Miracle-dz/UniFAR-FTUs huggingface dataset:
Lactonic Sophorolipids Increase Tumor Burden in Apcmin+/- Mice

Sophorolipids (SL) are amphiphilic biosurfactant molecules consisting of a disaccharide sophorose with one fatty acid at the C1 position and optional acetylation at the C6’and C6” positions. They exist in a closed ring lactonic (LSL) or open acidic (ASL) structure Sophorolipids are produced in crude mixtures in economically viable amounts by the yeast Starmerella bombicola and used in a variety of consumer products. Varying levels of anti- proliferative and anti-cancer activity of crude sophorolipid mixtures are described in a number of tumor cell lines in vitro. However, significant inter-study variation exists in the composition of sophorolipid species as well as other biologically active compounds in these mixtures, which makes interpretation of in vitro and in vivo studies difficult. We produced a 96% pure C18:1 lactonic sophorolipid that dose-dependently reduces the viability of colorectal cancer, as well as normal human colonic and lung cell lines in vitro. Oral administration of vehicle-only; or lactonic sophorolipids (50 mg/kg for 70 days), to Apcmin+/- mice resulted in an increase in the number (55.5 ± 3.3 vs 70.50 ± 7.8: p < 0.05) and size (modal size 2mm vs 4mm) of intestinal polyps. Lactonic administration resulted in a systematic effect via reduced hematocrit (49.5 ± 1.0 vs 28.2 ± 2.0 vs: p<0.03) and splenomegaly (0.56 ± 0.03g vs 0.71 ± 0.04g; p<0.01) confirming exacerbation of disease progression in this model.
Development of Eye Position Dependency of Slow Phase Velocity during Caloric Stimulation

The nystagmus in patients with vestibular disorders often has an eye position dependency, called Alexander’s law, where the slow phase velocity is higher with gaze in the fast phase direction compared with gaze in the slow phase direction. Alexander’s law has been hypothesized to arise either due to adaptive changes in the velocity-to-position neural integrator, or as a consequence of processing of the vestibular-ocular reflex. We tested whether Alexander’s law arises only as a consequence of non-physiologic vestibular stimulation. We measured the time course of the development of Alexander’s law in healthy humans with nystagmus caused by three types of caloric vestibular stimulation: cold (unilateral inhibition), warm (unilateral excitation), and simultaneous bilateral bithermal (one side cold, the other warm) stimulation, mimicking the normal push-pull pattern of vestibular stimulation. Alexander’s law, measured as a negative slope of the velocity versus position curve, was observed in all conditions. A reversed pattern of eye position dependency (positive slope) was found <10% of the time. The slope often changed with nystagmus velocity (cross-correlation of nystagmus speed and slope was significant in 50% of cases), and the average lag of the slope with the speed was not significantly different from zero. Our results do not support the hypothesis that Alexander’s law can only be observed with non-physiologic vestibular stimulation. Further, the rapid development of Alexander’s law, while possible for an adaptive mechanism, is nonetheless quite fast compared to most other ocular motor adaptations. These results suggest that Alexander’s law may not be a consequence of a true adaptive mechanism.
Testing probability distributions using conditional samples

We study a new framework for property testing of probability distributions, by considering distribution testing algorithms that have access to a conditional sampling oracle. * This is an oracle that takes as input a subset $S \subseteq [N]$ of the domain $[N]$ of the unknown probability distribution $D$ and returns a draw from the conditional probability distribution $D$ restricted to $S$. This new model allows considerable flexibility in the design of distribution testing algorithms; in particular, testing algorithms in this model can be adaptive. We study a wide range of natural distribution testing problems in this new framework and some of its variants, giving both upper and lower bounds on query complexity. These problems include testing whether $D$ is the uniform distribution $\mathcal{U}$; testing whether $D = D^\ast$ for an explicitly provided $D^\ast$; testing whether two unknown distributions $D_1$ and $D_2$ are equivalent; and estimating the variation distance between $D$ and the uniform distribution. At a high level our main finding is that the new "conditional sampling" framework we consider is a powerful one: while all the problems mentioned above have $\Omega(\sqrt{N})$ sample complexity in the standard model (and in some cases the complexity must be almost linear in $N$), we give $\mathrm{poly}(\log N, 1/\varepsilon)$-query algorithms (and in some cases $\mathrm{poly}(1/\varepsilon)$-query algorithms independent of $N$) for all these problems in our conditional sampling setting. *Independently from our work, Chakraborty et al. also considered this framework. We discuss their work in Subsection [1.4].
Named Entity Recognition for Telugu

This paper is about Named Entity Recognition (NER) for Telugu. Not much work has been done in NER for Indian languages in general and Telugu in particular. Adequate annotated corpora are not yet available in Telugu. We recognize that named entities are usually nouns. In this paper we therefore start with our experiments in building a CRF (Conditional Random Fields) based Noun Tagger. Trained on a manually tagged data of 13,425 words and tested on a test data set of 6,223 words, this Noun Tagger has given an F-Measure of about 92%. We then develop a rule based NER system for Telugu. Our focus is mainly on identifying person, place and organization names. A manually checked Named Entity tagged corpus of 72,157 words has been developed using this rule based tagger through bootstrapping. We have then developed a CRF based NER system for Telugu and tested it on several data sets from the Eenaadu and Andhra Prabha newspaper corpora developed by us here. Good performance has been obtained using the majority tag concept. We have obtained overall F-measures between 80% and 97% in various experiments.
Multi-class classification using support vector machines in decision tree architecture

A novel architecture of Support Vector Machine classifiers utilizing binary decision tree (SVM-DTA) for solving multiclass problems is proposed in this paper. A clustering algorithm was used to determine the hierarchy of binary decision subtasks performed by the SVM binary classifiers. The applied clustering model utilizes Mahalanobis distance measures at the kernel space for better consistency with the used SVM kernel. The proposed SVM based Binary Decision Tree architecture takes advantage of both the efficient computation of the decision tree architecture and the high classification accuracy of SVMs. The performance of the proposed SVM-DTA was estimated on a problem of recognition of handwritten digits and letters. The experiments were conducted with samples from Pendigit and Statlog databases of segmented digits and letters. The results of the experiments indicate that the proposed method is faster to be trained than the other methods. Also, due to its Log complexity, the proposed SVM-DTA is much faster than the widely used multi-class SVM methods like “one-against-one” and “one-against-all”, maintaining comparable accuracy. The experiments also showed that this method becomes more favorable as the number of classes in the recognition problem increases.
Effect of heparin on plasma fibrinopeptide A in patients with acute myocardial infarction.

The plasma level of fibrinopeptide A (fpA) was used as an index of thrombin action on fibrinogen in order to investigate the rates of fibrin formation and the effect of heparin on thrombin in patients with acute myocardial infarction. The fpA levels measured on admission in 19 patients with acute myocardial infarction ranged from 1.7 to 12.4 ng/ml and were elevated (greater than 2.5 ng/ml) in 16 patients. A loading dose of 5000 IU of heparin resulted in a significant decrease within 20 min of the mean fpA level (from 5.1 to 2.2 ng/ml; p less than .001) and in an fpA normalization in five of 16 patients. During the following continuous infusion of 20,000 IU of heparin per day, the mean fpA levels measured on day 0, 1, and 2 were 3.0, 3.2, and 3.4 ng/ml, respectively, with 16 of 46 fpA values within the normal range. In 10 additional patients, the effect of higher concentrations of heparin and the consequences of stopping heparin infusion were studied. An additional 5000 IU of heparin injected intravenously during continuous infusion of 20,000 IU of heparin per day resulted in a substantial decrease of the plasma fpA level in three of 10 measurements. The stopping of heparin infusion led to an impressive increase of the mean fpA level (from 3.1 to 12.9 ng/ml; p less than .001) within 2 hr. These data demonstrate increased fibrin formation in patients with acute myocardial infarction and neutralization of thrombin in vivo by heparin. (ABSTRACT TRUNCATED AT 250 WORDS)
Hungarian Corpus of Light Verb Constructions

The precise identification of light verb constructions is crucial for the successful functioning of several NLP applications. In order to facilitate the development of an algorithm that is capable of recognizing them, a manually annotated corpus of light verb constructions has been built for Hungarian. Basic annotation guidelines and statistical data on the corpus are also presented in the paper. It is also shown how applications in the fields of machine translation and information extraction can make use of such a corpus and an algorithm.
Differential Regulation of Granzyme and Perforin in Effector and Memory T Cells following Smallpox Immunization1

Primary immunization of healthy adults with vaccinia virus induces a local vesicle or “take” in the majority of vaccinees that previously has been shown to correlate with protection against smallpox. However, the immunologic mechanisms underlying this protective response in humans are not well characterized. We have studied human CD8+ T cells for the expression patterns of phenotypic markers and cytolytic effector molecules before and after primary smallpox immunization using nine-color polychromatic flow cytometry. One month after immunization, vaccinees developed vaccinia virus-specific CD8+ T cells with an effector cell phenotype containing both granzyme A and granzyme B. One year after immunization, we found a significant decrease in granzyme B containing cells and an increased memory cell phenotype in virus-specific CD8+ T cells. Perforin was rarely expressed directly ex vivo, but was highly expressed after Ag-specific activation in vitro. Together, these data suggest an important role for effector CD8+ T cells in controlling poxvirus infection, and have implications for our understanding of human CD8+ T cell differentiation.
Hierarchical Recurrent Attention Network for Response Generation

We study multi-turn response generation in chatbots where a response is generated according to a conversation context. Existing work has modeled the hierarchy of the context, but does not pay enough attention to the fact that words and utterances in the context are differentially important. As a result, they may lose important information in context and generate irrelevant responses. We propose a hierarchical recurrent attention network (HRAN) to model both the hierarchy and the importance variance in a unified framework. In HRAN, a hierarchical attention mechanism attends to important parts within and among utterances with word level attention and utterance level attention respectively.
Knowledge Representation and Reasoning on the Semantic Web : OWL

OWL is the ontology language recommended by the W3C. OWL is heavily based on the knowledge representation languages called Description Logic, which provide the basic representation features of OWL. OWL also includes facilities that integrate it into the mainstream of the Web, including use of IRIs as names, XML Schema datatypes, and ontologies as Web documents, which can then import other OWL ontologies over the Web. Because OWL is based on Description Logics, its constructs have a well defined meaning and there are tools that effectively perform inference within OWL, enabling the discovery of information that is not explicitly stated in OWL ontologies.
GOblet: a platform for Gene Ontology annotation of anonymous sequence data

GOblet is a comprehensive web server application providing the annotation of anonymous sequence data with Gene Ontology (GO) terms. It uses a variety of different protein databases (human, murines, invertebrates, plants, sp-trembl) and their respective GO mappings. The user selects the appropriate database and alignment threshold and thereafter submits single or multiple nucleotide or protein sequences. Results are shown in different ways, e.g. as survey statistics for the main GO categories for all sequences or as detailed results for each single sequence that has been submitted. In its newest version, GOblet allows the batch submission of sequences and provides an improved display of results with the aid of Java applets. All output data, together with the Java applet, are packed to a downloadable archive for local installation and analysis. GOblet can be accessed freely at http://goblet.molgen. mpg.de.
Minimizing cost of redundant sensor-systems with non-monotone and monotone search algorithms

This paper considers distributed multisensor applications, and finds redundant configurations that minimize system weight or cost while insuring system dependability. Given choices among different component types, fulfilling the system's operational requirements but having different dependability parameters and per item cost, two heuristics, one nonmonotone (tabu search) and one monotone (simulated annealing), are used to find configurations that minimize the chosen cost metric. The search is limited to a surface delimiting the solution space region fulfilling system dependability requirements. Experimental results are presented with cost savings of 20% compared with the least expensive system consisting of only one component type. A test case compares results from the two methods with an exhaustive search to verify that the heuristics provide reasonable solutions.
Towards a Strategic Multi-Country Model for Trade and Employment in the Baltics

The theoretical and empirical analysis in this paper is based on an extension of the familiar two-country Mundell-Fleming model to more than two countries and to the inclusion of country-specific labour market characteristics and wage price spirals. A first econometric estimation for the three Baltic economies (Estonia, Latvia and Lithuania) with a linearised and dynamic three-country version of this extension was already provided by Plasmans (1997) for the sample period 1991 I – 1995 IV.
Energy spectrum-based wavelet transform for non-intrusive demand monitoring and load identification

Though the wavelet transform coefficients (WTCs) contain plenty of information needed for turn-on/off transient signal identification of load events, adopting the WTCs directly has the drawbacks of taking a longer time and much memory for the identification process of non-intrusive load monitoring (NILM). To effectively reduce the number of features representing load turn-on/off transient signals, an energy spectrum of the WTCs in different scales calculated by the Parseval's Theorem are proposed and presented in this paper. The back-propagation (BP) classification system is then used for artificial neural network (ANN) constructions and load identifications. The high successful rates of load events recognition from both experiments and simulations have proved that the proposed algorithm is applicable in multiple load operations of non-intrusive demand monitoring applications.
CBR-Tagger: a case-based reasoning approach to the gene/protein mention problem

This work proposes a case-based classifier to tackle the gene/protein mention problem in biomedical literature. The so called gene mention problem consists of the recognition of gene and protein entities in scientific texts. A classification process aiming at deciding if a term is a gene mention or not is carried out for each word in the text. It is based on the selection of the best or most similar case in a base of known and unknown cases. The approach was evaluated on several datasets for different organisms and results show the suitability of this approach for the gene mention problem.
A tactile windowing system for blind users

Today's window systems present the information in a graphical and thereby a spatial manner making the text-only access of a standard Braille device insufficient to enable blind users an equivalent exploration of the data. In this paper we present the planar Braille Window System (BWS) designed for a tactile display consisting of a pin-matrix of 120 columns and 60 rows. The system is composed of six separate regions enabling the user to receive different types of information simultaneously. The content of the main region containing Braille windows can be shown in various manners (text-or graphics-based) through four different views. The interaction within our Braille Window System is implemented not only by keyboard shortcuts but also by the use of multitouch gestures. Therefore the user is able to interact directly on the touch-sensitive display. A study conducted with eight blind users has confirmed the concept of Braille windows, regions and views. Especially the gestural input for exploring details of the content offers new possibilities in interacting within a GUI.
Optimization of Test Cases using Fuzzy based Glowworm Swarm Algorithm

The objective of software engineering activity is to develop reliable software at a cost effective manner without compromising on its quality. But, software testing comprises of labour-intensive, confusing and error prone actions. To give cost-effective solutions for software test case optimization, issues like classification, minimization, selection, and prioritization are taken into consideration. Many researchers have analyzed about the fitness and optimization of test cases and have arrived at divergent results. This paper using Fuzzy Based Glowworm Swarm Algorithm, discusses about the glowworm, whose objective is to find the areas in the application with highest coverage and usage. This enables to detect failures at an earlier stage of Software Development. The proposed FGSO is a novel algorithm integrating fuzzy logic with the Glowworm Algorithm.This integration eliminates the drawbacks of all standard glowworm algorithms to a considerable degree. The proposed algorithm maximizes the convergence rate of a standard glowworm algorithm by enhancing the performance of its local search. The node with the highest usage that is to be covered by a given test case could be predetermined using this algorithm. Based on the proposed hybrid approach, an optimal result for test case execution is obtained. The performance of the proposed method is evaluated and is compared with other optimization techniques such as ACO, PSO, ABC and PSABC.
Strategic Planning and Vehicle Routing Algorithm for Newspaper Delivery Problem : Case study of Morning Newspaper , Bangkok , Thailand

of its variants, the vehicle routing problem with Time window (VRPTW) is studied. In the vehicle routing problem, a set of vehicles with limited capacity, are to be routed from a central depot to a set of geographically dispersed customers with known demands and predefined time windows. The VRPTW is NP-hard and best solved to optimum or near optimum is heuristic, so to solve this NP-hard vehicle problem Sweep Algorithm is developed. Our research aims to examine distribution problem of a morning newspaper and to minimize the total cost without violating the capacity and time window constraints. The problems consist of vehicle departure times, the routing of vehicles, the location of distribution centers and drop-off points and allocating drop-off points to distribution centers. Results provide evidence of the dramatic impact on costs achieved by developing routing and dispatching schedule simultaneously.
Adaptive multi-stage distance join processing

A spatial distance join is a relatively new type of operation introduced for spatial and multimedia database applications. Additional requirements for ranking and stopping cardinality are often combined with the spatial distance join in on-line query processing or internet search environments. These requirements pose new challenges as well as opportunities for more efficient processing of spatial distance join queries. In this paper, we first present an efficient k-distance join algorithm that uses spatial indexes such as R-trees. Bi-directional node expansion and plane-sweeping techniques are used for fast pruning of distant pairs, and the plane-sweeping is further optimized by novel strategies for selecting a sweeping axis and direction. Furthermore, we propose adaptive multi-stage algorithms for k-distance join and incremental distance join operations. Our performance study shows that the proposed adaptive multi-stage algorithms outperform previous work by up to an order of magnitude for both k-distance join and incremental distance join queries, under various operational conditions.
iPIXSAR: incremental clustering of indexed XML data

XML is one of the primary encoding schemes for data and knowledge. We investigate incremental physical data clustering for native XML data. We view the XML clustering problem as a sibling-augmented tree partitioning problem. In previous work we suggested an incremental algorithm that, based on workload, adjusts the storage structure so as to minimize page faults [24]. However, in addition to the XML augmented tree, there are also indices. The kind of index we consider is based on a XPath expression and it consists of index entries pointing to XML target nodes. Using such index entries one "jumps" directly to target nodes. Often, target XML nodes are accessed in temporal proximity and hence, for paging reasons, it is beneficial to store them on the same disk page. In other cases, such temporal proximity is absent and hence co-storing is not optimal. Designing an algorithm that views the XML data and indices as a sibling augmented tree with multiple roots (the additional roots correspond to indices) is complex. In this work we propose an extension to the PIXSAR algorithm, called iPIXSAR, which extends PIXSAR so as to make storing decisions of target XML nodes based on possible membership in more than one tree. We use an experimental data clustering system that includes a disk and File System simulator. Instead of implementing a query processor, we "record" logs of Saxon runs and emulate them in our system. To make experimentation feasible, we constructed a disk, simulator which operates in main memory. In [24], we compared PIXSAR to DFS (an efficient static storage scheme), on a simulated and on a real physical disk. We found that PIXSAR results are better than DFS by 20% to 50% on a simulated disk and usually by 60% on a real disk. In this work we experimentally show that in the presence of indices iPIXSAR is superior to PIXSAR by up to 8%.
Performance Diagnosis for Inefficient Loops

Writing efficient software is difficult. Design and implementation defects cancause severe performance degradation. Unfortunately, existing performance diagnosis techniques like profilers are still preliminary. They can locate code regions that consume resources, but not the ones that waste resources. In this paper, we first design a root-causeand fix-strategy taxonomy for inefficient loops, one of the most common performance problems in the field. We then design a static-dynamic hybrid analysis tool, LDoctor, toprovide accurate performance diagnosis for loops. We further use sampling techniques to lower the run-time overhead withoutdegrading the accuracy or latency of LDoctor diagnosis. Evaluation using real-world performanceproblems shows that LDoctor can provide better coverage and accuracy thanexisting techniques, with low overhead.