Alexander Russell

Currently, depression treatment relies on closely monitoring patients’ response to treatment and adjusting the treatment as needed. Using self-reported or physician-administrated questionnaires to monitor treatment response is, however, subjective, costly and suffers from recall bias. In this paper, we explore using location sensory data collected passively on smartphones to predict treatment outcome. To address heterogeneous data collection on Android and iOS phones, the two predominant smartphone platforms, we explore using domain adaptation techniques to map their data to a common feature space, and then use the data jointly to train machine learning models. We further explore integrating contrastive learning with domain adaptation to augment data and learn feature embeddings. These learned embeddings are then used to train machine learning models to predict depression treatment outcomes. Our evaluation shows that using the embeddings learned by jointly integrating contrastive learning and domain adaptation leads to the best prediction accuracy. In addition, our results show that using location features and baseline self-reported questionnaire score can lead to F1 score up to 0.76. This accuracy is comparable to that obtained using periodic self-reported questionnaires, indicating that using location data is a promising direction for predicting depression treatment outcome. Last, when all location and questionnaire data are used together, the F1 score further increases to 0.79.

Free-space satellite communication has significantly lower photon loss than terrestrial communication via optical fibers. Satellite-based quantum key distribution (QKD) leverages this advantage and provides a promising direction in achieving long-distance QKD. While the technological feasibility of satellite-based QKD has been demonstrated experimentally, optimizing the key rate remains a significant challenge. In this paper, we argue that improving classical post-processing is an important direction in increasing key rate in satellite-based QKD, while can also be easily incorporated in existing satellite systems. In particular, we explore one direction, blockwise post-processing, to address highly dynamic satellite channel conditions due to various environmental factors. This blockwise strategy divides the raw key bits into individual blocks that have similar noise characteristics, and processes them independently, in contrast to traditional non-blockwise strategy that treats all the raw key bits as a whole. Using a case study, we discuss the choice of blocks in blockwise strategy, and show that blockwise strategy can significantly outperform non-blockwise strategy. Our study demonstrates the importance of post-processing in satellite QKD systems, and presents several open problems in this direction.

The random oracle methodology has proven to be a powerful tool for designing and reasoning about cryptographic schemes. In this paper, we focus on the basic problem of correcting faulty or adversarially corrupted random oracles, so that they can be confidently applied for such cryptographic purposes. We prove that a simple construction can transform a "subverted" random oracle which disagrees with the original one at a small fraction of inputs into an object that is indifferentiable from a random function, even if the adversary is made aware of all randomness used in the transformation. Our results permit future designers of cryptographic primitives in typical kleptographic settings (i.e., those permitting adversaries that subvert or replace basic cryptographic algorithms) to use random oracles as a trusted black box.