nils-herrmann/clean_scirepeval huggingface dataset:
Recent Understandings of Biology, Prophylaxis and Treatment Strategies for Hypertrophic Scars and Keloids

Hypertrophic scars and keloids are fibroproliferative disorders that may arise after any deep cutaneous injury caused by trauma, burns, surgery, etc. Hypertrophic scars and keloids are cosmetically problematic, and in combination with functional problems such as contractures and subjective symptoms including pruritus, these significantly affect patients' quality of life. There have been many studies on hypertrophic scars and keloids; but the mechanisms underlying scar formation have not yet been well established, and prophylactic and treatment strategies remain unsatisfactory. In this review, the authors introduce and summarize classical concepts surrounding wound healing and review recent understandings of the biology, prevention and treatment strategies for hypertrophic scars and keloids.
A diffusion-neural-network for learning from small samples

Neural information processing models largely assume that the patterns for training a neural network are sufficient. Otherwise, there must exist a non-negligible error between the real function and the estimated function from a trained network. To reduce the error, in this paper, we suggest a diffusion-neural-network (DNN) to learn from a small sample consisting of only a few patterns. A DNN with more nodes in the input and layers is trained by using the deriving patterns instead of original patterns. In this paper, we give an example to show how to construct a DNN for recognizing a non-linear function. In our case, the DNN’s error is less than the error of the conventional BP network, about 48%. To substantiate the special case arguments, we also study other two non-linear functions with simulation technology. The results show that the DNN model is very effective in the case where the target function has a strong non-linearity or a given sample is very small.
Spanish Lymphoma Group (GELTAMO) guidelines for the diagnosis, staging, treatment, and follow-up of diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) accounts for approximately 30% of non-Hodgkin lymphoma (NHL) cases in adult series. DLBCL is characterized by marked clinical and biological heterogeneity, encompassing up to 16 distinct clinicopathological entities. While current treatments are effective in 60% to 70% of patients, those who are resistant to treatment continue to die from this disease. An expert panel performed a systematic review of all data on the diagnosis, prognosis, and treatment of DLBCL published in PubMed, EMBASE and MEDLINE up to December 2017. Recommendations were classified in accordance with the Grading of Recommendations Assessment Development and Evaluation (GRADE) framework, and the proposed recommendations incorporated into practical algorithms. Initial discussions between experts began in March 2016, and a final consensus was reached in November 2017. The final document was reviewed by all authors in February 2018 and by the Scientific Committee of the Spanish Lymphoma Group GELTAMO.
INTUITIONISTIC FUZZY GRAPH STRUCTURES

In this paper, we introduce the concept of an intuitionistic fuzzy graph structure (IFGS). We discuss certain notions, including intuitionistic fuzzy Bi-cycles, intuitionistic fuzzy Bi-trees and φ-complement of an intuitionistic fuzzy graph structure with several examples. We also present φ-complement of an intuitionistic fuzzy graph structure along with self-complementary and strong self-complementary intuitionistic fuzzy graph structures.
Modeling skin and proximity effects with reduced realizable RL circuits

On-chip conductors such as clock- and power-distribution networks require accurately modeling skin and proximity effects. Furthermore, to incorporate skin and proximity effects in the existing generic simulation tools such as SPICE, simple-frequency independent-lumped element-circuit models are needed. A rule based RL circuit model is proposed in this paper that is realizable and predicts skin and proximity effects accurately in the frequency range of interest. With this circuit model, wires are characterized by a few parallel branches of resistors and inductors while proximity effect is captured by mutual inductance between inductors in different RL circuits.
IOT based Parking Automation System

Technology has enabled us to imagine beyond our working capacities and think of solutions that can replace the monotonous work with automated machines and systems. This research paper is aimed at making the parking system agile, robust and more convenient for people. Albeit, several parking solutions are available, this system integrates all problems into one single idea that can be permanently embedded as a solution. The system will incorporate different modules like parking availability calculation, proximity estimation and payment service. The system will also guide the vehicle owners to navigate through the parking lot. Moreover, an analysis will be conducted to examine the benefits of the current project and how it can be improved.
Learning Taxonomies of Concepts and not Words using Contextualized Word Representations: A Position Paper

Taxonomies are semantic hierarchies of concepts. One limitation of current taxonomy learning systems is that they define concepts as single words. This position paper argues that contextualized word representations, which recently achieved state-of-the-art results on many competitive NLP tasks, are a promising method to address this limitation. We outline a novel approach for taxonomy learning that (1) defines concepts as synsets, (2) learns density-based approximations of contextualized word representations, and (3) can measure similarity and hypernymy among them.
Small Molecule Inhibitors of BAF; A Promising Family of Compounds in HIV-1 Latency Reversal

Persistence of latently infected cells in presence of Anti-Retroviral Therapy presents the main obstacle to HIV-1 eradication. Much effort is thus placed on identification of compounds capable of HIV-1 latency reversal in order to render infected cells susceptible to viral cytopathic effects and immune clearance. We identified the BAF chromatin remodeling complex as a key player required for maintenance of HIV-1 latency, highlighting its potential as a molecular target for inhibition in latency reversal. Here, we screened a recently identified panel of small molecule inhibitors of BAF (BAFi's) for potential to activate latent HIV-1. Latency reversal was strongly induced by BAFi's Caffeic Acid Phenethyl Ester and Pyrimethamine, two molecules previously characterized for clinical application. BAFi's reversed HIV-1 latency in cell line based latency models, in two ex vivo infected primary cell models of latency, as well as in HIV-1 infected patient's CD4 + T cells, without inducing T cell proliferation or activation. BAFi-induced HIV-1 latency reversal was synergistically enhanced upon PKC pathway activation and HDAC-inhibition. Therefore BAFi's constitute a promising family of molecules for inclusion in therapeutic combinatorial HIV-1 latency reversal.
Crisis and emergency risk communication as an integrative model.

This article describes a model of communication known as crisis and emergency risk communication (CERC). The model is outlined as a merger of many traditional notions of health and risk communication with work in crisis and disaster communication. The specific kinds of communication activities that should be called for at various stages of disaster or crisis development are outlined. Although crises are by definition uncertain, equivocal, and often chaotic situations, the CERC model is presented as a tool health communicators can use to help manage these complex events.
Locating the boundaries of Pareto fronts: A Many-Objective Evolutionary Algorithm Based on Corner Solution Search

In this paper, an evolutionary many-objective optimization algorithm based on corner solution search (MaOEACS) was proposed. MaOEA-CS implicitly contains two phases: the exploitative search for the most important boundary optimal solutions – corner solutions, at the first phase, and the use of angle-based selection  with the explorative search for the extension of PF approximation at the second phase. Due to its high efficiency and robustness to the shapes of PFs, it has won the CEC′2017 Competition on Evolutionary Many-Objective Optimization. In addition, MaOEA-CS has also been applied on two real-world engineering optimization problems with very irregular PFs. The experimental results show that MaOEACS outperforms other six state-of-the-art compared algorithms, which indicates it has the ability to handle real-world complex optimization problems with irregular PFs.
Motor unit number estimation: a technology and literature review.

Numerous methods for motor unit number estimation (MUNE) have been developed. The objective of this article is to summarize and compare the major methods and the available data regarding their reproducibility, validity, application, refinement, and utility.



Using specified search criteria, a systematic review of the literature was performed. Reproducibility, normative data, application to specific diseases and conditions, technical refinements, and practicality were compiled into a comprehensive database and analyzed.



The most commonly reported MUNE methods are the incremental, multiple-point stimulation, spike-triggered averaging, and statistical methods. All have established normative data sets and high reproducibility. MUNE provides quantitative assessments of motor neuron loss and has been applied successfully to the study of many clinical conditions, including amyotrophic lateral sclerosis and normal aging.



MUNE is an important research technique in human subjects, providing important data regarding motor unit populations and motor unit loss over time.
Oral hairy leukoplakia in HIV-negative patients: report of 10 cases.

Ten cases of oral hairy leukoplakia (OHL) in HIV- negative patients are presented. Eight of the 10 patients were on steroid treatment for chronic obstructive pulmonary disease, 1 patient was on prednisone as part of a therapeutic regimen for gastrointestinal stromal tumor, and 1 patient did not have any history of immunosuppression. There were 5 men and 5 women, ages 32-79, with mean age being 61.8 years. Nine out of 10 lesions were located unilaterally on the tongue, whereas 1 lesion was located at the junction of the hard and soft palate. All lesions were described as painless, corrugated, nonremovable white plaques (leukoplakias). Histologic features were consistent with Epstein-Barr virus-associated hyperkeratosis suggestive of OHL, and confirmatory in situ hybridization was performed in all cases. Candida hyphae and spores were present in 8 cases. Pathologists should be aware of OHL presenting not only in HIV-positive and HIV-negative organ transplant recipients but also in patients receiving steroid treatment, and more important, certain histologic features should raise suspicion for such diagnosis without prior knowledge of immunosuppression.
A Comprehensive Study of Fully Homomorphic Encryption Schemes

Organizations and individuals have been shifting to the Internet-based cloud computing technology to obtain more efficient and faster computing services in recent years. However, moving to the cloud services has created new security challenges. Confidential information becomes more vulnerable to be leaked due to computation outsourcing to the third-parties. The issue of data breaches could eliminate all the benefits that an organization may get by utilizing the cloud-based computing services. The primary goal of securing information is to provide confidentiality, authenticity, integrity and data privacy. Data encryption has been widely used to protect data during the communication and storage process. However, as users need to process data in the cloud, traditional cryptographic schemes cannot work well because it requires passing the secret keys to the cloud servers so that the servers can obtain the original data and perform the needed computations on the plaintext. Once encrypted data are disclosed for calculations, it can't guarantee the data confidentiality in the cloud; which poses a significant challenge for applications of cloud computing. Homomorphic Encryption (HE) has been proposed to overcome this security issue, which enables mathematical computations directly on encrypted data without the requirement of decryption in the cloud and can provide the same result as obtained by using calculations on the plaintext. Many Fully Homomorphic Encryption (FHE) schemes are designed, which can perform an arbitrary number of additions and multiplications on the ciphertext. In this paper, a comprehensive survey of homomorphic encryption technology using public key algorithms is introduced, then, FHE schemes are reviewed and analyzed. As researchers believe the advancement in the FHE technology will continue, this paper guides the state-of-the-art definitions, properties, algorithms, and schemes of FHE.
Data Extraction for Deep Web Using WordNet

Our survey shows that the techniques used in data extraction from deep webs need to be improved to achieve the efficiency and accuracy of automatic wrappers. Further investigations indicate that the development of a lightweight ontological technique using existing lexical database for English (WordNet) is able to check the similarity of data records and detect the correct data region with higher precision using the semantic properties of these data records. The advantages of this method are that it can extract three types of data records, namely, single-section data records, multiple-section data records, and loosely structured data records, and it also provides options for aligning iterative and disjunctive data items. Experimental results show that our technique is robust and performs better than the existing state-of-the-art wrappers. Tests also show that our wrapper is able to extract data records from multilingual web pages and that it is domain independent.
Design and Calibration of a New 6 DOF Haptic Device

For many applications such as tele-operational robots and interactions with virtual environments, it is better to have performance with force feedback than without. Haptic devices are force reflecting interfaces. They can also track human hand positions simultaneously. A new 6 DOF (degree-of-freedom) haptic device was designed and calibrated in this study. It mainly contains a double parallel linkage, a rhombus linkage, a rotating mechanical structure and a grasping interface. Benefited from the unique design, it is a hybrid structure device with a large workspace and high output capability. Therefore, it is capable of multi-finger interactions. Moreover, with an adjustable base, operators can change different postures without interrupting haptic tasks. To investigate the performance regarding position tracking accuracy and static output forces, we conducted experiments on a three-dimensional electric sliding platform and a digital force gauge, respectively. Displacement errors and force errors are calculated and analyzed. To identify the capability and potential of the device, four application examples were programmed.
Bidirectional DC-DC converter topologies and control strategies for interfacing energy storage systems in microgrids: An overview

A microgrid is defined as a local electric power distribution system with diverse distributed generation (DG), energy storage systems, and loads, which can operate as a part of the distribution system or when needed can operate in an islanded mode. Energy storage systems play a key role in improving security, stability, and power quality of the microgrid. During grid-connected mode, these storage units are charged from various DG sources as well as the main grid. During islanded mode, DG sources along with the storage units need to supply the load. Power electronic interfaces between the microgrid buses and the storage units should be able to detect the mode of operation, allow seamless transition between the modes, and allow power flow in both directions, while maintaining stability and power quality. An overview of bidirectional converter topologies relevant to microgrid energy storage application and their control strategies will be presented in this paper.
Nonlinear model & controller design for magnetic levitation system

This paper aims at development of non linear dynamic model for Magnetic Levitation System and proposed linear and nonlinear state space controllers. The linear controller was designed by linearizing the model around equilibrium point, while nonlinear controller was based on feedback linearization where a nonlinear state-space transformation is used to linearize the system exactly. Relative degree of the system was determined and conditions were found that ensure relative degree be well defined. Magnetic Levitation system considered in this study is taken as a ferromagnetic ball suspended in a voltage controlled magnetic field. Dynamic behaviour of the system was modeled by the study of electromagnetic and mechanical subsystems. State space model was derived from the system equations. Linear full state feedback controller along with linear observer was designed and was compared with nonlinear full state feedback with nonlinear observer. Both linear and nonlinear controllers were simulated using matlab and results are presented.  Magnetic Levitation System; Nonlinear Model; Exact Linearization; Electromagnet; unstable
Convolutional Neural Networks for Large-Scale Bird Song Classification in Noisy Environment

This paper describes a convolutional neural network based deep learning approach for bird song classification that was used in an audio record-based bird identification challenge, called BirdCLEF 2016. The training and test set contained about 24k and 8.5k recordings, belonging to 999 bird species. The recorded waveforms were very diverse in terms of length and content. We converted the waveforms into frequency domain and splitted into equal segments. The segments were fed into a convolutional neural network for feature learning, which was followed by fully connected layers for classification. In the official scores our solution reached a MAP score of over 40% for main species, and MAP score of over 33% for main species mixed with background species.
A Wideband Open-Slot Antenna With Dual-Band Circular Polarization

A wideband microstrip-fed open-slot antenna with dual-band circular polarization (CP) has been presented. A bent feeding structure and three slots, including a T-shaped and two inverted-L topologies etched in the ground plane, are designed to excite two orthogonal vectors with equal amplitude and 90 phase difference (PD) for a radiating right-hand circularly polarized (RHCP) wave at 1.57 GHz and left-hand circularly polarized (LHCP) wave at 2.33 GHz. The two bands are the operated bandwidth of the global positioning system (GPS) and satellite digital audio radio (SDAR) service system. To improve the impedance bandwidth at the low-frequency band, a parasitic rectangular patch is added on the top plane of the substrate. Several resonances are merged, and thus, a wide impedance bandwidth is obtained. The measured impedance bandwidth of a reflection coefficient <; -10 dB ranges from 1.45 to 3.93 GHz, and thus covers most of the commercial wireless communication systems, such as GPS, DCS, PCS, IMT-2000, WLAN, LTE, and WiMAX. The axial-ratio bandwidths are 65 MHz at the lower band (1.57 GHz) and 107 MHz at the upper band (2.33 GHz).
A 3-D-Point-Cloud System for Human-Pose Estimation

This paper focuses on human-pose estimation using a stationary depth sensor. The main challenge concerns reducing the feature ambiguity and modeling human poses in high-dimensional human-pose space because of the curse of dimensionality. We propose a 3-D-point-cloud system that captures the geometric properties (orientation and shape) of the 3-D point cloud of a human to reduce the feature ambiguity, and use the result from action classification to discover low-dimensional manifolds in human-pose space in estimating the underlying probability distribution of human poses. In the proposed system, a 3-D-point-cloud feature called viewpoint and shape feature histogram (VISH) is proposed to extract the 3-D points from a human and arrange them into a tree structure that preserves the global and local properties of the 3-D points. A nonparametric action-mixture model (AMM) is then proposed to model human poses using low-dimensional manifolds based on the concept of distributed representation. Since human poses estimated using the proposed AMM are in discrete space, a kinematic model is added in the last stage of the proposed system to model the spatial relationship of body parts in continuous space to reduce the quantization error in the AMM. The proposed system has been trained and evaluated on a benchmark dataset. Computer-simulation results showed that the overall error and standard deviation of the proposed 3-D-point-cloud system were reduced compared with some existing approaches without action classification.
Low Power Methodology Manual - for System-on-Chip Design

We may not be able to make you love reading, but low power methodology manual for system on chip design 2nd printing will lead you to love reading starting from now. Book is the window to open the new world. The world that you want is in the better stage and level. World will always guide you to even the prestige stage of the life. You know, this is some of how reading will give you the kindness. In this case, more books you read more knowledge you know, but it can mean also the bore is full.
Mission-level space situational awareness

Complex space systems typically provide the operator a means to understand the current state of system components. The operator often has to manually determine whether the system is able to perform a given set of high level objectives based on this information. The operations team needs a way for the system to quantify its capability to successfully complete a mission objective and convey that information in a clear, concise way. A mission-level space cyber situational awareness tool suite integrates the data into a complete picture to display the current state of the mission. The Johns Hopkins University Applied Physics Laboratory developed the Spyder tool suite for such a purpose. The Spyder space cyber situation awareness tool suite allows operators to understand the current state of their systems, allows them to determine whether their mission objectives can be completed given the current state, and provides insight into any anomalies in the system. Spacecraft telemetry, spacecraft position, ground system data, ground computer hardware, ground computer software processes, network connections, and network data flows are all combined into a system model service that serves the data to various display tools. Spyder monitors network connections, port scanning, and data exfiltration to determine if there is a cyber attack. The Spyder Tool Suite provides multiple ways of understanding what is going on in a system. Operators can see the logical and physical relationships between system components to better understand interdependencies and drill down to see exactly where problems are occurring. They can quickly determine the state of mission-level capabilities. The space system network can be analyzed to find unexpected traffic. Spyder bridges the gap between infrastructure and mission and provides situational awareness at the mission level.