Miracle-dz/UniFAR-FTUs huggingface dataset:
From Real to Virtual: A Comparison of Input Devices for Percussion Tasks

This paper presents an evaluation and comparison of four in put devices for percussion tasks: a standard tom drum, Roland V-Drum, and two established examples of gestural controllers: the Buchla Lightning II, and the Radio Baton. The primary goal of this study was to determine how players’ actions changed when moving from an acoustic instrument like the tom drum, to a gestural controller like the Buchla Lightning, which bears little resemblance to an acoustic percussion instrument. Motion capture data was analyzed by comparing a subject’s hand height variability and timing accuracy across the four instruments as they performed simple musical tasks. Results suggest that certain gestures such as hand height amplitude can be adapted to these gestural controllers with little change and that in general subjects’ timing variability is significantly affected when playing on the Lightning and Radio Baton when compared to the more familiar tom drum and VDrum. Possible explanations and other observations are also presented.
Echoes—how to generate, recognize, use or avoid them in MR‐imaging sequences. Part I: Fundamental and not so fundamental properties of spin echoes

Spin echoes have been known since 1950. Although their formal description by use of the Bloch equations is straightforward, it does not lead to an intuitive understanding of their behavior except for the special cases of 180° or 90° pulses, especially when many pulses are applied before the magnetization has returned into thermal equilibrium. The extended-phasegraph algorithm, which takes into account that the total magnetization in spin-echo sequences is a superposition of many isochromats, allows the recognition of all possible echo signals in arbitrary pulse sequences. Its application to multi-echo sequences leads to a number of surprising results. It can be demonstrated that refocusing pulses with flip angles much lower than 180° generate an unexpectedly high signal intensity after a few echo periods. Apart from leading to a simple algorithm for the exact calculation of echo intensities in arbitrary multi-pulse sequences, the phase-graph algorithm leads to a simple understanding of the contrast behavior of different gradient echo sequences and gives a rational means for the design of MR-imaging sequences that are free from spurious echoes.
Differentially Expressed miRNAs in Tumor, Adjacent, and Normal Tissues of Lung Adenocarcinoma

Lung cancer is the leading cause of cancer deaths. Non-small-cell lung cancer (NSCLC) is the major type of lung cancer. The aim of this study was to characterize the expression profiles of miRNAs in adenocarcinoma (AC), one major subtype of NSCLC. In this study, the miRNAs were detected in normal, adjacent, and tumor tissues by next-generation sequencing. Then the expression levels of differential miRNAs were quantified by quantitative reverse transcription-polymerase chain reaction (qRT-PCR). In the results, 259, 401, and 389 miRNAs were detected in tumor, adjacent, and normal tissues of pooled AC samples, respectively. In addition, for the first time we have found that miR-21-5p and miR-196a-5p were gradually upregulated from normal to adjacent to tumor tissues; miR-218-5p was gradually downregulated with 2-fold or greater change in AC tissues. These 3 miRNAs were validated by qRT-PCR. Lastly, we predicted target genes of these 3 miRNAs and enriched the potential functions and regulatory pathways. The aberrant miR-21-5p, miR-196a-5p, and miR-218-5p may become biomarkers for diagnosis and prognosis of lung adenocarcinoma. This research may be useful for lung adenocarcinoma diagnosis and the study of pathology in lung cancer.
The Firm Under Uncertainty: Capital Structure and Background Risk

This paper examines the interplay between the real and financial decisions of the competitive firm under output price uncertainty. The firm faces additional sources of uncertainty that are aggregated into a background risk. We show that the firm always chooses its optimal debt-equity ratio to minimize the weighted average cost of capital, irrespective of the risk attitude of the firm and the incidence of the underlying uncertainty. We further show that the firm's optimal input mix depends on its optimal debt-equity ratio, thereby rendering the interdependence of the real and financial decisions of the firm. When the background risk is either additive or multiplicative, we provide reasonable restrictions on the firm's preferences so as to ensure that the firm's optimal output is adversely affected upon the introduction of the background risk.
A demonstration of the FN packet marking probability

The effectiveness of queue management mechanisms relies on how good their control decisions will help in satisfying their goals regarding congestion avoidance and control. These decisions are implemented and compelled during the design of the packet mark probability and the mark activation functions. The design of Fast Congestion Notification (FN) drop/mark probability function enables the two control decisions, packet admissions and congestion control directing, to be made along with each other. This permits sending congestion avoidance notification as early as required even if the queue is almost empty, and preventing congestion notification even if the queue is almost full but the arrival rate is controllable. This leads to good buffer utilization and proper congestion detection. This paper demonstrates the drop/mark probability functions that the fast FN policy would exercise for different values of optimal queue size, and also for a specific optimal queue size value.
Remarks on Systems and Differential Operators on Groups

Complexity and large size of contemporary control, communication, and computer systems impose strong challenges to methods of their mathematical mod- elling, design, and testing. Classical approaches to their formal specication by ex- ploiting theory of systems on groups of real numbers R and complex numbers C, often do not fulll requirements of practice. For that reason, theory of systems on groups different from R and C has been developed. Differential operators and spectral (Fourier) analysis on groups play the same important role in such systems as in the case of systems modelled by signals dened on R and C. This paper rst briey reviews some aspects of research in system theory on groups and then presents an extension of the notion of Gibbs differentiation to matrix- valued functions on nite non-Abelian groups.
The Research Trends and Comparing about the Big Data between Korea and China Using Text Mining

Now, big data technology, big data analysis and big data utilization are the hot-issue in the academic world. And the studies about big data technology are increasing now. This aim of this study is to examine the research trends of Chinese big data paper and South Korean big data paper from 1990 to 2015 by using keyword analysis. A statistical analysis including chronological distribution, the frequency of key words, the network of keywords were conducted and compared. This paper retrieved the literatures of China and South Korea on big data from CNKI and RISS. The search method is that using the two fields of the "paper title" and "keyword" to search by these key words of "Big Data", "big data technology" and "big data analysis". By the use of Excel and NodeXL software, this paper gave words analysis and comparing with the literatures of China and South Korea on big data.
The Calibration of Stock Option Pricing Models Using Inverse Problem Methodology

We analyse the procedure for determining volatility presented by Lagnado and Osher, and explain in some detail where the scheme comes from. We present an alternative scheme which avoids some of the technical complications arising in Lagnado and Osher's approach. An algorithm for solving the resulting equations is given, along with a selection of numerical examples.
Friend-based secure routing through challenges in mobile ad hoc networks

This paper proposes a Friend-based security scheme for efficient routing in mobile ad hoc networks. The idea behind this technique is the establishment of friend networks in the same way as the formation of friends in real life scenarios. The algorithm provides authentication of nodes by sending challenges and creating friend lists of nodes that complete the challenge. Friends are rated on the basis of the amount of data they transfer through themselves and according to the rating of other friends, which is obtained during the friend list sharing process. The data is finally routed through the route with the greatest number of trusted friends. One of the greatest advantages of this technique is that the nodes do not need to work in the promiscuous mode. The information about the malicious nodes is gathered effectively by using challenges. This reduces the overhead on the network significantly. Through various simulation experiments it has been found that the proposed algorithm works very efficiently in terms of detecting the malicious nodes and also allowing lesser number of packets to be routed through these detected malicious nodes.
A New Data Driven Method for Robust Speech Recognition

The conventional view on the problem of robustness in speech recognition is that performance degradation in ASR systems is due to mismatch between training and test conditions. If problem of robustness in ASR systems were considered as a mismatch between the training and testing conditions the solution would be to find a way to reduce it. Common approaches are: Data-Driven methods such as speech signal enhancement and using robust features and model-based methods that alliterate or adapt model of speech signal. In this paper, we review some of data-driven and model-based methods and implement some of datadriven methods such as: spectral subtraction, cepstral mean normalization, cepstral mean and variance normalization, mapping noisy space to clean space and SNR-depndent cepstral normalization. We suggest a new data-driven method for robust speech recognition using neural networks and compare it to other methods. Key-Words: Robust speech recognition, cepstral normalization, neural networks
SURVEY ON PREDICTION OF HEART MORBIDITY USING DATA MINING TECHNIQUES

Data mining is the non trivial extraction of implicit, previously unknown and potentially useful information from data. Data mining technology provides a user- oriented approach to novel and hidden patterns in the data. This paper presents about the various existing techniques, the issues and challenges associated with them. The discovered knowledge can be used by the healthcare administrators to improve the quality of service and also used by the medical practitioners to reduce the number of adverse drug effect, to suggest less expensive therapeutically equivalent alternatives. In this paper we discuss the popular data mining techniques namely, Decision Trees, Naive Bayes and Neural Network that are used for prediction of disease.
A surveillance and safety system based on performance criteria and functional degradation for an autonomous vehicle

Autonomous driving in urban environments is potentially dangerous since a malfunction of vehicle guidance systems can lead to severe situations for passengers inside the autonomous vehicle and other road users. Therefore both, monitoring the current system operation state by a surveillance system, which is able to detect failures of software and hardware modules, and a safety system, which reacts on these failures immediately, is necessary. In this paper an approach based on performance criteria and functional degradation is proposed, which is used in the autonomous vehicle Leonie developed within the Stadtpilot project. The surveillance part of the system collects data from sensors, software modules, hardware, and the vehicle to combine this data with heuristics to performance criteria. Based on these criteria degradation actions are executed to keep the operation of Leonie in a safe state. The safety system can influence driving maneuvers like lane changes and turning maneuvers, modify driving parameters like maximum speed and safe time headway and even force driving maneuvers like emergency stops and controlled stops at the side of the road. Currently, the safety driver onboard of Leonie is the fallback solution in case of a system malfunction. Using the proposed safety system should reduce the number of situations where the safety driver has to take control over the vehicle though.
PM: A Proof Manager For HOL And Other Provers

Different theorem-proving systems have different things to recommend them: automatic proiiers such as the Boyer-Moore prover or a number of resolution systems minimize human intervention in proving simple first-order assertions, but systems such as HOL or. Nuprl that are based on a more powerful logic better support reasoning about higher abstractions. In typical verification systems, a verifier is limited to the choice of a single theorem-prover. The proof manager PM is intended to allow a verifier to choose among several theorem-proving systems during the course of a single proof. We report on its current status as a HOL interface, and our initial design of a translation scheme that, when possible, transforms HOL input into firstorder assertions suited to the Boyer-Moore prover or automatic first-order provers.
What Drives Private Saving Around the World?

The authors investigate the policy and non-policy factors behind saving disparities, using a large panel data set and an encompassing approach including several relevant determinants of private saving. They extend the literature in several dimensions, by: 1) Using the largest data set on aggregate saving assembled to date. 2) Using panel instrumental variable techniques to correct for endogeneity and heterogeneity. 3) Performing robustness checks on changes in estimation procedures, data samples, and model specification. Their main empirical findings: a) Private saving rates show considerable inertia (are highly serially correlated even after controlling for other relevant factors). b) Private sector rates rise with the level and growth rate of real per capita income. So policies that spur development are in indirect but effective way to raise private saving rates. c) Predictions of the life-cycle hypothesis are supported in that dependency ratios generally have a negative effect on private saving rates. d) The precautionary motive for saving is supported by the finding that inflation - conventionally taken as a summary measure of macroeconomic volatility - has a positive impact on private saving, holding other facts constant. e) Fiscal policy is a moderately effective tool for raising national saving. F) the direct effect of financial liberalization are largely detrimental to private saving rates. Greater availability of credit reduces the private saving rate; financial depth and higher real interest rates do not increase saving.
Unstructured 3-D Grid Partitioning Methods Based On Genetic Algorithms

In this paper, two methods that are capable to efficiently partition 3-D unstructured grids for paral- lel processing, will be presented. Implicit to either of these methods are a recursive bisection pattern, a multi- level acceleration scheme and local refinement heuristics. Although both are based on Genetic Algorithms (GAs), their concept and formulation are completely different. The first method uses binary coding, where 0's and 1's denote owning processor for each grid entity. The sec- ond method encodes a few control parameters that define a unique bisection of the grid, on the equivalent graph, where a physical problem is solved. Advantages and dis- advantages of both methods will be cited and a hybrid scheme will be suggested as a fast alternative partitioner.
Overexpression of the transcription factor Yap1 modifies intracellular redox conditions and enhances recombinant protein secretion

Oxidative folding of secretory proteins in the endoplasmic reticulum (ER) is a redox active process, which also impacts the redox conditions in the cytosol. As the transcription factor Yap1 is involved in the transcriptional response to oxidative stress, we investigate its role upon the production of secretory proteins, using the yeast Pichia pastoris as model, and report a novel important role of Yap1 during oxidative protein folding. Yap1 is needed for the detoxification of reactive oxygen species (ROS) caused by increased oxidative protein folding. Constitutive co-overexpression of PpYAP1 leads to increased levels of secreted recombinant protein, while a lowered Yap1 function leads to accumulation of ROS and strong flocculation. Transcriptional analysis revealed that more than 150 genes were affected by overexpression of YAP1, in particular genes coding for antioxidant enzymes or involved in oxidation-reduction processes. By monitoring intracellular redox conditions within the cytosol and the ER using redox-sensitive roGFP1 variants, we could show that overexpression of YAP1 restores cellular redox conditions of protein-secreting P. pastoris by reoxidizing the cytosolic redox state to the levels of the wild type. These alterations are also reflected by increased levels of oxidized intracellular glutathione (GSSG) in the YAP1 co-overexpressing strain. Taken together, these data indicate a strong impact of intracellular redox balance on the secretion of (recombinant) proteins without affecting protein folding per se. Re-establishing suitable redox conditions by tuning the antioxidant capacity of the cell reduces metabolic load and cell stress caused by high oxidative protein folding load, thereby increasing the secretion capacity.
Why Can't a Computer be more Like a Brain?

This paper discusses a theory of the neocortical algorithm called the hierarchical temporal memory (HTM). Hierarchical temporal memories are built around a hierarchy of nodes. The hierarchy and how it works are the most important features of HTM theory. In an HTM, knowledge is distributed across many nodes up and down the hierarchy. As an HTM is trained, the low-level nodes learn first. Representations in high-level nodes then share what was previously learned in low-level nodes
Application of MIMO system for telemetry, tracking command and monitoring subsystem to control the satellite

Telemetry, tracking, command and monitoring (TTC&M) system is one of major satellite subsystem for satellite communication purpose. It is the part of the satellite management task, which involves an earth station, the main function of satellite management are control the orbit and attitude of the satellite, monitor the status of all sensors and subsystem on the satellite, switch on or off section of communication system. TTC&M may replace by the TTC-MIMO, there are many advantage over the traditional system in order to increase the energy efficiency, diversity, data rate, channel capacity, spectral efficiency, signal to noise ratio.
Wealth, Industry and the Transition to Entrepreneurship

Although the debate about the effect of wealth on entrepreneurship is now almost two decades old, there is little consensus among researchers about the significance of wealth as a determinant for self-employment. We re-visit the relationship between wealth and entrepreneurship using data from the National Longitudinal Survey of Youth. Like Hurst and Lusardi (2004), our results suggest the relationship between wealth and the probability of entering entrepreneurship is nonlinear. However, unlike Hurst and Lusardi, we find the probability of entrepreneurship increases at an increasing rate with wealth, starting at lower quantiles of the wealth distribution. We also observe that the aggregate relationship masks differences among entrepreneurs with respect to their industry. While high capital requirement industries and professional services display a convex relationship between wealth and the probability of self-employment, low capital requirement industries display a concave relationship. Since we find a positive relationship between wealth and the probability of entering entrepreneurship at lower quantiles of the wealth distribution, it is critical to check whether this relationship is caused by wealth endogeneity. In order to account for the possible endogeneity of wealth we instrument for wealth using changes in housing equity and the value of unexpected inheritances. The results of instrumental variable estimation reveal that there is no significant relationship between wealth and entering entrepreneurship for the full sample as well as for each of the three industries.
GPU-accelerated Computation of 3D Laser Radar Range Imaging of Arbitrary Coarse Targets

We report here a backscattering model of average signal power function (SPF) for laser radar 3D range imagery obtained by arrays of detectors for arbitrary coarse targets. The model relates the average power seen by the receiver with laser pulse, target shape, optical scattering properties of surface material, incidence angle and other factors. The optical scattering property of the material is characterized by bidirectional reflectance distribution function (BRDF). The model can be used for demonstration of 3D laser radar system and can also be used to generate library of model data sets for automatic target recognition (ATR). Finally, Compute Unified Device Architecture (CUDA) is introduced for parallelizing these algorithms. The acceleration reaches 56 times speedup on single Fermi-generation NVIDIA GTX 480 as compared to the traditional CPU version of code on Intel i7 930 CPU.
Steady state characterization of current ripple in DCM interleaved power converters

Power converters that operate in Discontinuous Conduction Mode (DCM) are able to reduce switching losses, when compared to Continuous Conduction Mode (CCM) operation. This reduction is mainly due to zero current commutation and the reduction of the reverse recovery losses. However, DCM operation in high power converters is limited due to the increment in current ripple, which increases losses and volume in the differential mode (DM) filter. Multiphase DCM power converters can reduce total ripple by dividing total current among N phases and interleaving its ripples. Nevertheless, magnitude of ripple reduction as a function of the system parameters has not yet been completely determined. This information would be an important performance indicator and a useful tool for aiding in the design of key converter features, such as the number of phases and DM filter design, in order to meet total ripple, losses or electromagnetic interference specifications. In this sense, this paper proposes a methodology for the steady state characterization of input and output ripple in both buck and boost converters operating in DCM. Experimental tests on a 4-phase buck converter validate the proposal.
Detection of Sybil attacks in participatory sensing using Cloud based Trust Management System

Participatory sensing is a revolutionary paradigm in which volunteers collect and share information from their local environment using mobile phones. Different from other participatory sensing application challenges who consider user privacy and data trustworthiness, we consider network trustworthiness problem namely Sybil attacks in participatory sensing. Sybil attacks focus on creating multiple online user identities called Sybil identities and try to achieve malicious results through these identities. In this paper, we proposed a Cloud based Trust Management Scheme (CbTMS) framework for detecting Sybil attacks in participatory sensing network. Our CbTMS was proposed for performing Sybil attack characteristic check and trustworthiness management system to verify coverage nodes in the participatory sensing. To verify the proposed framework, we are currently developing the proposed scheme on OMNeT++ network simulator in multiple scenarios to achieve Sybil identities detection in our simulation environment.