Miracle-dz/UniFAR-FTUs huggingface dataset:
Ucl Ssees Centre for Comparative Economics Are Systems of Innovation in Eastern Europe Efficient? 1 Are Systems of Innovation in Eastern Europe Efficient?

This paper explores the determinants of the productivity in the countries of Eastern Europe (EE) through the perspective of 'narrow' and 'broad' national systems of innovation (NSI). Based on panel econometrics it examines the extent to which systems in EE could be considered '(in)efficient'. Our results suggest that the EE countries have lower levels of productivity than might be expected given their research and development (R&D), innovation and production capabilities. The inefficiencies of 'broad' NSI are compounded by the inefficiencies of 'narrow' NSI in terms of generating numbers of science and technology publications and resident patents relative to R&D employment, compared to the rest of the world. Our results point to an important distinction between technology and production capability as the drivers of productivity improvements, and provide some policy implications.
Note on Algorithm Differences Between Nonnegative Matrix Factorization And Probabilistic Latent Semantic Indexing

NMF and PLSI are two state-of-the-art unsupervised learning models in data mining, and both are widely used in many applications. References have shown the equivalence between NMF and PLSI under some conditions. However, a new issue arises here: why can they result in different solutions since they are equivalent? or in other words, their algorithm differences are not studied intensively yet. In this note, we explicitly give the algorithm differences between PLSI and NMF. Importantly, we find that even if starting from the same initializations, NMF and PLSI may converge to different local solutions, and the differences between them are born in the additional constraints in PLSI though NMF and PLSI optimize the same objective function.
The Purpose of Tutorial Groups: Social Influence and The Group As Means and Objective

The aim of this study was to investigate how first-year students view the purpose of tutorial groups in problem-based learning. In all, 147 students from 24 groups participated, providing 399 statements. Data were analysed using thematic analysis. The results showed a focus on both learning and social influence. Learning involved the tutorial as both an objective and as a means. Social influence is important for a tutorial to become a well-functioning group, together with opportunities to use the group as an objective in and of itself to learn to work in a group, cooperate, solve problems and communicate. Social support and feelings of togetherness create conditions for intrinsic motivation, as well as stronger identification with the group. A tutorial group as a well-functioning learning environment requires both the group as an objective and as a means.
Packet Erasure Multi-Channel Model for Video Streaming Applications

Transmission of a video stream over multiple channels is an emerging technology for improving the streaming quality over wireless and best-effort networks. However, these channels might be statistically dependent and may share resources such as bottleneck links, or may cause mutual interference. As a result, the packet loss rate and packet delay over a specific channel can increase while activating additional channels. Modeling such a system without consideration of the negative impact of the channels statistical dependency can lead to underestimation of service performance problems. We suggest a new simple Markov model for multiple channels with statistical dependency. The suggested model captures the performance degradation over the individual channels, while activating multiple channels. Traces of real data are used to confirm the model validity.
Productivity, Export, and Environmental Performance: Air Pollutants in the United States

This paper studies the firm-level relationship among productivity, decision to export, and environmental performance. The emerging theoretical and empirical literature suggests that trade has an important role in determining firms' heterogeneity: increased openness to trade induces a reallocation effect that increases within-industry efficiency, thereby linking firms' decisions to export and adopt newer (and cleaner) technology. We argue that this framework provides the following empirically-relevant predictions: there is an inverse relationship between firm productivity and pollution emissions per unit output; exporting firms have lower emissions per unit output; and larger firms have a lower emission intensity. To examine these implications empirically, we have assembled a uniquely detailed dataset of the U.S. manufacturing industry for the years 2002, 2005, and 2008 by matching facility-level air emission data from the U.S. Environmental Protection Agency with the facility's economic characteristics contained in the National Establishment Time Series database. The strategy is to first estimate a facility-level total factor productivity parameter as a plant-specific fixed effect. We then investigate how this estimated productivity parameter correlates with emission intensity on a pollutant-by-pollutant basis. Our empirical findings support the hypotheses suggested by the conceptual model. For each criteria air pollutant considered, we find a significant negative correlation between estimated facility productivity and emission intensity. Conditional on a facility's estimated productivity and other controls, exporting facilities have significantly lower emissions per value of sales than non-exporting facilities in the same industry. We also find that plant size is negatively and significantly related to emission intensity for all pollutants.
Idiosyncratic Volatility of Liquidity and Expected Stock Returns

We show that idiosyncratic liquidity risk is positively priced in the cross-section of stock returns. Our measure of idiosyncratic liquidity volatility is based on a ”market” model for stock liquidity. Idiosyncratic volatility of liquidity is priced in the presence of systematic liquidity risk: the covariance of stock returns with aggregate liquidity, the covariance of stock liquidity with aggregate liquidity, and the covariance of stock liquidity with the market return. Our results are puzzling in light of Acharya and Pedersen (2005) who develop a model in which only systematic liquidity risk affects returns.
Global and Local Models for Multi-Document Summarization

In this paper we study the effectiveness of combining corpus-level (global) tag-topic models and target document set level local models for multi-document summarization. Recently tag-topic models that exploit both word level annotation (e.g. named entity type) and/or document level metadata (e.g. words related to topic categories) have been proposed to model documents tagged from two different perspectives. We augment such models with an informative prior over document level latent topic proportions and conduct extensive experiments on multi-document summarization of newswire articles within the TAC 2010 and 2011 datasets. We train tag-topic models on the entire set of documents without knowledge of any document rele-vancy and ﬁt each sentence of the documents in the relevant document set to the models. Combining the model likelihoods of these sentences and their similarities to bag-of-words derived from key terms in the relevant document set resulted in summaries that significantly outperformed robust baselines. By augmenting this model with local Rhetorical Structure trees of these sentences, we are able to select the salient spans of the sentences which are used to generate bulleted list summaries. We empirically show that the standard ROUGE SU4 scores of such summaries are comparable to those obtained from human generated counterparts.
Spectrum and Energy Efficiency Evaluation of Two-Tier Femtocell Networks With Partially Open Channels

Two-tier femtocell networks are an efficient communication architecture that significantly improves throughput in indoor environments with low power consumption. Traditionally, a femtocell network is usually configured to be either completely open or completely closed in that its channels are either made available to all users or used by its own users only. This may limit network flexibility and performance. It is desirable for owners of femtocell base stations if a femtocell can partially open its channels for external-user access. In such scenarios, spectrum and energy efficiency becomes a critical issue in the design of femtocell network protocols and structure. In this paper, we conduct performance analysis for two-tier femtocell networks with partially open channels. In particular, we build a Markov chain to model the channel access in the femtocell network and then derive the performance metrics in terms of the blocking probabilities. Based on stationary state probabilities derived by Markov chain models, spectrum and energy efficiency is modeled and analyzed under different scenarios characterized by critical parameters, including number of femtocells in a macrocell, average number of users, and number of open channels in a femtocell. Numerical and Monte Carlo (MC) simulation results indicate that the number of open channels in a femtocell has an adverse impact on the spectrum and energy efficiency of two-tier femtocell networks. Results in this paper provide guidelines for trading off spectrum and energy efficiency of two-tier femtocell networks by configuring different numbers of open channels in a femtocell.
Using Overhearing for Local Group Formation François Legras

We present a framework for group formation within teams of non-selfish agents cooperating in a dynamic environment with local broadcast communication. This framework is based on a set of six types of propositional attitudes, the concept of overhearing and a mechanism that allows agents to track the organization of the team. We explain the principles of this framework, formalize the organization tracking mechanism and formally prove some of its properties. Finally, we present the results of a series of experiments conducted on an implementation of this framework.
Developing an extensible framework for content based searching in super peer p2p network

Searching is an important factor in p2p network for content retrieval. Most of the searches in p2p system are title-based with their limited functionality. Without knowing the unique filename we canpsilat retrieve the content of the file in title based search. Here super peer p2p network is designed that supports content-based search for relevant documents. At the beginning, a general and extensible framework is proposed which is based on hierarchical summary structure for searching similar documents in p2p network. The summary structure is formed by Vector Space Model (VSM), Latent Semantic Indexing (LSI) and Singular Value Decomposition (SVD) techniques. Than an effective document searching is developed by summarizing and maintaining all documents within the network with different factors. Finally at the end, the experimental result is verified on a real p2p prototype and large scale network. The results show the effectiveness, efficiency and scalability of the proposed system.
A Table-Based Application-Specific Prefetch Engine for Object-Oriented Embedded Systems

A table-based application-specific data prefetching mechanism is presented in this paper. This mechanism is proposed to improve the performance of the application specific instruction-set processors (ASIP) we develop customized to an object-oriented application. In this approach, we divide the data accesses of a class method into two conditional and unconditional parts. We supply the prefetch engine with the static information about each part to prefetch all data fields of an object required by a class method when the class method is invoked. Effective management of memory access patterns by dividing them based on the method to which they belong and storing the access information of nested loops using a simple structure are the merits of the proposed mechanism. In addition, by adding a prefetch flag to cache blocks, we eliminate a large number of prefetch related tag comparisons. The results show that the proposed mechanism reduces the cache miss ratio and prefetch related tag comparisons on average by 66% and 21%, respectively
Response to comments Interactive comment on “ Chemical and stable carbon isotopic composition of PM 2 . 5 from on-road vehicle emissions in the PRD region and implication for vehicle emission control policy

Vehicle emissions are a major source of urban air pollution. In recent decade, the Chinese government has introduced a range of policies to reduce vehicle emissions. In order to understand the chemical characteristics of PM2.5 from on-road vehicle emissions in the Pearl River Delta (PRD) region and to evaluate the effectiveness of control policies on vehicle emissions, the emission factors of PM2.5 mass, elemental carbon (EC), organic carbon (OC), watersoluble organic carbon (WSOC), water-soluble inorganic ions (WSII), metal elements, organic compounds and stable carbon isotopic composition were measured in the Zhujiang tunnel of Guangzhou, in the PRD region of China in 2013. Emission factors of PM2.5 mass, OC, EC and WSOC were 92.4, 16.7, 16.4 and 1.31 mg vehicle km respectively. Emission factors of WSII were 0.016 (F)∼ 4.17 (Cl)mg vehicle km, contributing about 9.8 % to the PM2.5 emissions. The sum of 27 measured metal elements accounted for 15.2 % of PM2.5 emissions. Fe was the most abundant metal element, with an emission factor of 3.91 mg vehicle km. Emission factors of organic compounds including n-alkanes, polycyclic aromatic hydrocarbons, hopanes and steranes were 91.9, 5.02, 32.0 and 7.59 μg vehicle km, respectively. Stable carbon isotopic composition δC value was −25.0 ‰ on average. An isotopic fractionation of 3.2 ‰ was found during fuel combustion. Compared to a previous study in Zhujiang tunnel in 2004, emission factors of PM2.5 mass, EC, OC, WSII except Cl and organic compounds decreased by 16.0∼ 93.4 %, which could be attributed to emission control policy from 2004 to 2013. However, emission factors of most of the metal elements increased significantly, which could be partially attributed to the changes in motor oil additives and vehicle conditions. There are no mandatory national standards to limit metal content from vehicle emissions, which should be a concern of the government. A snapshot of the 2013 characteristic emissions of PM2.5 and its constituents from the on-road vehicular fleet in the PRD region retrieved from our study would be helpful for the assessment of past and future implementations of vehicle emission control policy.
The Effect of Discount Frequency and Depth on Consumer Price Judgments

The intensity of price discounting by retailers and manufacturers raises important questions about consumer price judgments. In the extreme, discounting can take the form of frequent but shallow discounts or deep but infrequent discounts. The research reported here explores the effects of these strategies on consumer estimation of price levels for competing stores and brands. In an initial experiment in which subjects made brand choices over time, a depth effect was observed that contrasted with the frequency effect found in previous research. Subsequent experiments identified the conditions under which depth (vs. frequency) characteristics of price data dominate consumers' price-estimation judgments. Frequency information is more influential when sets of interstore or interbrand comparative prices exhibit complex and overlapping distributions (hence creating processing difficulty); in contrast, a depth bias occurs when prices have a simpler, dichotomous distribution. These results place pragmatically meaningful limitations on the influence of frequency information and illustrate the importance of context in determining consumer price judgments in a promotional environment. Copyright 1999 by the University of Chicago.
Flexible macroblock ordering as a content adaptation tool in H.264/AVC

H.264/AVC is the newest block based video coding standard from MPEG and VCEG. It not only provides superior and efficient video coding at various bit rates, it also has a "network-friendly" representation thanks to a series of new techniques which provide error robustness. Flexible Macroblock Ordering (FMO) is one of the new error resilience tools included in H.264/AVC. Here, we present an alternative use of flexible macroblock ordering, using its idea of combining non-neighboring macroblocks together in one slice. Instead of creating a scattered pattern, which is useful when transmitting the data over an error-prone network, we divide the picture into a number of regions of interest and one remaining region of disinterest. It is assumed that people watching the video will pay much more attention to the regions of interest than to the remainder of the video. So we compress the regions of interest at a higher bit rate than the regions of disinterest, thus lowering the overall bit rate. Simulations show that the overhead introduced by using rectangular regions of interest is minimal, while the bit rate can be reduced by 30% and more in most cases. Even at those reductions the video stays pleasant to watch. Transcoders can use this information as well by reducing only the quality of the regions of disinterest instead of the quality of the entire picture if applying SNR scalability. In extreme cases the regions of disinterest can even be dropped easily, thus reducing the overall bit rate even further.
Multi-dimension multimedia high-level semantic structure and locating algorithm

Derived from the thought of data cube, this paper presents a new structure model which organizes multimedia data in a multi-dimensional data cube. For the problem of semantic gap in retrieving multimedia information, the paper gives the definition and operation laws of matrix division method, which describes the high-level semantic structure for a lot of kinds of multimedia information. We retrieve multimedia information by using the high-level semantic structure to solve the problem of semantic gap. Based on the structure, the paper presents a partial priority algorithm by the results users locate multimedia information. The locating efficiency is improved by the algorithm based on the structure through modifying the high-level semantic branch weight by every result users locate multimedia information automatically.
The ASCoVeCo State Space Analysis Platform: Next Generation Tool Support for State Space Analysis (Invited Tutorial)

State space analysis is one of the main approaches to model-based verification of concurrent systems and is one of the most successfully applied analysis methods for Coloured Petri Nets (CP-nets or CPNs) [13, 16, 17]. The basic idea of state space exploration and analysis is to compute all reachable states and state changes of the concurrent system under consideration and represent these as a directed graph. From a constructed state space it is possible to verify and analyse a large class of behavioural properties by considering, e.g., the standard behavioural properties of CP-nets, traverse a constructed state space by means of user-defined queries, or conduct LTL and CTL model checking [4, 10]. Another main advantage of state space analysis is that it can be supported by computer tools in a highly automatic way, and it can provide counter examples demonstrating why the system does not have a certain property. The main limitation of using state spaces to verify behavioural properties of systems is the state explosion problem [24], i.e., that state spaces of systems may have an astronomical number of reachable states which means that they are too large to be handled with the available computing power (CPU speed and memory). Methods for alleviating this inherent complexity problem is an active area of research and has led to the development of a large collection of state space reduction methods. These methods have significantly broadened the class of systems that can be verified and state spaces can now be used to verify systems of industrial size. Some of these methods [2, 15, 14, 25] have been developed in the context of the CPN modelling language. Other methods (e.g., [23, 22, 26, 11]) have been developed outside the context of the CPN modelling language, but state space reduction methods are generally independent of any concrete modelling language and hence also applicable for CP-nets. A computer tool supporting state spaces must implement a wide range of state space reduction methods since no single reduction method works well on all systems. Both CPN Tools [5] and its predecessor Design/CPN [6] have supported state space analysis in it most basic form and experimental prototype libraries have been developed supporting state space reduction methods such as the symmetry method [15, 18], the equivalence method [14, 19], time condensed state spaces [3], and the sweep-line method [2, 21]. The software architectures of CPN Tools and Design/CPN have, however, made it difficult to support a collection of state space reduction methods in a coherent manner in these tools. This tutorial presents the ASCoVeCo State Space Analysis Platform (ASAP) which is currently being developed in the context of the ASCoVeCo research project [1]. ASAP represents the next generation of tool support for state space exploration and analysis of CPN
Keyword Spotting in Offline Chinese Handwritten Documents Using a Statistical Model

This paper proposes a method for keyword spotting in offline Chinese handwritten documents using a statistical model. On a text query word, the method measures the similarity between the query word and every candidate word in the document by combining a character classifier and four classifiers characterizing the geometric contexts. By over-segmenting text lines into primitive segments, candidate characters and words are generated by concatenating consecutive segments, and the beam search strategy is used to search all the candidate words. The character classifier and the model combining weights are trained by optimizing a one-vs-all discrimination objective so as to maximize the similarity of true words and minimize the similarity of imposters. In experiments on a test dataset containing 1,015 pages of 180 writers, the proposed methods yields promising performance. For retrieving four-characer words, the recall, precision and F-measure are 92.47%, 83.76% and 87.90%, respectively.
Ieee Transactions on Wireless Communications, Accepted for Publication 1 Single Hop Ieee 802.11 Dcf Analysis Revisited: Accurate Modeling of Channel Access Delay and Throughput for Saturated and Unsaturated Traffic Cases

—Analytical models for IEEE 802.11 Distributed Coordination Function (DCF) play important roles in performance estimation, protocol optimization and admission control for wireless networks utilizing this MAC protocol. While a myriad of models for IEEE 802.11 DCF channel access delay and through-put exist, such models' accuracy is generally limited to rather narrow ranges of scenario parameters. Our investigations single out the inaccuracy in modeling the backoff process as the primary reason for the said deviations. In this paper, we introduce two highly accurate models for IEEE 802.11 DCF protocol in a single-hop setting under both saturated and unsaturated traffic loads. First, a crucial augmentation to the classical model proposed by Bianchi [1] is presented for the saturation load analysis to account for channel state during the backoff countdown process, resulting in a highly accurate estimation of collision probability, channel throughput and channel access delay. We then extend this accurate model to unsaturated traffic cases through an iterative approach which similarly results in highly accurate performance metric estimations for a wide range of parameters. Both models have been evaluated through simulations and in comparison with existing analytical models.