Efficient ticket routing by resolution sequence mining

IT problem management calls for quick identification of resolvers to reported problems. The efficiency of this process highly depends on ticket routing—transferring problem ticket among various expert groups in search of the right resolver to the ticket. To achieve efficient ticket routing, wise decision needs to be made at each step of ticket transfer to determine which expert group is likely to be, or to lead to the resolver. In this paper, we address the possibility of improving ticket routing efficiency by mining ticket resolution sequences alone, without accessing ticket content. To demonstrate this possibility, a Markov model is developed to statistically capture the right decisions that have been made toward problem resolution, where the order of the Markov model is carefully chosen according to the conditional entropy obtained from ticket data. We also design a search algorithm, called Variable-order Multiple active State search (VMS), that generates ticket transfer recommendations based on our model. The proposed framework is evaluated on a large set of realworld problem tickets. The results demonstrate that VMS significantly improves human decisions: Problem resolvers can often be identified with fewer ticket transfers

Obstacle avoidance planning for redundant manipulator based on variational method

U svrhu smanjenja prekomjernog pomicanja zglobova, u radu se predlaže nova metoda za izbjegavanje prepreka kod redundantnog manipulatora. U toj smo metodi dizajnirali funkcionalnu procjenu pokreta kako bismo dobili najkraću stazu kretanja zgloba manipulatora i dobili indeks optimizacije vektora gradijenta primjenom varijacijske (variational) metode da bi za vrijeme izbjegavanja prepreke kretanje zgloba bilo minimalno. U međuvremenu, kako bismo izbjegli neuspjeh algoritma za izbjegavanje prepreke ili prekoraćenje krajnjih granica zglobova, do kojih je došlo zbog velike razlike između solucije najmanje norme i homogene solucije kinematike inverzne brzine manipulatora, u radu je primijenjena kontinuirana funkcija 2-norme da bi se dinamički podesio homogeni faktor rješenja metode projekcije gradijenta. Da bi se provjerila ispravnosti u radu predložene metode izbjegavanja prepreka, provedeni su simulacijski eksperimenti na 7-DOF redundantnom manipulatoru. Rezultati pokazuju da u usporedbi s tradicionalnom metodom projekcije gradijenta za izbjegavanje prepreka, u radu predloženim algoritmom smanjio se pomak zgloba 1 do zgloba 6 za 38,3 %, 83,3 %, 3,81 %, 7,85 %, 50,1 % i 45,6 %, a cjelokupni pomak prizmatičkih zglobova i okretnih (revolute) zglobova za 62,2 % i 26,4 %. U isto vrijeme, promjena brzine i ubrzanja zgloba 1 do zgloba 6 između početnog i završnog vremena tijekom izbjegavanja prepreke smanjila se 43,2 %, 97,3 %, 2,23 %, 36,6 %, 96,7 %, 72,7 % (brzina) i 91,04 %, 98,28 %, 73,33 %, 98,40 %, 93,86 % i 91,94 % (ubrzanje). Ispitivanje je pokazalo da je predložena metoda za izbjegavanje prepreka temeljena na varijacijskoj metodi, izvediva i praktična.To reduce the excessive joints movement, this paper proposed a new obstacle avoidance method for a redundant manipulator. In this method, we designed the performance evaluation functional to realize the shortest joints motion path of manipulator, and deduced the gradient vector optimizing index by variational method to make the joint movement minimum during obstacle avoiding. Meanwhile, in order to avoid the obstacle avoidance algorithm failure or joints exceeding their limits, which arose from the great difference between least-norm solution and homogeneous solution of velocity inverse kinematics of the manipulator, this paper used 2-norm continuous function to adjust homogeneous solution factor of gradient projection method dynamically. To verify the validity of the proposed obstacle avoidance method in the paper, simulation experiments were conducted on a 7-DOF redundant manipulator. The results show that, compared to the traditional gradient projection method for obstacle avoidance, the proposed algorithm in this paper has decreased the displacement of joint 1 to joint 6 by 38,3 %, 83,3 %, 3,81 %, 7,85 %, 50,1 % and 45,6 % respectively, and the total displacement of prismatic joints and revolute joints has reduced 62,2 % and 26,4 %. At the same time, the changes of joint 1 to joint 6’s velocity and acceleration between initial time and final time during obstacle avoiding has been decreased 43,2 %, 97,3 %, 2,23 %, 36,6 %, 96,7 %, 72,7 % (velocity) and 91,04 %, 98,28 %, 73,33 %, 98,40 %, 93,86 % and 91,94 % (acceleration) respectively. The test validated that the proposed obstacle avoidance method based on variational method is feasible and practicable

Interactions of Oxide Surfaces with Water Revealed with Solid-State NMR Spectroscopy.

Hydrous materials are ubiquitous in the natural environment and efforts have previously been made to investigate the structures and dynamics of hydrated surfaces for their key roles in various chemical and physical applications, with the help of theoretical modeling and microscopy techniques. However, an overall atomic-scale understanding of the water-solid interface, including the effect of water on surface ions, is still lacking. Herein, we employ ceria nanorods with different amounts of water as an example and demonstrate a new approach to explore the water-surface interactions by using solid-state NMR in combination with density functional theory. NMR shifts and relaxation time analysis provide detailed information on the local structure of oxygen ions and the nature of water motion on the surface: the amount of molecularly adsorbed water decreases rapidly with increasing temperature (from room temperature to 150 °C), whereas hydroxyl groups are stable up to 150 °C, and dynamic water molecules are found to instantaneously coordinate to the surface oxygen ions. The applicability of dynamic nuclear polarization for selective detection of surface oxygen species is also compared to conventional NMR with surface selective isotopic-labeling: the optimal method depends on the feasibility of enrichment and the concentration of protons in the sample. These results provide new insight into the interfacial structure of hydrated oxide nanostructures, which is important to improve performance for various applications

Unveiling hidden physics at the LHC

The field of particle physics is at the crossroads. The discovery of a Higgs-like boson completed the Standard Model (SM), but the lacking observation of convincing resonances Beyond the SM (BSM) offers no guidance for the future of particle physics. On the other hand, the motivation for New Physics has not diminished and is, in fact, reinforced by several striking anomalous results in many experiments. Here we summarise the status of the most significant anomalies, including the most recent results for the flavour anomalies, the multi-lepton anomalies at the LHC, the Higgs-like excess at around 96 GeV, and anomalies in neutrino physics, astrophysics, cosmology, and cosmic rays. While the LHC promises up to 4 ab−1 of integrated luminosity and far-reaching physics programmes to unveil BSM physics, we consider the possibility that the latter could be tested with present data, but that systemic shortcomings of the experiments and their search strategies may preclude their discovery for several reasons, including: final states consisting in soft particles only, associated production processes, QCD-like final states, close-by SM resonances, and SUSY scenarios where no missing energy is produced. New search strategies could help to unveil the hidden BSM signatures, devised by making use of the CERN open data as a new testing ground. We discuss the CERN open data with its policies, challenges, and potential usefulness for the community. We showcase the example of the CMS collaboration, which is the only collaboration regularly releasing some of its data. We find it important to stress that individuals using public data for their own research does not imply competition with experimental efforts, but rather provides unique opportunities to give guidance for further BSM searches by the collaborations. Wide access to open data is paramount to fully exploit the LHCs potential

Unveiling hidden physics at the LHC

The field of particle physics is at the crossroads. The discovery of a Higgs-like boson completed the Standard Model (SM), but the lacking observation of convincing resonances Beyond the SM (BSM) offers no guidance for the future of particle physics. On the other hand, the motivation for New Physics has not diminished and is, in fact, reinforced by several striking anomalous results in many experiments. Here we summarise the status of the most significant anomalies, including the most recent results for the flavour anomalies, the multi-lepton anomalies at the LHC, the Higgs-like excess at around 96 GeV, and anomalies in neutrino physics, astrophysics, cosmology, and cosmic rays. While the LHC promises up to 4 ab(-1) of integrated luminosity and far-reaching physics programmes to unveil BSM physics, we consider the possibility that the latter could be tested with present data, but that systemic shortcomings of the experiments and their search strategies may preclude their discovery for several reasons, including: final states consisting in soft particles only, associated production processes, QCD-like final states, close-by SM resonances, and SUSY scenarios where no missing energy is produced. New search strategies could help to unveil the hidden BSM signatures, devised by making use of the CERN open data as a new testing ground. We discuss the CERN open data with its policies, challenges, and potential usefulness for the community. We showcase the example of the CMS collaboration, which is the only collaboration regularly releasing some of its data. We find it important to stress that individuals using public data for their own research does not imply competition with experimental efforts, but rather provides unique opportunities to give guidance for further BSM searches by the collaborations. Wide access to open data is paramount to fully exploit the LHCs potential.Peer reviewe

Pleiotropy of genetic variants on obesity and smoking phenotypes: Results from the Oncoarray Project of The International Lung Cancer Consortium

Obesity and cigarette smoking are correlated through complex relationships. Common genetic causes may contribute to these correlations. In this study, we selected 241 loci potentially associated with body mass index (BMI) based on the Genetic Investigation of ANthropometric Traits (GIANT) consortium data and calculated a BMI genetic risk score (BMI-GRS) for 17,037 individuals of European descent from the Oncoarray Project of the International Lung Cancer Consortium (ILCCO). Smokers had a significantly higher BMI-GRS than never-smokers (p = 0.016 and 0.010 before and after adjustment for BMI, respectively). The BMI-GRS was also positively correlated with pack-years of smoking (p<0.001) in smokers. Based on causal network inference analyses, seven and five of 241 SNPs were classified to pleiotropic models for BMI/smoking status and BMI/pack-years, respectively. Among them, three and four SNPs associated with smoking status and pack-years (p<0.05), respectively, were followed up in the ever-smoking data of the Tobacco, Alcohol and Genetics (TAG) consortium. Among these seven candidate SNPs, one SNP (rs11030104, BDNF) achieved statistical significance after Bonferroni correction for multiple testing, and three suggestive SNPs (rs13021737, TMEM18; rs11583200, ELAVL4; and rs6990042, SGCZ) achieved a nominal statistical significance. Our results suggest that there is a common genetic component between BMI and smoking, and pleiotropy analysis can be useful to identify novel genetic loci of complex phenotypes

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Entityrank: Searching entities directly and holistically

As the Web has evolved into a data-rich repository, with the standard “page view, ” current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data “entities ” (e.g., phone number, paper PDF, date), today’s engines only take us indirectly to pages. While entities appear in many pages, current engines only find each page individually. Toward searching directly and holistically for finding information of finer granularity, we study the problem of entity search, a significant departure from traditional document retrieval. We focus on the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We evaluate our online prototype over a 2TB Web corpus, and show that EntityRank performs effectively. 1

General Terms

As the Web has evolved into a data-rich repository, with the standard “page view, ” current search engines are increasingly inadequate. While we often search for various data “entities ” (e.g. phone number, paper PDF, date), today’s engines only take us indirectly to pages. Therefore, we propose the concept of entity search, a significant departure from traditional document retrieval. Towards our goal of supporting entity search, in the WISDM 1 project at UIUC we build and evaluate our prototype search engine over a 2TB Web corpus. Our demonstration shows the feasibility and promise of a large-scale system architecture to support entity search