nils-herrmann/clean_scirepeval huggingface dataset:
The Co-Design Method for Robust Satisfactory H∞ / H2 Fault-TolerantEvent-Triggered Control of NCS with α - Safety Degree

Based on discrete event-triggered communication scheme (DETCS), this paper is concerned with the satisfactory H ! / H 2 event-triggered fault-tolerant control problem for networked control system (NCS) with α -safety degree and actuator saturation constraint from the perspective of improving satisfaction of fault-tolerant control and saving network resource. Firstly, the closed-loop NCS model with actuator failures and actuator saturation is built based on DETCS; Secondly, based on Lyapunov-Krasovskii function and the definition of α -safety degree given in the paper, a sufficient condition is presented for NCS with the generalized H2 and H! performance, which is the contractively invariant set of fault-tolerance with α -safety degree, and the co-design method for event-triggered parameter and satisfactory faulttolerant controller is also given in this paper. Moreover, the simulation example verifies the feasibility of improving system satisfaction and the effectiveness of saving network resource for the method. Finally, the compatibility analysis of the related indexes is also discussed and analyzed.
Unusually large interthalamic adhesion and its clinical importance

Interthalamic adhesion is a structure that connects the medial surfaces of the two thalami. It crosses the cavity of the third ventricle. It is roughly about 1 cm in diameter and is made up of nerve cell bodies and a few nerve fibers. Exact function of the adhesion is not known as its absence has not caused any functional deficits. It is absent in about 20% of the cases. When enlarged, it may cause hindrance to the flow of cerebrospinal fluid. We saw an unusually large interthalamic adhesion and the same is being reported here.  During regular dissections for undergraduate medical students at Melaka Manipal Medical College (Manipal Campus) we found an unusually large interthalamic adhesion . The variation was found in a male cadaver aged approximately 70 years. The interthalamic adhesion was coma shaped in sectional view and was about 3 cm in anteroposterior diameter. It connected most of the area on the medial surfaces of the two thalami. The cavity of the third ventricle was confined to the part below the hypothalamic sulcus. All the parts of the body and brain were normal other than the large size of the interthalamic adhesion.  Interthalamic adhesion is a variable structure and it may be totally absent . In the past, several studies have been conducted on the variations of thalamus and interthalamic Satheesha NAYAK B.  Soumya KV
Differences in perceptions of beauty and cosmetic procedures performed in ethnic patients.

The United States has become progressively more multicultural, with the ethnic population growing at record rates. The US Census Bureau projects that, by the year 2056, greater than 50% of the US population will be of non-Caucasian descent. Ethnic patients have different cosmetic concerns and natural features that are unique. The cosmetic concerns of ethnic patients also differ as the result of differences in skin pathophysiology, mechanisms of aging, and unique anatomic structure. There is no longer a single standard of beauty. We must now adapt to the more diverse population and understand how to accommodate the diversity of beauty in the United States. Ethnic patients do not necessarily want a Westernized look because what constitutes beauty is determined by racial, cultural, and environmental influences. We as leaders in skin care must understand these differences and adapt our practices accordingly. This article will focus on the differences in aging in different ethnic populations and highlight procedures unique to skin of color.
Thermo-Mechanical Simulation of Wire Bonding Joints in Power Modules

High voltage and high current power modules are key components for traction applications. In the future, railway locomotives, tramways and elevators will be equipped with Insulated Gate Bipolar Transistor (IGBT) modules. In this field of application high reliability is of decisive importance, especially for aluminum bond wires with diameters up to 500 μm, connecting the silicon device with the output pins. Reliability tests show that wire bonding and soldering may cause failures of the modules. In this work, we investigate the stress and strain distributions in the bonding zone after a temperature load step using finite-element analysis.
An Approach to Modeling Software Safety in Safety-Critical Systems

Software for safety-critical systems has to deal w ith the hazards identified by safety analysis in order to make the system safe, risk-free and fai l-safe. Software safety is a composite of many factors.  Existing software quality models like McCall’s and Boehm’s and ISO 9126 were inadequate in addressing the software saf ety issues of real time safety-critical embedded systems. At present there does not exist any standa rd framework that comprehensively addresses the Factors, Criteria and Metrics (FCM) approach of the quality models in respect of software safety.  We proposed a new model for software safety based on the McCall’s software quality model that specifically identifies the criteria cor responding to software safety in safety critical applications. The criteria in the proposed software safety model pertains to system hazard analysis, completeness of requirements, identification of sof tware-related safety-critical requirements, safetyconstraints based design, run-time issues managemen t and software safety-critical testing.  This model was applied to a prototype safety-critical so ftware-based Railroad Crossing Control System (RCCS). The results showed that all critical operat ions were safe and risk-free, capable of handling contingency situations.  Development of a safety-critical system based on ou r proposed software safety model significantly enhanced the sa f operation of the overall system.
Learning doubly sparse transforms for image representation

The sparsity of images in a fixed analytic transform domain or dictionary such as DCT or Wavelets has been exploited in many applications in image processing including image compression. Recently, synthesis sparsifying dictionaries that are directly adapted to the data have become popular in image processing. However, the idea of learning sparsifying transforms has received only little attention. We propose a novel problem formulation for learning doubly sparse transforms for signals or image patches. These transforms are a product of a fixed, fast analytic transform such as the DCT, and an adaptive matrix constrained to be sparse. Such transforms can be learnt, stored, and implemented efficiently. We show the superior promise of our approach as compared to analytical sparsifying transforms such as DCT for image representation.
Test Case Generation Based on Invariant Extraction

Software testing is an effective way to find software errors. Generating a good test suite is the key. A program invariant is a property that is true at a particular program point or points. The property could reflect the program’s execution over a test suite. Based on this point, we integrate the random test case generation technique and the invariant extraction technique, achieving automatic test case generation and selection. With the same invariants, compared with the traditional random test case generation technique, the experimental results show that the approach this paper describes can generate a smaller test suite. Keywords-software testing; random testing; test case; program invariant
A Deep Cascade of Convolutional Neural Networks for MR Image Reconstruction

The acquisition of Magnetic Resonance Imaging (MRI) is inherently slow. Inspired by recent advances in deep learning, we propose a framework for reconstructing MR images from undersampled data using a deep cascade of convolutional neural networks to accelerate the data acquisition process. We show that for Cartesian undersampling of 2D cardiac MR images, the proposed method outperforms the state-of-the-art compressed sensing approaches, such as dictionary learning-based MRI (DLMRI) reconstruction, in terms of reconstruction error, perceptual quality and reconstruction speed for both 3-fold and 6-fold undersampling. Compared to DLMRI, the error produced by the method proposed is approximately twice as small, allowing to preserve anatomical structures more faithfully. Using our method, each image can be reconstructed in 23ms, which is fast enough to enable real-time applications.
CNNs-based Acoustic Scene Classification using Multi-Spectrogram Fusion and Label Expansions

Spectrograms have been widely used in Convolutional Neural Networks based schemes for acoustic scene classification, such as the STFT spectrogram and the MFCC spectrogram, etc. They have different time-frequency characteristics, contributing to their own advantages and disadvantages in recognizing acoustic scenes. In this letter, a novel multi-spectrogram fusion framework is proposed, making the spectrograms complement each other. In the framework, a single CNN architecture is applied onto multiple spectrograms for feature extraction. The deep features extracted from multiple spectrograms are then fused to discriminate the acoustic scenes. Moreover, motivated by the inter-class similarities in acoustic scene datasets, a label expansion method is further proposed in which super-class labels are constructed upon the original classes. On the help of the expanded labels, the CNN models are transformed into the multitask learning form to improve the acoustic scene classification by appending the auxiliary task of super-class classification. To verify the effectiveness of the proposed methods, intensive experiments have been performed on the DCASE2017 and the LITIS Rouen datasets. Experimental results show that the proposed method can achieve promising accuracies on both datasets. Specifically, accuracies of 0.9744, 0.8865 and 0.7778 are obtained for the LITIS Rouen dataset, the DCASE Development set and Evaluation set respectively.
Absent Multiple Kernel Learning

Multiple kernel learning (MKL) optimally combines the multiple channels of each sample to improve classification performance. However, existing MKL algorithms cannot effectively handle the situation where some channels are missing, which is common in practical applications. This paper proposes an absent MKL (AMKL) algorithm to address this issue. Different from existing approaches where missing channels are firstly imputed and then a standard MKL algorithm is deployed on the imputed data, our algorithm directly classifies each sample with its observed channels. In specific, we define a margin for each sample in its own relevant space, which corresponds to the observed channels of that sample. The proposed AMKL algorithm then maximizes the minimum of all sample-based margins, and this leads to a difficult optimization problem. We show that this problem can be reformulated as a convex one by applying the representer theorem. This makes it readily be solved via existing convex optimization packages. Extensive experiments are conducted on five MKL benchmark data sets to compare the proposed algorithm with existing imputation-based methods. As observed, our algorithm achieves superior performance and the improvement is more significant with the increasing missing ratio. Disciplines Engineering | Science and Technology Studies Publication Details Liu, X., Wang, L., Yin, J., Dou, Y. & Zhang,  (2015). Absent multiple kernel learning. 2813). United States: IEEE. This conference paper is available at Research Online: http://ro.uow.edu.au/eispapers/5373 Absent Multiple Kernel Learning Xinwang Liu School of Computer National University of Defense Technology Changsha, China, 410073 Lei Wang School of Computer Science and Software Engineering University of Wollongong NSW, Australia, 2522 Jianping Yin, Yong Dou School of Computer National University of Defense Technology Changsha, China, 410073 Jian Zhang Faculty of Engineering and Information Technology University of Technology Sydney NSW, Australia, 2007
ALTERNATIVES TO SCATTERPLOTS

Emerging evidence suggests that people do not have difficulty judging covariation per se but rather have difficulty decoding standard displays such as scatterplots. Using the data analysis software Tinkerplots, I demonstrate various alternative representations that students appear to be able to use quite effectively to make judgments about covariation. More generally, I argue that data analysis instruction in K-12 should be structured according to how statistical reasoning develops in young students and should, for the time begin, not target specific graphical representations as objectives of instruction.  SOFTWARE FOR THE MIDDLE SCHOOL The computer's potential to improve the teaching of data analysis is now a well-known It includes its power to illuminate key concepts through simulations and multiple-linked representations. It also includes its ability to free students up, at the appropriate time, from time-intensive tasks—from what National Council of Teachers of Mathematics (1989) Standards referred to as the " narrow aspects of statistics " (p. 113). This potentially allows instruction to focus more attention on the processes of data analysis—exploring substantive questions of interest, searching for and interpreting patterns and trends in data, and communicating findings. However, as Biehler (1995) has suggested, the younger the student, the more difficult it is to design an appropriate tool for learning statistics. Most of the existing tools for young students have been developed from the " top down. " They provide a subset of conventional plots and thus are simpler than professional tools only in that they have fewer options. These " simplified professional tools " are ill-suited to younger students who " need a tool that is designed from their bottom-up perspective of statistical novices and can develop in various ways into a full professional tool (not vice versa) " (p.3). Tinkerplots is a data analysis tool for the middle school that we are designing " from the bottom up " (Konold & Miller, 2001). When a data set is first opened in Tinkerplots, a plot window appears showing a haphazard arrangement of data icons on the screen. As in Tabletop (see Hancock, Kaput & Goldsmith, 1992), each icon represents an individual case. But in Tinkerplots, rather than choosing from a menu of existing plot types (e.g., bar graph, pie chart, scatterplot), students progressively organize the data using a small set of intuitive operators including " stack, " " order, " and " separate ". By using these operators in different combinations, …
Assessing the Readability of Stacked Graphs

Stacked graphs are a visualization technique popular in casual scenarios for representing multiple time-series. Variations of stacked graphs have been focused on reducing the distortion of individual streams because foundational perceptual studies suggest that variably curved slopes may make it difficult to accurately read and compare values. We contribute to this discussion by formally comparing the relative readability of basic stacked area charts, ThemeRivers, streamgraphs and our own interactive technique for straightening baselines of individual streams in a ThemeRiver. We used both real-world and randomly generated datasets and covered tasks at the elementary, intermediate and overall information levels. Results indicate that the decreased distortion of the newer techniques does appear to improve their readability, with streamgraphs performing best for value comparison tasks. We also found that when a variety of tasks is expected to be performed, using the interactive version of the themeriver leads to more correctness at the cost of being slower for value comparison tasks.
D2D multi-hop relaying services towards disaster communication system

Recent events of multiple earthquakes in Nepal, Italy and New Zealand resulting loss of life and resources bring our attention to the ever growing significance of disaster management, especially in the context of large scale nature disasters such as earthquake and Tsunami. In this paper, we focus on how disaster communication system can benefit from recent advances in wireless communication technologies especially mobile technologies and devices. The paper provides an overview of how the new generation of telecommunications and technologies such as 4G/LTE, Device to Device (D2D) and 5G can improve the potential of disaster networks. D2D is a promising technology for 5G networks, providing high data rates, increased spectral and energy efficiencies, reduced end-to-end delay and transmission power. We examine a scenario of multi-hop D2D communications where one UE may help other UEs to exchange information, by utilizing cellular network technique. Results show the average energy-efficiency spectral- efficiency of these transmission types are enhanced when the number of hops used in multi-hop links increases. The effect of resource group allocation is also pointed out for efficient design of system.
How a qualitative approach to concept map analysis can be used to aid learning by illustrating patterns of conceptual development

This paper describes a qualitative approach to analysing students’ concept maps. The classi cation highlights three major patterns which are referred to as ‘spoke’, ‘chain’ and ‘net’ structures. Examples are given from Year 8 science classes. The patterns are interpreted as being indicators of progressive levels of understanding. It is proposed that identi cation of these differences may help the classroom teacher to focus teaching for more effective learning and may be used as a basis for structuring groups in collaborative settings. This approach to analysing concept maps is of value because it suggests teaching approaches that help students integrate new knowledge and build upon their existing naïve concepts. We also refer to the teacher’s scheme of work and to the National Curriculum for science in order to consider their in uence in the construction of understanding. These ideas have been deliberately offered for early publication to encourage debate and generate feedback. Further work is in progress to better understand how students with different conceptual structures can be most appropriately helped to achieve learning development.
Interactive 3D reconstruction from multiple images: A primitive-based approach

This paper proposes a fast and stable image-based modeling method which generates 3D models with high-quality face textures in a semi-automatic way. The modeler guides untrained users to quickly obtain 3D model data via several steps of simple user interface operations using predefined 3D primitives. The proposed method contains an iterative non-linear error minimization technique in the model estimation step with an error function based on finite line segments instead of infinite lines. The error corresponds to the difference between the observed structure and the predicted structure from current model parameters. Experimental results on real images validate the robustness and the accuracy of the algorithm.
Quasi-Z-Source Indirect Matrix Converter Fed Induction Motor Drive for Flow Control of Dye in Paper Mill

This paper describes a flow control of the dye in the paper mill with the quasi-Z-source indirect matrix converter (QZSIMC) fed induction motor drive. More than a decade the voltage-source inverter (VSI) and current-source inverter (CSI) have been used to control the speed of the induction motor, which in turns controls the flow of dye. Recently, the matrix converter (MC) has been an excellent competitor for the VSI or CSI for its compactness. The voltage transfer ratio of the VSI, CSI, and MC has been limited to 0.866. Thus, the efficiency of these converters is less. To improve the voltage transfer ratio the quasi-Z-source network (QZSN) is to be used between voltage source and indirect MC (IMC). The modification in the shoot-through duty ratio of the QZSN varies the voltage transfer ratio greater than 0.866. Different voltage transfer ratios are needed for different voltage sag conditions. For a variation of the duty ratio of the QZSN, the fuzzy logic controller has been presented. To control the IMC vector control with space vector modulation has been presented. This paper proposes the implementation of QZSIMC adjustable speed drive for the flow control of dye in paper mill during different voltage sag conditions. A 4-kW prototype has been built and the effectiveness of the proposed system is verified with simulation results and experimental setup. Simulation is done in MATLAB, Simulink platform. Experimental setup is done with the aid of a TMS320F2812 (Texas Instrument) processor. The experimental results validate the maintenance of the speed of an induction motor at the set condition, thus controlling the perfect flow of dye in paper manufacturing technology.
Urban growth patterns and growth management boundaries in the Central Puget Sound, Washington, 1986–2007

Many regions of the globe are experiencing rapid urban growth, the location and intensity of which can have negative effects on ecological and social systems. In some locales, planners and policy makers have used urban growth boundaries to direct the location and intensity of development; however the empirical evidence for the efficacy of such policies is mixed. Monitoring the location of urban growth is an essential first step in understanding how the system has changed over time. In addition, if regulations purporting to direct urban growth to specific locales are present, it is important to evaluate if the desired pattern (or change in pattern) has been observed. In this paper, we document land cover and change across six dates (1986, 1991, 1995, 1999, 2002, and 2007) for six counties in the Central Puget Sound, Washington State, USA. We explore patterns of change by three different spatial partitions (the region, each county, 2000 U.S. Census Tracks), and with respect to urban growth boundaries implemented in the late 1990’s as part of the state’s Growth Management Act. Urban land cover increased from 8 to 19% of the study area between 1986 and 2007, while lowland deciduous and mixed forests decreased from 21 to 13% and grass and agriculture decreased from 11 to 8%. Land in urban classes outside of the urban growth boundaries increased more rapidly (by area and percentage of new urban land cover) than land within the urban growth boundaries, suggesting that the intended effect of the Growth Management Act to direct growth to within the urban growth boundaries may not have been accomplished by 2007. Urban sprawl, as estimated by the area of land per capita, increased overall within the region, with the more rural counties within commuting distance to cities having the highest rate of increase observed. Land cover data is increasingly available and can be used to rapidly evaluate urban development patterns over large areas. Such data are important inputs for policy makers, urban planners, and modelers alike to manage and plan for future population, land use, and land cover changes.