What measurements of neutrino neutral current events can reveal

We show that neutral current (NC) measurements at neutrino detectors can play a valuable role in the search for new physics. Such measurements have certain intrinsic features and advantages that can fruitfully be combined with the usual well-studied charged lepton detection channels in order to probe the presence of new interactions or new light states. In addition to the fact that NC events are immune to uncertainties in standard model neutrino mixing and mass parameters, they can have small matter effects and superior rates since all three flavours participate. We also show, as a general feature, that NC measurements provide access to different combinations of CP phases and mixing parameters compared to CC measurements at both long and short baseline experiments. Using the Deep Underground Neutrino Experiment (DUNE) as an illustrative setting, we demonstrate the capability of NC measurements to break degeneracies arising in CC measurements, allowing us, in principle, to distinguish between new physics that violates three flavour unitarity and that which does not. Finally, we show that NC measurements can enable us to restrict new physics parameters that are not easily constrained by CC measurements.Comment: 22 pages, 10 figure

User Constrained Thumbnail Generation using Adaptive Convolutions

Thumbnails are widely used all over the world as a preview for digital images. In this work we propose a deep neural framework to generate thumbnails of any size and aspect ratio, even for unseen values during training, with high accuracy and precision. We use Global Context Aggregation (GCA) and a modified Region Proposal Network (RPN) with adaptive convolutions to generate thumbnails in real time. GCA is used to selectively attend and aggregate the global context information from the entire image while the RPN is used to predict candidate bounding boxes for the thumbnail image. Adaptive convolution eliminates the problem of generating thumbnails of various aspect ratios by using filter weights dynamically generated from the aspect ratio information. The experimental results indicate the superior performance of the proposed model over existing state-of-the-art techniques.Comment: International Conference on Acoustics, Speech, and Signal Processing(ICASSP), 201

LSND and MiniBooNE as guideposts to understanding the muon g−2g-2 results and the CDF II WW mass measurement

In recent times, several experiments have observed results that are in significant conflict with the predictions of the Standard Model (SM). Two neutrino experiments, LSND and MiniBooNE (MB) have reported electron-like signal excesses above backgrounds. Both the Brookhaven and the Fermilab muon $g-2$ collaborations have measured values of this parameter which, while consistent with each other, are in conflict with the SM. Recently, the CDF II collaboration has reported a precision measurement of the $W$-boson mass that is in strong conflict with the SM prediction. It is worthwhile to seek new physics which may underly all four anomalies. In such a quest, the neutrino experiments could play a crucial role, because once a common solution to these anomalies is sought, LSND and MB, due to their highly restrictive requirements and observed final states, help to greatly narrow the multiplicity of new physics possibilities that are otherwise open to the $W$ mass and muon $g-2$ discrepancies. Pursuant to this, earlier work has shown that LSND, MB and the muon $g-2$ results can be understood in the context of a scalar extension of the SM which incorporates a second Higgs doublet and a dark sector singlet. We show that the same model leads to a contribution to the $W$ mass which is consistent with the recent CDF II measurement. While the LSND, MB fits and the muon $g-2$ results help determine the masses of the light scalars in the model, the calculation of the oblique parameters $S$ and $T$ determines the allowed mass ranges of the heavier pseudoscalar and the charged Higgs bosons as well as the effective Weinberg angle and its new range.Comment: 11 pages, 8 figures, 1 table. Figues and related text added. References adde

Adversarially Learned Abnormal Trajectory Classifier

We address the problem of abnormal event detection from trajectory data. In this paper, a new adversarial approach is proposed for building a deep neural network binary classifier, trained in an unsupervised fashion, that can distinguish normal from abnormal trajectory-based events without the need for setting manual detection threshold. Inspired by the generative adversarial network (GAN) framework, our GAN version is a discriminative one in which the discriminator is trained to distinguish normal and abnormal trajectory reconstruction errors given by a deep autoencoder. With urban traffic videos and their associated trajectories, our proposed method gives the best accuracy for abnormal trajectory detection. In addition, our model can easily be generalized for abnormal trajectory-based event detection and can still yield the best behavioural detection results as demonstrated on the CAVIAR dataset.Comment: Accepted for the 16th Conference on Computer and Robot Vision (CRV) 201

A Comparative Analysis of Retrievability and PageRank Measures

The accessibility of documents within a collection holds a pivotal role in Information Retrieval, signifying the ease of locating specific content in a collection of documents. This accessibility can be achieved via two distinct avenues. The first is through some retrieval model using a keyword or other feature-based search, and the other is where a document can be navigated using links associated with them, if available. Metrics such as PageRank, Hub, and Authority illuminate the pathways through which documents can be discovered within the network of content while the concept of Retrievability is used to quantify the ease with which a document can be found by a retrieval model. In this paper, we compare these two perspectives, PageRank and retrievability, as they quantify the importance and discoverability of content in a corpus. Through empirical experimentation on benchmark datasets, we demonstrate a subtle similarity between retrievability and PageRank particularly distinguishable for larger datasets.Comment: Accepted at FIRE 202

Deep learning for reconstructing protein structures from cryo-EM density maps: recent advances and future directions

Cryo-Electron Microscopy (cryo-EM) has emerged as a key technology to determine the structure of proteins, particularly large protein complexes and assemblies in recent years. A key challenge in cryo-EM data analysis is to automatically reconstruct accurate protein structures from cryo-EM density maps. In this review, we briefly overview various deep learning methods for building protein structures from cryo-EM density maps, analyze their impact, and discuss the challenges of preparing high-quality data sets for training deep learning models. Looking into the future, more advanced deep learning models of effectively integrating cryo-EM data with other sources of complementary data such as protein sequences and AlphaFold-predicted structures need to be developed to further advance the field

Predicting Next Local Appearance for Video Anomaly Detection

We present a local anomaly detection method in videos. As opposed to most existing methods that are computationally expensive and are not very generalizable across different video scenes, we propose an adversarial framework that learns the temporal local appearance variations by predicting the appearance of a normally behaving object in the next frame of a scene by only relying on its current and past appearances. In the presence of an abnormally behaving object, the reconstruction error between the real and the predicted next appearance of that object indicates the likelihood of an anomaly. Our method is competitive with the existing state-of-the-art while being significantly faster for both training and inference and being better at generalizing to unseen video scenes.Comment: Accepted as an oral presentation for MVA'202