Optimal volume anomaly detection and isolation in large-scale IP networks using coarse-grained measurements

AbstractNeural mass models have been used since the 1970s to model the coarse-grained activity of large populations of neurons. They have proven especially fruitful for understanding brain rhythms. However, although motivated by neurobiological considerations they are phenomenological in nature, and cannot hope to recreate some of the rich repertoire of responses seen in real neuronal tissue. Here we consider a simple spiking neuron network model that has recently been shown to admit an exact mean-field description for both synaptic and gap-junction interactions. The mean-field model takes a similar form to a standard neural mass model, with an additional dynamical equation to describe the evolution of within-population synchrony. As well as reviewing the origins of this next generation mass model we discuss its extension to describe an idealised spatially extended planar cortex. To emphasise the usefulness of this model for EEG/MEG modelling we show how it can be used to uncover the role of local gap-junction coupling in shaping large scale synaptic waves.

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Large Scale Hierarchical Anomaly Detection and Temporal Localization

Proceedings of the 28th ACM International Conference on Multimedia ◽

10.1145/3394171.3416302 ◽

2020 ◽

Author(s):

Soumil Kanwal ◽

Vineet Mehta ◽

Abhinav Dhall

Keyword(s):

Anomaly Detection ◽

Large Scale ◽

Temporal Localization

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Multi-task learning based Encoder-Decoder: A comprehensive detection and diagnosis system for multi-sensor data

Advances in Mechanical Engineering ◽

10.1177/16878140211013138 ◽

2021 ◽

Vol 13 (5) ◽

pp. 168781402110131

Author(s):

Junfeng Wu ◽

Li Yao ◽

Bin Liu ◽

Zheyuan Ding ◽

Lei Zhang

Keyword(s):

Anomaly Detection ◽

Event Detection ◽

Large Scale ◽

Multivariate Time Series ◽

Sensor Data ◽

Unified Framework ◽

Diagnosis System ◽

Learning Framework ◽

Task Learning ◽

Detection And Diagnosis

As more and more sensor data have been collected, automated detection, and diagnosis systems are urgently needed to lessen the increasing monitoring burden and reduce the risk of system faults. A plethora of researches have been done on anomaly detection, event detection, anomaly diagnosis respectively. However, none of current approaches can explore all these respects in one unified framework. In this work, a Multi-Task Learning based Encoder-Decoder (MTLED) which can simultaneously detect anomalies, diagnose anomalies, and detect events is proposed. In MTLED, feature matrix is introduced so that features are extracted for each time point and point-wise anomaly detection can be realized in an end-to-end way. Anomaly diagnosis and event detection share the same feature matrix with anomaly detection in the multi-task learning framework and also provide important information for system monitoring. To train such a comprehensive detection and diagnosis system, a large-scale multivariate time series dataset which contains anomalies of multiple types is generated with simulation tools. Extensive experiments on the synthetic dataset verify the effectiveness of MTLED and its multi-task learning framework, and the evaluation on a real-world dataset demonstrates that MTLED can be used in other application scenarios through transfer learning.

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Simba

Communications of the ACM ◽

10.1145/3460227 ◽

2021 ◽

Vol 64 (6) ◽

pp. 107-116

Author(s):

Yakun Sophia Shao ◽

Jason Cemons ◽

Rangharajan Venkatesan ◽

Brian Zimmer ◽

Matthew Fojtik ◽

...

Keyword(s):

Deep Learning ◽

Large Scale ◽

Data Locality ◽

Coarse Grained ◽

Batch Size ◽

Peak Performance ◽

Large Scale Systems ◽

High Area ◽

On Chip ◽

And Storage

Package-level integration using multi-chip-modules (MCMs) is a promising approach for building large-scale systems. Compared to a large monolithic die, an MCM combines many smaller chiplets into a larger system, substantially reducing fabrication and design costs. Current MCMs typically only contain a handful of coarse-grained large chiplets due to the high area, performance, and energy overheads associated with inter-chiplet communication. This work investigates and quantifies the costs and benefits of using MCMs with finegrained chiplets for deep learning inference, an application domain with large compute and on-chip storage requirements. To evaluate the approach, we architected, implemented, fabricated, and tested Simba, a 36-chiplet prototype MCM system for deep-learning inference. Each chiplet achieves 4 TOPS peak performance, and the 36-chiplet MCM package achieves up to 128 TOPS and up to 6.1 TOPS/W. The MCM is configurable to support a flexible mapping of DNN layers to the distributed compute and storage units. To mitigate inter-chiplet communication overheads, we introduce three tiling optimizations that improve data locality. These optimizations achieve up to 16% speedup compared to the baseline layer mapping. Our evaluation shows that Simba can process 1988 images/s running ResNet-50 with a batch size of one, delivering an inference latency of 0.50 ms.

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Mobility patterns are associated with experienced income segregation in large US cities

Nature Communications ◽

10.1038/s41467-021-24899-8 ◽

2021 ◽

Vol 12 (1) ◽

Author(s):

Esteban Moro ◽

Dan Calacci ◽

Xiaowen Dong ◽

Alex Pentland

Keyword(s):

Large Scale ◽

Mobility Model ◽

Coarse Grained ◽

Spatial Proximity ◽

Mobility Patterns ◽

Data Set ◽

Residential Patterns ◽

Income Segregation ◽

Mobility Data ◽

Mobility Behavior

AbstractTraditional understanding of urban income segregation is largely based on static coarse-grained residential patterns. However, these do not capture the income segregation experience implied by the rich social interactions that happen in places that may relate to individual choices, opportunities, and mobility behavior. Using a large-scale high-resolution mobility data set of 4.5 million mobile phone users and 1.1 million places in 11 large American cities, we show that income segregation experienced in places and by individuals can differ greatly even within close spatial proximity. To further understand these fine-grained income segregation patterns, we introduce a Schelling extension of a well-known mobility model, and show that experienced income segregation is associated with an individual’s tendency to explore new places (place exploration) as well as places with visitors from different income groups (social exploration). Interestingly, while the latter is more strongly associated with demographic characteristics, the former is more strongly associated with mobility behavioral variables. Our results suggest that mobility behavior plays an important role in experienced income segregation of individuals. To measure this form of income segregation, urban researchers should take into account mobility behavior and not only residential patterns.

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Heterogeneous lamella structure unites ultrafine-grain strength with coarse-grain ductility

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1517193112 ◽

2015 ◽

Vol 112 (47) ◽

pp. 14501-14505 ◽

Cited By ~ 437

Author(s):

Xiaolei Wu ◽

Muxin Yang ◽

Fuping Yuan ◽

Guilin Wu ◽

Yujie Wei ◽

...

Keyword(s):

Large Scale ◽

Low Cost ◽

High Strength ◽

Back Stress ◽

Coarse Grained ◽

Ultrafine Grain ◽

Asymmetric Rolling ◽

Dislocation Hardening ◽

Ultrafine Grained ◽

Lamella Structure

Grain refinement can make conventional metals several times stronger, but this comes at dramatic loss of ductility. Here we report a heterogeneous lamella structure in Ti produced by asymmetric rolling and partial recrystallization that can produce an unprecedented property combination: as strong as ultrafine-grained metal and at the same time as ductile as conventional coarse-grained metal. It also has higher strain hardening than coarse-grained Ti, which was hitherto believed impossible. The heterogeneous lamella structure is characterized with soft micrograined lamellae embedded in hard ultrafine-grained lamella matrix. The unusual high strength is obtained with the assistance of high back stress developed from heterogeneous yielding, whereas the high ductility is attributed to back-stress hardening and dislocation hardening. The process discovered here is amenable to large-scale industrial production at low cost, and might be applicable to other metal systems.

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