Magnetic tight binding and the iron-chromium enthalpy anomaly

We describe a self-consistent magnetic tight-binding theory based in an expansion of the Hohenberg-Kohn density functional to second order, about a non-spin-polarized reference density. We show how a first order expansion about a density having a trial input magnetic moment leads to a fixed moment model. We employ a simple set of tight-binding parameters that accurately describes electronic structure and energetics, and show these to be transferable between first row transition metals and their alloys. We make a number of calculations of the electronic structure of dilute Cr impurities in Fe, which we compare with results using the local spin density approximation. The fixed moment model provides a powerful means for interpreting complex magnetic configurations in alloys; using this approach, we are able to advance a simple and readily understood explanation for the observed anomaly in the enthalpy of mixing.

Resolution of an ancient surface science anomaly: Work function change induced by N adsorption on W{100}

For many decades it has been assumed that an adsorbate centered above a metal surface and with a net negative charge should increase the work function of the surface. However, despite their electronegativity, N adatoms on W{100} cause a significant work function decrease. Here we present a resolution of this anomaly. Using density functional theory, we demonstrate that while the N atom carries a negative charge, of overriding importance is a reduction in the surface overspill electron density into the vacuum, when that charge is engaged in bonding to the adatom. This novel interpretation is fundamentally important in the general understanding of work function changes induced by atomic adsorbates.

Artificial gravity reveals that economy of action determines the stability of sensorimotor coordination.

Background
When we move along in time with a piece of music, we synchronise the downward phase of our gesture with the beat. While it is easy to demonstrate this tendency, there is considerable debate as to its neural origins. It may have a structural basis, whereby the gravitational field acts as an orientation reference that biases the formulation of motor commands. Alternatively, it may be functional, and related to the economy with which motion assisted by gravity can be generated by the motor system.
Methodology/Principal Findings
We used a robotic system to generate a mathematical model of the gravitational forces acting upon the hand, and then to reverse the effect of gravity, and invert the weight of the limb. In these circumstances, patterns of coordination in which the upward phase of rhythmic hand movements coincided with the beat of a metronome were more stable than those in which downward movements were made on the beat. When a normal gravitational force was present, movements made down-on-the-beat were more stable than those made up-on-the-beat.
Conclusions/Significance
The ubiquitous tendency to make a downward movement on a musical beat arises not from the perception of gravity, but as a result of the economy of action that derives from its exploitation.

The anomaly in the candidate microlensing event PA-99-N2

The light curve of PA-99-N2, one of the recently announced microlensing candidates toward M31, shows small deviations from the standard Paczynski form. We explore a number of possible explanations, including correlations with the seeing, the parallax effect, and a binary lens. We find that the observations are consistent with an unresolved red giant branch or asymptotic giant branch star in M31 being microlensed by a binary lens. We find that the best-fit binary lens mass ratio is similar to1.2x10(-2), which is one of the most extreme values found for a binary lens so far. If both the source and lens lie in the M31 disk, then the standard M31 model predicts the probable mass range of the system to be 0.02-3.6 M-circle dot (95% confidence limit). In this scenario, the mass of the secondary component is therefore likely to be below the hydrogen-burning limit. On the other hand, if a compact halo object in M31 is lensing a disk or spheroid source, then the total lens mass is likely to lie between 0.09 and 32 M-circle dot, which is consistent with the primary being a stellar remnant and the secondary being a low-mass star or brown dwarf. The optical depth (or, alternatively, the differential rate) along the line of sight toward the event indicates that a halo lens is more likely than a stellar lens, provided that dark compact objects comprise no less than 15% (or 5%) of halos.

Electronic stopping power in gold:The role of d electrons and the H/He anomaly

The electronic stopping power of H and He moving through gold is obtained to high accuracy using time-evolving density-functional theory, thereby bringing usual first principles accuracies into this kind of strongly coupled, continuum nonadiabatic processes in condensed matter. The two key unexplained features of what observed experimentally have been reproduced and understood: (i)The nonlinear behavior of stopping power versus velocity is a gradual crossover as excitations tail into the d-electron spectrum; and (ii)the low-velocity H/He anomaly (the relative stopping powers are contrary to established theory) is explained by the substantial involvement of the d electrons in the screening of the projectile even at the lowest velocities where the energy loss is generated by s-like electron-hole pair formation only. © 2012 American Physical Society.

A Lightweight Tool for Anomaly Detection in Cloud Data Centres

Cloud data centres are critical business infrastructures and the fastest growing service providers. Detecting anomalies in Cloud data centre operation is vital. Given the vast complexity of the data centre system software stack, applications and workloads, anomaly detection is a challenging endeavour. Current tools for detecting anomalies often use machine learning techniques, application instance behaviours or system metrics distribu- tion, which are complex to implement in Cloud computing environments as they require training, access to application-level data and complex processing. This paper presents LADT, a lightweight anomaly detection tool for Cloud data centres that uses rigorous correlation of system metrics, implemented by an efficient corre- lation algorithm without need for training or complex infrastructure set up. LADT is based on the hypothesis that, in an anomaly-free system, metrics from data centre host nodes and virtual machines (VMs) are strongly correlated. An anomaly is detected whenever correlation drops below a threshold value. We demonstrate and evaluate LADT using a Cloud environment, where it shows that the hosting node I/O operations per second (IOPS) are strongly correlated with the aggregated virtual machine IOPS, but this correlation vanishes when an application stresses the disk, indicating a node-level anomaly.

Experimental investigation of flow and segregation behaviour of bulk solids in silos under high gravity conditions

Many researchers have investigated the flow and segregation behaviour in model scale experimental silos at normal gravity conditions. However it is known that the stresses experienced by the bulk solid in industrial silos are high when compared to model silos. Therefore it is important to understand the effect of stress level on flow and segregation behaviour and establish the scaling laws governing this behaviour. The objective of this paper is to understand the effect of gravity on the flow and segregation behaviour of bulk solids in a silo centrifuge model. The materials used were two mixtures composed of Polyamide and glass beads. The discharge of two bi-disperse bulk solids in a silo centrifuge model were recorded under accelerations ranging from 1g to 15g. The velocity distribution during discharge was evaluated using Particle Image Velocimetry (PIV) techniques and the concentration distribution of large and small particles were obtained by imaging processing techniques. The flow and segregation behaviour at high gravities were then quantified and compared with the empirical equations available in the literature.

Anomaly Detection for Data with Spatial Attributes

The problem of detecting spatially-coherent groups of data that exhibit anomalous behavior has started to attract attention due to applications across areas such as epidemic analysis and weather forecasting. Earlier efforts from the data mining community have largely focused on finding outliers, individual data objects that display deviant behavior. Such point-based methods are not easy to extend to find groups of data that exhibit anomalous behavior. Scan Statistics are methods from the statistics community that have considered the problem of identifying regions where data objects exhibit a behavior that is atypical of the general dataset. The spatial scan statistic and methods that build upon it mostly adopt the framework of defining a character for regions (e.g., circular or elliptical) of objects and repeatedly sampling regions of such character followed by applying a statistical test for anomaly detection. In the past decade, there have been efforts from the statistics community to enhance efficiency of scan statstics as well as to enable discovery of arbitrarily shaped anomalous regions. On the other hand, the data mining community has started to look at determining anomalous regions that have behavior divergent from their neighborhood.In this chapter,we survey the space of techniques for detecting anomalous regions on spatial data from across the data mining and statistics communities while outlining connections to well-studied problems in clustering and image segmentation. We analyze the techniques systematically by categorizing them appropriately to provide a structured birds eye view of the work on anomalous region detection;we hope that this would encourage better cross-pollination of ideas across communities to help advance the frontier in anomaly detection.

LS-ADT: Lightweight and Scalable Anomaly Detection for Cloud Datacentres

Cloud data centres are implemented as large-scale clusters with demanding requirements for service performance, availability and cost of operation. As a result of scale and complexity, data centres typically exhibit large numbers of system anomalies resulting from operator error, resource over/under provisioning, hardware or software failures and security issus anomalies are inherently difficult to identify and resolve promptly via human inspection. Therefore, it is vital in a cloud system to have automatic system monitoring that detects potential anomalies and identifies their source. In this paper we present a lightweight anomaly detection tool for Cloud data centres which combines extended log analysis and rigorous correlation of system metrics, implemented by an efficient correlation algorithm which does not require training or complex infrastructure set up. The LADT algorithm is based on the premise that there is a strong correlation between node level and VM level metrics in a cloud system. This correlation will drop significantly in the event of any performance anomaly at the node-level and a continuous drop in the correlation can indicate the presence of a true anomaly in the node. The log analysis of LADT assists in determining whether the correlation drop could be caused by naturally occurring cloud management activity such as VM migration, creation, suspension, termination or resizing. In this way, any potential anomaly alerts are reasoned about to prevent false positives that could be caused by the cloud operator’s activity. We demonstrate LADT with log analysis in a Cloud environment to show how the log analysis is combined with the correlation of systems metrics to achieve accurate anomaly detection.

Video anomaly detection in real-time on a Power-Aware Heterogeneous Platform

FPGAs and GPUs are often used when real-time performance in video processing is required. An accelerated processor is chosen based on task-specific priorities (power consumption, processing time and detection accuracy), and this decision is normally made once at design time. All three characteristics are important, particularly in battery-powered systems. Here we propose a method for moving selection of processing platform from a single design-time choice to a continuous run time one.We implement Histogram of Oriented Gradients (HOG) detectors for cars and people and Mixture of Gaussians (MoG) motion detectors running across FPGA, GPU and CPU in a heterogeneous system. We use this to detect illegally parked vehicles in urban scenes. Power, time and accuracy information for each detector is characterised. An anomaly measure is assigned to each detected object based on its trajectory and location, when compared to learned contextual movement patterns. This drives processor and implementation selection, so that scenes with high behavioural anomalies are processed with faster but more power hungry implementations, but routine or static time periods are processed with power-optimised, less accurate, slower versions. Real-time performance is evaluated on video datasets including i-LIDS. Compared to power-optimised static selection, automatic dynamic implementation mapping is 10% more accurate but draws 12W extra power in our testbed desktop system.

Contextual anomaly detection in crowded surveillance scenes

This work addresses the problem of detecting human behavioural anomalies in crowded surveillance environments. We focus in particular on the problem of detecting subtle anomalies in a behaviourally heterogeneous surveillance scene. To reach this goal we implement a novel unsupervised context-aware process. We propose and evaluate a method of utilising social context and scene context to improve behaviour analysis. We find that in a crowded scene the application of Mutual Information based social context permits the ability to prevent self-justifying groups and propagate anomalies in a social network, granting a greater anomaly detection capability. Scene context uniformly improves the detection of anomalies in both datasets. The strength of our contextual features is demonstrated by the detection of subtly abnormal behaviours, which otherwise remain indistinguishable from normal behaviour.