U-DADA:Unsupervised Deep Action Domain Adaptation

The problem of domain adaptation has been extensively studied for object classification task. However, this problem has not been as well studied for recognizing actions. While, object recognition is well understood, the diverse variety of videos in action recognition make the task of addressing domain shift to be more challenging. We address this problem by proposing a new novel adaptation technique that we term as unsupervised deep action domain adaptation (U-DADA). The main concept that we propose is that of explicitly modeling density based adaptation and using them while adapting domains for recognizing actions. We show that these techniques work well both for domain adaptation through adversarial learning to obtain invariant features or explicitly reducing the domain shift between distributions. The method is shown to work well using existing benchmark datasets such as UCF50, UCF101, HMDB51 and Olympic Sports. As a pioneering effort in the area of deep action adaptation, we are presenting several benchmark results and techniques that could serve as baselines to guide future research in this area.</p

Successful network inference from time-series data using Mutual Information Rate

This work uses an information-based methodology to infer the connectivity of complex systems from observed time-series data. We first derive analytically an expression for the Mutual Information Rate (MIR), namely, the amount of information exchanged per unit of time, that can be used to estimate the MIR between two finite-length low-resolution noisy time-series, and then apply it after a proper normalization for the identification of the connectivity structure of small networks of interacting dynamical systems. In particular, we show that our methodology successfully infers the connectivity for heterogeneous networks, different time-series lengths or coupling strengths, and even in the presence of additive noise. Finally, we show that our methodology based on MIR successfully infers the connectivity of networks composed of nodes with different time-scale dynamics, where inference based on Mutual Information fails

Engineering hemoglobin to enable homogenous PEGylation without modifying protein functionality

In order to infuse hemoglobin into the vasculature as an oxygen therapeutic or blood substitute, it is necessary to increase the size of the molecule to enhance vascular retention. This aim can be achieved by PEGylation. However, using non-specific conjugation methods creates heterogenous mixtures and alters protein function. Site-specific PEGylation at the naturally reactive thiol on human hemoglobin (βCys93) alters hemoglobin oxygen binding affinity and increases its autooxidation rate. In order to avoid this issue, new reactive thiol residues were therefore engineered at sites distant to the heme group and the α/β dimer/dimer interface. The two mutants were βCys93Ala/αAla19Cys and βCys93Ala/βAla13Cys. Gel electrophoresis, size exclusion chromatography and mass spectrometry revealed efficient PEGylation at both αAla19Cys and βAla13Cys, with over 80% of the thiols PEGylated in the case of αAla19Cys. For both mutants there was no significant effect on the oxygen affinity or the cooperativity of oxygen binding. PEGylation at αAla19Cys had the additional benefit of decreasing the rates of autoxidation and heme release, properties that have been considered contributory factors to the adverse clinical side effects exhibited by previous hemoglobin based oxygen carriers. PEGylation at αAla19Cys may therefore be a useful component of future clinical products