6.28 mL (P = .02). There were no major complications, eg, septal perforation or saddle nose, and no revision surgery was needed.



Endonasal septal batten graft is a safe, useful, and effective technique to straighten moderate to severe septal cartilage deformities that are otherwise not correctable via conventional septoplasty techniques.
Genetic features of Mongolian ethnic groups revealed by Y-chromosomal analysis.

About 20 ethnic groups reside in Mongolia. On the basis of genetic and anthropological studies, it is believed that Mongolians have played a pivotal role in the peopling of Central and East Asia. However, the genetic relationships among these ethnic groups have remained obscure, as have their detailed relationships with adjacent populations. We analyzed 16 binary and 17 STR polymorphisms of human Y chromosome in 669 individuals from nine populations, including four indigenous ethnic groups in Mongolia (Khalkh, Uriankhai, Zakhchin, and Khoton). Among these four Mongolian populations, the Khalkh, Uriankhai, and Zakhchin populations showed relatively close genetic affinities to each other and to Siberian populations, while the Khoton population showed a closer relationship to Central Asian populations than to even the other Mongolian populations. These findings suggest that the major Mongolian ethnic groups have a close genetic affinity to populations in northern East Asia, although the genetic link between Mongolia and Central Asia is not negligible.
CubeSat Deployable Ka-Band Mesh Reflector Antenna Development for Earth Science Missions

CubeSats are positioned to play a key role in Earth Science, wherein multiple copies of the same RADAR instrument are launched in desirable formations, allowing for the measurement of atmospheric processes over a short evolutionary timescale. To achieve this goal, such CubeSats require a high-gain antenna (HGA) that fits in a highly constrained volume. This paper presents a novel mesh deployable Ka-band antenna design that folds in a 1.5 U (10 × 10 × 15 cm3) stowage volume suitable for 6 U (10 × 20 × 30 cm3) class CubeSats. Considering all aspects of the deployable mesh reflector antenna including the feed, detailed simulations and measurements show that 42.6-dBi gain and 52% aperture efficiency is achievable at 35.75 GHz. The mechanical deployment mechanism and associated challenges are also described, as they are critical components of a deployable CubeSat antenna. Both solid and mesh prototype antennas have been developed and measurement results show excellent agreement with simulations.
Neural correlates of the psychedelic state as determined by fMRI studies with psilocybin.

Psychedelic drugs have a long history of use in healing ceremonies, but despite renewed interest in their therapeutic potential, we continue to know very little about how they work in the brain. Here we used psilocybin, a classic psychedelic found in magic mushrooms, and a task-free functional MRI (fMRI) protocol designed to capture the transition from normal waking consciousness to the psychedelic state. Arterial spin labeling perfusion and blood-oxygen level-dependent (BOLD) fMRI were used to map cerebral blood flow and changes in venous oxygenation before and after intravenous infusions of placebo and psilocybin. Fifteen healthy volunteers were scanned with arterial spin labeling and a separate 15 with BOLD. As predicted, profound changes in consciousness were observed after psilocybin, but surprisingly, only decreases in cerebral blood flow and BOLD signal were seen, and these were maximal in hub regions, such as the thalamus and anterior and posterior cingulate cortex (ACC and PCC). Decreased activity in the ACC/medial prefrontal cortex (mPFC) was a consistent finding and the magnitude of this decrease predicted the intensity of the subjective effects. Based on these results, a seed-based pharmaco-physiological interaction/functional connectivity analysis was performed using a medial prefrontal seed. Psilocybin caused a significant decrease in the positive coupling between the mPFC and PCC. These results strongly imply that the subjective effects of psychedelic drugs are caused by decreased activity and connectivity in the brain's key connector hubs, enabling a state of unconstrained cognition.
Functional neuroimaging and the neuroanatomy of obsessive-compulsive disorder.

Functional neuroimaging studies have advanced the understanding of the brain mediation of OCD by orbitofrontal-subcortical circuitry, but much is still unknown. Phenotypic heterogeneity could account for many of the inconsistencies among previous neuroimaging studies of OCD. Current studies are seeking to find the neurobiological basis of OCD symptom subtypes and predictors of treatment response. Future studies combining genetics and basic neuroanatomic research with neuroimaging may clarify the cause and pathophysiology of OCD. Although many lines of evidence point to dysfunction of orbitofrontal-subcortical circuitry in patients with OCD, many questions remain unanswered. Some have suggested that orbitofrontal-subcortical hyperactivity in OCD may be the result of abnormal neuroanatomic development of these structures or a failure of pruning of neuronal connections between them, as occurs in normal development, but no postmortem neuroanatomic studies of OCD exist to delineate its pathophysiology. Interventions that directly alter the indirect-direct pathway balance within frontal-subcortical circuits will allow for direct testing of the pathophysiologic hypotheses presented here. The roles of various neurochemical systems in OCD are similarly unclear. Although an abundance of indirect evidence suggests serotonergic abnormalities in patients with OCD, no direct evidence demonstrates what those abnormalities are or whether they are primary or secondary phenomena in patients with OCD. Ongoing studies of 5-HT synthesis in the brains of patients with OCD may shed light on this question.
Non-fragile mixed H∞ and passive synchronization of Markov jump neural networks with mixed time-varying delays and randomly occurring controller gain fluctuation

This paper studies the non-fragile mixed H∞ and passive synchronization problem for Markov jump neural networks. The randomly occurring controller gain fluctuation phenomenon is investigated for non-fragile strategy. Moreover, the mixed time-varying delays composed of discrete and distributed delays are considered. By employing stochastic stability theory, synchronization criteria are developed for the Markov jump neural networks. On the basis of the derived criteria, the non-fragile synchronization controller is designed. Finally, an illustrative example is presented to demonstrate the validity of the control approach.
MRI of fetal GI tract abnormalities

We describe the magnetic resonance (MR) patterns of a variety of fetal gastrointestinal (GI) abnormalities. Thirty-two fetuses between 23 and 38 weeks’ gestation with abnormal appearance of the GI tract by ultrasound underwent MR imaging with T1- and T2-weighted sequences. The MR aspect of intestinal atresia (duodenal atresia, one case; small bowel atresia, nine cases) included dilatation of the bowel loops, accurate assessment of the normal bowel distal to the atresia (except in the patient with multiple atresia and apple-peel syndrome), and micro-rectum with decreased T1 signal (except in the patient with duodenal atresia). Megacystis-microcolon-intestinal hypoperistalsis syndrome (one case) was indicated by an abnormal signal of the entire bowel and an abnormal pattern for the urinary tract. Meconium pseudocysts (two cases) were easily differentiated from enteric cysts (two cases). High anorectal malformations with (two cases) or without (one case) urinary fistula and cloacal malformation (one case) are described and MR findings are discussed. The capability of MR imaging to demonstrate the normal bowel with intraperitoneal anomalies (e.g., congenital diaphragmatic hernia, and sacrococcygeal teratoma) is emphasized. MR imaging is informative in the diagnosis of GI tract abnormalities, especially the severe malformations, with much more accuracy than sonography.
"Shallow Draughts Intoxicate the Brain": Lessons from Cognitive Science for Cognitive Neuropsychology

This article presents a sobering view of the discipline of cognitive neuropsychology as practiced over the last three or four decades. Our judgment is that, although the study of abnormal cognition resulting from brain injury or disease in previously normal adults has produced a catalogue of fascinating and highly selective deficits, it has yielded relatively little advance in understanding how the brain accomplishes its cognitive business. We question the wisdom of the following three "choices" in mainstream cognitive neuropsychology: (a) single-case methodology, (b) dissociation between functions as the most important source of evidence, and (c) a central goal of diagramming the functional architecture of cognition rather than specifying how its components work. These choices may all stem from an excessive commitment to strict and fine-grained modularity. Although different brain regions are undoubtedly specialized for different functions, we argue that parallel and interactive processing is a better assumption about cognitive processing. The essential goal of specifying representations and processes can, we claim, be significantly assisted by computational modeling which, by its very nature, requires such specification.
Brain insulin resistance in type 2 diabetes and Alzheimer disease: concepts and conundrums

Considerable overlap has been identified in the risk factors, comorbidities and putative pathophysiological mechanisms of Alzheimer disease and related dementias (ADRDs) and type 2 diabetes mellitus (T2DM), two of the most pressing epidemics of our time. Much is known about the biology of each condition, but whether T2DM and ADRDs are parallel phenomena arising from coincidental roots in ageing or synergistic diseases linked by vicious pathophysiological cycles remains unclear. Insulin resistance is a core feature of T2DM and is emerging as a potentially important feature of ADRDs. Here, we review key observations and experimental data on insulin signalling in the brain, highlighting its actions in neurons and glia. In addition, we define the concept of 'brain insulin resistance' and review the growing, although still inconsistent, literature concerning cognitive impairment and neuropathological abnormalities in T2DM, obesity and insulin resistance. Lastly, we review evidence of intrinsic brain insulin resistance in ADRDs. By expanding our understanding of the overlapping mechanisms of these conditions, we hope to accelerate the rational development of preventive, disease-modifying and symptomatic treatments for cognitive dysfunction in T2DM and ADRDs alike.
An Ultralow Power Subthreshold CMOS Voltage Reference Without Requiring Resistors or BJTs

This brief presents a novel ultralow power CMOS voltage reference (CVR) with only 4.6-nW power consumption. In the proposed CVR circuit, the proportional-to-absolute-temperature voltage is generated by feeding the leakage current of a zero-<inline-formula> <tex-math notation="LaTeX">$V_{\mathrm {gs}}$ </tex-math></inline-formula> nMOS transistor to two diode-connected nMOS transistors in series, both of which are in subthreshold region; while the complementary-to-absolute-temperature voltage is created by using the body diodes of another nMOS transistor. Consequently, low-power operation can be achieved without requiring resistors or bipolar junction transistors, leading to small chip area consumption. The proposed CVR circuit is fabricated in a standard 0.18-<inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula> CMOS process. Measurement results show that the prototype design is capable of providing a 755 mV typical reference voltage with 34 ppm/°C from −15 °C to 140 °C. Moreover, the typical power consumption is only 4.6 nW at room temperature and the active area is only 0.0598 mm<sup>2</sup>.