Miracle-dz/UniFAR-FTUs huggingface dataset:
Picture extraction from digitized historical manuscripts

In this work we propose a system for automatic document segmentation to extract graphical elements from historical manuscripts and then to identify significant pictures from them, removing floral and abstract decorations. The system performs a block based analysis by means of color and texture features. The Gradient Spatial Dependency Matrix, a new texture operator particularly effective for this task, is proposed. The feature vectors are processed by an embedding procedure which allows increased performance in later SVM classification. Results for both feature extraction and embedding based classification are reported, supporting the effectiveness of the proposal.
Expression of the CD4 gene requires a Myb transcription factor

We have analyzed the control of developmental expression of the CD4 gene, which encodes an important recognition molecule and differentiation antigen on T cells. We have determined that the CD4 promoter alone functions at high levels in the CD4+ CD8- mature T cell but not at the early CD4+ CD8+ stage of T-cell development. In addition, the CD4 promoter functions only in T lymphocytes; thus, the stage and tissue specificity of the CD4 gene is mediated in part by its promoter. We have determined that a Myb transcription factor binds to the CD4 promoter and is critical for full promoter function. Thus, Myb plays an important role in the expression of T-cell-specific developmentally regulated genes.
Programming framework for clusters with heterogeneous accelerators

We describe a programming framework for high performance clusters with various hardware accelerators. In this framework, users can utilize the available heterogeneous resources productively and efficiently. The distributed application is highly modularized to support dynamic system configuration with changing types and number of the accelerators. Multiple layers of communication interface are introduced to reduce the overhead in both control messages and data transfers. Parallelism can be achieved by controlling the accelerators in various schemes through scheduling extension. The framework has been used to support physics simulation and financial application development. We achieve significant performance improvement on a 16-node cluster with FPGA and GPU accelerators.
20Gbps C-Based Complex Event Processing

This paper presents the world's fastest complex event processing system, designed to process a large number of events on FPGAs. Unlike conventional SQL-based approaches, our approach features logic automation constructed with a new C-based event language that supports regular expressions on the basis of C functions, so that a wide variety of event-processing applications can be efficiently mapped to FPGAs. Evaluations on an FPGA-based NIC show that we have achieved 20Gbps event processing performance in a financial trading application with only a small logic increase, 2.7%.
QUILTS: Multidimensional Data Partitioning Framework Based on Query-Aware and Skew-Tolerant Space-Filling Curves

Recently, massive data management plays an increasingly important role in data analytics because data access is a major bottleneck. Data skipping is a promising technique to reduce the number of data accesses. Data skipping partitions data into pages and accesses only pages that contain data to be retrieved by a query. Therefore, effective data partitioning is required to minimize the number of page accesses. However, it is an NP-hard problem to obtain optimal data partitioning given query pattern and data distribution. We propose a framework that involves a multidimensional indexing technique based on a space-filling curve. A space-filling curve is a way to define which portion of data can be stored in the same page. Therefore, the problem can be interpreted as selecting a curve that distributes data to be accessed by a query to minimize the number of page accesses. To solve this problem, we analyzed how different space-filling curves affect the number of page accesses. We found that it is critical for a curve to fit a query pattern and be robust against any data distribution. We propose a cost model for measuring how well a space-filling curve fits a given query pattern and tolerates data skew. Also we propose a method for designing a query-aware and skew-tolerant curve for a given query pattern. We prototyped our framework using the defined query-aware and skew-tolerant curve. We conducted experiments using a skew data set, and confirmed that our framework can reduce the number of page accesses by an order of magnitude for data warehousing (DWH) and geographic information systems (GIS) applications with real-world data.
In-depth investigations of parameters influencing the accuracy of orthoimages from high resolution satellites

Orthoimages are the most popular product from high spatial resolution satellite sensors. Various investigations, especially regarding Ikonos and Quickbird, have been published and achieved accuracies reported. However, these investigations are based on different input parameters (density and accuracy of DTM, number, distribution and accuracy of GCPs, sensor model, range of heights in the area, sensor elevation, type, amount and accuracy of information used to check the planimetric accuracy) and the published results are thus not comparable. Furthermore, the influence of the above mentioned parameters on the planimetric accuracy of the orthoimages has not been analysed using one common consistent dataset. In this work, we will try to cover this gap. In previous publications, we have presented a comparison of point positioning accuracies using various sensor models and different number and distribution of GCPs, so these aspects will not be treated in this work. Here, we have used in all cases as sensor model RPCs with a subsequent affine transformation and a small number of GCPs (8) with homogeneous distribution. The aspects that we analyse here is the influence of (a) the DTM, (b) the source and accuracy of GCPs, and (c) the sensor elevation.
Mapping High-Level Application Requirements onto Low-Level Cloud Resources

Cloud computing has created a paradigm shift that affects the way in which business applications are developed. Many business organizations use cloud infrastructures as platforms on which to deploy business applications. Increasing numbers of vendors are supplying the cloud marketplace with a wide range of cloud products. Different vendors offer cloud products in different formats. The cost structures for consuming cloud products can be complex. Finding a suitable set of cloud products that meets an application’s requirements and budget can be a challenging task. In this paper, an ontology-based resource mapping mechanism is proposed. Domain-specific ontologies are used to specify high-level application’s requirements. These are then translated into high-level infrastructure ontologies which then can be mapped onto low-level descriptions of cloud resources. Cost ontologies are proposed for cloud resources. An exemplar media transcoding and delivery service is studied in order to illustrate how high-level requirements can be modeled and mapped onto cloud resources within a budget constraint. The proposed ontologies provide an application-centric mechanism for specifying cloud requirements which can then be used for searching for suitable resources in a multi-provider cloud environment.
IT-enabled Knowledge Management in Primary Care Settings: An Absorptive Capacity Perspective

Primary care medical practices have made sizable IT investments in recent years, primarily deploying electronic medical record (EMR) systems as well as Web-based elearning applications. The basic assumption here is that developing IT-enabled knowledge management capabilities may significantly improve the innovation and clinical performance of these organizations. Increasing uncertainty in their environment requires them to develop greater absorptive capacity (ACAP), i.e. an organizational learning capability to deal with the external sources of this uncertainty. In applying ACAP theory to primary care settings, this study seeks to answer the following research questions: What are the e-learning and EMR capabilities developed by primary care medical practices in response to increasing environmental uncertainty? To what extent does the development of an e-learning capability influence the development of an EMR capability? To what extent does building ACAP contribute to positive outcomes in terms of medical practices' innovation and clinical performance?
A Hebbian learning rule gives rise to mirror neurons and links them to control theoretic inverse models

Mirror neurons are neurons whose responses to the observation of a motor act resemble responses measured during production of that act. Computationally, mirror neurons have been viewed as evidence for the existence of internal inverse models. Such models, rooted within control theory, map-desired sensory targets onto the motor commands required to generate those targets. To jointly explore both the formation of mirrored responses and their functional contribution to inverse models, we develop a correlation-based theory of interactions between a sensory and a motor area. We show that a simple eligibility-weighted Hebbian learning rule, operating within a sensorimotor loop during motor explorations and stabilized by heterosynaptic competition, naturally gives rise to mirror neurons as well as control theoretic inverse models encoded in the synaptic weights from sensory to motor neurons. Crucially, we find that the correlational structure or stereotypy of the neural code underlying motor explorations determines the nature of the learned inverse model: random motor codes lead to causal inverses that map sensory activity patterns to their motor causes; such inverses are maximally useful, by allowing the imitation of arbitrary sensory target sequences. By contrast, stereotyped motor codes lead to less useful predictive inverses that map sensory activity to future motor actions. Our theory generalizes previous work on inverse models by showing that such models can be learned in a simple Hebbian framework without the need for error signals or backpropagation, and it makes new conceptual connections between the causal nature of inverse models, the statistical structure of motor variability, and the time-lag between sensory and motor responses of mirror neurons. Applied to bird song learning, our theory can account for puzzling aspects of the song system, including necessity of sensorimotor gating and selectivity of auditory responses to bird's own song (BOS) stimuli.
Strategies for dielectrophoretic separation in laboratory‐on‐a‐chip systems

Dielectrophoresis – the induced motion of polarizable particles in nonuniform fields – has proven to be an effective method for the separation of bioparticles such as cells, viruses and proteins. In this paper, the application of dielectrophoresis in miniaturized laboratories‐on‐a‐chip is discussed, and strategies are described for using dielectrophoresis both for the binary separation of bioparticles and for the fractionation of many populations in such devices.
Adaptation and validation of the children’s cognitive triad inventory for Indonesian students

Depression causes student learning problems. Depressive symptoms are influenced by automatic thoughts that contain a negative cognitive triad. A valid and reliable instrument to measure the cognitive triad in adolescents is essential to identify one of the risk factors for depressive disorders in adolescents. At the same time, Indonesia has no instrument to measure this. This study aims to adapt the children’s cognitive triad inventory (CTI-C) into an Indonesian version and to examine its psychometric properties. Data were collected from 1,184 students aged 13-19 (mean=15.32, SD=1.62) using the 36-item CTI-C. The CTI-C instrument adaptation process refers to the international test commission (ITC). The results of confirmatory factor analysis (CFA) showed comparative fit index (CFI)=0.95, Tucker-Lewis’s index (TLI)=0.94, root mean square error of approximation (RMSEA)=0.071, standardized root mean square residual (SRMR)=0.056, and goodness of fit index (GFI)=0.82, with a load factor range of 0.43-0.80 for the view of self, 0.43-0.68 for the view of the world, and 0.37-0.70 for the view of the future. The Cronbach’s alpha coefficient ranged from 0.81 to 0.84 for the subscales and 0.93 for the total score. Thus, the Indonesian version of the CTI-C can measure the cognitive triad in Indonesian students aged 13-19.
Block-Data-Based Gradient Adaptive Minimum Bit Error Rate Beamforming using Variable Kernel Estimator

— The minimum bit error rate (MBER) beamforming is one of the intelligent adaptive beamforming techniques used in smart antenna (SA) system. It directly minimizes the BER performance. It is based on the approximating the probability density function (pdf) of the beamformer's output using block-data-adaptive MBER algorithm which iteratively minimizes the estimated BER by adjusting the beamformer weights. There are many pdf estimators can be utilized to approximate the unknown pdf from the observed data. A widely used of approximating a pdf is known as the kernel or Parzen window-based estimator. This approach, however, suffers from slight drawback when applied to the data from long-tailed distributions. In this paper, a variable kernel estimator is proposed to overcome this difficulty and enhance the BER performance. I. INTRODUCTION NEW wireless application demand smart antenna (SA) technology are growing exponentially. The mobile communication technology such as space division multiple access (SDMA) essentially is used to enhance the spectral utilization. Also the SA system is used to enhance the capacity achieving in communication system [1]. Adaptive beamformer referred to spatial filtering is a property of SA filtering the signal in spatial domain [2], which makes antenna able to distinguish automatically between the desired signal and the interference signals in spatial domain [3-4]. For a communication system, it is the achievable bit error rate (BER), not the minimum mean square error (MSE) performance that really matters. The system design should be based directly on minimizing the BER, rather than the MSE, which can be obtained by approximating the probability density function (pdf) of the beamformer's output [3]. Density estimation is the construction of an estimate of the density function from the observed data. The main methods for density estimation are explained in [5]. Chen [3], assumes binary phase shift keying (BPSK) scheme with additive Gaussian noise without incidence of intersymbol interference (ISI), and uses the kernel or Parzen window-based estimator to estimate unknown pdf of the beamformer's output due to noise effect only. The antenna array weights are intelligently
A VQ based speaker recognition system based in histogram distances. text independent and for noisy environments

In speaker recognition systems based on VQ, normally each speaker is assigned a codebook, and the classification is done by means of the a distortion distance of the utterance computed by means of each codebook. In [1] we proposed a system which instead of having a codebook for each speaker, had only one codebook for all the speakers, and for each speaker one histogram. This histogram was the occupancy rate of each codeword for a given speaker. This means that the information of the histogram of a given speaker is the probability that the speaker utters the information related to the codeword. So we approximated the pdf of each speaker by the normalized histogram. In this paper we present an exhaustive study of different measures for comparing histograms: Kullbach-Leiber, logdifference of each probability, geometrical distance, and the Euclidean distance. We have done also an exhaustive study of the properties of the system for each distance in the presence of noise (white and colored), and for different parameterizations: LPC, MFCC, LPC-Cepstrum-OSA (One sided autocorrelation sequence), LCP-Cepstrum. (Cepstrum with/without liftering). As the combination of experiments was high, the conclusions were drawn after an analisis of variance (ANOVA), and T-tests. Thus the conclusions, with significance levels, can be drawn about the differences and interactions between kind of. distance, parametrizacion, kind of noise and level of noise.
Multidimensional Dynamic Pricing for Welfare Maximization

We study the problem of a seller dynamically pricing d distinct types of indivisible goods, when faced with the online arrival of unit-demand buyers drawn independently from an unknown distribution. The goods are not in limited supply, but can only be produced at a limited rate and are costly to produce. The seller observes only the bundle of goods purchased at each day, but nothing else about the buyer's valuation function. Our main result is a dynamic pricing algorithm for optimizing welfare (including the seller's cost of production) that runs in time and a number of rounds that are polynomial in d and the approximation parameter. We are able to do this despite the fact that (i) the price-response function is not continuous, and even its fractional relaxation is a non-concave function of the prices, and (ii) the welfare is not observable to the seller. We derive this result as an application of a general technique for optimizing welfare over divisible goods, which is of independent interest. When buyers have strongly concave, Hölder continuous valuation functions over d divisible goods, we give a general polynomial time dynamic pricing technique. We are able to apply this technique to the setting of unit demand buyers despite the fact that in that setting the goods are not divisible, and the natural fractional relaxation of a unit demand valuation is not strongly concave. In order to apply our general technique, we introduce a novel price randomization procedure which has the effect of implicitly inducing buyers to "regularize'' their valuations with a strongly concave function. Finally, we also extend our results to a limited-supply setting in which the number of copies of each good cannot be replenished.
Ultrasound Elastography as a Diagnostic Tool for Peyronie’s Disease: A State-of-the-Art Review

Elastography is a noninvasive method that utilizes ultrasound imaging to assess the elasticity and stiffness of soft tissue. Peyronie’s disease (PD) is a chronic inflammatory condition that affects the male penis, causing the formation of fibrous plaques. This alters the penis’s elasticity and can lead to changes in its shape. Ultrasound elastography (UE) is an important advancement in the diagnosis of PD. It not only identifies plaques, but it also measures their rigidity, providing crucial information to monitor changes during and after treatment. We conducted a narrative review of the scientific literature to identify articles that discuss the use of elastography in the diagnostic study of PD. The purpose of this study was to describe the “state of the art” in the diagnostic use of ultrasound in combination with elastography to highlight any benefits in the diagnosis of PD. We found 12 relevant articles after searching PubMed, Embase, and Google Scholar using the keywords “ultrasound elastography” and “Peyronie’s disease”, including eight clinical studies, two case reports, and two review articles. The results of our review indicate that UE is a useful technique for identifying Peyronie-related plaques, particularly when they are not detectable using a standard ultrasound or physical examination. It is also helpful in monitoring improvements during and after conservative treatments. More research is required to confirm the effectiveness of ultrasound elastography in diagnosing Peyronie’s disease and to determine whether it is better than traditional ultrasound.
Carbonic fluid inclusions in a garnet–pyroxene granulite from Austhovde in the Lützow–Holm Complex, East Antarctica: Implications for a decompressional P–T path

A garnet–pyroxene granulite from Austhovde in the Lützow–Holm Complex, East Antarctica, contains two types of fluid inclusions: a small number of primary inclusions in garnet and quartz, and dominant secondary inclusions in garnet and plagioclase. The melting temperatures of the trapped fluids lie in a range of −57.2 to −56.4 °C, which are close to the triple–point temperature of pure CO2. The primary inclusions are homogenized at +1.8 to +12.6 °C, which correspond to densities of 0.842–0.917 g/cm3. However, the estimated pressure conditions (3–5 kbar at 850 °C) from the fluid densities of the primary inclusions are not consistent with the peak P–T conditions recorded in the rock (840–860 °C and 8.3–8.7 kbar). Homogenization of the secondary inclusions into the liquid phase occurs within a range of +11.5 to +25.2 °C, which correspond to low CO2 densities of 0.708 to 0.851 g/cm3. The results imply that fluid density decreased by partial leakage of the trapped fluid in the primary inclusions during post–peak exhumation stage, and the process is consistent with the occurrence of orthopyroxene + hornblende + plagioclase symplectite around garnet, suggesting post–peak rapid decompression.
Learning Structural Changes of Gaussian Graphical Models in Controlled Experiments

Graphical models are widely used in scienti fic and engineering research to represent conditional independence structures between random variables. In many controlled experiments, environmental changes or external stimuli can often alter the conditional dependence between the random variables, and potentially produce significant structural changes in the corresponding graphical models. Therefore, it is of great importance to be able to detect such structural changes from data, so as to gain novel insights into where and how the structural changes take place and help the system adapt to the new environment. Here we report an effective learning strategy to extract structural changes in Gaussian graphical model using l1-regularization based convex optimization. We discuss the properties of the problem formulation and introduce an efficient implementation by the block coordinate descent algorithm. We demonstrate the principle of the approach on a numerical simulation experiment, and we then apply the algorithm to the modeling of gene regulatory networks under different conditions and obtain promising yet biologically plausible results.