Breaking all the things:a systematic survey of firmware extraction and modification techniques for IoT devices

In this paper, we systematically review and categorize different hardware-based firmware extraction techniques, using 24 examples of real, wide-spread products, e.g. smart voice assistants (in particular Amazon Echo devices), alarm and access control systems, as well as home automation devices. We show that in over 45% of the cases, an exposed UART interface is sufficient to obtain a firmware dump, while in othercases, more complicated, yet still low-cost methods (e.g. JTAG or eMMC readout) are needed. In this regard, we perform an in-depth investigation of the security concept of the Amazon Echo Plus, which contains significant protection methods against hardware-level attacks. Based on the results of our study, we give recommendations for countermeasures to mitigate the respective methods

Monte Carlo Simulations for Ghost Imaging Based on Scattered Photons

X-ray based imaging modalities are widely used in research, industry, and in the medical field. Consequently, there is a strong motivation to improve their performances with respect to resolution, dose, and contrast. Ghost imaging (GI) is an imaging technique in which the images are reconstructed from measurements with a single-pixel detector using correlation between the detected intensities and the intensity structures of the input beam. The method that has been recently extended to X-rays provides intriguing possibilities to overcome several fundamental challenges of X-ray imaging. However, understanding the potential of the method and designing X-ray GI systems pose challenges since in addition to geometric optic effects, radiation-matter interactions must be considered. Such considerations are fundamentally more complex than those at longer wavelengths as relativistic effects such as Compton scattering become significant. In this work we present a new method for designing and implementing GI systems using the particle transport code FLUKA, that rely on Monte Carlo (MC) sampling. This new approach enables comprehensive consideration of the radiation-matter interactions, facilitating successful planning of complex GI systems. As an example of an advanced imaging system, we simulate a high-resolution scattered photons GI technique

Private Incremental Regression

Data is continuously generated by modern data sources, and a recent challenge in machine learning has been to develop techniques that perform well in an incremental (streaming) setting. In this paper, we investigate the problem of private machine learning, where as common in practice, the data is not given at once, but rather arrives incrementally over time. We introduce the problems of private incremental ERM and private incremental regression where the general goal is to always maintain a good empirical risk minimizer for the history observed under differential privacy. Our first contribution is a generic transformation of private batch ERM mechanisms into private incremental ERM mechanisms, based on a simple idea of invoking the private batch ERM procedure at some regular time intervals. We take this construction as a baseline for comparison. We then provide two mechanisms for the private incremental regression problem. Our first mechanism is based on privately constructing a noisy incremental gradient function, which is then used in a modified projected gradient procedure at every timestep. This mechanism has an excess empirical risk of $\approx\sqrt{d}$, where $d$ is the dimensionality of the data. While from the results of [Bassily et al. 2014] this bound is tight in the worst-case, we show that certain geometric properties of the input and constraint set can be used to derive significantly better results for certain interesting regression problems.Comment: To appear in PODS 201

Quantity makes quality: learning with partial views

In many real world applications, the number of examples to learn from is plentiful, but we can only obtain limited information on each individual example. We study the possibilities of efficient, provably correct, large-scale learning in such settings. The main theme we would like to establish is that large amounts of examples can compensate for the lack of full information on each individual example. The type of partial information we consider can be due to inherent noise or from constraints on the type of interaction with the data source. In particular, we describe and analyze algorithms for budgeted learning, in which the learner can only view a few attributes of each training example (Cesa-Bianchi, Shalev-Shwartz, and Shamir 2010a; 2010c), and algorithms for learning kernel-based predictors, when individual examples are corrupted by random noise (Cesa-Bianchi, Shalev-Shwartz, and Shamir 2010b)

Post-hospital medical respite care and hospital readmission of homeless persons

Medical respite programs offer medical, nursing, and other care as well as accommodation for homeless persons discharged from acute hospital stays. They represent a community-based adaptation of urban health systems to the specific needs of homeless persons. This article examines whether post-hospital discharge to a homeless medical respite program was associated with a reduced chance of 90-day readmission compared to other disposition options. Adjusting for imbalances in patient characteristics using propensity scores, respite patients were the only group that was significantly less likely to be readmitted within 90 days compared to those released to Own Care. Respite programs merit attention as a potentially efficacious service for homeless persons leaving the hospital

On conformal measures and harmonic functions for group extensions

We prove a Perron-Frobenius-Ruelle theorem for group extensions of topological Markov chains based on a construction of $\sigma$-finite conformal measures and give applications to the construction of harmonic functions.Comment: To appear in Proceedings of "New Trends in Onedimensional Dynamics, celebrating the 70th birthday of Welington de Melo

Averages of bb-hadron, cc-hadron, and τ\tau-lepton properties as of summer 2014

This article reports world averages of measurements of $b$-hadron, $c$-hadron, and $\tau$-lepton properties obtained by the Heavy Flavor Averaging Group (HFAG) using results available through summer 2014. For the averaging, common input parameters used in the various analyses are adjusted (rescaled) to common values, and known correlations are taken into account. The averages include branching fractions, lifetimes, neutral meson mixing parameters, $CP$ violation parameters, parameters of semileptonic decays and CKM matrix elements.Comment: 436 pages, many figures and tables. Online updates available at http://www.slac.stanford.edu/xorg/hfag