Search

A robust network intrusion detection system (NIDS) plays an important role in cyberspace security for protecting confidential systems from potential threats. In real world network, there exists complex correlations among the various types of network traffic information, which may be respectively attributed to different abnormal behaviors and should be make full utilized in NIDS. Regarding complex network traffic information, traditional learning based abnormal behavior detection methods can hardly meet the requirements of the real world network environment. Existing methods have not taken into account the impact of various modalities of data, and the mutual support among different data features. To address the concerns, this paper proposes a multi-dimensional feature fusion and stacking ensemble mechanism (MFFSEM), which can detect abnormal behaviors effectively. In order to accurately explore the connotation of traffic information, multiple basic feature datasets are established considering different aspects of traffic information such as time, space, and load. Then, considering the association and correlation among the basic feature datasets, multiple comprehensive feature datasets are set up to meet the requirements of real world abnormal behavior detection. In specific, stacking ensemble learning is conducted on multiple comprehensive feature datasets, and thus an effective multi-dimensional global anomaly detection model is accomplished. The experimental results on the dataset KDD Cup 99, NSL-KDD, UNSW-NB15, and CIC-IDS2017 have shown that MFFSEM significantly outperforms the basic and meta classifiers adopted in our method. Furthermore, its detection performance is superior to other well-known ensemble approaches.

The Internet of Things establishes the intimacy between the Internet and the physical world. Due to portable size, most IoT devices have limited computing and storage capabilities and are vulnerable to various malicious intrusions. Therefore, it is vital to have efficient approaches to distinguish the true IoT data from fake one, we term such methods as anomaly detection (AD). To detect anomalies accurately and efficiently, in this article a 3-hierarchy joint local and global anomaly detection framework, HADIoT, is proposed, in which IoT devices generate and transmit sensory data to their local edge servers for local AD after data refinement which includes re-framing, normalization, complexity reduction via Principal Component Analysis, and symbol mapping. High detection accuracy is achieved by jointly local and global ADs. The local AD focuses on the data pattern consistency of individual devices via the Gated Recurrent Unit, and the processed data is then forwarded from edge servers to the cloud server for global AD. The global AD focuses on the analysis of the data pattern correlations between different IoT devices, using the Conditional Random Fields. For the maintenance of cyber-security, the proposed anomaly detection framework HADIoT enables to provide an accurate and faster anomaly detection for IoT applications, compared with existing anomaly detection methods. The performance of the proposed method is also empirically evaluated through simulations, using a real dataset - the Information Security Center of Excellence (ISCX) 2012 dataset. Simulation results demonstrate the effectiveness of the proposed framework in terms of True Positive Rate, False Positive Rate, Precision, Accuracy and F_score, compared with three benchmark schemes.

Compartmentalization of hydrocarbon reservoirs, change in fluid contacts, effects of high permeable fractures around faults, and hydrocarbon traps created by seal faults make fault detection and extraction as a necessity in the analysis of seismic data. Faulting disrupts the smoothness trend of geological layers (reflections in seismic sections) and displaces layers along its plane. Thus, a fault could be considered as an abnormal phenomenon that globally deviates normal behavior of layers around its plane. In the present study, faults are considered as sparse global anomalies in a seismic section that can be extracted using Gaussian process regression. The Gaussian process regression is a nonparametric probabilistic model based on Bayesian statistics that can be used to model spatial properties as a regression problem. The Gaussian process usually is used to extract and describe normal interactions from the data set using smooth functions. The main idea of this study is to detect the global anomaly using Gaussian process regression. For this purpose, we considered geological layers as smooth normal events in seismic sections. Therefore, the location of the fault plane is where the Gaussian process gets an error during describing the layers. Abnormalities such as faults cause the Gaussian process to suffer an error near the anomaly. We used these errors and analyzed them to detect probable locations of fault edge. Finally, we used a consistent connection algorithm to separate most probable fault points and to connect them to an edge using morphological reconstruction algorithm. The proposed algorithm was evaluated based on the receiver operating characteristics analysis. Several synthetic seismic sections with different levels of signal to noise ratios were used to evaluate the algorithm in the presence of random noise. The results showed that all points predicted by a diagnostic test fell into the area above the diagonal of the receiver operating characteristics space, which represents a good diagnostic classification.

Disks are the main equipment for data storage in data centers. The prediction of disk failure is of great significance for the reliability and security of data. On account of the few abnormal samples in the disk datasets, it is difficult to satisfy the requirement of supervised and semi-supervised algorithms for the number of abnormal data while the unsupervised algorithms have poor performance on recall rate when solving the problems of local anomalies and wrapped a nomalies. This paper presents an incremental learning disk failure prediction model using the density metric of edge samples. An isolation region is built by searching the nearest neighbor of each sample. We calculate the nearest training point of the test point which is not a global anomaly and the nearest training point of the obtained nearest training point by Euclidean distance. The global metric of abnormal degree of the test sample comes from the ratio of the radius of the region where the two nearest training points are located. Then, the local metric of abnormal degree of the test sample comes from the ratio between the nearest distance from the test point to the edge of the training point region and the radius of the region. Abnormal scores of test points can be obtained by combining two measurements. We identify the SMART attributes that are significantly related to disk failures and promote their weights in the next time the attributes are inputted. The experiments are carried on the synthetic and public datasets which contain local anomalies and wrapped anomalies. The proposed method outperforms the typical unsupervised algorithms such as iNNE, iForest and LOF, and the achieved recall rates increase at most 7%. Furthermore, the contrast tests on the public disk datasets also verify the proposed method has better performance on recall rate.

In this note we review the role of homotopy groups in determining non-perturbative (henceforth `global') gauge anomalies, in light of recent progress understanding global anomalies using bordism. We explain why non-vanishing of $\pi_d(G)$ is neither a necessary nor a sufficient condition for there being a possible global anomaly in a $d$-dimensional chiral gauge theory with gauge group $G$. To showcase the failure of sufficiency, we revisit `global anomalies' that have been previously studied in 6d gauge theories with $G=SU(2)$, $SU(3)$, or $G_2$. Even though $\pi_6(G) \neq 0$, the bordism groups $\Omega_7^\mathrm{Spin}(BG)$ vanish in all three cases, implying there are no global anomalies. In the case of $G=SU(2)$ we carefully scrutinize the role of homotopy, and explain why any 7-dimensional mapping torus must be trivial from the bordism perspective. In all these 6d examples, the conditions previously thought to be necessary for global anomaly cancellation are in fact necessary conditions for the local anomalies to vanish., Comment: 36 pages, 6 figures. Footnotes added to clarify notation for eta-invariant. Matches version accepted for publication

We discuss anomaly cancellation in $U(2)$ gauge theories in four dimensions. For a $U(2)$ gauge theory defined with a spin structure, the vanishing of the bordism group $\Omega_5^{\text{Spin}}(BU(2))$ implies that there can be no global anomalies, in contrast to the related case of an $SU(2)$ gauge theory. We show explicitly that the familiar $SU(2)$ global anomaly is replaced by a local anomaly when $SU(2)$ is embedded in $U(2)$. There must be an even number of fermions with isospin $2r+1/2$, for $r\in \mathbb{Z}_{\geq 0}$, for this local anomaly to cancel. The case of a $U(2)$ theory defined without a choice of spin structure but rather using a spin-$U(2)$ structure, which is possible when all fermions (bosons) have half-integer (integer) isospin and odd (even) $U(1)$ charge, is more subtle. We find that the recently-discovered `new $SU(2)$ global anomaly' is also equivalent, though only at the level of the partition function, to a perturbative anomaly in the $U(2)$ theory, which is this time a combination of a mixed gauge anomaly with a gauge-gravity anomaly. This perturbative anomaly vanishes if there is an even number of fermions with isospin $4r+3/2$, for $r\in \mathbb{Z}_{\geq 0}$, recovering the condition for cancelling the new $SU(2)$ anomaly. Alternatively, this perturbative anomaly can be cancelled by a Wess--Zumino term, leaving a low-energy theory with a global anomaly, which can itself be cancelled by coupling to topological degrees of freedom., Comment: 23 pages, 2 figures. Description of the mapping tori for U(2) vs SU(2) has been made clearer

Detecting anomaly in images is challenging due to the high dimension nature of image data. While the previous learning-based anomaly detection approaches can detect a particular type of anomaly precisely, they often fail in detecting multiple types of abnormal samples simultaneously.We identify the two specific types of anomalies that can be precisely detected by either compress-based or reconstruction-based anomaly detection approaches, named global anomaly and local anomaly. We then propose Glad, an anomaly detector that can precisely detect both of them at the same time. Glad adopts a joint approach combining the density estimation and auto-encoder. Firstly, it designs a multimodal density estimation model to derive the latent representation probability for identifying the global anomaly. Then, it uses structural similarity to measure the reconstruction loss for characterizing local anomaly. Finally, both anomalies can be diagnosed according to the joint density of latent representation and reconstruction loss. Experimental results on public benchmark datasets demonstrate that Glad outperforms the state-of-the-art methods significantly.

There have been many studies in relationship between ultra-low frequency electromagnetic anomaly and earthquakes, while most of them judge anomaly using single feature. We propose a multi-feature anomaly detection method for AETA ULF electromagnetic disturbance signals based on Isolation Forest, with some feature extraction and selection method added. A statistical test method superposed epoch analysis (SEA) is used for its evaluation. The result shows that 6 of 12 selected stations show significant correlation between signal anomaly and earthquakes. A further comparison experiment shows that our method has better performance than traditional single-feature sliding IQR method, which indicates multi-feature might be a good choice in finding global anomaly points.

Global abnormal events form unique and distinct motion characteristics and category of anomalies at image level rather than pixel level with less complexity compared to local abnormal events. However, traditional anomaly detection approaches focused more on pixel-level feature extraction from foreground pixels and combine global and local anomaly detection in a single algorithm with equal degree of computational complexity. In this paper, we propose a novel framework for global anomaly detection via block-level feature extraction using context location (CL) and motion-rich STVs (MRSTVs). The histogram of optical flow orientation and motion magnitude features from spatio-temporal volumes (STVs) are used as global feature descriptor to capture motion characteristics of normal and abnormal events. Simple and cost-effective one-class SVM classifier is employed to learn normal behaviour from MRSTVs during training and detect abnormal STVs from test data. Thereafter, a spatio-temporal post-processing technique detects frame-level abnormal behaviour and reduces false alarm rate. We define CL to detect abnormal behaviour in an unexpected region. The proposed approach omits pixel-level feature extraction and background modelling by considering MRSTVs, thus enhances detection rate and reduces computational complexity. We have conducted experiments on widely used UMN and PETS2009 datasets to compare the performance of proposed approach with existing methods.

Logs are generated by systems to record the detailed runtime information about system operations, and log analysis plays an important role in anomaly detection at the host or network level. Most existing detection methods require a priori knowledge, which cannot be used to detect the new or unknown anomalies. Moreover, the growing volume of logs poses new challenges to anomaly detection. In this paper, we propose an integrated method using K-prototype clustering and k-NN classification algorithms, which uses a novel clustering-filtering-refinement framework to perform anomaly detection from massive logs. First, we analyze the characteristics of system logs and extract 10 features based on the session information to characterize user behaviors effectively. Second, based on these extracted features, the K-prototype clustering algorithm is applied to partition the data set into different clusters. Then, the obvious normal events which usually present as highly coherent clusters are filtered out, and the others are regarded as anomaly candidates for further analysis. Finally, we design two new distance-based features to measure the local and global anomaly degrees for these anomaly candidates. Based on these two new features, we apply the k-NN classifier to generate accurate detection results. To verify the integrated method, we constructed a log collection and anomaly detection platform in the campus network center of Xi’an Jiaotong University. The experimental results based on the data sets collected from the platform show our method has high detection accuracy and low computational complexity.

This paper presents a novel method for global anomaly detection in crowded scenes. The optical flow of frames is used to extract the foreground of areas with people motions in the crowd in the form of layers. The optical flow between two frames generates one layer. The proposed method applies the metaheuristic of artificial bacteria colony as a robust algorithm to optimize the extracted layers. Artificial bacteria cover all regions of interest that have high movement between frames. The artificial bacteria colony adapts quickly to the most varied scenarios. Moreover, the algorithm has low sensibility to noise and to sudden changes in video lighting as captured by optical flow. The bacteria population of the colonies, its food storage and the colony’s centroid position regarding each optical flow layer, are used as input to train a Kohonen’s neural network. Once trained the network is able to detect specific events based on behavior patterns similarity, as produced by the bacteria colony during such events. Experiments are conducted on available public dataset. The achieved results show that the proposed method captures the dynamics of the crowd behavior successfully, revealing that the proposed scheme outperforms the available state-of-the-art algorithms for global anomaly detection.

A combination of the magnetic field and the quantum anomaly leads to transport phenomena of chiral fermions. On the microscopic level, however, what really flows is a non-trivial question. I propose an answer to this question; the particle production affected by the magnetic field and the quantum anomaly has an anisotropic distribution in momentum space, which should be realized in the heavy-ion collision by a fast process occurring on top of color flux tubes in the glasma.

This book is about the light side of ecology, the non-forested open ecosystems of the world. More than a quarter of the world’s land area is dominated by open, non-forested ecosystems in climates which can support closed forests. They are particularly common in the tropics, making up grasslands and savannas, but also occur in other climate zones. Open ecosystems have been widely attributed to human deforestation. While deforestation is widespread and increasing in many regions, open ecosystems include ancient vegetation, in species, with traits divergent from closed forests. Using Cape fynbos, the world’s richest temperate flora, as an example, the ideas and explanations for these anomalously low biomass systems are introduced. The aim of this book is explained as introducing a wider readership to the still poorly known biology of open ecosystems on the light side. The structure and content of chapters is outlined.

We survey the Hirzebruch signature theorem as a special case of the Atiyah-Singer index theorem. The family version of the Atiyah-Singer index theorem in the form of the Riemann-Roch-Grothendieck-Quillen (RRGQ) formula is then applied to the complexified signature operators varying along the universal family of elliptic curves. The RRGQ formula allows us to determine a generalized cohomology class on the base of the elliptic fibration that is known in physics as (a measure of) the local and global anomaly. Combining several anomalous operators allows us to cancel the local anomaly on a Jacobian elliptic surface, a construction that is based on the construction of the Poincar\'e line bundle over an elliptic surface., Comment: 37 pages; minor typos corrected in version 2

Symmetry-protected phases are gapped phases of matter which are distinguished only in the presence of a global symmetry $G$. These quantum phases lack any symmetry-breaking or topological order and have short-range entangled ground states. Based on this short-range entanglement property, we give a general argument for the existence of an emergent antiunitary (and sometimes also a unitary) ${\mathbb{Z}}_{2}$ symmetry at a critical point separating two different bosonic symmetry-protected phases in any dimension. Often, the emergent symmetry group at criticality has a mixed global anomaly. For those phases classified by group cohomology, we identify a criterion for when such a mixed global anomaly is present, and write down representative cocycles for the corresponding anomaly class. We illustrate our results with a series of examples and make connections to recent results on $(2+1)$-dimensional beyond-Landau critical points.

We review a class of models which generalize the traditional Peccei-Quinn (PQ) axion solution by a St\"uckelberg pseudoscalar. Such axion models represent a significant variant with respect to earlier scenarios where axion fields were associated with global anomalies, because of the St\"uckelberg field, which is essential for the cancellation of gauge anomalies in the presence of extra $U(1)$ symmetries. The extra neutral currents associated to these models have been investigated in the past in orientifold models with intersecting branes, under the assumption that the St\"uckelberg scale was in the multi-TeV region. Such constructions, at the field theory level, are quite general and can be interpreted as the four-dimensional field theory realization of the Green-Schwarz mechanism of anomaly cancellation of string theory. We present an overview of models of this type in the TeV/multi TeV range in their original formulation and their recent embeddings into an ordinary GUT theory, presenting an $E_6\times U(1)_X$ model as an example. In this case the model contains two axions, the first corresponding to a Peccei-Quinn axion, whose misalignment takes place at the QCD phase transition, with a mass in the meV region and which solves the strong CP problem. The second axion is ultralight, in the $10^{-20}-10^{-22}$ eV region, due to a misalignment and a decoupling taking place at the GUT scale. The two scales introduced by the PQ solution, the PQ breaking scale and the misalignment scale at the QCD hadron transition, become the Planck and the GUT scales respectively, with a global anomaly replaced by a gauge anomaly. The periodic potential and the corresponding oscillations are related to a particle whose De Broglie wavelength can reach 10 kpc. Such a sub-galactic scale has been deemed necessary in order to resolve several dark matter issues at the astrophysical level., Comment: 33 pages, 5 figures, extended final version, to appear on Frontiers in physics " Phenomena Beyond the Standard Model: What do we expect for New Physics to look like?" Ed. S. Moretti. arXiv admin note: text overlap with arXiv:1005.5441

Abnormal event detection in public scenes is very important in recent society. In this paper, a method for global anomaly detection in video surveillance is proposed, which is based on a deep prediction neural network. The deep prediction neural network is built on the Convolutional Neural Network (CNN) and a variant of the Recurrent Neural Network (RNN)-Long Short-Term Memory (LSTM). Especially, the feature of a frame is the output of CNN, which is instead of the hand-crafted feature. First, the feature of a short video clip is obtained through CNN. Second, the predicted feature of the next frame can be gained by LSTM. Finally, the prediction error is introduced to detect that a frame is abnormal or not after the feature of the frame is achieved. Experimental results of global abnormal event detection show the effectiveness of our deep prediction neural network. Comparing with state-of-the-art methods, the model we proposed obtains superior detection results.

Existing anomaly detection (AD) approaches rely on various hand-crafted representations to represent video data and can be costly. The choice or designing of hand-crafted representation can be difficult when faced with a new dataset without prior knowledge. Motivated by feature learning, e.g. deep leaning and the ability to directly learn useful representations and model high-level abstraction from raw data, the authors investigate the possibility of using a universal approach. The objective is learning data-driven high-level representation for the task of video AD without relying on hand-crafted representation. A deep incremental slow feature analysis (D-IncSFA) network is constructed and applied to directly learning progressively abstract and global high-level representations from raw data sequence. The D-IncSFA network has the functionalities of both feature extractor and anomaly detector that make AD completion in one step. The proposed approach can precisely detect global anomaly such as crowd panic. To detect local anomaly, a set of anomaly maps, produced from the network at different scales, is used. The proposed approach is universal and convenient, working well in different types of scenarios with little human intervention and low memory and computational requirements. The advantages are validated by conducting extensive experiments on different challenge datasets.

A new approach is presented to discuss two-dimensional hydrodynamics with gauge and gravitational anomalies. Exact constitutive relations for the stress tensor and charge current are obtained. Also, a connection between response parameters and anomaly coefficients is discussed. These are new results which, in the absence of the gauge sector, reproduce the results found by the gradient expansion approach.

This paper presents a hierarchical framework for detecting local and global anomalies via hierarchical feature representation and Gaussian process regression (GPR) which is fully non-parametric and robust to the noisy training data, and supports sparse features. While most research on anomaly detection has focused more on detecting local anomalies, we are more interested in global anomalies that involve multiple normal events interacting in an unusual manner, such as car accidents. To simultaneously detect local and global anomalies, we cast the extraction of normal interactions from the training videos as a problem of finding the frequent geometric relations of the nearby sparse spatio-temporal interest points (STIPs). A codebook of interaction templates is then constructed and modeled using the GPR, based on which a novel inference method for computing the likelihood of an observed interaction is also developed. Thereafter, these local likelihood scores are integrated into globally consistent anomaly masks, from which anomalies can be succinctly identified. To the best of our knowledge, it is the first time GPR is employed to model the relationship of the nearby STIPs for anomaly detection. Simulations based on four widespread datasets show that the new method outperforms the main state-of-the-art methods with lower computational burden.

We point out that the $\text{USp}$ symmetry associated to a full twisted puncture of a class S theory of type $A_\text{even}$ has the global anomaly associated to $\pi_4(\text{USp})=\mathbb{Z}_2$. We discuss manifestations of this fact in the context of the superconformal field theory $R_{2,2N}$ introduced by Chacaltana, Distler and Trimm. For example, we find that this theory can be thought of as a natural ultraviolet completion of an infrared-free $\text{SO}(2N+1)$ gauge theory with $2N$ flavors, whose $\text{USp}(4N)$ symmetry clearly has the global anomaly., Comment: 14 pages, 5 figures; v2: minor changes

We recently developed a new, man-portable, electromagnetic induction (EMI) sensor designed to detect and classify small, unexploded sub-munitions and discriminate them from non-hazardous debris. The ability to distinguish innocuous metal clutter from potentially hazardous unexploded ordnance (UXO) and other explosive remnants of war (ERW) before excavation can significantly accelerate land reclamation efforts by eliminating time spent removing harmless scrap metal. The EMI sensor employs a multi-axis transmitter and receiver configuration to produce data sufficient for anomaly discrimination. A real-time data inversion routine produces intrinsic and extrinsic anomaly features describing the polarizability, location, and orientation of the anomaly under test. We discuss data acquisition and post-processing software development, and results from laboratory and field tests demonstrating the discrimination capability of the system. Data acquisition and real-time processing emphasize ease-of-use, quality control (QC), and display of discrimination results. Integration of the QC and discrimination methods into the data acquisition software reduces the time required between sensor data collection and the final anomaly discrimination result. The system supports multiple concepts of operations (CONOPs) including: 1) a non-GPS cued configuration in which detected anomalies are discriminated and excavated immediately following the anomaly survey; 2) GPS integration to survey multiple anomalies to produce a prioritized dig list with global anomaly locations; and 3) a dynamic mapping configuration supporting detection followed by discrimination and excavation of targets of interest.

Using the heat kernel method, we compute nonrelativistic trace anomalies for Schr\"odinger theories in flat spacetime, with a generic background gauge field for the particle number symmetry, both for a free scalar and a free fermion. The result is genuinely nonrelativistic, and it has no counterpart in the relativistic case. Contrary to the naive expectations, the anomaly is not gauge-invariant; this is similar to the non-gauge covariance of the non-abelian relativistic anomaly. We also show that, in the same background, the gravitational anomaly for a nonrelativistic scalar vanishes., Comment: 20 pages; V2 minor changes also in title, typos

Edge theories of symmetry-protected topological phases are well known to possess global symmetry anomalies. In this Letter we focus on two-dimensional bosonic phases protected by an on-site symmetry and analyze the corresponding edge anomalies in more detail. Physical interpretations of the anomaly in terms of an obstruction to orbifolding and constructing symmetry-preserving boundaries are connected to the cohomology classification of symmetry-protected phases in two dimensions. Using the tensor network and matrix product state formalism we numerically illustrate our arguments and discuss computational detection schemes to identify symmetry-protected order in a ground state wave function.

We obtain new constraints on the anomaly coefficients of 6D $$ \mathcal{N}=\left(1,0\right) $$ supergravity theories using local and global anomaly cancellation conditions. We show how these constraints can be strengthened if we assume that the theory is well-defined on any spin space-time with an arbitrary gauge bundle. We distinguish the constraints depending on the gauge algebra only from those depending on the global structure of the gauge group. Our main constraint states that the coefficients of the anomaly polynomial for the gauge group G should be an element of 2H4(BG; ℤ) ⊗ Λ S where Λ S is the unimodular string charge lattice. We show that the constraints in their strongest form are realized in F-theory compactifications. In the process, we identify the cocharacter lattice, which determines the global structure of the gauge group, within the homology lattice of the compactification manifold.

We use Pauli-Villars regularization to evaluate the conformal and chiral anomalies in the effective field theories from Z3 and Z7 compactifications of the heterotic string without Wilson lines. We show that parameters for Pauli-Villars chiral multiplets can be chosen in such a way that the anomaly is universal in the sense that its coefficient depends only on a single holomorphic function of the three diagonal moduli. It is therefore possible to cancel the anomaly by a generalization of the four-dimensional Green-Schwarz mechanism. In particular we are able to reproduce the results of a string calculation of the four-dimensional chiral anomaly for these two models., 26 pages

In a single eye movement experiment, we investigated the effects of context on the time course of local and global anomaly processing during reading in adults with Autism Spectrum Disorder (ASD). In one condition, short paragraph texts contained anomalous target words. Detection of the anomaly was only possible through evaluation of word meaning in relation to the global context of the whole paragraph ( Passage-Level Anomalies). In another condition, the anomaly could be detected via computation of a local thematic violation within a single sentence embedded in the paragraph ( Sentence-Level Anomalies). For the sentence-level anomalies, the ASD group, in contrast with the typically developing (TD) group, showed early detection of the anomaly as indexed by regressive eye movements from the critical target word upon fixation. Conversely, for the passage-level anomalies, and in contrast with the ASD group, the TD group showed early detection of the anomaly with increased regressive eye movements once the critical word had been fixated. The reversal of the pattern of regression path data for the two groups, for the sentence- and passage-level anomalies, is discussed in relation to cognitive accounts of ASD.

We prove a formula for the global gravitational anomaly of the self-dual field theory in the presence of background gauge fields, assuming the results of arXiv:1110.4639. Along the way, we also clarify various points about the self-dual field theory. In particular, we give a general definition of the theta characteristic entering its partition function and settle the issue of its possible metric dependence. We treat the cohomological version of type IIB supergravity as an example of the formalism: a mixed gauge-gravitational global anomaly, occurring when the B-field and Ramond-Ramond 2-form gauge fields have non-trivial Wilson lines, cancels provided a certain cobordism group vanishes., Comment: 38 pages. v3: Corrections in the discussion of the global anomaly cancellation in type IIB sugra. Typos corrected

Power loss is a serious problem for all power companies. To find effective means of abnormal electricity iidentification is a popular research field in recent years. This paper puts forward an anomalous electricity use model based on multi-dimensional compound features of electricity users. The support vector machine, local outlier factor, correlation measurement based on the similar user power load, and correlation change rate measurement based on the most relevant users—these four algorithms are adopted to extract four-dimensional compound features of anomalous electricity use from the perspective of global anomaly, local anomaly, regional space, and time sequence. Next, the logistic regression (LR) model is trained based on the compound features. After training, the LR model is adopted as the final anomalous electricity use identification model. Analysis of the practical power load data of users suggests that the LR model combine respective advantages of the four-dimensional compound features. Detection of anomalies using the LR model is an effective approach, which can reliably and accurately identify residents’ anomalous electricity use. From the accuracy rate, recall rate, precision rate and scores of F1, it can be seen that the LR model is significantly superior to SVM.

The concurrent influence of large-scale, coupled oceanic–atmospheric circulation patterns was established to have an effect on hydrologic variability across the world. El Nino–Southern Oscillation (ENSO) and Indian Ocean Dipole (IOD) are, in particular, important for Indian hydroclimatology. However, it is now established that rather than just a few well-known teleconnection patterns, a Global Climate Pattern (GCP) comprising of a global field of several climate anomalies are responsible for above-normal and below-normal precipitation events over entire India. The existence of a GCP for hydrological extremes in an even smaller spatial scale is illustrated in this study. The central part of India, consisting of the contiguous homogeneous meteorological subdivisions—West Madhya Pradesh, East Madhya Pradesh, Vidarbha, and Chattisgarh (hereinafter ‘central India’), is selected as the study area. Hydrological extremes (this study focus on precipitation) in the study area are identified in terms of the Standardized Precipitation Anomaly Index (SPAI), which is suitable for quantifying extreme events in a monsoon-dominated climatology. After investigation of the global anomaly fields of five climate variables, a set of 19 specific zones of climate anomalies from across the world are found to constitute the GCP for the hydrological extremes in the study region. The identified GCP is further utilized in a Support Vector Machine (SVM) model to investigate the potential of the GCP in foreseeing dry and wet extremes over the study area.

We propose a six-dimensional regularization of four dimensional chiral gauge theories. We consider a massive Dirac fermion in six dimensions with two different operators having domain-wall profiles in the fifth and the sixth directions, respectively. A Weyl fermion appears as a localized mode at the junction of the two domain-walls. In our formulation, the Stora-Zumino chain of the anomaly descent equations, starting from the axial $U(1)$ anomaly in six-dimensions to the gauge anomaly in four-dimensions, is naturally embedded. Moreover, a similar inflow of the global anomalies is found. The anomaly free condition is equivalent to requiring that the axial $U(1)$ anomaly and the parity anomaly are canceled among the six-dimensional Dirac fermions. Putting the gauge field at the four- dimensional junction and extending it to the bulk using the Yang-Mills gradient flow, as recently proposed by Grabowska and Kaplan, we define the four-dimensional path integral of the target chiral gauge theory., Comment: 14pages, 2figures, Proceedings of the 34th annual International Symposium on Lattice Field Theory. PoS(LATTICE2016)330, References added

We derive a selection rule among the $(1+1)$-dimensional SU(2) Wess-Zumino-Witten theories, based on the global anomaly of the discrete $\mathbb{Z}_2$ symmetry found by Gepner and Witten. In the presence of both the SU(2) and $\mathbb{Z}_2$ symmetries, a renormalization-group flow is possible between level-$k$ and level-$k'$ Wess-Zumino-Witten theories only if $k\equiv k' \mod{2}$. This classifies the Lorentz-invariant, SU(2)-symmetric critical behavior into two "symmetry-protected" categories corresponding to even and odd levels,restricting possible gapless critical behavior of translation-invariant quantum spin chains.

This paper presents a novel method for global anomaly detection in crowded scenes. The optical flow of frames is used to extract the foreground of areas with people motions in crowd. The optical flow between two frames generates one layer. The proposed method applies the metaheuristic of artificial bacteria colony as a robust algorithm to optimize the layers from optical flow. The artificial bacteria colony has the ability to adapt quickly to the most varied scenarios, extracting just relevant information from regions of interest. Moreover, the algorithm has low sensibility to noise and to sudden changes in video lighting as captured by optical flow. The bacteria population of colonies, its food storage and the colony’s centroid position regarding each optical flow layer, are used as input to train a Kohonen’s neural network. Once trained the network is able to detect specific events based on behavior patterns similarity, as produced by the bacteria colony during such events. Experiments are conducted on publicly available dataset. The achieved results show that the proposed method captures the dynamics of the crowd behavior successfully, revealing that the proposed scheme outperforms the available state-of-the-art algorithms for global anomaly detection.

The gauge dependence of the conformal anomaly for spin 3/2 and spin 2 fields in non-conformal supergravities has been a long standing puzzle. In this Letter we argue that the `correct' gauge choice is the one that follows from requiring all terms that would imply a violation of the Wess-Zumino consistency condition to be absent in the counterterm, because otherwise the usual link between the anomaly and the one-loop divergence becomes invalid. Remarkably, the `good' choice of gauge is the one that confirms our previous result that a complete cancellation of conformal anomalies in D=4 can only be achieved for N-extended (Poincar\'e) supergravities with $N\geq 5$.

The trace anomaly in six-dimensional space is given by the local terms which have six derivatives of the metric. We find the effective action which is responsible for the anomaly. The result is presented in non-local covariant form and also in the local covariant form which employs two auxiliary scalar fields., Comment: 8 pages

In view of the recent applications of chiral anomaly to various fields beyond particle physics, we discuss some basic aspects of chiral anomaly which may help deepen our understanding of chiral anomaly in particle physics also. It is first shown that Berry's phase (and its generalization) for the Weyl model $H =v_{F} \vec{\sigma}\cdot \vec{p}(t)$ assumes a monopole form at the exact adiabatic limit but deviates from it off the adiabatic limit and vanishes in the high frequency limit of the Fourier transform of $\vec{p}(t)$ for bounded $|\vec{p}(t)|$. An effective action, which is consistent with the non-adiabatic limit of Berry's phase, combined with the Bjorken-Johnson-Low prescription gives normal equal-time space-time commutators and no chiral anomaly. In contrast, an effective action with a monopole at the origin of the momentum space, which describes Berry's phase in the precise adiabatic limit but fails off the adiabatic limit, gives anomalous space-time commutators and a covariant anomaly to the gauge current. We regard this anomaly as an artifact of the postulated monopole and not a consequence of Berry's phase. As for the recent application of the chiral anomaly to the description of effective Weyl fermions in condensed matter and nuclear physics, which is closely related to the formulation of lattice chiral fermions, we point out that the chiral anomaly for each species doubler separately vanishes for a finite lattice spacing, contrary to the common assumption. Instead a general form of pair creation associated with the spectral flow for the Dirac sea with finite depth takes place. This view is supported by the Ginsparg-Wilson fermion, which defines a single Weyl fermion without doublers on the lattice and gives a well-defined index (anomaly) even for a finite lattice spacing., Comment: 14 pages. Some explanations are added. This version is to be published in PRD

We study perturbative and global anomalies at the boundaries of bosonic analogues of integer quantum Hall (BIQH) and topological insulator (BTI) phases using a description of the boundaries of these phases in terms of a nonlinear sigma model (NLSM) with Wess-Zumino term. One of the main results of the paper is that these anomalies are robust against arbitrary smooth deformations of the target space of the NLSM which describes the phase, provided that the deformations also respect the symmetry of the phase. In the first part of the paper we discuss the perturbative $U(1)$ anomaly at the boundary of BIQH states in all odd (spacetime) dimensions. In the second part we study global anomalies at the boundary of BTI states in even dimensions. In a previous work [Phys. Rev. B 95, 035149 (2017)] we argued that the boundary of the BTI phase exhibits a global anomaly which is an analogue of the parity anomaly of Dirac fermions in three dimensions. Here we elevate this argument to a proof for the boundary of the two-dimensional BTI state by explicitly computing the partition function of the gauged NLSM describing the boundary. We then use the powerful equivariant localization technique to show that this global anomaly is robust against all smooth deformations of the target space of the NLSM which preserve the $U(1)\rtimes\mathbb{Z}_2$ symmetry of the BTI state. We also comment on the difficulties of generalizing this latter proof to higher dimensions. Finally, we discuss the expected low energy behavior of the boundary theories studied in this paper when the coupling constants are allowed to flow under the renormalization group., Comment: 22 pages, three appendices, v2: 24 pages, three appendices, and a new section (Sec. IV). To appear in PRB

The automatic detection of anomalies in Active and Assisted Living (AAL) environments is important for monitoring the wellbeing and safety of the elderly at home. The integration of smart domotic sensors (e.g. presence detectors) and those ones equipping modern mobile robots (e.g. RGB-D cameras) provides new opportunities for addressing this challenge. In this paper, we propose a novel solution to combine local activity levels detected by a single RGB-D camera with the global activity perceived by a network of domotic sensors. Our approach relies on a new method for computing such a global activity using various presence detectors, based on the concept of entropy from information theory. This entropy effectively shows how active a particular room or environment's area is. The solution includes also a new application of Hybrid Markov Logic Networks (HMLNs) to merge different information sources for local and global anomaly detection. The system has been tested with a comprehensive dataset of RGB-D and domotic data containing data entries from 37 different domotic sensors (presence, temperature, light, energy consumption, door contact), which is made publicly available. The experimental results show the effectiveness of our approach and its potential for complex anomaly detection in AAL settings.

We investigate the constraints imposed by global gravitational anomalies on parity odd induced transport coefficients in even dimensions for theories with chiral fermions, gravitinos and self dual tensors. The $\eta$-invariant for the large diffeomorphism corresponding to the $T$ transformation on a torus constraints the coefficients in the thermal effective action up to mod 2. We show that the result obtained for the parity odd transport for gravitinos using global anomaly matching is consistent with the direct perturbative calculation. In $d=6$ we see that the second Pontryagin class in the anomaly polynomial does not contribute to the $\eta$-invariant which provides a topological explanation of this observation in the `replacement rule'. We then perform a direct perturbative calculation for the contribution of the self dual tensor in $d=6$ to the parity odd transport coefficient using the Feynman rules proposed by Gaum\'{e} and Witten. The result for the transport coefficient agrees with that obtained using matching of global anomalies., Comment: 53 pages, Mathematica code for Wick contractions available on request

In this paper we present a novel framework based on multi-feature extraction for anomaly detection in video surveillance which global anomaly and local anomaly are detected separately. To detect global anomaly, we define kinetic energy Ek and compute the first derivative of Ek and then derive a global anomaly score of each test frame. As for local anomaly detection, three kinds of local anomaly are defined namely appearance anomaly, location anomaly and velocity anomaly where different kinds of features are extracted respectively and finally fused into a unified framework. At last, an improved Normality Sensitive Hashing method is proposed to classify abnormal instances from normal instances. The experiment results demonstrate that our method can detect global and local anomaly with a comparative performance.

We review the concept of anomaly field theory, namely the fact that the anomalies of a $d$-dimensional field theory can be encoded in a $d+1$-dimensional field theory functor. We give numerous examples of anomaly field theories, explain how classical facts about anomalies are recovered from the anomaly field theory, and review recent work on global anomaly cancellation in 6d supergravity where this concept was instrumental. We also sketch the status of global anomaly cancellation checks in string theory. This paper is based on a talk given at the Durham Symposium `Higher Structures in M-theory' in August 2018.

Crowd motions usually play a basic role of analyzing and understanding abnormal events. However, only using these visible features cannot fully describe the scenarios because the influence caused by an abnormal event is not considered from the social psychological point of view. Although the invisible influences cannot be directly observed through the video, they objectively exist and have precise definitions and specific analytical methods in sociology and psychology. Therefore, a mid-level representation named global event influence (GEI) for global anomaly detection in dense crowds is introduced. The proposed GEI integrates the crowd motions and social psychology attributes to improve the description of crowds. For this, low-level motion features are abstracted as crowd attributes of scale, velocity, and disorder. Then, the detailed definitions and mathematical expressions of GEI are presented through calculating the convolution of rise factor and decay factor. Based on GEI, a model for global anomaly detection is proposed. Compared with most previous methods, our proposed model is robust to detect not only the occurrence of anomalous events but also elimination time of the event influence. Accordingly, strategies for event occurrence detection and influence elimination detection are proposed, respectively. In addition, a dataset of dense crowds is introduced and used for evaluation. The experimental comparison on benchmark datasets shows that the performance of our GEI model has not only the competitive accuracy of event occurrence detection but also the claimed effectiveness of influence elimination, which is more advanced than others.

This work extends the emerging field of hyperspectral imagery (HSI) anomaly detectors and employs independent component (IC) analysis (ICA) to identify anomalous pixels. Using new techniques to fully automate feature extraction, feature selection, and anomaly pixel identification, an Autonomous Global Anomaly Detector has been developed for employment in an operational environment for real-time processing of HSI. Dimensionality reduction, which is the initial feature extraction prior to ICA, is effected through a geometric solution that estimates the number of retained principal components. The solution is based on the theory of the shape of the eigenvalue curve of the covariance matrix of spectral data containing noise. This research presents two new features, namely, potential anomaly signal-to-noise ratio and maximum pixel score, both of which are computed for each of the ICs to create a new 2-D feature space where the overlap between anomaly and nonanomaly classes is reduced. After anomaly feature selection, adaptive noise filtering is applied iteratively to suppress the background. Finally, a zero-detection histogram method is applied to the smoothed signals to identify anomaly locations to the user. After the algorithm is fully developed, a set of designed experiments are conducted to identify a reasonable set of input parameters.