Miracle-dz/UniFAR-FTUs huggingface dataset:
Innovation and Performance of SMEs in Italy: The Relevance of Spatial Aspects

The tripartite model of an old-industrial northwestern part of the country, of an efficient and dynamic Third Italy, mainly driven by small and medium firms' agglomeration, and a peripheral lagging South still influences the economic debate on regional development: small firms tend to be regarded as the most dynamic industrial organisation, and for this reason, regions with a high proportion of small firms tend to have higher economic growth. The aim of the paper is to present the Italian reality on the basis of some recent data on regional growth and innovation patterns. The results are surprising. Among them, that regional industrial structure in Italy seems to depart from the traditional tripartite model and that they diverge with the traditional tripartite model even in the level of competitiveness of Italian regions.
A relevance feedback perspective to image search result diversification

An efficient information retrieval system should be able to provide search results which are in the same time relevant for the query but which cover different aspects, i.e., diverse, of it. In this paper we address the issue of image search result diversification. We propose a new hybrid approach that integrates both the automatization power of the machines and the intelligence of human observers via an optimized multi-class Support Vector Machine (SVM) classifier-based relevance feedback (RF). In contrast to existing RF techniques which focus almost exclusively on improving the relevance of the results, the novelty of our approach is in considering in priority the diversification. We designed several diversification strategies which operate on top of the SVM RF and exploit the classifiers' output confidence scores. Experimental validation conducted on a publicly available image retrieval diversification dataset show the benefits of this approach which outperforms other state-of-the-art methods.
AN SVD UPDATING ALGORITHM FOR SUBSPACE TRACKING

In this paper, we extend the well known QR-updating scheme to a similar but more versatile and generally applicable scheme for updating the singular value decomposition (SVD). This is done by supplementing the QR-updating with a Jacobi-type SVD procedure, where apparently only a few SVD steps after each QR-update suce in order to restore an acceptable approximation for the SVD. This then results in a reduced computational cost, comparable to the cost for merely QR-updating. We examine the usefulness of such an approximate updating scheme when applied to subspace tracking. It is shown how an O(n 2) SVD updating algorithm can restore an acceptable approximation at every stage, with a fairly small tracking error of approximately the time variation in O(n) time steps. Finally, an error analysis is performed, proving that the algorithm is stable, when supplemented with a Jacobi-type re-orthogonalization procedure, which can easily be incorporated into the updating scheme. 1. Introduction. In many signal processing applications, it is necessary to continuously update matrix decompositions as new measurement vectors are appended as additional rows. Such problems frequently occur in beamforming, direction nding, spectral analysis, etc. [21]. Ecient updating techniques have long been known for the QR-decomposition [8], while the more dicult problem of updating the (ordinary) singular value decomposition (SVD) has only recently been addressed [1, 2, 4, 9, 20]. Previously described techniques for row updating of the SVD mostly reduce to computing the rank-one modication of the corresponding symmetric eigenvalue problem [1, 2, 9]. A major drawback is the necessary knowledge of the exact eigenstructure of the original matrix in order to compute the updated eigenstructure. For real-time applications, where in each time step an exact updating is thus to be performed, this results in an unacceptably heavy computational load, viz. O(n 3) per update. Moreover, round-o errors due to the use of nite precision arithmetic are likely to accumulate unboundedly. In this paper, we derive a fast SVD updating technique as a combination of QR-updating on the one hand and a Jacobi-type SVD procedure on the other hand. The updating scheme provides only an approximate decomposition after each update. It has a low computational complexity {O(n 2) per update{ and it is particularly suited for parallel implementation [18]. When combined with exponential weighting, such an algorithm is seen to be highly applicable to subspace tracking problems. SVD methods are known to be extremely reliable in this respect, …
Local Histograms of Character N-grams for Authorship Attribution

This paper proposes the use of local histograms (LH) over character n-grams for authorship attribution (AA). LHs are enriched histogram representations that preserve sequential information in documents; they have been successfully used for text categorization and document visualization using word histograms. In this work we explore the suitability of LHs over n-grams at the character-level for AA. We show that LHs are particularly helpful for AA, because they provide useful information for uncovering, to some extent, the writing style of authors. We report experimental results in AA data sets that confirm that LHs over character n-grams are more helpful for AA than the usual global histograms, yielding results far superior to state of the art approaches. We found that LHs are even more advantageous in challenging conditions, such as having imbalanced and small training sets. Our results motivate further research on the use of LHs for modeling the writing style of authors for related tasks, such as authorship verification and plagiarism detection.
The First Spatially Resolved Mid-Infrared Spectra of NGC 1068 Obtained at Diffraction-limited Resolution with the Keck I Telescope Long Wavelength Spectrometer

We present spatially resolved mid-infrared (mid-IR) spectra of NGC 1068 with a diffraction-limited resolution of 0. ″25 using the Long Wavelength Spectrometer (LWS) at the Keck I telescope. The mid-IR image of NGC 1068 is extended along the north-south direction. Previous imaging studies have shown that the extended regions are located inside the ionization cones, indicating that the mid-IR emission arises perhaps from the inner regions of the narrow-line clouds instead of the proposed dusty torus itself. The spatially resolved mid-IR spectra were obtained at two different slit position angles, +8. °0 and -13. °0 across the elongated regions in the mid-IR. From these spectra, we found only weak silicate absorption toward the northern extended regions but strong absorption in the nucleus and the southern extended regions. This is consistent with a model of a slightly inclined cold obscuring torus that covers much of the southern regions but is behind the northern extension. While a detailed analysis of the spectra requires a radiative transfer model, the lack of silicate emission from the northern extended regions prompts us to consider a dual dust population model as one of the possible explanations in which a different dust population exists in the ionization cones compared to that in the dusty torus. Dust inside the ionization cones may lack small silicate grains, giving rise to only a featureless continuum in the northern extended regions, while dust in the dusty torus has plenty of small silicate grains to produce the strong silicate absorption lines toward the nucleus and the southern extended regions.
Pose Calibration using Approximately Planar Urban Structure

We introduce an algorithm that automatically aligns images with partial wireframe models to compute extrinsic camera parameters with respect to the model reference frame. Aligned imagery is fused with the model to incorporate high-resolution textures and to facilitate context sensitive image processing. The technique is designed to exploit the approximately planar structure commonly found in human-made environments such as building façades, interior walls, parking lots, and roadways. Initially, images are processed to extract sets of lines that are consistent with a common vanishing point, called line pencils, that may be the result of planar structure in the perspective view. Pose is computed using a symbolic model matching technique that minimizes error between extracted line pencils and the projected wireframe model. Results from the three-dimensional city modeling domain show that the technique is capable of automatic alignment to a partial wireframe model. Calibrated imagery can be processed to segment planar structure based such as building façades, and to refine the wireframe model through context sensitive detection of façade details such as windows and doors.
Hierarchical modeling of spatio-temporally misaligned data: relating traffic density to pediatric asthma hospitalizations

Bayes and empirical Bayes methods have proven effective in smoothing crude maps of disease risk, eliminating the instability of estimates in low-population areas while maintaining overall geographic trends and patterns. Recent work applies these methods to the analysis of areal data which are spatially misaligned, i.e., involving variables (typically counts or rates) which are aggregated over differing sets of regional boundaries. In this paper we extend this hierarchical modeling approach to the spatio-temporal case, so that misalignment can arise either within a given timepoint, or across timepoints (as when the regional boundaries themselves evolve over time). Implemented using Markov chain Monte Carlo computing methods, our approach sensibly combines the relevant data sources and imposes the necessary constraints over the misaligned regional grids. We illustrate the method through an analysis of the dataset that motivated the method, which relates traffic density to pediatric asthma hospitalizations in San Diego County, California. We compare two different measures of the traffic covariate (neither of which is aligned with the zip code-level asthma data), mapping the resulting fitted risk estimates in the Geographic Information System (GIS) ARC/INFO. Results in both cases are consistent with those of several previous authors who have investigated the traffic–asthma link. Copyright © 2000 John Wiley & Sons, Ltd.
Fundamental groups of moduli stacks of stable curves of compact type

Mg;n , for 2g 2Cn> 0, be the moduli stack of n‐pointed, genus g , stable complex curves of compact type. Various characterizations and properties are obtained of both the topological and algebraic fundamental groups of the stack A Mg;n . For instance, in Theorem 3.20, we show that the topological fundamental groups are linear, extending to all n 0 previous results of Morita and Hain for g 2 and nD 0;1. Let Ag;n , for 2g 2Cn> 0, be the Teichmuller group associated with a compact Riemann surface of genus g with n points removed Sg;n , ie the group of homotopy classes of diffeomorphisms of Sg;n which preserve the orientation of Sg;n and a given order of its punctures. Let Kg;n be the normal subgroup of Ag;n generated by Dehn twists along separating simple closed curves (briefly s.c.c.) on Sg;n . The above theory yields a characterization of Kg;n for all n 0, improving Johnson’s classical results for closed and one-punctured surfaces in [13]. The Torelli group Tg;n is the kernel of the natural representation Ag;n! Sp 2g . Z/. The abelianization of the Torelli group Tg;n is determined for all g 1 and n 1, thus completing classical results of Johnson [14] and Mess [18] for closed and one-punctured surfaces. We also prove that a connected finite etale cover A M of A Mg;n , for g 2, has a
An Alternative Investigation of Weak Form Efficiency in Dhaka Stock Exchange Based on Technical Analysis

One highly documented method to test a capital market for weak form efficiency is to identify the return predictability of technical trading rules in that market. Studies on these tests are fewer in number in emerging markets than that of in developed markets and most of the tests have drawn conclusion by including only trend indicators in their trading rules. But it has already been recognized in some previous developed markets studies that trend indicators generally fail to identify sufficient information content in the past prices; hence practitioners very often use these trend indicators combined with confirming indicator (Loh 2007). The current study has investigated Dhaka Stock Exchange, an emerging market of South Asia, for weak form market efficiency by approaching the tests of technical trading rules and has confirmed the profitability of these rules up to 2.15 percent costs per transaction. Here it has used stochastic oscillator as a confirming indicator combined with moving averages (trend indicators) which is the first study of its kind in this market, and has found that it can improve the return predictability only for the short length moving averages.
Multi-object filtering for pairwise Markov chains

The Probability Hypothesis Density (PHD) Filter is a recent solution to the multi-target filtering problem which consists in estimating an unknown number of targets and their states. The PHD filter equations are derived under the assumption that the dynamics of the targets and associated observations follow a Hidden Markov Chain (HMC) model. HMC models have been recently extended to Pairwise Markov Chains (PMC) models. In this paper, we focus on multi-target filtering when targets and associated measurements follow a PMC model, and we extend the classical PHD filter to such models. We also propose a Gaussian Mixture (GM) implementation of our PMC PHD filter for linear and Gaussian PMC. Our approach enables to extend multi-object filtering to more general tracking scenarios, and also enables to deduce an estimate of the measurement associated to each target.
An Interface for Supporting Asynchronous Multi-Level Collaborative Information Retrieval

A great deal of research into Collaborative Information Retrieval (CIR) has assumed that search team members have the same level of unrestricted access to information. However, case studies and observations from different domains including government, healthcare and legal, have suggested that CIR sometimes involves people with unequal access to information. This type of scenario has been referred to as Multi-Level CIR (MLCIR). In addition to supporting collaboration, MLCIR systems must ensure that there is no unintended disclosure of sensitive information, this is an under investigated area of research. In this paper we present results of an evaluation of an interface we have designed for MLCIR scenarios. Pairs of participants used the interface under 3 different information access scenarios for a variety of search tasks. These scenarios included 1 CIR and 2 MLCIR scenarios, namely: full access (FA), document removal (DR) and term blacklisting (TR). Design interviews were conducted post evaluation to obtain qualitative feedback from participants. Evaluation results showed that our interface performed well for both DR and FA scenarios but for TR, team members with less access had a negative influence on their partner's search performance, demonstrating insights into how different MLCIR scenarios should be supported. Design interview results showed that our interface helped the participants to reformulate their queries, understand their partner's performance, reduce duplicated work and review their team's search history without disclosing sensitive information.
Increased Robustness and Security of Digital Watermarking Using DS-CDMA

Digital Watermarking is a popular method for discouraging illicit copying of multimedia data. In this paper a blind, robust and secure watermarking technique based on DS-CDMA is presented. By using this technique a watermark can be extracted at very high PSNR value with very low BER. An adaptive approach is used to select the coefficients in the DCT domain, where the watermark is to hide. This significantly reduces BER. Multiple spreading codes can be used to make this scheme secure and more robust against JPEG compression, scaling and cropping.
Modeling contexts by RFID-sensor fusion

Once devices have been distributed around us, it is essential to model the context in which we interact on a daily basis in an appropriate way. In this sense, we should ensure that the devices chosen should be completely suited to needs. Our aim is to create applications to facilitate the user's daily activities, without any extra effort in terms of interaction, if possible. In this work we present an approach to the modeling of contexts through RFID technology, identifying users as implicit inputs to the system and offering services as implicit outputs. The particular contexts studied are the classroom and the conference site
A Light-weight Solution for Real-Time Dengue Detection using Mobile Phones

Dengue is a disease transmitted mostly from mosquito bites. Viral outbreaks have spread vastly across the world, however mostly found in warm climate areas. Innovative solutions have been developed to combat outbreaks. In developing countries, dengue detection kits are not affordable or always accessible. Thus traditional approaches are still used that are slow to diagnose and treat in time. We present a dengue detection solution that uses vision sensors in cellular phones, a lightweight object identification algorithm, and a novel paper based patch currently being manufactured by researchers at the Harvard School of Medicine and the Dengue Relief Foundation. Our dengue detection algorithm rapidly and economically diagnose dengue and leverage the power of mobile phones to transmit results to the Center for Disease Control (CDC) for further analysis. This novel approach is cost effective and can improve the quality of life in developing countries by accurately detecting dengue and providing data to the CDC for monitoring of dengue epidemics.
Incremental Multi-Dimensional Scaling

Multi-Dimensional Scaling (MDS) is a widely used method for embedding a given distance matrix into a low dimensional space, used both as a preprocessing step for many machine learning problems, as well as a visualization tool in its own right. In this paper, we present an incremental version of MDS (iMDS). In iMDS, d-dimensional data points are presented in a stream, and the task is to embed the current d-dimensional data point into a kdimensional space for k < d such that distances from the current point to the previous points are preserved. Let {x1, . , xt−1} ∈ R be the data points at time step t that have already been embedded into a k-dimensional space, {r1, . , rt−1} be the given distances computed in R, then objective of iMDS is to find a point xt:
Evaluation of Import Vector Machines for Classifying Hyperspectral Data

Zusammenfassung We evaluate the performance of Import Vector Machines (IVM), a sparse Kernel Logistic Regression approach, for the classification of hyperspectral data. The IVM classifier is applied on two different data sets, using different number of training samples. The performance of IVM to Support Vector Machines (SVM) is compared in terms of accuracy and sparsity. Moreover, the impact of the training sample set on the accuracy and stability of IVM was investigated. The results underline that the IVM perform similar when compared to the popular SVM in terms of accuracy. Moreover, the number of import vectors from the IVM is significantly lower when compared to the number of support vectors from the SVM. Thus, the classification process of the IVM is faster. These findings are independent from the study site, the number of training samples and specific classes. Consequently, the proposed IVM approach is a promising classification method for hyperspectral imagery.
Keep on Rockin’ in a (Plastic-)Free World: Collective Efficacy and Pro-Environmental Intentions as a Function of Task Difficulty

Collective efficacy—the belief that one’s group is capable of affecting relevant aspects of its environment—has been highlighted as an important predictor of sustainable behavior. It increases people’s collective action tendencies, and is important for fostering environmental behavioral change beyond self-efficacy beliefs. The current study addresses two primary goals. First, we tested whether the difficulty of a task increased collective efficacy, and thereby environmental intentions. Second, we explored how collective and self-efficacy in concert predict such intentions. In a combined field-and-survey study, 165 voluntary participants took part in a plastic reduction challenge that was pretested as easy, moderate, or difficult. After being confronted with the task, participants completed an online questionnaire in which, among other variables, specific and general self-efficacy, collective efficacy, and pro-environmental intentions were measured—both general and plastic-reduction specific. Results revealed that (a) collective efficacy was significantly stronger when task difficulty was moderate rather than easy or difficult; and (b) that through specific collective and self-efficacy perceptions, sustainable intentions were gauged—even when controlling for attitudes and social norms. These findings suggest that collective efficacy beliefs are particularly relevant for attaining environmental goals that are neither too easy nor too difficult, and could thus be valuable for communication and policy strategies.
Trekking Through Siberia: Managing Cold Data in a Memory-Optimized Database

Main memories are becoming sufficiently large that most OLTP databases can be stored entirely in main memory, but this may not be the best solution. OLTP workloads typically exhibit skewed access patterns where some records are hot (frequently accessed) but many records are cold (infrequently or never accessed). It is still more economical to store the coldest records on secondary storage such as flash. This paper introduces Siberia, a framework for managing cold data in the Microsoft Hekaton main-memory database engine. We discuss how to migrate cold data to secondary storage while providing an interface to the user to manipulate both hot and cold data that hides the actual data location. We describe how queries of different isolation levels can read and modify data stored in both hot and cold stores without restriction while minimizing number of accesses to cold storage. We also show how records can be migrated between hot and cold stores while the DBMS is online and active. Experiments reveal that for cold data access rates appropriate for main-memory optimized databases, we incur an acceptable 7-14% throughput loss.