The excited nuclear states of 125Te were populated in a fusion-evaporation reaction using a target of 124Sn and alpha beam at 31 and 35 MeV energies. All the gamma-gamma coincidences were recorded using the Indian National Gamma Array at VECC, India, and processed by a digital data acquisition system. A total of 92 new gamma rays were identified and placed within the level scheme. Six new structures, three of positive parity and three with negative parity, have been identified. Previously observed level scheme has been verified and extended up to approximate to 5.7 MeV. The results were interpreted in the framework of the particle rotor model and possible configurations were assigned to the structures.

The yrast and nonyrast states of 126 Te were populated in a fusion -evaporation reaction using a target of 124 Sn and alpha beam at 31 MeV energy. All the gamma - gamma coincidences were recorded using the Indian National Gamma Array at the Variable Energy Cyclotron Centre, India, and processed by a digital data -acquisition system. The level scheme was enriched with the addition of 65 new transitions and the identification of five new structures. Spin and parity assignments were made and the subsequent results were interpreted in the framework of the nuclear shell model using the large-scale shell -model code ANTOINE .

Atreyee Dey1,∗ Anwesha Basu, A.K Singh, S.Nag, G. Mukherjee , S.Bhattacharyya, R. Banik, S. Nandi, S. Bhattacharya, R. Raut, S. S. Ghugre, S. Das, S. Samanta, S. Chatterjee, A.Goswami †, S. Ali, H. Pai, and S. Rajbanshi Indian Institute of Technology, Kharagpur-721302, India Indian Institute of Technology(BHU), Varanasi-221005, India Variable Energy Cyclotron Centre, Kolkata700064, India 4 UGC-DAE CSR, Kolkata Centre, Kolkata700098, India Saha Institute of Nuclear Physics, Kolkata700064, India and Presidency University, Kolkata-700073, India

Background: The influence of shell effect on the dynamics of the fusion fission process and its evolution with excitation energy in the preactinide Hg-Pb region in general is a matter of intense research in recent years. In particular, a strong ambiguity remains for the neutron shell closed Po-210 nucleus regarding the role of shell effect in fission around approximate to 30-40 MeV of excitation energy. Purpose: We havemeasured the fission fragment mass distribution of Po-210 populated using fusion of He-4 + Pb-206 at different excitation energies and compare the result with recent theoretical predictions as well as with our previous measurement for the same nucleus populated through a different entrance channel. Mass distribution in the fission of the neighboring nuclei At-213 is also studied for comparison. Methods: Two large area multiwire proportional counters (MWPC) were used for complete kinematical measurement of the coincident fission fragments. The time of flight differences of the coincident fission fragments were used to directly extract the fission fragment mass distributions. Results: The measured fragment mass distribution for the reactions He-4 + Pb-206 and He-4 + Bi-209 were symmetric and the width of the mass distributions were found to increase monotonically with excitation energy above 36.7 MeV and 32.9 MeV, respectively, indicating the absence of shell effects at the saddle. However, in the fission of Po-210, we find minor deviation from symmetric mass distributions at the lowest excitation energy (30.8 MeV). Conclusion: Persistence of shell effect in fission fragment mass distribution of Po-210 was observed at the excitation energy approximate to 31 MeV as predicted by the theory; at higher excitation energy, however, the present study reaffirms the absence of any shell correction in the fission of Po-210.

We address the problem of mining interesting phrases from subsets of a text corpus where the subset is specied using a set of features such as keywords that form a query. Previous algorithms for the problem have proposed solutions that involve sifting through a phrase dictionary based index or a document-based index where the solution is linear in either the phrase dictionary size or the size of the document subset. We propose the usage of an independence assumption between query keywords given the top correlated phrases, wherein the pre-processing could be reduced to discovering phrases from among the top phrases per each feature in the query. We then outline an indexing mechanism where per-keyword phrase lists are stored either in disk or memory, so that popular aggregation algorithms such as No Random Access and Sort-merge Join may be adapted to do the scoring at real-time to identify the top interesting phrases. Though such an approach is expected to be approximate, we empirically illustrate that very high accuracies (of over 90%) are achieved against the results of exact algorithms. Due to the simplied list-aggregation, we are also able to provide response times that are orders of magnitude better than state-of-the-art algorithms. Interestingly, our disk-based approach outperforms the in-memory baselines by up to hundred times and sometimes more, conrming the superiority of the proposed method.

Master data management (MDM) integrates data from multiple structured data sources and builds a consolidated 360-degree view of business entities such as customers and products. Today's MDM systems are not prepared to integrate information from unstructured data sources, such as news reports, emails, call-center transcripts, and chat logs. However, those unstructured data sources may contain valuable information about the same entities known to MDM from the structured data sources. Integrating information from unstructured data into MDM is challenging as textual references to existing MDM entities are often incomplete and imprecise and the additional entity information extracted from text should not impact the trustworthiness of MDM data. In this paper, we present an architecture for making MDM text-aware and showcase its implementation as IBM Info-Sphere MDM Extension for Unstructured Text Correlation, an add-on to IBM InfoSphere Master Data Management Standard Edition. We highlight how MDM benefits from additional evidence found in documents when doing entity resolution and relationship discovery. We experimentally demonstrate the feasibility of integrating information from unstructured data sources into MDM.

Predicate selectivity estimates are subject to considerable run-time variation relative to their compile-time estimates, often leading to poor plan choices that cause inflated response times. We present here a parametrized family of plan generation and selection algorithms that replace, whenever feasible, the optimizer's solely cost-conscious choice with an alternative plan that is (a) guaranteed to be near-optimal in the absence of selectivity estimation errors, and (b) likely to deliver comparatively stable performance in the presence of arbitrary errors. These algorithms have been implemented within the PostgreSQL optimizer, and their performance evaluated on a rich spectrum of TPC-H and TPC-DS-based query templates in a variety of database environments. Our experimental results indicate that it is indeed possible to identify robust plan choices that substantially curtail the adverse effects of erroneous selectivity estimates. In fact, the plan selection quality provided by our algorithms is often competitive with those obtained through apriori knowledge of the plan search and optimality spaces. The additional computational overheads incurred by the replacement approach are miniscule in comparison to the expected savings in query execution times. We also demonstrate that with appropriate parameter choices, it is feasible to directly produce anorexic plan diagrams, a potent objective in query optimizer design.

Modern query optimizers choose their execution plans prima rily on a cost-minimization basis, assuming that the inputs to the costing process, such as relational selectivities, are accurate. However, in practice, these inputs are subject to considera ble run-time variation relative to their compile-time estimates, often leading to poor plan choices that cause inflated response times. We present in this paper a parametrized family of online plan ge eration and selection algorithms that substitute, whenever feasible, the optimizer’ s solely cost-conscious choice with an alternative plan that is (a) guaranteed to be near-optimal in the a bs nce of selectivity estimation errors, and (b) likely to deliver comparatively stable performance in the presence of arbitrary errors. The proposed algorithms have been implemented within the Postg reSQL optimizer, and their performance evaluated on a rich spectrum of TPC-H and TPC-DS-based query templates in a variety of database environments. Our experimental results indicate th it is indeed possible to identify robust plan choices that substantially curtail the adverse effect s of erroneous selectivity estimates. In fact, the plan selection quality provided by our online algorithm s is often competitive with those obtained through apriori knowledge of the plan search and optimality spaces. Further, the additional optimization overheads incurred by our algorithms are miniscul e in comparison to the expected savings in query execution times. Finally, we also demonstrate that with appropriate parameter choices, it is feasible to directly produce anorexic plan diagrams, a po tent objective in query optimizer design.

Given a parametrized n-dimensional SQL query template and a choice of query optimizer, a plan diagram is a color-coded pictorial enumeration of the execu tion plan choices of the optimizer over the query parameter space. These diagrams have proved to be a powerful metaphor for the analysis and redesign of modern optimizers, and are gaining currency in diverse industrial and academic institutions. However, their utility is adversely impacte d by the impractically large computational overheads incurred when standard brute-force exhaustive a ppro ches are used for producing highdimension and high-resolution diagrams. In this paper, we investigate strategies for efficiently pro ducing high-quality approximate plan diagrams that have low plan-identity and plan-location err ors. Our techniques are customized to the features available in the optimizer’s API, ranging from the generic optimizers that provide only the optimal plan for a query to those which also support costing o f sub-optimal plans and enumerating rank-ordered lists of plans. The techniques collectively f ature both random and grid sampling, as well as interpolation techniques based on kNN classifiers , p arametric query optimization and plan cost monotonicity. Extensive experimentation with a representative set of TPC -H and TPC-DS-based query templates on industrial-strength optimizers indicates that o ur techniques are capable of meeting identity and location error bounds as low as 10% while incurring less t han 15% of the computational overheads of the exhaustive approach. In fact, for full-feature d optimizers, we can guarantee zero error with overheads of less than 10%. These approximation techni ques have been implemented in the publicly available Picasso optimizer visualization tool.

Given a parametrized n-dimensional SQL query template and a choice of query optimizer, a plan diagram is a color-coded pictorial enumeration of the execution plan choices of the optimizer over the query parameter space. These diagrams have proved to be a powerful metaphor for the analysis and redesign of modern optimizers, and are gaining currency in diverse industrial and academic institutions. However, their utility is adversely impacted by the impractically large computational overheads incurred when standard bruteforce exhaustive approaches are used for producing fine-grained diagrams on high-dimensional query templates.In this paper, we investigate strategies for efficiently producing close approximations to complex plan diagrams. Our techniques are customized to the features available in the optimizer's API, ranging from the generic optimizers that provide only the optimal plan for a query, to those that also support costing of sub-optimal plans and enumerating rank-ordered lists of plans. The techniques collectively feature both random and grid sampling, as well as inference techniques based on nearest-neighbor classifiers, parametric query optimization and plan cost monotonicity.Extensive experimentation with a representative set of TPC-H and TPC-DS-based query templates on industrial-strength optimizers indicates that our techniques are capable of delivering 90% accurate diagrams while incurring less than 15% of the computational overheads of the exhaustive approach. In fact, for full-featured optimizers, we can guarantee zero error with less than 10% overheads. These approximation techniques have been implemented in the publicly available Picasso optimizer visualization tool.