Explore the vast range of titles available.

The integration of advanced sensor technology and control technology has gradually improved the operational efficiency of traditional power systems. Due to the undetectability of these attacks using traditional chi-square detection techniques, the state estimation of power systems is vulnerable to cyber–physical attacks, For this reason, this paper presents a novel detection and identification framework for detecting malicious attacks in power systems from the perspective of cyber–physical symmetry. To consider the undetectability of cyber–physical attacks, a physical dynamics detection model using the unknown input observers (UIOs) and cosine similarity theorem is proposed. Through the design of UIO parameters, the influence of attacks on state estimation can be eliminated. A cosine similarity value-based detection criterion is proposed to replace the traditional detection threshold. To further cut down the effects caused by malicious attacks, an observer combination-based attack identification framework is established. Finally, simulations are given to demonstrate that the proposed security method can detect and identify the injected malicious attacks quickly and effectively.

Quantized vortices are key features of quantum fluids such as superfluid helium and Bose—Einstein condensates. The reconnection of quantized vortices and subsequent emission of Kelvin waves along the vortices are thought to be central to dissipation in such systems. By visualizing the motion of submicron particles dispersed in superfluid 4 He, we have directly observed the emission of Kelvin waves from quantized vortex reconnection. We characterize one event in detail, using dimensionless similarity coordinates, and compare it with several theories. Finally, we give evidence for other examples of wavelike behavior in our system.

A conceptual framework is proposed to evaluate the relative importance of beta diversity, nestedness and agreement in species richness in presence—absence data matrices via partitioning pairwise gamma diversity into additive components. This is achieved by calculating three complementary indices that measure similarity, relative species replacement, and relative richness difference for all pairs of sites, and by displaying the results in a two-dimensional simplex diagram, or ternary plot. By summing two terms at a time, three one-dimensional simplices are derived correspondig to different contrasts: beta diversity versus similarity, species replacement versus nestedness and, finally, richness difference versus richness agreement. The simplex diagrams can be used to interpret underlying data structures by showing departure from randomness towards well-interpretable directions, as demonstrated by artificial and actual examples. In particular, one may appreciate how far data structure deviates from three extreme model situations: perfect nestedness, anti-nestedness and perfect gradient. Throughout the paper, we pay special attention to the measurement and interpetation of beta diversity and nestedness for pairs of sites, because these concepts have been in focus of ecological reseach for decades. The novel method can be used in community ecology, conservation biology, and biogeography, whenever the objective is to recover explanatory ecological processes behind patterns conveyed by presence-absence data.

The unique way in which each of us perceives the world must arise from our brain representations. If brain imaging could reveal an individual’s unique mental representation, it could help us understand the biological substrate of our individual experiential worlds in mental health and disease. However, imaging studies of object vision have focused on commonalities between individuals rather than individual differences and on category averages rather than representations of particular objects. Here we investigate the individually unique component of brain representations of particular objects with functional MRI (fMRI). Subjects were presented with unfamiliar and personally meaningful object images while we measured their brain activity on two separate days. We characterized the representational geometry by the dissimilarity matrix of activity patterns elicited by particular object images. The representational geometry remained stable across scanning days and was unique in each individual in early visual cortex and human inferior temporal cortex (hIT). The hIT representation predicted perceived similarity as reflected in dissimilarity judgments. Importantly, hIT predicted the individually unique component of the judgments when the objects were personally meaningful. Our results suggest that hIT brain representational idiosyncrasies accessible to fMRI are expressed in an individual's perceptual judgments. The unique way each of us perceives the world thus might reflect the individually unique representation in high-level visual areas. Significance Everyone is different. Understanding the unique way an individual perceives the world is a fundamental goal of psychology and brain science. Using novel methods for analyzing functional MRI (fMRI) data, we show that each person viewing a set of objects represents the objects uniquely in his or her brain. Moreover, given an individual’s measured brain-activity patterns, idiosyncrasies in his or her perception of the similarities among the objects can be predicted. Prediction accuracy is modest using current technology. However, our results demonstrate that fMRI has the power to reveal individually unique representations of particular objects in the human brain. The novel method might help us understand the biological substrate of individual experience in mental health and disease.

Clustering data by identifying a subset of representative examples is important for processing sensory signals and detecting patterns in data. Such \"exemplars\" can be found by randomly choosing an initial subset of data points and then iteratively refining it, but this works well only if that initial choice is close to a good solution. We devised a method called \"affinity propagation,\" which takes as input measures of similarity between pairs of data points. Real-valued messages are exchanged between data points until a high-quality set of exemplars and corresponding clusters gradually emerges. We used affinity propagation to cluster images of faces, detect genes in microarray data, identify representative sentences in this manuscript, and identify cities that are efficiently accessed by airline travel. Affinity propagation found clusters with much lower error than other methods, and it did so in less than one-hundredth the amount of time.

Turbulence is well represented by atmospheric models at very fine grid sizes, from 10 to 100 m, for which turbulent movements are mainly resolved, and by atmospheric models with grid sizes greater than 2 km, for which those movements are entirely parameterized. But what happens at intermediate scales, Wyngaard’s so-called terra incognita? Here an original method is presented that provides a new diagnostic by calculating the subgrid and resolved parts of five variables at different scales: turbulent kinetic energy (TKE), heat and moisture fluxes, and potential temperature and mixing ratio variances. They are established at intermediate scales for dry and cumulus-topped convective boundary layers. The similarity theorem allows the determination of the dimensionless variables of the problem. When the subgrid and resolved parts are studied, a new dimensionless variable, the dimensionless mesh size , needs to be added to the Deardorff free convective scaling variables, where h is the boundary layer height and hc is the height of the cloud layer. Similarity functions for the subgrid and resolved parts are assumed to be the product of the similarity function of the total (subgrid plus resolved) variables and a “partial” similarity function that depends only on . In order to determine the partial similarity function form, large-eddy simulations (LES) of five dry and cloudy convective boundary layers are used. The resolved and subgrid parts of the variables at coarser grid sizes are then deduced from the LES fields. The evolution of the subgrid and resolved parts in the boundary layer with is as follows: fine grids mainly resolve variables. As the mesh becomes coarser, more eddies are subgrid. Finally, for very large meshes, turbulence is entirely subgrid. A scale therefore exists for which the subgrid and resolved parts are equal. This is obtained for in the case of TKE, 0.4 for the potential temperature variance, and 0.8 for the mixing ratio variance, indicating that the velocity structures are smaller than those for the potential temperature, which are smaller than those for the mixing ratio. Furthermore, boundary layers capped by convective clouds have structures larger than dry boundary layer ones as displayed by the scaling in the partial similarity functions. This new diagnostic gives a reference for evaluating current and future parameterizations at kilometric scales. As an illustration, the parameterizations of a mesoscale model are eventually evaluated at intermediate scales. In its standard version, the model produces too many resolved movements, as the turbulence scheme does not sufficiently represent the impact of the subgrid thermal. This is not true when a mass-flux scheme is introduced. However in this case, a completely subgrid thermal is modeled leading to an overestimation of the subgrid part.

Significance The cerebral cortex can be divided into a number of distinct areas on the basis of anatomy and function. Understanding the complex pattern of connections among these areas is essential to uncovering how the brain performs its distributed computations. We report a systematic relation between the connectivity and functional similarity of cortical areas in the monkey, human, and mouse cortex. Motivated by observations that the cortical areal network is densely connected and that connections have a strong dependence on wiring length, we introduce a spatially embedded, generative model of the areal network that accounts for many observed features of cortical connectivity. Recent anatomical tracing studies have yielded substantial amounts of data on the areal connectivity underlying distributed processing in cortex, yet the fundamental principles that govern the large-scale organization of cortex remain unknown. Here we show that functional similarity between areas as defined by the pattern of shared inputs or outputs is a key to understanding the areal network of cortex. In particular, we report a systematic relation in the monkey, human, and mouse cortex between the occurrence of connections from one area to another and their similarity distance. This characteristic relation is rooted in the wiring distance dependence of connections in the brain. We introduce a weighted, spatially embedded random network model that robustly gives rise to this structure, as well as many other spatial and topological properties observed in cortex. These include features that were not accounted for in any previous model, such as the wide range of interareal connection weights. Connections in the model emerge from an underlying distribution of spatially embedded axons, thereby integrating the two scales of cortical connectivity—individual axons and interareal pathways—into a common geometric framework. These results provide insights into the origin of large-scale connectivity in cortex and have important implications for theories of cortical organization.

We describe approaches for distances between pairs of two-dimensional surfaces (embedded in three-dimensional space) that use local structures and global information contained in interstructure geometric relationships. We present algorithms to automatically determine these distances as well as geometric correspondences. This approach is motivated by the aspiration of students of natural science to understand the continuity of form that unites the diversity of life. At present, scientists using physical traits to study evolutionary relationships among living and extinct animals analyze data extracted from carefully defined anatomical correspondence points (landmarks). Identifying and recording these landmarks is time consuming and can be done accurately only by trained morphologists. This necessity renders these studies inaccessible to nonmorphologists and causes phenomics to lag behind genomics in elucidating evolutionary patterns. Unlike other algorithms presented for morphological correspondences, our approach does not require any preliminary marking of special features or landmarks by the user. It also differs from other seminal work in computational geometry in that our algorithms are polynomial in nature and thus faster, making pairwise comparisons feasible for significantly larger numbers of digitized surfaces. We illustrate our approach using three datasets representing teeth and different bones of primates and humans, and show that it leads to highly accurate results.

The use of similarity theorems for simulation of the technological process of mineral extraction is considered. The list of parameters that significantly influence the process of underground leaching of minerals is defined. Using these parameters and fundamental laws of physics and chemistry, mathematical functions are determined to describe the processes’ behaviour under these conditions. The obtained mathematical functions make it possible to develop a computer model of polymetallic ores leaching. This allows for the prediction of the volume of extracted concentrate with minerals from the ore mass with the associated compounds. The obtained results of calculations showed a change in the volume of minerals extracted from the rock mass depending on the mass of the working agent, the volume of leached ore and the solvent percolation rate. The results of the research can be used at mining enterprises to extract polymetallic ores by underground leaching. Also, they allow for the estimation of the economic issues from mining the ore reserves.

We conducted a field study of 71 action teams to examine the relationship between team mental model similarity and accuracy and the performance of real-world teams. We used Pathfinder to operationalize team members' taskwork mental models (describing team procedures, tasks, and equipment) and teamwork mental models (describing team interaction processes) and examined team performance as evaluated by expert team assessment center raters. Both taskwork mental model and teamwork mental model similarity predicted team performance. Team mental model accuracy measures were also predictive of team performance. We discuss the implications of our findings and directions for future research.