101 research outputs found
CharBot: A Simple and Effective Method for Evading DGA Classifiers
Domain generation algorithms (DGAs) are commonly leveraged by malware to
create lists of domain names which can be used for command and control (C&C)
purposes. Approaches based on machine learning have recently been developed to
automatically detect generated domain names in real-time. In this work, we
present a novel DGA called CharBot which is capable of producing large numbers
of unregistered domain names that are not detected by state-of-the-art
classifiers for real-time detection of DGAs, including the recently published
methods FANCI (a random forest based on human-engineered features) and LSTM.MI
(a deep learning approach). CharBot is very simple, effective and requires no
knowledge of the targeted DGA classifiers. We show that retraining the
classifiers on CharBot samples is not a viable defense strategy. We believe
these findings show that DGA classifiers are inherently vulnerable to
adversarial attacks if they rely only on the domain name string to make a
decision. Designing a robust DGA classifier may, therefore, necessitate the use
of additional information besides the domain name alone. To the best of our
knowledge, CharBot is the simplest and most efficient black-box adversarial
attack against DGA classifiers proposed to date
CharBot : a simple and effective method for evading DGA classifiers
Domain generation algorithms (DGAs) are commonly leveraged by malware to create lists of domain names, which can be used for command and control (C&C) purposes. Approaches based on machine learning have recently been developed to automatically detect generated domain names in real-time. In this paper, we present a novel DGA called CharBot, which is capable of producing large numbers of unregistered domain names that are not detected by state-of-the-art classifiers for real-time detection of the DGAs, including the recently published methods FANCI (a random forest based on human-engineered features) and LSTM.MI (a deep learning approach). The CharBot is very simple, effective, and requires no knowledge of the targeted DGA classifiers. We show that retraining the classifiers on CharBot samples is not a viable defense strategy. We believe these findings show that DGA classifiers are inherently vulnerable to adversarial attacks if they rely only on the domain name string to make a decision. Designing a robust DGA classifier may, therefore, necessitate the use of additional information besides the domain name alone. To the best of our knowledge, the CharBot is the simplest and most efficient black-box adversarial attack against DGA classifiers proposed to date
Acupuncture for menstruation-related migraine prophylaxis:A multicenter randomized controlled trial
OBJECTIVE: The aim of this study was to evaluate the efficacy of acupuncture, an alternative medicine therapy, as a preventive treatment for menstruation-related migraine (MRM). PATIENTS AND METHODS: This was a prospective, multicenter, double-dummy, participant-blinded, randomized controlled clinical trial conducted in China between 1 April 2013, and 30 April 2014. The participants were enrolled from four study centers and randomized to into either the acupuncture group, which received 24 sessions of acupuncture at traditional acupoints plus placebo, or the medication group, which received sham acupuncture plus naproxen. The primary endpoint was change from the baseline average number of migraine days per perimenstrual period over cycles 1−3. The secondary endpoints included changes from the baseline average number of migraine days outside the perimenstrual period, mean number of migraine hours during and outside the perimenstrual period, mean visual analog scale score during and outside the perimenstrual period, ≥50% migraine responder rate, and the proportion of participants who used acute pain medication over cycles 1−3 and 4−6. RESULTS: A total of 172 women with MRM were enrolled; 170 in the intention-to-treat analyses. Our primary outcome reported a significant between-group difference that favored the acupuncture group (95% CI, 0.17–0.50; P < 0.001), with the average reduction of migraine days per perimenstrual period from the baseline was 0.94 (95% CI, 0.82–1.07) in the acupuncture group and 0.61 (95% CI, 0.50–0.71) in the medication group over cycles 1−3. CONCLUSION: This study showed that compared to medication, acupuncture reduces the number of migraine days experienced by patients with MRM. For patients who received the acupuncture treatment over three cycles, the preventive effect of the therapy was sustained for six cycles. CLINICAL TRIAL REGISTRATION: [https://www.isrctn.com/ISRCTN57133712], identifier [ISRCTN15663606]
2-point statistics covariance with fewer mocks
We present an approach for accurate estimation of the covariance of 2-point
correlation functions that requires fewer mocks than the standard mock-based
covariance. This can be achieved by dividing a set of mocks into jackknife
regions and fitting the correction term first introduced in Mohammad & Percival
(2022), such that the mean of the jackknife covariances corresponds to the one
from the mocks. This extends the model beyond the shot-noise limited regime,
allowing it to be used for denser samples of galaxies. We test the performance
of our fitted jackknife approach, both in terms of accuracy and precision,
using lognormal mocks with varying densities and approximate EZmocks mimicking
the DESI LRG and ELG samples in the redshift range of z = [0.8, 1.2].
We find that the Mohammad-Percival correction produces a bias in the 2-point
correlation function covariance matrix that grows with number density and that
our fitted jackknife approach does not. We also study the effect of the
covariance on the uncertainty of cosmological parameters by performing a
full-shape analysis. We find that our fitted jackknife approach based on 25
mocks is able to recover unbiased and as precise cosmological parameters as the
ones obtained from a covariance matrix based on 1000 or 1500 mocks, while the
Mohammad-Percival correction produces uncertainties that are twice as large.
The number of mocks required to obtain an accurate estimation of the covariance
for 2-point correlation function is therefore reduced by a factor of 40-60.Comment: 13 pages, 14 figures, submitted to MNRA
PROVABGS: The Probabilistic Stellar Mass Function of the BGS One-Percent Survey
We present the probabilistic stellar mass function (pSMF) of galaxies in the
DESI Bright Galaxy Survey (BGS), observed during the One-Percent Survey. The
One-Percent Survey was one of DESI's survey validation programs conducted from
April to May 2021, before the start of the main survey. It used the same target
selection and similar observing strategy as the main survey and successfully
observed the spectra and redshifts of 143,017 galaxies in the
magnitude-limited BGS Bright sample and 95,499 galaxies in the fainter surface
brightness and color selected BGS Faint sample over . We derive pSMFs
from posteriors of stellar mass, , inferred from DESI photometry and
spectroscopy using the Hahn et al. (2022a; arXiv:2202.01809) PRObabilistic
Value-Added BGS (PROVABGS) Bayesian SED modeling framework. We use a
hierarchical population inference framework that statistically and rigorously
propagates the uncertainties. Furthermore, we include correction weights
that account for the selection effects and incompleteness of the BGS
observations. We present the redshift evolution of the pSMF in BGS as well as
the pSMFs of star-forming and quiescent galaxies classified using average
specific star formation rates from PROVABGS. Overall, the pSMFs show good
agreement with previous stellar mass function measurements in the literature.
Our pSMFs showcase the potential and statistical power of BGS, which in its
main survey will observe >100 more galaxies. Moreover, we present the
statistical framework for subsequent population statistics measurements using
BGS, which will characterize the global galaxy population and scaling relations
at low redshifts with unprecedented precision.Comment: 25 pages, 12 figures; data used to generate figures is available at
https://doi.org/10.5281/zenodo.8018936; submitted to Ap
Validation of semi-analytical, semi-empirical covariance matrices for two-point correlation function for Early DESI data
We present an extended validation of semi-analytical, semi-empirical
covariance matrices for the two-point correlation function (2PCF) on simulated
catalogs representative of Luminous Red Galaxies (LRG) data collected during
the initial two months of operations of the Stage-IV ground-based Dark Energy
Spectroscopic Instrument (DESI). We run the pipeline on multiple extended
Zel'dovich (EZ) mock galaxy catalogs with the corresponding cuts applied and
compare the results with the mock sample covariance to assess the accuracy and
its fluctuations. We propose an extension of the previously developed formalism
for catalogs processed with standard reconstruction algorithms. We consider
methods for comparing covariance matrices in detail, highlighting their
interpretation and statistical properties caused by sample variance, in
particular, nontrivial expectation values of certain metrics even when the
external covariance estimate is perfect. With improved mocks and validation
techniques, we confirm a good agreement between our predictions and sample
covariance. This allows one to generate covariance matrices for comparable
datasets without the need to create numerous mock galaxy catalogs with matching
clustering, only requiring 2PCF measurements from the data itself. The code
used in this paper is publicly available at
https://github.com/oliverphilcox/RascalC.Comment: 19 pages, 1 figure. Code available at
https://github.com/oliverphilcox/RascalC, table and figure data available at
https://dx.doi.org/10.5281/zenodo.775063
Long-term follow-up observations of extreme coronal line emitting galaxies
We present new spectroscopic and photometric follow-up observations of the
known sample of extreme coronal line emitting galaxies (ECLEs) identified in
the Sloan Digital Sky Survey (SDSS). With these new data, observations of the
ECLE sample now span a period of two decades following their initial SDSS
detections. We confirm the nonrecurrence of the iron coronal line signatures in
five of the seven objects, further supporting their identification as the
transient light echoes of tidal disruption events (TDEs). Photometric
observations of these objects in optical bands show little overall evolution.
In contrast, mid-infrared (MIR) observations show ongoing long-term declines.
The remaining two objects had been classified as active galactic nuclei (AGN)
with unusually strong coronal lines rather than being TDE related, given the
persistence of the coronal lines in earlier follow-up spectra. We confirm this
classification, with our spectra continuing to show the presence of strong,
unchanged coronal-line features and AGN-like MIR colours and behaviour. We have
constructed spectral templates of both subtypes of ECLE to aid in
distinguishing the likely origin of newly discovered ECLEs. We highlight the
need for higher cadence, and more rapid, follow-up observations of such objects
to better constrain their properties and evolution. We also discuss the
relationships between ECLEs, TDEs, and other identified transients having
significant MIR variability.Comment: Submitted to MNRAS. 33 pages, 15 figure
The DESI One-Percent Survey: Exploring the Halo Occupation Distribution of Luminous Red Galaxies and Quasi-Stellar Objects with AbacusSummit
We present the first comprehensive Halo Occupation Distribution (HOD)
analysis of the DESI One-Percent survey Luminous Red Galaxy (LRG) and
Quasi-Stellar Object (QSO) samples. We constrain the HOD of each sample and
test possible HOD extensions by fitting the redshift-space galaxy 2-point
correlation functions in 0.15 < r < 32 Mpc/h in a set of fiducial redshift
bins. We use AbacusSummit cubic boxes at Planck 2018 cosmology as model
templates and forward model galaxy clustering with the AbacusHOD package. We
achieve good fits with a standard HOD model with velocity bias, and we find no
evidence for galaxy assembly bias or satellite profile modulation at the
current level of statistical uncertainty. For LRGs in 0.4 < z < 0.6, we infer a
satellite fraction of fsat = 11+-1%, a mean halo mass of log10 Mh =
13.40+0.02-0.02, and a linear bias of blin = 1.93+0.06-0.04. For LRGs in 0.6 <
z < 0.8, we find fsat = 14+-1%, log10 Mh = 13.24+0.02-0.02, and blin =
2.08+0.03-0.03. For QSOs, we infer fsat = 3+8-2%, log10 Mh = 12.65+0.09-0.04,
and blin = 2.63+0.37-0.26 in redshift range 0.8 < z < 2.1. Using these fits, we
generate a large suite of high-fidelity galaxy mocks. We also study the
redshift-evolution of the DESI LRG sample from z = 0.4 up to z = 1.1, revealing
significant and interesting trends in mean halo mass, linear bias, and
satellite fraction.Comment: Submitted to MNRAS, comments welcom
mTOR signalling, embryogenesis and the control of lung development
The existence of a nutrient sensitive “autocatakinetic” regulator of embryonic tissue growth has been hypothesised since the early 20th century, beginning with pioneering work on the determinants of foetal size by the Australian physiologist, Thorburn Brailsford-Robertson. We now know that the mammalian target of rapamycin complexes (mTORC1 and 2) perform this essential function in all eukaryotic tissues by balancing nutrient and energy supply during the first stages of embryonic cleavage, the formation of embryonic stem cell layers and niches, the highly specified programmes of tissue growth during organogenesis and, at birth, paving the way for the first few breaths of life. This review provides a synopsis of the role of the mTOR complexes in each of these events, culminating in an analysis of lung branching morphogenesis as a way of demonstrating the central role mTOR in defining organ structural complexity. We conclude that the mTOR complexes satisfy the key requirements of a nutrient sensitive growth controller and can therefore be considered as Brailsford-Robertson's autocatakinetic centre that drives tissue growth programmes during foetal development
Local primordial non-Gaussianity from the large-scale clustering of photometric DESI luminous red galaxies
We use angular clustering of luminous red galaxies from the Dark Energy
Spectroscopic Instrument (DESI) imaging surveys to constrain the local
primordial non-Gaussianity parameter fNL. Our sample comprises over 12 million
targets, covering 14,000 square degrees of the sky, with redshifts in the range
0.2< z < 1.35. We identify Galactic extinction, survey depth, and astronomical
seeing as the primary sources of systematic error, and employ linear regression
and artificial neural networks to alleviate non-cosmological excess clustering
on large scales. Our methods are tested against log-normal simulations with and
without fNL and systematics, showing superior performance of the neural network
treatment in reducing remaining systematics. Assuming the universality
relation, we find fNL at 68\%(95\%) confidence.
With a more aggressive treatment, including regression against the full set of
imaging maps, our maximum likelihood value shifts slightly to fNL and
the uncertainty on fNL increases due to the removal of large-scale clustering
information. We apply a series of robustness tests (e.g., cuts on imaging,
declination, or scales used) that show consistency in the obtained constraints.
Despite extensive efforts to mitigate systematics, our measurements indicate
fNL > 0 with a 99.9 percent confidence level. This outcome raises concerns as
it could be attributed to unforeseen systematics, including calibration errors
or uncertainties associated with low-\ell systematics in the extinction
template. Alternatively, it could suggest a scale-dependent fNL model--causing
significant non-Gaussianity around large-scale structure while leaving cosmic
microwave background scales unaffected. Our results encourage further studies
of fNL with DESI spectroscopic samples, where the inclusion of 3D clustering
modes should help separate imaging systematics.Comment: 19 pages, 15 figures, 6 tables (Appendix excluded). Submitted to
MNRA
- …