Miracle-dz/UniFAR-FTUs huggingface dataset:
Efficient Wavelet-Based Compression for Hyperspectral Images Using Precompression Algorithm

Since it's difficult for DWT-based compression scheme to efficiently and directly utilize the various properties of hyperspectral images, such as their statistic characteristics, spectral correlation, and spatial correlation, a new framework of DWT-based compression is proposed and a so-called precompression process is built and introduced into it. Firstly, general relation between bands is deduced in accordance with the high spectral correlation. Based on the relation, precompression algorithm is designed to segment bands of the images into groups and remove spectral correlation and spatial correlation within each group by extremely few bits consumption. Afterwards, algorithm based on 3D packet DWT is adopted subsequently for further compression. Experiments show that the proposed schemes derived from the new DWT-based framework can significantly improve performance compared with the best known techniques, especially at low bitrates. And the new framework has excellent extensibility.
Visualizing multiple evolution metrics

Observing the evolution of very large software systems needs the analysis of large complex data models and visualization of condensed views on the system. For visualization software metrics have been used to compute such condensed views. However, current techniques concentrate on visualizing data of one particular release providing only insufficient support for visualizing data of several releases.In this paper we present the RelVis visualization approach that concentrates on providing integrated condensed graphical views on source code and release history data of up to n releases. Measures of metrics of source code entities and relationships are composed in Kiviat diagrams as annual rings. Diagrams highlight the good and bad times of an entity and facilitate the identification of entities and relationships with critical trends. They represent potential refactoring candidates that should be addressed first before further evolving the system. The paper provides needed background information and evaluation of the approach with a large open source software project.
Ju n 20 01 New Analyses of Star-to-Star Abundance Variations Among Bright Giants in the Mildly Metal-Poor Globular Cluster M 5 1

We present a chemical composition analysis of 36 giant stars in the mildly metalpoor (<[Fe/H]> = –1.21) globular cluster M5 (NGC 5904). The analysis makes use of high resolution data acquired for 25 stars at the Keck I telescope, as well as a reanalysis of the high resolution spectra for 13 stars acquired for an earlier study at Lick Observatory. We employed two analysis techniques: one, adopting standard spectroscopic constraints, including setting the surface gravity from the ionization equilibrium of iron, and two, subsequent to investigating alternative approaches, adopting an analysis consistent with the non-LTE precepts as recently described by Thévenin & Idiart. The abundance ratios we derive for magnesium, silicon, calcium, scandium, titanium, vanadium, nickel, barium and europium in M5 show no significant abundance variations and the ratios are comparable to those of halo field stars. However, large variations are seen in the abundances of oxygen, sodium and aluminum, the elements that are sensitive to proton-capture nucleosynthesis. These variations are well-correlated with the CN bandstrength index S(3839). Surprisingly, in M5 the dependence of the abundance variations on log g is in the opposite sense to that discovered in M13 by the Lick-Texas group where the relationship provided strong evidence in support of the evolutionary scenario. The present analysis of M5 giants does not necessarily rule out an evolutionary scenario, but it provides no support for it either. In comparing the abundances of M5 and M4 (NGC 6121), another mildly metal-poor (<[Fe/H]> = –1.08) globular cluster, we find that silicon, aluminum, barium and lanthanum are overabundant in M4 with respect to what is seen in M5, confirming and expanding the results of previous studies. In comparing the abundances between these two clusters and others having comparable metallicities, we find that the anti-correlations observed in M5 are similar Department of Astronomy and McDonald Observatory, University of Texas, RLM 15.308, Mail Code c1400, Austin, TX 78712; iivans@astro.as. utexas.edu; chris@verdi.as. utexas.edu Research School of Astronomy & Astrophysics, Australian National University, Mount Stromlo Observatory, Cotter Road, Weston ACT 2611, Australia UCO/Lick Observatory, University of California, Santa Cruz, CA 95064; kraft@ucolick.org; graeme@ucolick.org Dept. of Physics & Astronomy, UCLA, Math-Sciences 8979, Los Angeles, CA 90095-1562; rmr@astro.ucla. edu McDonald Observatory, University of Texas, HC 75, Box 1337 L, Fort Davis, TX 79734; shetrone@astro.as. utexas.edu
An Efficient Algorithm for Finding Frequent Items in a Stream

Most of the existing algorithms for mining frequent items over data streams do not emphasis the importance of the more recent data items. We present an efficient algorithm where a fading factor lambda is used for computing frequency counts exceeding a user-specified threshold over data streams. Our algorithm lambda-Miner can detect epsilon-approximate frequent items of a data stream using O(epsilon−1) memory space and the processing time for each data item is O(1). Experimental results on several artificial data sets and real data sets show that lambda-Miner performs better than lambda-LC in terms with precision, memory requirement and time cost.
An Improved Systematic Approach to Predicting Transcription Factor Target Genes Using Support Vector Machine

Biological prediction of transcription factor binding sites and their corresponding transcription factor target genes (TFTGs) makes great contribution to understanding the gene regulatory networks. However, these approaches are based on laborious and time-consuming biological experiments. Numerous computational approaches have shown great potential to circumvent laborious biological methods. However, the majority of these algorithms provide limited performances and fail to consider the structural property of the datasets. We proposed a refined systematic computational approach for predicting TFTGs. Based on previous work done on identifying auxin response factor target genes from Arabidopsis thaliana co-expression data, we adopted a novel reverse-complementary distance-sensitive n-gram profile algorithm. This algorithm converts each upstream sub-sequence into a high-dimensional vector data point and transforms the prediction task into a classification problem using support vector machine-based classifier. Our approach showed significant improvement compared to other computational methods based on the area under curve value of the receiver operating characteristic curve using 10-fold cross validation. In addition, in the light of the highly skewed structure of the dataset, we also evaluated other metrics and their associated curves, such as precision-recall curves and cost curves, which provided highly satisfactory results.
K-Best Spanning Tree Dependency Parsing With Verb Valency Lexicon Reranking

A novel method for hybrid graph-based dependency parsing of natural language text is proposed. It is based on k-best maximum spanning tree dependency parsing and evaluation of the spanning trees by using a verb valency lexicon for a given language as a reranking knowledge base. The approach is compared with existing state-of-the-art transition-based and graph-based approaches to dependency parsing. As the proposed generic method was developed specifically for improving the accuracy of Croatian dependency parsing, Croatian Dependency Treebank and CROVALLEX verb valency lexicon are used in the experiment. The suggested approach scored approximately 77.21% LAS, outperforming the tested state-of-the-art approaches by at least 2.68% LAS.
Stimulating High Quality Social Media through Knowledge Barter-Auctioning

Incentives play a pivotal role in stimulating user-generated content (UGC), which is critical to the viability and success of today's social computing services. Non-financial social incentives are generally effective in boosting the quantity, but have limited effect on the quality. Conversely, financial incentives generally motivate better quality, but often complicate the efforts to attract quantity. In this paper, we propose knowledge barter-auctioning, a non-financial remunerative mechanism that is particularly effective in stimulating the quality of UGC yet without detriment to its quantity. This mechanism provides an optimal way for the knowledge vendor to choose the best barter partner in order to maximise their expected revenue, which is an extrinsic motivation for the triumph of quality as UGC of higher quality will enable the vendor to attract more bidders and consequently make a higher revenue through the barter auction. We have conducted a series of experiments using a real-world dataset to analyse the ramifications of UGC quality in knowledge bartering processes.
p53 upregulated modulator of apoptosis sensitizes drug-resistant U251 glioblastoma stem cells to temozolomide through enhanced apoptosis

Malignant glioma is a highly aggressive brain tumor with a poor prognosis. Chemotherapy has been observed to prolong overall survival rate and temozolomide (TMZ), a promising chemotherapeutic agent for treating glioblastoma (GBM), possesses the most effective clinical activity at present, although drug resistance limits its clinical outcome. Growing evidence supports the concept that initial and recurrent GBM may derive from glioblastoma stem cells, which may be responsible for drug resistance. However, the molecular mechanisms underlying this resistance remain to be elucidated. In the present study, a TMZ-resistant GBM cell line, U251R, was developed and subsequently divided into two subpopulations according to the CD133 immunophenotype. No significant difference was identified in the expression of O6-methylguanine-DNA-methyltransferase (MGMT) between CD133+ U251R cells and CD133− U251R cells, whereas the CD133+ cell population was more resistant to TMZ-induced growth inhibition and cell death. TMZ achieves its cytotoxic effect by inducing DNA lesions and p53 upregulated modulator of apoptosis (PUMA) is an essential mediator of DNA damage-induced apoptosis independently of p53 status. Therefore, whether PUMA effectively enhances growth suppression and induces apoptosis when combined with TMZ was investigated. Consequently, it was found that adenoviruses expressing wild-type-PUMA not only lead to the apoptosis of CD133+ U251R cells alone, but also significantly increase their sensitivity toward TMZ by elevating the Bcl-2-associated X protein/B-cell lymphoma-2 ratio without alterations in MGMT expression. Therefore, PUMA may be a suitable target for intervention to improve the therapeutic efficacy of TMZ.
Gridless pin access in detailed routing

In the physical design of VLSI circuits, routing is one of the most important tasks. Usually done towards the end of the design process, especially the detailed routing phase has to obey virtually all design rules. As the feature size become ever smaller, shifts towards gridless design paradigms are necessary and a formerly trivial task, namely pin access, now becomes difficult.
A Gazetteer and Georeferencing for Historical English Documents

We report on a newly available gazetteer of historical English place-names and describe how it was created from a recent digitisation of the Survey of English Place-Names, published by the English Place-Name Society (EPNS). The gazetteer resource is accessible via a number of routes, not currently as linked data but in formats that do provide connections between a number of different datasets. In particular, connections between the historical gazetteer and the Unlock 1 and GeoNames 2 gazetteer services have been established along with links to the Key to English Place-Names database 3 . The gazetteer is available via the Unlock API and in the final part of the paper we describe how the Edinburgh Geoparser, which forms the basis of Unlock Text, has been adapted to allow users to georeference historical texts.
Learning Structural Equation Models for fMRI

Structural equation models can be seen as an extension of Gaussian belief networks to cyclic graphs, and we show they can be understood generatively as the model for the joint distribution of long term average equilibrium activity of Gaussian dynamic belief networks. Most use of structural equation models in fMRI involves postulating a particular structure and comparing learnt parameters across different groups. In this paper it is argued that there are situations where priors about structure are not firm or exhaustive, and given sufficient data, it is worth investigating learning network structure as part of the approach to connectivity analysis. First we demonstrate structure learning on a toy problem. We then show that for particular fMRI data the simple models usually assumed are not supported. We show that is is possible to learn sensible structural equation models that can provide modelling benefits, but that are not necessarily going to be the same as a true causal model, and suggest the combination of prior models and learning or the use of temporal information from dynamic models may provide more benefits than learning structural equations alone.
Ileal Amino Acid Digestibility of Broken Rice Fed to Postweaned Piglets with or without Multicarbohydrase and Phytase Supplementation

Most of amino acid (AA) digestibility values for feed ingredients are obtained using pigs cannulated in the distal ileum. The ileal-cannulated pig model uses pigs older than six weeks due to difficulties related to implanting the T-cannula in distal ileum of younger pigs and complications during the post-surgical recovery. However, to properly formulate the diet of weaned pigs, the nutritive value of feed ingredients should be determined with younger pigs. Thus, 25 weaned pigs were used to determine the apparent total tract digestibility (ATTD) of nutrients, energy, and apparent ileal digestibility (AID) and standardized ileal digestibility (SID) ileal AA digestibility of broken rice (BR), with or without multicarbohydrase (MC) and phytase (Phy) supplementation. Piglets were weaned at 23 d of age and individually housed in digestibility cages until 45 d of age. The trial consisted of 7 d of adaptation to the experimental diets and 3 d of excreta (feces and urine) collection. Ileal digesta was collected at slaughter (about 6 weeks of age). A completely randomized experimental design was used to determine the effects of MC and Phy. Reference diets (RD, 5% casein) was replaced by 30% of BR with or without MC, Phy, or MC+Phy. The RD was used to quantify endogenous AA losses. BR with Phy supplied had increased the ATTD of dry matter (p<0.05) and SID of histidine (p = 0.05), arginine, leucine, lysine, valine, alanine, and proline (p<0.05). BR with MC had been increased digestible energy and protein and SID for histidine (p<0.05). There was no interaction between Phy and MC on the BR nutrient digestibilities. Standardized amino acid digestibilities of BR, without enzymes, were lower than those values reported in the literature. The MC and Phy improved the digestibility of some nutrients and energy of BR in post-weaned piglet diets.
Capacity and delay of heterogeneous wireless networks with correlated mobility

Although the capacity of wireless ad hoc networks has been extensively studied under different mobility models and network settings, few work has been done on the effect of heterogeneous mobile nodes and correlated mobility. In this paper, we consider the heterogeneous wireless networks consisting of two types of nodes, called user nodes and master nodes. Specifically, user nodes are combined into groups and each group is equipped with a more powerful master node serving as relay for packet transmissions among groups. By proposing a simple, asymptotically optimal scheduling and routing scheme, we present the maximum per-node throughput and the end-to-end delay in order sense, respectively. We also explore the trade-offs between capacity and delay by adjusting network settings.
Rolling horizon evolution versus tree search for navigation in single-player real-time games

In real-time games, agents have limited time to respond to environmental cues. This requires either a policy defined up-front or, if one has access to a generative model, a very efficient rolling horizon search. In this paper, different search techniques are compared in a simple, yet interesting, real-time game known as the Physical Travelling Salesman Problem (PTSP). We introduce a rolling horizon version of a simple evolutionary algorithm that handles macro-actions and compare it against Monte Carlo Tree Search (MCTS), an approach known to perform well in practice, as well as random search. The experimental setup employs a variety of settings for both the action space of the agent as well as the algorithms used. We show that MCTS is able to handle very fine-grained searches whereas evolution performs better as we move to coarser-grained actions; the choice of algorithm becomes irrelevant if the actions are even more coarse-grained. We conclude that evolutionary algorithms can be a viable and competitive alternative to MCTS.
Structural Health Monitoring

Structural Health Monitoring (SHM) is a method in which sensing instruments are used to collect, process and interpret data for a structural integrity monitoring system through the life-cycle of an asset. Structural health monitoring is a stepped process that promotes more effectiveness in asset integrity management by allowing the asset owner to understand operational conditions and implement appropriate maintenance schedules that reduce costs, promote safety and ensure long-term operation. Systems for structural health are used to measure current parameters of building performance and compare these to engineering-design specifications, as well as past building performance.
Multiscale Stochastic Volatility Asymptotics

In this paper we propose to use a combination of regular and singular perturbations to analyze parabolic PDEs that arise in the context of pricing options when the volatility is a stochastic process that varies on several characteristic time scales. The classical Black--Scholes formula gives the price of call options when the underlying is a geometric Brownian motion with a constant volatility. The underlying might be the price of a stock or an index, say, and a constant volatility corresponds to a fixed standard deviation for the random fluctuations in the returns of the underlying. Modern market phenomena make it important to analyze the situation when this volatility is not fixed but rather is heterogeneous and varies with time. In previous work (see, for instance, [J. P. Fouque, G. Papanicolaou, and K. R. Sircar, Derivatives in Financial Markets with Stochastic Volatility, Cambridge University Press, Cambridge, UK, 2000]), we considered the situation when the volatility is fast mean reverting. Using a.
Design of an aspect-supported generic modeling tool

The Compter Supported Cooperative Work(CSCW) is one of the basic features of modern industry. Every industry area requires their own software system to meet specific application businesses and needs. The development of domain-specific software inevitably requests collaboration of domain experts, end users, system analysts and designers, programmers and other related persons. So, how to build a precise, consistent and comprehensible system model becomes the key factor of the cooperative work. This paper describes the design of a generic modeling tool (GMT). The GMT, adopting the ideas of two-phase modeling, can depict different application domains, with description accuracy of each. To solve the problems of model complexity and componentization, the Aspect-Oriented technology is applied to the Model Driven Architecture (MDA), hoping to improve the cohesion and coupling of system design and to enhance the software maintainability.
Profit Persistence in the US : Evidence from a New Dataset

This paper analyzes the persistence of corporate rates of return for 187 surviving US firms from 1950-1999 using a new dataset. The results go in line with the existing literature bringing more evidence for profit persistence. The problem of profit cycles is treated using autoregressive models up to order four. A surprising result is that among the firms that did not converge to the norm there were more firms that performed below the norm than above.
Impact of a 5-Day Expedition to Machu Picchu on Persons with Multiple Sclerosis

Persons with multiple sclerosis (MS) are less physically active than nondiseased persons and often report low self-efficacy levels. In the context of an awareness project to promote physical activity and participation in MS, we addressed the impact of training for and participation in a unique expedition. Medical events, relapses, and self-reported neurological worsening were followed from 6 months before and up to 4 months afterwards. Validated patient-reported outcome measures were used to assess fatigue, self-efficacy in exercising, walking abilities, and illness perception. Nine participants completed the training, expedition, and observational study. Minor events, relapses, and/or neurological worsening were reported in six participants. The three participants with mild disability and no cardiovascular risk factors or comorbidities were free of medical and neurological events. We found a significant reduction of motor fatigue at last when compared with the first assessment. The reduction tended to be more evident in participants with mild disability (Expanded Disability Status Scale (EDSS) <4 at baseline). Cognitive fatigue, self-efficacy, and self-reported walking abilities did not change significantly. Illness perceptions tended to be reduced over time in the domains of consequences, identity, and concerns. Overall, no major adverse events occurred.
A Comprehensive Comparison of the Sun to Other Stars: Searching for Self-Selection Effects

If the origin of life and the evolution of observers on a planet is favored by atypical properties of a planet’s host star, we would expect our Sun to be atypical with respect to such properties. The Sun has been described by previous studies as both typical and atypical. In an effort to reduce this ambiguity and quantify how typical the Sun is, we identify 11 maximally independent properties that have plausible correlations with habitability and that have been observed by, or can be derived from, sufficiently large, currently available, and representative stellar surveys. By comparing solar values for the 11 properties to the resultant stellar distributions, we make the most comprehensive comparison of the Sun to other stars. The two most atypical properties of the Sun are its mass and orbit. The Sun is more massive than 95% ± 2% of nearby stars, and its orbit around the Galaxy is less eccentric than 93% ± 1% of FGK stars within 40 pc. Despite these apparently atypical properties, a χ2 analysis of the Sun’s values for 11 properties, taken together, yields a solar χ2☉ = 8.39 ± 0.96. If a star is chosen at random, the probability that it will have a lower value (i.e., be more typical) than the Sun, with respect to the 11 properties analyzed here, is only 29% ± 11% . These values quantify, and are consistent with, the idea that the Sun is a typical star. If we have sampled all reasonable properties associated with habitability, our result suggests that there are no special requirements for a star to host a planet with life.