In recent years, virtual reality (VR) has emerged as a new safe and effective tool for neurorehabilitation of different childhood and adulthood conditions. VR-based therapies can induce cortical reorganization and promote the activation of different neuronal connections over a wide range of ages, leading to contrasted improvements in motor and functional skills. The use of VR for the visual rehabilitation in amblyopia has been investigated in the last years, with the potential of using serious games combining perceptual learning and dichoptic stimulation. This combination of technologies allows the clinician to measure, treat, and control changes in interocular suppression, which is one of the factors leading to cortical alterations in amblyopia. Several clinical researches on this issue have been conducted, showing the potential of promoting visual acuity, contrast sensitivity, and stereopsis improvement. Indeed, several systems have been evaluated for amblyopia treatment including the use of different commercially available types of head mounted displays (HMDs). These HMDs are mostly well tolerated by patients during short exposures and do not cause significant long-term side effects, although their use has been occasionally associated with some visual discomfort and other complications in certain types of subjects. More studies are needed to confirm these promising therapies in controlled randomized clinical trials, with special emphasis on the definition of the most adequate planning for obtaining an effective recovery of the visual and binocular function.

Abstract.A bitext, or bilingual parallel corpus, consists of two texts, each one in a different language, that are mutual translations. Bitexts are very useful in linguistic engineering because they are used as source of knowledge for different purposes. In this paper we propose a strategy to efficiently compress and use bitexts, saving, not only space, but also processing time when exploiting them. Our strategy is based on a two-level structure for the vocabularies, and on the use of biwords, a pair of associated words, one from each language, as basic symbols to be encoded with an ETDC [2] compressor. The resulting compressed bitext needs around 20% of the space and allows more efficient implementations of the different types of searches and operations that linguistic engineerings need to perform on them. In this paper we discuss and provide results for compression, decompression, different types of searches, and bilingual snippets extraction.

Large bilingual parallel texts (also known as bitexts) are usually stored in a compressed form, and previous work has shown that they can be more efficiently compressed if the fact that the two texts are mutual translations is exploited. For example, a bitext can be seen as a sequence of biwords ---pairs of parallel words with a high probability of co-occurrence--- that can be used as an intermediate representation in the compression process. However, the simple biword approach described in the literature can only exploit one-to-one word alignments and cannot tackle the reordering of words. We therefore introduce a generalization of biwords which can describe multi-word expressions and reorderings. We also describe some methods for the binary compression of generalized biword sequences, and compare their performance when different schemes are applied to the extraction of the biword sequence. In addition, we show that this generalization of biwords allows for the implementation of an efficient algorithm to look on the compressed bitext for words or text segments in one of the texts and retrieve their counterpart translations in the other text ---an application usually referred to as translation spotting--- with only some minor modifications in the compression algorithm

The amount of information that is stored in digital form in more than one language is growing very fast as a consequence of the globalization. Furthermore, there are countries and supra-national entities whose legislation enforces the translation (and storage) of all the official texts into all their official languages.Two texts that are mutual translations are usually referred to as a bilingual parallel corpus or, in short, as a bitext. Compressing independently the two texts of a bitext is far form efficient, since the information conveyed by both texts, the meaning, is similar. We take advantage of this fact to devise a bitext compression algorithm that compresses and stores the two texts that form a bitext simultaneously.In our approach, a single model is used to represent both bitext components. For this purpose, we define a biword as a pair made of two words, each one from a different text, that are mutual translations in the bitext. This new concept allows one to represent with a single symbol two words with high mutual information.The algorithm consists of a simple processing pipeline with two stages. The first one (preprocessing) performs a text (sentence and word) alignment in which no preexisting resources are used; it takes as input the bitext and outputs its biword-based representation. The second stage (compression) implements a customization of the mPPM model in which biwords are used as symbols and a limited length dictionary is used to obtain two-byte codewords.We carried out an exhaustive experimentation with seven language pairs (es-ca, es-gl, es-en, es-fr, es-it, es-pt and fr-en) and bitexts of different sizes. For comparison purposes we compressed the concatenation of the two texts of each bitext using general-purpose compressors. Large improvements are obtained for bitexts of closely-related languages (up to 82.73% for es-gl) due to the monotonicity of the word alignments obtained in the preprocessing stage. For less-related language pairs smaller improvements, between 11.12% (es-pt) and 2.48% (fr-en), are achieved because some non-monotonic word alignments are discarded to ensure that the original bitext can be fully recovered when decompressing. Multiword units may be used in the biwords in order to reduce the number of discarded word alignments. * Funded by Spanish projects TIN2006-15071-C03-01, TIN2006-15071-C03-02 and VA012B08. Miguel A. Martínez-Prieto is granted by JCyL and ESF. We thank Mikel L. Forcada for inspiration.

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