nils-herrmann/clean_scirepeval huggingface dataset:
Autophagy: A Novel Therapeutic Target for Diabetic Nephropathy

Diabetic nephropathy is a leading cause of end stage renal disease and its occurance is increasing worldwide. The most effective treatment strategy for the condition is intensive treatment to strictly control glycemia and blood pressure using renin-angiotensin system inhibitors. However, a fraction of patients still go on to reach end stage renal disease even under such intensive care. New therapeutic targets for diabetic nephropathy are, therefore, urgently needed. Autophagy is a major catabolic pathway by which mammalian cells degrade macromolecules and organelles to maintain intracellular homeostasis. The accumulation of damaged proteins and organelles is associated with the pathogenesis of diabetic nephropathy. Autophagy in the kidney is activated under some stress conditions, such as oxidative stress and hypoxia in proximal tubular cells, and occurs even under normal conditions in podocytes. These and other accumulating findings have led to a hypothesis that autophagy is involved in the pathogenesis of diabetic nephropathy. Here, we review recent findings underpinning this hypothesis and discuss the advantages of targeting autophagy for the treatment of diabetic nephropathy.
Hydraulic erosion

This paper presents a generalized solution to modelling hydraulic erosion using ideas from fluid mechanics. The model is based on the Navier–Stokes equations, which provide the dynamics of velocity and pressure. These equations form the basis for the model to balance erosion and deposition that determine changes in the layers between water and erosion material. The eroded material is captured and relocated by water according to a material transport equation. The resulting model is fully 3D and is able to simulate a variety of phenomena including river meanders, low hill sediment wash, natural water springs and receding waterfalls. The simulations show the terrain morphogenesis and can be used for animations as well as for static scene generation.
FACTORS INFLUENCING COSMETICS PURCHASE INTENTION IN THAILAND : A STUDY ON THE RELATIONSHIP OF CREDIBILITYAND REPUTATION WITH THE PERSUASIVE CAPABILITIES OF BEAUTY BLOGGERS

The purpose of this study is to examine the factors affecting beauty bloggers’ credibility and how credibility itself influences cosmetic purchase intentions of Thai people. As identified by Ohanian (1990), three variables impacting one’s decision-making process (trustworthiness, expertise and attractiveness) are considered along with two additional variables determined by this researcher through focus group interviews to fit with and complement them. These are reputation and persuasive capabilities. Demographic factors of the respondents (age, gender, etc) were investigated as part as understanding differences, if any, in the cosmetic purchase intentions of Thai people. The Pearson Correlation analysis indicates that all independent variables have a positive relationship with purchase intention. Moreover, the results from the one-way ANOVA analysis show that the demographic factors variously affect the purchase intention of cosmetics of respondents.
Transmit power adaptation for multiuser OFDM systems

In this paper, we develop a transmit power adaptation method that maximizes the total data rate of multiuser orthogonal frequency division multiplexing (OFDM) systems in a downlink transmission. We generally formulate the data rate maximization problem by allowing that a subcarrier could be shared by multiple users. The transmit power adaptation scheme is derived by solving the maximization problem via two steps: subcarrier assignment for users and power allocation for subcarriers. We have found that the data rate of a multiuser OFDM system is maximized when each subcarrier is assigned to only one user with the best channel gain for that subcarrier and the transmit power is distributed over the subcarriers by the water-filling policy. In order to reduce the computational complexity in calculating water-filling level in the proposed transmit power adaptation method, we also propose a simple method where users with the best channel gain for each subcarrier are selected and then the transmit power is equally distributed among the subcarriers. Results show that the total data rate for the proposed transmit power adaptation methods significantly increases with the number of users owing to the multiuser diversity effects and is greater than that for the conventional frequencydivision multiple access (FDMA)-like transmit power adaptation schemes. Furthermore, we have found that the total data rate of the multiuser OFDM system with the proposed transmit power adaptation methods becomes even higher than the capacity of the AWGN channel when the number of users is large enough.
TurkDeck: Physical Virtual Reality Based on People

TurkDeck is an immersive virtual reality system that reproduces not only what users see and hear, but also what users feel. TurkDeck produces the haptic sensation using props, i.e., when users touch or manipulate an object in the virtual world, they simultaneously also touch or manipulate a corresponding object in the physical world. Unlike previous work on prop-based virtual reality, however, TurkDeck allows creating arbitrarily large virtual worlds in finite space and using a finite set of physical props. The key idea behind TurkDeck is that it creates these physical representations on the fly by making a group of human workers present and operate the props only when and where the user can actually reach them. TurkDeck manages these so-called "human actuators" by displaying visual instructions that tell the human actuators when and where to place props and how to actuate them. We demonstrate TurkDeck at the example of an immersive 300m2 experience in 25m2 physical space. We show how to simulate a wide range of physical objects and effects, including walls, doors, ledges, steps, beams, switches, stompers, portals, zip lines, and wind. In a user study, participants rated the realism/immersion of TurkDeck higher than a traditional prop-less baseline condition (4.9 vs. 3.6 on 7 item Likert).
Knowledge Graph Representation via Similarity-Based Embedding

Knowledge graph, a typical multi-relational structure, includes large-scale facts of the world, yet it is still far away from completeness. Knowledge graph embedding, as a representation method, constructs a low-dimensional and continuous space to describe the latent semantic information and predict themissing facts. Among various solutions, almost all embeddingmodels have high time andmemory-space complexities and, hence, are difficult to apply to large-scale knowledge graphs. Some other embedding models, such as TransE and DistMult, although with lower complexity, ignore inherent features and only use correlations between different entities to represent the features of each entity. To overcome these shortcomings, we present a novel low-complexity embedding model, namely, SimE-ER, to calculate the similarity of entities in independent and associated spaces. In SimE-ER, each entity (relation) is described as two parts. The entity (relation) features in independent space are represented by the features entity (relation) intrinsically owns and, in associated space, the entity (relation) features are expressed by the entity (relation) features they connect. And the similarity between the embeddings of the same entities in different representation spaces is high. In experiments, we evaluate our model with two typical tasks: entity prediction and relation prediction. Compared with the stateof-the-art models, our experimental results demonstrate that SimE-ER outperforms existing competitors and has low time and memory-space complexities.
A Recurrent Neural Network for Musical Structure Processing and Expectation

Research in cognitive neuroscience has identified neural activity correlated with subjects hearing an unexpected event in a musical sequence. However, there is a lack of models attempting to explain how these computations are carried out in the human brain. Using an augmented data set consisting of music from the western tradition (originally 371 Bach chorales), we trained a Long Short-Term Memory (LSTM) and two Recurrent Neural Network (RNN) architectures to ask: will a neural network show a larger perplexity when presented with an unexpected event in a musical sequence? Could this higher perplexity highlight similar computations carried out by the human brain? Our results indicate that our methods to train different neural network architectures with a corpus of music were effective. Perplexity values for the LSTM architecture reached low values in the order of 50. Visualization of the TSNE-reduced chord embedding matrix showed separation toward the edges common chords in our vocabulary compared to less common ones. Moreover, perplexity for an unexpected chord in an otherwise well-formed chord sequence was larger compared to an expected chord, but not as large as we originally hypothesized. Finally, out of all the architectures we trained, the clockwork RNN was the one less prone to overfitting the data, and the one that generated the most sensical chord progressions after training. Our results show a promising path moving toward the development of a neural network model of musical structure processing and
Internal Coupled-Fed Dual-Loop Antenna Integrated With a USB Connector for WWAN/LTE Mobile Handset

A coupled-fed dual-loop antenna capable of providing eight-band WWAN/LTE operation and suitable to integrate with a USB connector in the mobile handset is presented. The antenna integrates with a protruded ground, which is extended from the main ground plane of the mobile handset to accommodate a USB connector functioning as a data port of the handset. To consider the presence of the integrated protruded ground, the antenna uses two separate shorted strips and a T-shape monopole encircled therein as a coupling feed and a radiator as well. The shorted strips are short-circuited through a common shorting strip to the protruded ground and coupled-fed by the T-shape monopole to generate two separate quarter-wavelength loop resonant modes to form a wide lower band to cover the LTE700/GSM850/900 operation (704-960 MHz). An additional higher-order loop resonant mode is also generated to combine with two wideband resonant modes contributed by the T-shape monopole to form a very wide upper band of larger than 1 GHz to cover the GSM1800/1900/UMTS/LTE2300/2500 operation (1710-2690 MHz). Details of the proposed antenna are presented. For the SAR (specific absorption rate) requirement in practical mobile handsets to meet the limit of 1.6 W/kg for 1-g human tissue, the SAR values of the antenna are also analyzed.
Current Phase Lead Compensation in Single-Phase PFC Boost Converters With a Reduced Switching Frequency to Line Frequency Ratio

Traditional design of the current loop controller in a single-phase power factor correction boost converter is not suitable for applications with higher line frequencies (up to 800 Hz) because of the zero-crossing distortion and high harmonic content due to the current phase lead effect. Increasing the control bandwidth and switching frequency in order to avoid this effect would reduce converter efficiency and is objectionable. The paper presents the leading-phase admittance cancellation (LPAC) technique, which improves the current-shaping control structure and eliminates the current phase lead without increasing the bandwidth requirement. The LPAC method extends the allowable line frequency range from 1/150 to 1/5 of the current loop bandwidth. The LPAC method is load-invariant and superior to other previously proposed methods. The LPAC network can be added to existing designs, which would require only two passive components in the simplest case
Ternary cache: Three-valued MLC STT-RAM caches

Spin-transfer torque random access memory (STT-RAM) has become a promising non-volatile memory technology for cache memories. Recently, 2-bit multi-level cell (MLC) STT-RAM has been proposed to enhance data density, but it suffers from low reliability of its read and write operations. In this paper, we propose a novel cache design called Ternary cache. In Ternary cache, a memory cell can store three values (i.e., 0,1,2) while MLC STT-RAM can store four values. In this way, Ternary cache achieves much higher read stability than MLC STT-RAM-based caches. To enhance writability, a write operation is performed with high current and terminated as soon as the data is written. Evaluation results show that Ternary cache achieves the data density benefit of MLC STT-RAM and the reliability benefit of SLC STT-RAM.
Position and Force Control Based on Mathematical Models of Pneumatic Artificial Muscles Reinforced by Straight Glass Fibers

This paper reports on the position and force control of pneumatic artificial muscles reinforced by straight glass fibers. This type of artificial muscle has a greater contraction ratio and power and a longer lifetime than conventional McKibben types. However, these muscles are highly non-linear; hence, it is difficult to use them in a mechanical system. Furthermore, this actuator has a high compliance characteristic. Though this characteristic is useful for human interactions, the position and force of this actuator cannot be easily controlled. In this paper, a mathematical model of this type of artificial muscle is proposed, and the relationship between design parameters and control specifications is realized. In addition, the position and force based on the mathematical model are determined and applied to artificial muscle linearization.
A new estimation model of the lightning shielding performance of transmission lines using a fractal approach

The path of the lightning discharge shows characteristics of branching and tortuosity, and the lightning shielding failure of the transmission line also has statistical features. In this paper, an improved leader progression model using a fractal approach is introduced, which considers both the deterministic and stochastic features of the downward lightning leader. Based on the proposed model, the statistical features of lightning shielding failure on transmission lines will be analyzed. First, the fractal dimensions of the downward lightning trajectory in simulation are calculated and the value of breakdown probability constant η will be determined based on the fractal dimensions of natural lightning. Second, using the proposed model the shielding failure probability contour around the transmission line space is also obtained and used to describe the shielding failure zone and then the contour curve is applied to analyzing the characteristics of transmission lines that suffer from direct lightning strokes. Moreover, a method to estimate the lightning shielding failure rate of transmission lines is put forward, and the influence of the line height and the shielding angle of ground wires on the shielding failure rate of transmission lines are also investigated.
Open/Closed Eye Analysis for Drowsiness Detection

Drowsiness detection is vital in preventing traffic accidents. Eye state analysis - detecting whether the eye is open or closed - is critical step for drowsiness detection. In this paper, we propose an easy algorithm for pupil center and iris boundary localization and a new algorithm for eye state analysis, which we incorporate into a four step system for drowsiness detection: face detection, eye detection, eye state analysis, and drowsy decision. This new system requires no training data at any step or special cameras. Our eye detection algorithm uses Eye Map, thus achieving excellent pupil center and iris boundary localization results on the IMM database. Our novel eye state analysis algorithm detects eye state using the saturation (S) channel of the HSV color space. We analyze our eye state analysis algorithm using five video sequences and show superior results compared to the common technique based on distance between eyelids.
Verifiable Computation in Smart Contracts

Recent developments in blockchain technologies have seen the popularisation of smart contracts, that is, distributed programs which store their state on a blockchain. Usage of these programs has practical restrictions, however, including both hard and soft limits on the number of calculations they can perform. This project aims to provide a proof-of-concept implementation of how techniques from verifiable computation may be used to reduce the practical costs of using smart contracts, enabling them to be used in a wider range of applications. Verifiable computation is typically seen from the perspective of a user wishing to outsource computation to a third party, but wanting guarantees that the party provides the correct results. In the case of smart contracts, this scenario is flipped, with the user wishing to perform the computation themselves, and guarantee the correctness to the rest of the network. This project provides a functioning means of utilising verifiable computation in an Ethereum fork, and determines the challenges in extending this into a practical, widely used technology.
The use of IMU MEMS-sensors for designing of motion capture system for control of robotic objects

Developed HMI is a system consisting of a set of sensors, placed in a special suit or item of clothing. Dressed in a special suit human operator is able to control mechatronic object in the distance. Mechatronic object (for example an anthropomorphic robot) imitates the motion of the human operator by performing the technological operations on removal. So, to do it motion sensors are installed in the body areas which are suitable to copy the human movements. Then the information from sensors is processed and going to a special unit which transmits control signals wirelessly to the control object. Human-machine interface is represented as a hardware and software suite. The hardware suite are INS (inertial modules attached to various body parts), the communication channel between the modules, processing channel and wireless data transmission modules on a desktop computer. INS contain three-axis MEMS gyroscope, accelerometer and magnetometer.
Detection and Localization of Texture-less Objects with Deep Neural Networks

This thesis studies Faster R-CNN, the state-of-art method for object detection in RGB images, and proposes its extension to RGB-D images. Solutions to the following problems are proposed and evaluated: filling missing values in depth images, depth encoding (raw depth vs. surface normals), extension of the CNN architecture to accept the extra depth information, and initialization of weights in the extended network. The overall best results were achieved with a network that accepts an extra depth channel, pre-processed by the iterative median filter to fill in the missing values, and has the depth weights in the first convolutional layer initialized with the mean of the color weights that were pretrained on ImageNet. However, the improvement over the original method using only RGB channels is not significant (mAP was increased by 1 − 2%), which suggests a need for different incorporation of the depth information.
Internet of Things and Fog Computing

The Internet of Things (IoT) is widely used in advanced logistic systems. Safety and security of such systems are utmost important to guarantee the quality of their services. However, such systems are vulnerable to cyber-attacks. Development of lightweight anomaly based intrusion detection systems (IDS) is one of the key measures to tackle this problem. In this paper, we present a new distributed and lightweight IDS based on an Artificial Immune System (AIS). The IDS is distributed in a three-layered IoT structure including the cloud, fog and edge layers. In the cloud layer, the IDS clusters primary network traffic and trains its detectors. In the fog layer, we take advantage of a smart data concept to analyze the intrusion alerts. In the edge layer, we deploy our detectors in edge devices. Smart data is a very promising approach for enabling lightweight and efficient intrusion detection, providing a path for detection of silent attacks such as botnet attacks in IoT-based systems.
Open Drug Discovery Toolkit (ODDT): a new open-source player in the drug discovery field

There has been huge progress in the open cheminformatics field in both methods and software development. Unfortunately, there has been little effort to unite those methods and software into one package. We here describe the Open Drug Discovery Toolkit (ODDT), which aims to fulfill the need for comprehensive and open source drug discovery software.



The Open Drug Discovery Toolkit was developed as a free and open source tool for both computer aided drug discovery (CADD) developers and researchers. ODDT reimplements many state-of-the-art methods, such as machine learning scoring functions (RF-Score and NNScore) and wraps other external software to ease the process of developing CADD pipelines. ODDT is an out-of-the-box solution designed to be easily customizable and extensible. Therefore, users are strongly encouraged to extend it and develop new methods. We here present three use cases for ODDT in common tasks in computer-aided drug discovery.



Open Drug Discovery Toolkit is released on a permissive 3-clause BSD license for both academic and industrial use. ODDT's source code, additional examples and documentation are available on GitHub (https://github.com/oddt/oddt).
Quantification of mRNA using real-time reverse transcription PCR (RT-PCR): trends and problems.

The fluorescence-based real-time reverse transcription PCR (RT-PCR) is widely used for the quantification of steady-state mRNA levels and is a critical tool for basic research, molecular medicine and biotechnology. Assays are easy to perform, capable of high throughput, and can combine high sensitivity with reliable specificity. The technology is evolving rapidly with the introduction of new enzymes, chemistries and instrumentation. However, while real-time RT-PCR addresses many of the difficulties inherent in conventional RT-PCR, it has become increasingly clear that it engenders new problems that require urgent attention. Therefore, in addition to providing a snapshot of the state-of-the-art in real-time RT-PCR, this review has an additional aim: it will describe and discuss critically some of the problems associated with interpreting results that are numerical and lend themselves to statistical analysis, yet whose accuracy is significantly affected by reagent and operator variability.
Modelling and analysis of convergence of wireless sensor network and passive optical network using queueing theory

Wireless sensor networks (WSNs) are being deployed for an ever-widening range of applications, particularly due to their low power consumption and reliability. In most applications, the sensor data must be sent over the Internet or core network, which results in the WSN backhaul problem. Passive optical networks (PONs), a next-generation access network technology, are well suited form a backhaul which interfaces WSNs to the core network. In this paper a new structure is proposed which converges WSNs and PONs. It is modelled and analyzed through queuing theory by employing two M/M/1 queues in tandem. Numerical results show how the WSN and PON dimensions affect the average queue length, thus serving as a guideline for future resource allocation and scheduling of such a converged network.