Anomaly detection using Deep Autoencoders for the assessment of the quality of the data acquired by the CMS experiment

The certification of the CMS experiment data as usable for physics analysis is a crucial task to ensure the quality of all physics results published by the collaboration. Currently, the certification conducted by human experts is labor intensive and based on the scrutiny of distributions integrated on several hours of data taking. This contribution focuses on the design and prototype of an automated certification system assessing data quality on a per-luminosity section (i.e. 23 seconds of data taking) basis. Anomalies caused by detector malfunctioning or sub-optimal reconstruction are difficult to enumerate a priori and occur rarely, making it difficult to use classical supervised classification methods such as feedforward neural networks. We base our prototype on a semi-supervised approach which employs deep autoencoders. This approach has been qualified successfully on CMS data collected during the 2016 LHC run: we demonstrate its ability to detect anomalies with high accuracy and low false positive rate, when compared against the outcome of the manual certification by experts. A key advantage of this approach over other machine learning technologies is the great interpretability of the results, which can be further used to ascribe the origin of the problems in the data to a specific sub-detector or physics objects

Mitochondrial membrane potential in raloxifene treated parasites.

<p><i>L. amazonensis</i> promastigotes preincubated with raloxifene in Hank's balanced salt solution supplemented with glucose for 20 min were loaded with 0.3 µg/mL Rh123, and the fluorescence level was measured by flow cytometry. Parasites treated with 100 µM FCCP were used as a positive control. Untreated parasites (NT) and parasites incubated with the highest volume of drug diluent (DMSO 1.2%) were used as negative controls. (A) Representative fluorescence histograms with untreated parasites (gray), 11 µM raloxifene (blue), 120 µM raloxifene (red) or 100 µM FCCP (black). (B) Mean fluorescence intensity compared to the control in parasites treated with increasing concentrations of raloxifene. Bars represent the mean and standard deviation of triplicates in an experiment representative of three independent experiments.</p

A distributed decision framework for building clusters with different heterogeneity settings

In the past few decades, extensive research has been conducted to develop operation and control strategy for smart buildings with the purpose of reducing energy consumption. Besides studying on single building, it is envisioned that the next generation buildings can freely connect with one another to share energy and exchange information in the context of smart grid. It was demonstrated that a network of connected buildings (aka building clusters) can significantly reduce primary energy consumption, improve environmental sustainability and building's resilience capability. However, an analytic tool to determine which type of buildings should form a cluster and what is the impact of building clusters' heterogeneity based on energy profile to the energy performance of building clusters is missing. To bridge these research gaps, we propose a self-organizing map clustering algorithm to divide multiple buildings to different clusters based on their energy profiles, and a homogeneity index to evaluate the heterogeneity of different building clusters configurations. In addition, a bi-level distributed decision model is developed to study the energy sharing in the building clusters. To demonstrate the effectiveness of the proposed clustering algorithm and decision model, we employ a dataset including monthly energy consumption data for 30 buildings where the data is collected every 15. min. It is demonstrated that the proposed decision model can achieve at least 13% cost savings for building clusters. The results show that the heterogeneity of energy profile is an important factor to select battery and renewable energy source for building clusters, and the shared battery and renewable energy are preferred for more heterogeneous building clusters