We investigate how the anticipated neutral current rate from SNO will sharpen our understanding of the solar neutrino anomaly. Quantitative analyses are performed with representative values of this rate in the expected range of 0.8− 1.2. This would provide a 5− 10 σ signal for νe transition into a state containing an active neutrino component. Assuming this state to be purely active one can est...

A new set of Skyrme parameters is obtained from a fit to the binding energies, rms charge radii, and single-particle energies of both normal and exotic spherical nuclei. Nuclear matter and neutron matter properties are used to put constraints on the parameters which are not well determined from the nuclear data. Special problems with the Nolen-Schiffer anomaly and the spin-orbit interaction are...

Supervised target detection and anomaly detection are widely used in various applications, depending upon the availability of target spectral signature. Basically, they are based on a similar linear process, which makes them highly correlated. In this paper, we propose a novel adjusted spectral matched filter (ASMF) for hyperspectral target detection, which aims to effectively improve target de...

Anomaly detection is an important data analysis task. It is used to identify interesting and emerging patterns, trends and anomalies from data. Anomaly detection is an important tool to detect abnormalities in many different domains including financial fraud detection, computer network intrusion, human behavioural analysis, gene expression analysis and many more. Recently, in the financial sect...

Cloud computing has become an attractive target for attackers as the mainstream technologies in the cloud, such as the virtualization and multitenancy, permit multiple users to utilize the same physical resource, thereby posing the so-called problem of internal facing security. Moreover, the traditional network-based intrusion detection systems (IDSs) are ineffective to be deployed in the cloud...

Network anomaly detection has become the promising aspect of intrusion detection. The existing anomaly detection models depict the detection profiles with a static way, which lack good adaptability and interoperability. Furthermore, the detection rate is low, so they are difficult to be deployed the realtime detection under the high-speed network environment. In this paper, the excellent mechan...

Anomaly detection is a popular problem in many fields. We investigate an anomaly detection method based on probability density function (PDF) of different status. The constructed PDF only require few training data based on Kullback–Leibler Divergence method and small signal assumption. The measurement matrix was deduced according to principal component analysis (PCA). And the statistical detect...

Due to the rapid growth of high speed network, the risk of credit-card attacks on the complex networks are also increases accordingly. Anomaly discovery from the database is a process of filtering uncertain features, so that it can be used wide variety of applications. Since the online distributed data is the communication between the remote client and the centralized server, it is difficult to...

Detecting the anomaly behaviors such as network failure or Internet intentional attack in the large-scale Internet is a vital but challenging task. While numerous techniques have been developed based on Internet traffic in past years, anomaly detection for structured datasets by complex network have just been of focus recently. In this paper, a anomaly detection method for large-scale Internet ...

Anomaly detection is a fundamental problem for which a wide variety of algorithms have been developed. However, compared to supervised learning, there has been very little work aimed at understanding the sample complexity of anomaly detection. In this paper, we take a step in this direction by introducing a Probably Approximately Correct (PAC) framework for anomaly detection based on the identi...