Efficient Classification for Metric Data

Recent advances in large-margin classification of data residing in general metric spaces (rather than Hilbert spaces) enable classification under various natural metrics, such as string edit and earthmover distance. A general framework developed for this purpose by von Luxburg and Bousquet [JMLR, 2004] left open the questions of computational efficiency and of providing direct bounds on generalization error. We design a new algorithm for classification in general metric spaces, whose runtime and accuracy depend on the doubling dimension of the data points, and can thus achieve superior classification performance in many common scenarios. The algorithmic core of our approach is an approximate (rather than exact) solution to the classical problems of Lipschitz extension and of Nearest Neighbor Search. The algorithm's generalization performance is guaranteed via the fat-shattering dimension of Lipschitz classifiers, and we present experimental evidence of its superiority to some common kernel methods. As a by-product, we offer a new perspective on the nearest neighbor classifier, which yields significantly sharper risk asymptotics than the classic analysis of Cover and Hart [IEEE Trans. Info. Theory, 1967].Comment: This is the full version of an extended abstract that appeared in Proceedings of the 23rd COLT, 201

Update of MRST parton distributions.

We discuss the latest update of the MRST parton distributions in response to the most recent data. We discuss the areas where there are hints of difficulties in the global fit, and compare to some other updated sets of parton distributions, particularly CTEQ6. We briefly discuss the issue of uncertainties associated with partons

MRST global fit update.

We discuss the impact of the most recent data on the MRST global analysis - in particular the new high-ET jet data and their implications for the gluon and the new small x structure function data. In the light of these new data we also consider the uncertainty in predictions for physical quantities depending on parton distributions, concentrating on the W cross-section at hadron colliders

Labelings vs. Embeddings: On Distributed Representations of Distances

We investigate for which metric spaces the performance of distance labeling and of $\ell_\infty$-embeddings differ, and how significant can this difference be. Recall that a distance labeling is a distributed representation of distances in a metric space $(X,d)$, where each point $x\in X$ is assigned a succinct label, such that the distance between any two points $x,y \in X$ can be approximated given only their labels. A highly structured special case is an embedding into $\ell_\infty$, where each point $x\in X$ is assigned a vector $f(x)$ such that $\|f(x)-f(y)\|_\infty$ is approximately $d(x,y)$. The performance of a distance labeling or an $\ell_\infty$-embedding is measured via its distortion and its label-size/dimension. We also study the analogous question for the prioritized versions of these two measures. Here, a priority order $\pi=(x_1,\dots,x_n)$ of the point set $X$ is given, and higher-priority points should have shorter labels. Formally, a distance labeling has prioritized label-size $\alpha(.)$ if every $x_j$ has label size at most $\alpha(j)$. Similarly, an embedding $f: X \to \ell_\infty$ has prioritized dimension $\alpha(.)$ if $f(x_j)$ is non-zero only in the first $\alpha(j)$ coordinates. In addition, we compare these their prioritized measures to their classical (worst-case) versions. We answer these questions in several scenarios, uncovering a surprisingly diverse range of behaviors. First, in some cases labelings and embeddings have very similar worst-case performance, but in other cases there is a huge disparity. However in the prioritized setting, we most often find a strict separation between the performance of labelings and embeddings. And finally, when comparing the classical and prioritized settings, we find that the worst-case bound for label size often ``translates'' to a prioritized one, but also a surprising exception to this rule

MRST partons and uncertainties.

We discuss uncertainties in the extraction of parton distributions from global analyses of DIS and related data. We present conservative sets of partons, at both NLO and NNLO, which are stable to x,Q2,W2 cuts on the data. We give the corresponding values of S(M2 Z) and the cross sections for W production at the Tevatron

Ecopsychosocial Interventions in Cognitive Decline and Dementia:A New Terminology and a New Paradigm

Dementia is a major medical and social scourge. Neither pharmacological nor nonpharmacological interventions and treatments have received sufficient funding to be meaningful in combatting this tsunami. Because the term—“nonpharmacological”—refers to what these interventions are not, rather than what they are, nonpharmacological treatments face a special set of challenges to be recognized, accepted, funded, and implemented. In some ways, the current situation is analogous to using the term “nonhate” to mean “love.” This article presents a carefully reasoned argument for using the terminology “ecopsychosocial” to describe the full range of approaches and interventions that fall into this category. These include interventions such as educational efforts with care partners, social support programs for individuals with various levels of dementia, efforts to improve community awareness of dementia, an intergenerational school where persons with dementia teach young children, and the design of residential and community settings that improve functioning and can reduce behavioral symptoms of dementia. The proposed terminology relates to the nature of the interventions themselves, rather than their outcomes, and reflects the broadest range of interventions possible under the present rubric—nonpharmacological. The goal of this new label is to be better able to compare interventions and their outcomes and to be able to see the connections between data sets presently not seen as fitting together, thereby encouraging greater focus on developing new ecopsychosocial interventions and approaches that can improve the lives of those with dementia, their care partners, and the broader society. </jats:p