Miracle-dz/UniFAR-FTUs huggingface dataset:
Solving Italian crosswords using the Web Technical Report

We designed and implemented a software system, called WebCrow, that represents the first solver for Italian crosswords and the first system that tackles a language game using the Web as knowledge base. Its core feature is the Web Search Module that produces a special form of web-based question answering that we call clue-answering. This paper will focus its attention on this task. The web-search approach has proved itself to be very consistent: using a limited set of documents (30 for each clue) the clue-answering process is able to retrieve over two thirds of the correct answers. In many cases the targeted word is given in output among the very first most probable candidates and in nearly 15% of clues the correct answer appears in first position. To complete the crossword solving problem the system has to fill the grid with the best set of word answers. Currently, WebCrow performances are interesting: crosswords that are “easy” for expert humans (i.e. crosswords from the cover pages of La Settimana EnigmisticaTM) are solved, in a 15 minutes time limit, with 80% of correct words and over 90% of correct letters. Crosswords that are designed for experts (i.e. examples by S. & A. Bartezzaghi both in La Settimana Enigmistica and in La Repubblica) WebCrow places correctly two thirds of the words and around 80% of the letters.
Survey on Techniques Used for M-fish Image Segmentation for Classification of Chromosomes

Multicolor Fluorescence Insitu Hybridization is a newly developed chromosome imaging method where eachchromosome class appears to have a different color. This methodnot only simplifies the detection of subtle chromosomal abnormalitiesbut also makes the analysis of chromosome images easier; bothfor human inspection and computerized examination. The segmentation and classification of Multicolor Fluorescence Insitu Hybridization (M-FISH) images can be used to detect chromosomal aberrations that can be used for cancer and genetic disease analysis. In this paper various methods to the problem of image segmentation and classification are explored. Here the possibility of using these algorithms to segment general images is presented and the issues involved in such algorithms are discussed.
Contour detection using Binary Partition Trees

Contour detection is a hard, challenging, and of paramount importance problem in image processing. State-of-the-art algorithms are approaching human performance but usually entail complex and tailored image models and arduous training. Binary Partition Tree is a hierarchical region-based image model that has been proven to have a wide range of applications in image filtering, information retrieval, object detection, etc. In this paper we propose a contour detection technique based on this versatile image model, extracting the contour information available in the tree yet outperforming one of the most widely-used contour detector.
FLIP the (Flow) table: Fast lightweight policy-preserving SDN updates

We propose FLIP, a new algorithm for SDN network updates that preserve forwarding policies. FLIP builds upon the dualism between replacements and additions of switch flow-table rules. It identifies constraints on rule replacements and additions that independently prevent policy violations from occurring during the update. Moreover, it keeps track of alternative constraints, avoiding the same policy violation. Then, it progressively explores the solution space by swapping constraints with their alternatives, until it reaches a satisfiable set of constraints. Extensive simulations show that FLIP outperforms previous proposals. It achieves a much higher success rate than algorithms based on rule replacements only, and massively reduces the memory overhead with respect to techniques solely relying on rule additions.
Compaction-free compressed cache for high performance multi-core system

Compressed cache was used in shared last level cache (LLC) to increase the effective capacity. However, because of various data compression sizes, fragmentation problem of storage is inevitable in this cache design. When it happens, usually, a compaction process is invoked to make contiguous storage space. This compaction process induces extra cycle penalty and degrades the effectiveness of compressed cache design. In this paper, we propose a compaction-free compressed cache architecture which can completely eliminate the time for executing compaction. Based on this cache design, we demonstrate that our results, compared with the conventional cache, have system performance improvement by 16% and energy reduction by 16%. Compared with the work by Alameldeen et al. [1], our design has 5% more performance improvement and 3% more energy reduction. Compared with the work by Sardashti et al. [2], our design has 3% more performance improvement and 2% more energy reduction.
A Novel Approach for Amplification and Purification of Mouse Oligodendrocyte Progenitor Cells

Although transgenic and knockout mice are widely used to study the specification and differentiation of oligodendrocyte precursor cells (OPCs), mouse primary OPCs are difficult to be purified and maintained, and many in vitro studies have to resort to rat OPCs as substitutes. In this study, we reported that mouse O4 negative early-stage OPCs can be obtained by culturing cortical tissue blocks, and the simultaneous treatment of OPCs with Platelet Derived Growth Factor-AA (PDGFaa), basic fibroblast growth factor (bFGF), and epidermal growth factor (EGF) is the key for the propagation of mouse OPCs in culture. EGF was found to be a potent mitogen for OPCs and cooperate with PDGFaa to extend cell division and inhibit their differentiation. EGF also collaborates with PDGFaa and bFGF to convert bipolar or tripolar OPCs to more vital fibroblast-like OPCs without compromising their oligodendrocyte differentiation potential. In addition, EGF promoted the survival and proliferation of glial progenitor cells (GPCs) derived from primary OPC cultures, and a mixture of GPCs and OPCs can be obtained and propagated in the presence of EGF, bFGF, and PDGFaa. Once EGF is withdrawn, GPC population decreased sharply and fibroblast-like OPCs changed into typical OPCs morphology, then homogeneous OPCs were obtained subsequently.
On the Nature of Strategic Communications

Abstract : Strategic communications, as now generally understood within the Department of Defense (DOD), encompass (to use the bureaucratic terms of art) public affairs, "defense support for public diplomacy," and military psychological operations (PSYOP). That there has been something less than smooth cooperation among these various components is hardly a secret. In the fall of 2001, for instance, the Pentagon established an Office of Strategic Influence (OSI) with significant funding of its own to plan and coordinate a joint and coalition campaign to shape the communications battlefield in the war on terror. This promising initiative promptly blew up. In early 2002, Secretary Donald Rumsfeld felt compelled to disestablish OSI when press accounts alleged that the office was placing so-called disinformation in the American media (later investigation showed the charges to be false or grossly misleading). Evidently, these attacks were inspired from within the Pentagon itself by elements of the DOD public affairs community. Continuing tension between the PSYOP and public affairs communities over the fundamental nature of strategic communications remains perhaps the most serious impediment to more effective action by the Defense Department in this critical arena.
Register promotion by sparse partial redundancy elimination of loads and stores

An algorithm for register promotion is presented based on the observation that the circumstances for promoting a memory location's value to register coincide with situations where the program exhibits partial redundancy between accesses to the memory location. The recent SSAPRE algorithm for eliminating partial redundancy using a sparse SSA representation forms the foundation for the present algorithm to eliminate redundancy among memory accesses, enabling us to achieve both computational and live range optimality in our register promotion results. We discuss how to effect speculative code motion in the SSAPRE framework. We present two different algorithms for performing speculative code motion: the conservative speculation algorithm used in the absence of profile data, and the the profile-driven speculation algorithm used when profile data are available. We define the static single use (SSU) form and develop the dual of the SSAPRE algorithm, called SSUPRE, to perform the partial redundancy elimination of stores. We provide measurement data on the SPECint95 benchmark suite to demonstrate the effectiveness of our register promotion approach in removing loads and stores. We also study the relative performance of the different speculative code motion strategies when applied to scalar loads and stores.
Illumination invariant face recognition using Discrete Cosine Transform and Principal Component Analysis

Face recognition technology has evolved as a popular identification technique to perform verification of human identity. By using the feature extraction methods and dimensionality reduction techniques in the pattern recognition applications, a number of facial recognition systems has been produced with distinct measure of success. Various face recognition algorithms and their extensions, have been proposed in the past three decades. However, face recognition faces challenging problems in real life applications because of the variation in the illumination of the face images. In the recent years, the research is focused towards Illumination-invariant face recognition system and many approaches have been proposed. But, there are several issues in face recognition across illumination variation which still remains unsolved. This paper provides a research on an efficient illumination-invariant face recognition system using Discrete Cosine Transform (DCT) and Principal Component Analysis (PCA). For processing the illumination invariant image, low frequency components of DCT are used to normalize the illuminated image, odd and even components of DCT is used for compensation in illumination variation and PCA is used for recognition of face images. The existing approaches in illumination Invariant face recognition are comprehensively reviewed and discussed. The proposed approach is validated with Yale Face Database B. Experimental results demonstrate the effectiveness of this approach in the performance of face recognition.
HIGH DIMENSIONAL DATA CLUSTERING THROUGH EFFICIENT EVOLUTIONARY ALGORITHM

Dimensionality reduction is essential in multidimensional data mining since the dimensionality of real time data could easily extend to higher dimensions. Most recent efforts on dimensionality reduction, however, are not adequate for multidimensional data due to lack of scalability. In this paper, we use the evolutionary algorithm for the dimension reduction process. Initially, our proposed evolutionary algorithm gets the values of a number of clusters and the total number of dimensions of the database. For each number of dimensions, we generate a solution. The next step of our proposed algorithm is selection of centroid for each cluster from the dataset as per the dimension of each solution. The next step is to evaluate each solution through our proposed fitness function and store the values of best one subsequently the solutions are updated through the proposed attribute evaluation process. As a consequence of the proposed evolutionary algorithm selects the best set of dimension with the best set of centroids which will apply for the K-Means clustering algorithm to create the clusters. Finally, our proposed hybrid optimization algorithm is implemented using JAVA and we compare our proposed algorithm with the standard dimension reduction algorithm in terms of clustering accuracy.
Markov Latent Feature Models

We introduce Markov latent feature models (MLFM), a sparse latent feature model that arises naturally from a simple sequential construction. The key idea is to interpret each state of a sequential process as corresponding to a latent feature, and the set of states visited between two null-state visits as picking out features for an observation. We show that, given some natural constraints, we can represent this stochastic process as a mixture of recurrent Markov chains. In this way we can perform correlated latent feature modeling for the sparse coding problem. We demonstrate two cases in which we define finite and infinite latent feature models constructed from first-order Markov chains, and derive their associated scalable inference algorithms. We show empirical results on a genome analysis task and an image denoising task.
TRAF2 Exerts Its Antiapoptotic Effect by Regulating the Expression of Krüppel-Like Factor LKLF

ABSTRACT Tumor necrosis factor receptor (TNFR)-associated factor 2 (TRAF2) is one of the key factors that mediate TNF signaling. The deletion of TRAF2 renders cells more sensitive to TNF-induced apoptosis. Although TRAF2 is known to be required for TNF-induced JNK and NF-κB activation, the underlying mechanism of the increased sensitivity of TRAF2 null cells (TRAF2−/−) to TNF-induced apoptosis is not fully understood. To study the underlying mechanism, we examined the difference in gene expression between TRAF2−/− and wild-type fibroblast cells by using microarray technology. We found that one of the genes whose expression was dramatically decreased in TRAF2−/− cells was the lung Krüppel-like factor (LKLF). Our results indicate that the expression of LKLF requires TRAF2 but is independent of TNF signaling. Although it appears that TRAF2 regulates the expression of the LKLF gene at the transcription level, TRAF2 does not function as a transcription factor itself. Our results suggest that TRAF2 regulates LKLF expression through the mitogen-activated protein kinase p38 pathway. More importantly, ectopic expression of LKLF in TRAF2−/− cells protected cells against TNF-induced apoptosis. These results reveal a novel aspect of TRAF2 function: by regulating the expression of genes, such as LKLF, TRAF2 controls cell sensitivity to apoptosis.
Adaptive Group Organization Cooperative Evolutionary Algorithm for TSK-type Neural Fuzzy Networks Design

This paper proposes a novel adaptive group organization cooperative evolutionary algorithm (AGOCEA) for TSK-type neural fuzzy networks design. The proposed AGOCEA uses group-based cooperative evolutionary algorithm and self- organizing technique to automatically design neural fuzzy networks. The group-based evolutionary divided populations to several groups and each group can evolve itself. In the proposed self-organizing technique, it can automatically determine the parameters of the neural fuzzy networks, and therefore some critical parameters have no need to be assigned in advance. The simulation results are shown the better performance of the proposed algorithm than the other learning algorithms. Keywords—TSK-type Neural Fuzzy Networks; Evolutionary Algorithm; Group based Symbiotic; Self Organization; System Identification I. INTRODUCTION In the field of artificial intelligence, neural fuzzy network (1-3) refers to combinations of neural networks and fuzzy logic. Because the advantages of neural fuzzy networks are powerful computation ability and human-like reasoning ability from neural networks and fuzzy systems, respectively, it has good performance for solving complex nonlinear problems. The neural fuzzy networks can perform the nonlinear mapping once the system parameters are trained based on a sequence of input and desired response pairs, and it does not require mathematical descriptions of system. Therefore, the determination of parameters is a critical issue for neural fuzzy networks. The backpropagation (BP) (4, 5) is a common method and widely used for training neural fuzzy networks. It is well known that BP is an approximate steepest descent algorithm. The steepest descent algorithm is the simplest, and the minimization method. The advantage of steepest decent algorithm is very simple, requiring calculation only of the gradient, the disadvantage of steepest descent algorithm is that training time is generally longer than other algorithms; based on initial weight values, it is often to find the local optimal solution but not global optimal solution. Besides, BP training performance depends on the initial values of the system parameters, and for different network topologies one has to derive new mathematical expressions for each network layer. Considering the aforementioned disadvantages one may be faced with suboptimal performance even for a suitable neural fuzzy network topology. Hence, the capability of training parameters and finding the global solution while optimizing the overall structure are important. The evolutionary methods using for training the fuzzy model has become a popular research field (6-20) because evolutionary methods simultaneously evaluate many points in the search space and are more likely to converge toward the global solution. The evolutionary fuzzy model is a learning process to generate a fuzzy model automatically by incorporating evolutionary learning procedures.
A Software-Pipelined Approach to Multicore Execution of Timing Predictable Multi-threaded Hard Real-Time Tasks

Multicore processors can deliver higher performance than single-core processors by exploiting thread level parallelism (TLP): applications are split into independent threads, each of which is mapped into a different core, reducing the execution time and potentially its worst-case execution time (WCET). Unfortunately, inter-thread interferences generated by simultaneous accesses to shared resources from different threads may completely destroy the performance benefits brought by TLP. This paper proposes a software/hardware cache partitioning approach that reduces the inter-thread memory interferences generated in hard real-time software-pipelined parallel applications. Our results show that our approach effectively reduces memory interferences, while still guaranteeing a predictable timing behaviour, achieving a WCET estimation reduction of 28% for a software pipelined version of the LU decomposition application with respect to the single-threaded version.
Robust and Eecient Detection of Convex Groups 1

This paper describes an algorithm that robustly locates convex collections of line segments in an image. The algorithm is guaranteed to nd all convex sets of line segments in which the length of the line segments accounts for at least some xed proportion of the length of their convex hull. This enables the algorithm to nd convex groups whose contours are partially occluded or missing due to noise. We perform an expected case analysis of the algorithm's performance that shows that its run time is O(n 2 log(n) + nm), when we wish to nd the m most salient groups in an image with n line segments. We support this analysis with experiments on real data. Our analysis not only reveals the circumstances under which our algorithm is eecient, but also tells us when the groups found are unlikely to occur at random, and so are likely to capture the underlying structure of a scene. We also demonstrate the grouping system as a module in an eecient recognition system that combines grouping with indexing 2 .
Incorporating Estimation Error into Optimal Portfolio allocation

In this report, we consider the problem concerned with incorporating estimation error into optimal consumption and portfolio selection in continuous time. The original optimal consumption and asset allocation problem in continuous time was solved by Merton in a series of papers [4, 5] and became widely known as “Merton’s Problem”. Merton made the assumption that the asset price processes {Si} N i=1 are given by Geometric Brownian Motion (GBM), where the parameter values are known. He was able to prove that the investment opportunity set can be generated by two portfolios or mutual funds of assets, which themselves obey Geometric Brownian Motion. This result is sometimes known as a two-fund separation theorem, and does not depend on the market being in equilibrium. Merton utilized the separation theorem in his development of the Inter-temporal Capital Asset Pricing Model in [6], but the mutual fund theorem is only dependent on the assumed properties of the asset price processes. Subsequent work on this problem has sought to generalize Merton’s work in numerous ways. For instance, some authors have considered more general asset price processes than GBM, e.g. Ito processes with deterministic (and even stochastic) time-dependent drift and diffusion parameters, and other general diffusion and Markov processes, or general semimartingales. In the latter case, the additional assumption that the market is complete (or more generally, effectively complete) is required, and the method of solution uses the so-called Cox-Huang-Pliska method [9], which involves the use of the Martingale Representation Theorem. Another generalization of the Merton problem involves the inclusion of
Conceptualising Space and Place On the role of agency, memory and identity in the construction of space from the Upper Palaeolithic to the Iron Age in Europe

This volume contains papers from two sessions at the 15th uispp congress - The creation of 'significant places' and 'landscapes' in the Northwestern half of the Iberia, during Pre and Proto-historic times. Theoretical, recording and interpretation issues from cases in this region; and Space, Memory and Identity in the European Bronze Age' . Papers come from a wide range of theoretical perspectives including neo-marxist, processualist, phenomenological and contextual.
Implementing subtopic recall and plummeting shadowing in Suffix Tree clustering

Mining data to find patterns has been studied for some time and current work on automatically grouping documents has roots in this research. We propose a dexterous partitioning strategy for Web search result presentation. We identify a canon to which a clustering must stick on to perk up the user's search experience, avoid the redundant effect of query aspect recurrence, which is called shadowing. We present measures to quantify the shadowing effect, and we introduce an algorithm SSTC Subtopic Suffix Tree Clustering that optimizes the identified principles. The key idea of this is a dynamic reorganization of a clustering, similar to a faceted navigation system, We have also concentrated on the elimination of excessive clustering, i.e., either the number of cluster labels or the number of documents per cluster should not exceed the size of the result list Evaluations are done using the AMBIENT corpus and demonstrate the latent of our approach by a comparison with two well-known clustering search result algorithms STC and Lingo.
Exploring virtual ecosystems

Browsing In Time & Space (BITS) is an interface designed to explore virtual ecosystems. A virtual ecosystem includes a three dimensional terrain model background, collections of man-made and natural objects, and behavior and interaction rules between the objects and the background. BITS is based on a virtual notepad and pen metaphor and is inspired in the concept of logging. Physical props are used to represent the notepad and the pen. The notepad includes a Time & Space Slider to facilitate time and space traveling, a set of buttons and a list of commands to control the interaction and enable the manipulation of objects, and a Notes Area. The handwritten notes can be referenced in time and space with the use of logging marks. BITS is being implemented on a PC-based architecture using sensors to track the pen's movement and the notepad's position. BITS major problem is related to the poor representation of the notes written in the notepad using the sensor based tracking system.