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We report the detection of a total of 135 bursts from a recently discovered active, repeating fast radio burst, FRB 20240114A with the GMRT over a frequency range of 300$-$750 MHz. The bursts were detected with intrinsic widths ranging from 0.308 to 39.364 ms, a median scattering timescale of 2.059 ms at 400 MHz and 1.372 ms at 650 MHz. The fluences of the detected bursts range from 36.81 mJy ms to 7.47 Jy ms. Both the energy and waiting time distributions of the bursts can be fitted with broken power laws, indicating the presence of two distinct populations of bursts. The energy distributions were modeled via broken power law with $\alpha_{1} = -0.62 \pm 0.01$ and $\alpha_{2} = -1.98 \pm 0.1$, while the waiting time distribution was modeled via a broken power law with $\alpha_{1} = -0.71 \pm 0.01$ and $\alpha_{2} = -2.09 \pm 0.09$. Both the energy and waiting time distributions of FRB 20240114A are comparable to high-energy bursts from magnetars, and giant radio pulses from pulsars, indicating that such objects could be likely progenitors.

The COVID-19 pandemic has escalated mental health crises worldwide, with social isolation and economic instability contributing to a rise in suicidal behavior. Suicide can result from social factors such as shame, abuse, abandonment, and mental health conditions like depression, Post-Traumatic Stress Disorder (PTSD), Attention-Deficit/Hyperactivity Disorder (ADHD), anxiety disorders, and bipolar disorders. As these conditions develop, signs of suicidal ideation may manifest in social media interactions. Analyzing social media data using artificial intelligence (AI) techniques can help identify patterns of suicidal behavior, providing invaluable insights for suicide prevention agencies, professionals, and broader community awareness initiatives. Machine learning algorithms for this purpose require large volumes of accurately labeled data. Previous research has not fully explored the potential of incorporating explanations in analyzing and labeling longitudinal social media data. In this study, we employed a model explanation method, Layer Integrated Gradients, on top of a fine-tuned state-of-the-art language model, to assign each token from Reddit users' posts an attribution score for predicting suicidal ideation. By extracting and analyzing attributions of tokens from the data, we propose a methodology for preliminary screening of social media posts for suicidal ideation without using large language models during inference., Comment: Presented at ICMLA 2023, Special Session: Machine Learning in Health, 8 pages, 6 figures, 7 tables

Integrating renewable energy sources into the power grid is becoming increasingly important as the world moves towards a more sustainable energy future in line with SDG 7. However, the intermittent nature of renewable energy sources can make it challenging to manage the power grid and ensure a stable supply of electricity, which is crucial for achieving SDG 9. In this paper, we propose a deep learning-based approach for predicting energy demand in a smart power grid, which can improve the integration of renewable energy sources by providing accurate predictions of energy demand. Our approach aligns with SDG 13 on climate action, enabling more efficient management of renewable energy resources. We use long short-term memory networks, well-suited for time series data, to capture complex patterns and dependencies in energy demand data. The proposed approach is evaluated using four historical short-term energy demand data datasets from different energy distribution companies, including American Electric Power, Commonwealth Edison, Dayton Power and Light, and Pennsylvania-New Jersey-Maryland Interconnection. The proposed model is also compared with three other state-of-the-art forecasting algorithms: Facebook Prophet, Support Vector Regression, and Random Forest Regression. The experimental results show that the proposed REDf model can accurately predict energy demand with a mean absolute error of 1.4%, indicating its potential to enhance the stability and efficiency of the power grid and contribute to achieving SDGs 7, 9, and 13. The proposed model also has the potential to manage the integration of renewable energy sources in an effective manner.

Convolutional Neural Networks have been used in a variety of image related applications after their rise in popularity due to ImageNet competition. Convolutional Neural Networks have shown remarkable results in applications including face recognition, moving target detection and tracking, classification of food based on the calorie content and many more. Designing of Convolutional Neural Networks requires experts having a cross domain knowledge and it is laborious, which requires a lot of time for testing different values for different hyperparameter along with the consideration of different configurations of existing architectures. Neural Architecture Search is an automated way of generating Neural Network architectures which saves researchers from all the brute-force testing trouble, but with the drawback of consuming a lot of computational resources for a prolonged period. In this paper, we propose an automated Neural Architecture Search framework DQNAS, guided by the principles of Reinforcement Learning along with One-shot Training which aims to generate neural network architectures that show superior performance and have minimum scalability problem., Comment: 15 Pages, 6 Tables, 9 figures

We characterize the intrinsic properties of any FRB using its redshift $z$, spectral index $\alpha$ and energy $E_{33}$ in units of $10^{33} \, {\rm J}$ emitted across $2128 - 2848\; {\rm MHz}$ in the FRB's rest frame. Provided that $z$ is inferred from the measured extra-galactic dispersion measure $DM_{\rm Ex}$, the fluence $F$ of the observed event defines a track in $(\alpha, E_{33})$ space which we refer to as the "energy track". Here we consider the energy tracks for a sample of $254$ non-repeating low dispersion measure FRBs from the CHIME/FRB Catalog-1, and use these to determine $n(E_{33} \mid \alpha)$ the conditional energy distribution i.e. the number of FRBs in the interval $\Delta E_{33}$ given a value of $\alpha$. Considering $-10 \le \alpha \le 10$, we find that the entire energy scale shifts to higher energy values as $\alpha$ is increased. For all values of $\alpha$, we can identify two distinct energy ranges indicating that there are possibly two distinct FRB populations. At high energies, the distribution is well fitted by a modified Schechter function whose slope and characteristic energy both increase with $\alpha$. At low energies, the number of FRBs are in excess of the predictions of the modified Schechter function indicating that we may have a distinctly different population of low-energy FRBs. We have checked that our main findings are reasonably robust to the assumptions regarding the Galactic Halo and Host galaxy contributions to the dispersion measure., Comment: 7 pages, 9 figures, Submitted to MNRAS