1,473 research outputs found
Parallel Exhaustive Search without Coordination
We analyze parallel algorithms in the context of exhaustive search over
totally ordered sets. Imagine an infinite list of "boxes", with a "treasure"
hidden in one of them, where the boxes' order reflects the importance of
finding the treasure in a given box. At each time step, a search protocol
executed by a searcher has the ability to peek into one box, and see whether
the treasure is present or not. By equally dividing the workload between them,
searchers can find the treasure times faster than one searcher.
However, this straightforward strategy is very sensitive to failures (e.g.,
crashes of processors), and overcoming this issue seems to require a large
amount of communication. We therefore address the question of designing
parallel search algorithms maximizing their speed-up and maintaining high
levels of robustness, while minimizing the amount of resources for
coordination. Based on the observation that algorithms that avoid communication
are inherently robust, we analyze the best running time performance of
non-coordinating algorithms. Specifically, we devise non-coordinating
algorithms that achieve a speed-up of for two searchers, a speed-up of
for three searchers, and in general, a speed-up of
for any searchers. Thus, asymptotically, the speed-up is only four
times worse compared to the case of full-coordination, and our algorithms are
surprisingly simple and hence applicable. Moreover, these bounds are tight in a
strong sense as no non-coordinating search algorithm can achieve better
speed-ups. Overall, we highlight that, in faulty contexts in which coordination
between the searchers is technically difficult to implement, intrusive with
respect to privacy, and/or costly in term of resources, it might well be worth
giving up on coordination, and simply run our non-coordinating exhaustive
search algorithms
Thermal conductance of thin film YIG determined using Bayesian statistics
Thin film YIG (YFeO) is a prototypical material for
experiments on thermally generated pure spin currents and the spin Seebeck
effect. The 3-omega method is an established technique to measure the
cross-plane thermal conductance of thin films, but can not be used in YIG/GGG
(GaGdO) systems in its standard form. We use two-dimensional
modeling of heat transport and introduce a technique based on Bayesian
statistics to evaluate measurement data taken from the 3-omega method. Our
analysis method allows us to study materials systems that have not been
accessible with the conventionally used 3-omega analysis. Temperature dependent
thermal conductance data of thin film YIG are of major importance for
experiments in the field of spin-caloritronics. Here we show data between room
temperature and 10 K for films covering a wide thickness range as well as the
magnetic field effect on the thermal conductance between 10 K and 50 K
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Draft Genome Sequence of the Caffeine-Degrading Methylotroph Methylorubrum populi Pinkel.
A pink-pigmented facultative methylotroph, Methylorubrum populi Pinkel, was isolated from compost by selective enrichment with caffeine (3,5,7-trimethylxanthine) as the sole carbon, nitrogen, and energy source. We report here its high-quality draft genome sequence, assembled in 35 contigs totaling 5,630,907 bp. We identified 5,681 protein-coding sequences, including those putatively involved in caffeine degradation
Are You Tampering With My Data?
We propose a novel approach towards adversarial attacks on neural networks
(NN), focusing on tampering the data used for training instead of generating
attacks on trained models. Our network-agnostic method creates a backdoor
during training which can be exploited at test time to force a neural network
to exhibit abnormal behaviour. We demonstrate on two widely used datasets
(CIFAR-10 and SVHN) that a universal modification of just one pixel per image
for all the images of a class in the training set is enough to corrupt the
training procedure of several state-of-the-art deep neural networks causing the
networks to misclassify any images to which the modification is applied. Our
aim is to bring to the attention of the machine learning community, the
possibility that even learning-based methods that are personally trained on
public datasets can be subject to attacks by a skillful adversary.Comment: 18 page
CacheZoom: How SGX Amplifies The Power of Cache Attacks
In modern computing environments, hardware resources are commonly shared, and
parallel computation is widely used. Parallel tasks can cause privacy and
security problems if proper isolation is not enforced. Intel proposed SGX to
create a trusted execution environment within the processor. SGX relies on the
hardware, and claims runtime protection even if the OS and other software
components are malicious. However, SGX disregards side-channel attacks. We
introduce a powerful cache side-channel attack that provides system adversaries
a high resolution channel. Our attack tool named CacheZoom is able to virtually
track all memory accesses of SGX enclaves with high spatial and temporal
precision. As proof of concept, we demonstrate AES key recovery attacks on
commonly used implementations including those that were believed to be
resistant in previous scenarios. Our results show that SGX cannot protect
critical data sensitive computations, and efficient AES key recovery is
possible in a practical environment. In contrast to previous works which
require hundreds of measurements, this is the first cache side-channel attack
on a real system that can recover AES keys with a minimal number of
measurements. We can successfully recover AES keys from T-Table based
implementations with as few as ten measurements.Comment: Accepted at Conference on Cryptographic Hardware and Embedded Systems
(CHES '17
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