nils-herrmann/clean_scirepeval huggingface dataset:
Fast PixelCNN: Based on network acceleration cache and partial generation network

Single image super resolution is one of the most important topic in computer vision and image processing research, many convolutional neural networks (CNN) based super resolution algorithms were proposed and achieved advanced performance, especially in recovering image details, in which PixelCNN is the most representative one. However, due to the intensive computation requirement of PixelCNN model, running time remains a major challenge, which limited its wider application. In this paper, several modifications are proposed to improve PixelCNN based recursive super resolution model. First, a discrete logistic mixture likelihood is adopted, then a cache structure for generating process is proposed, with these modifications, numerous redundant computations are removed without loss of accuracy. Finally, a partial generating network is proposed for higher resolution generation. Experiments on CelebA dataset demonstrate the effectiveness the superiority of the proposed method.
Power-law voltage acceleration: A key element for ultra-thin gate oxide reliability

The properties of the so-called time dependent dielectric breakdown (TDDB) of silicon dioxide-based gate dielectric for microelectronics technology have been investigated and reviewed. Experimental data covering a wide range of oxide thickness, stress voltage, temperature, and for the two bias polarities were gathered using structures with a wide range of gate oxide areas, and over very long stress times. Thickness dependence of oxide breakdown was shown to be in excellent agreement with statistical models founded in the percolation theory which explain the drastic reduction of the time-to-breakdown with decreasing oxide thickness. The voltage dependence of time-to-breakdown was found to follow a power-law behavior rather than an exponential law as commonly assumed. Our investigation on the inter-relationship between voltage and temperature dependencies of oxide breakdown reveals that a strong temperature activation with non-Arrhenius behavior is consistent with the power-law voltage dependence. The power-law voltage dependence in combination with strong temperature activation provides the most important reliability relief in compensation for the strong decrease of time-to-breakdown resulting from the reduction of the oxide thickness. Using the maximum energy of injected electrons at the anode interface as breakdown variable, we have resolved the polarity gap of timeand charge-to-breakdown (TBD and QBD), confirming that the fluency and the electron energy at anode interface are the fundamental quantities controlling oxide breakdown. Combining this large database with a recently proposed cell-based analytical version of the percolation model, we extract the defect generation efficiency responsible for breakdown. Following a review of different breakdown mechanisms and models, we discuss how the release of hydrogen through the coupling between vibrational and electronic degrees of freedom can explain the power-law dependence of defect generation efficiency. On the basis of these results, a unified and global picture of oxide breakdown is constructed and the resulting model is applied to project reliability limits. In this regard, it is concluded that SiO2-based dielectrics can provide reliable gate dielectric, even to a thickness of 1 nm, and that CMOS scaling may well be viable for the 50 nm technology node.  E-mail address: eywu@us.ibm.com (E.Y. Wu).
Workflow and toolchain for developing the automotive software according AUTOSAR standard at a Virtual-ECU

The increasing demand of new functionalities in next generation vehicles, leads to a growth of the complexity level for the E/E automotive systems. On the same way, the automotive software also tends to follow the same pace, so new methods should be adopted to deal with this scenario of complexity. The next generation of automotive embedded software is rapidly migrating to the AUTOSAR standard, which is an architectural composition of software components idealized to establish an open industry standard for the automotive industry. AUTOSAR aims to increase the reuse of these software components, in particular between different vehicle platforms, and between OEMs and suppliers. Inside this development process, software control suppliers are able to check if the system functionalities are attending to the requirements already in preliminary phases, even if the ECU is not yet available. In this paper the authors show the workflow to develop a virtual validation based on the AUTOSAR standard with a Virtual ECU (V-ECU) using a toolchain consisting of dSPACE (SystemDesk, VEOS and TargetLink) and MathWorks (Matlab, Simulink and Stateflow) software. The simulation of the architecture has been realized considering the communication inside a same V-ECU and also between two different V-ECUs considering a distributed architecture. As result, a point-to-point explanation about AUTOSAR methodology is done to show how the process is done.
LTE security, protocol exploits and location tracking experimentation with low-cost software radio

The Long Term Evolution (LTE) is the latest mobile standard being implemented globally to provide connectivity and access to advanced services for personal mobile devices. Moreover, LTE networks are considered to be one of the main pillars for the deployment of Machine to Machine (M2M) communication systems and the spread of the Internet of Things (IoT). As an enabler for advanced communications services with a subscription count in the billions, security is of capital importance in LTE. Although legacy GSM (Global System for Mobile Communications) networks are known for being insecure and vulnerable to rogue base stations, LTE is assumed to guarantee confidentiality and strong authentication. However, LTE networks are vulnerable to security threats that tamper availability, privacy and authentication. This manuscript, which summarizes and expands the results presented by the author at ShmooCon 2016 , investigates the insecurity rationale behind LTE protocol exploits and LTE rogue base stations based on the analysis of real LTE radio link captures from the production network. Implementation results are discussed from the actual deployment of LTE rogue base stations, IMSI catchers and exploits that can potentially block a mobile device. A previously unknown technique to potentially track the location of mobile devices as they move from cell to cell is also discussed, with mitigations being proposed.
Effects of Internet and Smartphone Addictions on Depression and Anxiety Based on Propensity Score Matching Analysis

The associations of Internet addiction (IA) and smartphone addiction (SA) with mental health problems have been widely studied. We investigated the effects of IA and SA on depression and anxiety while adjusting for sociodemographic variables. In this study, 4854 participants completed a cross-sectional web-based survey including socio-demographic items, the Korean Scale for Internet Addiction, the Smartphone Addiction Proneness Scale, and the subscales of the Symptom Checklist 90 Items-Revised. The participants were classified into IA, SA, and normal use (NU) groups. To reduce sampling bias, we applied the propensity score matching method based on genetics matching. The IA group showed an increased risk of depression (relative risk 1.207; p < 0.001) and anxiety (relative risk 1.264; p < 0.001) compared to NUs. The SA group also showed an increased risk of depression (relative risk 1.337; p < 0.001) and anxiety (relative risk 1.402; p < 0.001) compared to NCs. These findings show that both, IA and SA, exerted significant effects on depression and anxiety. Moreover, our findings showed that SA has a stronger relationship with depression and anxiety, stronger than IA, and emphasized the need for prevention and management policy of the excessive smartphone use.
Image recognition and processing using Artificial Neural Network

There are several techniques for image recognition. Among those methods, application of soft computing models on digital image has been considered to be an approach for a better result. The main objective of the present work is to provide a new approach for image recognition using Artificial Neural Networks. Initially an original gray scale intensity image has been taken for transformation. The Input image has been added with Salt and Peeper noise. Adaptive median Filter has been applied on noisy image such that the noise can be removed and the output image would be considered as filtered Image. The estimated Error and average error of the values stored in filtered image matrix have been calculated with reference to the values stored in original data matrix for the purpose of checking of proper noise removal. Now each pixel data has been converted into binary number (8 bit) from decimal values. A set of four pixels has been taken together to form a new binary number with 32 bits and it has been converted into a decimal. This process continues to produce new data matrix with new different set of values. This data matrix has been taken as original data matrix and saved in data bank. Now for recognition, a new test image has been taken and the same steps as salt & pepper noise insertion, removal of noise using adaptive median filter as mentioned earlier have been applied to get a new test matrix. Now the average error of the second image with respect to original image has been calculated based on the both generated matrices. If the average error is more than 45% then a conclusion can be made that the images are different and cannot be matched. But if the value of average error has been found to be less than or equal to 45%, an effort has been made to use the artificial neural network on test data matrix with reference to original data matrix thereby producing a new matrix of the second image(test image). The total average error has been calculated on generated data matrix produced after the application of artificial neural networks on test data matrix to check whether proper identification can be made or not. It has been observed that the value of average error is less than that of test image without application of artificial neural network. Further it has been observed that the test image is matching and recognized with respect to original image.
Usability Evaluation of Web Based Educational Multimedia by Eye- Tracking Technique

In cases that education processes are supported by the internet and web technologies, usability is one of the factors that can affect success of students and technology usage status of teachers. In this study, usability factors for effective design and usage of web based multimedia packages created for educational purposes are surveyed. During the research, experiments and analysis are done in the Human – Computer Interaction Laboratory with nine prospective teachers. According to literature review, it is generally possible to find 75% of the usability problems by testing only 5 participants. Experiment data are collected by using screen and voice records, which contain eye and mouse motions. For analysis of data, fixation count, sequence and dwell time values in gridded area of interest, heat map values in whole are considered. The present study demonstrates that large number of fixations and the disperse nature of fixation locations make it hard for users to find the task. It was also found based on eye-movement data that size of some objects and words were too small to notice. There should be options that enable users to decide on the size of objects according to their preference. Finally, this study makes recommendations aimed at making the multimedia more effective, efficient and of higher quality to ensure user satisfaction. It can be said that results obtained from this study can also be used for design and validation of computer aided materials such as learning objects, web based applications and serious games.
Trade space evaluation of ascent and return architectures for a Mars Sample Return mission

The concept of Mars Sample Return (MSR) has been considered since the 1960s and is still a top priority for the planetary science community.  Although a plan on the number and types of samples to be collected for MSR has been outlined, as articulated in the Mars 2020 Science Definition Team report , the trade space of options to return this sample from the surface of Mars to the surface of the Earth is still being explored. One of the main challenges with MSR is that it is inherently a multi-vehicle system where each vehicle's design impacts that of the others. Defining the trade space must therefore be treated as a System of Systems (SoS) problem. The work presented puts forward a framework to rapidly explore such spatially and temporally distributed systems. It investigates the possible vehicle and technology options for MSR, assuming that a packaged sample has been left on the surface of Mars. It also evaluates how launch sequencing choices affect the expected return on investment of different architectures. The paper explores eight key trades, including different types of landing and propulsion systems, as well as low-cost direct return options. A large set of architectures are compared to the baseline proposed in the Planetary Science Decadal Survey  for MSR, which consists of a stationary lander, a small fetch rover, a Mars Ascent Vehicle (MAV), and a return orbiter with chemical propulsion. Overall, the baseline is found to be well optimized, although a few options, including the use of solar electric propulsion and of a roving vehicle carrying the MAV to the sample, are shown to offer a better return on investment. Furthermore, when considering only the goals of MSR, an approach where the lander is sent to Mars at least one launch window ahead of the return orbiter is demonstrated to be preferable.
Characterization of UWB Transmit-Receive Antenna System

An ultra-wide-band (UWB), stripline-fed Vivaldi antenna is characterized both numerically and experimentally. Three-dimensional far-field measurements are conducted and accurate antenna gain and efficiency as well as gain variation versus frequency in the boresight direction are measured. Using two Vivaldi antennas, a free-space communication link is set up. The impulse response of the cascaded antenna system is obtained using full-wave numerical electromagnetic time-domain simulations. These results are compared with frequency-domain measurements using a network analyzer. Full-wave numerical simulation of the free-space channel is performed using a two step process to circumvent the computationally intense simulation problem. Vector transfer function concept is used to obtain the overall system transfer function and the impulse response.
Time Series Classification by Sequence Learning in All-Subsequence Space

Existing approaches to time series classification can be grouped into shape-based (numeric) and structure-based (symbolic). Shape-based techniques use the raw numeric time series with Euclidean or Dynamic Time Warping distance and a 1-Nearest Neighbor classifier. They are accurate, but computationally intensive. Structure-based methods discretize the raw data into symbolic representations, then extract features for classifiers. Recent symbolic methods have outperformed numeric ones regarding both accuracy and efficiency. Most approaches employ a bag-of-symbolic-words representation, but typically the word-length is fixed across all time series, an issue identified as a major weakness in the literature. Also, there are no prior attempts to use efficient sequence learning techniques to go beyond single words, to features based on variable-length sequences of words or symbols. We study an efficient linear classification approach, SEQL, originally designed for classification of symbolic sequences. SEQL learns discriminative subsequences from training data by exploiting the all-subsequence space using greedy gradient descent. We explore different discretization approaches, from none at all to increasing smoothing of the original data, and study the effect of these transformations on the accuracy of SEQL classifiers. We propose two adaptations of SEQL for time series data, SAX-VSEQL, can deal with X-axis offsets by learning variable-length symbolic words, and SAX-VFSEQL, can deal with X-axis and Y-axis offsets, by learning fuzzy variable-length symbolic words. Our models are linear classifiers in rich feature spaces. Their predictions are based on the most discriminative subsequences learned during training, and can be investigated for interpreting the classification decision.
Tadehaginosides A-J, Phenylpropanoid Glucosides from Tadehagi triquetrum, Enhance Glucose Uptake via the Upregulation of PPARγ and GLUT-4 in C2C12 Myotubes.

Ten new phenylpropanoid glucosides, tadehaginosides A-J (1-10), and the known compound tadehaginoside (11) were obtained from Tadehagi triquetrum. These phenylpropanoid glucosides were structurally characterized through extensive physical and chemical analyses. Compounds 1 and 2 represent the first set of dimeric derivatives of tadehaginoside with an unusual bicyclo[2.2.2]octene skeleton, whereas compounds 3 and 4 contain a unique cyclobutane basic core in their carbon scaffolds. The effects of these compounds on glucose uptake in C2C12 myotubes were evaluated. Compounds 3-11, particularly 4, significantly increased the basal and insulin-elicited glucose uptake. The results from molecular docking, luciferase analyses, and ELISA indicated that the increased glucose uptake may be due to increases in peroxisome proliferator-activated receptor γ (PPARγ) activity and glucose transporter-4 (GLUT-4) expression. These results indicate that the isolated phenylpropanoid glucosides, particularly compound 4, have the potential to be developed into antidiabetic compounds.
Complementary Split-Ring Resonators for Measuring Dielectric Constants and Loss Tangents

A noninvasive planar complementary split-ring resonator (CSRR) for measuring the dielectric constants and loss tangents of material is presented in this letter. Measurements were developed to extract the complex permittivity of the material under test (MUT) using the equivalent RLC resonator circuit model. This technique enables a single-step measurement of the MUT without the need for machining or reshaping. The method requires calculating only two parameters, the resonant frequency (fr) and the magnitude (dB) response, thus achieving a substantial reduction in cost and computation time. A CSRR sensor operating in the 1.8 to 2.8 GHz band is fabricated and tested for verification. The measurement errors in the dielectric constant were less than 7.6%. An efficient technique of CSRR measuring complex permittivity is experimentally verified.
Cooperative Multi-agent Reinforcement Learning for Flappy Bird *

The advent of Google’s Deep Q-Learning Network ushered in a new generation of reinforcement learning systems that learn control policies directly from raw sensory data. Two important innovations– using a deep convolutional neural network to approximate the action-value function, and stateful ”experience-replay” mechanisms–allowed agents to tractably learn successful strategies on Atari games, like Pong. Researchers are working to generalize these techniques to handle the complex challenges faced by human agents in the real world. To this end, we are the first to investigate multi-agent reinforcement learning to elicit cooperative play in the game Flappy Bird. In our experiments, each agent is independent and only observes the actions of the other player from the raw environment pixels. We report that two agents are able to achieve a score over 1000 after being trained on over 20,000 rounds of play.
New analysis and results for the Frank-Wolfe method

Wepresent new results for the Frank–Wolfemethod (also known as the conditional gradient method). We derive computational guarantees for arbitrary step-size sequences, which are then applied to various step-size rules, including simple averaging and constant step-sizes. We also develop step-size rules and computational guarantees that depend naturally on the warm-start quality of the initial (and subsequent) iterates. Our results include computational guarantees for both duality/bound gaps and the so-calledFWgaps. Lastly,wepresent complexity bounds in the presence of approximate computation of gradients and/or linear optimization subproblem solutions.
Ab interno approach to the subconjunctival space using a collagen glaucoma stent.

This review considers a minimally invasive ab interno approach to glaucoma filtration surgery. Glaucoma filtration surgery can be defined as an attempt to lower intraocular pressure (IOP) by the surgical formation of an artificial drainage pathway from the anterior chamber to the subconjunctival space. Subconjunctival drainage of aqueous fluid has been a cornerstone of glaucoma surgery for more than a century. Varying techniques have been deployed to provide access to this space. Yet, despite numerous innovations in filtering surgery to achieve safe IOP reduction, too many short-term and long-term complications are associated with this surgery. This article describes the development of a new, soft, and permanent ab interno collagen implant (XEN gel stent) to optimize aqueous drainage to the subconjunctival space. Specific characteristics are critical in designing such an implant. Determining the optimum size of the device lumen to avoid hypotony while maximizing long-term outflow is crucial. Other topics discussed include material, length, diameter, flexibility, stability, and biocompatibility of the implant. Preclinical and human eye testing shows that the implant does not seem to occlude inside the lumen and the implant material does not appear to cause tissue reaction in the eye. The ab interno placement of the stent offers an alternative for lowering IOP with a minimally invasive procedure, minimum conjunctival tissue disruption, restricted flow to avoid hypotony, and long-term safety.


FINANCIAL DISCLOSURE
Dr. Lewis received financial support from Aquesys, Inc. as a consultant.
The second wave of synthetic biology: from modules to systems

Synthetic biology is a research field that combines the investigative nature of biology with the constructive nature of engineering. Efforts in synthetic biology have largely focused on the creation and perfection of genetic devices and small modules that are constructed from these devices. But to view cells as true 'programmable' entities, it is now essential to develop effective strategies for assembling devices and modules into intricate, customizable larger scale systems. The ability to create such systems will result in innovative approaches to a wide range of applications, such as bioremediation, sustainable energy production and biomedical therapies.
PEEP: Perceptually Enhanced Exploration of Pictures

We present a novel simple technique for rapidly creating and presenting interactive immersive 3D exploration experiences of 2D pictures and images of natural and artificial landscapes. Various application domains, ranging from virtual exploration of works of art to street navigation systems, can benefit from the approach. The method, dubbed PEEP, is motivated by the perceptual characteristics of the human visual system in interpreting perspective cues and detecting relative angles between lines. It applies to the common perspective images with zero or one vanishing points, and does not require the extraction of a precise geometric description of the scene. Taking as input a single image without other information, an automatic analysis technique fits a simple but perceptually consistent parametric 3D representation of the viewed space, which is used to drive an indirect constrained exploration method capable to provide the illusion of 3D exploration with realistic monocular (perspective and motion parallax) and binocular (stereo) depth cues. The effectiveness of the method is demonstrated on a variety of casual pictures and exploration configurations, including mobile devices.