nils-herrmann/clean_scirepeval huggingface dataset:
Acupuncture modulates resting state connectivity in default and sensorimotor brain networks

Previous studies have defined low-frequency, spatially consistent networks in resting fMRI data which may reflect functional connectivity. We sought to explore how a complex somatosensory stimulation, acupuncture, influences intrinsic connectivity in two of these networks: the default mode network (DMN) and sensorimotor network (SMN). We analyzed resting fMRI data taken before and after verum and sham acupuncture. Electrocardiography data were used to infer autonomic modulation through measures of heart rate variability (HRV). Probabilistic independent component analysis was used to separate resting fMRI data into DMN and SMN components. Following verum, but not sham, acupuncture there was increased DMN connectivity with pain (anterior cingulate cortex (ACC), periaqueductal gray), affective (amygdala, ACC), and memory (hippocampal formation, middle temporal gyrus) related brain regions. Furthermore, increased DMN connectivity with the hippocampal formation, a region known to support memory and interconnected with autonomic brain regions, was negatively correlated with acupuncture-induced increase in a sympathetic related HRV metric (LFu), and positively correlated with a parasympathetic related metric (HFu). Following verum, but not sham, acupuncture there was also increased SMN connectivity with pain-related brain regions (ACC, cerebellum). We attribute differences between verum and sham acupuncture to more varied and stronger sensations evoked by verum acupuncture. Our results demonstrate for the first time that acupuncture can enhance the post-stimulation spatial extent of resting brain networks to include anti-nociceptive, memory, and affective brain regions. This modulation and sympathovagal response may relate to acupuncture analgesia and other potential therapeutic effects.
Small footprint implementation of dual-microphone delay-and-sum beamforming for in-car speech enhancement

For effective speech processing in an automotive environment, speech enhancement is necessary due to significant levels of background noise. In this paper, we present a cost effective small footprint implementation of one particular speech enhancement technique: dual microphone delay-and-sum beamforming. In order to save resources, the implementation utilizes the overlapping frame property used in speech processing systems. The implementation also exhibits a simple interconnection structure leading to even greater resource saving. Experiment results show that the proposed design can produce enhanced output very close to that generated by a theoretical (floating-point) model while only requiring a modest hardware resource usage.
SENSOR-LESS CONTROL OF A PERMANENT MAGNET SYNCHRONOUS MOTOR USING A NOVEL FOUR SWITCH THREE PHASE INVERTER AND EXTENDED KALMAN FILTER

Department of Electrical Engineering, University of Engineering and Technology, Lahore, Pakistan Contact: : trueusama@gmail.com mustafeezulhassan@yahoo.com abdulrahman@uet.edu.pk  This paper presents the sensor-less control of a PMSM using extended Kalman Filter and a four switch three phase inverter implemented using FPGA Spartan 3-e. The system was modeled in Matlab/Simulink® and verified using experimental setup. System-Generator along with Xilinx® was used to develop control on Spartan 3-e plat form. The dynamic model of permanent magnet synchronous motor simulated in Matlab/Simulink® environment was validated and used to extract parameters for the design of extended Kalman filter. The EKF was tuned to estimate the speed and rotor position and results have been presented to ensure the performance of estimation of EKF. The estimation using EKF has reduced the parametric variation effect on speed and rotor position. The estimated parameters were used to implement the speed and current loops for vector control of PMSM using space vector pulse width modulation based four switch three phase inverter. The authentic working of this four switch three phase inverter was also checked against the usual six switch inverter. The reduction of power switches by two reduced the initial cost, switching losses and made inverter configuration compact. The complete System Generator model has been presented along with the result to demonstrate the performance of the PMSM drive using EKF based estimation on FPGA plate form.
Development of a continuum robot using pneumatic artificial muscles

This paper presents the design concept of an intrinsic spatial continuum robot, of which arm segments are actuated by pneumatic artificial muscles. Since pneumatic artificial muscles supported the arm segments of the robot instead of a passive spring, the developed continuum robot was not only capable of following a given trajectory, but also varying its stiffness according to given external environment. Experiment results revealed that the proposed continuum robot showed versatile movements by controlling the pressure of supply air to pneumatic artificial muscles, and the well-coordinated pattern matching scheme based on low-cost webcams gave good performance in estimating the position and orientation of the end-effector of the spatial continuum robot.
Signal Recovery by Proximal Forward-Backward Splitting

We show that various inverse problems in signal recovery can be formulated as the generic problem of minimizing the sum of two convex functions with certain regularity properties. This formulation makes it possible to derive existence, uniqueness, characterization, and stability results in a unified and standardized fashion for a large class of apparently disparate problems. Recent results on monotone operator splitting methods are applied to establish the convergence of a forward-backward algorithm to solve the generic problem. In turn, we recover, extend, and provide a simplified analysis for a variety of existing iterative methods. Applications to geometry/texture image decomposition schemes are also discussed. A novelty of our framework is to use extensively the notion of a proximity operator, which was introduced by Moreau in the 1960s.
Automatic Extraction of Acronym-meaning Pairs from MEDLINE Databases

Acronyms are widely used in biomedical and other technical texts. Understanding their meaning constitutes an important problem in the automatic extraction and mining of information from text. Here we present a system called ACROMED that is part of a set of Information Extraction tools designed for processing and extracting information from abstracts in the Medline database. In this paper, we present the results of two strategies for finding the long forms for acronyms in biomedical texts. These strategies differ from previous automated acronym extraction methods by being tuned to the complex phrase structures of the biomedical lexicon and by incorporating shallow parsing of the text into the acronym recognition algorithm. The performance of our system was tested with several data sets obtaining a performance of 72 % recall with 97 % precision. These results are found to be better for biomedical texts than the performance of other acronym extraction systems designed for unrestricted text.
WiFi RSS fingerprinting indoor localization for mobile devices

While WiFi-based indoor localization is attractive, the need for a significant degree of pre-deployment effort is a key challenge. In this paper, indoor localization with no pre-deployment effort in an indoor space, such as an office building corridor, with WiFi coverage but no a priori knowledge of the placement of the access points(APs) is implemented for mobile devices. WiFi Received Signal Strength(RSS) in the considered environment is used to build radio maps using WiFi fingerprinting approach. An offline RSS fingerprint of the environment is compared with an online RSS measurement to estimate the location of a user. Two architectures are developed based on this localization algorithm. The first one involves a client-server approach where the localization algorithm runs on the server whereas the second one is a standalone architecture and the algorithm runs on the SD card of the mobile device. Experimental results in the considered environment validate the approach for both architectures.
Retinal Vessel Segmentation Based on Conditional Deep Convolutional Generative Adversarial Networks

血管作为血液循环流动的管道,遍布人体的 各个部位,尤其是眼底中含有大量的动脉血管, 是人身体内唯独采用无损伤手段就能够直接观察 到的较深层次的微血管系统,为医生提供了有关 眼睛状况和一般系统状态的丰富信息。 眼科医 生可以检测到高血压和糖尿病引起的全身血管负 荷增加以及视网膜血管疾病如视网膜静脉阻塞和 视网膜动脉阻塞等血管结构异常的早期征兆,血 管和血管系统引起的疾病会造成患者失明。随着 技术的发展,探索一种自动化方法分割视网膜血 管已经被广泛研究。从而辅助医师诊断、分析患 者病情,对眼底疾病做出尽早的预防和诊治,可 有效避免绝病变所引发的视觉损失。 目前在国内外的血管分割算法有匹配滤波器 法[1]、多阈值的血管检测[2]、以形态学为基础技 基于条件深度卷积生成对抗网络的视网膜血管分割 蒋芸;谭宁 (西北师范大学 计算机科学与工程学院,兰州 730000) 摘 要 视网膜血管的分割帮助医生对眼底疾病进行诊断有着重要的意义。但现有方法对视网膜血管的分割存在着 各种问题,例如对血管分割不足,抗噪声干扰能力弱,对病灶敏感等。针对现有血管分割方法的缺陷,本文提出使 用条件深度卷积生成对抗网络的方法对视网膜血管进行分割。我们主要对生成器的网络结构进行了改进,在卷积层 引入残差模块进行差值学习使得网络结构对输出的改变变得敏感,从而更好的对生成器的权重进行调整。为了降低 参数数目和计算,在使用大卷积核之前使用小卷积对输入特征图的通道数进行减半处理。通过使用跳远连接将卷积 层的输出与反卷积层的输出进行连接从而避免低级信息共享。通过在DRIVE和STARE数据集上对本文的方法进行 了验证,其分割准确率分别为96.08%、97.71%,敏感度分别达到了82.74%、85.34%,F-measure分别达到了82.08% 和85.02%,灵敏度比R2U-Net的灵敏度分别高了4.82%,2.4%。
Indoor deployment of low-power wide area networks (LPWAN): A LoRaWAN case study

The last decade saw the emergence of the Internet of Things (IoT) paradigm, which aims to connect any object to the Internet. In this context, a new type of wireless communication network emerged known as Low-Power Wire-Area Network (LPWAN). By contrast to well-known short range and multi-hop wireless networks, LPWAN networks allow long range communications at a low bit rate. Furthermore, LPWAN networks are considered to be integrated into 5G. Among LPWAN networks, the LoRaWAN technology gains more and more interest from the research and industrial communities. In this article, we have led a thorough experimental performance evaluation of LoRaWAN in an indoor environment. From this study, we quantify the limits of this technology and expose the merits of using LoRaWAN for IoT communications in the context of 5G.
Conductive polymers: towards a smart biomaterial for tissue engineering.

Developing stimulus-responsive biomaterials with easy-to-tailor properties is a highly desired goal of the tissue engineering community. A novel type of electroactive biomaterial, the conductive polymer, promises to become one such material. Conductive polymers are already used in fuel cells, computer displays and microsurgical tools, and are now finding applications in the field of biomaterials. These versatile polymers can be synthesised alone, as hydrogels, combined into composites or electrospun into microfibres. They can be created to be biocompatible and biodegradable. Their physical properties can easily be optimized for a specific application through binding biologically important molecules into the polymer using one of the many available methods for their functionalization. Their conductive nature allows cells or tissue cultured upon them to be stimulated, the polymers' own physical properties to be influenced post-synthesis and the drugs bound in them released, through the application of an electrical signal. It is thus little wonder that these polymers are becoming very important materials for biosensors, neural implants, drug delivery devices and tissue engineering scaffolds. Focusing mainly on polypyrrole, polyaniline and poly(3,4-ethylenedioxythiophene), we review conductive polymers from the perspective of tissue engineering. The basic properties of conductive polymers, their chemical and electrochemical synthesis, the phenomena underlying their conductivity and the ways to tailor their properties (functionalization, composites, etc.) are discussed.
Damage in the anca-associated vasculitides: long-term data from the European vasculitis study group (EUVAS) therapeutic trials.

To describe short-term (up to 12 months) and long-term (up to 7 years) damage in patients with newly diagnosed antineutrophil-cytoplasm antibody-associated vasculitis (AAV).



Data were combined from six European Vasculitis Study group trials (n=735). Long-term follow-up (LTFU) data available for patients from four trials (n=535). Damage accrued was quantified by the Vasculitis Damage Index (VDI). Sixteen damage items were defined a priori as being potentially treatment-related.



VDI data were available for 629 of 735 patients (85.6%) at baseline, at which time 217/629 (34.5%) had ≥1 item of damage and 32 (5.1%) ≥5 items, reflecting disease manifestations prior to diagnosis and trial enrolment. LTFU data were available for 467/535 (87.3%) at a mean of 7.3 years postdiagnosis. 302/535 patients (56.4%) had VDI data at LTFU, with 104/302 (34.4%) having ≥5 items and only 24 (7.9%) no items of damage. At 6 months and LTFU, the most frequent items were proteinuria, impaired glomerular filtration rate, hypertension, nasal crusting, hearing loss and peripheral neuropathy. The frequency of damage, including potentially treatment-related damage, rose over time (p<0.01). At LTFU, the most commonly reported items of treatment-related damage were hypertension (41.5%; 95% CI 35.6 to 47.4%), osteoporosis (14.1%; 9.9 to 18.2%), malignancy (12.6%; 8.6 to 16.6%), and diabetes (10.4%; 6.7 to 14.0%).



In AAV, renal, otolaryngological and treatment-related (cardiovascular, disease, diabetes, osteoporosis and malignancy) damage increases over time, with around one-third of patients having ≥5 items of damage at a mean of 7 years postdiagnosis.
An Image Matching Algorithm Based on SIFT and Improved LTP

SIFT is one of the most robust and widely used image matching algorithms based on local features. But the key-points descriptor of SIFT algorithm have 128 dimensions. Aiming to the problem of its high dimension and complexity, a novel image matching algorithm is proposed. The descriptors of SIFT key-points are constructed by the rotation invariant LTP, city-block distance is also employed to reduce calculation of key-points matching. The experiment is achieved through different lighting, blur changes and rotation of images, the results show that this method can reduce the processing time and raise image matching efficiency.
ReBudget: Trading Off Efficiency vs. Fairness in Market-Based Multicore Resource Allocation via Runtime Budget Reassignment

Efficiently allocating shared resources in computer systems is critical to optimizing execution. Recently, a number of market-based solutions have been proposed to attack this problem. Some of them provide provable theoretical bounds to efficiency and/or fairness losses under market equilibrium. However, they are limited to markets with potentially important constraints, such as enforcing equal budget for all players, or curve-fitting players' utility into a specific function type. Moreover, they do not generally provide an intuitive "knob" to control efficiency vs. fairness. In this paper, we introduce two new metrics, Market Utility Range (MUR) and Market Budget Range (MBR), through which we provide for the first time theoretical bounds on efficiency and fairness of market equilibria under arbitrary budget assignments. We leverage this result and propose ReBudget, an iterative budget re-assignment algorithm that can be used to control efficiency vs. fairness at run-time. We apply our algorithm to a multi-resource allocation problem in multicore chips. Our evaluation using detailed execution-driven simulations shows that our budget re-assignment technique is intuitive, effective, and efficient.
CIRCULAR POLARIZATION DUAL FEED MICROSTRIP PATCH ANTENNA WITH 3 dB HYBRID COUPLER FOR WLAN ( 2 . 4 GHZ

Microstrip patch antennas represent one family of compact antennas that offer a conformal nature and the capability of ready integration with communication system’s printed circuitry. In this project, a 2.4 GHz circular polarization microstrip antenna is designed, constructed and measured. The selected microstrip antenna is a dual –fed circular polarized microstrip antenna. The antenna consists of square patch and 3 dB hybrid coupler. The dual – fed circular polarized microstrip antenna is etched on a FR4 with dielectric substrate of 4.6 with the height of 1.6 mm. Circular polarization is obtained when two orthogonal modes are equally excited with 90° phase difference between them. Circular polarization is important because regardless of the receiver orientation, it will always be receiving a component of the signal. This is due to the resulting wave having an angular variation.
Cloud-assisted industrial cyber-physical systems: An insight

The development of industrialization and information communication technology (ICT) has deeply changed our way of life. In particular, with the emerging theory of ‘‘Industry 4.0”, the integration of cloud technologies and industrial cyber-physical systems (ICPS) becomes increasingly important, as this will greatly improve the manufacturing chain and business services. In this paper, we first describe the development and character of ICPS. ICPS will inevitably play an important role in manufacturing, sales, and logistics. With the support of the cloud, ICPS development will impact value creation, business models, downstream services, and work organization. Then, we present a service-oriented ICPS model. With the support of the cloud, infrastructure platform and service application, ICPS will promote the manufacturing efficiency, increase quality of production, enable a sustainable industrial system and more environmentally friendly businesses. Thirdly, we focus on some key enabling technologies, which are critical in supporting smart factories. These key enabling technologies will also help companies to realize high quality, high output, and low cost. Finally, we talk about some challenges of ICPS implementation and the future work.
Deep Learning Human Mind for Automated Visual Classification

What if we could effectively read the mind and transfer human visual capabilities to computer vision methods? In this paper, we aim at addressing this question by developing the first visual object classifier driven by human brain signals. In particular, we employ EEG data evoked by visual object stimuli combined with Recurrent Neural Networks (RNN) to learn a discriminative brain activity manifold of visual categories in a reading the mind effort. Afterward, we transfer the learned capabilities to machines by training a Convolutional Neural Network (CNN)&#x2013;based regressor to project images onto the learned manifold, thus allowing machines to employ human brain&#x2013;based features for automated visual classification. We use a 128-channel EEG with active electrodes to record brain activity of several subjects while looking at images of 40 ImageNet object classes. The proposed RNN-based approach for discriminating object classes using brain signals reaches an average accuracy of about 83%, which greatly outperforms existing methods attempting to learn EEG visual object representations. As for automated object categorization, our human brain&#x2013;driven approach obtains competitive performance, comparable to those achieved by powerful CNN models and it is also able to generalize over different visual datasets.
Automatic Array Alignment in Data-Parallel Programs

Data-parallel languages like Fortran 90 express parallelism in the form of operations on data aggregates such as arrays. Misalignment of the operands of an array operation can reduce program performance on a distributed-memory parallel machine by requiring nonlocal data accesses. Determining array alignments that reduce communication is therefore a key issue in compiling such languages.
We present a framework for the automatic determination of array alignments in data-parallel languages such as Fortran 90. Our language model handles array sectioning, reductions, spreads, transpositions, and masked operations. We decompose alignment functions into three constituents: axis, stride, and offset. For each of these subproblems, we show how to solve the alignment problem for a basic block of code, possibly containing common subexpressions. Alignments are generated for all array objects in the code, both named program variables and intermediate results. The alignments obtained by our algorithms are more general than those provided by the “owner-computes” rule. Finally, we present some ideas for dealing with control flow, replication, and dynamic alignments that depend on loop induction variables.
Capability maturity model, version 1.1

The capability maturity model (CMM), developed to present sets of recommended practices in a number of key process areas that have been shown to enhance software-development and maintenance capability, is discussed. The CMM was designed to help developers select process-improvement strategies by determining their current process maturity and identifying the issues most critical to improving their software quality and process. The initial release of the CMM, version 1.0, was reviewed and used by the software community during 1991 and 1992. A workshop on CMM 1.0, held in April 1992, was attended by about 200 software professionals. The current version of the CMM is the result of the feedback from that workshop and ongoing feedback from the software community. The technical report that describes version 1.1. is summarised.<<ETX>>
Reconfigurable antennas in cognitive radio that can think for themselves?

This paper discusses the use of reconfigurable antennas in cognitive radio. Most of the emphasis on cognitive radio so far has been in the area of spectral estimation and signal classification. In this paper we show that once a cognitive device manages to learn the RF environment (cognition part), from past observations and decisions using machine learning techniques, we can use the collected data to train reconfigurable antennas to adapt to any change in the RF environment.
Minimal Cost Attribute Reduction through Backtracking

Test costs and misclassification costs are two most important types in cost-sensitive learning. In decision systems with both costs, there is a tradeoff between them while building a classifier. Generally, with more attributes selected and more information available, the test cost increases, and the misclassification cost decreases. We shall deliberately select an attribute subset such that the total cost is minimal. Existing decision tree approaches deal with this issue from a local perspective. They benefit from immediately available test results, therefore objects falling into different branches may experience different tests. In this paper, we consider the situation where tests have delayed results. Since we need to choose a test set for all objects, the attribute reduction problem is defined from a global perspective. We propose a backtrack algorithm with three pruning techniques to find a minimal cost reduct. Experimental results indicate that the pruning techniques are effective, and the algorithm is efficient on a medium sized dataset Mushroom.
Chip-level and board-level CDM ESD tests on IC products

The electrostatic discharge (ESD) transient currents and failure analysis (FA) between chip-level and board-level charged-device-model (CDM) ESD tests are investigated in this work. The discharging current waveforms of three different printed circuit boards (PCBs) are characterized first. Then, the chip-level and board-level CDM ESD tests are performed to an ESD-protected dummy NMOS and a high-speed receiver front-end circuit, respectively. Scanning electron microscope (SEM) failure pictures show that the board-level CDM ESD test causes much severer failure than that caused by the chip-level CDM ESD test.