Miracle-dz/UniFAR-FTUs huggingface dataset:
On the Automatic Generation of Instruction SelectorUsing Bottom-Up Tree Pattern

Code generation in uences the e ciency of not only the compiled program but also the compilation process. The appearence of the many new processors has created the need of automating the code generation phase. The instruction selection is a important component to be poduced automatically in the code generation phase. The tools for automating the instruction selection usually use the tree-structure to represent the intermediate representation. The algorithms to manipulate the tree structure are simple. And the powerful tree rewriting tools can be automatically generated for transforming trees to target machine code. Among many technologies for the aumatic generation, BURS technology produces the fastest code generators. But, BURS technology often require long time and much space to process a complex machine description. Much e orts has gone to make BURS technology more attractive: e cient in points of time and space. In this proposal, we closely inspect BURS technology and propose a new method which may solve the problems of BURS technology.
The Characterization of the Wideband and Ultra-Wideband Mobile Channels and Their Equalization using Neuro-Fuzzy Techniques

The 3G mobile and 4G mobile systems have generated ever-increasing interest over the last few years, fuelled by a demand for high frequency utilization and a large number of users requiring simultaneous multidimensional high data rate access for applications of wireless Internet and e-commerce. The characterization of mobile cellular channels and their equalization had been a topic of intense research the world over. We have linear time variant (LTV) models of channels appropriate for different classes/generations of systems like GSM, CDMA, and WCDMA. The need for more bandwidth resulting from the proliferation of users demands better ways to model the mobile channel and mitigate the aggravated co-channel interference (CCI). In this paper we present a novel approach for the modeling of mobile cellular channels in the wideband and ultra wideband frequencies. It should be noted that at the above frequency bands, the channel impulse response is both frequency dependent and time varying. The classical methods support narrow band characterization of these channels especially when parameter spread of channel matrix is less significant. Neuro-fuzzy modeling provides for a higher spread of these parameters making the wideband analysis easier. Therefore the use of Neuro-Fuzzy model for the channel equalizer design is more elegant and simple. Simulations do indicate that this paradigm is indeed valid.
Towards Preferences in Virtual Environment Interfaces

Virtual Environment interfaces are designed by implementing an interaction metaphor and comparing it to existing implementations. This technique has proven effective in desktop interfaces but the difficulty of working inside a VE remains because VE interfaces do not understand what the user is trying to do, only what the metaphor wants to do. To handle this problem, we investigated a lower-level approach in interface design of letting the user work as they wish and the interface adapting to the user's method of interaction. Two exploratory experiments were performed on the task of selection to learn how users want to work, with the results being that users do not know until guided by affordances and feedback. Discussed is the intelligent capturing and dealing with VE interface data in terms of Nuances that can represent the details of the interface.
I NTERNATIONAL C ENTER FOR F INANCIAL A SSET M ANAGEMENT AND

We aim at accommodating the existing affine jump-diffusion and quadratic models under the same roof, namely the linear-quadratic jump-diffusion (LQJD) class. We give a complete characterization of the dynamics underlying this class of models as well as identification constraints, and compute standard and extended transforms relevant to asset pricing. We also show that the LQJD class can be embedded into the affine class through use of an augmented state vector. We further establish that an equivalence relationship holds between both classes in terms of transform analysis. An option pricing application to multifactor stochastic volatility models reveals that adding nonlinearity into the model significantly reduces pricing errors, and further addition of a jump component in the stock price largely improves goodness-of-fit for in-the-money calls but less for out-of-the-money ones.
Parallel, massive processing in SuperMatrix—A general tool for distributional semantic analysis of corpus

The paper presents an extended version of the SuperMatrix system-a general tool supporting automatic acquisition of lexical semantic relations from corpora. Extensions focus mainly on parallel processing of massive amounts of data. The construction of the system is discussed. Three distributed parts of the system are presented, i.e., distributed construction of co-incidence matrices from corpora, computation of similarity matrix and parallel solving of synonymy tests. An evaluation of a proposed approach to parallel processing is shown. Parallelization of similarity matrix computation demonstrates almost linear speedup. The smallest improvements were achieved for construction of matrices, as this process is mostly bound by reading huge amounts of data. Also, a few areas in which functionality of SuperMatrix was improved are described.
TimeWarping Invariant Echo State Networks

Echo State Networks (ESNs) is a recent simple and powerful approach to training recurrent neural networks (RNNs). In this report we present a modification of ESNs-time warping invariant echo state networks (TWIESNs) that can effectively deal with time warping in dynamic pattern recognition. The standard approach to classify time warped input signals is to align them to candidate prototype patterns by a dynamic programming method and use the alignment cost as a classification criterion. In contrast, we feed the original input signal into specifically designed ESNs which intrinsically are invariant to time warping in the input. For this purpose, ESNs with leaky integrator neurons are required, which are here presented for the first time, too. We then explain the TWIESN architecture and demonstrate their functioning on very strongly warped, synthetic data sets.
SAR Interferometry -Novelty in Real Scenario

A lot of efforts have been put in so far in the InSAR signal processing to develop easier yet accurate methods of generating topographic maps and other products. In order to facilitate better understanding and application of the SAR interferometry, however, there still remain short comings while taking into account certain important factors during various steps. In this paper efforts have been made to encompass all areas in the conventional InSAR signal processing which possess an appropriate reference of relationship with the processing in the real scenario. Methods and relationships have been developed to enable retrieve the accurate results as much as possible. The relevant processing steps have been identified and recommendations have been made to bridge the gaps between the novel and the real InSAR signal processing. The necessary steps involved in processing the received data to the geometrically compensated digital models have been explained in an easy and comprehensible way.
Calibration of active antenna arrays using a sky brightness model

Low‐frequency arrays (120–400 MHz) can be calibrated using a sky model based on sky maps. For a single antenna the variation of measured power with the Earth's rotation is needed to separate receiver gain and noise temperature. For an array an instantaneous calibration can be made using normalized correlations or normalized beam power for which the receiver gain cancels out. Examples of the sky model for various antenna orientations are shown along with measured data used to perform a calibration. The effects of unmodeled mutual coupling of the received signals and receiver noise on the calibration method are analyzed and are found to result in errors of only a few percent in an array of 5 × 5 crossed dipoles designed to observe the deuterium line at 327 MHz.
Making E-Learning Better Through Machine Learning

Due to massive information overload on the web it’s hard to index and reuse existing learning resources. Classifying learning resources according to domain specific concept hierarchies could address the problem of indexing and reusability. Manual classification is a tedious task and, as a result, automatic classifiers are in high demand. For this task we present an automated approach based on machine learning technique to exploit hierarchal knowledge in order to classify learning resources in a given hierarchy of concepts. We show by experimentation that using hierarchical information and content of unclassified documents provides better accuracy.
The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT 2015

This paper describes the submission of the University of Edinburgh and the Johns Hopkins University for the shared translation task of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation (WMT 2015). We set up phrase-based statistical machine translation systems for all ten language pairs of this year’s evaluation campaign, which are English paired with Czech, Finnish, French, German, and Russian in both translation directions. Novel research directions we investigated include: neural network language models and bilingual neural network language models, a comprehensive use of word classes, and sparse lexicalized reordering features.
Automatic Soccer Video Analysis and Summarization

We propose a fully automatic and computationally efficient framework for analysis and summarization of soccer videos using cinematic and object-based features. The proposed framework includes some novel low-level soccer video processing algorithms, such as dominant color region detection, robust shot boundary detection, and shot classification, as well as some higher-level algorithms for goal detection, referee detection, and penalty-box detection. The system can output three types of summaries: i) all slow-motion segments in a game, ii) all goals in a game, and iii) slow-motion segments classified according to object-based features. The first two types of summaries are based on cinematic features only for speedy processing, while the summaries of the last type contain higher-level semantics. The proposed framework is efficient, effective, and robust for soccer video processing. It is efficient in the sense that there is no need to compute object-based features when cinematic features are sufficient for the detection of certain events, e.g. goals in soccer. It is effective in the sense that the framework can also employ object-based features when needed to increase accuracy (at the expense of more computation). The efficiency, effectiveness, and the robustness of the proposed framework are demonstrated over a large data set, consisting of more than 13 hours of soccer video, captured at different countries and conditions.
Contourlet Based MR Image Reconstruction via Reweighted L1-Minimization

How to improve MR imaging speed by using Compressive Sensing (CS) theory, which reduce imaging time by sampling only a small bit of K-space data, has become a research focus of MR reconstruction. The measurement required in CS theory relies on the sparsity of sparse transform and the objective function of reconstruction. In this paper, we introduce contourlet transform into MR reconstruction based on CS, which achieves a sparser representation of image in contrast with orthogonal transforms such as wavelet. In addition, a reweighted L1- minimization scheme is introduced as the objective function instead of the traditional unweighted L1-minimization to further improve reconstruction performance. The experimental results prove the effectiveness of the proposed approach.
What Protects Adolescents from Suicidal Attempt: A Qualitative Study

Background: Suicidalattemptsarecommonamongadolescentsaroundtheworld. Manycountrieshavetakenmeasurestoreduce thehighincidenceof suicidalattemptsandpreventpeoplefromkillingthemselves. Theyalsoprovidecaretoacceleratetheprocess of recovery among those who have attempted suicide. Objectives: The aim of this study was to qualitatively evaluate the protective factors against suicidal attempts among adolescents who recently did so. Methods: In this qualitative study, 18 single adolescents, with a mean age of 17.44 ± 1.15, who were referred to 2 hospitals in Shiraz (southernIran),wereselectedviaconvenientsamplingprocedure. Datawerecollectedviasemi-structuredin-depthinterviewsand analyzed using conventional approaches to qualitative content analysis. Results: Three main categories and 10 sub-categories were extracted from the data. The main categories included: (a) individual factors (lack of mental conﬂict, religious beliefs, fear of death, thinking about family, hope for the future, spiritual counseling), (b) social factors (interaction with friends, social stigma of suicidal attempt, sense of family cohesion), and (c) professional advice and support (psychological counseling). Conclusions: Theﬁndingsof thisstudycanguidehealthprofessionalstotakemeasurestopreventsuicidalattempts,communicate withpeoplewhoareatriskof attemptedsuicide,andpreventrelapseof thosewhoareinrecoveryperiodafteranattemptedsuicide.
Association of extraintestinal manifestations of inflammatory bowel disease in a province of western Hungary with disease phenotype: results of a 25-year follow-up study.

AIM IBD is a systemic disease associated with a large number of extraintestinal manifestations (EIMs). Our aim was to determine the prevalence of EIMs in a large IBD cohort in Veszprem Province in a 25-year follow-up study. METHODS Eight hundred and seventy-three IBD patients were enrolled (ulcerative colitis/UC/: 619, m/f: 317/302, mean age at presentation: 38.3 years, average disease duration: 11.2 years; Crohn's disease/CD/: 254, m/f: 125/129, mean age at presentation: 32.5 years, average disease duration: 9.2 years). Intestinal, extraintestinal signs and laboratory tests were monitored regularly. Any alteration suggesting an EIMs was investigated by a specialist. RESULTS A total of 21.3% of patients with IBD had EIM (UC: 15.0%, CD: 36.6%). Age at presentation did not affect the likelihood of EIM. Prevalence of EIMs was higher in women and in CD, ocular complications and primary sclerosing cholangitis (PSC) were more frequent in UC. In UC there was an increased tendency of EIM in patients with a more extensive disease. Joint complications were more frequent in CD (22.4% vs UC 10.2%, P<0.01). In UC positive family history increased the risk of joint complications (OR:3.63). In CD the frequency of type-1 peripheral arthritis was increased in patients with penetrating disease (P=0.028). PSC was present in 1.6% in UC and 0.8% in CD. Dermatological complications were present in 3.8% in UC and 10.2% in CD, the rate of ocular complications was around 3% in both diseases. Rare complications were glomerulonephritis, autoimmune hemolytic anaemia and celiac disease. CONCLUSION Prevalence of EIM in Hungarian IBD patients is in concordance with data from Western countries. The high number of EIM supports a role for complex follow-up in these patients.
Melodic characterization of monophonic recordings for expressive tempo transformations

The work described in this paper aims at developing a system that could perform expressive tempo transformations in monophonic instrument phrases. We have first developed a melodic description subsystem that extracts a set of acoustic features from monophonic recordings. This set of features is structured and stored following a description scheme that is derived from the current MPEG-7 standard. These performance descriptions are then compared with their corresponding scores, using edit distance techniques, for automatically annotating the expressive transformations performed by the musician. Then, these annotated performance descriptions are incorporated in a case-based reasoning (CBR) subsystem in order to build an expressive tempo transformations case base. The transformation subsystem will use this CBR system to perform tempo transformations in an expressive manner. Saxophone performances of jazz standards played by a professional performer have been recorded for this study. In this paper, we first describe which are the melodic features that have been extracted and how they are structured and stored. Then, we explain the analysis methods that have been implemented to extract this set of features from audio signals and how they are processed by the CBR subsystem.
Blind source separation in noisy environment and applications in satellite communication anti-jamming

Aiming at the problem of satellite communication signal and jamming blind separation in noisy environment, a new method is presented which combined Joint Approximate Diagonalization (JAD) of the fourth order cumulant matrix and post-position wavelet de-noising. The new method has solved the problem that the performance of previous ones become worse at Jamming-to-Signal-Ratio (JSR) exceeded 30dB when they are used for satellite communication anti-jamming. Bit Error Ratio (BER) performance simulations are used to provide quantitative evaluations of the efficiency of the method.
Advances in Brief Characterization of a Breast Cancer Cell Line Derived from a GermLine BRCA 1 Mutation Carrier 1

A tumor cell line, HCC1937, was established from a primary breast carcinoma from a 24-year-old patient with a germ-line //AT t / mutation. A corresponding B-lymphoblastoid cell line was established from the patient's peripheral blood lymphocytes. BRCA1 analysis revealed that the tumor cell line is homozygous for the BRCA1 5382insC mutation, whereas the patient's lymphocyte DNA is heterozygous for the same mutation, as are at least two other family members' lymphocyte DNA. The tumor cell line is marked by multiple additional genetic changes including a high degree of aneuploidy, an acquired mutation of TP53 with wild-type alÃ-ele loss, an acquired homozygous deletion of the PTEN gene, and loss of heterozygosity at multiple loci known to be involved in the pathogenesis of breast cancer. Comparison of the primary tumor with the cell line re vealed the same lÃ¬ IHM mutation and an identical pattern of alÃ-eleloss at multiple loci, indicating that the cell line had maintained many of the properties of the original tumor. This breast tumor-derived cell line may provide a useful model system for the study of familial breast cancer pathogenesis and for elucidating BRCA1 function and localization.
Scientific Analysis by Queries in Extended SPARQL over a Scalable e-Science Data Store

Data-intensive applications in e-Science require scalable solutions for storage as well as interactive tools for analysis of scientific data. It is important to be able to query the data in a storage-independent way, and to be able to obtain the results of the data-analysis incrementally (in contrast to traditional batch solutions). We use the RDF data model extended with multidimensional numeric arrays to represent the results, parameters, and other metadata describing scientific experiments, and SciSPARQL, an extension of the SPARQL language, to combine massive numeric array data and metadata in queries. To address the scalability problem we present an architecture that enables the same SciSPARQL queries to be executed on the RDF dataset whether it is stored in a relational DBMS or mapped over a specialized geographically distributed e-Science data store. In order to minimize access and communication costs, we represent the arrays with proxy objects, and retrieve their content lazily. We formulate typical analysis tasks from a computational biology application in terms of SciSPARQL queries, and compare the query processing performance with manually written scripts in MATLAB.
Order in the Court: How Firm Status and Reputation Shape the Outcomes of Employment Discrimination Suits

This article explores the mechanisms by which corporate prestige produces distorted legal outcomes. Drawing on social psychological theories of status, we suggest that prestige influences audience evaluations by shaping expectations, and that its effect will differ depending on whether a firm’s blameworthiness has been firmly established. We empirically analyze a unique database of more than 500 employment discrimination suits brought between 1998 and 2008. We find that prestige is associated with a decreased likelihood of being found liable (suggesting a halo effect in assessments of blameworthiness), but with more severe punishments among organizations that are found liable (suggesting a halo tax in administrations of punishment). Our analysis allows us to reconcile two ostensibly contradictory bodies of work on how organizational prestige affects audience evaluations by showing that prestige can be both a benefit and a liability, depending on whether an organization’s blameworthiness has been firmly established.
Viewpoint and Sentiment towards the ISO / IEC 29110 Standard : A Two Country Grounded Theory Study

The ISO/IEC 29110 standard has at its core a Management and Engineering Guide [1] which are targeted at very small entities (enterprises, organizations, departments or projects) having up to 25 people [2], to assist them unlock the potential benefits of using standards which are specifically designed to address their needs. This paper is concerned with understanding the issues that affect the adoption of software process standards by Very Small Entities (VSEs), their needs from process standards and their willingness to engage with the new ISO/IEC 29110 standard in particular. This paper bring together two complimentary studies undertaken in Ireland and Ecuador which pose questions to VSE management regarding opinions, attitude and sentiment towards the adoption of the VSE designed standard ISO/IEC 29110. A series of interviews were untaken in both countries counties with qualitative data analysis utilizing the grounded theory coding mechanisms, to produce a picture of the current situation. This paper serves as a roadmap for both researchers wishing to understand the issues of process standards adoption by very small companies and also for the software process standards community.
Texture classification with a dictionary of basic image features

Many successful recent approaches to texture classification model texture images as distributions over a set of discrete features, or textons, which correspond to a partitioning of the space of responses to local descriptors such as filter banks or image patches. This partitioning is learned by unsupervised clustering of descriptor responses taken from the dataset to be analysed. Here, we explore a quantization of filter responses into a dictionary of discrete features which is based on geometrical, rather than statistical, considerations, resulting in a simple texture description based on a dictionary of dasiavisual wordspsila which is independent of the images to be described. A multi-scale classification scheme built on this dictionary is evaluated. The results presented are, to the best of our knowledge, state-of-the-art for the UIUCTex and KTH-TIPS datasets, and close to the state-of-the-art for CUReT, despite using a less sophisticated classifier.
Towards a portable lazy functional declarative debug-ger

Declarative (or algorithmic) debugging is a promising technique for debugging lazy functional programs. This paper addresses two important reasons why it is not more widespread: the diiculty of writing a declarative debugger for a lazy functional language in the language itself and the eeciency of the debugger. Using the source language to implement the debugger is desirable for portability and we discuss the reasons why this is very diicult to do. We propose a system in which nearly all the code is in the source language but there is one function which must be written at lower level. We also show how this function can be the key to signiicantly improving the eeciency of declarative debuggers for such languages.