Miracle-dz/UniFAR-FTUs huggingface dataset:
The Statistical Properties of Coordinate Noun Phrases

• Traditional notion of coordination as an internally constrained relation: combine two things that are alike in some respect • Recent theoretical work on the syntax of coordination suggests that there are no internal categorical constraints operative between conjuncts • I show that a strong non-categorical constraint operates between NP conjuncts. Conjuncts tend toward parallelism – they are unusually similar to one another. This pattern holds at a variety of granularity levels. • With modern syntactically annotated corpora, we can investigate these patterns across genres and languages
Role of 18F-FDG PET/CT in the Carcinoma of the Uterus: A Review of Literature

In the present review we reported the value of 18F-fluorodeoxyglucose (FDG) PET/CT in face of uterine cancer, in terms of sensitivity, specificity and accuracy. Moreover, we made a comparison with the other imaging techniques currently used to evacuate these tumors including contrast-enhanced CT, contrast enhanced-MRI and transvaginal ultrasonography. FDG PET/CT has been reported to be of particular value in detecting occult metastatic lesions, in prediction of response to treatment and as a pro-gnostic factor.
Some dynamics of signaling games

Information transfer is a basic feature of life that includes signaling within and between organisms. Owing to its interactive nature, signaling can be investigated by using game theory. Game theoretic models of signaling have a long tradition in biology, economics, and philosophy. For a long time the analyses of these games has mostly relied on using static equilibrium concepts such as Pareto optimal Nash equilibria or evolutionarily stable strategies. More recently signaling games of various types have been investigated with the help of game dynamics, which includes dynamical models of evolution and individual learning. A dynamical analysis leads to more nuanced conclusions as to the outcomes of signaling interactions. Here we explore different kinds of signaling games that range from interactions without conflicts of interest between the players to interactions where their interests are seriously misaligned. We consider these games within the context of evolutionary dynamics (both infinite and finite population models) and learning dynamics (reinforcement learning). Some results are specific features of a particular dynamical model, whereas others turn out to be quite robust across different models. This suggests that there are certain qualitative aspects that are common to many real-world signaling interactions.
Unsupervised Feature Learning in Computer Vision

Much of computer vision has been devoted to the question of representation through feature extraction. Ideal features transform raw pixel intensity values to a representation in which common problems such as object identification, tracking, and segmentation are easier to solve. Recently, deep feature hierarchies have proven to be immensely successful at solving many problems in computer vision. In the supervised setting, these hierarchies are trained to solve specific problems by minimizing an objective function of the data and problem specific label information. Recent findings suggest that despite being trained on a specific task, the learned features can be transferred across multiple visual tasks. These findings suggests that there exists a generically useful feature representation for natural visual data. This work aims to uncover the principles that lead to these generic feature representations in the unsupervised setting, which does not require problem specific label information. We begin by reviewing relevant prior work, particularly the literature on autoencoder networks and energy based learning. We introduce a new regularizer for autoencoders that plays an analogous role to the partition function in probabilistic graphical models. Next we explore the role of specialized encoder architectures for sparse inference. The remainder of the thesis explores visual feature learning from video. We establish a connection between slow-feature learning and metric learning, and experimentally demonstrate that semantically coherent metrics can be learned from natural videos. Finally, we posit that useful features linearize natural image transformations in video. To this end, we introduce a new architecture and loss for training deep feature hierarchies that linearize the transformations observed in unlabeled natural video sequences by learning to predict future frames in the presence of uncertainty.
Tools and Applications for Teaching and Research in Computer Graphics

In this paper we present the work in progress along with some preliminary research results in the field of Computational Geometry and Mesh Processing obtained by the Computer Graphics Group of the University of Cagliari, Italy. We focus on the work in mesh analysis by introducing the development of a lightweight visualization and processing tool that helped expanding the aims of the group by letting the students from the University move their first steps in Computer Graphics. We show some results obtained by the group with the focus on the usefulness of a common framework of reference.
Swarm Intelligence for Non-Negative Matrix Factorization

The Non-negative Matrix Factorization (NMF) is a special low-rank approximation which allows for an additive parts-based and interpretable representation of the data. This article presents efforts to improve the convergence, approximation quality, and classification accuracy of NMF using five different meta-heuristics based on swarm intelligence. Several properties of the NMF objective function motivate the utilization of meta-heuristics: this function is non-convex, discontinuous, and may possess many local minima. The proposed optimization strategies are two-fold: On the one hand, a new initialization strategy for NMF is presented in order to initialize the NMF factors prior to the factorization; on the other hand, an iterative update strategy is proposed, which improves the accuracy per runtime for the multiplicative update NMF algorithm. The success of the proposed optimization strategies are shown by applying them on synthetic data and data sets coming from the areas of spam filtering/email classification, and evaluate them also in their application context. Experimental results show that both optimization strategies are able to improve NMF in terms of faster convergence, lower approximation error, and better classification accuracy. Especially the initialization strategy leads to significant reductions of the runtime per accuracy ratio for both, the NMF approximation as well as the classification results achieved with NMF.
A 2/spl mu/m InGaP/GaAs HBT matrix amplifier with 15dB gain and 41GHz bandwidth

A broadband matrix amplifier occupying a chip area of 1.5/spl times/1.6 mm/sup 2/ implemented with commercially available 2/spl mu/m InGaP/GaAs HBT technology is demonstrated with a gain of 15dB and 41GHz 3-dB bandwidth, as well as a gain-bandwidth product of 231. At a biasing collector current density (J/sub c/) of 50 kA/cm/sup 2/, the 2 /spl mu/m InGaP/GaAs HBT has f/sub t/ and f/sub max/ of 55 and 62GHz respectively with MTTF of 2.8/spl times/10/sup 6/ hrs or approximately 320 years. Comparing to other published results using the figure of merits of gain-bandwidth product (GBP) per transistor f/sub t/ and f/sub max/, this amplifier is believed to be among the best in single chip distributed and matrix amplifiers implemented in any bipolar technologies.
Long-Lived Distributed Relative Localization of Robot Swarms

This paper studies the problem of having mobile robots in a multi-robot system maintain an estimate of the relative position and relative orientation of near-by robots in the environment. This problem is studied in the context of large swarms of simple robots which are capable of measuring only the distance to near-by robots. We present two distributed localization algorithms with different trade-offs between their computational complexity and their coordination requirements. The first algorithm does not require the robots to coordinate their motion. It relies on a non-linear least squares based strategy to allow robots to compute the relative pose of near-by robots. The second algorithm borrows tools from distributed computing theory to coordinate which robots must remain stationary and which robots are allowed to move. This coordination allows the robots to use standard trilateration techniques to compute the relative pose of near-by robots. Both algorithms are analyzed theoretically and validated through simulations.
Nonlinear Blind Source Separation by Variational Bayesian Learning

Blind separation of sources from their linear mixtures is a well understood problem. However, if the mixtures are nonlinear, this problem becomes generally very difficult. This is because both the nonlinear mapping and the underlying sources must be learned from the data in a blind manner, and the problem is highly ill-posed without a suitable regularization. In our approach, multilayer perceptrons are used as nonlinear generative models for the data, and variational Bayesian (ensemble) learning is applied for finding the sources. The variational Bayesian technique automatically provides a reasonable regularization of the nonlinear blind separation problem. In this paper, we first consider a static nonlinear mixing model, with a successful application to real-world speech data compression. Then we discuss extraction of sources from nonlinear dynamic processes, and detection of abrupt changes in the process dynamics. In a difficult test problem with chaotic data, our approach clearly outperforms currently available nonlinear prediction and change detection techniques. The proposed methods are computationally demanding, but they can be applied to blind nonlinear problems of higher dimensions than other existing approaches.
A Proactive Mechanism for Efficient Information Discovery

In relation to decentralized search on top of P2P communication paradigm, we study a proactive mechanism that is based on the dissemination of advertisements for new sources that join a system. The system design goal of limiting the state maintained by each peer and ensuring search efficiency is the driving reason for exploiting the small-world idea, and in particular the hierarchical network model that provides theoretical bounds on search time. Starting from the study of this theoretical model, we then tried to reflect it into protocol design. Simulation results testify the theoretical bounds on search time and provide a quantification view on the search time in relation to the directory capacity requirements of the peers
Markedly divergent estimates of Amazon forest carbon density from ground plots and satellites

Aim The accurate mapping of forest carbon stocks is essential for understanding the global carbon cycle, for assessing emissions from deforestation, and for rational land-use planning. Remote sensing (RS) is currently the key tool for this purpose, but RS does not estimate vegetation biomass directly, and thus may miss significant spatial variations in forest structure. We test the stated accuracy of pantropical carbon maps using a large independent field dataset. Location Tropical forests of the Amazon basin. The permanent archive of the field plot data can be accessed at: http://dx.doi. org/10.5521/FORESTPLOTS.NET/2014_1 Methods Two recent pantropical RS maps of vegetation carbon are compared to a unique ground-plot dataset, involving tree measurements in 413 large inventory plots located in nine countries. The RS maps were compared directly to field plots, and kriging of the field data was used to allow area-based comparisons. Results The two RS carbon maps fail to capture the main gradient in Amazon forest carbon detected using 413 ground plots, from the densely wooded tall forests of the north-east, to the light-wooded, shorter forests of the south-west. The differences between plots and RS maps far exceed the uncertainties given in these studies, with whole regions over- or under-estimated by > 25%, whereas regional uncertainties for the maps were reported to be < 5%. Main conclusions Pantropical biomass maps are widely used by governments and by projects aiming to reduce deforestation using carbon offsets, but may have significant regional biases. Carbon-mapping techniques must be revised to account for the known ecological variation in tree wood density and allometry to create maps suitable for carbon accounting. The use of single relationships between tree canopy height and above-ground biomass inevitably yields large, spatially correlated errors. This presents a significant challenge to both the forest conservation and remote sensing communities, because neither wood density nor species assemblages can be reliably mapped from space.
Market Reaction of the Restricted Shares Reducing: Mechanism and Features

This paper analyzes macro and micro transmission mechanism of market reaction of restricted shares. Logical analysis finds that market reaction mechanism of the lift of restricted shares can be decomposed into cumulative effect, early response and reducing effect, which are proved by empirical test. The test also shows: cumulative effect is one of critical factors of market fluctuation; there are significantly negative early response and reducing effect, and market reaction is negative overall; early response is obviously stronger than reducing effect, and there is lift day phenomenon; most of the pressure of lift information is digested in advance, and the lift impact on investors’ psychology is far greater than that on reduction itself.
Pre-initialized composition for large-vocabulary speech recognition

This paper describes a modified composition algorithm that is used for combining two finite-state transducers, representing the context-dependent lexicon and the language model respectively, in large vocabulary speech recogntion. This algorithm is a hybrid between the static and dynamic expansion of the resultant transducer, which maps from context-dependent phones to words and is searched during decoding. The approach is to pre-compute part of the recognition transducer and leave the balance to be expanded during decoding. This method allows for a fine-grained trade-off between space and time in recognition. For example, the time overhead of purely dynamic expansion can be reduced by over six-fold with only a 20% increase in memory in a collection of large-vocabulary recognition tasks available on the Google Android platform.
Interactive Animation of 4D Performance Capture

A 4D parametric motion graph representation is presented for interactive animation from actor performance capture in a multiple camera studio. The representation is based on a 4D model database of temporally aligned mesh sequence reconstructions for multiple motions. High-level movement controls such as speed and direction are achieved by blending multiple mesh sequences of related motions. A real-time mesh sequence blending approach is introduced, which combines the realistic deformation of previous nonlinear solutions with efficient online computation. Transitions between different parametric motion spaces are evaluated in real time based on surface shape and motion similarity. Four-dimensional parametric motion graphs allow real-time interactive character animation while preserving the natural dynamics of the captured performance.
Research of Pump-Jack Fault Diagnosis Method Based on the Negative Selection Algorithm

This paper proposes a fault diagnosis method a based on the biology immune principle, in which fault is regarded as antigen. First initial antibodies are generated using negative selection algorithm, then based on clone, mutation, clone selection, self adjustment and immune memory of the biology immune system, the initial antibodies are evolved to generate memory antibodies which can express and identify the antigen or fault. The method is used to detect the faults of pump-jacks of an oil field. The results show that the effect of fault diagnosis is good and can satisfy the needs
Rule discovery : error measures and conditional rule probability

Many promising rule discovery algorithms have been proposed. These algorithms use their proprietary methods to measure the goodness (or error) of rules. The goodness of rules is used to guide the search for the "best" rule set. This paper first compares various error (or goodness) measures, including average error, mean square error, probability difference and prediction factor, to evaluate their appropriateness for rule discovery. Secondly, we study a method of estimating conditional probabilities for a single rule and as a novelty, for rule sets. Results regarding how conditional probabilities affect the goodness of the discovered knowledge are presented. The investigations are accomplished by using a general purpose algorithm to discover nonpropositional rules. This algorithm has minimal inter-dependency between different modules such as the partial ordering (specialization) used to navigate in the search space, and the chosen error measure. This assures fair comparison of the error measures and assures objective evaluation of conditional probabilities.
A new spatio-temporal MRF framework for video-based object segmentation

In this paper we propose a general framework for videobased object segmentation using a new spatio-temporal Markov Random Field (MRF) model. Video-based object segmentation has the potential to improve the performance of static image segmentation by fusing information over the temporal scale. Built upon a spatio-temporal MRF model, our method offers three advantages in a unified framework. First, our model is defined on a flexible graph structure induced by the local motion information instead of the regular 3D grid, allowing the model nodes to be connected to more reliable temporal neighbors. Second, the segmentation task is considered in unison with foreground/background modeling, leading to more accurate appearance models suitable for object segmentation. Third, the inclusion of shape priors as a top-down highlevel object constraints as guides for the bottom-up low-level image cues leads to improved segmentation. The object segmentation is solved as an MRF-MAP inference problem by the Loopy Belief Propagation (LBP) algorithm effectively and efficiently. Estimation of the model parameters can be accomplished simultaneously with the inference problem, using an Expectation Maximization (EM) algorithm. In our experiments, we show promising object segmentation results by combining multiple modules in
A three-gene model to robustly identify breast cancer molecular subtypes.

BACKGROUND Single sample predictors (SSPs) and Subtype classification models (SCMs) are gene expression-based classifiers used to identify the four primary molecular subtypes of breast cancer (basal-like, HER2-enriched, luminal A, and luminal B). SSPs use hierarchical clustering, followed by nearest centroid classification, based on large sets of tumor-intrinsic genes. SCMs use a mixture of Gaussian distributions based on sets of genes with expression specifically correlated with three key breast cancer genes (estrogen receptor [ER], HER2, and aurora kinase A [AURKA]). The aim of this study was to compare the robustness, classification concordance, and prognostic value of these classifiers with those of a simplified three-gene SCM in a large compendium of microarray datasets. METHODS Thirty-six publicly available breast cancer datasets (n = 5715) were subjected to molecular subtyping using five published classifiers (three SSPs and two SCMs) and SCMGENE, the new three-gene (ER, HER2, and AURKA) SCM. We used the prediction strength statistic to estimate robustness of the classification models, defined as the capacity of a classifier to assign the same tumors to the same subtypes independently of the dataset used to fit it. We used Cohen κ and Cramer V coefficients to assess concordance between the subtype classifiers and association with clinical variables, respectively. We used Kaplan-Meier survival curves and cross-validated partial likelihood to compare prognostic value of the resulting classifications. All statistical tests were two-sided. RESULTS SCMs were statistically significantly more robust than SSPs, with SCMGENE being the most robust because of its simplicity. SCMGENE was statistically significantly concordant with published SCMs (κ = 0.65-0.70) and SSPs (κ = 0.34-0.59), statistically significantly associated with ER (V = 0.64), HER2 (V = 0.52) status, and histological grade (V = 0.55), and yielded similar strong prognostic value. CONCLUSION Our results suggest that adequate classification of the major and clinically relevant molecular subtypes of breast cancer can be robustly achieved with quantitative measurements of three key genes.
Network discovery by generalized random walks

We investigate network exploration by random walks defined via stationary and adaptive transition probabilities on large graphs. We derive an exact formula valid for arbitrary graphs and arbitrary walks with stationary transition probabilities (STP), for the average number of discovered edges as a function of time. We show that for STP walks site and edge exploration obey the same scaling ∼nλ as a function of time n. Therefore, edge exploration on graphs with many loops is always lagging compared to site exploration, the revealed graph being sparse until almost all nodes have been discovered. We then introduce the edge explorer model (EEM), which presents a novel class of adaptive walks, that perform faithful network discovery even on dense networks.
An application scheduler for bioinformatics applications

RAJAGOPALAN, RAVI. An Application Scheduler for Bioinformatics Applications. (Under the direction of Dr. Gregory T Byrd and Dr. Ralph A Dean) Bioinformatics analyses are unique as they usually comprise of a large number of small, computationally intensive but fairly independent processes. This gives them a high degree of parallelism and a very small cost of synchronization among the different tasks. However, the time to schedule each task is often a whole lot more than the execution time of each independent task. To reduce the overhead associated with scheduling, tasks are grouped together increasing the average life of a job and also reducing the number of jobs that need to be scheduled. Grouping also reduces the variation in the lifetime of a job, allowing better predictions of the time that a resource needs to be allocated for a task. While increasing the number of tasks in a group improves the predictability of a task, tasks within a group are executed sequentially, reducing the amount of parallelism available with the pool of tasks. This can increase the total execution time of an analysis, degrading performance of the application. An analysis pipeline for theA. fumigatusgenome, consisting of BLASTn analyses against 11 genomes, two gene prediction algorithms and a RepeatMasker analysis, was developed in the DeCIFR tool for comparative genomics. Performance tests were carried out on a homogenous single resource grid consisting of 14 Dell Servers running dual 1Ghz Intel Xeon processors with 2GB of RAM. Performance improvements of upto 52% were seen due to grouping. The variation in the CPU time for a job reduced from around 75% to about 20% by grouping just 20 tasks together. The best case improvement in CPU time variation was from 87% to 13%. Over-grouping of tasks led to poor utilization of resource degrading the performance by about 40%. The grouping of tasks is dependent on the average life of the task and the ratio of tasks to be completed to the number of resources available. AN APPLICATION SCHEDULER FOR BIOINFORMATICS APPLICATIONS