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We present the detection and characterization of fluctuations in linearly polarized emission from the atmosphere above the South Pole. These measurements make use of Austral winter survey data from the SPT-3G receiver on the South Pole Telescope in three frequency bands centered at 95, 150, and 220 GHz. We use the cross-correlation between detectors to produce an unbiased estimate of the power in Stokes I, Q, and U parameters on large angular scales. Our results are consistent with the polarized signal being produced by the combination of Rayleigh scattering of thermal radiation from the ground and thermal emission from a population of horizontally aligned ice crystals with an anisotropic distribution described by Kolmogorov turbulence. The signal is most significant at large angular scales, high observing frequency, and low elevation angle. Polarized atmospheric emission has the potential to significantly impact observations on the large angular scales being targeted by searches for inflationary B-mode CMB polarization. We present the distribution of measured angular power spectrum amplitudes in Stokes Q and I for 4 years of winter observations, which can be used to simulate the impact of atmospheric polarization and intensity fluctuations at the South Pole on a specified experiment and observation strategy. For the SPT-3G data, downweighting the small fraction of significantly contaminated observations is an effective mitigation strategy. In addition, we present a strategy for further improving sensitivity on large angular scales where maps made in the 220 GHz band are used to measure and subtract the polarized atmosphere signal from the 150 GHz band maps. In observations with the SPT-3G instrument at the South Pole, the polarized atmospheric signal is a well-understood and sub-dominant contribution to the measured noise after implementing the mitigation strategies described here., Comment: 32 pages, 28 figures

Software-Defined Networks (SDN) are the standard architecture for network deployment. Intrusion Detection Systems (IDS) are a pivotal part of this technology as networks become more vulnerable to new and sophisticated attacks. Machine Learning (ML)-based IDS are increasingly seen as the most effective approach to handle this issue. However, IDS datasets suffer from high class imbalance, which impacts the performance of standard ML models. We propose Sequential Binary Classification (SBC) - an algorithm for multi-class classification to address this issue. SBC is a hierarchical cascade of base classifiers, each of which can be modelled on any general binary classifier. Extensive experiments are reported on benchmark datasets that evaluate the performance of SBC under different scenarios.

We present the discovery of a unique Lyman-continuum (LyC) emitter at z=3.088. The LyC emission were detected using the Hubble Space Telescope (HST) WFC3/UVIS F336W filter, covering a rest-frame wavelength range of 760-900 Angstrom. The peak signal-to-noise ratio (SNR) of LyC emission is 3.9 in a r=0.24'' aperture and is spatially offset by 0.29''+/-0.04'' (~ 2.2+/-0.3 kpc) from the rest-UV emission peak (F606W). By combining imaging and spectroscopic data from the James Webb Space Telescope (JWST) JADES, FRESCO and JEMS surveys, along with VLT/MUSE data from the MXDF survey, we estimate that the probability of random alignment with an interloper galaxy causing the LyC emission is less than 6x10^-5. The interstellar medium (ISM) conditions in the galaxy are similar to other LyC emitters at high redshift (12+log(O/H)=7.79+/-0.06, logU =-3.27+/-0.14, O32 = 3.65+/-0.22), although the single-peaked Lyman-alpha profile and lack of rest-UV emission lines suggest an optically thick ISM. We think that LyC photons are leaking through a narrow cone of optically thin neutral ISM, most likely created by a past merger (as evidenced by medium-band F210M and F182M images). Using the escape fraction constraints from individual leakers and a simple model, we estimate that the opening half-angle of ionization cones can be as low as 16^deg (2% ionised fraction) to reproduce some of the theoretical constraints on the average escape fraction for galaxies. The narrow opening angle required can explain the low number density of confirmed LyC leakers., Comment: 12 pages, 8 figures, and 1 table. Submitted to The Astrophysical Journal

California's significant role as the second-largest consumer of energy in the United States underscores the importance of accurate energy consumption predictions. With a thriving industrial sector, a burgeoning population, and ambitious environmental goals, the state's energy landscape is dynamic and complex. This paper presents a comprehensive analysis of California's energy consumption trends and provides detailed forecasting models for different energy sources and sectors. The study leverages ARIMA and ARIMAX models, considering both historical consumption data and exogenous variables. We address the unique challenges posed by the COVID-19 pandemic and the limited data for 2022, highlighting the resilience of these models in the face of uncertainty. Our analysis reveals that while fossil fuels continue to dominate California's energy landscape, renewable energy sources, particularly solar and biomass, are experiencing substantial growth. Hydroelectric power, while sensitive to precipitation, remains a significant contributor to renewable energy consumption. Furthermore, we anticipate ongoing efforts to reduce fossil fuel consumption. The forecasts for energy consumption by sector suggest continued growth in the commercial and residential sectors, reflecting California's expanding economy and population. In contrast, the industrial sector is expected to experience more moderate changes, while the transportation sector remains the largest energy consumer.

The surge in counterfeit signatures has inflicted widespread inconveniences and formidable challenges for both individuals and organizations. This groundbreaking research paper introduces SigScatNet, an innovative solution to combat this issue by harnessing the potential of a Siamese deep learning network, bolstered by Scattering wavelets, to detect signature forgery and assess signature similarity. The Siamese Network empowers us to ascertain the authenticity of signatures through a comprehensive similarity index, enabling precise validation and comparison. Remarkably, the integration of Scattering wavelets endows our model with exceptional efficiency, rendering it light enough to operate seamlessly on cost-effective hardware systems. To validate the efficacy of our approach, extensive experimentation was conducted on two open-sourced datasets: the ICDAR SigComp Dutch dataset and the CEDAR dataset. The experimental results demonstrate the practicality and resounding success of our proposed SigScatNet, yielding an unparalleled Equal Error Rate of 3.689% with the ICDAR SigComp Dutch dataset and an astonishing 0.0578% with the CEDAR dataset. Through the implementation of SigScatNet, our research spearheads a new state-of-the-art in signature analysis in terms of EER scores and computational efficiency, offering an advanced and accessible solution for detecting forgery and quantifying signature similarities. By employing cutting-edge Siamese deep learning and Scattering wavelets, we provide a robust framework that paves the way for secure and efficient signature verification systems., Comment: 7 pages, 8 figures

Generative Adversarial Networks (GANs) have risen to prominence in the field of deep learning, facilitating the generation of realistic data from random noise. The effectiveness of GANs often depends on the quality of feature extraction, a critical aspect of their architecture. This paper introduces L-WaveBlock, a novel and robust feature extractor that leverages the capabilities of the Discrete Wavelet Transform (DWT) with deep learning methodologies. L-WaveBlock is catered to quicken the convergence of GAN generators while simultaneously enhancing their performance. The paper demonstrates the remarkable utility of L-WaveBlock across three datasets, a road satellite imagery dataset, the CelebA dataset and the GoPro dataset, showcasing its ability to ease feature extraction and make it more efficient. By utilizing DWT, L-WaveBlock efficiently captures the intricate details of both structural and textural details, and further partitions feature maps into orthogonal subbands across multiple scales while preserving essential information at the same time. Not only does it lead to faster convergence, but also gives competent results on every dataset by employing the L-WaveBlock. The proposed method achieves an Inception Score of 3.6959 and a Structural Similarity Index of 0.4261 on the maps dataset, a Peak Signal-to-Noise Ratio of 29.05 and a Structural Similarity Index of 0.874 on the CelebA dataset. The proposed method performs competently to the state-of-the-art for the image denoising dataset, albeit not better, but still leads to faster convergence than conventional methods. With this, L-WaveBlock emerges as a robust and efficient tool for enhancing GAN-based image generation, demonstrating superior convergence speed and competitive performance across multiple datasets for image resolution, image generation and image denoising., Comment: 12 figures, 8 pages

Lung diseases such as COVID-19, tuberculosis (TB), and pneumonia continue to be serious global health concerns that affect millions of people worldwide. In medical practice, chest X-ray examinations have emerged as the norm for diagnosing diseases, particularly chest infections such as COVID-19. Paramedics and scientists are working intensively to create a reliable and precise approach for early-stage COVID-19 diagnosis in order to save lives. But with a variety of symptoms, medical diagnosis of these disorders poses special difficulties. It is essential to address their identification and timely diagnosis in order to successfully treat and prevent these illnesses. In this research, a multiclass classification approach using state-of-the-art methods for deep learning and image processing is proposed. This method takes into account the robustness and efficiency of the system in order to increase diagnostic precision of chest diseases. A comparison between a brand-new convolution neural network (CNN) and several transfer learning pre-trained models including VGG19, ResNet, DenseNet, EfficientNet, and InceptionNet is recommended. Publicly available and widely used research datasets like Shenzen, Montogomery, the multiclass Kaggle dataset and the NIH dataset were used to rigorously test the model. Recall, precision, F1-score, and Area Under Curve (AUC) score are used to evaluate and compare the performance of the proposed model. An AUC value of 0.95 for COVID-19, 0.99 for TB, and 0.98 for pneumonia is obtained using the proposed network. Recall and precision ratings of 0.95, 0.98, and 0.97, respectively, likewise met high standards., Comment: 11 pages, 18 figures, Under review in Discover Artificial Intelligence Journal by Springer Nature

This research paper presents a novel approach to pothole detection using Deep Learning and Image Processing techniques. The proposed system leverages the VGG16 model for feature extraction and utilizes a custom Siamese network with triplet loss, referred to as RoadScan. The system aims to address the critical issue of potholes on roads, which pose significant risks to road users. Accidents due to potholes on the roads have led to numerous accidents. Although it is necessary to completely remove potholes, it is a time-consuming process. Hence, a general road user should be able to detect potholes from a safe distance in order to avoid damage. Existing methods for pothole detection heavily rely on object detection algorithms which tend to have a high chance of failure owing to the similarity in structures and textures of a road and a pothole. Additionally, these systems utilize millions of parameters thereby making the model difficult to use in small-scale applications for the general citizen. By analyzing diverse image processing methods and various high-performing networks, the proposed model achieves remarkable performance in accurately detecting potholes. Evaluation metrics such as accuracy, EER, precision, recall, and AUROC validate the effectiveness of the system. Additionally, the proposed model demonstrates computational efficiency and cost-effectiveness by utilizing fewer parameters and data for training. The research highlights the importance of technology in the transportation sector and its potential to enhance road safety and convenience. The network proposed in this model performs with a 96.12 % accuracy, 3.89 % EER, and a 0.988 AUROC value, which is highly competitive with other state-of-the-art works., Comment: 6 pages, 5 figures, Accepted at the IEEE 7th Conference on Communication and Information Technology 2023

Abell 3266 is a massive and complex merging galaxy cluster that exhibits significant substructure. We present new, highly sensitive radio continuum observations of Abell 3266 performed with the Australian Square Kilometre Array Pathfinder (0.8$-$1.1 GHz) and the Australia Telescope Compact Array (1.1$-$3.1 GHz). These deep observations provide new insights into recently-reported diffuse non-thermal phenomena associated with the intracluster medium, including a 'wrong-way' relic, a fossil plasma source, and an as-yet unclassified central diffuse ridge, which we reveal comprises the brightest part of a large-scale radio halo detected here for the first time. The 'wrong-way' relic is highly atypical of its kind: it exhibits many classical signatures of a shock-related radio relic, while at the same time exhibiting strong spectral steepening. While radio relics are generally consistent with a quasi-stationary shock scenario, the 'wrong-way' relic is not. We study the spectral properties of the fossil plasma source; it exhibits an ultra-steep and highly curved radio spectrum, indicating an extremely aged electron population. The larger-scale radio halo fills much of the cluster centre, and presents a strong connection between the thermal and non-thermal components of the intracluster medium, along with evidence of substructure. Whether the central diffuse ridge is simply a brighter component of the halo, or a mini-halo, remains an open question. Finally, we study the morphological and spectral properties of the multiple complex radio galaxies in this cluster in unprecedented detail, tracing their evolutionary history., Comment: Main article contains 26 pages, 15 figures, 6 tables. Supplementary material contains figures illustrating the improvement gain by applying third-generation calibration and imaging techniques to ATCA and ASKAP data. Manuscript published Open Access in MNRAS on August 1st

Reconstruction of the sky brightness measured by radio interferometers is typically achieved through gridding techniques, or histograms in spatial Fourier space. For Epoch of Reionisation (EoR) 21 cm power spectrum measurements, extreme levels of gridding resolution are required to reduce spectral contamination, as explored in other works. However, the role of the shape of the Fourier space spreading function, or kernel, also has consequences in reconstructed power spectra. We decompose the instrumental Murchison Widefield Array (MWA) beam into a series of Gaussians and simulate the effects of finite kernel extents and differing shapes in gridding/degridding for optimal map making analyses. For the MWA, we find that the kernel must extend out to 0.001--0.0001% of the maximum value in order to measure the EoR using foreground avoidance. This requirement changes depending on beam shape, with compact kernels requiring far smaller extents for similar contamination levels at the cost of less-optimal errors. However, simple calibration using pixelated degridding results, regardless of shape of the kernel, cannot recover the EoR due to catastrophic errors caused by the pixel resolution. Including an opaque horizon with widefield beams also causes significant spectral contamination via a beam--horizon interaction that creates an infinitely extended kernel in Fourier space, which cannot be represented well. Thus, our results indicate that simple calibration via degridded models and optimal map making for extreme widefield instrumentation are not feasible., Comment: Accepted in ApJ. 16 pages, 6 figures

Current attempts to measure the 21cm Power Spectrum of neutral hydrogen during the Epoch of Reionization are limited by systematics which produce measured upper limits above both the thermal noise and the expected cosmological signal. These systematics arise from a combination of observational, instrumental, and analysis effects. In order to further understand and mitigate these effects, it is instructive to explore different aspects of existing datasets. One such aspect is the choice of observing field. To date, MWA EoR observations have largely focused on the EoR0 field. In this work, we present a new detailed analysis of the EoR1 field. The EoR1 field is one of the coldest regions of the Southern radio sky, but contains the very bright radio galaxy Fornax-A. The presence of this bright extended source in the primary beam of the interferometer makes the calibration and analysis of EoR1 particularly challenging. We demonstrate the effectiveness of a recently developed shapelet model of Fornax-A in improving the results from this field. We also describe and apply a series of data quality metrics which identify and remove systematically contaminated data. With substantially improved source models, upgraded analysis algorithms and enhanced data quality metrics, we determine EoR power spectrum upper limits based on analysis of the best $\sim$14-hours data observed during 2015 and 2014 at redshifts 6.5, 6.8 and 7.1, with the lowest $2\sigma$ upper limit at z=6.5 of $\Delta^2 \leq (73.78 ~\mathrm{mK)^2}$ at $k=0.13~\mathrm{h~ Mpc^{-1}}$, improving on previous EoR1 measurement results., Comment: 18 pages, 17 figures, Accepted for publication in MNRAS