Miracle-dz/UniFAR-FTUs huggingface dataset:
Planning and matchmaking for the interoperation of information processing agents

In today’s open, distributed environments, there is an increasing need for systems to assist the interoperation of tools and information resources. This paper describes a multi-agent system, DALEKS, that supports such activities for the information processing domain. With this system, information processing tasks are accomplished by the use of an agent architecture incorporating task planning and information agent matchmaking components. We discuss the characteristics of planning in this domain and describe how information processing tools are specified for the planner. We also describe the manner in which planning, agent matchmaking, and information task execution are interleaved in the DALEKS system. An example application taken from the domain of university course administration is provided to illustrate some of the activities performed in this system.
Hepatic caudate lobe neuroendocrine carcinoma diagnosed by EUS-guided core biopsy (with video)

A 56-year-old man presented with upper abdominal pain. Upper gastrointestinal endoscopy was unremarkable. Imaging with computed tomography (CT) and magnetic resonance imaging [Figure 1] revealed a 2-cm lesion in the caudate lobe of the liver. CT-guided biopsy was unsuccessful as there was no safe window to access the lesion. The patient underwent the EUS-guided biopsy of the caudate lobe through a transduodenal approach using a 20-gauge Procore® biopsy needle (Cook Medical Inc., Winston-Salem, North Carolina, United States) [Video 1]. Abundant core tissue was obtained for histology [Figure 2], which demonstrated poorly differentiated carcinoma with high nucleus: cytoplasmic ratio, nuclear molding, conspicuous apoptotic bodies, and necrosis and showing synaptophysin positivity on immunohistochemistry, consistent with small cell carcinoma (poorly differentiated neuroendocrine carcinoma). The Ki-67 proliferation index was 90%. The tumor was strongly positive for thyroid transcription factor-1 on immunohistochemistry, suggesting a lung primary carcinoma needed to be excluded, followed by other potential sites before managing it as a solitary primary liver small cell carcinoma which is very rare. [1] He was referred to the medical oncologist for management. Although it may be difficult to access the caudate lobe from a percutaneous approach, it can be easily accessed and punctured under EUS guidance, a fact that non-EUS practitioners may not be aware of. [2,3] A dedicated 19-gauge biopsy needle has been shown to achieve a high yield of core tissue for histology. [4] The 20-gauge biopsy needle is less stiff than the 19-gauge needle, and thus facilitates the process of transduodenal puncture. Although cytology obtained Images and Videos
Zoledronic acid in the management of metastatic bone disease

Many patients with advanced cancer experience decreased bone strength due to metastatic foci, underlying osteoporosis and/or cancer treatment induced bone loss. The clinical consequences of metastatic disease involving the skeleton are widespread. This review focuses on the efficacy, pharmacology, and safety when using intravenous biphosphonate such a zoledronic acid for cancer bone metastases. Zoledronic acid is the gold standard for the medical management of metastatic bone disease. The indications for treatment include prevention of skeletal relevant events (SRE), osteoporotic complications, and palliation of bone pain, among others. Zoledronic acid is the only bisphosphonate effective in decreasing SREs associated with bone metastases from advanced renal cell carcinoma and prostate cancer. Regarding prostate cancer, zoledronic acid effectively prevents both bone loss in patients with locally advanced disease receiving androgen deprivation therapy and SREs in men with hormone-refractory or hormone-sensitive metastatic disease. Zoledronic acid has an acceptable safety profile and tolerability, and has been effective at significantly decreasing the incidence, delaying the onset, and reducing the overall risk of experiencing an SRE compared to placebo. It is the only bisphosphonate currently approved for the prevention and treatment of skeletal complications in patients with bone metastases due to all solid tumors.
Selected bioactive components in fluted pumpkin (Telfairia occidentalis) and amaranth (Amaranthus caudatus) leaves.

Aims: This study was carried out to assess the contents of total phenolics, flavonoids, condensed tannins, saponins and alkaloids in Telfairiaoccidentalisand Amaranthus caudatusleaves. Study Design:The experiment employed a 2◊2◊5 factorial design.
Twenty-Hour Sleep Deprivation Does Not Affect Perceived Vection Strength

We examined the effect of sleep deprivation on self-motion perception (vection). We measured the strength of vection, its latency, and its duration in two conditions: Sleep-Deprivation and Normal-Sleep (by using the between-subject design). For the Sleep-Deprivation condition, participants did not sleep for about 20 hours. We also recorded subjective sleepiness with a subjective rating scale that was filled out by the participants. Results showed that vection strength did not differ between the two conditions. Sleep deprivation did not have any clear effect on vection. As expected, subjective sleepiness significantly increased following sleep deprivation. Further, subjective sleepiness significantly correlated with vection latency and duration only in the Normal-Sleep condition. Vection was immune to sleep deprivation. We conclude that when people are not deprived of sleep, sleepiness can enhance the perceived strength of vection.
Lqr and Predictive Control Tuned via Bdu 1. Least-squares Is Sensitive to Data Errors

This work presents the BDU technique (Bounded Data Uncertainties) and the tuning of the linear quadratic regulator (LQR) via this technique, which considers models with bounded uncertainties. The BDU method is based on constrained game-type formulations, and allows the designer to explicitly incorporate a priori information about bounds on the sizes of the uncertainties into the problem statement. Thus, on the one hand the uncertainty effect is not over-emphasized, avoiding an overly conservative design and on the other hand the uncertainty effect is not under-emphasized, avoiding an overly sensitive to errors design. A feature of this technique consists of its geometric interpretation. The structure of the paper is the following, in the first section some problems about the least-squares method in the presence of uncertainty are introduced. The BDU technique is shown in the second section and the LQR controller in the third. After that a new guided way of tuning the LQR is offered, taking into account the uncertainties bounds via the BDU. The consequence of this method is that both recursive and algebraic Riccati equations are modified. Finally, some examples are shown and the main conclusiones and future work are commented.
Generalization error for multi-class margin classification

In this article, we study rates of convergence of the generalization error of multi-class margin classifiers. In particular, we develop an upper bound theory quantifying the generalization error of various large margin classifiers. The theory permits a treatment of general margin losses, convex or nonconvex, in presence or absence of a dominating class. Three main results are established. First, for any fixed margin loss, there may be a trade-off between the ideal and actual generalization performances with respect to the choice of the class of candidate decision functions, which is governed by the trade-off between the approximation and estimation errors. In fact, different margin losses lead to different ideal or actual performances in specific cases. Second, we demonstrate, in a problem of linear learning, that the convergence rate can be arbitrarily fast in the sample size $n$ depending on the joint distribution of the input/output pair. This goes beyond the anticipated rate $O(n^{-1})$. Third, we establish rates of convergence of several margin classifiers in feature selection with the number of candidate variables $p$ allowed to greatly exceed the sample size $n$ but no faster than $\exp(n)$.
Membrane-associated actin from the microvillar membranes of ascites tumor cells

A membrane fraction (MF2) has been purified from isolated microvilli of the MAT-C1 subline of the 13762 rat mammary ascites adenocarcinoma under conditions which cause F-actin depolymerization. This membrane preparation contains actin as a major component, although no filamentous structures are observed by transmission electron microscopy. Membranes were extracted with a Triton X-100-containing actin-stabilizing buffer (S buffer) or actin-destabilizing buffer (D buffer). In D buffer greater than 90% of metabolically labeled protein and glycoprotein was extracted, and 80-90% of these labeled species was extracted in S buffer. When S buffer extracts of MF2 were fractionated by either gel filtration on Sepharose 6 B or rate-zonal sucrose density gradient centrifugation, most of the actin was found to be intermediate in size between G- and F-actin. In D buffer most of the MF2 actin behaved as G-actin. Extraction and gel filtration of intact microvilli in S buffer also showed the presence of the intermediate form of actin, indicating that it did not arise during membrane preparation. When [35S]methionine-labeled G-actin from ascites cells was added to S buffer extracts of MF2 and chromatographed, all of the radioactivity chromatographed as G-actin, indicating that the intermediate form of actin did not result from an association of G-actin molecules during extraction or chromatography. The results of this study suggest that the microvillar membrane fraction is enriched in an intermediate form of actin smaller than F-actin and larger than G-actin.
Classification of Group Potency Levels of Software Development Student Teams

This paper describes the use of an automatic classifier to model group potency levels within software development projects. A set of machine learning experiments that looked at different group characteristics and various collaboration measures extracted from a team’s communication activities were used to predict overall group potency levels. These textual communication exchanges were collected from three software development projects involving students living in the US, Turkey and Panama. Based on the group potency literature, group-level measures such as skill diversity, cohesion, and collaboration were developed and then collected for each team. A regression analysis was originally performed on the continuous group potency values to test the relationships between the group-level measures and group potency levels. This method, however, proved to be ineffective. As a result, the group potency values were converted into binary labels and the relationships between the group-level measures and group potency were re-analyzed using machine learning classifiers. Results of this new analysis indicated an improvement in the accuracy of the model. Thus, we were able to successfully characterize teams as having either low or high potency levels. Such information can prove useful to both managers and leaders of teams in any setting.
Visualizing Statistical Properties of Smoothly Brushed Data Subsets

In many application fields, the statistical properties of data sets are of great interest for data analysts. Since local variations can occur especially in large datasets, it is useful to visualize not only global values, but also the properties of user-defined subsets. Hence, we present in this paper a visualization of the statistical characteristics of subsets, with an emphasis on the temporal development. The visualization is coordinated with other views. They provide details about the data and allow for smooth brushing of subsets, a concept that introduces a continuous transition from data in focus to context data. Our approach accounts for smooth brushing in both the derivation and the visualization of statistical properties in order to visualize variations within subsets. To analyze differences between multiple subsets, our approach further integrates visualization concepts for the comparison of statistical properties. An application example from the simulation of biological systems demonstrates the benefits of our approach.
Speech input-output system in Indian farming sector

In this research paper, we have developed a Speech input-output System in Indian language that can be useful in bridging the gap between lab and land. This voice enabled system will be very helpful to Indian farmers to retrieve the cultivation information like availability of seeds, fertilizers, pesticides and methods of farming. The speech input-output system is very beneficial in two angles for Indian cultivation domain, first it needs the input query of farmers in voice mode which completely avoids the end users to be literate persons and secondly the response of the system also in voice mode which is more comprehensive for every class of end-user. Speech input-output system was developed using CMU Sphinx4 for understanding the enduser query and speech synthesis was achieved by playing the prerecorded sound files for providing the response to the spoken query. We have collected the speech corpus related to Indian cultivation for this research work in Odia language which is one Indian official language. The performance evaluation of the system is measured in terms of percentage of word accuracy and word error rate as well as using mean opinion score(MOS) test. The word accuracy for known and new users is found to be 86.87 % and 75.13% respectively. The MOS test for speech output is found as 4.62 on an average.
Cyber Surveillance for Flood Disasters

Regional heavy rainfall is usually caused by the influence of extreme weather conditions. Instant heavy rainfall often results in the flooding of rivers and the neighboring low-lying areas, which is responsible for a large number of casualties and considerable property loss. The existing precipitation forecast systems mostly focus on the analysis and forecast of large-scale areas but do not provide precise instant automatic monitoring and alert feedback for individual river areas and sections. Therefore, in this paper, we propose an easy method to automatically monitor the flood object of a specific area, based on the currently widely used remote cyber surveillance systems and image processing methods, in order to obtain instant flooding and waterlogging event feedback. The intrusion detection mode of these surveillance systems is used in this study, wherein a flood is considered a possible invasion object. Through the detection and verification of flood objects, automatic flood risk-level monitoring of specific individual river segments, as well as the automatic urban inundation detection, has become possible. The proposed method can better meet the practical needs of disaster prevention than the method of large-area forecasting. It also has several other advantages, such as flexibility in location selection, no requirement of a standard water-level ruler, and a relatively large field of view, when compared with the traditional water-level measurements using video screens. The results can offer prompt reference for appropriate disaster warning actions in small areas, making them more accurate and effective.
Generation times in wild chimpanzees and gorillas suggest earlier divergence times in great ape and human evolution

Fossils and molecular data are two independent sources of information that should in principle provide consistent inferences of when evolutionary lineages diverged. Here we use an alternative approach to genetic inference of species split times in recent human and ape evolution that is independent of the fossil record. We first use genetic parentage information on a large number of wild chimpanzees and mountain gorillas to directly infer their average generation times. We then compare these generation time estimates with those of humans and apply recent estimates of the human mutation rate per generation to derive estimates of split times of great apes and humans that are independent of fossil calibration. We date the human–chimpanzee split to at least 7–8 million years and the population split between Neanderthals and modern humans to 400,000–800,000 y ago. This suggests that molecular divergence dates may not be in conflict with the attribution of 6- to 7-million-y-old fossils to the human lineage and 400,000-y-old fossils to the Neanderthal lineage.
Winter is coming: hibernation reverses the outcome of sperm competition in a fly

Sperm commonly compete within females to fertilize ova, but research has focused on short‐term sperm storage: sperm that are maintained in a female for only a few days or weeks before use. In nature, females of many species store sperm for months or years, often during periods of environmental stress, such as cold winters. Here we examine the outcome of sperm competition in the fruit fly Drosophila pseudoobscura, simulating the conditions in which females survive winter. We mated females to two males and then stored the female for up to 120 days at 4°C. We found that the outcome of sperm competition was consistent when sperm from two males was stored for 0, 1 or 30 days, with the last male to mate fathering most of the offspring. However, when females were stored in the cold for 120 days, the last male to mate fathered less than 5% of the offspring. Moreover, when sperm were stored long term the first male fathered almost all offspring even when he carried a meiotic driving sex chromosome that drastically reduces sperm competitive success under short‐term storage conditions. This suggests that long‐term sperm storage can radically alter the outcome of sperm competition.
Salient object segmentation based on graph cut

Salient object segmentation is an important technique for many content based applications. This paper presents an unsupervised salient object segmentation method under the graph cut optimization framework. First, we exploit a kernel density estimation based saliency model to generate the saliency map, which provides the useful cues for object segmentation. Then we exploit the saliency map to adaptively define the region cost term, the boundary cost term and their balancing weight in the cost function, which is minimized using graph cut to obtain a binary segmentation of salient objects. Experimental results on a variety of images demonstrate the better segmentation performance of our approach.
The clinical and economic burden of chronic obstructive pulmonary disease in the USA

Chronic obstructive pulmonary disease (COPD) is the third most common cause of death in the USA. In 2010, the cost of COPD in the USA was projected to be approximately US$50 billion, which includes $20 billion in indirect costs and $30 billion in direct health care expenditures. These costs can be expected to continue to rise with this progressive disease. Costs increase with increasing severity of disease, and hospital stays account for the majority of these costs. Patients are diagnosed with COPD following a multifactorial assessment that includes spirometry, clinical presentation, symptomatology, and risk factors. Smoking cessation interventions are the most influential factor in COPD management. The primary goal of chronic COPD management is stabilization of chronic disease and prevention of acute exacerbations. Bronchodilators are the mainstay of COPD therapy. Patients with few symptoms and low exacerbation risk should be treated with a short-acting bronchodilator as needed for breathlessness. Progression of symptoms, as well as possible decline in forced expiratory volume in the first second of expiration (FEV1), warrant the use of long-acting bronchodilators. For patients with frequent exacerbations with or without consistent symptoms, inhaled corticosteroids should be considered in addition to a long-acting beta2-agonist (LABA) or long-acting muscarinic antagonist (LAMA) and may even consist of “triple therapy” with all three agents with more severe disease. Phosphodiesterase-4 inhibitors may be an option in patients with frequent exacerbations and symptoms of chronic bronchitis. In addition to a variety of novel ultra-LABAs, LAMAs and combination bronchodilator and inhaled corticosteroid (ICS) therapies, other bronchodilators with a variety of mechanisms are also being considered, to expand therapeutic options for the treatment of COPD. With more than 50 new medications in the pipeline for the treatment of COPD, optimal management will continue to evolve and grow more complex as benefits of therapy are balanced with the limitations and needs of each patient.
SQL Injection Attack prevention based on decision tree classification

In real world as dependence on World Wide Web applications increasing day by day they transformed vulnerable to security attacks. Out of all the different attacks the SQL Injection Attacks are the most common. In this paper we propose SQL injection vulnerability prevention by decision tree classification technique. The proposed model make use famous decision tree classification model to prevent the SQL injection attacks. The proposed model will filter the sent HTTP request by using a decision tree classification based attack signatures. We test our proposed model on synthetic data which given satisfactory results.
Problems and Results in Combinatorial Analysis

I gave many lectures by this and similar titles, many in fact in these conferences and I hope in my lecture in 1978 I will give a survey of the old problems and describe what happened to them. In the first part of this paper I discuss some new problems in Ramsey theory and in the second part I discuss some miscellaneous old and new problems. problems and a fairly complete list of my combinatorial problem papers. Denote by f2 r) (n,a) the smallest integer for which it is possible to split the r-tuples of a set S, ISI = n into k classes, so . that for every S 1 cS, IS1 I e fkr) (n,a) every class contains more than a 5 1 r-tuples of 3 1 (0 <_ a < k). The reader I hope will forgive the somewhat clumsy notation-perhaps mistakenly I feel that this is the best way to state the results and problems. For a=O f (2) (n,0) is the familiar Ramsey function, the smallest integer t=f (2) (n,O) for which there is a graph on n vertices which does not contain a complete subgraph or independent set of size i (I realise that this is a somewhat unusual way of formulating the well-known Ramsey theorems). It is well-known that (1) log 2 log n < f (2) (n,0) < 2log 2 log n and more generally I. (2) c 1 (a) log n < f (2) (n,a) < c 2 (a)log n The lower bound in (1) and (2) is given by the probabilistic method (see P. Erdős and J. Spencer [11). Thus no great mysteries remain for the case r=2 (there are similar formulas for k>2). It would of course be very desirable to obtain an