79 research outputs found
Spiking Optical Patterns and Synchronization
We analyze the time resolved spike statistics of a solitary and two mutually
interacting chaotic semiconductor lasers whose chaos is characterized by
apparently random, short intensity spikes. Repulsion between two successive
spikes is observed, resulting in a refractory period which is largest at laser
threshold. For time intervals between spikes greater than the refractory
period, the distribution of the intervals follows a Poisson distribution. The
spiking pattern is highly periodic over time windows corresponding to the
optical length of the external cavity, with a slow change of the spiking
pattern as time increases. When zero-lag synchronization between the two lasers
is established, the statistics of the nearly perfectly matched spikes are not
altered. The similarity of these features to those found in complex interacting
neural networks, suggests the use of laser systems as simpler physical models
for neural networks
Mind The Edge: Refining Depth Edges in Sparsely-Supervised Monocular Depth Estimation
Monocular Depth Estimation (MDE) is a fundamental problem in computer vision
with numerous applications. Recently, LIDAR-supervised methods have achieved
remarkable per-pixel depth accuracy in outdoor scenes. However, significant
errors are typically found in the proximity of depth discontinuities, i.e.,
depth edges, which often hinder the performance of depth-dependent applications
that are sensitive to such inaccuracies, e.g., novel view synthesis and
augmented reality. Since direct supervision for the location of depth edges is
typically unavailable in sparse LIDAR-based scenes, encouraging the MDE model
to produce correct depth edges is not straightforward. In this work we propose
to learn to detect the location of depth edges from densely-supervised
synthetic data, and use it to generate supervision for the depth edges in the
MDE training. %Despite the 'domain gap' between synthetic and real data, we
show that depth edges that are estimated directly are significantly more
accurate than the ones that emerge indirectly from the MDE training. To
quantitatively evaluate our approach, and due to the lack of depth edges ground
truth in LIDAR-based scenes, we manually annotated subsets of the KITTI and the
DDAD datasets with depth edges ground truth. We demonstrate significant gains
in the accuracy of the depth edges with comparable per-pixel depth accuracy on
several challenging datasets
Constraining Baryon-Dark-Matter Scattering with the Cosmic Dawn 21-cm Signal.
The recent detection of an anomalously strong 21-cm signal of neutral hydrogen from cosmic dawn by the EDGES low-band radio experiment can be explained if cold dark matter particles scattered off the baryons draining excess energy from the gas. In this Letter we explore the expanded range of the 21-cm signal that is opened up by this interaction, varying the astrophysical parameters as well as the properties of dark matter particles in the widest possible range. We identify models consistent with current data by comparing to both the detection in the low-band region and the upper limits from the EDGES high-band antenna. We find that consistent models predict a 21-cm fluctuation during cosmic dawn that is between 3 and 30 times larger than the largest previously expected without dark matter scattering. The expected power spectrum exhibits strong baryon acoustic oscillations imprinted by the velocity-dependent cross section. The latter signature is a conclusive evidence of the velocity-dependent scattering and could be used by interferometers to verify the dark matter explanation of the EDGES detection
Sec-Lib: Protecting Scholarly Digital Libraries From Infected Papers Using Active Machine Learning Framework
Researchers from academia and the corporate-sector rely on scholarly digital libraries to access articles. Attackers take advantage of innocent users who consider the articles\u27 files safe and thus open PDF-files with little concern. In addition, researchers consider scholarly libraries a reliable, trusted, and untainted corpus of papers. For these reasons, scholarly digital libraries are an attractive-target and inadvertently support the proliferation of cyber-attacks launched via malicious PDF-files. In this study, we present related vulnerabilities and malware distribution approaches that exploit the vulnerabilities of scholarly digital libraries. We evaluated over two-million scholarly papers in the CiteSeerX library and found the library to be contaminated with a surprisingly large number (0.3-2%) of malicious PDF documents (over 55% were crawled from the IPs of US-universities). We developed a two layered detection framework aimed at enhancing the detection of malicious PDF documents, Sec-Lib, which offers a security solution for large digital libraries. Sec-Lib includes a deterministic layer for detecting known malware, and a machine learning based layer for detecting unknown malware. Our evaluation showed that scholarly digital libraries can detect 96.9% of malware with Sec-Lib, while minimizing the number of PDF-files requiring labeling, and thus reducing the manual inspection efforts of security-experts by 98%
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