Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userThe Troublesome Broadband Evolution of GRB 061126: Does a Grey Burst Imply Grey Dust?
63
0
0.0
(
0
)
Ask ChatGPT about the research
No Arabic abstract
We report on observations of a gamma-ray burst (GRB 061126) with an extremely bright (R ~ 12 mag at peak) early-time optical afterglow. The optical afterglow is already fading as a power law 22 seconds after the trigger, with no detectable prompt contribution in our first exposure, which was coincident with a large prompt-emission gamma-ray pulse. The optical--infrared photometric spectral energy distribution is an excellent fit to a power law, but it exhibits a moderate red-to-blue evolution in the spectral index at about 500 s after the burst. This color change is contemporaneous with a switch from a relatively fast decay to slower decay. The rapidly decaying early afterglow is broadly consistent with synchrotron emission from a reverse shock, but a bright forward-shock component predicted by the intermediate- to late-time X-ray observations under the assumptions of standard afterglow models is not observed. Indeed, despite its remarkable early-time brightness, this burst would qualify as a dark burst at later times on the basis of its nearly flat optical-to-X-ray spectral index. Our photometric spectral energy distribution provides no evidence of host-galaxy extinction, requiring either large quantities of grey dust in the host system (at redshift 1.1588 +/- 0.0006, based upon our late-time Keck spectroscopy) or separate physical origins for the X-ray and optical afterglows.
rate research
Read More
We design and implement from scratch a new fuzzer called SIVO that refines multiple stages of grey-box fuzzing. First, SIVO refines data-flow fuzzing in two ways: (a) it provides a new taint inference engine that requires only logarithmic in the input size number of tests to infer the dependency of all program branches on the input bytes, and (b) it deploys a novel method for inverting branches by solving directly and efficiently systems of inequalities. Second, our fuzzer refines accurate tracking and detection of code coverage with simple and easily implementable methods. Finally, SIVO refines selection of parameters and strategies by parameterizing all stages of fuzzing and then dynamically selecting optimal values during fuzzing. Thus the fuzzer can easily adapt to a target program and rapidly increase coverage. We compare our fuzzer to 11 other state-of-the-art grey-box fuzzers on 27 popular benchmarks. Our evaluation shows that SIVO scores the highest both in terms of code coverage and in terms of number of found vulnerabilities.
Swift triggered on a precursor to the main burst of GRB 061121 (z=1.314), allowing observations to be made from the optical to gamma-ray bands. Many other telescopes, including Konus-Wind, XMM-Newton, ROTSE and the Faulkes Telescope North, also observed the burst. The gamma-ray, X-ray and UV/optical emission all showed a peak ~75s after the trigger, although the optical and X-ray afterglow components also appear early on - before, or during, the main peak. Spectral evolution was seen throughout the burst, with the prompt emission showing a clear positive correlation between brightness and hardness. The Spectral Energy Distribution (SED) of the prompt emission, stretching from 1eV up to 1MeV, is very flat, with a peak in the flux density at ~1keV. The optical-to-X-ray spectra at this time are better fitted by a broken, rather than single, power-law, similar to previous results for X-ray flares. The SED shows spectral hardening as the afterglow evolves with time. This behaviour might be a symptom of self-Comptonisation, although circumstellar densities similar to those found in the cores of molecular clouds would be required. The afterglow also decays too slowly to be accounted for by the standard models. Although the precursor and main emission show different spectral lags, both are consistent with the lag-luminosity correlation for long bursts. GRB 061121 is the instantaneously brightest long burst yet detected by Swift. Using a combination of Swift and Konus-Wind data, we estimate an isotropic energy of 2.8x10^53 erg over 1keV - 10MeV in the GRB rest frame. A probable jet break is detected at ~2x10^5s, leading to an estimate of ~10^51 erg for the beaming-corrected gamma-ray energy.
Foresight of CO$_2$ emissions from fuel combustion is essential for policy-makers to identify ready targets for effective reduction plans and further to improve energy policies and plans. For the purpose of accurately forecasting the future development of Chinas CO$_2$ emissions from fuel combustion, a novel continuous fractional nonlinear grey Bernoulli model is developed in this paper. The fractional nonlinear grey Bernoulli model already in place is known that has a fixed first-order derivative that impairs the predictive performance to some extent. To address this problem, in the newly proposed model, a flexible variable is introduced into the order of derivative, freeing it from integer-order accumulation. In order to further improve the performance of the newly proposed model, a meta-heuristic algorithm, namely Grey Wolf Optimizer (GWO), is determined to the emerging coefficients. To demonstrate the effectiveness, two real examples and Chinas fuel combustion-related CO$_2$ emissions are used for model validation by comparing with other benchmark models, the results show the proposed model outperforms competitors. Thus, the future development trend of fuel combustion-related CO$_2$ emissions by 2023 are predicted, accounting for 10039.80 Million tons (Mt). In accordance with the forecasts, several suggestions are provided to curb carbon dioxide emissions.
Mobile apps are extensively involved in cyber-crimes. Some apps are malware which compromise users devices, while some others may lead to privacy leakage. Apart from them, there also exist apps which directly make profit from victims through deceiving, threatening or other criminal actions. We name these apps as CULPRITWARE. They have become emerging threats in recent years. However, the characteristics and the ecosystem of CULPRITWARE remain mysterious. This paper takes the first step towards systematically studying CULPRITWARE and its ecosystem. Specifically, we first characterize CULPRITWARE by categorizing and comparing them with benign apps and malware. The result shows that CULPRITWARE have unique features, e.g., the usage of app generators (25.27%) deviates from that of benign apps (5.08%) and malware (0.43%). Such a discrepancy can be used to distinguish CULPRITWARE from benign apps and malware. Then we understand the structure of the ecosystem by revealing the four participating entities (i.e., developer, agent, operator and reaper) and the workflow. After that, we further reveal the characteristics of the ecosystem by studying the participating entities. Our investigation shows that the majority of CULPRITWARE (at least 52.08%) are propagated through social media rather than the official app markets, and most CULPRITWARE (96%) indirectly rely on the covert fourth-party payment services to transfer the profits. Our findings shed light on the ecosystem, and can facilitate the community and law enforcement authorities to mitigate the threats. We will release the source code of our tools to engage the community.
The cerebellar grey matter morphology is an important feature to study neurodegenerative diseases such as Alzheimers disease or Downs syndrome. Its volume or thickness is commonly used as a surrogate imaging biomarker for such diseases. Most studies about grey matter thickness estimation focused on the cortex, and little attention has been drawn on the morphology of the cerebellum. Using ex vivo high-resolution MRI, it is now possible to visualise the different cell layers in the mouse cerebellum. In this work, we introduce a framework to extract the Purkinje layer within the grey matter, enabling the estimation of the thickness of the cerebellar grey matter, the granular layer and molecular layer from gadolinium-enhanced ex vivo mouse brain MRI. Application to mouse model of Downs syndrome found reduced cortical and layer thicknesses in the transchromosomic group.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
