Miracle-dz/UniFAR-FTUs huggingface dataset:
A Wideband 341-386 GHz Transmitter in SiGe BiCMOS Technology

A transmitter with an output frequency range from 341 to 386 GHz is presented. Power output varies from 0.1 to -15.8 dBm along its operating range, while it remains above -2.9 dBm from 341 to 353 GHz. The high-frequency signal is generated using a wideband push-push voltage-controlled oscillator (VCO) with coarse and fine frequency tuning control, a 3-stage power amplifier, and a frequency doubler. Additionally, a frequency divider is integrated to provide a second low- frequency output for measurement purposes and to enable later the addition of a phase-locked loop (PLL) for the stabilization of the VCO. The obtained frequency tuning range of 12.4% is a record value for silicon-based transmitters above 300 GHz. The total power consumption is 790 mW. The chip is fabricated in a 130nm SiGe BiCMOS technology with fT/fmax = 250/370 GHz.
Reversible Defects in Natural Killer and Memory Cd8 T Cell Lineages in Interleukin 15–Deficient Mice

C57BL/6 mice genetically deficient in interleukin 15 (IL-15−/− mice) were generated by gene targeting. IL-15−/− mice displayed marked reductions in numbers of thymic and peripheral natural killer (NK) T cells, memory phenotype CD8+ T cells, and distinct subpopulations of intestinal intraepithelial lymphocytes (IELs). The reduction but not absence of these populations in IL-15−/− mice likely reflects an important role for IL-15 for expansion and/or survival of these cells. IL-15−/− mice lacked NK cells, as assessed by both immunophenotyping and functional criteria, indicating an obligate role for IL-15 in the development and functional maturation of NK cells. Specific defects associated with IL-15 deficiency were reversed by in vivo administration of exogenous IL-15. Despite their immunological defects, IL-15−/− mice remained healthy when maintained under specific pathogen-free conditions. However, IL-15−/− mice are likely to have compromised host defense responses to various pathogens, as they were unable to mount a protective response to challenge with vaccinia virus. These data reveal critical roles for IL-15 in the development of specific lymphoid lineages. Moreover, the ability to rescue lymphoid defects in IL-15−/− mice by IL-15 administration represents a powerful means by which to further elucidate the biological roles of this cytokine.
A Cultural Algorithm Based on Artificial Bee Colony Optimization for Community Detection in Signed Social Networks

In this paper, we propose a cultural algorithm for detecting communities in signed networks based on artificial bee colony optimization, which is a process of dual inheritance from both a micro-evolutionary level and a macro-evolutionary level. We define the population space and belief space of the algorithm which are fit for community detection problem in signed social networks. To validate the performance of the algorithm we make experiments on two real-world networks and comparative experiments with three existing detecting algorithms on synthetic networks. The experimental results show that our algorithm gets a good performance on real-world networks and outperforms other algorithms on most of synthetic networks.
A Unified Kernel Approach for Learning Typed Sentence Rewritings

Many high level natural language processing problems can be framed as determining if two given sentences are a rewriting of each other. In this paper, we propose a class of kernel functions, referred to as type-enriched string rewriting kernels, which, used in kernel-based machine learning algorithms, allow to learn sentence rewritings. Unlike previous work, this method can be fed external lexical semantic relations to capture a wider class of rewriting rules. It also does not assume preliminary syntactic parsing but is still able to provide a unified framework to capture syntactic structure and alignments between the two sentences. We experiment on three different natural sentence rewriting tasks and obtain state-of-the-art results for all of them.
Spatial sound generation using HRTF created by the use of recursive filters

Generating spatial sound and playing it through headphones is a demanding task, since two important factors, ILD - inter-aural level difference and ITD - inter-aural time difference, need to be taken into consideration. The problem can be solved by the use of head related transfer functions (HRTF) which represent a set of empirically measured functions, one for each spatial direction. The complete reconstruction of HRTF is possible through the use of finite impulse response (FIR) filters with 512 coefficients each. Since the spectrum of HRTF consists of distinctive maximums and minimums, the spectrum could be approximated by the use of resonators and notch filters. The approximation of the complete spectrum (20 Hz - 20 kHz) could be done by the use of six resonators and one notch filter. Our approach to spatial sound generation using HRTF created by the use of recursive (IIR) filters presents a practical and computationally effective solution. It also indicates a way to uniformly model all factors connected to spatial sound perception.
A sequential adder using recurrent networks

D.E. Rumelhart et al.'s proposal (1986) of how symbolic processing is achieved in PDP (parallel distributed processing) networks is tested by training two types of recurrent networks to learn to add two numbers of arbitrary lengths. A method of combining old and new training sets is developed which enables the network to learn and generalize with very large training sets. Through this model of addition, these networks demonstrated capability to do simple conditional branching, while loops, and sequences, mechanisms essential for a universal computer. Differences between the two types of recurrent networks are discussed, as well as implications for human learning. <<ETX>>
Multi-feature and DAG-Based Multi-tree Matching Algorithm for Automatic Web Data Mining

Web data extraction has received considerable attention and study in recent decades. To improve efficiency, many automatic Web data record mining approaches have been proposed. Among these approaches, each complete approach involves data record identification as well as data item alignment. In this paper, we propose a new multi-feature and DAG (Directed Acyclic Graph) based multi-tree matching algorithm for automatic data item alignment. Our algorithm improves alignment accuracy in two aspects. First, it combines multiple features to cope with the limitations of existing algorithms, second, it employs a DAG-based method to deduce the global alignment of data items with high accuracy. Experimental results show that our algorithm outperforms state-of-the-art data item alignment algorithms.
Storm in a rice bowl: Rice reform and poverty in Vietnam in the 1990s

This paper analyses the poverty impact of Vietnam’s reforms, especially with reference to the rice economy, during the 1990s. It reviews trade and institutional reforms that directly impinged on the rice sector, and the trends in rice production, consumption and prices throughout the 1990s. We then analyse the impact of rice production on poverty dynamics by estimating a multinomial logit model based on a panel of 4,302 households. Our results show that the increased rice production as a result of various reforms contributed to poverty reduction. We also explore whether households that are disproportionately heavy consumers of rice suffered from the price increase. We examine whether allowing the poverty line to reflect household-specific consumption bundles would change the identification of poverty dynamics. We conclude that unless the large changes in relative prices are accompanied by massive differences in consumption baskets across households accounting for differences in consumption preferences would make little difference in assessing the poverty implications of reform.
Training a Personal Alert System for Research Information Recommendation

Information Systems, and in particular Current Re- search Information Systems (CRISs), are usually quite difficult to query when looking for specific information, due to the huge amounts of data they contain. To solve this problem, we propose to use a per- sonal search agent that uses fuzzy and rough sets to inform the user about newly available information. Additionally, in order to auto- mate the operation of our solution and to provide it with sufficient in- formation, a document classification module is developed and tested. This module also generates fuzzy relations between research domains that are used by the agent during the mapping process.
On the Aging Effects Due to Concurrency Bugs: A Case Study on MySQL

This study investigates software aging effects caused by the activation of concurrency bugs in a wellknown database management system (DBMS), namely MySQL. Experiments with different workloads are performed in order to reproduce the most likely conditions for concurrency bugs activation. Besides the typical aging effects observed in many operational systems (i.e., a gradual degradation over time), results highlight that both available resources and DBMS performance (e.g. service rate, service time, and connection latency) can decrease with time in a hard-to-predict way. We observed that, due to the activation of concurrency bug, the DBMS enters a degraded state in which: i) the estimation of Time-To-Failure (TTF) by means of memory depletion trend analysis is highly inaccurate, and ii) the failure rate does not depend on the instantaneous and/or mean accumulated work. Results suggest that, in such cases, finer-grained indicators and/or different techniques need to be taken into account for properly preventing failures.
Distances Correlation for Re-ranking in Content-Based Image Retrieval

Content-based image retrieval relies on the use of efficient and effective image descriptors. One of the most important components of an image descriptor is concerned with the distance function used to measure how similar two images are. This paper presents a clustering approach based on distances correlation for computing the similarity among images. Conducted experiments involving shape, color, and texture descriptors demonstrate the effectiveness of our method.
An Efficient Method for Eye Detection in Color Images using Morphological Operations

This paper presents an efficient method for detecting human eye in a full frontal face color image. Eye regions are characterized by low illumination, high density edges and high contrast as compared to other parts in frontal face image. This method uses basic color image processing and morphological operation to detect eyes. Using color based training algorithm, this technique first extracts skin region of the input image. Then morphological operation is applied for detection of eye boundary and light source reflection of eye, also known as eye dot. It gives finite number of eye candidates from which noise is subsequently removed. For full frontal face images, this method is found highly efficient and accurate for eye detection.
Optimal Resource Allocation for Genetic Algorithm Based Multi-Objective Optimization with 1000 Simulations

1. Abstract This study pertains to practical application of the GA for industrial applications where only a limited number of simulations can be afforded. Specifically, an attempt is made to find the optimal allocation of the total simulation budget (population size and number of generations) for constrained multi-objective optimization. A study is conducted to seek improvements while restricting the number of simulations to 1000. Parallelization is exploited using concurrent simulations for each GA generation on a HP quad-core cluster, and resulted in a significant time savings. Furthermore, the optimal distribution of computational effort to achieve the greatest improvement in performance was explored. Two analytical examples as well as an automotive crashworthiness simulation of a finite element model with 58,000 elements were used as test examples. Various population sizes and numbers of generations were tried while limiting the total number of simulations to 1000. The optimization performance was compared with Monte-Carlo and space filling sampling methods. It was observed that using the GA, many feasible and trade-off solutions could be found. It is shown that it is beneficial to allow a large number of generations to get good trade-off solutions. For the vehicle design, significant improvements in the performance were observed while this example also suggests that, for problems with a small feasible region, the number of feasible solutions can be significantly increased in the first few generations involving about 200 simulations.
Model and analysis of local decision based on cooperative self-organization for problem solving

This paper presents an approach based on cooperative self-organization for artificial systems, in order to tackle openness and dynamics. In this work, cooperation is used as a local criterion enabling parts of the system  the cooperative agents  to reorganize as to autonomously modify their interactions and then the global function. The difficulty in defining cooperation and the means to reestablish a cooperative state within a non cooperative system are underlined and analyzed. Two cooperative agents' behaviors are expounded, at the boundary between altruism and selfishness. This approach is also illustrated by modeling, from a local viewpoint, a classical constraint satisfaction or optimization problems.
Field trial of an information-providing robot in a shopping mall

We developed an information-providing robot for a shopping mall. It moves around and hands over flyers about the mall and when people are interested or approach the robot to obtain some information, it starts the information-providing service. During the information-providing service, it provides directions and recommendations of shops in the mall. The robot is developed to be fully autonomous, but due to low performance in automatic speech recognition, this function is overtaken by a human operator. In our field trial, the robot worked for 53.4 hours (over 11 days) without any major failure (83.5% of time autonomously operated). We conducted interviews with visitors, and revealed that the majority of them found the information from the robot useful, and wanted to use it again.
Learning support order for manipulation in clutter

Understanding positional semantics of the environment plays an important role in manipulating an object in clutter. The interaction with surrounding objects in the environment must be considered in order to perform the task without causing the objects fall or get damaged. In this paper, we learn the semantics in terms of support relationship among different objects in a cluttered environment by utilizing various photometric and geometric properties of the scene. To manipulate an object of interest, we use the inferred support relationship to derive a sequence in which its surrounding objects should be removed while causing minimal damage to the environment. We believe, this work can push the boundary of robotic applications in grasping, object manipulation and picking-from-bin, towards objects of generic shape and size and scenarios with physical contact and overlap. We have created an RGBD dataset that consists of various objects used in day-to-day life present in clutter. We explore many different settings involving different kind of object-object interaction. We successfully learn support relationships and predict support order in these settings.
Law and the Economics of Entrepreneurship

I show how the law interacts with entrepreneurship in two principal ways. First, legal structures shape organizational forms in entrepreneurship. Second, legal rules and institutions carry public policy implications for entrepreneurship in at least three areas: regulation; bankruptcy legislation; and the broad area of property rights, corruption, and the efficiency of courts. This article reviews the literature on each of these issues.
Modelling Abrupt Shift in Time Series Using Indicator Variable: Evidence of Nigerian Insurance Stock

This study models abrupt shift in time series using indicator variable. Seven symmetric and five asymmetric models were considered by incorporating an indicator variable in the variance equation to monitor the changes of some selected Nigerian insurance stocks. The results showed that the daily returns were stationary but not normally distributed and eight out of ten stocks considered for the study showed evidence of ARCH effect. The performance of the different models was evaluated using the RMSE, MAE and MAPE. The model ARCH (1) proved to be the most suitable among the twelve competing volatility models considered. When the regime changes are incorporated into the model, it is found that the highly persistent volatility of the insurance stock return rate is reduced for most of the stocks.
EXPLORING THE DYNAMICAL NATURE OF THE LOWER SOLAR CHROMOSPHERE

We examine spectral time-series of two lowerchromospheric lines (Ni 1319Å and Cii 1335Å) observed with the SUMER instrument on the SOHO spacecraft. We point out differences between (intensity and velocity) power spectra of network and internetwork regions and argue that the behaviour resembles that of Ca ii power spectra. No significant phase differences are found between the intensities of both lines. However, when phase spectra are averaged along the slit there is some evidence that the C ii intensity lags that of Ni by 16 sec near 3 mHz. Intensity power spectra of C ii are affected at higher frequencies by streams of emitting structures. Using contrastenhanced time slices we show that 1) there exists a grain-like pattern which is found in both network and internetwork regions; 2) streams of supersonically moving structures probably outline a wave interference pattern; 3) the sizes of structures observed in Ni are smaller than when observed in C ii. At various points our findings disagree with earlier results from SUMER. A cookbook formalism is presented to derive confidence levels for power, phase, gain and coherency spectra.
Sparse Representation for Image Similarity Assessment using SIFT Algorithm

Assessment of image similarity is fundamentally important to numerous multimedia applications. The goal of similarity assessment is to automatically assess the similarities among images in a perceptually consistent manner. In this project, we interpret the image similarity assessment problem as an information fidelity problem. More specifically, we propose a feature-based approach to quantify the information that is present in a reference image and how much of this information can be extracted from a test image to assess the similarity between the two images. Here, we extract the feature points and their descriptors from an image, followed by learning the dictionary/basis for the descriptors in order to interpret the information present in this image. Then, we formulate the problem of the image similarity assessment in terms of sparse representation. The prime importance of this project is even after the image is subjected to geometrical variations like rotating, shifting, change in orientation, change in background, still we can able to correctly identify the similarity between two image. To evaluate the applicability of the proposed featurebased sparse representation for image similarity assessment (FSRISA) technique, we apply FSRISA to three popular applications, namely, image copy detection, retrieval, and recognition by properly formulating them to sparse representation problems.
Classification and Discrimination for Traditional Chinese Medicine Nature Based on OSC-OPLS/O2PLS-DA

the research for Chinese herbs’ warm and cold natures classification is a significative thing for clinical. The paper put forward a new model classification and discrimination for Traditional Chinese medicine(TCM)' nature based on orthogonal signal correction-orthogonal partial least squares-discriminant analysis (OSC-OPLS/O2PLS-DA) after normalization. The first, data preprocessing and normalization for the metabolites sample space’s data, and the results data 135 multiply 839 dimension consist of three sections: warm nature’s normalization sample, cold nature’s normalization sample, blank group normalization sample; The second, OSC, dimension reduction and noise reduction for the metabolites sample space’s data; the third, OPLS/O2PLS-DA, Generate classification and discrimination Method for Traditional Chinese medicine(TCM)' nature. The model was proved to be feasible and effective after tested with 6 type’s warm nature’s herbs, 6 type’s cold nature’s herbs.
Work stress and coronary heart disease: what are the mechanisms?

AIMS To determine the biological and behavioural factors linking work stress with coronary heart disease (CHD). METHODS AND RESULTS A total of 10 308 London-based male and female civil servants aged 35-55 at phase 1 (1985-88) of the Whitehall II study were studied. Exposures included work stress (assessed at phases 1 and 2), and outcomes included behavioural risk factors (phase 3), the metabolic syndrome (phase 3), heart rate variability, morning rise in cortisol (phase 7), and incident CHD (phases 2-7) on the basis of CHD death, non-fatal myocardial infarction, or definite angina. Chronic work stress was associated with CHD and this association was stronger among participants aged under 50 (RR 1.68, 95% CI 1.17-2.42). There were similar associations between work stress and low physical activity, poor diet, the metabolic syndrome, its components, and lower heart rate variability. Cross-sectionally, work stress was associated with a higher morning rise in cortisol. Around 32% of the effect of work stress on CHD was attributable to its effect on health behaviours and the metabolic syndrome. CONCLUSION Work stress may be an important determinant of CHD among working-age populations, which is mediated through indirect effects on health behaviours and direct effects on neuroendocrine stress pathways.
Cyclical Bias in GovernmentSpending: Evidence fromthe OECD

This paper studies the role of business cycles in the phenomenon of increasing government spending/GDP ratios in the OECD countries. Using a panel data set covering the 1975-1995 period, the mainnd- ing is that the prolonged rise in this ratio is linked to a cyclical bias; the spending/GDP ratio increased during recessions and stayed ap- proximately constant during expansions. Also analyzed are the cycli- cal changes in the composition of government spending (goods and services, transfers and subsidies, and capital expenditure), in tax rev- enues, and a possible link between the cyclical bias and an index of government weakness. Journal of Economic Literature Classication Numbers: E62, H50, H60.
Extracting RESTful services from Web applications

The Web contains large amount of information and services primarily intended for human users. A Web application offers high user experience and responsiveness. A user performs different task, such as reserving flight tickets from a Web application. A task is a set of activities required for a user to achieve a goal. Similar tasks are often used in different websites. Therefore, facilitating their reuse would improve development productivity and ease maintenance of Web applications. However, designing a reusable Web application component is often neglected by Web developers due to the pressure for the time-to-market. To circumvent this limitation, we propose an approach to interactively identify tasks from Web applications and represent these tasks as services.