Behavior subtraction

Background subtraction has been a driving engine for many computer vision and video analytics tasks. Although its many variants exist, they all share the underlying assumption that photometric scene properties are either static or exhibit temporal stationarity. While this works in many applications, the model fails when one is interested in discovering changes in scene dynamics instead of changes in scene's photometric properties; the detection of unusual pedestrian or motor traffic patterns are but two examples. We propose a new model and computational framework that assume the dynamics of a scene, not its photometry, to be stationary, i.e., a dynamic background serves as the reference for the dynamics of an observed scene. Central to our approach is the concept of an event, which we define as short-term scene dynamics captured over a time window at a specific spatial location in the camera field of view. Unlike in our earlier work, we compute events by time-aggregating vector object descriptors that can combine multiple features, such as object size, direction of movement, speed, etc. We characterize events probabilistically, but use low-memory, low-complexity surrogates in a practical implementation. Using these surrogates amounts to behavior subtraction, a new algorithm for effective and efficient temporal anomaly detection and localization. Behavior subtraction is resilient to spurious background motion, such as due to camera jitter, and is content-blind, i.e., it works equally well on humans, cars, animals, and other objects in both uncluttered and highly cluttered scenes. Clearly, treating video as a collection of events rather than colored pixels opens new possibilities for video analytics.Accepted manuscrip

Rules and principles in cognitive diagnoses

Cognitive simulation is concerned with constructing process models of human cognitive behavior. Our work on the ACM system (Automated Cognitive Modeler) is an attempt to automate this process. The basic assumption is that all goal-oriented cognitive behavior involves search through some problem space. Within this framework, the task of cognitive diagnosis is to identify the problem space in which the subject is operating, identify solution paths used by the subject, and find conditions on the operators that explain those solution paths and that predict the subject's behavior on new problems. The work presented in this paper uses techniques from machine learning to automate the tasks of finding solution paths and operator conditions. We apply this method to the domain of multi-column subtraction and present results that demonstrate ACM's ability to model incorrect subtraction strategies. Finally, we discuss the difference between procedural bugs and misconceptions, proposing that errors due to misconceptions can be viewed as violations of principles for the task domain

Chaotic to ordered state transition of cathode-sheath instabilities in DC glow discharge plasmas

Transition from chaotic to ordered state has been observed during the initial stage of a discharge in a cylindrical dc glow discharge plasma. Initially it shows a chaotic behavior but increasing the discharge voltage changes the characteristics of the discharge glow and shows a period substraction of order 7 period $\to$ 5 period $\to$3 period $\to$1 period i.e. the system goes to single mode through odd cycle subtraction. On further increasing the discharge voltage, the system goes through period doubling, like 1 period $\to$ 2 period $\to$ 4 period. On further increasing the voltage, the system goes to stable state without having any oscillations.Comment: chathode-sheath, instabilities, chaos, period-subtraction, bifurcation, dc-discharg

Collective T=0 pairing in N=Z nuclei? Pairing vibrations around 56Ni revisited

We present a new analysis of the pairing vibrations around 56Ni, with emphasis on odd-odd nuclei. This analysis of the experimental excitation energies is based on the subtraction of average properties that include the full symmetry energy together with volume, surface and Coulomb terms. The results clearly indicate a collective behavior of the isovector pairing vibrations and do not support any appreciable collectivity in the isoscalar channel.Comment: RevTeX, two-column, 5 pages, 4 figure

Spin glass behavior in an interacting gamma-Fe2O3 nanoparticle system

In this paper we investigate the superspin glass behavior of a concentrated assembly of interacting maghemite nanoparticles and compare it to that of canonical atomic spin glass systems. ac versus temperature and frequency measurements show evidence of a superspin glass transition taking place at low temperature. In order to fully characterize the superspin glass phase, the aging behavior of both the thermo-remanent magnetization (TRM) and ac susceptibility has been investigated. It is shown that the scaling laws obeyed by superspin glasses and atomic spin glasses are essentially the same, after subtraction of a superparamagnetic contribution from the superspin glass response functions. Finally, we discuss a possible origin of this superparamagnetic contribution in terms of dilute spin glass models