High-stakes testing : truth or consequential validity.

This study consisted of a comprehensive review of the consequential aspects of validity of a grade 10 mathematics assessment. This test is part of a larger state-mandated assessment system in which the studied test is one of two assessments that a student must pass in order to graduate from high school in the state of Massachusetts. Validity evidence was collected using three rigorous measurement methods. Qualitative and quantitative procedures were used to ensure a more complete collection and analyses of validity evidence. A survey was developed and administered to all participating high school mathematics teachers and key education personnel. Fifty-six percent of the surveys were completed and analyzed. In addition, focus group and one-on-one interviews were conducted within each participating school district. The results indicated that the Massachusetts\u27 education reform initiative had created significant changes in high school mathematics curriculum and instruction. In addition, many positive and negative intended and unintended consequences connected to this assessment system were identified. The results were discussed based on a classification system in which a representative sample of school districts was selected from the state population. In this study, a comprehensive analysis of a few specific consequential validity questions was addressed using sound quantitative and qualitative research methods. This type of research, examining the consequential aspects of validity of a state mandated test as a component of a larger assessment system, represents a huge undertaking. The social, politic, and educational implications involved in any reform effort are complex and difficult to document. As education reform affects more and more students across this nation, answers to the outlined questions may assist key administrators in the state of Massachusetts, perhaps even other states in the middle of similar reform efforts, in making important mid-course corrections, and/or merely provide needed validity evidence regarding intended and unintended consequences of the program using solid, data-driven information

Distributed graph clustering and sparsification

Graph clustering is a fundamental computational problem with a number of applications in algorithm design, machine learning, data mining, and analysis of social networks. Over the past decades, researchers have proposed a number of algorithmic design methods for graph clustering. Most of these methods, however, are based on complicated spectral techniques or convex optimisation and cannot be directly applied for clustering many networks that occur in practice, whose information is often collected on different sites. Designing a simple and distributed clustering algorithm is of great interest and has comprehensive applications for processing big datasets. In this article, we present a simple and distributed algorithm for graph clustering: For a wide class of graphs that are characterised by a strong cluster-structure, our algorithm finishes in a poly-logarithmic number of rounds and recovers a partition of the graph close to optimal. One of the main procedures behind our algorithm is a sampling scheme that, given a dense graph as input, produces a sparse subgraph that provably preserves the cluster-structure of the input. Compared with previous sparsification algorithms that require Laplacian solvers or involve combinatorial constructions, this procedure is easy to implement in a distributed setting and runs fast in practice.</jats:p

Philosophy and childhood: theory and practice

An introduction to the Special Issu

Random walks on dynamic graphs: Mixing times, hitting times, and return probabilities

We establish and generalise several bounds for various random walk quantities including the mixing time and the maximum hitting time. Unlike previous analyses, our derivations are based on rather intuitive notions of local expansion properties which allows us to capture the progress the random walk makes through t-step probabilities. We apply our framework to dynamically changing graphs, where the set of vertices is fixed while the set of edges changes in each round. For random walks on dynamic connected graphs for which the stationary distribution does not change over time, we show that their behaviour is in a certain sense similar to static graphs. For example, we show that the mixing and hitting times of any sequence of d-regular connected graphs is O(n^2), generalising a well-known result for static graphs. We also provide refined bounds depending on the isoperimetric dimension of the graph, matching again known results for static graphs. Finally, we investigate properties of random walks on dynamic graphs that are not always connected: we relate their convergence to stationarity to the spectral properties of an average of transition matrices and provide some examples that demonstrate strong discrepancies between static and dynamic graphs

Gene expression profiling of Mycobacterium avium subsp. paratuberculosis in simulated multi-stress conditions and within THP-1 cells reveals a new kind of interactive intramacrophage behaviour

Recent studies have identified in Mycobacterium avium subsp. paratuberculosis (MAP), already known as a pathogen in ruminants, a potential zoonotic agent of some autoimmune diseases in humans. Therefore, considering the possible risk for public health, it is necessary a thorough understanding of MAP's gene expression during infection of human host as well as the identification of its immunogenic and/or virulence factors for the development of appropriate diagnostic and therapeutic tools.In order to characterize MAP's transcriptome during macrophage infection, we analyzed for the first time the whole gene expression of a human derived strain of MAP in simulated intraphagosomal conditions and after intracellular infection of the human macrophage cell line THP-1 by using the DNA-microarray technology. Results showed that MAP shifts its transcriptome to an adaptive metabolism for an anoxic environment and nutrient starvation. It up-regulates several response factors to oxidative stress or intracellular conditions and allows, in terms of transcription, a passive surface peptidoglycan spoliation within the macrophage along with an intensification of the anabolic activity for lipidic membrane structures.These results indicate a possible interactive system between MAP and its host cell based on the internal mimicry unlike other intracellular pathogens, bringing new hypothesis in the virulence and pathogenicity of MAP and its importance in human health

Partitioning Well-clustered Graphs with k-Means and Heat Kernel

We study a suitable class of well-clustered graphs that admit good k-way partitions and present the first almost-linear time algorithm for with almost-optimal approximation guarantees partitioning such graphs. A good k-way partition is a partition of the vertices of a graph into disjoint clusters (subsets) $\{S_i\}_{i=1}^k$, such that each cluster is better connected on the inside than towards the outside. This problem is a key building block in algorithm design, and has wide applications in community detection and network analysis. Key to our result is a theorem on the multi-cut and eigenvector structure of the graph Laplacians of these well-clustered graphs. Based on this theorem, we give the first rigorous guarantees on the approximation ratios of the widely used k-means clustering algorithms. We also give an almost-linear time algorithm based on heat kernel embeddings and approximate nearest neighbor data structures