Miracle-dz/UniFAR-FTUs huggingface dataset:
The relation between AGN hard X-ray emission and mid-infrared continuum from ISO spectra: Scatter and unification aspects ,

We use mid-infrared spectral decomposition to separate the 6 μ m mid-infrared AGN continuum from the host emission in the ISO low resolution spectra of 71 active galaxies and compare the results to observed and intrinsic 2-10 keV hard X-ray fluxes from the literature. We find a correlation between mid-infrared luminosity and absorption corrected hard X-ray luminosity, but the scatter is about an order of magnitude, significantly larger than previously found with smaller statistics. Main contributors to this scatter are likely variations in the geometry of absorbing dust, and AGN variability in combination with non-simultaneous observations. There is no significant difference between type 1 and type 2 objects in the average ratio of mid-infrared and hard X-ray emission, a result which is not consistent with the most simple version of a unified scheme in which an optically and geometrically thick torus dominates the mid-infrared AGN continuum. Most probably, significant non-torus contributions to the AGN mid-IR continuum are masking the expected difference between the two types of AGN.
RETIREMENT, FINANCIAL INCENTIVES

This paper aims to assess empirically the relative size of incentive effects and health for the retirement decision. We specify and estimate a dynamic model for retirement behaviour that explicitly takes account of health and eligibility conditions and replacement rates of alternative exit routes from the labour force. A range of health instruments are constructed from estimates of a model for health dynamics and these are used to assess the effect of reporting errors and of endogeneity of health on the estimates of the retirement model. Our results provide evidence that health and retirement are endogenously related. Health matters but the size of the health effect depends crucially on the health measure used. We find that subjective health measures overstate the effect of health on retirement and that endogeneity of health suppresses the health effect. Incentive effects are relatively insensitive to alternative specifications for health. The incentive effects are strong for Early Retirement schemes. There is evidence that income streams in alternative exit routes are compared in the retirement decision and that alternative exit routes act as substitutes.
Automated test generation from a behavioral model

The challenge for testers: reduce the testing interval without reducing quality. One answer: find a new way to approach test design and test generation. This paper will discuss an ongoing Lucent Technologies experiment in automated test generation from a behavioral model of the software product under test. Results indicate that our new approach can increase the effectiveness of our testing while reducing the cost of test design and generation.
A Neuro-Fuzzy Approach for Equity Valuation Based on Fundamental Analysis

Equity valuation by fundamental analysis is modeled by a neuro-fuzzy approach in this paper. This neuro-fuzzy approach is a combination of a fuzzy inference system and a neural network. The fuzzy inference system enables the analyst to qualitatively describe his/her assessment of the influential factors in equity valuation. The neural learning can identify the relation between the influential factors and the equity value through the sample data, and also obtain a quantitative description of the qualitative assessment of influential factors. Stock prices and accounting information of listed companies in Taiwan are collected as sample data for constructing the equity valuation model by the proposed approach
A review of assistive devices for arm balancing

Due to neuromuscular disorders (e.g., Duchenne Muscular Dystrophy) people often loose muscle strength and become wheelchair bound. It is important to use muscles as much as possible. To allow this, and to increase independency of patients, an arm orthoses can be used to perform activities of daily life. The orthoses compensates for the gravity force of the arm, allowing people to perform movements with smaller muscle forces. For patients, the aesthetics of the orthosis is one of the critical issues. This paper presents the state-of-the-art in passive and wearable active arm orthoses, and investigates how to proceed towards a suitable structure for a wearable passive arm orthosis, that is able to balance the arm within its natural range of motion and is inconspicuous; in the ideal case it fits underneath the clothes. Existing devices were investigated with respect to the body interface, the volume, and the workspace. According to these evaluation metrics it is investigated to what extent the devices are wearable and inconspicuous. Furthermore, the balancing principle of the devices, the architecture, force transmission through the devices, and alignment with the body joints are investigated. It appears that there is only one wearable passive orthosis presented in literature. This orthosis can perform throughout the natural workspace of the arm, but is still too bulky to be inconspicuous. The other passive orthoses were conspicuous and mounted to the wheelchair. Except one, the wearable active orthoses were all conspicuous and heavy due to a large backpack to enclose the actuators. They also could not achieve the entire natural workspace of the human arm. A future design of an inconspicuous, wearable, passive arm orthoses should stay close to the body, be comfortable to wear, and supports pronation and supination.
Tensor term indexing: An application of HOSVD for document summarization

In this paper, a new method for text summarization is proposed by using an extended version of the Tensor Term Importance (TTI) model. This method summarizes documents by extracting important sentences from a document. It improves the per document summarization efficiency by incorporating additional information of the whole document set referring to the same topic (or coherent documents). The basic idea of this approach is to represent the whole document set in a uniform form, in the term-sentence-document tensor, and to use higher-order singular value decomposition (HOSVD) to highlight the important terms in each document. Here, we present two different methods of summarization. In the first method, the sentences having the highly weighted terms are extracted as the important sentences representing the document. The important sentences identified by selecting those that contains more from the important terms. The second model uses a so-called super sentence and uses that to extract other sentences having high similarity with it. Unlike in Latent Semantic Analysis (LSA) where SVD is applied for compressing the sparse term-document matrix and defining latent semantic links between terms, in TTI SVD is used to reduce noise and to highlight the important term-document relations in the document. Our evaluation results show that our TTI based methods are more similar to human generated summaries than other automated summarizers which work on single documents at a time.
Low-Rate Signal Compression Incorporating Decimation

The paper presents results for PCM- and DPCM-like signal compression schemes that incorporate lowpass -filtering and downsampling prior to the actual encoder, and corresponding upsampling and filtering to reconstruct the signal to the original rate after the decoder. This scheme is suggested by rate-distortion theory for coding signals with monotonically decaying spectra at low rate. The cutoff frequencies are optimized for best performance
Adaptat ion Agents : Providing Uniform Adaptat ions in Open Serv ice Archi tectures

We present the notion of adaptivity agents, agents that can be used to adapt an open range of services to a common user model, using a common adaptation scheme. The idea is illustrated with a specific adaptation agent, and exemplified with the KIMSAC system, an agentbased system for an open range of public information services to citizens. The usage of adaptation agents has several advantages to existing work on user-adaptive systems. The user model is shared between services, allowing services to make use of each other's inferences about the user. The adaptation agent is intended to be owned by the user, who can inspect and control its content and decide how information can be distributed to services. This way, an important privacy issue for open service architectures is addressed. The adaptation agent also provides a common model of user adaptations to services. This way, the user needs not learn a new interaction model each time a service is added. The functionality of the adaptation agent is determined by its service contract with services. The service contract specifies which adaptations will be made, and what information needs to be exchanged between agents to achieve these adaptations. We provide a first step towards a truly open service environment, by providing an analysis of the ontologies needed for communication between services and adaptation agents.
Face Recognition at a Distance: a study of super resolution

We evaluate the performance of face recognition using images with di�er- ent resolution. The experiments are conducted on Face Recognition Grand Challenge version one (FRGC v1.0) database and Surveillance Cameras Face (SCface) Database. Three recognition methods are used, namely Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA) and Local Binary Pattern (LBP). To improve the performance of face images with low- resolution (LR), two state-of-art super-resolution (SR) methods are applied. One is called Discriminative Super-resolution (DSR). It �nds the relation- ship from low-resolution images to their corresponding high-resolution (HR) images so that the reconstructed super-resolution images would be close to the HR images which belongs to the same subject with them and far away from others. The other SR method uses Nonlinear Mappings on Coherent Features (NMCF). Canonical Correlation analysis is applied to compute the coherent features between the PCA features of HR and LR images. Then Radial Basis Functions (RBFs) is used to �nd the mapping from LR fea- tures to HR features in the coherent feature space. The two SR methods are compared on both FRGC and SCface databases as well.
Object management strategy for an unified high level automotive sensor fusion framework

An important requirement in autonomous driving for many complex scenarios is to correctly detect static and dynamic targets under various states of motion. The possibility of fulfilling this requirement depends upon the availability of different sensor data to the sensor fusion module. This paper uses data from sensors with built-in tracking modules and our objective is to make the resultant of two different sensor fusion modules that use the same sensor tracked data, to be statistically relevant based on the respective operational requirements despite this commmon prior set-up. In our case, we have two sensor fusion modules. One sensor fusion module deals with dynamic targets with well-defined object representation and other module deals only with static targets of undefined shapes. The authors have developed different concepts to manage the relevancy of the deliverables of the two modules. A novel approach based on multi-hypothesis tracking is presented. The results are evaluated using simulation and as well as with real world sensor data with reference ground truth target data.
A Performance Model of Zoned Disk Drives with I/O Request Reordering

Disk drives are a common performance bottleneck in modern storage systems. To alleviate this, disk manufacturers employ a variety of I/O request scheduling strategies which aim to reduce disk head positioning time by dynamically reordering queueing requests. An analytical model of this phenomenon is best represented by an M/G/1 queue with queue length dependent service times. However, there is no general exact result for the response time distribution of this variety of queue with generalised service time distributions. In this paper, we present a novel approximation for the response time distribution of sucha queue. We then apply this method to the specific case of azoned disk drive which implements I/O request reordering. A key contribution is the derivation of realistic service time distributions with minimised positioning time. We derive analytical results for calculating not only the mean but also higher moments and the full distribution of I/O request response time. We validate our model against measurements from a real disk to demonstrate the accuracy of our approximation.
Performance of Interconnection Network in Multithreaded Architectures ?

In this paper, we analyze the performance of interconnec-tion networks in a multithreaded multiprocessor using a closed queuing network model. Proposed integrated model of the multiprocessor system captures the interaction among subsystems faithfully. Our study reveals a strong relationship of workload parameters to the network performance and brings out a feedback eeect of network response on message rate to the network.
Does the PI polymorphism alone control alpha-1-antitrypsin expression?

Whether genetic factors other than the protease-inhibitor (PI) polymorphism itself contribute to variation in alpha-1-antitrypsin is of both theoretical and practical interest. We have measured the quantity of alpha-1-antitrypsin (by an immunoturbidometric assay) and its activity (by assaying elastase inhibitory capacity [EIC]) in 583 individuals from 114 twin kinships who were also typed for PI by isoelectric focusing. Models of variation were fitted directly to the raw observations by a maximum-likelihood method. Specification of phenotypic means led to highly significant improvements in fit over models including only individual environment variance and additive genetic variance. The 29 phenotype means could also be described as the appropriate additive combinations of the 12 allelic effects. Only small improvements in fit could then be obtained by addition of polygenic components of variance. We conclude that nearly all genetic variation in alpha-1-antitrypsin quantity and activity can be explained by detectable variation at the PI locus and that this variance is largely additive. Bivariate analysis of alpha-1-antitrypsin and EIC revealed marginal evidence for differences in specific activities of molecules coded by different PI alleles. The correlation between environmental deviations for the two measures was only . 63, which may reflect, in part, the rather low reliability of the assays and account for the modest heritabilities (less than .5) of the two measures. An intriguing finding was the presence of significant differences in E1 variance for different PI types, suggesting that different phenotypes have differing capacities to react to environmental challenges.
Social Work Training in the Use of Evidence-Based Treatments for Children: What Works?

ABSTRACT Despite the benefits of evidence-based treatments (EBTs) for client outcomes, social service systems have struggled to disseminate EBTs. Effective graduate-level training in EBTs is critical to increase use, given the strong effect of initial training on future practices. This study examined outcomes of a graduate-level training program in EBTs. Questionnaires while in the program and postgraduation were administered to 66 students. Regression analyses indicated an increase in postgraduation use occurred after the program incorporated strategies to support the use of specific EBTs. Consistent with the implementation literature, initial planning with field sites, behavioral rehearsal of specific practice skills, focusing on initiation of EBTs, and targeted support for use of specific practices appear to support graduates’ uptake of EBTs in mental health settings.
The Limitation of MapReduce: A Probing Case and a Lightweight Solution

MapReduce is arguably the most successful parallelization framework especially for processing large data sets in datacenters comprising commodity computers. However, difficulties are observed in porting sophisticated applications to MapReduce, albeit the existence of numerous parallelization opportunities. Intrinsically, the MapReduce design allows a program to scale up to handle extremely large data sets, but constrains a program’s ability to process smaller data items and exploit variable-degrees of parallelization opportunities which are likely to be the common case in general application. In this paper, we analyze the limitations of MapReduce and present the design and implementation of a new lightweight parallelization framework, MRlite. MRlite can efficiently process moderatesize data with dependences among numerous computational steps. In the mean time, the parallelization on each step emulates the MapReduce model. Hence, the MRlite framework can also scale up for large data sets if massive parallelism with minimal dependence exists. MRlite can significantly improve the flexibility and parallel execution performance for a number of typical programs. Our evaluation shows that MRlite is one order of magnitude faster than Hadoop on problems that MapReduce has difficulty in handling. Keywords-Distributed computing; Parallel architectures
Keeping the background low: Production and testing of the GERDA phase II detectors

The GERmanium Detector Array (GERDA) experiment at the INFN Laboratori Nazionali del Gran Sasso searches for neutrinoless double beta decay of 76Ge. The first phase using ~15 kg of coaxial germanium detectors is ongoing. In a second phase, additional ~20 kg of newly produced Broad Energy Germanium (BEGe) detectors will be deployed. To limit the generation of cosmogenically induced radioisotopes, the exposure of the germanium to cosmic radiation during the detector production and testing was minimized. An acceptance and characterization campaign of the newly produced detectors was carried out at the HEROICA facility in the HADES underground laboratory in Mol, Belgium. An overview over the complete production process, from isotopic enrichment of the material to the detector testing protocol, is given.
yMGV: helping biologists with yeast microarray data mining

yMGV (yeast Microarray Global Viewer) was designed to provide biologists with meaningful information from genome-wide yeast expression data. The database includes most of the available expression data published on yeast microarrays over the last 4 years. It provides customizable tools for the rapid visualization of expression profiles associated with a set of genes from all published experiments. It also allows users to compare the results from different publications so that they can identify genes with common expression profiles. We used yMGV to perform global analyses to find a gene expression profile specific for given biological conditions and to locate functional gene clusters on chromosomes. Other organisms will be added to this database. yMGV is accessible on the web at http://transcriptome.ens. fr/ymgv.
Hybrid architecture for simultaneous localization and map building in large outdoor areas

This paper address the problem of navigating in very large outdoor unstructured environments. It presents solutions to the problem of closing large loops in simultaneous localization and map building applications. A hybrid architecture is presented that make use of the extended Kalman filter to perform SLAM in an efficient form and a Monte Carlo type filter to resolve the data association problem present when closing large loops. The proposed algorithm incorporates integrity to the standard SLAM algorithms by allowing multimode distribution to be handled in real time. Experimental results in outdoor environments are also presented.
Interference and energy aware topology control

Topology control in wireless sensor networks has been well studied, and a huge amount of algorithms for topology construction have been proposed. Among those algorithms, only a few methods take interference into account. In this paper, with minimizing energy consumption and communication interference in mind, we present a Minimum Spanning Tree (MST) based algorithm, which has following properties: (1) only localized information is needed for topology construction; (2) both energy consumption and interference are minimized simultaneously; (3) connectivity of the network is almost preserved. We also show by simulation that the above design goals are all fulfilled.
Trust, Risk and Benefit in Electronic Commerce: What Are the Relationships?

What does trust mean? Is trust really important to a consumer as it relates to electronic commerce? What are the relationships between consumer trust and other variables such as a consumer’s perception of risk and a consumer’s perception of benefit? To answer these questions, this paper proposes three alternative models related to consumer trust, risk, and benefit, and tests the relationships proposed from the models using consumer behavior data collected via a Web based survey.
K-Similar Conditional Random Fields for Semi-supervised Sequence Labeling

Sequence labeling tasks, such as named entity recognition and part of speech tagging, are the fundamental compositions of the information extraction system, and thus received attentions these years. This paper proposes k-similar conditional random fields for semi-supervised sequence labeling, and makes use of unlabeled data to calculate the similarity between words with distributional clustering. The named entity recognition experiments show that this method can improve the performance through unlabeled data.
Principles and Practice of Constraint Programming

In this work, we propose a two-stage approach to strengthen piecewise McCormick relaxations for mixed-integer nonlinear programs (MINLP) with multi-linear terms. In the ﬁrst stage, we exploit Constraint Programing (CP) techniques to contract the variable bounds. In the second stage we partition the variables domains using a dynamic multivariate partitioning scheme. Instead of equally partitioning the domains of variables appearing in multi-linear terms, we construct sparser partitions yet tighter relaxations by iteratively partitioning the variable domains in regions of interest. This approach decouples the number of partitions from the size of the variable domains, leads to a signiﬁ-cant reduction in computation time, and limits the number of binary variables that are introduced by the partitioning. We demonstrate the performance of our algorithm on well-known benchmark problems from MINLPLIB and discuss the computational beneﬁts of CP-based bound tightening procedures.
The design of LLVM-based shader compiler for embedded architecture

The increasing hand-held devices has resulted in widespread use of Apps. To sustain the Apps performance, most developers use Graphic Processing Units (GPUs) as hardware accelerator recently. As a result, GPUs become the basic system requirement in the smart hand-held devices. With the improvement of GPUs techniques, the rendering pipeline has become programmable by using the shading language proposed from DirectX and OpenGL. A shader compiler is essential to compile the shading language into GPU assembly in order to run on programmable rendering pipelines. In this thesis, we describe a case study for the process to implement an OpenGL-ES 2.0 shader compiler for new GPU designed by an ITRI team. The compiler is based on LLVM and OpenGL framework Mesa. We use the front-end from Mesa that translates the OpenGL-ES shading language (GLSL ES) 2.0 to TGSI intermediate form and the Gallium3D device driver framework in Mesa will translate the TGSI to LLVM bitcode. Finally, we use the LLVM as backend to compile the bitcode to assembly NV gpu program4. In our preliminary experimental results, we use the gpu simulator provided from ITRI and GLES benchmark to show that our LLVM based shader compiler is able to generate reliable codes for basic OpenGL-ES programs.