Miracle-dz/UniFAR-FTUs huggingface dataset:
Dynamic Cutoff Prediction in Multi-Stage Retrieval Systems

Modern multi-stage retrieval systems are comprised of a candidate generation stage followed by one or more reranking stages. In such an architecture, the quality of the final ranked list may not be sensitive to the quality of the initial candidate pool, especially in terms of early precision. This provides several opportunities to increase retrieval efficiency without significantly sacrificing effectiveness. In this paper, we explore a new approach to dynamically predicting the size of an initial result set in the candidate generation stage, which can directly affect the overall efficiency and effectiveness of the entire system. Previous work exploring this tradeoff has focused on global parameter settings that apply to all queries, even though optimal settings vary across queries. In contrast, we propose a technique that makes a parameter prediction to maximize efficiency within an effectiveness envelope on a per query basis, using only static pre-retrieval features. Experimental results show that substantial efficiency gains are achievable. In addition, our framework provides a versatile tool that can be used to estimate the effectiveness-efficiency tradeoffs that are possible before selecting and tuning algorithms to make machine-learned predictions.
Glucose induces lipolytic cleavage of a glycolipidic plasma membrane anchor in yeast

In the yeast Saccharomyces cerevisiae an amphiphilic cAMP-binding protein has been found recently to be anchored to plasma membranes by virtue of a glycolipid structure (Muller and Bandlow, 1991a, 1992). The cAMP-binding parameters of this protein are affected by the lipolytic removal of the glycosylphosphatidylinositol (GPI) membrane anchor by exogenous (G)PI-specific phospholipases C or D (PLC or PLD) (Muller and Bandlow, 1993) suggesting a regulatory role of glycolipidic membrane anchorage. Here we report that transfer of yeast cells from lactate to glucose medium results in the conversion of the amphiphilic form of the cAMP receptor protein into a hydrophilic version accompanied by the rapid loss of fatty acids from the GPI anchor of the [14C]palmitic acid- labeled protein. Analysis of the cleavage site identifies [14C]inositol phosphate as the major product after treatment of the soluble, [14C]inositol-labeled protein with nitrous acid which destroys the glucosamine constituent of the anchor. Together with the observed cross- reactivity of the hydrophilic fragment with antibodies directed against the cross-reacting determinant of soluble trypanosomal variable surface glycoproteins (i.e., myo-inositol-1,2-cyclic phosphate) this demonstrates that, in membrane release, the initial cleavage event is catalyzed by an intrinsic GPI-PLC activated upon transfer of cells to glucose medium. Release from the plasma membrane in soluble form requires, in addition, the presence of high salt or alpha-methyl mannopyranoside, or the removal of the carbohydrate moieties, because otherwise the protein remains associated with the membrane presumably at least in part via its N-glycosidic carbohydrate side chains. The data point to the possibility that cleavage of the anchor could play a role in the transfer of the signal for the nutritional situation to the interior of the cell.
BLM SUMOylation regulates ssDNA accumulation at stalled replication forks

Polymerase stalling results in uncoupling of DNA polymerase and the replicative helicase, which generates single-stranded DNA (ssDNA). After stalling, RAD51 accumulates at stalled replication forks to stabilize the fork and to repair by homologous recombination (HR) double-strand breaks (DSBs) that accumulate there. We showed recently that SUMO modification of the BLM helicase is required in order for RAD51 to accumulate at stalled forks. In order to investigate how BLM SUMOylation controls RAD51 accumulation, we characterized the function of HR proteins and ssDNA-binding protein RPA in cells that stably expressed either normal BLM (BLM+) or SUMO-mutant BLM (SM-BLM). In HU-treated SM-BLM cells, mediators BRCA2 and RAD52, which normally substitute RAD51 for RPA on ssDNA, failed to accumulate normally at stalled forks; instead, excess RPA accumulated. SM-BLM cells also exhibited higher levels of HU-induced chromatin-bound RPA than BLM+ cells did. The excess RPA did not result from excessive intrinsic BLM helicase activity, because in vitro SUMOylated BLM unwound similar amounts of replication-fork substrate as unSUMOylated BLM. Nor did BLM SUMOylation inhibit binding of RPA to BLM in vitro; however, in immunoprecipitation experiments, more BLM-RPA complex formed in HU-treated SM-BLM cells, indicating that BLM SUMOylation controls the amount of BLM-RPA complex normally formed at stalled forks. Together, these results showed that BLM SUMOylation regulates the amount of ssDNA that accumulates during polymerase stalling. We conclude that BLM SUMOylation functions as a licensing mechanism that permits and regulates HR at damaged replication forks.
Design decision rationale: experiences and steps ahead towards systematic use

Design decisions crucially influence the success of every software project. While the resulting design is typically documented quite well, the situation is usually different for the underlying rationale and decision-making process. Despite being recognized as a helpful approach in general, the explicit documentation of Design Decision Rationale (DDR) is not yet largely utilized due to some inhibitors (e.g., additional documentation effort). Experience with other qualities, e.g. software reusability, evidently shows that an improvement of these qualities only pays off on a large scale and therefore has to be pursued in a strategic, pre-planned, and carefully focused way. In this paper we argue that this also has to be considered for documenting DDR. To this end the paper presents: (i) the Decision, Goal, and Alternatives (DGA) DDR framework, (ii) experience in dealing with DGA, (iii) motivators and inhibitors of using DDR, and (iv) an approach for systematic DDR use that follows value-based software engineering principles.
Association of healthcare expenditures with aggressive versus palliative care for cancer patients at the end of life: a cross-sectional study using claims data in Japan.

BACKGROUND End-of-life (EOL) care imposes heavy economic burdens on patients and health insurers. Little is known about the association between the types of EOL care and healthcare costs for cancer patients across various providers. OBJECTIVE To explore the association of healthcare expenditures with benchmarking indicators of aggressive versus palliative care among terminally ill cancer patients, from the perspective of health insurers. DESIGN Cross-sectional retrospective study using health insurance claims data. SETTING participants Cancer patients who had died in Kyoto prefecture, Japan, between April 2009 and May 2010. Main outcome measure Claims data were analyzed using multilevel generalized linear models to examine whether aggressive care and palliative care were associated with expenditures during the last 3 months of life, after adjusting for patient characteristics, hospital characteristics and other non-indicator procedures. RESULTS We analyzed 3143 decedents from 54 hospitals. Median expenditure per patient during the last 3 months was US$13 030. Higher expenditures were associated with the aggressive care indicators of higher mortality at acute-care hospitals and use of chemotherapy in the last month of life, as well as with the palliative care indicators of increased hospice care and opioid use in the last 3 months of life. However, increased physician home care in the last 3 months was associated with lower expenditure. CONCLUSIONS Indicators of both aggressive and palliative EOL care were associated with higher healthcare expenditures. These results may support the coherent development of measures to optimize aggressive care and reduce the financial burdens of terminal cancer care.
STELLAR METALLICITY OF THE EXTENDED DISK AND DISTANCE OF THE SPIRAL GALAXY NGC 3621

Low resolution (∼4.5 Å) ESO VLT/FORS spectra of blue supergiant stars are analyzed to determine stellar metallicities (based on elements such as iron, titanium, and magnesium) in the extended disk of the spiral galaxy, NGC 3621. Mildly subsolar metallicity (−0.30 dex) is found for the outer objects beyond 7 kpc, independent of galactocentric radius and compatible with the absence of a metallicity gradient, confirming the results of a recent investigation of interstellar medium H ii region gas oxygen abundances. The stellar metallicities are slightly higher than those from the H ii regions when based on measurements of the weak forbidden auroral oxygen line at 4363 Å but lower than the ones obtained with the R23 strong line method. It is shown that the present level of metallicity in the extended disk cannot be the result of chemical evolution over the age of the disk with the present rate of in situ star formation. Additional mechanisms must be involved. In addition to metallicity, stellar effective temperatures, gravities, interstellar reddening, and bolometric magnitudes are determined. After the application of individual reddening corrections for each target, the flux-weighted gravity–luminosity relationship of blue supergiant stars is used to obtain a distance modulus of 29.07 ± 0.09 mag (distance D = 6.52 ± 0.28 Mpc). This new distance is discussed in relation to Cepheid and the tip of the red giant branch distances.
Pipeline Implementation of New Segmentation Based on Cognate Neighborhood Approach

In this paper, we cast the segmentation problem as the maximization of cognate information between the row and column pairs of the neighborhood. For this, the maximum and minimum gradient on pairs of row and column of the local neighborhood are calculated and applied on the original image. The maximum and minimum row and column pair of the local neighborhood forms the contribution of primitive morphological operations, dilation and erosion respectively. For extracting strong edges edge increase and edge decrease are applied on local neighborhood. The final segmentation is obtained after applying the above preprocessing steps by using a new approach of cognate neighborhood. To test the above process of segmentation the method is applied on brodatz textures, leena and brain MRI images, which resulted a good segmentation.
The Lasserre Hierarchy in Almost Diagonal Form

The Lasserre hierarchy is a systematic procedure for constructing a sequence of increasingly tight relaxations that capture the convex formulations used in the best available approximation algorithms for a wide variety of optimization problems. Despite the increasing interest, there are very few techniques for analyzing Lasserre integrality gaps. Satisfying the positive semi-definite requirement is one of the major hurdles to constructing Lasserre gap examples. We present a novel characterization of the Lasserre hierarchy based on moment matrices that differ from diagonal ones by matrices of rank one (almost diagonal form). We provide a modular recipe to obtain positive semi-definite feasibility conditions by iteratively diagonalizing rank one matrices. Using this, we prove strong lower bounds on integrality gaps of Lasserre hierarchy for two basic capacitated covering problems. For the min-knapsack problem, we show that the integrality gap remains arbitrarily large even at level $n-1$ of Lasserre hierarchy. For the min-sum of tardy jobs scheduling problem, we show that the integrality gap is unbounded at level $\Omega(\sqrt{n})$ (even when the objective function is integrated as a constraint). These bounds are interesting on their own, since both problems admit FPTAS.
Decoupling the CGAL 3D Triangulations from the Underlying Space

The Computational Geometry Algorithms Library gal currently provides packages to compute triangulations in R2 and R3. In this paper we describe a new design for the 3D triangulation package that permits to easily add functionality to compute triangulations in other spaces. These design changes have been implemented, and validated on the case of the periodic space T3. We give a detailed description of the realized changes together with their motivation. Finally, we show benchmarks to prove that the new design does not affect the efficiency.
Retroactive ordering heuristics for Asynchronous Backtracking on DisCSPs ?

A new type of ordering heuristics for dynamic ordering asynchronous backtracking( ABT DO ) on DisCSPs is presented. Agents can be moved to a position that is higher than that of the target of the backtrack ( Nogood ). This new type of heuristics do not follow the restrictions on the heuristics of previous versions of ABT DO . The ﬂexibility of the new type of heuristics is dependent on the size of additional Nogood storage agents are allowed to keep. This size is deﬁned by a parameter k which limits the size of stored Nogoods . The performance of the retroactive ordered ABT is found to be worse when larger Nogood storage is used. The best performing version is the one that uses a speciﬁc min-domain heuristic with no additional storage of Nogoods .
A wireless-sensor-network energy-efficient medium-access-control protocol with overhearing avoidance

This paper proposes an energy-efficient medium-accesscontrol (MAC) protocol for wireless sensor networks with aggressive energy saving without throughput degradation. The proposal provides a throughput and energy-aware solution to the unproductive consumption of energy caused by idle listening and overhearing. The proposed contention-based approach dynamically sets up sleeping and waking time to reduce idle listening and puts a sensor to “nap” to avoid overhearing. The inter-node communication is supported by a synchronization mechanism inherited from the proposed scheme. This built-in synchronization, in turn, serves as an environment friendly to scalability and adaptability. Simulation experiments demonstrate the effectiveness of this throughput-aware approach to solving the energy problem in MAC for wireless sensor networks.
An Efficient Adaptive Deadlock-Free Routing Algorithm for Torus Networks

A deadlock-free minimal routing algorithm called clue is first proposed for VCT (virtual cut-through)-switched tori. Only two virtual channels are required. One channel is applied in the deadlock-free routing algorithm for the mesh subnetwork based on a known base routing scheme, such as, negative-first or dimension-order routing. The other channel is similar to an adaptive channel. This combination presents a novel fully adaptive minimal routing scheme because the first channel does not supply routing paths for every source-destination pair. Other two algorithms named flow controlled clue and wormhole clue are proposed. Flow controlled clue is proposed for VCT-switched tori, which is fully adaptive minimal deadlock-free with no virtual channel. Each input port requires at least two buffers, each of which is able to keep a packet. A simple but well-designed flow control function is used in the proposed flow controlled clue routing algorithm to avoid deadlocks. Wormhole clue is proposed for wormhole-switched tori. It is partially adaptive because we add some constraints to the adaptive channels for deadlock avoidance. It is shown that clue and flow controlled clue work better than the bubble flow control scheme under several popular traffic patterns in 3-dimensional (3D) torus. In a wormhole-switched tori, the advantage of wormhole clue over Duato's protocol is also very apparent.
Using Business Intelligence to Analyze and Share Health System Infrastructure Data in a Rural Health Authority

Background Health care organizations gather large volumes of data, which has been traditionally stored in legacy formats making it difficult to analyze or use effectively. Though recent government-funded initiatives have improved the situation, the quality of most existing data is poor, suffers from inconsistencies, and lacks integrity. Generating reports from such data is generally not considered feasible due to extensive labor, lack of reliability, and time constraints. Advanced data analytics is one way of extracting useful information from such data. Objective The intent of this study was to propose how Business Intelligence (BI) techniques can be applied to health system infrastructure data in order to make this information more accessible and comprehensible for a broader group of people. Methods An integration process was developed to cleanse and integrate data from disparate sources into a data warehouse. An Online Analytical Processing (OLAP) cube was then built to allow slicing along multiple dimensions determined by various key performance indicators (KPIs), representing population and patient profiles, case mix groups, and healthy community indicators. The use of mapping tools, customized shape files, and embedded objects further augment the navigation. Finally, Web forms provide a mechanism for remote uploading of data and transparent processing of the cube. For privileged information, access controls were implemented. Results Data visualization has eliminated tedious analysis through legacy reports and provided a mechanism for optimally aligning resources with needs. Stakeholders are able to visualize KPIs on a main dashboard, slice-and-dice data, generate ad hoc reports, and quickly find the desired information. In addition, comparison, availability, and service level reports can also be generated on demand. All reports can be drilled down for navigation at a finer granularity. Conclusions We have demonstrated how BI techniques and tools can be used in the health care environment to make informed decisions with reference to resource allocation and enhancement of the quality of patient care. The data can be uploaded immediately upon collection, thus keeping reports current. The modular design can be expanded to add new datasets such as for smoking rates, teen pregnancies, human immunodeficiency virus (HIV) rates, immunization coverage, and vital statistical summaries.
Reintroduction of English as Medium of Instruction in Sri Lanka: With Special Reference to Jaffna

English is now introduced in grade 6 once the children finish their primary education in vernacular. The main aspect of the way in which English is used as medium of instruction is that in government schools some subjects are taught in English while some other subjects are taught in Tamil. This helps the students be balanced bilinguals because they constantly learn their first language while acquiring mastery in L2. Eventually, it is assumed that this will facilitate the additive bilingualism and children would be balanced bilinguals. This paper tried to appraise the potential success of one decade –long English medium program and also see the different perspectives of the stakeholders involved in the English medium education. This study captured the views of parents, teachers and children: English medium and vernacular children.
Improving the Efficiency of a Color-based Image Retrieval System

This is to certify that I have examined this copy of a master's thesis by Anand Sivaraman and have found that it is complete and satisfactory in all respects, and that any and all revisions required by the final examining committee have been made. ABSTRACT Content-based image retrieval systems retrieve images from a database that are
A review of recent developments in Simultaneous Localization and Mapping

Simultaneous Localization and Mapping (SLAM) problem has been an active area of research in robotics for more than a decade. Many fundamental and practical aspects of SLAM have been addressed and some impressive practical solutions have been demonstrated. The aim of this paper is to provide a review of the current state of the research on feature based SLAM, in particular to examine the current understanding of the fundamental properties of the SLAM problem and associated issues with the view to consolidate recent achievements.
A selective incremental approach for transductive nearest neighbor classification

Transductive learning is a special case of semi-supervised learning, where class labels to the test patterns alone are found. That is, the domain of the learner is the test set alone. Often, transductive learners achieve a better classification accuracy, since additional information in the form of test patterns location in the feature-space is used. For several inductive learners, there exists corresponding transductive learners; like for SVMs there exists transductive SVMs (TSVMs). For nearest neighbor based classifiers, their corresponding transductive methods are achieved through graph mincuts or spectral graph mincuts. It is shown that these solutions achieve low leave-one-out cross-validation (LOOCV) error with respect to nearest neighbor based classifiers. It is formally shown in the paper that, through a clustering method, it is possible to get various solutions having zero LOOCV error with respect to nearest neighbor based classifiers. Some solutions can have low classification accuracy. The paper proposes, instead of optimizing LOOCV error, to optimize a margin like criterion. This criterion is based on the observation that similar labeled patterns should be nearer to each other, while dissimilar labeled patterns should be far away. An approximate method to solve the proposed optimization problem is given in the paper which is called selective incremental transductive nearest neighbor classifier (SI-TNNC). SI-TNNC finds the test pattern from the test set which is very close to one class of training patterns and at the same time very much away from the other class of training examples. The selected test pattern is given its nearest neighbor's label and is added to the training set. This pattern is removed from the test set. The process is repeated with the next best test pattern, and is stopped only when the test set becomes empty. An algorithm to implement SI-TNNC method is given in the paper which has a quadratic time complexity. Other related solutions have either cubic time complexity or are NP-hard. Experimentally, using several standard data-sets, it is shown that the proposed transductive learner achieves on-par or better classification accuracy than its related competitors.
Using Citizen Science Data to Model the Distributions of Common Songbirds of Turkey Under Different Global Climatic Change Scenarios

In this study, we evaluated the potential impact of climate change on the distributions of Turkey’s songbirds in the 21st century by modelling future distributions of 20 resident and nine migratory species under two global climate change scenarios. We combined verified data from an ornithological citizen science initiative (www.kusbank.org) with maximum entropy modeling and eight bioclimatic variables to estimate species distributions and projections for future time periods. Model predictions for resident and migratory species showed high variability, with some species projected to lose and others projected to gain suitable habitat. Our study helps improve the understanding of the current and potential future distributions of Turkey’s songbirds and their responses to climate change, highlights effective strategies to maximize avian conservation efforts in the study region, and provides a model for using citizen science data for biodiversity research in a large developing country with few professional field biologists. Our results demonstrate that climate change will not affect every species equally in Turkey. Expected range reductions in some breeding species will increase the risk of local extinction, whereas others are likely to expand their ranges.