Bayesian Approach for Computing Free Energy on Perturbation Graphs with Cycles.

A common approach for computing free energy differences among multiple states is to build a perturbation graph connecting the states and compute free energy differences on all edges of the graph. Such perturbation graphs are often designed to have cycles. Because free energy is a function of states, the free energy around any cycle is zero, which we refer to as the cycle consistency condition.

Using deep learning to detect digitally encoded DNA trigger for Trojan malware in Bio-Cyber attacks.

This article uses Deep Learning technologies to safeguard DNA sequencing against Bio-Cyber attacks. We consider a hybrid attack scenario where the payload is encoded into a DNA sequence to activate a Trojan malware implanted in a software tool used in the sequencing pipeline in order to allow the perpetrators to gain control over the resources used in that pipeline during sequence analysis. The scenario considered in the paper is based on perpetrators submitting synthetically engineered DNA samples that contain digitally encoded IP address and port number of the perpetrator's machine in the DNA.

Search for lepton flavour violation in the continuum with the ATLAS detector in [Formula: see text] collisions at the LHC.

This paper presents a search for the -channel exchange of an -parity violating scalar top quark ([Formula: see text]) in the continuum using 2.1 fb of data collected by the ATLAS detector in [Formula: see text] collisions at the Large Hadron Collider. Data are found to be consistent with the expectation from the Standard Model backgrounds.

Search for a heavy particle decaying into an electron and a muon with the ATLAS detector in sqrt[s] = 7 TeV pp collisions at the LHC.

This Letter presents the first search for a heavy particle decaying into an e ± μ(-/+) final state in sqrt[s] = 7 TeV pp collisions at the LHC. The data were recorded by the ATLAS detector during 2010 and correspond to a total integrated luminosity of 35 pb(-1). No excess above the standard model background expectation is observed.