A detailed review of the education sector in Australia as in the data provided by the 2006 edition of the OECD's annual publication, 'Education at a Glance' is presented. While the data has shown that in almost all OECD countries educational attainment levels are on the rise, with countries showing impressive gains in university qualifications, it also reveals that a large of share of young people still do not complete secondary school, which remains a baseline for successful entry into the labour market.

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Education at a Glance

The ubiquity of communication devices such as smartphones has led to the emergence of context-aware services that are able to respond to specific user activities or contexts. These services allow communication providers to develop new, added-value services for a wide range of applications such as social networking, elderly care and near-emergency early warning systems. At the core of these services is the ability to detect specific physical settings or the context a user is in, using either internal or external sensors. For example, using built-in accelerometers, it is possible to determine whether a user is walking or running at a specific time of day. By correlating this knowledge with GPS data, it is possible to provide specific information services to users with similar daily routines. This article presents a survey of the techniques for extracting this activity information from raw accelerometer data. The techniques that can be implemented in mobile devices range from classical signal processing techniques such as FFT to contemporary string-based methods. We present experimental results to compare and evaluate the accuracy of the various techniques using real data sets collected from daily activities.

/pdf/preprocessing-techniques-for-context-recognition-from-3o0k2lk1mr.pdf

Preprocessing techniques for context recognition from accelerometer data

As rates of new coronavirus disease 2019 (COVID-19) cases decline across Europe owing to nonpharmaceutical interventions such as social distancing policies and lockdown measures, countries require guidance on how to ease restrictions while minimizing the risk of resurgent outbreaks. We use mobility and case data to quantify how coordinated exit strategies could delay continental resurgence and limit community transmission of COVID-19. We find that a resurgent continental epidemic could occur as many as 5 weeks earlier when well-connected countries with stringent existing interventions end their interventions prematurely. Further, we find that appropriate coordination can greatly improve the likelihood of eliminating community transmission throughout Europe. In particular, synchronizing intermittent lockdowns across Europe means that half as many lockdown periods would be required to end continent-wide community transmission.

https://science.sciencemag.org/content/sci/369/6510/1465.full.pdf

Assessing the impact of coordinated COVID-19 exit strategies across Europe

On Parallel Algorithms of some Orthogonal Transforms (S.S. Agaian and D.Z. Gevorkian). An Efficient Algorithm for Finding Peripheral Nodes (I. Arany). Computational Aspects of Assigning Characteristic Semigroup of Asynchronous Automata and Their Extensions (S. Bocian and B. Mikolajczak). Reichenbach's Propositional Logic in Algorithmic Form (P. Borowik). The Complexity of Weighted Multi-Constrained Spanning Tree Problems (P. Camerini, G. Galbiati and F. Maffioli). An Algorithm for Finding SC-Preimages of a Deterministic Finite Automaton (K. Chmiel). On Entropy Decomposition Methods and Algorithm Design (Th. Fischer). An Efficient Algorithm for Dynamic String-Storage Allocation (D. Fox). Covering Intervals with Intervals under Containment Constraints (M.R. Garey and R.Y. Pinter). How to Construct Random Functions (O. Goldreich, S. Goldwasser and S. Micali). Four Pebbles Don't Suffice to Search Planar Infinite Labyrinths (F. Hoffmann). Parallel Algorithms: The Impact of Communication Complexity (F. Hossfeld). Tight Worst-Case Bounds for Bin-Packing Algorithms (A. Ivanyi). Hypergraph Planarity and the Complexity of Drawing Venn Diagrams (D.S. Johnson and H.O. Pollak). Convolutional Charaterization of Computability and Complexity of Computations (S. Jukna). Succinct Data Representations and the Complexity of Computations (S. Jukna). Lattices, Basis Reduction and the Shortest Vector Problem (R. Kannan). The Characterization of Some Complexity Classes by Recursion Schemata (M. Liskiewicz, K. Lorys and M. Piotrow). Some Algorithmic Problems on Lattices (L. Lovasz). Linear Proofs in the Non-Negative Cone (J. Moravek). Characterizing Some Low Arithmetic Classes (J.B. Paris, W.G. Handley and A.J. Wilkie). Constructing a Simplex Form of a Rational Matrix (A. Rycerz and J. Jegier). Computing N with a Few Number of Additions (I. Ruzsa and Zs. Tuza). A Hierarchy of Polynomial Time Basis Reduction Algorithms (C.P. Schnorr). A Topological View of Some Problems in Complexity Theory (M. Sipser). v-Computations on Turing Machines and the Accepted Languages (L. Staiger). On the Greedy Algorithm for an Edge-Partitioning Problem (Gy. Turan). The Complexity of Linear Quadtrees (T.R. Walsh).

Theory of algorithms

The article aims at describing processes of building Business Intelligence (BI) systems. Taking the BI systems specifics into consideration, the authors present a suggested methodology of the systems creation and implementation in organisations. The considerations are focused on objectives and functional areas of the BI in organisations. Hence, in this context the approach to be used while building and implementing the BI involves two major stages that are of interactive nature, i.e. BI creation and BI "consumption". A large part of the article is devoted to presenting objectives and tasks that are realised while building and implementing BI.

Approach to Building and Implementing Business Intelligence Systems

A quantum particle evolving by Schrodinger's equation contains, from the kinetic energy of the particle, a term in its Hamiltonian proportional to Laplace's operator. In discrete space, this is replaced by the discrete or graph Laplacian, which gives rise to a continuous-time quantum walk. Besides this natural definition, some quantum walk algorithms instead use the adjacency matrix to effect the walk. While this is equivalent to the Laplacian for regular graphs, it is different for non-regular graphs and is thus an inequivalent quantum walk. We algorithmically explore this distinction by analyzing search on the complete bipartite graph with multiple marked vertices, using both the Laplacian and adjacency matrix. The two walks differ qualitatively and quantitatively in their required jumping rate, runtime, sampling of marked vertices, and in what constitutes a natural initial state. Thus the choice of the Laplacian or adjacency matrix to effect the walk has important algorithmic consequences.

Laplacian versus adjacency matrix in quantum walk search

We examine the effect of network heterogeneity on the performance of quantum search algorithms. To this end, we study quantum search on a tree for the oracle Hamiltonian formulation employed by continuous-time quantum walks. We use analytical and numerical arguments to show that the exponent of the asymptotic running time $\ensuremath{\sim}{N}^{\ensuremath{\beta}}$ changes uniformly from $\ensuremath{\beta}=0.5$ to $\ensuremath{\beta}=1$ as the searched-for site is moved from the root of the tree towards the leaves. These results imply that the time complexity of the quantum search algorithm on a balanced tree is closely correlated with certain path-based centrality measures of the searched-for site.

Continuous-Time Quantum Search on Balanced Trees

The growing processing capabilities of mobile devices coupled with portable and wearable sensors have enabled the development of context-aware services tailored to the user environment and its daily activities. The problem of determining the user context at each particular point in time is one of the main challenges in this area. In this paper, we describe the approach pursued in the UPCASE project, which makes use of sensors available in the mobile device as well as sensors externally connected via Bluetooth. We describe the system architecture from raw data acquisition to feature extraction and context inference. As a proof of concept, the inference of contexts is based on a decision tree to learn and identify contexts automatically and dynamically at runtime. Preliminary results suggest that this is a promising approach for context inference in several application scenarios.

/pdf/context-inference-for-mobile-applications-in-the-upcase-1oflhu46ni.pdf

Context Inference for Mobile Applications in the UPCASE Project

The choice of a fixed step size in adaptive filtering algorithms implies a conflict between the convergence rate and the steady-state performance. In order to address this tradeoff more effectively, variable step-size schemes have been proposed. The efficiency evaluation of such techniques requires comparisons of the resulting step-size values with theoretical optimum values obtained from a statistical analysis of the adaptive algorithm convergence. The analysis generally employs statistical approximations, the most critical being the assumption of independence between the input signal and the filter coefficients. In this letter, it is argued that such a comparison can be misleading because the supposedly optimal step-size sequence sometimes induces divergence in the initial phase of learning. This occurs most often when the input signal is colored and/or heavy tailed. This instability trend can be explained by a convergence analysis that does not employ the independence hypothesis. In addition, the use of this exact analysis implies an optimal step-size sequence that can be significantly different from that obtained with standard analysis methods. This approach can be used to improve the design process of variable step-size adaptive filtering algorithms.

Exact Expectation Evaluation and Design of Variable Step-Size Adaptive Algorithms

A quantum particle evolving by Schrodinger's equation contains, from the kinetic energy of the particle, a term in its Hamiltonian proportional to Laplace's operator. In discrete space, this is replaced by the discrete or graph Laplacian, which gives rise to a continuous-time quantum walk. Besides this natural definition, some quantum walk algorithms instead use the adjacency matrix to effect the walk. While this is equivalent to the Laplacian for regular graphs, it is different for non-regular graphs, and is thus an inequivalent quantum walk. We algorithmically explore this distinction by analyzing search on the complete bipartite graph with multiple marked vertices, using both the Laplacian and adjacency matrix. The two walks differ qualitatively and quantitatively in their required jumping rate, runtime, sampling of marked vertices, and in what constitutes a natural initial state. Thus the choice of the Laplacian or adjacency matrix to effect the walk has important algorithmic consequences.

Luís Tarrataca

Papers

Laplacian versus adjacency matrix in quantum walk search

Continuous-Time Quantum Search on Balanced Trees

Context Inference for Mobile Applications in the UPCASE Project

Exact Expectation Evaluation and Design of Variable Step-Size Adaptive Algorithms

Laplacian versus Adjacency Matrix in Quantum Walk Search