Miracle-dz/UniFAR-FTUs huggingface dataset:
Convergence rates of sub-sampled Newton methods

We consider the problem of minimizing a sum of $n$ functions over a convex parameter set $\mathcal{C} \subset \mathbb{R}^p$ where $n\gg p\gg 1$. In this regime, algorithms which utilize sub-sampling techniques are known to be effective. In this paper, we use sub-sampling techniques together with low-rank approximation to design a new randomized batch algorithm which possesses comparable convergence rate to Newton's method, yet has much smaller per-iteration cost. The proposed algorithm is robust in terms of starting point and step size, and enjoys a composite convergence rate, namely, quadratic convergence at start and linear convergence when the iterate is close to the minimizer. We develop its theoretical analysis which also allows us to select near-optimal algorithm parameters. Our theoretical results can be used to obtain convergence rates of previously proposed sub-sampling based algorithms as well. We demonstrate how our results apply to well-known machine learning problems. Lastly, we evaluate the performance of our algorithm on several datasets under various scenarios.
Segmentation, classification and clustering of an Italian broadcast news corpus

This work reports on preliminary activity at ITC-irst on the problem of acoustic segmentation, classification and clustering of an Italian audio broadcast news corpus. The approach is based on the following stages. First, the input data stream is segmented by detecting spectral changes through the Bayesian Information Criterion (BIC). Second, segments are classified in terms of acoustic conditions, modeled by mixtures of Gaussians. Finally, segments from the same speakers are clustered, by using again the BIC. The scheme proposed for the automatic segmentation, classification and clustering causes a degradation of the recognition error rate, with respect to the fully supervisioned experiment, equal to 1.3% before adaptation, and 3.4% after adaptation.
Measurement of 19 F(,) 15 N with a bubble chamber and a bremsstrahlung beam

We have devised a new technique for determining thermonuclear reaction rates of astrophysical importance. By measuring (γ,α) cross sections we will determine the (α,γ) reaction rate of 12C(α,γ)16O with a considerable improvement in sensitivity from previous experiments. Adopting ideas from dark matter search experiments with bubble detectors, we have found that a superheated liquid is sensitive to α-particle and heavy ion recoils produced from a γ ray beam impinging on the nuclei in the liquid. The main advantage of the new target-detector system is a density factor of 4-6 orders of magnitude higher than conventional gas targets. In addition, the inverse reaction (γ,α) has approximately 100 times the cross section of the (α,γ) reaction in this energy region. Also, the detector is virtually insensitive to the γ-ray beam itself, thus allowing us to detect only the products of the nuclear reaction of interest. This proposal requests 336 hours of beam time for measuring the 16O(γ,α)12C reaction.
Abstract Dialectical Frameworks Revisited

We present various new concepts and results related to abstract dialectical frameworks (ADFs), a powerful generalization of Dung's argumentation frameworks (AFs). In particular, we show how the existing definitions of stable and preferred semantics which are restricted to the subcase of so-called bipolar ADFs can be improved and generalized to arbitrary frameworks. Furthermore, we introduce preference handling methods for ADFs, allowing for both reasoning with and about preferences. Finally, we present an implementation based on an encoding in answer set programming.
Clinical study of tumor angiogenesis and perfusion imaging using multi-slice spiral computed tomography for breast cancer.

OBJECTIVES To explore the correlation between multi-slice spiral CT (MSCT) perfusion parameters and the expression of vascular endothelial growth factor (VEGF) as well as matrix metalloproteinase-2 (MMP- 2) in breast cancer. METHODS Forty five breast cancer patients and 16 patients with benign breast tumor, both confirmed by pathology examination, were enrolled. All underwent MSCT perfusion imaging to obtain perfusion maps and data for parameters including blood flow (BF), blood volume (BV) and permeability surface (PS). Cancer patients did not receive treatment prior to surgery. The expression of VEGF and MMP-2 were examined with both immunohistochemistry and Western blotting. RESULTS The levels of VEGF and MMP-2 by immunohistochemistry were significantly higher in the breast cancer group (P < 0.01) than the benign tumor group. Relative OD values from Western blotting were also higher in cancer cases (P < 0.05). Similarly, the mean MSCT perfusion parameters (BF, BV, PS) were significantly higher in the breast cancer group (P < 0.01), BF and BV positively correlating with VEGF expression (r = 0.878 and 0.809 respectively, P < 0.01); PS and VEGF and MMP-2 expression were also positively correlated (r= 0.860, 0.786 respectively, P < 0.01). CONCLUSION There is a correlation between breast cancer MSCT perfusion parameters and VEGF andMMP-2 expression, which might be useful for detection of breast lesions, qualitative diagnosis of breast cancer, and evaluation of breast cancer treatment.
A REGION-BASED SYSTEM FOR THE AUTOMATIC EVALUATION OF PAGE SEGMENTATION ALGORITHMS

A method for automatically evaluating the quality of document page segmentation algorithms is described. Page segmentation involves decomposing a page into its structural and logical units such as paragraphs, halftones, captions and tables. These units are then ordered and logically associated. These two steps are very important in a document recognition strategy. Many di erent techniques have been proposed in literature to segment document pages, but since no robust method to benchmark them is available, it is impossible to evaluate them reliably. Our proposed page segmentation benchmarking strategy is a region-based approach, in which segmentation results are compared with manually generated "ground truth les", containing a synthetic description of all possible correct segmentations. It is described in detail and we give a set of guidelines to generate ground truth les and propose a general format to represent them. The algorithm is simple and fast, and provides a multi-level output for each segmentation, from the page level to the region level.
What's in a message?: interpreting geo-referenced data for the visually-impaired

In this paper we describe content determination issues involved in the Atlas.txt project, which aims to automatically describe geo-referenced information such as census data as text for the visually-impaired (VI). Texts communicating geo-referenced census information contain census data abstractions and their corresponding geographic references. Because visually impaired users find interpreting geographic references hard, we hypothesized that an introduction message about the underlying geography should help the users to interpret the geographic references easily. We performed user studies to design and evaluate the introduction message. An initial evaluation study with several sighted users and one partially sighted user showed that an introduction message is certainly preferred by most participants. Many of them used an introduction message themselves when they described maps textually. But the study also showed that the introduction message made no difference when the participants were asked to draw maps using the information in the textual descriptions.
COMPARING THE USE OF FIVE-PACKAGE OF MATH IN THE 2011 NATIONAL EXAMINATION BETWEEN SMA AND MADRASAH ALIYAH: A CASE STUDY IN JAKARTA

This study aims to compare five question packages used in the 2011 National Examination, specifically for Maths for Social Studies subject between public senior high school (SMA) and Islamic senior high school (MA) in Jakarta. It employed simple random sampling as its data collection technique. The independent variables used were the category of high schools and the Five Question Packages used in the National Examination. The schools were divided into two: regular senior high school (SMA) and Islamic senior high school or Madrasah Aliyah (MA). The packages chosen to be analyzed were package number: 12, 25, 39, 46 and 54. The data was analyzed by two-factor ANOVA for its 11 Differential Item Function (DIF) and 29 non Differential Item Function (non-DIF) items. The items used for this study are the items that fit the three-parameter logistic model. This study found that no difference between the scores on both the DIF and non-DIF items in either for SMA or Ma is found.
IL-17 and IL-11 GCF Levels in Aggressive and Chronic Periodontitis Patients: Relation to PCR Bacterial Detection

Objectives. This study evaluated IL-17 and IL-11 in gingival crevicular fluid (GCF) of generalized chronic periodontitis (GCP) and generalized aggressive periodontitis (GAgP) patients in relation to periodontopathic bacteria. Subjects and Methods. GCF samples were collected from 65 subjects including 25 CP, 25 GAgP, and 15 controls (C) and analyzed for IL-17 and IL-11 by an enzyme-linked immunosorbent assay. Molecular detection of bacteria in the dental plaque was determined by polymerase chain reaction. Results. The total amount of IL-17 was significantly higher in GAgP group than in GCP and C groups (P < 0.001). The IL-11 concentration was significantly higher in C and GCP groups than GAgP group (P < 0.001). The IL-11/IL-17 ratio was significantly higher in the C group than in GCP and GAgP groups (P < 0.05). Moreover, GAgP group showed lower ratios of IL-11/IL-17 when compared to GCP group. The high positivity of P. gingivalis in the dental plaque was associated with significantly increased GCF levels of IL-17 in GCP and GAgP patients. Conclusions. The increased IL-17 level in GCF of GAgP suggests a potential role in the aetiopathogenesis. Meanwhile, the decreased ratio of IL-11/IL-17 might reflect an imbalance between the proinflammatory and anti-inflammatory cytokines in different periodontal diseases.
Convex Multi-Task Learning by Clustering

We consider the problem of multi-task learning in which tasks belong to hidden clusters. We formulate the learning problem as a novel convex optimization problem in which linear classiers are combinations of (a small number of) some basis. Our formulation jointly learns both the basis and the linear combination. We propose a scalable optimization algorithm for nding the optimal solution. Our new methods outperform existing stateof-the-art methods on multi-task sentiment classication tasks.
Perceptually Discriminating Chunks in Business Process Models

Perceptual Discriminability can be used to help distinguishing modeling constructs in conceptual models as well as to produce parallel processing which makes these constructs pop-out from the rest of the model. Prior research has already shown advantages of a pop-out for single constructs and for matching operators. This paper focusses on constructs that are perceived as a single chunk of information. Based on experiences from chunking and template theory relevant BPM-chunks are derived and moved towards parallel processing. We further report on the results of an experiment that tests the impact on comprehension speed for novices for this approach.
Saliency detection via boundary and center priors

This paper proposes a novel method for saliency detection in HSL color space. We firstly divide the traditional global contrast based saliency into background saliency and foreground saliency. Background saliency is then simplified into boundary saliency according to background priors. Furthermore, upper and lower boundary saliency maps are obtained. To overcome the defects of foreground saliency, we redefine it in this paper. Besides, an efficient method is proposed to pick up the center region to obtain center saliency and use it to represent foreground saliency. Finally, a well-founded fusion is proceeded to get the saliency map. Extensive experiments on MSRA dataset show that our algorithm has lower computation cost and higher performance compared with the other 10 state-of-the-art saliency detection methods.
A Dynamic Resource Allocation Optimization for SMT Processors

The threads on simultaneous multithreading (SMT) processors compete for the common resources rather than share them, and meanwhile they take on changing program phases. It is a challenge to meet the changing resource requirements of the threads by making resource allocation according to limited pipeline information. To avoid the stagnation of resource allocation optimization in the local suboptimal resource distributions and perform quick capture on the new optimal resource distributions, this paper proposes a dynamic resource allocation optimization (DRAO) for SMT processors, which optimizes the resource allocation targeting only the throughput performance. An optimization model with consideration of both throughput and fairness performance is designed for DRAO to steer the exploitation on historical allocation and the exploration on new allocation solutions. Results from experiment show that DRAO obtains potential improvement of fairness performance as well as good improvement of throughput performance. These are attributed to the well-balanced exploitation and exploration activities.
Exploratory visualization based on multidimensional transfer functions and star coordinates

Exploration and analysis of multivariate data play an important role in different domains. This work proposes a simple interface prototype that allows a human user to visually explore multivariate spatial objects, such as images, sequence of images or volume. It uses star coordinates as a widget to display the multivariate data on the computer 2D screen. The user then identifies a feature on this powerful coordinate system by mapping a selected feature region on that widget to a color and opacity. As a visual result, the feature is rendered on the object's space composing the use of this maps and the star coordinates projection. Some examples illustrate the potential of this interface
Generation and Extension of Linear Network Coding at Different Multicast Rates

By studying the intrinsic mechanism of linear network coding, this paper proposes a concept of generation and extension between two coding schemes at different multicast rates. We find out that a coding scheme at lower multicast rate is a generation of one at higher multicast rate,and a coding scheme at higher multicast rate is an extension of one at lower multicast rate. Moreover, we find out a determinate relationship between channels’ global encoding vectors under two generation-extension coding schemes. Furthermore, by combining with random linear network coding, several important properties are derived, which are helpful to implement linear network coding for single-source multicast network. Some related applications are enumerated.
Research on Medical Image Registration Based on QPSO and Powell Algorithm

This paper proposed a multi-resolution search optimization algorithm combining QPSO and Powell algorithm,it can solve the Powell algorithm shortcoming effectively. In this paper, the algorithm were used in two-dimensional MRI images registration.The experimental results show that the algorithm can effectively overcome the problem of local extremum of mutual information function, and improve the precision and speed of registration.
Accelerating Equi-Join on a CPU-FPGA Heterogeneous Platform

Accelerating database applications using FPGAs has recently been an area of growing interest in both academia and industry. Equi-join is one of the key database operations whose performance highly depends on sorting, which exhibits high memory usage on FPGA. A fully pipelined N-key merge sorter consists of log N sorting stages using O(N) memory totally. For large data sets, external memory has to be employed to perform data buffering between the sorting stages. This introduces pipeline stalls as well as several iterations between FPGA and external memory, causing significant performance degradation. In this paper, we speed-up equi-join using a hybrid CPU-FPGA heterogeneous platform. To alleviate the performance impact of limited memory, we propose a merge sort based hybrid design where the first few sorting stages in the merge sort tree are replaced with "folded" bitonic sorting networks. These "folded" bitonic sorting networks operate in parallel on the FPGA. The partial results are then merged on the CPU to produce the final sorted result. Based on this hybrid sorting design, we develop two streaming join algorithms by optimizing the classic CPU-based nested-loop join and sort-merge join algorithms. On a rangeof data set sizes, our design achieves throughput improvement of 3.1x and 1.9x compared with software-only and FPGA only implementations, respectively. Our design sustains 21.6% of thepeak bandwidth, which is 3.9x utilization obtained by the state-of-the-art FPGA equi-join implementation.
CoMMix profile for modeling and analysis embedded systems

The objective of this paper is to present a new profile for the design of embedded systems at the system level, appointed CoMMix Profile. This profile offers unified concepts for designing the systems level. It privileges a component-based approach based inspired by AADL (Architecture Analysis and Design Language) and as well SysML and UML-Marte standards. Modeling CoMMix system is described as an assembly of the components and architectural mappings.
Unifying Data and Constraint Repairs

Integrity constraints play an important role in data design. However, in an operational database, they may not be enforced for many reasons. Hence, over time, data may become inconsistent with respect to the constraints. To manage this, several approaches have proposed techniques to repair the data by finding minimal or lowest cost changes to the data that make it consistent with the constraints. Such techniques are appropriate for applications where only the data changes, but schemas and their constraints remain fixed. In many modern applications, however, constraints may evolve over time as application or business rules change, as data are integrated with new data sources or as the underlying semantics of the data evolves. In such settings, when an inconsistency occurs, it is no longer clear if there is an error in the data (and the data should be repaired) or if the constraints have evolved (and the constraints should be repaired). In this work, we present a novel unified cost model that allows data and constraint repairs to be compared on an equal footing. We consider repairs over a database that is inconsistent with respect to a set of rules, modeled as functional dependencies (FDs). FDs are the most common type of constraint and are known to play an important role in maintaining data quality. We propose modifications to the data and to the FDs such that the data and the constraints are better aligned. We evaluate the quality and scalability of our repair algorithms over synthetic and real datasets. The results show that our repair algorithms not only scale well for large datasets but also are able to accurately capture and correct inconsistencies and accurately decide when a data repair versus a constraint repair is best.
Detection and tracking of occluded humans in three-camera network

Human tracking is one of the most important topics in surveillance systems. Increment of system's ability to detect and track humans in both indoor and outdoor crowded environments leads to a safer environment. In this paper color and shape information are fused based on particle filter framework to track humans. Histogram of oriented gradient (HOG) is a shape descriptor that is used as a feature to detect humans using support vector machine (SVM) classifier. The first step of human detection is mixture of Gaussian method that is used to find moving regions of the scene, then HOG feature of these regions is extracted and finally SVM is used to distinguish human from non-human. This algorithm leads to a fewer computational complexity against traditional method of human detection that used sliding windows to detect humans. Human motion is non-Linear and non-Gaussian so a particle filter framework is used to track human. Color and HOG histograms are used to model humans. Occlusion is one of the most important tracking challenges. According to increment of surveillance requirements, three-camera system is used to handle occlusion. Experimental results show the effectiveness of the proposed algorithm.
Scalable spin locks for multiprogrammed systems

Synchronization primitives for large shared-memory multiprocessors need to minimize latency and contention. Software queue-based locks address these goals, but suffer if a process near the end of the queue waits for a preempted processes ahead of it. To solve this problem, the authors present two queue-based locks that recover from in-queue preemption. The simpler, faster lock employs an extended kernel interface that shares information in both directions across the user-kernel boundary. Results from experiments with real and synthetic applications on SGI and KSR multiprocessors demonstrate that high-performance software spin locks are compatible with multiprogramming on both large-scale and bus-based machines. <<ETX>>
Robust Estimation of Radar Pulse Modulation

In this paper the problem of estimating a common modulation from a group of intercepted radar pulses is addressed. A robust M-estimation technique is proposed. The M-estimation approach provides tolerance against preprocessing errors as well as to other model failures. The performance of the M-estimation technique is compared to a maximum likelihood estimation method through simulation experiments
Hybrid Systems in Process Control : Challenges , Methods and Limits

Hybrid dynamics in process control arise for various reasons. The main source is the interaction between computer-realized discrete control functions and the continuous physico-chemical processes in the plant. But also physical or operational constraints lead to discrete phenomena in otherwise continuous processes. The modeling, the analysis and the design of hybrid control systems pose new problems for which the conventional systems and control theory is not an appropriate tool. The paper identifies these challenges using illustrative case studies and gives a survey of recent approaches to a formal treatment of hybrid systems. Particular emphasis is put on methods which were developed in computer science in the last years. We will discuss to what extent these techniques can support the analysis and design of hybrid systems in process control or automation and what the limits are.
ANN Based Classification of Unknown Genome Fragments Using Chaos Game Representation

Classification of organisms into different categories using their genomic sequences has found importance in study of evolutionary characteristics, specific identification of previously unknown organisms, study of mutual relationships between organisms and many other aspects in the study of living things. Chaos game representation (CGR) uniquely represents DNA sequences and reveals hidden patterns in it. Frequency-CGR (FCGR) derived from CGR, shows the frequency of sub-sequences present in the DNA sequence. In this paper, a novel method for classification of organisms based on a combination of FCGR and Artificial Neural network (ANN) is proposed. Eight categories from the taxonomical distribution of Eukaryotic organisms are taken and ANN is used for classification. Different configurations of ANN are tested and good accuracy is obtained. The way the fractal nature of DNA helps in classification, is also investigated.
Composing extended top-down tree transducers

A composition procedure for linear and nondeleting extended top-down tree transducers is presented. It is demonstrated that the new procedure is more widely applicable than the existing methods. In general, the result of the composition is an extended top-down tree transducer that is no longer linear or nondeleting, but in a number of cases these properties can easily be recovered by a post-processing step.