Space- and Time-Efficient Algorithm for Maintaining Dense Subgraphs on One-Pass Dynamic Streams

While in many graph mining applications it is crucial to handle a stream of updates efficiently in terms of {\em both} time and space, not much was known about achieving such type of algorithm. In this paper we study this issue for a problem which lies at the core of many graph mining applications called {\em densest subgraph problem}. We develop an algorithm that achieves time- and space-efficiency for this problem simultaneously. It is one of the first of its kind for graph problems to the best of our knowledge. In a graph $G = (V, E)$, the "density" of a subgraph induced by a subset of nodes $S \subseteq V$ is defined as $|E(S)|/|S|$, where $E(S)$ is the set of edges in $E$ with both endpoints in $S$. In the densest subgraph problem, the goal is to find a subset of nodes that maximizes the density of the corresponding induced subgraph. For any $\epsilon>0$, we present a dynamic algorithm that, with high probability, maintains a $(4+\epsilon)$-approximation to the densest subgraph problem under a sequence of edge insertions and deletions in a graph with $n$ nodes. It uses $\tilde O(n)$ space, and has an amortized update time of $\tilde O(1)$ and a query time of $\tilde O(1)$. Here, $\tilde O$ hides a O(\poly\log_{1+\epsilon} n) term. The approximation ratio can be improved to $(2+\epsilon)$ at the cost of increasing the query time to $\tilde O(n)$. It can be extended to a $(2+\epsilon)$-approximation sublinear-time algorithm and a distributed-streaming algorithm. Our algorithm is the first streaming algorithm that can maintain the densest subgraph in {\em one pass}. The previously best algorithm in this setting required $O(\log n)$ passes [Bahmani, Kumar and Vassilvitskii, VLDB'12]. The space required by our algorithm is tight up to a polylogarithmic factor.Comment: A preliminary version of this paper appeared in STOC 201

Performance of the cross-product steering law for the transearth injection phase

Steering law for transearth injection between circular lunar orbit and vector

Understanding the role of promoters in catalysis: operando XAFS/DRIFTS study of CeO_x/Pt/Al₂O₃ during CO oxidation

A combined operando XAFS/DRIFTS study on CeOx/Pt/Al2O3 catalysts has been performed during CO oxidation and provides insights into the changes in nanoparticle structure and adsorbed species during the reaction profile. The onset of CO2 formation is shown to be concurrent with a rapid re-oxidation of the Pt nanoparticles, evidenced by XAFS spectroscopy, and the loss of bridge bonded CO adsorbed on Pt, as shown by simultaneous DRIFTS acquisition. The continued appearance of linear bound CO on the catalyst surface is shown to remain long after catalytic light off. The interaction of Pt and CeOx is evidenced by the improved performance towards CO oxidation, compared to the non-CeOx modified Pt/Al2O3, and changes in the CO adsorption properties on Pt previously linked to Pt-CeO2 interfaces

Transportation noise pollution - Control and abatement

Control and abatement of transportation noise pollutio

Public Health Informatics in Local and State Health Agencies: An Update From the Public Health Workforce Interests and Needs Survey

OBJECTIVE: To characterize public health informatics (PHI) specialists and identify the informatics needs of the public health workforce. DESIGN: Cross-sectional study. SETTING: US local and state health agencies. PARTICIPANTS: Employees from state health agencies central office (SHA-COs) and local health departments (LHDs) participating in the 2017 Public Health Workforce Interests and Needs Survey (PH WINS). We characterized and compared the job roles for self-reported PHI, "information technology specialist or information system manager" (IT/IS), "public health science" (PHS), and "clinical and laboratory" workers. MAIN OUTCOME MEASURE: Descriptive statistics for demographics, income, education, public health experience, program area, job satisfaction, and workplace environment, as well as data and informatics skills and needs. RESULTS: A total of 17 136 SHA-CO and 26 533 LHD employees participated in the survey. PHI specialist was self-reported as a job role among 1.1% and 0.3% of SHA-CO and LHD employees. The PHI segment most closely resembled PHS employees but had less public health experience and had lower salaries. Overall, fewer than one-third of PHI specialists reported working in an informatics program area, often supporting epidemiology and surveillance, vital records, and communicable disease. Compared with PH WINS 2014, current PHI respondents' satisfaction with their job and workplace environment moved toward more neutral and negative responses, while the IT/IS, PHS, and clinical and laboratory subgroups shifted toward more positive responses. The PHI specialists were less likely than those in IT/IS, PHS, or clinical and laboratory roles to report gaps in needed data and informatics skills. CONCLUSIONS: The informatics specialists' role continues to be rare in public health agencies, and those filling that role tend to have less public health experience and be less well compensated than staff in other technically focused positions. Significant data and informatics skills gaps persist among the broader public health workforce

ROAM: a Radial-basis-function Optimization Approximation Method for diagnosing the three-dimensional coronal magnetic field

The Coronal Multichannel Polarimeter (CoMP) routinely performs coronal polarimetric measurements using the Fe XIII 10747 $\AA$ and 10798 $\AA$ lines, which are sensitive to the coronal magnetic field. However, inverting such polarimetric measurements into magnetic field data is a difficult task because the corona is optically thin at these wavelengths and the observed signal is therefore the integrated emission of all the plasma along the line of sight. To overcome this difficulty, we take on a new approach that combines a parameterized 3D magnetic field model with forward modeling of the polarization signal. For that purpose, we develop a new, fast and efficient, optimization method for model-data fitting: the Radial-basis-functions Optimization Approximation Method (ROAM). Model-data fitting is achieved by optimizing a user-specified log-likelihood function that quantifies the differences between the observed polarization signal and its synthetic/predicted analogue. Speed and efficiency are obtained by combining sparse evaluation of the magnetic model with radial-basis-function (RBF) decomposition of the log-likelihood function. The RBF decomposition provides an analytical expression for the log-likelihood function that is used to inexpensively estimate the set of parameter values optimizing it. We test and validate ROAM on a synthetic test bed of a coronal magnetic flux rope and show that it performs well with a significantly sparse sample of the parameter space. We conclude that our optimization method is well-suited for fast and efficient model-data fitting and can be exploited for converting coronal polarimetric measurements, such as the ones provided by CoMP, into coronal magnetic field data.Comment: 23 pages, 12 figures, accepted in Frontiers in Astronomy and Space Science

Data-Optimized Coronal Field Model: I. Proof of Concept

Deriving the strength and direction of the three-dimensional (3D) magnetic field in the solar atmosphere is fundamental for understanding its dynamics. Volume information on the magnetic field mostly relies on coupling 3D reconstruction methods with photospheric and/or chromospheric surface vector magnetic fields. Infrared coronal polarimetry could provide additional information to better constrain magnetic field reconstructions. However, combining such data with reconstruction methods is challenging, e.g., because of the optical-thinness of the solar corona and the lack and limitations of stereoscopic polarimetry. To address these issues, we introduce the Data-Optimized Coronal Field Model (DOCFM) framework, a model-data fitting approach that combines a parametrized 3D generative model, e.g., a magnetic field extrapolation or a magnetohydrodynamic model, with forward modeling of coronal data. We test it with a parametrized flux rope insertion method and infrared coronal polarimetry where synthetic observations are created from a known "ground truth" physical state. We show that this framework allows us to accurately retrieve the ground truth 3D magnetic field of a set of force-free field solutions from the flux rope insertion method. In observational studies, the DOCFM will provide a means to force the solutions derived with different reconstruction methods to satisfy additional, common, coronal constraints. The DOCFM framework therefore opens new perspectives for the exploitation of coronal polarimetry in magnetic field reconstructions and for developing new techniques to more reliably infer the 3D magnetic fields that trigger solar flares and coronal mass ejections.Comment: 14 pages, 6 figures; Accepted for publication in Ap

Food-conditioned odour rejection in the late stages of the meal, mediating learnt control of meal volume by aftereffects of food consumption

In a two-bottle choice test, rats drank more of the fluid having a novel odour than that having an odour which had previously been presented in the later part of meals on concentrated maltodextrin solution. Rats are normally more averse to a novel odour than to a familiar odour; therefore, the conditioned reaction to the odour acquired in these circumstances is likely to be an ingestive aversion, rather than merely a lack of preference. Furthermore, this learnt odour rejection was seen only in the second half of the meal, indicating that it is dependent on an ingestion-induced state of repletion. Together then, these observations are evidence that the volume of meals rich in carbohydrate can be controlled by learnt rejection of particular food flavours in the presence of visceral cues specific to repletion (previously dubbed "conditioned satiety"), the only known mechanism by which aftereffects of ingested energy could reduce meal volume

Distance-generalized Core Decomposition

The $k$-core of a graph is defined as the maximal subgraph in which every vertex is connected to at least $k$ other vertices within that subgraph. In this work we introduce a distance-based generalization of the notion of $k$-core, which we refer to as the $(k,h)$-core, i.e., the maximal subgraph in which every vertex has at least $k$ other vertices at distance $\leq h$ within that subgraph. We study the properties of the $(k,h)$-core showing that it preserves many of the nice features of the classic core decomposition (e.g., its connection with the notion of distance-generalized chromatic number) and it preserves its usefulness to speed-up or approximate distance-generalized notions of dense structures, such as $h$-club. Computing the distance-generalized core decomposition over large networks is intrinsically complex. However, by exploiting clever upper and lower bounds we can partition the computation in a set of totally independent subcomputations, opening the door to top-down exploration and to multithreading, and thus achieving an efficient algorithm

The synergistic and dynamic relationship between learning design and learning analytics

The synergistic relationship between learning design and learning analytics has the potential for improving learning and teaching in near real-time. The potential for integrating the newly available and dynamic information from ongoing analysis into learning design requires new perspectives on learning and teaching data processing and analysis as well as advanced theories, methods, and tools for supporting dynamic learning design processes. Three perspectives of learning analytics design provide summative, real-time, and predictive insights. In a case study with 3,550 users, the navigation sequence and network graph analysis demonstrate the potential of learning analytics design. The study aims to demonstrate how the analysis of navigation patterns and network graph analysis could inform the learning design of self-guided digital learning experiences. Even with open-ended freedom, only 608 sequences were evidenced by learners out of a potential number of hundreds of millions of sequences. Advancements of learning analytics design have the potential for mapping the cognitive, social and even physical states of the learner and optimise their learning environment on the fly