Miracle-dz/UniFAR-FTUs huggingface dataset:
Venue Recommendation and Web Search Based on Anchor Text

Abstract : This paper presents the University of Amsterdam's participation in TREC 2014. For the Contextual Suggestion Track, we experimented with the use of anchor text representations in the language modeling framework, and base our runs either on full ClueWeb12 or the subset of touristic aggregators (e.g., tripadvisor) provided by the organizers of the track. We also look at the effectiveness of priors (in particular, PageRank) and ways of formulating the query based on the context. Our main finding is that the anchor text representation is effective for retrieving candidate attractions, and performs better than a standard document text index. A linear combination of both anchor and document text leads to further improvement. For the Web Track, we continued our experiment with the fusion of anchor text relative to the text-based baseline run. Our main finding is, again, that the combination of anchor and document text leads to improvement, and we demonstrate how the fusion weight can be used as a handle to tune the amount of risk acceptable for the risk sensitive evaluation.
The Theory Underlying the Equation

Instrumental variables estimation on a single equation is used to estimate the causal effects of inflation on growth of output in this article by Robert J. Barro. He is certainly aware of the theoretical foundations of the equation he is estimating, and knows that justifying instrumental variables estimation requires at least informal discussion of the mechanisms that complete the system determining the endogenous variables. He does not discuss the theory underlying the equation much, however, and he gives only partial and informal indications of how he might complete the system. In this comment, I fill in these aspects of the analysis. The conclusion is that the coefficient on inflation that Barro estimates cannot reliably be interpreted as he seems to interpret it. Barro also does not pay much attention to the fit of his model. That is, he does not ask how well it accords with the data compared with other models whose predictions of the effect of inflation on growth might be different. Though a comment does not provide enough space to do much along these lines, I make some remarks on this point, suggesting that theories that give no causal role to anti-inflation policy per se in promoting growth are apparently consistent with what we see in the data.
Multi-Rate Expert Systems in Supply Chain Simulation for Telecommunication Industry

In modern telecommunications industry, demand & supply chain management needs reliable design and versatile tools to control the material flow. The objective for efficient supply chain management is reducing inventory, lead times and related costs in order to assure reliable and on-time deliveries from manufacturing units towards customers. In this paper we present multi-rate expert system based methodology for developing tools that would enable optimal demand & supply chain management for modern telecommunication manufacturing environment.
Expression of the SOMATIC EMBRYOGENESIS RECEPTOR-LIKE KINASE1 (SERK1) gene is associated with developmental change in the life cycle of the model legume Medicago truncatula

SOMATIC EMBRYOGENESIS RECEPTOR-LIKE KINASE (SERK) genes have been demonstrated to play a role in somatic embryogenesis in several plant species. As more is learnt about these genes, the view of their role in plant development has broadened. The Medicago truncatula MtSERK1 gene has been associated with somatic embryogenesis and in vitro root formation. In order to study the role of MtSERK1 in development further, the MtSERK1 promoter sequence has been isolated and cloned into a promoter–GUS analysis vector. SERK1 promoter-driven GUS expression was studied in A. tumefaciens-transformed cultures and regenerated plants, in A. rhizogenes-transformed root clones, and in nodulation. In embryogenic cultures, GUS staining is detected after 2 d of culture at the edge of the explant and around vascular tissue. Expression at the explant edge intensifies over subsequent days and then is lost from the edge as callus formation moves inward. MtSERK1 expression appears to be associated with new callus formation. When somatic embryos form, GUS staining occurs throughout embryo development. Zygotic embryos show expression until the heart stage. The in planta studies reveal a number of interesting expression patterns. There appear to be three types. (i) Expression associated with the primary meristems of the root and shoot and the newly formed meristems of the lateral roots and nodule. (ii) Expression at the junction between one type of tissue or organ and another. (iii) Expression associated with the vascular tissue procambial cells. The data led us to conclude that MtSERK1 expression is associated with developmental change, possibly reflecting cellular reprogramming.
Low-Rank Spatio-Temporal Video Segmentation

Recently, a great deal of interest has been generated by the technique known as Robust Principle Component Analysis (RPCA) of Candes et al. [1], which addresses the problem of separating a matrix into a low-rank and a sparse component. This very general formulation can be used for tasks such as background estimation in videos and face recognition. In the case of background estimation, the low-rank matrix models the background, and the sparse matrix corresponds to the foreground. A considerable drawback of this approach is its poor robustness to local lighting conditions. If lighting conditions vary locally, one of two things may happen. Either the method incorporates the lighting variation into the foreground, which is clearly undesirable, or the rank of the background model is allowed to increase. Unfortunately, this second option means that the true foreground is likely to become included in the background, especially for objects which are static for a short while. Here, we propose to model the background as a piece-wise low-rank matrix. In this manner, it will be possible to extract several localised models which correspond to coherent lighting conditions. However, for this we need to segment the input video into such coherent regions. We refer to this problem as a low-rank spatio-temporal video segmentation. We present an algorithm to address this segmentation problem, based on region merging and spectral clustering techniques. We show that by carrying out a local RPCA in each region, the results of foreground/background separation are greatly improved, in comparison with both the standard RPCA and several other well-known background estimation techniques. Let X ∈ Rm×n represent an input video, in matrix form. Each frame contains m pixels, and there are a total of n frames in our video. The goal of RPCA is to decompose X as X≈ L+S, where L is the low-rank matrix and S is the sparse matrix. Unfortunately, the rank of a matrix is a non-convex function, so a surrogate function, the nuclear norm is used. Thus, the background/foreground separation problem may be formulated as follows:
Nordic welfare states in the European context

1. Introduction: How Distinct are the Nordic Welfare States? 2. Still Different? Income Distribution in the Nordic Countries in a European Comparison 3. Nordic Health Inequalities in the European Context 4. Gender Policies and Gender Equality 5.Gender Equality in Earnings at Work and at Home 6. Changing Family Patterns - A Challenge to Social Security 7. Health and Social Care Systems: How Different is the Nordic Model? 8. Toward Activation? The Changing Relationship Between Social Protection and Employment in Western Europe 9. On Condition of Work: Increasing Work Requirements in Unemployment Compensation Schemes 10. Moving Closer? Convergence in Financing of Welfare States 11. Conclusion: The Nordic Welfare States in the European Context
Handout B: Linear Algebra Cheat Sheet 1 Basic Notations, Definitions and Results 1.1 Vectors and Matrices

where aij ∈ R for i = 1, . ,m and j = 1, . , n. A row vector is a matrix with m = 1, and a column vector is a matrix with n = 1. The word vector will always mean a column vector unless otherwise stated. The set of all n–dimensional real vectors is denoted by Rn, and an element x ∈ Rn can be written as x = (x1, . . . , xn). Note that we still view x = (x1, . . . , xn) as a column vector, even though typographically it does not appear so. The reason for such a notation is simply to save space. Now, given an m× n matrix A of the form (1), its transpose AT is defined as the following n×m matrix:
Hierarchical Cluster Analysis On Security Design Patterns

Integrating security into enterprise level applications requires expertise and experience. Frameworks, tools and guidelines have been developed for the incorporation of security. Expert guidance is also encapsulated in the form of security design patterns which provide reusable solutions to recurring security problems. This field has been greatly enriched by contributions from various researchers since the emergence of the first security design patterns in 1997. However, the pattern landscape is very vast and unorganized in terms of the number of patterns and also their classification. This makes it difficult to apply design patterns in practice, leading to their poor adoption. Analytical tools can be applied on patterns if they are classified according to a common set of criteria. Hierarchical clustering is a widely used data analytical tool for creating groups of similar data items. Grouping of patterns will give additional insights into their relationships and hierarchy; thereby improving their usefulness and applicability. In this paper, we have applied hierarchical clustering to a set of security design patterns which are classified according to a chosen set of criteria.
Interoperable Digital Musicology Research via music 21 Web Applications

Digital humanities practices applied to musical scores h ave the potential to open up vast new d atasets and avenu es for research in musicology and are beginning to transform the field of musical research. Yet beyond the common difficulties of all digital humanities projects, significant problems arise in d igital musicology that are unique to the structure of musical scores and the lack of available tools for manip ulating scor es. Performing an alysis tasks of ten requires spec ialized tools th at h ave high barr iers to entr y, su ch as compiling, choosing a par ticular operating system, and converting data between divergent formats. The “webapps” module of the open-source music21 toolk it p rovides the ar chitecture to connect var ious digital musicolog y projects. I t m akes standard b ut time-consuming musicological tools availab le to less technolo gically sophisticated users while providing tremendously var ied developmental options to technically-inclined researchers . The authors propose a JSON format for encod ing bo th score d ata and manipulations to/analysis of scores that can easily be used by backend systems besides music21, whether specialized for musical analysis or for other digital humanities and machine learning tasks. The article ends by stressing, with examples, the continued need for standalone musical analysis systems even in a world of easily available web systems.
The Determinants of the Inflation Rate in Transition Countries:A Panel Data Analysis

The theoretical literature points out that inflation targeting and the exchange rate peg have the advantage of lowering the inflation rate. Controlling for the other relevant variables, this paper estimates the effects of these policies on the inflation rate in 20 transition countries during 1995-2003 by using regressions on panel data. The main finding is that inflation targeting and the exchange rate peg appear to have been effective in lowering inflation rate even in transition countries.
Freedom: Online Activity Recognition via Dictionary-Based Sparse Representation of RFID Sensing Data

Understanding and recognizing the activities performed by people is a fundamental research topic for a wide range of important applications such as fall detection of elderly people. In this paper, we present the technical details behind Freedom, a low-cost, unobtrusive system that supports independent livingof the older people. The Freedom system interprets what aperson is doing by leveraging machine learning algorithmsand radio-frequency identification (RFID) technology. To dealwith noisy, streaming, unstable RFID signals, we particularlydevelop a dictionary-based approach that can learn dictionariesfor activities using an unsupervised sparse coding algorithm. Our approach achieves efficient and robust activity recognitionvia a more compact representation of the activities. Extensiveexperiments conducted in a real-life residential environmentdemonstrate that our proposed system offers a good overallperformance (e.g., achieving over 96% accuracy in recognizing23 activities) and has the potential to be further developed tosupport the independent living of elderly people.
Customer Satisfaction: A Comparative Analysis of Public and Private Sector Banks in India

As economic globalization intensifies competition and creates a climate of constant change, winning and keeping customers has become all the more important. Nowadays banks have realized that cost of attracting a new customer is much more than retaining existing customers, so banks are emphasizing more upon customer satisfaction. These days customers demand for top quality services and products served with minimum wait time, so customers prefer techno- savvy banks as well bankers. At this backdrop, the main problem today before the commercial banks, more particularly the public sector banks in India which were earlier operating in a sheltered regime after nationalization, is their long-run survival, tapping quality customers and forging way ahead by retaining their valued customers. The current research paper attempts to make a comparative analysis of level of customer satisfaction towards services provided by public and private sector banks. The study has been conducted in Chandigarh city. This study is based on questionnaire method. A sample of 160 customers has been selected using convenient sampling method. The statistical tests are conducted at 5% and 1% level of significant the main statistical tools are used. This study concluded that private sector banks are more preferred by majority of the customer as they emphasize more upon relationship building with their clients and are better equipped with modern infrastructure as compared to public sector banks. Keywords: Customer satisfaction, retail banking, service quality, bank’s performance
Interaction prediction of PDZ domains using a machine learning approach

Protein interaction domains play crucial roles in many complex cellular pathways. PDZ domains are one of the most common protein interaction domains. Prediction of binding specificity of PDZ domains by a computational manner could eliminate unnecessary, time-consuming experiments. In this study, interactions of PDZ domains are predicted by using a machine learning approach in which only primary sequences of PDZ domains and peptides are used. In order to encode feature vectors for each interaction, trigram frequencies of primary sequences of PDZ domains and corresponding peptides are calculated. After construction of numerical interaction dataset, we compared different classifiers and ended up with Random Forest (RF) algorithm which gave the top performance. We obtained very high prediction accuracy (91.4%) for binary interaction prediction which outperforms all previous similar methods.
Pattern of Lymph Node Metastasis Correlates with Tumor Location in Bladder Cancer

Purpose Resection of a large number of lymph nodes (LNs) increases survival in patients with LN-positive disease; however, morbidity also increases. Here, we investigated the correlation between tumor location and LN metastasis in bladder cancer. Materials and Methods Thirty-six patients with pathological N1 or higher bladder cancer, who underwent radical cystectomy with extended lymphadenectomy, were reviewed retrospectively. The tumor location was classified as right, left, front, posterior, or whole bladder. The LN metastasis pattern was classified as right, left, or bilateral. The correlation between tumor location and LN metastasis was determined by chi-square test. Survival rates were compared by Kaplan-Meier analysis. Results The numbers of patients with a tumor on the right (group 1), left (group 2), posterior (group 3), or whole (group 4) bladder were 16 (44.4%), 16 (44.4%), 2 (5.6%), and 2 (5.6%), respectively. In group 1, 14 patients (87.5%) had right-sided ipsilateral LNs, and 2 patients (12.5%) had left-sided contralateral LNs. In group 2, 4 patients (25%) had right-sided contralateral LNs, and 12 patients (75%) had left-sided ipsilateral LNs. In group 3, both patients (100%) had right-sided posterior LNs. In group 4, both patients (100%) had positive LNs on both sides. Tumor location and LN metastasis were significantly correlated (p<0.05). Moreover, the survival rate was significantly higher in patients with no LN metastasis than in patients with ipsilateral or contralateral LN-positive bladder cancer. Conclusions The location of the bladder tumor and direction of metastatic spread were significantly correlated. Mandatory bilateral lymphadenectomy during radical cystectomy should be questioned.
Design and Implementation of Repair-aware Test Flow for Multi-Memory

A complex SoC typically consists of numerous of memories in today's digital systems. This paper presents a test/ repair flow based on memory grouping strategy and a revised distributed BIST structure for complex SoC devices. A gated selecting method is added to the distributed BIST structure. Also, this paper for the first time proposes a robust post repair stage based on BIRA and memory grouping in test flow. Simulation results by mathematical method show that the proposed test flow has achieved a significant increase in yield of memories.
Correlations of Differentially Expressed Gap Junction Connexins Cx26, Cx30, Cx32, Cx43 and Cx46 with Breast Cancer Progression and Prognosis

Background and Aims Connexins and their cell membrane channels contribute to the control of cell proliferation and compartmental functions in breast glands and their deregulation is linked to breast carcinogenesis. Our aim was to correlate connexin expression with tumor progression and prognosis in primary breast cancers. Materials and Methods Meta-analysis of connexin isotype expression data of 1809 and 1899 breast cancers from the Affymetrix and Illumina array platforms, respectively, was performed. Expressed connexins were also monitored at the protein level in tissue microarrays of 127 patients equally representing all tumor grades, using immunofluorescence and multilayer, multichannel digital microscopy. Prognostic correlations were plotted in Kaplan-Meier curves and tested using the log-rank test and cox-regression analysis in univariate and multivariate models. Results The expression of GJA1/Cx43, GJA3/Cx46 and GJB2/Cx26 and, for the first time, GJA6/Cx30 and GJB1/Cx32 was revealed both in normal human mammary glands and breast carcinomas. Within their subfamilies these connexins can form homo- and heterocellular epithelial channels. In cancer, the array datasets cross-validated each other’s prognostic results. In line with the significant correlations found at mRNA level, elevated Cx43 protein levels were linked with significantly improved breast cancer outcome, offering Cx43 protein detection as an independent prognostic marker stronger than vascular invasion or necrosis. As a contrary, elevated Cx30 mRNA and protein levels were associated with a reduced disease outcome offering Cx30 protein detection as an independent prognostic marker outperforming mitotic index and necrosis. Elevated versus low Cx43 protein levels allowed the stratification of grade 2 tumors into good and poor relapse free survival subgroups, respectively. Also, elevated versus low Cx30 levels stratified grade 3 patients into poor and good overall survival subgroups, respectively. Conclusion Differential expression of Cx43 and Cx30 may serve as potential positive and negative prognostic markers, respectively, for a clinically relevant stratification of breast cancers.
A Comparison of University Academics Job Factors: Satisfaction and Dissatisfaction

The aim of this paper is to compare the university academics job satisfaction factors and job dissatisfaction factors. This aim is achieved by identifying the factors that affect academics job satisfaction and dissatisfaction. The main results of this study were concluded by comparing the satisfaction factors and dissatisfaction factors for university academics from the existing literature. The novelty of this study is the use of a significant body of the existing literature has focused on the factors affecting university academics in terms of job satisfaction and job dissatisfaction to get the aim. The findings would help to know for university academic factors affect for satisfaction and dissatisfaction.
Array Bounds Check Elimination for Java Based on Sparse Representation

As a type-safe program language, Java requires bounds checks of array accesses. Whenever an array element is accessed, a cmp (compare) instruction is executed to check whether the index value is within the valid bounds. Array bounds checks may prevent many useful optimizations because of precise exception. We present a new ABCE (Array Bounds Check Elimination) algorithm to eliminate redundant checks based on sparse representation for a Java static compiler. In contrast to other approaches performing in JVMs, we adhere to the design principle of the static compiler to optimize scientific Java applications. The algorithm is a light-weight algorithm working on an intermediate representation in Static Single Assignment form. It fully removes bounds checks if it can be proven that they never fail. Whenever possible, it moves bounds checks out of loops to reduce the total number of executed checks. If such a check fails, the executing program branches into the unmodified loop to preserve the exception semantics of Java. For the scientific SciMark 2.0 benchmark suite, this algorithm removes on average 76% of bounds check instructions. The evaluation shows a speedup near to the theoretical maximum for LU test case.
Feature Selection via Sparse Approximation for Face Recognition

Inspired by biological vision systems, the over-complete local features with huge cardinality are increasingly used for face recognition during the last decades. Accordingly, feature selection has become more and more important and plays a critical role for face data description and recognition. In this paper, we propose a trainable feature selection algorithm based on the regularized frame for face recognition. By enforcing a sparsity penalty term on the minimum squared error (MSE) criterion, we cast the feature selection problem into a combinatorial sparse approximation problem, which can be solved by greedy methods or convex relaxation methods. Moreover, based on the same frame, we propose a sparse Ho-Kashyap (HK) procedure to obtain simultaneously the optimal sparse solution and the corresponding margin vector of the MSE criterion. The proposed methods are used for selecting the most informative Gabor features of face images for recognition and the experimental results on benchmark face databases demonstrate the effectiveness of the proposed methods.