S. Ali

R. Abbasi, M. Ackermann, J. Adams et al.

IceCube is a cubic-kilometer-scale neutrino detector located at the geographic South Pole. A precise directional reconstruction of IceCube neutrinos is vital for associations with astronomical objects. In this context, we discuss neural posterior estimation of the neutrino direction via a transformer encoder that maps to a normalizing flow on the 2-sphere. It achieves a new state-of-the-art angular resolution for the two main event morphologies in IceCube - tracks and showers - while being significantly faster than traditional B-spline-based likelihood reconstructions. All-sky scans can be performed within seconds rather than hours, and take constant computation time, regardless of whether the posterior extent is arc-minutes or spans the whole sky. We utilize a combination of $C^2$-smooth rational-quadratic splines, scale transformations and rotations to define a novel spherical normalizing-flow distribution whose parameters are predicted as a whole as the output of the transformer encoder. We test several structural choices diverting from the vanilla transformer architecture. In particular, we find dual residual streams, nonlinear QKV projection and a separate class token with its own cross-attention processing to boost test-time performance. The angular resolution for both showers and tracks improves substantially over the whole trained energy range from 100 GeV to 100 PeV. At 100 TeV deposited energy, for example, the median angular resolution improves by a factor of $1.3$ for throughgoing tracks, by a factor of $1.7$ for showers and by a factor of $2.5$ for starting tracks compared to state-of-the art likelihood reconstructions based on B-splines. While previous machine-learning (ML) efforts have managed to obtain competitive shower resolutions, this is the first time an ML-based method outperforms likelihood-based muon reconstructions above 100 GeV.

S. Ali, A. S. Ryzhikov, D. A. Derkach et al.

In the realm of high-energy physics, the longevity of calorimeters is paramount. Our research introduces a deep learning strategy to refine the calibration process of calorimeters used in particle physics experiments. We develop a Wasserstein GAN inspired methodology that adeptly calibrates the misalignment in calorimeter data due to aging or other factors. Leveraging the Wasserstein distance for loss calculation, this innovative approach requires a significantly lower number of events and resources to achieve high precision, minimizing absolute errors effectively. Our work extends the operational lifespan of calorimeters, thereby ensuring the accuracy and reliability of data in the long term, and is particularly beneficial for experiments where data integrity is crucial for scientific discovery.

S. Ali, M. Elgharabawy, Q. Duchaussoy et al.

For parents of young children and adolescents, the digital age has introduced many new challenges, including excessive screen time, inappropriate online content, cyber predators, and cyberbullying. To address these challenges, many parents rely on numerous parental control solutions on different platforms, including parental control network devices (e.g., WiFi routers) and software applications on mobile devices and laptops. While these parental control solutions may help digital parenting, they may also introduce serious security and privacy risks to children and parents, due to their elevated privileges and having access to a significant amount of privacy-sensitive data. In this paper, we present an experimental framework for systematically evaluating security and privacy issues in parental control software and hardware solutions. Using the developed framework, we provide the first comprehensive study of parental control tools on multiple platforms including network devices, Windows applications, Chrome extensions and Android apps. Our analysis uncovers pervasive security and privacy issues that can lead to leakage of private information, and/or allow an adversary to fully control the parental control solution, and thereby may directly aid cyberbullying and cyber predators.