1,047 research outputs found
Avian Haemosporidian blood parasite infections at a migration hotspot in Eilat, Israel
Haemosporidian blood parasites are frequent amongst passerines. Though they often do not cause detectable consequences to host health, however, their presence or absence and also their prevalence across host populations may potentially carry meaningful information about the health, stress, body condition and viability of bird individuals or populations. The study of migratory birds captured in Eilat, Israel, allowed us to evaluate the prevalence of blood parasite infections in a wide range of both migrant and resident species in spring (N = 1,950) and autumn (N = 538) of 2004 and 2005. According to blood film microscopy, Haemoproteus spp. and Leucocytozoon spp. were more prevalent in the spring than in the autumn (0.289, 0.082 vs. 0.132, 0.033, respectively), whilst Plasmodium spp. exhibited a slight opposite trend (0.034, 0.056). All other parasites (such as trypanosomes, microfilaria and haemococcidians) were rare. During the spring seasons, prevalences were significantly higher in migrant than in resident species, whilst this difference was only marginally significant in the autumn. Given that Eilat is a migration hotspot for several Palearctic passerine species, the present descriptive study may hopefully serve to set the baseline values for future long-term epidemiological monitoring
Distributed NEGF Algorithms for the Simulation of Nanoelectronic Devices with Scattering
Through the Non-Equilibrium Green's Function (NEGF) formalism, quantum-scale
device simulation can be performed with the inclusion of electron-phonon
scattering. However, the simulation of realistically sized devices under the
NEGF formalism typically requires prohibitive amounts of memory and computation
time. Two of the most demanding computational problems for NEGF simulation
involve mathematical operations with structured matrices called semiseparable
matrices. In this work, we present parallel approaches for these computational
problems which allow for efficient distribution of both memory and computation
based upon the underlying device structure. This is critical when simulating
realistically sized devices due to the aforementioned computational burdens.
First, we consider determining a distributed compact representation for the
retarded Green's function matrix . This compact representation is exact
and allows for any entry in the matrix to be generated through the inherent
semiseparable structure. The second parallel operation allows for the
computation of electron density and current characteristics for the device.
Specifically, matrix products between the distributed representation for the
semiseparable matrix and the self-energy scattering terms in
produce the less-than Green's function . As an illustration
of the computational efficiency of our approach, we stably generate the
mobility for nanowires with cross-sectional sizes of up to 4.5nm, assuming an
atomistic model with scattering
Are Accuracy and Robustness Correlated?
Machine learning models are vulnerable to adversarial examples formed by
applying small carefully chosen perturbations to inputs that cause unexpected
classification errors. In this paper, we perform experiments on various
adversarial example generation approaches with multiple deep convolutional
neural networks including Residual Networks, the best performing models on
ImageNet Large-Scale Visual Recognition Challenge 2015. We compare the
adversarial example generation techniques with respect to the quality of the
produced images, and measure the robustness of the tested machine learning
models to adversarial examples. Finally, we conduct large-scale experiments on
cross-model adversarial portability. We find that adversarial examples are
mostly transferable across similar network topologies, and we demonstrate that
better machine learning models are less vulnerable to adversarial examples.Comment: Accepted for publication at ICMLA 201
Adversarial Diversity and Hard Positive Generation
State-of-the-art deep neural networks suffer from a fundamental problem -
they misclassify adversarial examples formed by applying small perturbations to
inputs. In this paper, we present a new psychometric perceptual adversarial
similarity score (PASS) measure for quantifying adversarial images, introduce
the notion of hard positive generation, and use a diverse set of adversarial
perturbations - not just the closest ones - for data augmentation. We introduce
a novel hot/cold approach for adversarial example generation, which provides
multiple possible adversarial perturbations for every single image. The
perturbations generated by our novel approach often correspond to semantically
meaningful image structures, and allow greater flexibility to scale
perturbation-amplitudes, which yields an increased diversity of adversarial
images. We present adversarial images on several network topologies and
datasets, including LeNet on the MNIST dataset, and GoogLeNet and ResidualNet
on the ImageNet dataset. Finally, we demonstrate on LeNet and GoogLeNet that
fine-tuning with a diverse set of hard positives improves the robustness of
these networks compared to training with prior methods of generating
adversarial images.Comment: Accepted to CVPR 2016 DeepVision Worksho
Adversarial Robustness: Softmax versus Openmax
Deep neural networks (DNNs) provide state-of-the-art results on various tasks
and are widely used in real world applications. However, it was discovered that
machine learning models, including the best performing DNNs, suffer from a
fundamental problem: they can unexpectedly and confidently misclassify examples
formed by slightly perturbing otherwise correctly recognized inputs. Various
approaches have been developed for efficiently generating these so-called
adversarial examples, but those mostly rely on ascending the gradient of loss.
In this paper, we introduce the novel logits optimized targeting system (LOTS)
to directly manipulate deep features captured at the penultimate layer. Using
LOTS, we analyze and compare the adversarial robustness of DNNs using the
traditional Softmax layer with Openmax, which was designed to provide open set
recognition by defining classes derived from deep representations, and is
claimed to be more robust to adversarial perturbations. We demonstrate that
Openmax provides less vulnerable systems than Softmax to traditional attacks,
however, we show that it can be equally susceptible to more sophisticated
adversarial generation techniques that directly work on deep representations.Comment: Accepted to British Machine Vision Conference (BMVC) 201
Henri Temianka Correspondence; (rozsa)
This collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/4160/thumbnail.jp
Henri Temianka Correspondence; (rozsa)
This collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/4159/thumbnail.jp
Henri Temianka Correspondence; (rozsa)
This collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/4154/thumbnail.jp
Henri Temianka Correspondence; (rozsa)
This collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/4152/thumbnail.jp
Henri Temianka Correspondence; (rozsa)
This collection contains material pertaining to the life, career, and activities of Henri Temianka, violin virtuoso, conductor, music teacher, and author. Materials include correspondence, concert programs and flyers, music scores, photographs, and books.https://digitalcommons.chapman.edu/temianka_correspondence/4153/thumbnail.jp
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