Generalized h-index for Disclosing Latent Facts in Citation Networks

What is the value of a scientist and its impact upon the scientific thinking? How can we measure the prestige of a journal or of a conference? The evaluation of the scientific work of a scientist and the estimation of the quality of a journal or conference has long attracted significant interest, due to the benefits from obtaining an unbiased and fair criterion. Although it appears to be simple, defining a quality metric is not an easy task. To overcome the disadvantages of the present metrics used for ranking scientists and journals, J.E. Hirsch proposed a pioneering metric, the now famous h-index. In this article, we demonstrate several inefficiencies of this index and develop a pair of generalizations and effective variants of it to deal with scientist ranking and with publication forum ranking. The new citation indices are able to disclose trendsetters in scientific research, as well as researchers that constantly shape their field with their influential work, no matter how old they are. We exhibit the effectiveness and the benefits of the new indices to unfold the full potential of the h-index, with extensive experimental results obtained from DBLP, a widely known on-line digital library.Comment: 19 pages, 17 tables, 27 figure

STELAR: Spatio-temporal Tensor Factorization with Latent Epidemiological Regularization

Accurate prediction of the transmission of epidemic diseases such as COVID-19 is crucial for implementing effective mitigation measures. In this work, we develop a tensor method to predict the evolution of epidemic trends for many regions simultaneously. We construct a 3-way spatio-temporal tensor (location, attribute, time) of case counts and propose a nonnegative tensor factorization with latent epidemiological model regularization named STELAR. Unlike standard tensor factorization methods which cannot predict slabs ahead, STELAR enables long-term prediction by incorporating latent temporal regularization through a system of discrete-time difference equations of a widely adopted epidemiological model. We use latent instead of location/attribute-level epidemiological dynamics to capture common epidemic profile sub-types and improve collaborative learning and prediction. We conduct experiments using both county- and state-level COVID-19 data and show that our model can identify interesting latent patterns of the epidemic. Finally, we evaluate the predictive ability of our method and show superior performance compared to the baselines, achieving up to 21% lower root mean square error and 25% lower mean absolute error for county-level prediction.Comment: AAAI 202

First results on the performance of the CMS global calorimeter trigger

The CMS Global Calorimeter Trigger (GCT) uses data from the CMS calorimeters to compute a number kinematical quantities which characterize the LHC event. The GTC output is used by the Global Trigger (GT) along with data from the Global Muon Trigger (GMT) to produce the Level-1 Accept (L1A) decision. The design for the current GCT system commenced early in 2006. After a rapid development phase all the different GCT components have been produced and a large fraction of them have been installed at the CMS electronics cavern (USC-55). There the GCT system has been under test since March 2007. This paper reports results from tests which took place at the USC-55. Initial tests aimed to test the integrity of the GCT data and establish that the proper synchronization had been achieved both internally within GCT as well as with the Regional Calorimeter Trigger (RCT) which provides the GCT input data and with GT which receives the GCT results. After synchronization and data integrity had been established, Monte Carlo Events with electrons in the final state were injected at the GCT inputs and were propagated to the GCT outputs. The GCT output was compared with the predictions of the GCT emulator model in the CMS Monte Carlo and were found to be identical

The Hypanis fluvial deltaic system in Xanthe Terra: a candidate ExoMars 2018 Rover landing site

The search for life on Mars is a cornerstone of international solar system exploration. In 2018, the European Space agency will launch the ExoMars Rover to further this goal. The key science objectives of the ExoMars Rover are to: 1) search for signs of past and present life on Mars; 2) investigate the water/geochemical environment as a function of depth in the shallow subsurface; and 3) to characterize the surface environment. ExoMars will drill into the sub-surface to look for indicators of past life using a variety of complementary techniques, including assessment of morphology (potential fossil organisms), mineralogy (past environments) and a search for organic molecules and their chirality (biomarkers). The choice of landing site is vital if the objectives are to be met. The landing site must: (i) be ancient (≥3.6 Ga); (ii) show abundant morphological and mineral evidence for long-term, or frequently reoccurring, aqueous activity; (iii) include numerous sedimentary outcrops that (iv) are distributed over the landing region (the typical Rover traverse range is a few km, but ellipse size is ~ 104 by 19 km). Various ‘engineering constraints’ also apply, including: (i) latitude limited to 5º S to 25º N; (ii) maximum altitude of the landing site 2 km below Mars’s datum; and (iii) few steep slopes within the ellipse

Performance of the CMS Global Calorimeter Trigger

The CMS Global Calorimeter Trigger system performs a wide-variety of calorimeter data processing functions required by the CMS Level-1 trigger. It is responsible for finding and classifying jets and tau-jets, calculating total and missing transverse energy, total transverse energy identified within jets, sorting e/$\gamma$ candidates, and calculating several quantities based on forward calorimetry for minimum-bias triggers. The system is based on high-speed serial optical links and large FPGAs. The system has provided CMS with calorimeter triggers during commissioning and cosmic runs throughout 2008. The performance of the system in validation tests and cosmic runs is presented here

Dynamin 2 mutations in Charcot-Marie-Tooth neuropathy highlight the importance of clathrin-mediated endocytosis in myelination

Mutations in dynamin 2 (DNM2) lead to dominant intermediate Charcot-Marie-Tooth neuropathy type B, while a different set of DNM2 mutations cause autosomal dominant centronuclear myopathy. In this study, we aimed to elucidate the disease mechanisms in dominant intermediate Charcot-Marie-Tooth neuropathy type B and to find explanations for the tissue-specific defects that are associated with different DNM2 mutations in dominant intermediate Charcot-Marie-Tooth neuropathy type B versus autosomal dominant centronuclear myopathy. We used tissue derived from Dnm2-deficient mice to establish an appropriate peripheral nerve model and found that dominant intermediate Charcot-Marie-Tooth neuropathy type B-associated dynamin 2 mutants, but not autosomal dominant centronuclear myopathy mutants, impaired myelination. In contrast to autosomal dominant centronuclear myopathy mutants, Schwann cells and neurons from the peripheral nervous system expressing dominant intermediate Charcot-Marie-Tooth neuropathy mutants showed defects in clathrin-mediated endocytosis. We demonstrate that, as a consequence, protein surface levels are altered in Schwann cells. Furthermore, we discovered that myelination is strictly dependent on Dnm2 and clathrin-mediated endocytosis function. Thus, we propose that altered endocytosis is a major contributing factor to the disease mechanisms in dominant intermediate Charcot-Marie-Tooth neuropathy type