Justin Lloyd's Thinkering

Today I read a paper titled “A High Quality Text-To-Speech System Composed of Multiple Neural Networks”

The abstract is:
While neural networks have been employed to handle several different text-to-speech tasks, ours is the first system to use neural networks throughout, for both linguistic and acoustic processing.

We divide the text-to-speech task into three subtasks, a linguistic module mapping from text to a linguistic representation, an acoustic module mapping from the linguistic representation to speech, and a video module mapping from the linguistic representation to animated images.

The linguistic module employs a letter-to-sound neural network and a postlexical neural network.

The acoustic module employs a duration neural network and a phonetic neural network.

The visual neural network is employed in parallel to the acoustic module to drive a talking head.

The use of neural networks that can be retrained on the characteristics of different voices and languages affords our system a degree of adaptability and naturalness heretofore unavailable.

Today I read a paper titled “Integration Of Visual Inter-word Constraints And Linguistic Knowledge In Degraded Text Recognition”

The abstract is:
Degraded text recognition is a difficult task.

Given a noisy text image, a word recognizer can be applied to generate several candidates for each word image.

High-level knowledge sources can then be used to select a decision from the candidate set for each word image.

In this paper, we propose that visual inter-word constraints can be used to facilitate candidate selection.

Visual inter-word constraints provide a way to link word images inside the text page, and to interpret them systematically..

Today I read a paper titled “A characterization of the set of fixed points of the Quicksort transformation”

The abstract is:
The limiting distribution \mu of the normalized number of key comparisons required by the Quicksort sorting algorithm is known to be the unique fixed point of a certain distributional transformation T — unique, that is, subject to the constraints of zero mean and finite variance.

We show that a distribution is a fixed point of T if and only if it is the convolution of \mu with a Cauchy distribution of arbitrary center and scale.

In particular, therefore, \mu is the unique fixed point of T having zero mean.

Today I read a paper titled “MOO: A Methodology for Online Optimization through Mining the Offline Optimum”

The abstract is:
Ports, warehouses and courier services have to decide online how an arriving task is to be served in order that cost is minimized (or profit maximized).

These operators have a wealth of historical data on task assignments; can these data be mined for knowledge or rules that can help the decision-making? MOO is a novel application of data mining to online optimization.

The idea is to mine (logged) expert decisions or the offline optimum for rules that can be used for online decisions.

It requires little knowledge about the task distribution and cost structure, and is applicable to a wide range of problems.

This paper presents a feasibility study of the methodology for the well-known k-server problem.

Experiments with synthetic data show that optimization can be recast as classification of the optimum decisions; the resulting heuristic can achieve the optimum for strong request patterns, consistently outperforms other heuristics for weak patterns, and is robust despite changes in cost model.

Today I read a paper titled “Designing an interface to optimize reading with small display windows”

The abstract is:
The electronic presentation of text in small display windows is mushrooming.

In the present paper, four ways of presenting text in a small display window were examined and compared to a Normal Page condition: rapid serial visual presentation (RSVP), RSVP with a Completion Meter, Sentence-by-Sentence presentation, and Sentence-by-Sentence presentation with a Completion Meter.

Dependent measures were reading efficiency – speed and comprehension – and preference.

For designers of hardware or software with small display windows, the results suggest the following: (1) Though RSVP is disliked by readers, the present methods of allowing self-pacing and regressions in RSVP, unlike earlier tested methods, are efficient and feasible.

(2) Slower reading in RSVP should be achieved by increasing pauses between sentences or by repeating sentences, not by decreasing the within-sentence rate.

(3) Completion meters do not interfere with performance, and are usually preferred.

(4) The space-saving Sentence-by-Sentence format is as efficient and as preferred as the Normal Page format.

Today I read a paper titled “Software Agents: Completing Patterns and Constructing User Interfaces”

The abstract is:
To support the goal of allowing users to record and retrieve information, this paper describes an interactive note-taking system for pen-based computers with two distinctive features.

First, it actively predicts what the user is going to write.

Second, it automatically constructs a custom, button-box user interface on request.

The system is an example of a learning-apprentice software- agent.

A machine learning component characterizes the syntax and semantics of the user’s information.

A performance system uses this learned information to generate completion strings and construct a user interface.

Description of Online Appendix: People like to record information.

Doing this on paper is initially efficient, but lacks flexibility.

Recording information on a computer is less efficient but more powerful.

In our new note taking softwre, the user records information directly on a computer.

Behind the interface, an agent acts for the user.

To help, it provides defaults and constructs a custom user interface.

The demonstration is a QuickTime movie of the note taking agent in action.

The file is a binhexed self-extracting archive.

Macintosh utilities for binhex are available from mac.archive.umich.edu.

QuickTime is available from ftp.apple.com in the dts/mac/sys.soft/quicktime..