Learning Privacy Preserving Encodings through Adversarial Training

We present a framework to learn privacy-preserving encodings of images that inhibit inference of chosen private attributes, while allowing recovery of other desirable information. Rather than simply inhibiting a given fixed pre-trained estimator, our goal is that an estimator be unable to learn to accurately predict the private attributes even with knowledge of the encoding function. We use a natural adversarial optimization-based formulation for this---training the encoding function against a classifier for the private attribute, with both modeled as deep neural networks. The key contribution of our work is a stable and convergent optimization approach that is successful at learning an encoder with our desired properties---maintaining utility while inhibiting inference of private attributes, not just within the adversarial optimization, but also by classifiers that are trained after the encoder is fixed. We adopt a rigorous experimental protocol for verification wherein classifiers are trained exhaustively till saturation on the fixed encoders. We evaluate our approach on tasks of real-world complexity---learning high-dimensional encodings that inhibit detection of different scene categories---and find that it yields encoders that are resilient at maintaining privacy.Comment: To appear in WACV 201

Low-level Vision by Consensus in a Spatial Hierarchy of Regions

We introduce a multi-scale framework for low-level vision, where the goal is estimating physical scene values from image data---such as depth from stereo image pairs. The framework uses a dense, overlapping set of image regions at multiple scales and a "local model," such as a slanted-plane model for stereo disparity, that is expected to be valid piecewise across the visual field. Estimation is cast as optimization over a dichotomous mixture of variables, simultaneously determining which regions are inliers with respect to the local model (binary variables) and the correct co-ordinates in the local model space for each inlying region (continuous variables). When the regions are organized into a multi-scale hierarchy, optimization can occur in an efficient and parallel architecture, where distributed computational units iteratively perform calculations and share information through sparse connections between parents and children. The framework performs well on a standard benchmark for binocular stereo, and it produces a distributional scene representation that is appropriate for combining with higher-level reasoning and other low-level cues.Comment: Accepted to CVPR 2015. Project page: http://www.ttic.edu/chakrabarti/consensus

Absolute frequency measurements of the D2D_2 line and fine-structure interval in 39^{39}K

We report a value for the $D_2$-line frequency of $^{39}$K with 0.25 ppb uncertainty. The frequency is measured using an evacuated ring-cavity resonator whose length is calibrated against a reference laser. The $D_2$ line presents a problem in identifying the line center because the closely-spaced energy levels of the excited state are not resolved. We use computer modelling of the measured spectrum to extract the line center and obtain a value of 391 015 578.040(75) MHz. In conjunction with our previous measurement of the $D_1$ line, we determine the fine-structure interval in the $4P$ state to be 1 729 997.132(90) MHz. The results represent significant improvement over previous values.Comment: 4 pages, 3 figure

Quantifying the effects of spatial resolution and noise on galaxy metallicity gradients

Metallicity gradients are important diagnostics of galaxy evolution, because they record the history of events such as mergers, gas inflow and star-formation. However, the accuracy with which gradients can be measured is limited by spatial resolution and noise, and hence measurements need to be corrected for such effects. We use high resolution (~20 pc) simulation of a face-on Milky Way mass galaxy, coupled with photoionisation models, to produce a suite of synthetic high resolution integral field spectroscopy (IFS) datacubes. We then degrade the datacubes, with a range of realistic models for spatial resolution (2 to 16 beams per galaxy scale length) and noise, to investigate and quantify how well the input metallicity gradient can be recovered as a function of resolution and signal-to-noise ratio (SNR) with the intention to compare with modern IFS surveys like MaNGA and SAMI. Given appropriate propagation of uncertainties and pruning of low SNR pixels, we show that a resolution of 3-4 telescope beams per galaxy scale length is sufficient to recover the gradient to ~10-20% uncertainty. The uncertainty escalates to ~60% for lower resolution. Inclusion of the low SNR pixels causes the uncertainty in the inferred gradient to deteriorate. Our results can potentially inform future IFS surveys regarding the resolution and SNR required to achieve a desired accuracy in metallicity gradient measurements.Comment: 21 pages, 11 figures, 20 pages Supplementary Online Material provided with 10 additional figures, accepted for publication in MNRA

The birth of a bacterial tRNA gene by large-scale, tandem duplication events

Organisms differ in the types and numbers of tRNA genes that they carry. While the evolutionary mechanisms behind tRNA gene set evolution have been investigated theoretically and computationally, direct observations of tRNA gene set evolution remain rare. Here, we report the evolution of a tRNA gene set in laboratory populations of the bacterium ̑extitPseudomonas fluorescens} SBW25. The growth defect caused by deleting the single-copy tRNA gene, ̑extit{serCGA}, is rapidly compensated by large-scale (45–290 kb) duplications in the chromosome. Each duplication encompasses a second, compensatory tRNA gene (̑extit{serTGA}) and is associated with a rise in tRNA-Ser(UGA) in the mature tRNA pool. We postulate that tRNA-Ser(CGA) elimination increases the translational demand for tRNA-Ser(UGA), a pressure relieved by increasing ̑extit{serTGA copy number. This work demonstrates that tRNA gene sets can evolve through duplication of existing tRNA genes, a phenomenon that may contribute to the presence of multiple, identical tRNA gene copies within genomes

The holographic spectral function in non-equilibrium states

We develop holographic prescriptions for obtaining spectral functions in non-equilibrium states and space-time dependent non-equilibrium shifts in the energy and spin of quasi-particle like excitations. We reproduce strongly coupled versions of aspects of non-equilibrium dynamics of Fermi surfaces in Landau's Fermi-liquid theory. We find that the incoming wave boundary condition at the horizon does not suffice to obtain a well-defined perturbative expansion for non-equilibrium observables. Our prescription, based on analysis of regularity at the horizon, allows such a perturbative expansion to be achieved nevertheless and can be precisely formulated in a universal manner independent of the non-equilibrium state, provided the state thermalizes. We also find that the non-equilibrium spectral function furnishes information about the relaxation modes of the system. Along the way, we argue that in a typical non-supersymmetric theory with a gravity dual, there may exist a window of temperature and chemical potential at large N, in which a generic non-equilibrium state can be characterized by just a finitely few operators with low scaling dimensions, even far away from the hydrodynamic limit.Comment: revtex; 43 pages, 2 figures; typos corrected, accepted for publication in PR

Managing the content of LinkedIn posts: Influence on B2B customer engagement and sales?

This study investigates whether LinkedIn content in a business-to-business (B2B) service setting affects how firms generate engagement and sales revenue. Drawing on social media marketing theoretical underpinnings, we explain how a new post typology (sales, technical, and social) and customer engagement (likes, clicks, shares, and comments) are relevant to increase firm performance. We specify a VAR model with exogenous variables (VARX) using 106 weeks of data from a new, steadily growing B2B firm. We focus on the cumulative effects (i.e., short- and long-term effects) of the types of posts, website visits, new followers, and a composite of engagement behaviors over time and compute elasticities with impulse response functions (IRFs). Our findings indicate that followers and website visits positively affect the amount of sales revenue, and sales posts and website visits drive the number of followers. In addition, we find that social posts, new followers, and sales revenue positively influence engagement. These findings demonstrate the utility of LinkedIn at the firm level, preventing top management from perceiving social media as an ornamental accessory, and provide guidance for B2B marketers about what content to post on LinkedIn