Awareness of quality assurance procedures in digital preservation

Awareness and implementation of appropriate quality assurance procedures at each stage in the process of digital preservation is vital for achieving the goals of long-term access and integrity of electronic information, and maximising the return on the high levels of investment being made in digital preservation. This paper outlines the four stages of quality assurance within the digitisation process suggested in the UK by the JISC QA Focus, and identifies issues to be considered at each stage

Multi-excitons in self-assembled InAs/GaAs quantum dots: A pseudopotential, many-body approach

We use a many-body, atomistic empirical pseudopotential approach to predict the multi-exciton emission spectrum of a lens shaped InAs/GaAs self-assembled quantum dot. We discuss the effects of (i) The direct Coulomb energies, including the differences of electron and hole wavefunctions, (ii) the exchange Coulomb energies and (iii) correlation energies given by a configuration interaction calculation. Emission from the groundstate of the $N$ exciton system to the $N-1$ exciton system involving $e_0\to h_0$ and $e_1\to h_1$ recombinations are discussed. A comparison with a simpler single-band, effective mass approach is presented

Movement of suspended particle and solute concentrations with inflow and tidal action

There are no author-identified significant results in this report

From Stochastic Mixability to Fast Rates

Empirical risk minimization (ERM) is a fundamental learning rule for statistical learning problems where the data is generated according to some unknown distribution $\mathsf{P}$ and returns a hypothesis $f$ chosen from a fixed class $\mathcal{F}$ with small loss $\ell$. In the parametric setting, depending upon $(\ell, \mathcal{F},\mathsf{P})$ ERM can have slow $(1/\sqrt{n})$ or fast $(1/n)$ rates of convergence of the excess risk as a function of the sample size $n$. There exist several results that give sufficient conditions for fast rates in terms of joint properties of $\ell$, $\mathcal{F}$, and $\mathsf{P}$, such as the margin condition and the Bernstein condition. In the non-statistical prediction with expert advice setting, there is an analogous slow and fast rate phenomenon, and it is entirely characterized in terms of the mixability of the loss $\ell$ (there being no role there for $\mathcal{F}$ or $\mathsf{P}$). The notion of stochastic mixability builds a bridge between these two models of learning, reducing to classical mixability in a special case. The present paper presents a direct proof of fast rates for ERM in terms of stochastic mixability of $(\ell,\mathcal{F}, \mathsf{P})$, and in so doing provides new insight into the fast-rates phenomenon. The proof exploits an old result of Kemperman on the solution to the general moment problem. We also show a partial converse that suggests a characterization of fast rates for ERM in terms of stochastic mixability is possible.Comment: 21 pages, accepted to NIPS 201