Despite widespread assertions that enthusiasm is an important quality of effective teaching, empirical research on the effect of enthusiasm on learning and memory is mixed and largely inconclusive. To help resolve these inconsistencies, we conducted a carefully-controlled laboratory experiment, investigating whether enthusiastic instructions for a memory task would improve recall accuracy. Scri...

In this paper, time series prediction is considered as a problem of missing values. A new method for the determination of the missing time series values is presented. The new method is based on two projection methods: a nonlinear one (Self-Organized Maps) and a linear one (Empirical Orthogonal Functions). The presented global methodology combines the advantages of both methods to get accurate c...

Learnability has always been one of the most central problems in learning theory. Most previous studies on this issue were based on the assumption that the samples are drawn independently and identically according to an underlying (unknown) distribution. The i.i.d. assumption, however, does not hold in many real applications. In this paper, we study the learnability of problems where the sample...

The Ruedenberg type formula relating the total molecular energy to the sum of orbital energies is examined by using SCC-MO and ab initio D Z M O eigenvalues. Comparison with rigorous ab initio D Z energy expectation values indicates that Ruedenberg 's formula in its original form can not provide semiquantitative information on molecular energetics. Much more promissing in this respect is the el...

Recall that: L(w) = 1 n E‖Xw − Y ‖ = 1 n E‖Xw − E[Y ]‖ + σ Define our “empirical loss” as: L̂(w) = 1 n ‖Xw − Y ‖ which has no expectation over Y . Note that for a fixed w E[L̂(w)] = L(w) e.g. the empirical loss is an unbiased estimate of the true loss. Suppose we knew the support size q. One algorithm is to simply find the estimator which minimizes the empirical loss and has support only on q coo...

In this paper, we present the latest version of our system for identifying linguistic code switching in Arabic text. The system relies on Language Models and a tool for morphological analysis and disambiguation for Arabic to identify the class of each word in a given sentence. We evaluate the performance of our system on the test datasets of the shared task at the EMNLP workshop on Computationa...

Following the results in Rüschendorf and Uckelmann (2002), we introduce the completely mixable distributions on R and prove that distributions with monotone density and moderate mean are completely mixable. Using this method we solve the minimization problem minXi∼P Ef(X1 + · · ·+ Xn) for convex functions f and marginal distributions P with monotone density. Our results also provide valuable im...

We present novel minibatch stochastic optimization methods for empirical risk minimization problems, the methods efficiently leverage variance reduced first-order and sub-sampled higherorder information to accelerate the convergence speed. For quadratic objectives, we prove improved iteration complexity over state-of-the-art under reasonable assumptions. We also provide empirical evidence of th...

Let F be a set of M classification procedures with values in [−1, 1]. Given a loss function, we want to construct a procedure which mimics at the best possible rate the best procedure in F . This fastest rate is called optimal rate of aggregation. Considering a continuous scale of loss functions with various types of convexity, we prove that optimal rates of aggregation can be either ((logM)/n)...