Sonnets /

Mode of access: Internet.

The Continental Congress and the American Revolution (1775-1781) : a chronology with bibliography and appendix /

"March 28, 1975."

Memorial meeting, Walter Hines Page : held at the Brick Presbyterian Church, Fifth Avenue and Thirty-seventh Street, New York, on April the twenty-fifth, 1919.

Merrill, W. P. The prayer -- Alderman, E. A. Address -- Wilson, W. Message -- Lansing, R. Message -- Isaacs, R. D., Earl of Reading. Address -- McAdoo, W. G. Address -- Abbott, L. Address.

Biographical studies,

The character of Sir Robert Pell.--Lord Brougham.--Mr. Gladstone.--William Pitt.--Bolingbroke as a statesman.--Sir George Cornewall Lewis.--Adam Smith as a person.--Lord Althorp and the reform act of 1832.--Addenda: The Prince Consort. What Lord Lyndhurst really was. The tribute at Hereford to Sir G. C. Lewis. Mr. Cobden. Lord Palmerston. The Earl of Clarendon. Mr. Lowe as chancellor of the Exchequer. Monsieur Guizot. Professor Cairnes. Mr. Disraeli as a member of the House of commons.

Walpole.

Mode of access: Internet.

Lamp yr ysgol sul : a chydymaith y geiriadur ysgrythyrol.

Mode of access: Internet.

The Germans and the small nations; an interview with Lord French.

Interview by E. P. Bell.

The locomotive : its failures and remedies /

Mode of access: Internet.

Tennent's nautical almanac, tide register for the Pacific Coast and marine digest

"By Thomas Tennent."

The Western Reserve law journal.

Mode of access: Internet.

The National recovery administration; an analysis and appraisal,

Mode of access: Internet.

The Cecil family, /

"The greater part of this record is taken up with the life story of three great men--Lord Burghley, Sir Robert Cecil and the third Marquess of Salisbury."--Pref.

Lord Beaconsfield /

Includes index.

Regression with input-dependent noise: A Bayesian treatment

In most treatments of the regression problem it is assumed that the distribution of target data can be described by a deterministic function of the inputs, together with additive Gaussian noise having constant variance. The use of maximum likelihood to train such models then corresponds to the minimization of a sum-of-squares error function. In many applications a more realistic model would allow the noise variance itself to depend on the input variables. However, the use of maximum likelihood to train such models would give highly biased results. In this paper we show how a Bayesian treatment can allow for an input-dependent variance while overcoming the bias of maximum likelihood.

Dynamic variability in adaptive systems

One of the reasons for using variability in the software product line (SPL) approach (see Apel et al., 2006; Figueiredo et al., 2008; Kastner et al., 2007; Mezini & Ostermann, 2004) is to delay a design decision (Svahnberg et al., 2005). Instead of deciding on what system to develop in advance, with the SPL approach a set of components and a reference architecture are specified and implemented (during domain engineering, see Czarnecki & Eisenecker, 2000) out of which individual systems are composed at a later stage (during application engineering, see Czarnecki & Eisenecker, 2000). By postponing the design decisions in such a manner, it is possible to better fit the resultant system in its intended environment, for instance, to allow selection of the system interaction mode to be made after the customers have purchased particular hardware, such as a PDA vs. a laptop. Such variability is expressed through variation points which are locations in a software-based system where choices are available for defining a specific instance of a system (Svahnberg et al., 2005). Until recently it had sufficed to postpone committing to a specific system instance till before the system runtime. However, in the recent years the use and expectations of software systems in human society has undergone significant changes.Today's software systems need to be always available, highly interactive, and able to continuously adapt according to the varying environment conditions, user characteristics and characteristics of other systems that interact with them. Such systems, called adaptive systems, are expected to be long-lived and able to undertake adaptations with little or no human intervention (Cheng et al., 2009). Therefore, the variability now needs to be present also at system runtime, which leads to the emergence of a new type of system: adaptive systems with dynamic variability.