16,004 research outputs found
Linear and Range Counting under Metric-based Local Differential Privacy
Local differential privacy (LDP) enables private data sharing and analytics
without the need for a trusted data collector. Error-optimal primitives (for,
e.g., estimating means and item frequencies) under LDP have been well studied.
For analytical tasks such as range queries, however, the best known error bound
is dependent on the domain size of private data, which is potentially
prohibitive. This deficiency is inherent as LDP protects the same level of
indistinguishability between any pair of private data values for each data
downer.
In this paper, we utilize an extension of -LDP called Metric-LDP or
-LDP, where a metric defines heterogeneous privacy guarantees for
different pairs of private data values and thus provides a more flexible knob
than does to relax LDP and tune utility-privacy trade-offs. We show
that, under such privacy relaxations, for analytical workloads such as linear
counting, multi-dimensional range counting queries, and quantile queries, we
can achieve significant gains in utility. In particular, for range queries
under -LDP where the metric is the -distance function scaled by
, we design mechanisms with errors independent on the domain sizes;
instead, their errors depend on the metric , which specifies in what
granularity the private data is protected. We believe that the primitives we
design for -LDP will be useful in developing mechanisms for other analytical
tasks, and encourage the adoption of LDP in practice
Dispersive Elastodynamics of 1D Banded Materials and Structures: Design
Within periodic materials and structures, wave scattering and dispersion
occur across constituent material interfaces leading to a banded frequency
response. In an earlier paper, the elastodynamics of one-dimensional periodic
materials and finite structures comprising these materials were examined with
an emphasis on their frequency-dependent characteristics. In this work, a novel
design paradigm is presented whereby periodic unit cells are designed for
desired frequency band properties, and with appropriate scaling, these cells
are used as building blocks for forming fully periodic or partially periodic
structures with related dynamical characteristics. Through this multiscale
dispersive design methodology, which is hierarchical and integrated, structures
can be devised for effective vibration or shock isolation without needing to
employ dissipative damping mechanisms. The speed of energy propagation in a
designed structure can also be dictated through synthesis of the unit cells.
Case studies are presented to demonstrate the effectiveness of the methodology
for several applications. Results are given from sensitivity analyses that
indicate a high level of robustness to geometric variation.Comment: 33 text pages, 27 figure
Statistical inference of the mechanisms driving collective cell movement
Numerous biological processes, many impacting on human health, rely on collective cell
movement. We develop nine candidate models, based on advection-diffusion partial differential equations, to describe various alternative mechanisms that may drive cell movement. The parameters of these models were inferred from one-dimensional projections of laboratory observations of Dictyostelium discoideum cells by sampling from the posterior distribution using the delayed rejection adaptive Metropolis algorithm (DRAM). The best model was selected using the Widely Applicable Information Criterion (WAIC). We conclude that cell movement in our study system was driven both by a self-generated gradient in an attractant that the cells could deplete locally, and by chemical interactions between the cells
Not all adversarial examples require a complex defense : identifying over-optimized adversarial examples with IQR-based logit thresholding
Detecting adversarial examples currently stands as one of the biggest challenges in the field of deep learning. Adversarial attacks, which produce adversarial examples, increase the prediction likelihood of a target class for a particular data point. During this process, the adversarial example can be further optimized, even when it has already been wrongly classified with 100% confidence, thus making the adversarial example even more difficult to detect. For this kind of adversarial examples, which we refer to as over-optimized adversarial examples, we discovered that the logits of the model provide solid clues on whether the data point at hand is adversarial or genuine. In this context, we first discuss the masking effect of the softmax function for the prediction made and explain why the logits of the model are more useful in detecting over-optimized adversarial examples. To identify this type of adversarial examples in practice, we propose a non-parametric and computationally efficient method which relies on interquartile range, with this method becoming more effective as the image resolution increases. We support our observations throughout the paper with detailed experiments for different datasets (MNIST, CIFAR-10, and ImageNet) and several architectures
Inference of the drivers of collective movement in two cell types: Dictyostelium and melanoma
Collective cell movement is a key component of many important biological processes, including wound healing, the immune response and the spread of cancers. To understand and influence these movements, we need to be able to identify and quantify the contribution of their different underlying mechanisms. Here, we define a set of six candidate models—formulated as advection–diffusion–reaction partial differential equations—that incorporate a range of cell movement drivers. We fitted these models to movement assay data from two different cell types: Dictyostelium discoideum and human melanoma. Model comparison using widely applicable information criterion suggested that movement in both of our study systems was driven primarily by a self-generated gradient in the concentration of a depletable chemical in the cells' environment. For melanoma, there was also evidence that overcrowding influenced movement. These applications of model inference to determine the most likely drivers of cell movement indicate that such statistical techniques have potential to support targeted experimental work in increasing our understanding of collective cell movement in a range of systems
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