Miracle-dz/UniFAR-FTUs huggingface dataset:
The GALEX Ultraviolet Luminosity Function of the Cluster of Galaxies A1367

We present the Galaxy Evolution Exlorer (GALEX) near-ultraviolet (2310 Å) and far-ultraviolet (1530 Å) galaxy luminosity functions of the nearby cluster of galaxies A1367 in the magnitude range -20.3 ≤ MAB ≤ -13.3. The luminosity functions are consistent with previous (~2 mag shallower) estimates based on the FOCA and FAUST experiments, but they display a steeper faint-end slope than the GALEX luminosity function for local field galaxies. Using spectrophotometric optical data, we select star-forming systems from quiescent galaxies and study their separate contributions to the cluster luminosity function. We find that the UV luminosity function of cluster star-forming galaxies is consistent with the field. The difference between the cluster and field luminosity functions is entirely due to the contribution at low luminosities (MAB > -16 mag) of non-star-forming, early-type galaxies that are significantly overdense in clusters.
Knitting and Deep Niches : The Importance for SMEs of Focus in R & D and Product Development

Small and Medium-sized Enterprises (SMEs) face major challenges in trying to remain competitive in the global market place. An identified strategic ploy to maximise effectiveness in product design and R&D is the ‘deep niche strategy’. This paper demonstrates the use of system dynamics diagramming processes in articulating this strategy, as observed in case studies of two very high-tech SMEs and a niche lingerie company, and in conveying the critical importance of this ploy to SME entrepreneurs, consultants and academics.
Reliability analysis reloaded: How will we survive?

In safety related applications and in products with long lifetimes reliability is a must. Moreover, facing future technology nodes of integrated circuit device level reliability may decrease, i.e., counter-measures have to be taken to ensure product level reliability. But assessing the reliability of a large system is not a trivial task. This paper revisits the state-of-the-art in reliability evaluation starting from the physical device level, to the software system level, all the way up to the product level. Relevant standards and future trends are discussed.
START-UP SIZE AND SUBSEQUENT GROWTH : ENGLISH AND SPANISH NEW BUSINESSES COMPARED

The aims of this research are to determine and to compare the factors which influence start-up size and the subsequent employment change of new businesses over four years. A comparison between new firms started in selected areas of England and Spain is made. Data was collected by face-to-face interviews with 231 English and 182 Spanish firm founders using the same questionnaire in the two countries. In order to provide a full explanation of start-up size and employment change, explanatory variables are based on conditions observable at the pre-, atand post-start stages. First, a multiple regression analysis is estimated to identify the determinants of initial size of the firm. Results show that start-up size is strongly influenced by the starting resources of the entrepreneur and industry effects. However, different human capital variables influence start-up size in the two selected countries. Second, four groups of firms are identified in terms of their employment change in both samples. Surprisingly, the biggest firms at start-up are those that have job losses. These firms converge over time with a group of slow growers. Neutral employment change is observed in a group of mainly sole owners and there is also a group of fast growers. The determinants of these four employment change patterns are then analysed using an ordered probit model. Results show that start-up size is highly significant in explaining all patterns of change. Fast-growing firms are reflected in the same factors in both countries, such as formal planning, workforce training and owner managerial skills. Different results are found regarding founder-specific characteristics, use of external advice and sources of finance. Findings indicate that new firms learn as they age and there is therefore an adjustment process of firm size over time. Findings also suggest that new firm growth will depend on the entrepreneur’s ability to acquire skills through learning-by-doing. 1 We thank Andrew Burke, Stuart Fraser and David Storey for helpful comments and suggestions. 2 Departament d’Economia de l’Empresa, Universitat Autònoma de Barcelona. E-mail: joanlluis.capelleras@uab.es 3 Centre for Small and Medium Sized Enterprises, Warwick Business School, University of Warwick. E-mail: kevin.mole@wbs.ac. uk
Bayesian Constituent Context Model for Grammar Induction

Constituent Context Model (CCM) is an effective generative model for grammar induction, the aim of which is to induce hierarchical syntactic structure from natural text. The CCM simply defines the Multinomial distribution over constituents, which leads to a severe data sparse problem because long constituents are unlikely to appear in unseen data sets. This paper proposes a Bayesian method for constituent smoothing by defining two kinds of prior distributions over constituents: the Dirichlet prior and the Pitman-Yor Process prior. The Dirichlet prior functions as an additive smoothing method, and the PYP prior functions as a back-off smoothing method. Furthermore, a modified CCM is proposed to differentiate left constituents and right constituents in binary branching trees. Experiments show that both the proposed Bayesian smoothing method and the modified CCM are effective, and combining them attains or significantly improves the state-of-the-art performance of grammar induction evaluated on standard treebanks of various languages.
Binocular Perception of Slant about Oblique Axes Relative to a Visual Frame of Reference

From the literature it is known that the processing of disparity for slant is different in the presence and in the absence of a visual frame of reference. The experimental finding that vertical disparity is not processed for slant perception in the presence of a visual reference is elaborated. This theoretical analysis results in a reduction of the three basic first-order transformations between the retinal half images (divergence, rotation, and deformation) to only two basic orthogonal transformations. The first of these, horizontal scale, results in slant perception about the vertical axis, whereas the second, horizontal shear, results in slant perception about the horizontal axis. These transformations are based primarily on horizontal disparity. It is shown experimentally that in the presence of a frame of reference the amount of vertical transformation that is added to the two basic transformations (horizontal scale and shear) of a random-dot stimulus is indeed irrelevant for slant perception. It is suggested that, in the presence of a visual reference, slant perception about oblique axes is based solely on linear combinations of the horizontal-scale and horizontal-shear transformations. Subjects are able to reproduce slants about oblique axes experimentally merely by combining horizontal scale and shear.
SNR Scalability Based on Matching Pursuits

In this paper, SNR scalable representations of video signals are studied. The investigated codecs are well suited for communications applications because they are all based on backward motion-compensated predictive coding, which provides the necessary low-delay property. In a very-low bit rate context (VLBR), the matching pursuits (MP) signal representation algorithm is used to represent the displaced frame difference (DFD) of each layer of a multilevel decomposition of the video signal. A number of conventional prediction schemes that can be generalized to any DFD representation technique are considered. They are compared with an original and MP specific DFD prediction method. Two scenari have been considered. In the first scenario, an enhancement layer is built on a base layer that has been encoded using a classical, i.e., nonscalable scheme. In that case, all methods appear to be comparable, In the second scenario, the fact that the base layer is used as a reference for an enhancement layer is taken into account to build it. In that case, the proposed MP prediction method clearly outperforms all other conventional approaches, Additional lessons can be drawn from this work. The same motion vectors can be used in both SNR layers, and the DFD prediction between layers improves coding efficiency. Moreover, the MP representation of the signal enable us to measure the predictability of the high SNR layer DFD from the low SNR layer DFD, i.e., to quantify the part of the low SNR layer information that also belongs to the high SNR layer.
: From Deep Representation to Surface

We realize the full generation pipeline, from the deep (= semantic) representation (SemR), over the shallow (= surface-syntactic) representation (SSyntR) to the surface. To account systematically for the non-isomorphic projection between SemR and SSyntR, we introduce an intermediate representation: the so-called deep-syntactic representation (DSyntR), which does not contain yet (all) function words (as SemR), but which already contains grammatical function relation labels (as SSyntR).
Less Is More: Trading a Little Bandwidth for Ultra-Low Latency in the Data Center

Traditional measures of network goodness--goodput, quality of service, fairness--are expressed in terms of bandwidth. Network latency has rarely been a primary concern because delivering the highest level of bandwidth essentially entails driving up latency--at the mean and, especially, at the tail. Recently, however, there has been renewed interest in latency as a primary metric for mainstream applications. In this paper, we present the HULL (High-bandwidth Ultra-Low Latency) architecture to balance two seemingly contradictory goals: near baseline fabric latency and high bandwidth utilization. HULL leaves 'bandwidth headroom' using Phantom Queues that deliver congestion signals before network links are fully utilized and queues form at switches. By capping utilization at less than link capacity, we leave room for latency sensitive traffic to avoid buffering and the associated large delays. At the same time, we use DCTCP, a recently proposed congestion control algorithm, to adaptively respond to congestion and to mitigate the bandwidth penalties which arise from operating in a bufferless fashion. HULL further employs packet pacing to counter burstiness caused by Interrupt Coalescing and Large Send Offloading. Our implementation and simulation results show that by sacrificing a small amount (e.g., 10%) of bandwidth, HULL can dramatically reduce average and tail latencies in the data center.
A cross-layer approach for resiliency and energy efficiency in Near Threshold Computing

Energy constrained systems become the cornerstone of emerging energy harvested or battery-limited applications in Internet of Thing (IoT) platforms. A promising approach is to operate at near threshold voltage ranges, which can significantly reduce energy per operation. However, due to increased sensitivity to variations and reduced noise margin at low voltages, resiliency becomes a major challenge. In this paper we provide a cross layer approach, from compiler all the way to circuit design, to maximize the energy efficiency as well as the resiliency of functional units. The key idea is to identify the instructions which become timing critical at low voltages and address them by a combination of circuit redesign, multi-cycle execution and code replacement. This allows us to significantly reduce timing failures and at the same time limit leakage energy, which becomes considerable at low voltages. This approach enables resilient and energy efficient operation in a wide voltage range to trade off energy and performance.
Price Elasticity in Electronic Markets: Evaluating Quality and Product Information for Search and Experience Products

Evidence has shown that the provision of product information in electronic markets decreases the price elasticity of demand due to the ‘fit’ cost. This effect, however, could differ according to how consumers perceive the value of the product information to their quality evaluation procedures. If the information has very limited value, then they may not rely on it; thus, the demand elasticity may not decrease as predicted. The value of information to the quality evaluation procedure is determined by the consumer’s difficulty in judging product quality. Specifically, product attributes related to the quality of experience products cannot be ascertained by prior search and the value of information in this case is therefore low. Based on this prediction, this research investigates how the price elasticity of demand differs in relation to the difficulty of evaluating quality and how it affects the influence of product information provided in electronic markets on elasticity. Groupon sales data are used to empirically test these questions. The findings confirmed that elasticity is lower for experience products than for search products. This also suggests that the provision of product information lowers elasticity in differentiated product markets and that its effect is stronger for search products than experience products.
Tool integration using OSLC

In Software development process various tools are used in different phases of product lifecycle. They are either developed by any vendor or custom in-house solutions. To collaborate in development processes, these tools are required to communicate and transform data values. Different solutions like data object, data transformers and data translators are designed to communicate and transform data between tools. But as with technology evolves (new tools, APIs) it could become less viable that these integrators could address tool integration challenges and facilitate in the development processes. This leads to require a better solution. Open Service Life Cycle (OSLC) appears to overcome these issues. OSLC standardize open resources in the form of URIs to integrate them between different tools. In this thesis work, a careful study of OSLC and comparison with other tool integration mechanisms, practical implementation and the data interoperability is established between ViTAL (Framework for EAST-ADL model-verification using UPPAAL-PORT) and Farkle (Testing environment) tools using OSLC. This OSLC tool integration solution for Quality Management domain shows that it facile the resource availability and transformations of data for targeted tools.
Efficient energy management and data recovery in sensor networks using latent variables based tensor factorization

A key factor in a successful sensor network deployment is finding a good balance between maximizing the number of measurements taken (to maintain a good sampling rate) and minimizing the overall energy consumption (to extend the network lifetime). In this work, we present a data-driven statistical model to optimize this tradeoff. Our approach takes advantage of the multivariate nature of the data collected by a heterogeneous sensor network to learn spatio-temporal patterns. These patterns enable us to employ an aggressive duty cycling policy on the individual sensor nodes, thereby reducing the overall energy consumption. Our experiments with the OMNeT++ network simulator using realistic wireless channel conditions, on data collected from two real-world sensor networks, show that we can sample just 20% of the data and can reconstruct the remaining 80% of the data with less than 9% mean error, outperforming similar techniques such is distributed compressive sampling. In addition, energy savings ranging up to 76%, depending on the sampling rate and the hardware configuration of the node.
Tuning complex event processing rules using the prediction-correction paradigm

There is a growing need for the use of active systems, systems that act automatically based on events. In many cases, providing such active functionality requires materializing (inferring) the occurrence of relevant events. A widespread paradigm for enabling such materialization is Complex Event Processing (CEP), a rule based paradigm, which currently relies on domain experts to fully define the relevant rules. These experts need to provide the set of basic events which serves as input to the rule, their inter-relationships, and the parameters of the events for determining a new event materialization. While it is reasonable to expect that domain experts will be able to provide a partial rules specification, providing all the required details is a hard task, even for domain experts. Moreover, in many active systems, rules may change over time, due to the dynamic nature of the domain. Such changes complicate even further the specification task, as the expert must constantly update the rules. As a result, we seek additional support to the definition of rules, beyond expert opinion. This work presents a mechanism for automating both the initial definition of rules and the update of rules over time. This mechanism combines partial information provided by the domain expert with machine learning techniques, and is aimed at improving the accuracy of event specification and materialization. The proposed mechanism consists of two main repetitive stages, namely rule parameter prediction and rule parameter correction. The former is performed by updating the parameters using an available expert knowledge regarding the future changes of parameters. The latter stage utilizes expert feedback regarding the actual past occurrence of events and the events materialized by the CEP framework to tune rule parameters. We also include possible implementations for both stages, based on a statistical estimator and evaluate our outcome using a case study from the intrusion detection domain.
Global Analytic Solution for Variational Bayesian Matrix Factorization

Bayesian methods of matrix factorization (MF) have been actively explored recently as promising alternatives to classical singular value decomposition. In this paper, we show that, despite the fact that the optimization problem is non-convex, the global optimal solution of variational Bayesian (VB) MF can be computed analytically by solving a quartic equation. This is highly advantageous over a popular VBMF algorithm based on iterated conditional modes since it can only find a local optimal solution after iterations. We further show that the global optimal solution of empirical VBMF (hyperparameters are also learned from data) can also be analytically computed. We illustrate the usefulness of our results through experiments.
Robust Matching by Partial Correlation

Stereo matching by correlation near occlusions is a very challenging problem. When a partial occlusion occurs, most of the standard methods fail to produce acceptable results. This is because the techniques used do not take into account the presence of the occluding region. We propose a robust technique which we call partial correlation. This technique makes use of the least median square method which has recently been used for vision problems such as robust surface reconstruction. It performs better than standard methods near occlusions. It works by first of all disambiguating between the occluding region and the object region in the template and in the candidate window. A binary weighted correlation is then performed on the object regions. We present a comparative study between our approach and two other techniques. Experiment results validate our approach.
Jaysingpur Clustering Web Search Results-A Review

The rapid growth of the Internet has made the Web a popular place for collecting information. Today, Internet user access billions of web pages online using search engines. Information in the Web comes from many sources, including websites of companies, organizations, communications and personal homepages, etc. Effective representation of Web search results remains an open problem in the Information Retrieval (IR) community. Web search result clustering has been emerged as a method which overcomes these drawbacks of conventional information retrieval (IR) community. It is the clustering of results returned by the search engines into meaningful, thematic groups. This paper gives issues that must be addressed in the development of a Web clustering engine and categorizes various techniques that have been used in clustering of web search results. Search results clustering, the core of the system, has specific requirements that cannot be addressed by classical clustering algorithms. We emphasize the role played by the quality of the cluster labels as opposed to optimizing only the clustering structure. 1. INTRODUCTION Clustering of Web search results has been in the focus of the information retrieval community since the early days of the Web. There are two reasons for clustering of search results. The first is that the IR research community has long recognized the validity of the clustering approach in top-ranked documents, i.e. similar documents tend to be relevant to the same request. A second reason is that the ranked list is usually too large and contains many documents that are irrelevant to the particular meaning of the query the user had in mind. Thus, it would be beneficial to group search results by various meanings of the query. The attempts have made the clustering of search results for many web users. [1] A way of assisting users in finding what they are looking for quickly is to group the search results by topic. The user does not have to reformulate the query, but can merely click on the topic most accurately describing his or her specific information need. This grouping of result is called Clustering. More specifically, it is a process of grouping similar documents into clusters so that documents of one cluster are different from the documents of other clusters. There are many web clustering engines available on the web such asCarrot2, Vivisimo, SnakeT, Grouper etc which give the search results in forms of clusters. A web clustering engine takes the …
Activation of Protein Kinase C Induces γ-Aminobutyric Acid Type A Receptor Internalization in Xenopus Oocytes*

The inhibition of γ-aminobutyric acid (GABA)-gated chloride currents by the protein kinase C (PKC) activator 4β-phorbol 12-myristate 13-acetate (PMA) was investigated using recombinant human GABAA receptors expressed inXenopus oocytes. PMA (5 nm) reduced the GABA response in oocytes expressing the α1β2γ2L receptor construct, as measured by the two-electrode voltage-clamp method. GABA responses declined to approximately 25% of their pretreatment value within 45 min. GABA responses in oocytes expressing a receptor construct from which the known PKC phosphorylation sites were absent, α1β2(S410A), were comparably inhibited. Phorbol 12-monomyristate (PMM; 5 nm), which does not activate PKC, did not alter the GABA response in either construct, while the PKC inhibitor calphostin C (0.5 μm) prevented the PMA effect. To further investigate PMA inhibition of the GABA response, a GABAAreceptor α1 subunit/green fluorescent protein (GFP) chimera (α1GFP) was used to visualize GABAA receptor distribution. Similar to the wild type constructs, PMA robustly decreased GABA responses in oocytes expressing α1GFPβ2γ2L and α1GFPβ2(S410A) receptor constructs. Following PMA treatment, GFP fluorescence in the oocyte plasma membrane was decreased to approximately 45% of the pretreatment values indicating GABAA receptor internalization. This effect of PMA was prevented by calphostin C and was not produced by PMM. Experiments with bd24, a monoclonal antibody which recognizes an extracellular epitope of the α1 subunit, were used to demonstrate that PMA, but not PMM, decreases α1 subunit immunoreactivity in the plasma membrane of intact oocytes expressing the α1β2γ2L construct, thus confirming the results obtained with the chimeric receptor. It is concluded that, in Xenopusoocytes, PMA induces an internalization of the GABAAreceptor through PKC-mediated phosphorylation of an unidentified protein(s) and that this contributes to the decrease in electrophysiological responses to GABA following PKC activation.