Evaluating Interaction Techniques in an Interactive Workspace: Comparing the Effectiveness of a Textual Interface, Virtual Paths Interface, and ARIS

ARIS is an interface that enables users to visually relocate applications and redirect input among myriad devices in an interactive workspace. While we previously claimed that ARIS is more effective than other interfaces for performing these tasks, this work seeks to empirically validate our claim. We compared the use of ARIS to an interaction design of a text-based and virtual paths interface for relocating applications and redirecting input in an interactive workspace. Results show that (i) users can relocate applications and redirect input faster with ARIS than a text-based interface, (ii) users commit fewer errors with ARIS than a text-based interface, (iii) users experience less workload and are more satisfied with ARIS than a text-based interface, and (iv) ARIS was comparable to the use of a virtual paths interface. ARIS is more effective than an interaction design that requires a user to mentally map and select textual identifiers, while supporting functionality beyond that of a virtual paths interface

Inflation, interest rates, and seasonality

Inflation (Finance) ; Interest rates ; Seasonal variations (Economics)

Phase transitions in optimal unsupervised learning

We determine the optimal performance of learning the orientation of the symmetry axis of a set of P = alpha N points that are uniformly distributed in all the directions but one on the N-dimensional sphere. The components along the symmetry breaking direction, of unitary vector B, are sampled from a mixture of two gaussians of variable separation and width. The typical optimal performance is measured through the overlap Ropt=B.J* where J* is the optimal guess of the symmetry breaking direction. Within this general scenario, the learning curves Ropt(alpha) may present first order transitions if the clusters are narrow enough. Close to these transitions, high performance states can be obtained through the minimization of the corresponding optimal potential, although these solutions are metastable, and therefore not learnable, within the usual bayesian scenario.Comment: 9 pages, 8 figures, submitted to PRE, This new version of the paper contains one new section, Bayesian versus optimal solutions, where we explain in detail the results supporting our claim that bayesian learning may not be optimal. Figures 4 of the first submission was difficult to understand. We replaced it by two new figures (Figs. 4 and 5 in this new version) containing more detail

Finite-size effects in on-line learning of multilayer neural networks

We complement recent advances in thermodynamic limit analyses of mean on-line gradient descent learning dynamics in multi-layer networks by calculating fluctuations possessed by finite dimensional systems. Fluctuations from the mean dynamics are largest at the onset of specialisation as student hidden unit weight vectors begin to imitate specific teacher vectors, increasing with the degree of symmetry of the initial conditions. In light of this, we include a term to stimulate asymmetry in the learning process, which typically also leads to a significant decrease in training time