Miracle-dz/UniFAR-FTUs huggingface dataset:
Title: Incremental Visual Queries

Exploratory data analysis is a human-centered process whose goal is to develop intuition and insight into a dataset. Usually initial findings will suggest new questions, leading to extensive iteration. Statistical graphics play an important role, but a flexible means for populating them with relevant data is also needed. Database query languages are one means, but formulating queries is a distraction from the primary task of understanding the data. This paper uses the concept of isets, which embody both a description and a set of entities that satisfy it, to bridge the gap between the expressive power of queries and the concreteness of data. During exploration, isets can be incrementally modified, related to other isets, or used to define new isets. This last operation uses the metaphor of navigation from an existing iset as the means of arriving at the new related iset, which situates the analyst as actively moving through the world of the data. The navigation path among isets resembles a graphical query language. However, visualizations give continuous feedback as the graph is incrementally constructed, rather than only after an " execute " command. Navigation and nine other operations are meant to effectively support iterative exploration.
Prognostic and therapeutic value of mitochondrial serine hydroxyl-methyltransferase 2 as a breast cancer biomarker

Mitochondrial serine hydroxylmethyltransferase 2 (SHMT2) is a key enzyme in the serine/glycine synthesis pathway. SHMT2 has been implicated as a critical component for tumor cell survival. The aim of the present study was to evaluate the prognostic value and efficiency of SHMT2 as a biomarker in patients with breast cancer. Individual and pooled survival analyses were performed on five independent breast cancer microarray datasets. Gene signatures enriched by SHMT2 were also analyzed in these datasets. SHMT2 protein expression was detected using immunohistochemistry (IHC) assay in 128 breast cancer cases. Gene set enrichment analysis revealed that SHMT2 was significantly associated with gene signatures of mitochondrial module, cancer invasion, metastasis and poor survival among breast cancer patients (p<0.05). The clinical relevance of SHMT2 was validated on IHC data. The mitochondrial localization of SHMT2 protein was visualized on IHC staining. Independent and pooled analysis confirmed that SHMT2 expression was associated with breast cancer tumor aggressiveness (TNM staging and Elson grade) in a dose-dependent manner (p<0.05). The prognostic performance of SHMT2 mRNA was comparable to other gene signatures and proved superior to TNM staging. Further analysis results indicated that SHMT2 had better prognostic value for estrogen receptor (ER)-negative breast cancer patients, compared to ER-positive patients. In cases involving stage IIb breast cancer, chemotherapy significantly extended survival time among patients with high SHMT2 expression. These results indicate that SHMT2 may be a valuable prognostic biomarker in ER-negative breast cancer cases. Furthermore, SHMT2 may be a potential target for breast cancer treatment and drug discovery.
Sender based adaptive VoIP quality improvement using constructive feedback

QoS analysis of VoIP traffic is inevitable in quality improvement of multimedia converged networks. Therefore, adaptive speech quality improvement is vital for managing the VoIP communication quality in order to meet users' quality expectations and decrease utilized resources. This paper proposes a new approach for augmenting the real-time VoIP communication quality and tries to explore its impact on VoIP QoS parameters. The scheme includes different voice codecs and different voice payload sizes for each codec. Simulation results show that the proposed scheme leads to superior VoIP quality compared to some existing algorithms.
Boosted sparse nonlinear distance metric learning

This paper proposes a boosting‐based solution addressing metric learning problems for high‐dimensional data. Distance measures have been used as natural measures of (dis)similarity and have served as the foundation of various learning methods. The efficiency of distance‐based learning methods heavily depends on the chosen distance metric. With increasing dimensionality and complexity of data, however, traditional metric learning methods suffer from poor scalability, and the limitation due to linearity as the true signals are usually embedded within a low‐dimensional nonlinear subspace. In this paper, we propose a nonlinear sparse metric learning algorithm via boosting. We restructure a global optimization problem into a forward stage‐wise learning of weak learners based on a rank‐one decomposition of the weight matrix in the Mahalanobis distance metric. A gradient‐boosting algorithm is devised to obtain a sparse rank‐one update of the weight matrix at each step. Nonlinear features are learned by a hierarchical expansion of interactions incorporated within the boosting algorithm. Meanwhile, an early stopping rule is imposed to control the overall complexity of the learned metric. As a result, our approach guarantees three desirable properties of the final metric: positive semi‐definiteness, low rank and element‐wise sparsity. Numerical experiments show that our learning model compares favorably with the state‐of‐the‐art methods in the current literature of metric learning.
Study of Personal Location-Based State Distribution Inference

It is very important to study LBS (Location-based Service) personal location inference for the privacy protection and the value extraction of LBS big data. According to study the characteristics of the target user’s state in day view belongs to Gaussian distribution, an effective method to infer the user's home and work location is proposed in the article. Experimental results show that the inference method based on the user’s state distribution has the higher inference accuracy than the current inference method based on the frequency statistics.
Soft-Input, Iterative, Reed-Solomon Decoding using Redundant Parity-Check Equations

In this work we present a practical approach to the iterative decoding of Reed-Solomon (RS) codes. The presented methodology utilizes an architecture in which the output produced by steps of belief-propagation (BP) is successively applied to a legacy decoding algorithm. Due to the highly suboptimal performance of BP conducted on the inherently dense RS parity-check matrix, a method is first provided for the construction of reduced-density, binary, parity-check equations. Iterative decoding is then conducted utilizing a subset of a redundant set of parity-check equations to minimize the number of connections into the least-reliable bits. Simulation results show that performance comparable to (and exceeding) the best known practical RS decoding techniques is achievable with the presented methodology. The complexity of the proposed algorithm is orders of magnitude lower than these existing procedures and permits a practical implementation in hardware.
Managerial incentives for compliance with environmental information disclosure programs

Publicly reported information on the environmental behavior of firms can increase the efficacy of private markets as a mechanism to control environmental malfeasance through liability for harm, consumer demand response, and shareholder reaction. In the case of mandatory information disclosure programs, firms are required to report information that is potentially damaging to them. We argue that a firm’s internal organizational structure alters the incentives faced by decision-makers and therefore has the potential to affect their compliance decisions. We adapt a theoretical model developed by Gilpatric (2005) to examine these incentives and test the resulting hypotheses using experimental data. Experimental results confirm theoretical predictions of increased non-compliance when audit probabilities increase, but in contrast to theory there is no discernable effect of changing penalties for non-compliance.
On Finding the Point Where There Is No Return: Turning Point Mining on Game Data

Gaming expertise is usually accumulated through playing or watching many game instances, and identifying critical moments in these game instances called turning points. Turning point rules (shorten as TPRs) are game patterns that almost always lead to some irreversible outcomes. In this paper, we formulate the notion of irreversible outcome property which can be combined with pattern mining so as to automatically extract TPRs from any given game datasets. We specifically extend the well-known PrefixSpan sequence mining algorithm by incorporating the irreversible outcome property. To show the usefulness of TPRs, we apply them to Tetris, a popular game. We mine TPRs from Tetris games and generate challenging game sequences so as to help training an intelligent Tetris algorithm. Our experiment results show that 1) TPRs can be found from historical game data automatically with reasonable scalability, 2) our TPRs are able to help Tetris algorithm perform better when it is trained with challenging game sequences.
Adequacy, Accuracy, Scalability, and Uncertainty of Architecture-based Software Reliability: Lessons Learned from Large Empirical Case Studies

Our earlier research work on applying architecture-based software reliability models on a large scale case study allowed us to test how and when they work, to understand their limitations, and to outline the issues that need future research. In this paper we first present an additional case study which confirms our earlier findings. Then, we present uncertainty analysis of architecture-based software reliability for both case studies. The results show that Monte Carlo method scales better than the method of moments. The sensitivity analysis based on Monte Carlo method shows that (1) small number of parameters contribute to the most of the variation in system reliability and (2) given an operational profile, components' reliabilities have more significant impact on system reliability than transition probabilities. Finally, we summarize the lessons learned from conducting large scale empirical case studies for the purpose of architecture-based reliability assessment and uncertainty analysis
DEMOCRACY ’ S SPREAD : Partisan and Opportunistic Political Business Cycle Effects on Sovereign Bond Spreads in Developing Countries *

We use partisan and opportunistic political business cycle (“PBC”) considerations to develop and test a framework for explaining election-period changes in market-determined credit spreads for developing country sovereign bonds (“bond spreads”). Analyzing daily bond spread observations for 19 presidential elections held in 12 developing countries from 1994-2000, we find that pre-election bond spread trends are significantly linked both to the partisan orientation of incumbents facing election and to expectations of incumbent victory. We find that bond spreads for right-wing (left-wing) incumbents increase (decrease) as the likelihood of left-wing (right-wing) challenger victory increases. For right-wing incumbents, our results suggest that partisan and opportunistic PBC effects bondholder risk perceptions are mutually reinforcing. For left-wing incumbents, our results suggest that partisan PBC effects dominate bondholder risk perceptions compared to opportunistic PBC effects. Over all, our results suggest that bondholders and, perhaps, other non-voting financial actors behave consistently with PBC considerations. In certain cases, their PBC behaviors exact a surcharge on the cost of capital vital to developing country investment and economic growth.
Fixed Points and Stability of Density Evolution

Density evolution is a dynamic system in a space of probability distributions repre- senting the progress of iterative decoders in the infinite block length limit. In this paper we establish some basic results concering this process. In particular we show that the decoding threshold is equivalent to to appearance of non-trivial fixed point solutions to the density evolution equations. In the case of LDPC codes we prove the sufficiency of the previously published stability condition for stability of the δ∞ fixed point and slightly strengthen the necessity result.
A Color Images Steganography Method by Multiple Embedding Strategy Based on Sobel Operator

In this paper, we propose a color images steganography. Firstly, because modification of pixel of image edges is less sensitive to human eye, the edge or texture in one plane is detected by Sobel edge detection operator and the largest certain number of gradients are selected as embedding positions. Secondly, because of strong relevance in gradient among R, G, and B planes, the corresponding LSB (Least Significant Bit) of pixel values to hide the secret data in other planes is modified. Finally, combine the stego-planes and restore the color image. Furthermore, multi-times embedding can be adapted to obtain high data capacity. Experimental results show that stego-images keep good quality and high capacity. For example, there is no noticeable degradation after three times hiding process, the average PSNR is about 45db and data capacity is 6.3bpp. The method also can extract the secret data accurately.
Classification of child and adulthood using GLCM based on diagonal LBP

This paper derives a child and adulthood classification technique by integrating the statistical and structural approaches. The structural approaches are derived on a 3 × 3 window based on Local binary pattern (LBP) approach. The proposed approach divides the LBP in to two structural patterns. The present paper derives two distinct patterns called Left Diagonal (LD) and Right Diagonal (RD) LBP's. The given image is converted into binary by comparing the average value of the 3 × 3 neighborhood with its neighbors. Then LD-LBP and RD-LBP codes are evaluated. The range of these code values will be 0 to 23-1, since three pixels form the above proposed patterns. Based on LD and RD-LBP the present paper derived left and right diagonal-GLCM (LRD-GLCM) and features are evaluated. For efficient age classification chi-square distance method is used. To overcome the data dependency problem, the proposed method is implemented on three different facial namely FG-NET, Google and scanned images. The experimental results indicate a significant age classification rate over the existing methods.
Interactive event detection in crowd scenes

As an important aspect in video content analysis, event detection is still an open problem. In particular, the study on detecting interactive events in crowd scenes is still limited. In this paper, we investigate detecting interactive events between persons, e.g. PeopleMeet, PeopleSplitUp and Embrace in complex scenes using a sequence learning based approach. By sequence learning, the spatial-temporal context information is introduced in the learning stage. Experiments have been performed over TRECVid Event Detection 2010 dataset, which contains totally 144 hours surveillance video of London Gatwick airport. According to the TRECVid-ED 2010 formal evaluation, our approach obtains promising results, with the top performance (NDCR) for PeopleMeet and PeopleSplit-Up, and second-best performance (NDCR) for Embrace.
The Gold Standard in Corpus Annotation

Trustworthy corpora are necessary for training and meaningful evaluation of algorithms which use annotations. These standard collections are called Gold Standard Corpora (GSC). However the construction of GSC is a laborious and time-consuming process and size, quality and most of all availability of task-specific GSC directly influence the development of machine learning based natural language processing algorithms. This paper provides an introduction to gold standard corpus construction in the context of natural language processing and gives an overview of alternative approaches.
Multiscale Filtering and Neural Network Classification for Segmentation and Analysis of Retinal Vessels.

The blood vessel segmentation method described in this paper aims at minimizing the false positive rate, while maintaining high accuracy. Though developed for color retinal images, it can be applied to other tree-like vascular images as well. A computer vision approach is devised that mimics the image reading by expert people, based on a two stage process, perception and interpretation. The first stage adopts multiscale filtering to detect objects of different sizes: a two scale Laplacian of Gaussian scheme is used with the related sigma values chosen according to the smallest and greatest vessel widths. An approximate segmentation is achieved simply by means of the Laplacian sign. The interpretation stage is application-specific and accomplishes classification and quantitative analysis. The skeleton of the binary structures is subdivided in vessel segments, their features (position, orientation, length and width) are fed into an art i f ic ial neural network, after back-propagation training. The segments classified as vessels are assembled into the vascular tree by rule-based tracking. Results are evaluated on STARE and DRIVE data bases. Accuracy is 95% and the false positive rate is decreased to about 1%. The application on fundus images of infants with retinopathy of prematurity is also described.
Recursive Markov chains, stochastic grammars, and monotone systems of nonlinear equations

We define Recursive Markov Chains (RMCs), a class of finitely presented denumerable Markov chains, and we study algorithms for their analysis. Informally, an RMC consists of a collection of finite-state Markov chains with the ability to invoke each other in a potentially recursive manner. RMCs offer a natural abstract model for probabilistic programs with procedures. They generalize, in a precise sense, a number of well-studied stochastic models, including Stochastic Context-Free Grammars (SCFG) and Multi-Type Branching Processes (MT-BP). We focus on algorithms for reachability and termination analysis for RMCs: what is the probability that an RMC started from a given state reaches another target state, or that it terminates? These probabilities are in general irrational, and they arise as (least) fixed point solutions to certain (monotone) systems of nonlinear equations associated with RMCs. We address both the qualitative problem of determining whether the probabilities are 0, 1 or in-between, and the quantitative problems of comparing the probabilities with a given bound, or approximating them to desired precision. We show that all these problems can be solved in PSPACE using a decision procedure for the Existential Theory of Reals. We provide a more practical algorithm, based on a decomposed version of multi-variate Newton's method, and prove that it always converges monotonically to the desired probabilities. We show this method applies more generally to any monotone polynomial system. We obtain polynomial-time algorithms for various special subclasses of RMCs. Among these: for SCFGs and MT-BPs (equivalently, for 1-exit RMCs) the qualitative problem can be solved in P-time; for linearly recursive RMCs the probabilities are rational and can be computed exactly in P-time. We show that our PSPACE upper bounds cannot be substantially improved without a breakthrough on long standing open problems: the square-root sum problem and an arithmetic circuit decision problem that captures P-time on the unit-cost rational arithmetic RAM model. We show that these problems reduce to the qualitative problem and to the approximation problem (to within any nontrivial error) for termination probabilities of general RMCs, and to the quantitative decision problem for termination (extinction) of SCFGs (MT-BPs).
Results of the ESO-SEST Key Programme on CO in the Magellanic Clouds X. CO emission from star formation regions in LMC and SMC

We present J = 1 0a ndJ = 2 1 12 CO maps of several star-forming regions in both the Large and the Small Magellanic Cloud, and briefly discuss their structure. Many of the detected molecular clouds are relatively isolated and quite small with dimensions of typically 20 pc. Some larger complexes have been detected, but in all cases the extent of the molecular clouds sampled by CO emission is significantly less than the extent of the ionized gas of the star-formation region. Very little diuse extended CO emission was seen; diuse CO in between or surrounding the detected discrete clouds is either very weak or absent. The majority of all LMC lines of sight detected in 13 CO has an isotopic emission ratio I( 12 CO)=I( 13 CO) of about 10, i.e. twice higher than found in Galactic star-forming complexes. At the lowest 12 CO intensities, the spread of isotopic emission ratios rapidly increases, low ratios representing relatively dense and cold molecular gas and high ratios marking CO photo- dissociation at cloud edges.
Time-Aware Rank Aggregation for Microblog Search

We tackle the problem of searching microblog posts and frame it as a rank aggregation problem where we merge result lists generated by separate rankers so as to produce a final ranking to be returned to the user. We propose a rank aggregation method, TimeRA, that is able to infer the rank scores of documents via latent factor modeling. It is time-aware and rewards posts that are published in or near a burst of posts that are ranked highly in many of the lists being aggregated. Our experimental results show that it significantly outperforms state-of-the-art rank aggregation and time-sensitive microblog search algorithms.
Molecular Phylogeny and Ontogeny of a New Ciliate Genus, Paracladotricha salina n. g., n. sp. (Ciliophora, Hypotrichia)

A hypotrichous ciliate, Paracladotricha salina n. g. , n. sp. , was discovered in hypersaline waters (salinity about 80‰) from Qingdao, China. Its morphology and some major ontogenetic stages were studied and the phylogenetic position was estimated using standard methods. Paracladotricha salina is characterized by a flexible, more or less slender body (size 50–120 × 20–35 μm), a gonostomatid oral apparatus, one short and two long frontoventral rows, four macronuclear nodules, almost completely reduced dorsal kineties 1–3, and a loss of several parts of the ciliature, namely, the slightly shortened ciliary row of the adoral membranelles, the paroral, and the buccal, the postoral and pretransverse ventral, the transverse, and the caudal cirri. The ontogenesis is rather simple: anlage II of both filial products and anlage III of the opisthe originate de novo, while anlagen IV and V are formed within the parental rows. This combination of features requires the establishment of a new genus, Paracladotricha, which is, according to the morphological data, closely related to Schmidingerothrix and Cladotricha. The small‐subunit rRNA gene was sequenced, indicating that P. salina is, as also demonstrated by the oral apparatus, a member of the gonostomatids. We provide a first, vague hypothesis about the phylogenetic relationships of the Gonostomatidae, Cladotrichidae, and Schmidingerotrichidae. However, since molecular data of the type species of these higher taxa are lacking, their validity and relationships remain obscure.
Constraint satisfaction, packet routing, and the lovasz local lemma

Constraint-satisfaction problems (CSPs) form a basic family of NP-hard optimization problems that includes satisfiability. Motivated by the sufficient condition for the satisfiability of SAT formulae that is offered by the Lovasz Local Lemma, we seek such sufficient conditions for arbitrary CSPs. To this end, we identify a variable-covering radius--type parameter for the infeasible configurations of a given CSP, and also develop an extension of the Lovasz Local Lemma in which many of the events to be avoided have probabilities arbitrarily close to one; these lead to a general sufficient condition for the satisfiability of arbitrary CSPs. One primary application is to packet-routing in the classical Leighton-Maggs-Rao setting, where we introduce several additional ideas in order to prove the existence of near-optimal schedules; further applications in combinatorial optimization are also shown.