Spoken Language Translation (SLT) is becoming more widely used and becoming a communication tool that helps in crossing language barriers. One of the challenges of SLT is the translation from a language without gender agreement to a language with gender agreement such as English to Arabic. In this paper, we introduce an approach to tackle such limitation by enabling a Neural Machine Translation system to produce gender-aware translation. We show that NMT system can model the speaker/listener gender information to produce gender-aware translation. We propose a method to generate data used in adapting a NMT system to produce gender-aware. The proposed approach can achieve significant improvement of the translation quality by 2 BLEU points.

This work examines important issues in probabilistic temporal representation and reasoning using Bayesian networks (also known as belief networks). The representation proposed here utilizes temporal (or dynamic) probabilities to represent facts, events, and the effects of events. The architecture of a belief network may change with time to indicate a different causal context. Probability variations with time capture temporal properties such as persistence and causation. They also capture event interaction, and when the interaction between events follows known models such as the competing risks model, the additive model, or the dominating event model, the net effect of many interacting events on the temporal probabilities can be calculated efficiently. This representation of reasoning also exploits the notion of temporal degeneration of relevance due to information obsolescence to improve the efficiency.

This paper motivates the use of Qualitative Probabilistic Networks (QPNs) in conjunction with or in lieu of Bayesian Networks (BNs) for reconstructing gene regulatory networks from microarray expression data. QPNs are qualitative abstractions of Bayesian Networks that replace the conditional probability tables associated with BNs by qualitative influences, which use signs to encode how the values of variables change. We demonstrate that the qualitative influences defined by QPNs exhibit a natural mapping to naturally-occurring patterns of connections, termed network motifs, embedded in Gene Regulatory Networks and present a model that maps QPN constructs to such motifs.The contribution of this paper is that of discovering motifs by mapping their time-series experimental data to QPN influences and using the discovered motifs to aid the process of reconstructing the corresponding gene regulatory network via Dynamic Bayesian Networks (DBNs). The general aim is to compile a model that uses qualitative equivalents of Dynamic Bayesian Networks to explore gene expression networks and their regulatory mechanisms. Although this aim remains under development, the results we have obtained shows success for the discovery of regulatory motifs in Saccharomyces Cerevisiae and their effectiveness in improving the results obtained in terms of reconstruction using DBNs.

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The impact of different physiological fluids on the rheological properties of gellan gum is investigated using a commercially available rheometer with a modified lower plate. The power of this method is demonstrated by measuring in real time, the rapid gelation kinetics, and gel strength of gellan gum exposed to simulated gastric fluid, lacrimal fluid, saliva, and wound fluid (all having a different ionic composition), highlighting potential use in the intelligent design of in situ gelling delivery systems. Changes in rheological behavior are examined in situ, gelation kinetics are modeled, and microstructure analyzed in the different simulated physiological environments.

This paper extends a mereotopological theory of spatiotemporal reasoning to vague "egg-yolk" regions. In this extension, the egg and its yolk are allowed to move and change over time. We present a classification of motion classes for vague regions as well as composition tables for reasoning about moving vague regions. We also discuss the formation of scrambled eggs when it becomes impossible to distinguish the yolk from the white and examine how to incorporate temporally and spatially dispersed observations to recover the yolk and white from a scrambled egg. Egg splitting may occur as a result of the recovery process when available information supports multiple egg recovery alternatives. Egg splitting adds another dimension of uncertainty to reasoning with vague regions.

Parallel corpora available for building machine translation (MT) models for dialectal Arabic (DA) are rather limited. The scarcity of resources has prompted the use of Modern Standard Arabic (MSA) abundant resources to complement the limited dialectal resource. However, clitics often differ between MSA and DA. This paper compares morphologyaware DA word segmentation to other word segmentation approaches like Byte Pair Encoding (BPE) and Sub-word Regularization (SR). A set of experiments conducted on Egyptian Arabic (EA), Levantine Arabic (LA), and Gulf Arabic (GA) show that a sufficiently accurate morphology-aware segmentation used in conjunction with BPE or SR outperforms the other word segmentation approaches.

This paper demonstrates the use of qualitative probabilistic networks (QPNs) to aid Dynamic Bayesian Networks (DBNs) in the process of learning the structure of gene regulatory networks from microarray gene expression data. We present a study which shows that QPNs define monotonic relations that are capable of identifying regulatory interactions in a manner that is less susceptible to the many sources of uncertainty that surround gene expression data. Moreover, we construct a model that maps the regulatory interactions of genetic networks to QPN constructs and show its capability in providing a set of candidate regulators for target genes, which is subsequently used to establish a prior structure that the DBN learning algorithm can use and which 1) distinguishes spurious correlations from true regulations, 2) enables the discovery of sets of coregulators of target genes, and 3) results in a more efficient construction of gene regulatory networks. The model is compared to the existing literature using the known gene regulatory interactions of Drosophila Melanogaster.