Miracle-dz/UniFAR-FTUs huggingface dataset:
An Empirical Analysis of Joint Household Decisions, with Taxes and Quantity Constraints

This study applies an econometric framework that allows for complex non-convex budget sets, highly nonlinear labour supply curves and imperfect markets with institutional constraints. A married couple's version of the model is estimated on Italian microdata. The empirical results show that male labour supply is rather inelastic while labour supply among females, especially participation, is considerably more elastic. The elasticities depend strongly on household income. The largest elasticities are found for females living in poor households. The results of the tax simulations suggest that there are only modest labour supply responses from replacing the 1987 system by proportional taxes.
Compensation of De-saturation Effect in HDR Images Based on Real Scene Adaptation Analysis

Chromatic adaptation transform is used to many color appearance models. Also, the iCAM06 model, a highdynamic range (HDR) rendering process contains chromatic adaptation and is based on the CIECAM02 model. The D factor or degree of adaptation in chromatic adaptation of the iCAM06 model is the function of surround luminance or adapting luminance level, but does not consider visual white point shift according to the variation of luminance level. The iCAM06 model acquires a white image from the Gaussian low-passed image. So, each pixel has different white value, and when some of colors cover the whole image, de-saturation effect appears around these colors. In addition, the scale factor used to reduce de-saturation effect causes unsuitable change of adaptation degree. Therefore, we propose the new method to find global illuminant information and degree of adaptation in the HDR image rendering process. By comparing the proposed with the conventional method, we confirm that the proposed method has better performance in illuminant estimation and color representation.
Design of a System for Safe Driving Based on the Internet of Vehicles and the Fusion of Multi-aspects Information

To alleviate the growing transportation safety tension, this paper proposes a system to guarantee the security of the drivers while driving. The system achieves the goal from two aspects: a) to remind the drivers the dangerous driving behaviors of themselves and analyses the drivers' performances based on a large number of statistics to regulate the drivers' driving habits, b) to prevent the potential risks from the adjacent vehicles. The system use Multi-aspects information fusion and the Internet of Vehicle not only to be of efficiency of ensuring the safe driving and preventing the drivers from the dangers, but also reduce the related expenditure of personnel and fund.
A Case for Generic Interfaces in Cognitive Radio Networks

Cognitive wireless networks are envisioned as a solution for intelligent ubiquitous networks that are capable of adapting to the dynamic environment through programmable radio devices. Each functional module and protocol accomplishes such learning by observing the various environmental, networking and application related parameters and adapts its behaviour to improve the performance. While access to a variety of parameters provide a large margin to improve the performance, it hinders the practical use of the system by restricting portability. In this paper, we argue that standard generic interfaces can be effectively used to overcome the above problem and enable seamless communication between modules at different layers and radio technologies. We examine the various parts of a cognitive wireless network, categorise different types of generic interfaces, analyse the suitable abstractions, and propose architectural principles for a cognitive radio system. We discuss four such categories of generic interfaces that abstract different layers of networking, application utility and policy enforcement. Various business benefits arising from such standard generic interfaces are briefly discussed. We believe that the proposed architecture and the benefits greatly enhance a feasible realisation of cognitive wireless networks.
On the location of hotspots in the framework of mantle convection

Putative mechanisms that have been proposed to explain intraplate “hotspot” volcanism extensively depart from the early plume theory, and many do not involve deep mantle flow. Here, we look for a relationship between hotspot volcanism and mantle flow using flow models excited by density anomalies inferred from seismic tomography. We show that previously identified major hotspots are preferentially located, to a high degree of statistical significance, above regions of positive divergence of horizontal shear tractions beneath the lithosphere. This observation renders it difficult to discard some contribution of mantle flow as a control on hotspot volcanism and instead suggests that mantle plumes are drawn toward, and conveyed by, mantle upwellings (either active or passive), which are revealed by the positive stress divergence. This allows us to exclude a variety of external or shallow mechanisms for the major hotspots. Because we also find that many secondary hotspots do fall at random locations with respect to mantle flow, we emphasize that alternative processes are also required to trigger the less productive volcanism.
A Novel Framework for Optimal RGB to Grayscale Image Conversion

Nowadays most images are shot as color images. Yet in situations such as printing or pattern recognition they also need to be converted to grayscale images. The most important problem in this conversion process is to preserve the image contrast. In this paper we make two contributions. In the simple yet popular line projection approach, which is also adopted in Matlab, we propose an entropy-based optimization framework to choose the optimal line direction so that all the pixel color vectors in an image have the most spread-out projections, thus increasing the grayscale image contrast. Secondly, we make use of histogram specification on all the projection points to further increase the image contrast. Experimental results show that the proposed framework produces enhanced results compared to typical other methods.
Human body pose estimation using silhouette shape analysis

We describe a system for human body pose estimation from multiple views that is fast and completely automatic. The algorithm works in the presence of multiple people by decoupling the problems of pose estimation of different people. The pose is estimated based on a likelihood function that integrates information from multiple views and thus obtains a globally optimal solution. Other characteristics that make our method more general than previous work include: (1) no manual initialization; (2) no specification of the dimensions of the 3D structure; (3) no reliance on some learned poses or patterns of activity; (4) insensitivity to edges and clutter in the background and within the foreground. The algorithm has applications in surveillance and promising results have been obtained.
Consciousness and Unconsciousness of Artificial Intelligence

This paper presents the author’s attempt to justify the need for understanding the problem of multilevel mind in artificial intelligence systems. Thus, it is assumed that consciousness and the unconscious are not equal in natural mental processes. The human conscious is supposedly a “superstructure” above the unconscious automatic processes. Nevertheless, it is the unconscious that is the basis for the emotional and volitional manifestations of the human psyche and activity. At the same time, the alleged mental activity of Artificial Intelligence may be devoid of the evolutionary characteristics of the human mind. Several scenarios are proposed for the possible development of a “strong” AI through the prism of creation (or evolution) of the machine unconscious. In addition, we propose two opposite approaches regarding the relationship between the unconscious and the conscious.
A S D F : Automated, Online Fingerpointing for Hadoop1

Localizing performance problems (or fingerpointing) is essential for distributed systems such as Hadoop that support long-running, parallelized, data-intensive computations over a large cluster of nodes. Manual fingerpointing does not scale in such environments because of the number of nodes and the number of performance metrics to be analyzed on each node. ASDF is an automated, online fingerpointing framework that transparently extracts and parses different time-varying data sources (e.g., sysstat, Hadoop logs) on each node, and implements multiple techniques (e.g., log analysis, correlation, clustering) to analyze these data sources jointly or in isolation. We demonstrate ASDF’s online fingerpointing for documented performance problems in Hadoop, under different workloads; our results indicate that ASDF incurs an average monitoring overhead of 0.38% of CPU time, and exhibits average online fingerpointing latencies of less than 1 minute with false-positive rates of less than 1%. 1ASDF stands for Automated System for Diagnosing Failures Acknowledgements: The authors would like to acknowledge Julio Lopez for discussions on Hadoop, Kathleen Carley for her insights on visualization, and Christos Faloutsos for discussions on data mining. This material is based on research sponsored in part by the National Science Foundation, via CAREER grant CCR-0238381 and grant CNS-0326453.
Outlier Detection with One-class Kernel Fisher Discriminants

The problem of detecting "atypical objects" or "outliers" is one of the classical topics in (robust) statistics. Recently, it has been proposed to address this problem by means of one-class SVM classifiers. The main conceptual shortcoming of most one-class approaches, however, is that in a strict sense they are unable to detect outliers, since the expected fraction of outliers has to be specified in advance. The method presented in this paper overcomes this problem by relating kernelized one-class classification to Gaussian density estimation in the induced feature space. Having established this relation, it is possible to identify "atypical objects" by quantifying their deviations from the Gaussian model. For RBF kernels it is shown that the Gaussian model is "rich enough" in the sense that it asymptotically provides an unbiased estimator for the true density. In order to overcome the inherent model selection problem, a cross-validated likelihood criterion for selecting all free model parameters is applied.
Massless BTZ black holes in minisuperspace

We study aspects of the propagation of strings on BTZ black holes. After performing a careful analysis of the global spacetime structure of generic BTZ black holes, and its relation to the geometry of the SL(2,) group manifold, we focus on the simplest case of the massless BTZ black hole. We study the SL(2,) Wess-Zumino-Witten model in the worldsheet minisuperspace limit, taking into account special features associated to the lorentzian signature of spacetime. We analyse the two- and three-point functions in the pointparticle limit. To lay bare the underlying group structure of the correlation functions, we derive new results on Clebsch-Gordan coefficients for SL(2,) in a parabolic basis. We comment on the application of our results to string theory in singular time-dependent orbifolds, and to a lorentzian version of the AdS/CFT correspondence.
VIDEO IN TEACHING READING FOR BUSINESS PURPOSES: INTEGRATED- SKILLS APPROACH

______________ The paper provides practical suggestions for organizing reading instruction in a course of English for students of Business and Economics. The instruction is based on the integrated-skills approach, and video is used as one of the principal technological tools employed at the pre-reading stage for implementing this approach. It serves both for stimulating reading and as an introduction to the themes and contents of the texts to be read.
A Foundational Approach to Generalising the Maximum Entropy Inference Process to the Multi-Agent Context

The present paper seeks to establish a logical foundation for studying axiomatically multi-agent probabilistic reasoning over a discrete space of outcomes. We study the notion of a social inference process which generalises the concept of an inference process for a single agent which was used by Paris and Vencovska to characterise axiomatically the method of maximum entropy inference. Axioms for a social inference process are introduced and discussed, and a particular social inference process called the Social Entropy Process, or SEP, is defined which satisfies these axioms. SEP is justified heuristically by an information theoretic argument, and incorporates both the maximum entropy inference process for a single agent and the multi–agent normalised geometric mean pooling operator.
Control design of concentric self-servo track writing systems for hard disk drives

Modern hard disk drives (HDDs) generate the head position error signal (PES) by reading special magnetic patterns called servo patterns, which are written in designated areas on the disk surface known as servo sectors, and measure the position of the head relative to the center of the data track. Servo sectors are typically created during the HDD manufacturing process and are not subsequently overwritten or erased. The process of writing servo sector patterns is known as servo track writing (STW). The accuracy and precision of the servo track writing process plays a crucial role in dictating the ultimate track density and areal storage density of the HDD. There are several mechanisms for performing servo track writing. Conventional servo track writing processes use additional sensing and positioning equipment external to the HDD. In contrast, self-servo track writing processes only use the HDD's reading and writing heads and servo system; thus avoiding many of the manufacturing cost increases and productivity losses associated with conventional servo track writing. This paper presents two novel controller synthesis methodologies for performing concentric self-servo track using a feedforward control structure. In the first methodology, it is assumed that a conventional track-following causal feedback compensator has been designed. A non-causal feedforward compensator, which utilizes the stored error signal that was created while writing the previous track, is subsequently designed using standard H∞ control synthesis techniques, in order to achieve good disturbance attenuation while preventing the propagation of tracking errors from previous tracks. In the second methodology, both the track-following feedback compensator and the feedforward compensator are simultaneously designed via a mixed H2/H∞ control scheme, which involves the solution of a set of linear matrix inequalities. Simulation results confirm that the two proposed control synthesis methodologies prevent error propagation from the previously written tracks and significantly improve self-servo track writing performance.
Numerical Solutions Of American Options With Dividends Using Finite Difference Methods

We study the Black-Scholes model for American options with dividends. We cast the problem as a free-boundary problem for heat equations and use transformations to rewrite the problem in linear complementarity form. We use explicit and implicit finite difference methods to obtain numerical solutions. We implement and test the methods on a particular example in MATLAB. The effects of dividend payments on option pricing are also considered.
Impact of channel estimation errors on lattice reduction gains in MIMO systems

In this paper, we propose an approach based on Lattice Reduction (LR) algorithm which preserves the channel norm in the presence of estimation errors. We analyze the channel norm of perfect and imperfect channel by employing LR algorithm on both perfect and imperfect channels in MIMO systems with 2, 4, 8 and 16 antennas for various error variances. We conclude that effective detection can be achieved even with imperfect channel by employing LR on those channels.
Combination of an Allosteric Akt Inhibitor MK-2206 with Etoposide or Rapamycin Enhances the Antitumor Growth Effect in Neuroblastoma

Purpose: Activation of Akt is a marker of decreased event-free or overall survival in neuroblastoma patients. MK-2206, a novel allosteric Akt inhibitor, is now tested in clinical trials in adult cancers. In this study, effect of MK-2206 on tumor growth and murine survival, alone or in combination, with etoposide or rapamycin was evaluated. Experimental Design: The anticell proliferation effect of MK-2206 was tested in eight neuroblastoma cell lines by MTS assay. Caspase-3/7 activity, cell-cycle analysis, and reactive oxygen species (ROS) production were determined. Effect of MK-2206 combined with etoposide or rapamycin was evaluated in vitro and in vivo. Akt phosphorylation was measured by Western blotting in neuroblastoma cells and tumors. Results: In vitro, MK-2206 treatment inhibited neuroblastoma cell proliferation, which was accompanied by a cell line selective G1 arrest of cell cycle or production of ROS. A synergistic effect between MK-2206 and etoposide was detected in four tested neuroblastoma cell lines via caspase-dependent apoptosis, whereas increased inhibition of cell growth induced by combination of MK-2206 and rapamycin was mediated by ROS production. In vivo, MK-2206 alone decreased tumor growth and increased murine survival at dose that inhibited Akt phosphorylation in tumors. MK-2206, in combination with etoposide or rapamycin, caused a significant decrease in tumor growth and increase of murine survival compared with MK-2206 alone. Conclusion: Akt inhibition by MK-2206 increased the efficacy of etoposide or rapamycin. Our study supports future clinical evaluation of MK-2206 in combination with conventional cytotoxic therapy or with rapamycin in high-risk neuroblastoma patients. Clin Cancer Res; 18(13); 3603–15. ©2012 AACR.
MultiClassifier: A combination of DPI and ML for application-layer classification in SDN

In traditional campus network, application-layer classification is often achieved by using specific devices that support application-layer classification. Since different vendors have different realizations, even the same flow may have different results with different devices. Thus it's hard to set a global consistent application-layer management policy for the whole network. The idea of separating the control plane and the data plane comes up with Software Defined Network have opened a gate for solving this problem. In the SDN paradigm, the control plane have a global view over the whole network, thus it can do application-layer classification and set policies globally. In this paper, we identify problems with the current application-layer classification in campus network and analyze the advantage of doing application-layer classification with SDN. And based on SDN, we show a new approach to do application-layer classification combining different classifiers: Deep Packet Inspection and Machine Learning based Packet Classification. Our experiments show that with this approach, we can archive a high classification speed while maintain an acceptable accuracy rate.
Haar wavelet method for solving lumped and distributed-parameter systems

An operational matrix of integration based on Haar wavelets is established, and a procedure for applying the matrix to analyse lumped and distributed-parameters dynamic systems is formulated. The technique can be interpreted from the incremental and multiresolution viewpoint. Crude as well as accurate solutions can be obtained by changing the parameter m; in the mean time, the main features of the solution are preserved. Several nontrivial examples are included for demonstrating the fast, flexible and convenient capabilities of the new method.
Design of mobile robot teleoperation system based on virtual reality

As a rising subject, virtual reality is the best human-machine interface technology so far. This paper submits the architectural structure of the robot teleoperation system based on virtual reality and after analyzing the principle of the system's operation, it sets up the model of hierarchical control. Subsequently this paper elaborates critical techniques such as telerobot, creating virtual environment, designing video supervising system and so on. Bring technology of virtual reality in robot teleopration system, not only strengthens the working ability of the actuator but also widens application areas of the device. Manipulator can safely and cozily completes tasks by teleoperating the robot.
MSMS Peak Identification and its Applications

* To whom correspondence should be addressed. ABSTRACT A peak detection algorithm for Tandem Mass Spectra is presented that scores a fragment using intensity and isotopic distribution. It classifies each fragment in a spectrum as noise or signal based on a maximum likelihood estimate derived from the distribution observed in a training set of 12,000 validated spectra. This is the largest such database known to the authors. We present three tools which apply this algorithm: the Quality Filter removes noisy spectra, Mod-Pro profiles modifications and amino acids in a sample and Spectrimilarity scores similarity of two spectra. Contact: njaitly@caprion.com
Nonlinearities in the Relationship between Debt and Growth: Evidence from Co-Summability Testing

This paper employs novel time series methods to investigate the presence of nonlinearities in the long-run relationship between public debt and growth, analysing annual series for the United States, Great Britain, Japan and Sweden from the 1800s to 2008. We find only limited evidence for a nonlinear long-run relationship in these countries and further cannot support the notion that the equilibrium debt-growth relationship is identical across countries. Both results weaken the case for a common 90% or indeed any common debt/GDP threshold recently popularised by the work of Reinhart and Rogoff (2010) and others.
Improving the Quality of Life in Persons with Dementia through a Pilot Study of a Creative Dance Movement Programme in an Asian Setting

Methods: A prospective, cohort study was performed outpatient, on ten community-dwelling patients with mildmoderate dementia, with their care-givers. All the study participants underwent a creative dance movement programme (Everyday Waltzes) by two dance therapists, weekly for six weeks. The Alzheimer’s disease Quality of Life Inventory (ADQoL) scores of both patients and their caregivers’ impression of the patient’s quality of life in each of the 13 items, and combinations of items to obtain physical, social and psychological domains, pre and post intervention were recorded in a numerical scale. Wilcoxon’s signed rank test was used to determine the statistical significance of any change in summated score of each of the domains and overall score, post intervention in both patients and caregivers.
A real-world recording database for ad hoc microphone arrays

We report on a recently-recorded database for use in processing of ad hoc microphone constellations. Twenty-four microphones were positioned in various locations at a central table in a large room, and their outputs were recorded while 4 target talkers at the table both read from a list of sentences in a constrained way and also maintained a natural conversation for several minutes. This was done in the quiet and in the presence of 8, 24, and 56 other simultaneous talkers surrounding the central table at various distances. We also recorded without the 4 target talkers active in each of these conditions, and used a loudspeaker to measure impulse responses to the microphones from various positions in the room. We provide details of the recording setup and demonstrate use of this database via an application of linearly constrained minimum variance beam-forming. The database will become available to researchers in the field.