Online Nonparametric Anomaly Detection based on Geometric Entropy Minimization

We consider the online and nonparametric detection of abrupt and persistent anomalies, such as a change in the regular system dynamics at a time instance due to an anomalous event (e.g., a failure, a malicious activity). Combining the simplicity of the nonparametric Geometric Entropy Minimization (GEM) method with the timely detection capability of the Cumulative Sum (CUSUM) algorithm we propose a computationally efficient online anomaly detection method that is applicable to high-dimensional datasets, and at the same time achieve a near-optimum average detection delay performance for a given false alarm constraint. We provide new insights to both GEM and CUSUM, including new asymptotic analysis for GEM, which enables soft decisions for outlier detection, and a novel interpretation of CUSUM in terms of the discrepancy theory, which helps us generalize it to the nonparametric GEM statistic. We numerically show, using both simulated and real datasets, that the proposed nonparametric algorithm attains a close performance to the clairvoyant parametric CUSUM test.Comment: to appear in IEEE International Symposium on Information Theory (ISIT) 201

An entropy based heuristic model for predicting functional sub-type divisions of protein families

Multiple sequence alignments of protein families are often used for locating residues that are widely apart in the sequence, which are considered as influential for determining functional specificity of proteins towards various substrates, ligands, DNA and other proteins. In this paper, we propose an entropy-score based heuristic algorithm model for predicting functional sub-family divisions of protein families, given the multiple sequence alignment of the protein family as input without any functional sub-type or key site information given for any protein sequence. Two of the experimented test-cases are reported in this paper. First test-case is Nucleotidyl Cyclase protein family consisting of guanalyate and adenylate cyclases. And the second test-case is a dataset of proteins taken from six superfamilies in Structure-Function Linkage Database (SFLD). Results from these test-cases are reported in terms of confirmed sub-type divisions with phylogeny relations from former studies in the literature

Financial intermediaries, leverage ratios, and business cycles

I document cyclical properties of aggregate measures of liabilities, equity, and leverage ratio in the U.S. financial sector and those of credit spread. I find that (i) liabilities and equity are procyclical, leverage ratio is acyclical, and credit spread is countercyclical, (ii) financial variables are three to ten times more volatile than output, and (iii) financial variables lead the business cycle. I present a dynamic stochastic general equilibrium model with profit maximizing banks where bank equity mitigates a moral hazard problem between banks and their depositors. The driving sources of business cycles are shocks to bank equity as well as standard productivity shocks. The model generates real and financial fluctuations consistent with the U.S. data. The model also delivers some policy prescriptions about capital adequacy requirements of banks.Banks; Financial Fluctuations; Credit Frictions; Bank Equity; Real Fluctuations

Towards a Traffic Measuring System Utilizing Tire Pressure Monitoring Systems (TPMS)

The number of wireless devices influencing everyday life communication behaviour increases continuously. This trend encourages traffic engineers to develop systems which utilize the identifier of wireless communication standards to measure traffic. The principle to derive traffic parameters by querying the device’s address via the Bluetooth® interface is well known and frequently tested by the ITS community. Additionally, the DLR develops methods for measuring travel times in a road network on the basis of Wi-Fi. A new field of DLR’s research is to detect and recognize vehicles via wireless networks integrated in modern automobiles. Tire pressure monitoring systems (TPMS) are thought to be promising in-vehicle sensor systems for purposes of traffic. They evaluate inflation pressure of tires and transmit the measured data along with its static identifier to an electronic control unit (ECU) wirelessly. After being mandatory in the U.S., in the European Union all vehicles type approved after November 1st, 2012 have to be equipped with TPMS as well as all vehicles manufactured after November 1st, 2014. Thus, the dissemination level of motor vehicles coming with TPMS will theoretically increase up to 100 percent within the upcoming years. Since it is mandatory for every sold run-flat tire system, already today car dealers sell vehicle equipped with TPMS. For proof of concept the authors selected an aftermarket TPMS consisting of four sensors and display unit. Its sensors were easy to mount by replacing the valve caps with the sensor caps. The sensors proved to be advantageous for our purposes since by removing and reinstating the battery, a measurement and transmission can be triggered. Thus the moment of transmission was known, the raw signal data is easy to capture. Since the protocols do not rely on cryptographic mechanisms, the modulation scheme was determined: the data transmission of the selected TPMS utilizes an amplitude shift keying in the 433 MHz band. Next, we resolved the encoding scheme. The sensors employed Manchester encoding, which is a binary signalling mechanism that combines data and clock into bit-symbols. In order to understand the message’s bitfield, we varied pressure and temperature and observed which bits changed