Group Testing with Blocks of Positives and Inhibitors.

The main goal of group testing is to identify a small number of specific items among a large population of items. In this paper, we consider specific items as positives and inhibitors and non-specific items as negatives. In particular, we consider a novel model called group testing with blocks of positives and inhibitors. A test on a subset of items is positive if the subset contains at least one positive and does not contain any inhibitors, and it is negative otherwise. In this model, the input items are linearly ordered, and the positives and inhibitors are subsets of small blocks (at unknown locations) of consecutive items over that order. We also consider two specific instantiations of this model. The first instantiation is that model that contains a single block of consecutive items consisting of exactly known numbers of positives and inhibitors. The second instantiation is the model that contains a single block of consecutive items containing known numbers of positives and inhibitors. Our contribution is to propose efficient encoding and decoding schemes such that the numbers of tests used to identify only positives or both positives and inhibitors are less than the ones in the state-of-the-art schemes. Moreover, the decoding times mostly scale to the numbers of tests that are significantly smaller than the state-of-the-art ones, which scale to both the number of tests and the number of items.

Progressive Transmission of Medical Images via a Bank of Generative Adversarial Networks.

The healthcare sector is currently undergoing a major transformation due to the recent advances in deep learning and artificial intelligence. Despite a significant breakthrough in medical imaging and diagnosis, there are still many open issues and undeveloped applications in the healthcare domain. In particular, transmission of a large volume of medical images proves to be a challenging and time-consuming problem, and yet no prior studies have investigated the use of deep neural networks towards this task. The purpose of this paper is to introduce and develop a deep-learning approach for the efficient transmission of medical images, with a particular interest in the progressive coding of bit-planes. We establish a connection between bit-plane synthesis and image-to-image translation and propose a two-step pipeline for progressive image transmission. First, a bank of generative adversarial networks is trained for predicting bit-planes in a top-down manner, and then prediction residuals are encoded with a tailored adaptive lossless compression algorithm. Experimental results validate the effectiveness of the network bank for generating an accurate low-order bit-plane from high-order bit-planes and demonstrate an advantage of the tailored compression algorithm over conventional arithmetic coding for this special type of prediction residuals in terms of compression ratio.

Aspects of privacy for electronic health records.

Patients' medical data have been originally generated and maintained by health professionals in several independent electronic health records (EHRs). Centralized electronic health records accumulate medical data of patients to improve their availability and completeness; EHRs are not tied to a single medical institution anymore. Nowadays enterprises with the capacity and knowledge to maintain this kind of databases offer the services of maintaining EHRs and adding personal health data by the patients. These enterprises get access on the patients' medical data and act as a main point for collecting and disclosing personal data to third parties, e.g. among others doctors, healthcare service providers and drug stores. Existing systems like Microsoft HealthVault and Google Health comply with data protection acts by letting the patients decide on the usage and disclosure of their data. But they fail in satisfying essential requirements to privacy. We propose a privacy-protecting information system for controlled disclosure of personal data to third parties. Firstly, patients should be able to express and enforce obligations regarding a disclosure of health data to third parties. Secondly, an organization providing EHRs should neither be able to gain access to these health data nor establish a profile about patients.