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Aims. We combined the LOw-Frequency ARray (LOFAR) Two-metre Sky Survey (LoTSS) second data release (DR2) catalogue with gravitational lensing maps from the cosmic microwave background (CMB) to place constraints on the bias evolution of LoTSS-detected radio galaxies, and on the amplitude of matter perturbations.

Methods. We constructed a flux-limited catalogue from LoTSS DR2, and analysed its harmonic-space cross-correlation with CMB lensing maps fromPlanck,C_ℓ^gk, as well as its auto-correlation,C_ℓ^gg. We explored the models describing the redshift evolution of the large-scale radio galaxy bias, discriminating between them through the combination of bothC_ℓ^gkandC_ℓ^gg. Fixing the bias evolution, we then used these data to place constraints on the amplitude of large-scale density fluctuations, parametrised byσ₈.

Results. We report the significance of theC_ℓ^gksignal at a level of 26.6σ. We determined that a linear bias evolution of the formb_g(z) =b_g,D/D(z), whereD(z) is the growth rate, is able to provide a good description of the data, and we measuredb_g,D= 1.41 ± 0.06 for a sample that is flux limited at 1.5 mJy, for scalesℓ< 250 forC_ℓ^gg, andℓ< 500 forC_ℓ^gk. At the sample’s median redshift, we obtainedb(z= 0.82) = 2.34 ± 0.10. Usingσ₈as a free parameter, while keeping other cosmological parameters fixed to thePlanckvalues, we found fluctuations of σ₈= 0.75_−0.04^+0.05. The result is in agreement with weak lensing surveys, and at 1σdifference withPlanckCMB constraints. We also attempted to detect the late-time-integrated Sachs-Wolfe effect with LOFAR data; however, with the current sky coverage, the cross-correlation with CMB temperature maps is consistent with zero. Our results are an important step towards constraining cosmology with radio continuum surveys from LOFAR and other future large radio surveys.

 

As the popularity of the internet continues to grow, along with the use of web browsers and browser extensions, the threat of malicious browser extensions has increased and therefore demands an effective way to detect and in turn prevent the installation of these malicious extensions. These extensions compromise private user information (including usernames and passwords) and are also able to compromise the user’s computer in the form of Trojans and other malicious software. This paper presents a method which combines machine learning and feature engineering to detect malicious browser extensions. By analyzing the static code of browser extensions and looking for features in the static code, the method predicts whether a browser extension is malicious or benign with a machine learning algorithm. Four machine learning algorithms (SVM, RF, KNN, and XGBoost) were tested with a dataset collected by ourselves in this study. Their detection performance in terms of different performance metrics are discussed. 

Covering $\sim 5600\, \deg ^2$ to rms sensitivities of ∼70−100 $\mu$Jy beam−1, the LOFAR Two-metre Sky Survey Data Release 2 (LoTSS-DR2) provides the largest low-frequency (∼150 MHz) radio catalogue to date, making it an excellent tool for large-area radio cosmology studies. In this work, we use LoTSS-DR2 sources to investigate the angular two-point correlation function of galaxies within the survey. We discuss systematics in the data and an improved methodology for generating random catalogues, compared to that used for LoTSS-DR1, before presenting the angular clustering for ∼900 000 sources ≥1.5 mJy and a peak signal-to-noise ≥ 7.5 across ∼80 per cent of the observed area. Using the clustering, we infer the bias assuming two evolutionary models. When fitting angular scales of $0.5 \le \theta \lt 5{^\circ }$, using a linear bias model, we find LoTSS-DR2 sources are biased tracers of the underlying matter, with a bias of $b_{\rm C}= 2.14^{+0.22}_{-0.20}$ (assuming constant bias) and $b_{\rm E}(z=0)= 1.79^{+0.15}_{-0.14}$ (for an evolving model, inversely proportional to the growth factor), corresponding to $b_{\rm E}= 2.81^{+0.24}_{-0.22}$ at the median redshift of our sample, assuming the LoTSS Deep Fields redshift distribution is representative of our data. This reduces to $b_{\rm C}= 2.02^{+0.17}_{-0.16}$ and $b_{\rm E}(z=0)= 1.67^{+0.12}_{-0.12}$ when allowing preferential redshift distributions from the Deep Fields to model our data. Whilst the clustering amplitude is slightly lower than LoTSS-DR1 (≥2 mJy), our study benefits from larger samples and improved redshift estimates.

 

Security has become a serious problem for Android system as the number of Android malware increases rapidly. A great amount of effort has been devoted to protect Android devices against the threats of malware. Majority of the existing work use two-class classification methods which suffer the overfitting problem due to the lack of malicious samples. This will result in poor performance of detecting zero-day malware attacks. In this paper, we evaluated the performance of various one-class feature selection and classification methods for zero-day Android malware detection. Unlike two-class methods, one-class methods only use benign samples to build the detection model which overcomes the overfitting problem. Our results demonstrate the capability of the one-class methods over the two-class methods in detecting zero-day Android malware attacks. 

Smart grids are facing many challenges including cyber-attacks which can cause devastating damages to the grids. Existing machine learning based approaches for detecting cyber-attacks in smart grids are mainly based on supervised learning, which needs representative instances from various attack types to obtain good detection models. In this paper, we investigated semi-supervised outlier detection algorithms for this problem which only use instances of normal events for model training. Data collected by phasor measurement units (PMUs) was used for training the detection model. The semi-supervised outlier detection algorithms were augmented with deep feature extraction for enhanced detection performance. Our results show that semi-supervised outlier detection algorithms can perform better than popular supervised algorithms. Deep feature extraction can significantly improve the performance of semi-supervised algorithms for detecting cyber-attacks in smart grids 

Naturally synthesized marine organohalogens (MOH) and their anthropo- genic homologs produced as disinfection byproducts (DBP) are an emerging environmental health concern because several have been identified to exhibit potent biological activities in model systems, including cytotoxicity, genotox- icity, carcinogenicity and developmental toxicity. The molecular mechanisms mediating toxicity are poorly understood. Recently we discovered that several specific MOH and DBP measured in environmental and biological samples, including halopyrroles, halobipyrroles, haloindoles, and hydroxylated poly- brominated diphenylethers directly modify ryanodine receptors and SERCA pump activity, two key proteins anchored within sarcoplasmic/endoplasmic reticulum (SR/ER) that work in physiological opposition to tightly regulate net ER/SR Ca2+ dynamics and thereby shape meaningful Ca2+-dependent cel- lular processes. Using intact HEK293 cells null for ryanodine receptors (RyRs) expression and those that stably express RyR1, we demonstrate that tetra- bromopyrrole (TBP) selectively sensitizes RyR1 channels to caffeine-triggered Ca2+ release only in RyR1-expressing cells. TBP at higher concentrations also depletes of SR/ER Ca2+ stores in both null and RyR1 expressing cells com- mensurate with its lower potency to inhibitory SERCA in biochemical assays. Exposure of primary neuronal/glial co-cultures derived from newborn mice shows that TBP inhibits the frequency and amplitude of spontaneous Ca2+ oscillations (IC50=246 and 426nM, respectively), whereas >1μM produces a sustained rise in cytoplasmic Ca2+. Subchronic (24HR) exposure to TBP caused loss of neuronal/glial viability using the MTT assay (EC50=12.4μM). These re- sults show that nM TBP selectively targets RyR-mediated Ca2+ dynamics in a manner that has been shown to affect neurodevelopment, whereas low-μM exposures causes overt neurotoxicity, likely mediated by the combination of RyR activation and SERCA inhibition. 

A search for the nonresonant production of Higgs boson pairs in the$HH\to b\overline{b}{\tau }^{+}{\tau }^{-}$channel is performed using$140{\mathrm{fb}}^{-1}$of proton-proton collisions at a center-of-mass energy of 13 TeV recorded by the ATLAS detector at the CERN Large Hadron Collider. The analysis strategy is optimized to probe anomalous values of the Higgs boson self-coupling modifier${\kappa }_{\lambda }$and of the quartic$HHVV$($V=W,Z$) coupling modifier${\kappa }_{2V}$. No significant excess above the expected background from Standard Model processes is observed. An observed (expected) upper limit${\mu }_{HH}<5.9(3.3)$is set at 95% confidence-level on the Higgs boson pair production cross section normalized to its Standard Model prediction. The coupling modifiers are constrained to an observed (expected) 95% confidence interval of$-3.1<{\kappa }_{\lambda }<9.0$($-2.5<{\kappa }_{\lambda }<9.3$) and$-0.5<{\kappa }_{2V}<2.7$($-0.2<{\kappa }_{2V}<2.4$), assuming all other Higgs boson couplings are fixed to the Standard Model prediction. The results are also interpreted in the context of effective field theories via constraints on anomalous Higgs boson couplings and Higgs boson pair production cross sections assuming different kinematic benchmark scenarios.

© 2024 CERN, for the ATLAS Collaboration2024CERN 

The ATLAS trigger system is a crucial component of the ATLAS experiment at the LHC. It is responsible for selecting events in line with the ATLAS physics programme. This paper presents an overview of the changes to the trigger and data acquisition system during the second long shutdown of the LHC, and shows the performance of the trigger system and its components in the proton-proton collisions during the 2022 commissioning period as well as its expected performance in proton-proton and heavy-ion collisions for the remainder of the third LHC data-taking period (2022–2025).