Miracle-dz/UniFAR-FTUs huggingface dataset:
A Statistical Diagnosis Approach for Analyzing Design–Silicon Timing Mismatch

Explaining the mismatch between predicted timing behavior from modeling and simulation, and the observed timing behavior measured on silicon chips can be very challenging. Given a list of potential sources, the mismatch can be the aggregate result caused by some of them both individually and collectively, resulting in a very large search space. Furthermore, observed data are always corrupted by some unknown statistical random noises. In this paper, we examine how trying to explain the mismatch observed on silicon can be classified as an ill-posed problem, where ill posed means that the solution may not be unique or stable. Thus, a small change in the observed response can have a large change in the predicted solution. To solve ill-posed problems, a statistical learning theory uses a principle called regularization. This paper proposes using a statistical learning method called support vector (SV) analysis to statistically analyze all known sources of uncertainty with the objective to rank which sources contribute the most to the observed mismatch. Experimental results are presented under different error assumption models to compare two kinds of SV ranking approaches to four other ranking approaches, where some use the idea of regularization and others do not. This paper is concluded by showing a self cross-validation approach to validate the ranking results when there is no true ranking available, as the case with actual silicon.
Criteria for continuous-variable quantum teleportation

Abstract We derive an experimentally testable criterion for the teleportation of quantum states of continuous variables. This criterion is especially relevant to the recent experiment of Furusawa et al. where an input—output fidelity of 0.58 ± 0.02 was achieved for optical coherent stages. Our derivation demonstrates that fidelities greater than 1/2 could not have been achieved through the use of a classical channel alone; quantum entanglement was a crucial ingredient in the experiment.
Development of a representative volume element of lithium-ion batteries for thermo-mechanical integrity

The importance of Lithium-ion batteries continues to grow with the introduction of more electronic devices, electric cars, and energy storage. Yet the optimization approach taken by the manufacturers and system designers is one of test and build, an approach that nearly every other industry has long abandoned. A computational model is required to reduce the expensive buildtest cycle and allow safer, cheaper batteries to be built. The path to building this computational model will involve many different processes and one of those processes dictates the homogenizing of the interior of the battery casing by treating the interior as a homogenized Representative Volume Element. This thesis explains this process and outlines a procedure for the development of this particular model for both cylindrical and prismatic/pouch cells.
Silicon Baroreceptors : Modeling CardiovascularPressure Transductionin Analog

This paper describes an analog VLSI model of the representation of blood pressure used in a neural control system in the cardiovascular system. The chip computes the representation in real time, using analog continuous-time processing. We apply a variety of pressure signals to the chip, connrm-ing that the chip models known physiological responses. We are modeling this representation as a rst step to understanding the neural computation of cardiovascular control; we hope to apply this knowledge to nonlinear dy-namical control problems in the chemical industry.
How Universal is the Negative Correlation between Education and Fertility

A negative correlation between education and fertility has been described with great regularity in modern societies. The present investigation examines the strength of this relationship with data from the 1990, 1995 and 2000 waves of the World Values Survey covering 78 countries with a combined sample size of up to 181,728 respondents. The negative correlation is present in nearly all countries, is stronger in females than males, is greater for educational level than for length of schooling, and is not mediated by personal wealth. It is strongest at relatively low levels of economic, social and cognitive development and becomes weaker in the most advanced societies. However, it is also less than maximal in the least developed countries. The relationship is strongest in Latin America and the Middle East, where the typical correlations for cohorts with completed fertility are -. 31 for females and -. 24 for males, and weakest in Protestant Europe, where average correlations are -. 10 for females and -. 01 for males. The negative relationship persists in the younger generation of advanced societies, who are reproducing under conditions of sub-replacement fertility.
New fast wavelet packet transform algorithms for frame synchronized speech processing

The author presents orthogonal overlapped block transforms as a frame synchronized signal analysis tool with the capability of arbitrary multiresolution time-spectral decomposition of speech signals. His prime interest is in the representation of nonstationary discrete-time signals in terms of wavelet packets, and he concentrates on their fast transform algorithms. Wavelet packet representations provide a local time-spectral description which reveals the nonstationary nature of a signal. They allow the speech signal to be accurately parameterised for such applications as speech and speaker recognition, where a front-end is responsible for the frame synchronized feature extraction. In this case the fast overlapped block transform algorithms represent an elegant and efficient solution to the implementation of wavelet packet transforms, since their FFT-like lattice block structure provides all possible multiresolution time-spectral coefficients. The frame synchronization is also preserved in subbands which allows a new subband-based approach for speech recognition.
Bio-Sensing Systems and Bio-Feedback Systems for Interactive Media Arts

This is a report of research and some experimental applications of human-computer interaction in multi-media performing arts. The human performer and the computer systems perform computer graphic and computer music interactively in real-time. In general, many sensors are used for the interactive communication as interfaces, and the performer receives the output of the system via graphics, sounds and physical reactions of interfaces like musical instruments. I have produced many types of interfaces, not only with physical/electrical sensors but also with biological/physiological sensors. This paper is intended as an investigation of some special approaches: (1) 16-channel electromyogram sensor called "MiniBioMuse-III" and its application work called "BioCosmicStorm-II" performed in Paris, Kassel and Hamburg in 2001, (2) sensing/reacting with "breathing" in performing arts, (3) 8-channel electric-feedback system and its experiments of "body-hearing sounds" and "body-listening to music".
Improvements of coefficient learning in BPNN for image restoration

In this paper, we propose a method dealing with the problem of image restoration based on the conception of Back propagation Neural Network Algorithm. Conventional Back Propagation Algorithm has its inherited drawbacks, i.e. slow convergence rate, long training time, hard to achieve global minima etc. Recently, several methods introduced the dynamic learning rate and the dynamic momentum coefficient. Our new method applied in this paper improves the effect of learning coefficient η by using a new way to modify the value dynamically. The experimental results show that this helps improving the efficiency overall both in visual effect and quality analysis.
Unified decoding and feature representation for improved speech recognition

In this paper we propose a unified framework for decoding and feature representation based on the Maximum A Posterior (MAP) principle. The search space is augmented with an additional feature stream dimension such that different feature representations can be utilized for different phonetic context under the HMM decoding framework. We also provide a theoretic explanation for the unified framework. It gives us “supervised” signal processing and feature extraction for the recognition system, which has reduced the word recognition error rate by 15% on a large-vocabulary continuous speech recognition task when multiple feature streams are used simultaneously.
USING THE PARTICLE LEVEL SET METHOD AND A SECOND ORDER ACCURATE PRESSURE BOUNDARY CONDITION FOR FREE SURFACE FLOWS

In this paper, we present an enhanced resolution capturing method for topologically complex two and three dimensional incompressible free surface flows. The method is based upon the level set method of Osher and Sethian to represent the interface combined with two recent advances in the treatment of the interface, a second order accurate discretization of the Dirichlet pressure boundary condition at the free surface (2002, J. Comput. Phys. 176, 205) and the use of massless marker particles to enhance the resolution of the interface through the use of the particle level set method (2002, J. Comput. Phys., 183, 83). Use of these methods allow for the accurate movement of the interface while at the same time preserving the mass of the the liquid, even on coarse computational grids. Also, these methods complement the level set method in its ability to handle changes in interface topology in a robust manner. Surface tension effects can be easily included in our method. The method is presented in three spatial dimensions, with numerical examples in both two and three spatial dimensions.
A Comparison of the VISSIM and CORSIM Traffic Simulation Models

As transportation systems have become more complex and frequently congested, simulation modeling has gained recognition as an effective approach for quantifying traffic operations. Traffic simulation packages like CORSIM and VISSIM can address these types of network issues, and are frequently used as tools for analyzing traffic. However, there is little information available to the analysts applying these models about the most appropriate models to use, or even detailed information about the accuracy of individual models. To address that need, this paper provides a detailed, technical comparison of two popular traffic simulation models (CORSIM and VISSIM). Overall, CORSIM and VISSIM are perhaps more similar than they are different. Both models are designed to model any combination of surface street and freeway facilities, including most signal control and other operational strategies. Both models provide detailed and focused output, both in tabular format and via animated graphics. The main differences between the two models are in vehicle and driver behavior, primarily in the car-following and gap acceptance logic. However, while the nature of these differences is known, the impacts have not been documented. To address these issues, a series of investigations was conducted to compare the two models on specific measures like throughput and LOS. CORSIM and VISSIM give similar results for performance data on the standard measures examined here. At an intersection level, the throughput and LOS predictions from the two models were similar, although the results were different in some cases from the HCM predictions. The biggest difference observed is the variability of the models. Fortunately, this issue is easily addressed by making multiple runs. The comparisons were also good for an analysis of design alternatives on a complex, congested urban street network. Bloomberg and Dale page 2
Sources of organic carbon in PM1 in Helsinki urban air

Sources of organic carbon in PM1 in Helsinki urban air S. Saarikoski, H. Timonen, K. Saarnio, M. Aurela, L. Järvi, P. Keronen, V. -M. Kerminen, and R. Hillamo Finnish Meteorological Institute, Air Quality, P.O. Box 503, 00101 Helsinki, Finland University of Helsinki, Department of Physics, P.O. Box 64, 00014 Helsinki, Finland Received: 11 January 2008 – Accepted: 25 February 2008 – Published: 21 April 2008 Correspondence to: S. Saarikoski (sanna.saarikoski@fmi.fi) Published by Copernicus Publications on behalf of the European Geosciences Union.
Neuroendocrine mechanisms that connect feeding behavior and stress

Research during the past decade highlights the strong link between appetitive feeding behavior, reward, and motivation. Interestingly, stress levels can affect feeding behavior by manipulating hypothalamic circuits and brain dopaminergic reward pathways. Indeed, animals and people will increase or decrease their feeding responses when stressed. In many cases, acute stress leads to a decrease in food intake, yet chronic social stressors are associated to increases in caloric intake and adiposity. Interestingly, mood disorders and the treatments used to manage these disorders are also associated with changes in appetite and body weight. These data suggest a strong interaction between the systems that regulate feeding and metabolism and those that regulate stress and ultimately mood. This Research Topic compiles a number of review and research articles that focus how hormonal mechanisms regulate the nexus between feeding behavior and stress. It highlights the hormonal regulation of hypothalamic circuits and/or brain dopaminergic systems, as the potential sites controlling the converging pathways between feeding behavior and stress.
Formin-Dependent Synaptic Growth: Evidence That Dlar Signals via Diaphanous to Modulate Synaptic Actin and Dynamic Pioneer Microtubules

The diaphanous gene is the founding member of a family of Diaphanous-related formin proteins (DRFs). We identified diaphanous in a screen for genes that are necessary for the normal growth and stabilization of the Drosophila neuromuscular junction (NMJ). Here, we demonstrate that diaphanous mutations perturb synaptic growth at the NMJ. Diaphanous protein is present both presynaptically and postsynaptically. However, genetic rescue experiments in combination with additional genetic interaction experiments support the conclusion that dia is necessary presynaptically for normal NMJ growth. We then document defects in both the actin and microtubule cytoskeletons in dia mutant nerve terminals. In so doing, we define and characterize a population of dynamic pioneer microtubules within the NMJ that are distinct from the bundled core of microtubules identified by the MAP1b-like protein Futsch. Defects in both synaptic actin and dynamic pioneer microtubules are correlated with impaired synaptic growth in dia mutants. Finally, we present genetic evidence that Dia functions downstream of the presynaptic receptor tyrosine phosphatase Dlar and the Rho-type GEF (guanine nucleotide exchange factor) trio to control NMJ growth. Based on the established function of DRFs as Rho-GTPase-dependent regulators of the cell cytoskeleton, we propose a model in which Diaphanous links receptor tyrosine phosphatase signaling at the plasma membrane to growth-dependent modulation of the synaptic actin and microtubule cytoskeletons.
Efficient action spotting based on a spacetime oriented structure representation

This paper addresses action spotting, the spatiotemporal detection and localization of human actions in video. A novel compact local descriptor of video dynamics in the context of action spotting is introduced based on visual spacetime oriented energy measurements. This descriptor is efficiently computed directly from raw image intensity data and thereby forgoes the problems typically associated with flow-based features. An important aspect of the descriptor is that it allows for the comparison of the underlying dynamics of two spacetime video segments irrespective of spatial appearance, such as differences induced by clothing, and with robustness to clutter. An associated similarity measure is introduced that admits efficient exhaustive search for an action template across candidate video sequences. Empirical evaluation of the approach on a set of challenging natural videos suggests its efficacy.
Pre-Service Teachers’ Perceptions of Argumentation: Impact of a Teacher Education Project in Rwanda

In Rwanda, the national curriculum is increasingly promoting the engagement of students in active learning strategies and scientific inquiry. Related to this goal, the articulation of argumentation in teaching and learning is a significant topic. Argumentation involves the coordination of evidence and theory to support or refute an explanatory conclusion, model or prediction. Despite the research and policy rhetoric, the implementation of argumentation in everyday classrooms remains far from reality. In this project, we drew on evidence from research on professional development on argumentation to develop a pre-service teacher education program in Rwanda. This study was guided by the following key question: what is the impact of a series of workshops about teaching and learning of argumentation on Rwandan pre-service teachers’ perceptions of argumentation? The study was conducted with 25 pre-service teachers who participated in argumentation workshops that aimed to facilitate their understanding of the nature and teaching of scientific argumentation. As argumentation is a form of discourse practice, the participants’ perceptions of the role of language and discourse in learning were also investigated. The results indicate that majority of pre-service teachers had positive perceptions of the use of argumentation in science lessons. Further results on pre-service teachers’ perceptions on argumentation are discussed with implications for teacher education in Rwanda.
Visualizing uncertainty in reliability functions with application to aero engine overhaul

Summary. We propose an approach for estimating the date of lost confidence of jet engines, which are devices with multiple components subject to disruption. A mixed Weibull distribution is estimated from a large data set subject to censoring at various times. Parametric uncertainty is derived analytically and mapped visually onto the functions of use in reliability theory, including the hazard function. We demonstrate the use of the method on a database of disruption times for components in 325 jet engines.
Hybrid attributes similarity measurement for spectral clustering

Similarity measurement for spectral clustering has been well-studied in recent years due to its crucial role on describing the intrinsic structure of data points. In this paper, we propose a hybrid attributes similarity measure method to process the Gaussian kernel affinity matrix. Compared with traditional global or local scale methods, our new similarity measurement has a rather robustness to reflect the multi-scale and complex structure dataset, and the affinity matrix is clearly block diagonal. Experiment results show that our algorithm can successfully obtain higher performance on both synthetic and real life dataset than the existing similarity measure methods.
From the Publisher Making an Educated Guess

What does estimation mean? Having spent 30 years as an engineer and manager in the electronics and software marketplace, I thought, "Oh, that's easy! You simply guess how long it is going to take you to do this job, based on your last experience with the same or a similar job, and then double that guess. " Two things are clear with this oft-used algorithm. The first is that to have any kind of chance to be in the ballpark, you need some knowledge or historical precedence for having done something similar in the not-too-distant past. The second is a realization that the accuracy of any such estimate is not very good. If your business success does not rely on the accuracy of this type of estimate, this algorithm will probably continue to be used. Only when a business' existence and success rely on something a little more accurate is it likely a little more effort will be applied to make cost estimates more realistic and reliable. As readers will find in this month's CROSSTALK, cost estimation has become an essential feature of any successful software business development or sustainment venture. However, as quoted in Reducing Bias in Software Project Estimates by David Peeters and George Dewey on page 20, some still believe that "software estimating is definitely a black art." The authors note that a large percentage of software projects continue to finish behind schedule and over budget. The article discusses some ways to identify and reduce biases in software cost estimation to help make estimates more accurate. The dramatic increase in the quantity and complexity of software that drives so many of today's defense and consumer products is also a major driver in the need to develop more robust estimation methodologies and tools. Barry Boehm and his co-authors write in Future Trends, Implications in Software Cost Estimation Models on page 4 that software development trends have turned from the Waterfall process model toward evolutionary, incremental, and spiral models. It was found that the very useful Constructive Cost Model (COCOMO) estimation techniques required some significant changes to keep pace with the changing nature of this software development evolution. Product line management approaches to software reuse, and graphic user interface builder tools that made traditional-size metrics such as source lines of code inappropriate are examples of new software design methods that required new techniques. This article also discusses how continuing development extensions are being made to COCOMO II to address emerging trends such as Rapid Application Development and commercial-off-the-shelf integration. We hope that readers will find these and the other articles in this month's CROSSTALK to be valuable resources as they continually improve their organization's ability to accurately predict project schedule and cost.