Set Of Templates Research Articles

Return of the templates: Revisiting the Galactic Center excess with multimessenger observations

The Galactic center excess (GCE) remains one of the most intriguing discoveries from the Fermi Large Area Telescope (LAT) observations. We revisit the characteristics of the GCE by first producing a new set of high-resolution galactic diffuse gamma-ray emission templates. This diffuse emission, which accounts for the bulk of the observed gamma rays, is ultimately due to cosmic-ray interactions with the interstellar medium. Using recent high-precision cosmic-ray observations, in addition to the continuing Fermi-LAT observations and observations from lower energy photons, we constrain the properties of the galactic diffuse emission. We describe a large set of diffuse gamma-ray emission templates which account for a very wide range of initial assumptions on the physical conditions in the inner galaxy. The broad properties of the GCE that we find in this work are qualitatively unchanged despite the introduction of this new set of templates, though its quantitative features appear mildly different than those obtained in previous analyses. In particular, we find a high-energy tail at higher significance than previously reported. This tail is very prominent in the northern hemisphere, and less so in the southern hemisphere. This strongly affects one prominent interpretation of the excess: known millisecond pulsars are incapable of producing this high-energy emission, even in the relatively softer southern hemisphere, and are therefore disfavored as the sole explanation of the GCE. The annihilation of dark matter particles of mass ${40}_{\ensuremath{-}7}^{+10}\text{ }\text{ }\mathrm{GeV}$ (95% CL) to $b$ quarks with a cross-section of $⟨{\ensuremath{\sigma}}_{A}v⟩=1.{4}_{\ensuremath{-}0.3}^{+0.6}\ifmmode\times\else\texttimes\fi{}{10}^{\ensuremath{-}26}\text{ }\text{ }{\mathrm{cm}}^{3}\text{ }{\mathrm{s}}^{\ensuremath{-}1}$ provides a good fit to the excess especially in the relatively cleaner southern sky. Dark matter of the same mass range annihilating to $b$ quarks or heavier dark matter particles annihilating to heavier Standard Model bosons can combine with millisecond pulsars to provide a good fit to the southern hemisphere emission as well, as can a broken power-law spectrum which would be related to recent cosmic-ray burst activity. As part of this paper, we make publicly available all of our templates and the data covariance matrix we have generated to account for systematic uncertainties.

Hierarchical approach to matched filtering using a reduced basis

Searching for gravitational waves from compact binary coalescence (CBC) is performed by matched filtering the observed strain data from gravitational-wave observatories against a discrete set of waveform templates designed to accurately approximate the expected gravitational-wave signal, and are chosen to efficiently cover a target search region. The computational cost of matched filtering scales with both the number of templates required to cover a parameter space and the in-band duration of the waveform. Both of these factors increase in difficulty as the current observatories improve in sensitivity, especially at low frequencies, and may pose challenges for third-generation observatories. Reducing the cost of matched filtering would make searches of future detector data more tractable. In addition, it would be easier to conduct searches that incorporate the effects of eccentricity, precession or target light sources (e.g. subsolar). We present a hierarchical scheme based on a reduced basis method to decrease the computational cost of conducting a matched-filter based search. Compared to the current methods, we estimate without any loss in sensitivity, a speedup by a factor of ~ 10 for sources with signal-to-noise ratio (SNR) of at least =6.0, and a factor of ~ 6 for SNR of at least 5. Our method is dominated by linear operations which are highly parallelizable. Therefore, we implement our algorithm using graphical processing units (GPUs) and evaluate commercially motivated metrics to demonstrate the efficiency of GPUs in CBC searches. Our scheme can be extended to generic CBC searches and allows for efficient matched filtering using GPUs.

Proof transfer for fast certification of multiple approximate neural networks

Developers of machine learning applications often apply post-training neural network optimizations, such as quantization and pruning, that approximate a neural network to speed up inference and reduce energy consumption, while maintaining high accuracy and robustness. Despite a recent surge in techniques for the robustness verification of neural networks, a major limitation of almost all state-of-the-art approaches is that the verification needs to be run from scratch every time the network is even slightly modified. Running precise end-to-end verification from scratch for every new network is expensive and impractical in many scenarios that use or compare multiple approximate network versions, and the robustness of all the networks needs to be verified efficiently. We present FANC, the first general technique for transferring proofs between a given network and its multiple approximate versions without compromising verifier precision. To reuse the proofs obtained when verifying the original network, FANC generates a set of templates – connected symbolic shapes at intermediate layers of the original network – that capture the proof of the property to be verified. We present novel algorithms for generating and transforming templates that generalize to a broad range of approximate networks and reduce the verification cost. We present a comprehensive evaluation demonstrating the effectiveness of our approach. We consider a diverse set of networks obtained by applying popular approximation techniques such as quantization and pruning on fully-connected and convolutional architectures and verify their robustness against different adversarial attacks such as adversarial patches, L 0 , rotation and brightening. Our results indicate that FANC can significantly speed up verification with state-of-the-art verifier, DeepZ by up to 4.1x.

Face templates for the Chicago Face Database.

Researchers often need to manipulate faces, such as developing a continuum between two faces or averaging a set of faces. In order to do so, researchers use morphing software, but they first need to fit a template to the idiosyncratic landmarks in each face. In this paper, we present a set of landmark templates for the Chicago Face Database (CFD; Ma, D. S., Correll, J., & Wittenbrink, B. (2015). The Chicago Face Database: A free stimulus set of faces and norming data. Behavior Research Methods, 47(4), 1122-1135). The CFD is a free online face database containing images of faces of people from various races and genders. We provide templates for each of 597 neutral (non-expressive) faces in version two of the CFD. Our templates are unique because the facial landmarks were hand placed by researchers. Hand placing facial landmarks allows for more accurate placement of landmarks than a computer-generated template. Historically, hand-placed templates were created by individual labs and not shared. In this paper, we describe how our templates were created, and some possible uses for the templates. We hope that our templates ease the burden for other researchers to manipulate faces.

Integrating Handcrafted Features with Deep Representations for Smartphone Authentication

Recent research demonstrates the potential of touch dynamics as a usable and privacy-preserving scheme for smartphone authentication. Most existing approaches rely on handcrafted features since deep models may be vulnerable to behavioral uncertainty due to the lack of consistent semantic information. Toward this end, we propose an approach to integrating handcrafted features into two phases of the deep learning process. On one hand, we present three fine-grained behavior representations by encoding semantic handcrafted features into the raw touch actions. On the other hand, we devise a deep Feature Regularization Net (FRN) architecture to combine the complementary information in both handcrafted and deep features. FRN involves handcrafted features as regularization to guide the learning process of deep features and selectively fuses these two feature types through a feature re-weighting mechanism. Experimental findings demonstrate that FRN outperforms the existing handcrafted or deep features even with smaller training and template sets. The framework also works for SOTA deep models and further boosts the accuracy. Results show that our approach is more reliable to alleviate behavioral variability and is competitively robust to statistical attacks compared with the most effective handcrafted features, suggesting a promising mechanism to improve the effectiveness and usability of behavioral authentication for multi-touch enabled mobile devices.

In vivo symmetric multi-contrast MRI brain templates and atlas for spontaneously hypertensive rats.

Spontaneously hypertensive rats (SHRs) are a valuable animal model of essential hypertension. The increasing use of SHRs in neuroimaging has generated an urgent demand for a template set that provides a reference for advanced data analysis. Structural T2-weighted magnetic resonance imaging (MRI), diffusion tensor imaging (DTI) and functional MRI scans that were used to build the template set were obtained from 8 SHRs longitudinally scanned in vivo at 10, 24 and 52weeks of age. These symmetric multi-contrast templates were constructed by iterative registration and averaging. The cortical atlas was derived from the Tohoku atlas, and the subcortical regions were manually delineated based on the templates. A set of SHR brain images named the Hebei Medical University rat brain template set (HRT) comprised 3D symmetric T2WI, raw T2-weighted signal with no added diffusion weighting (B0), fractional anisotropy (FA), mean diffusivity (MD) and blood oxygen level-dependent (BOLD) templates; tissue probability maps (TPMs) of gray matter (GM), white matter (WM) and cerebrospinal fluid (CSF); and a whole-brain atlas with 163 labels. We quantitatively validated the template and characterized the longitudinal changes in brain morphology in different brain tissues as SHRs aged. To our knowledge, the HRT is the first MRI template set for SHRs. We believe that the HRT can serve as a beneficial tool for precise analysis of the SHR brain using structural and functional MRI, which can promote neuroimaging studies on essential hypertension.

Pioneering easy-to-use forestry data with Forest Explorer

Forest Explorer is a web tool that can be used to easily browse the contents of the Cross-Forest dataset, a Linked Open Data resource containing the forestry inventory and land cover map from Spain. The tool is purposed for domain experts and lay users to facilitate the exploration of forestry data. Since these two groups are not knowledgable on Semantic Web, the user interface is designed to hide the complexity of RDF, OWL or SPARQL. An interactive map is provided for this purpose, allowing users to navigate to the area of interest and presenting forestry data with different levels of detail according to the zoom level. Forest Explorer offers different filter controls and is localized to English and Spanish. All the data is retrieved from the Cross-Forest and DBpedia endpoints through the Data manager. This component feeds the different Feature managers with the data needed to be displayed in the map. The Data manager uses a reduced set of SPARQL templates to accommodate any data request of the Feature managers. Caching and smart geographic querying are employed to limit data exchanges with the endpoint. A live version of the tool is freely available for everybody that wants to try it – any device with a modern browser should be sufficient to test it. Since December 2019, more than 3,200 users have employed Forest Explorer and it has appeared 12 times in the Spanish media. Results from a user study with 28 participants (mainly domain experts) show that Forest Explorer can be used to easily navigate the contents of the Cross-Forest dataset. No important limitations were found, only feature requests such as the integration of new datasets from other countries that are part of our future work.

Multi-Stage Refinement Feature Matching Using Adaptive ORB Features for Robotic Vision Navigation

Feature matching is a crucial technique to estimate robot pose in the light of natural landmarks for a vision navigation system. It is difficult for the present feature matching approaches to balance the accuracy, robustness and efficiency for a vision-based robot in complex workspaces. A multi-stage KNN-TF-BF-EN-RANSAC (KTBER) refinement matching technique is proposed to combine with the adaptive ORB feature extraction method, in order to develop a novel KTBER_AORB model that can outperform the state-of-the-art feature extraction and matching techniques. Firstly, the adaptive ORB (AORB) features are extracted in the Laplacian Transformation of Gaussian (LTOG) pyramid by using a set of variable-radius templates. The template radius is adjusted in terms of different brightness and contrast conditions. Then, the Eigenvector Norm (EN) and Bidirectional Filtering (BF) techniques are devised to establish a novel KTBER model, which can match AORB feature pairs not only according to the local appearance-based similarity of feature descriptors in the Hamming space, but also by means of the similarity of local image structure and global grayscale information for feature pairs in the Euclidean space. Finally, a large number of feature matching experiments are conducted on five public datasets and our mobile robot dataset, in order to test the algorithm performances for different challenging scenarios, including illumination change, motion blur, image rotation, viewpoint change, and low texture. On the one hand, an intensive experimental analysis of our model with different configurations is made to check the specific effects of different algorithm elements. On the other hand, the KTBER_AORB model is further compared with four state-of-the-art techniques, e.g., SIFT, ORB-SLAM, MP-ORB, and LAAT. The experimental results demonstrate that the KTBER_AORB model outperforms other state-of-the-art feature matching techniques on accuracy and robustness, still preserving a higher efficiency than most counterparts.

Age-Specific Adult Rat Brain MRI Templates and Tissue Probability Maps.

Age-specific resources in human MRI mitigate processing biases that arise from structural changes across the lifespan. There are fewer age-specific resources for preclinical imaging, and they only represent developmental periods rather than adulthood. Since rats recapitulate many facets of human aging, it was hypothesized that brain volume and each tissue's relative contribution to total brain volume would change with age in the adult rat. Data from a longitudinal study of rats at 3, 5, 11, and 17 months old were used to test this hypothesis. Tissue volume was estimated from high resolution structural images using a priori information from tissue probability maps. However, existing tissue probability maps generated inaccurate gray matter probabilities in subcortical structures, particularly the thalamus. To address this issue, gray matter, white matter, and CSF tissue probability maps were generated by combining anatomical and signal intensity information. The effects of age on volumetric estimations were then assessed with mixed-effects models. Results showed that herein estimation of gray matter volumes better matched histological evidence, as compared to existing resources. All tissue volumes increased with age, and the tissue proportions relative to total brain volume varied across adulthood. Consequently, a set of rat brain templates and tissue probability maps from across the adult lifespan is released to expand the preclinical MRI community's fundamental resources.

Online Signature Verification Using Locally Weighted Dynamic Time Warping via Multiple Fusion Strategies

Online signature verification is the process of using a dynamic signature verification system to confirm the writer’s identity. It can be used as a security system to confirm entrance applications and password substitutes, as well as a forensic tool to assist an expert’s investigation. This study proposes a novel online signature verification system based on a single-template strategy to improve performance in real-world scenarios. It employs discriminative mean signature template sets as well as fusion strategies of multiple local weighting and warping schemes, for dynamic time warping (DTW). The first step is to generate a set of user-specific mean signature templates for each feature using a recent time-series averaging method, namely, Euclidean barycenter-based DTW barycenter averaging. Then, using multiple and direct matching points between the mean signature templates and references for dependent and independent DTW, we obtain a local weighting estimate considering local stability sequences. Furthermore, we develop fusion strategies for calculating locally weighted DTW sets and concatenating them as a feature vector for each warping, followed by the construction of a support vector machine (SVM) classifier. Finally, in the verification phase, we use the single-template technique to compute a discriminative fused score using SVMs between the mean signature template sets and a query sample. The effectiveness of the proposed method is demonstrated by extensive experimental results obtained using three public online signature datasets: SVC2004 Task1/Task2 and MCYT-100.

A simulation tool for building and lighting design considering ipRGC-influenced light responses

The ipRGCs in the human eye mediate several effects on people, such as alertness and circadian rhythms. The spectral, temporal and spatial characteristics of light influence the magnitude of these effects. To facilitate the anticipation of the ipRGC-influenced light (IIL) responses in building design, new simulation tools are needed. This paper presents an extension to the open-access simulation tool Lark, available as a plugin to Grasshopper for Rhino. The proposed extension includes a set of additional options, components, and templates that incorporate currently available knowledge on the IIL responses. The applicability of the extension is demonstrated in a simulation workflow.

Information support for decision making in problem situation description

Objectives. The problem of automating the user information support in decision making system at the stage of describing the problem situation is solved. The relevance of the problem is associated with the need to collect and process significant amounts of information, since in the presence of a large number of factors, a person's capabilities are often insufficient to search and organize the necessary information.When solving the problem of user information support at the stage of describing the problem situation, three main goals are pursued: building a mathematical model of the corresponding processes; formalization of the set of basic concepts of the model; development of the algorithms for implementation of user interaction with the information system.Methods. Methods of set theory, probability theory and graph theory are used.Results. A mathematical model of user information support at the stage of describing a problem situation has been developed. In the process of interacting with the user, the system suggests special templates of sentences and texts for filling. Along with templates, the user receives help texts from the system. They are generated on the basis of the previously developed model of knowledge representation in the form of verbal associations, that is, semantic links between words and phrases corresponding to associative relationships between the entities they designate in the real world.Conclusion. As an implementation of the proposed model, the following algorithms have been developed: an algorithm for creating a dictionary of communicative fragments; algorithms for creating fragment-slot templates for sentences, texts and subject areas; an algorithm of user information support. The vocabulary of communicative fragments is created in four steps in accordance with their formal definition. At each step, four conditions from the given definition are tested sequentially. Fragment-slot templates of sentences are formed by replacing their basic communicative fragments with slots, and text templates - as tuples of templates of their sentences. Fragment-slot templates of subject areas are created in the form of implementation of binary relations reductions on the sets of sentence templates from the corresponding thematic text corpora. Each thematic body of texts defines a certain subject area.

Influence of Template Size, Canonicalization, and Exclusivity for Retrosynthesis and Reaction Prediction Applications.

Heuristic and machine learning models for rank-ordering reaction templates comprise an important basis for computer-aided organic synthesis regarding both product prediction and retrosynthetic pathway planning. Their viability relies heavily on the quality and characteristics of the underlying template database. With the advent of automated reaction and template extraction software and consequently the creation of template databases too large for manual curation, a data-driven approach to assess and improve the quality of template sets is needed. We therefore systematically studied the influence of template generality, canonicalization, and exclusivity on the performance of different template ranking models. We find that duplicate and nonexclusive templates, i.e., templates which describe the same chemical transformation on identical or overlapping sets of molecules, decrease both the accuracy of the ranking algorithm and the applicability of the respective top-ranked templates significantly. To remedy the negative effects of nonexclusivity, we developed a general and computationally efficient framework to deduplicate and hierarchically correct templates. As a result, performance improved considerably for both heuristic and machine learning template ranking models, as well as multistep retrosynthetic planning models. The canonicalization and correction code is made freely available.

Efficient Online Log Parsing with Log Punctuations Signature

Logs, recording the system runtime information, are frequently used to ensure software system reliability. As the first and foremost step of typical log analysis, many data-driven methods have been proposed for automated log parsing. Most existing log parsers work offline, requiring a time-consuming training progress and retraining as the system upgrades. Meanwhile, the state of the art online log parsers are tree-based, which still have defects in robustness and efficiency. To overcome such limitations, we abandon the tree structure and propose a hash-like method. In this paper, we propose LogPunk, an efficient online log parsing method. The core of LogPunk is a novel log signature method based on log punctuations and length features. According to the signature, we can quickly find a small set of candidate templates. Further, the most suitable template is returned by traversing the candidate set with our log similarity function. We evaluated LogPunk on 16 public datasets from the LogHub comparing with five other log parsers. LogPunk achieves the best parsing accuracy of 91.9%. Evaluation results also demonstrate its superiority in terms of robustness and efficiency.

Glacial processes and landforms

AbstractFrom 1965 to 2000 glacial geomorphology became increasingly specialized and developed significantly due to technological improvements, particularly in remote sensing, surveying and field-based glaciological process studies. The better understanding of basal thermal regimes in ice sheets and glaciers led to the development of concepts such as spatial and temporal migration of ice divides in dynamic ice sheets that could overprint subglacial landform assemblages, debris entrainment processes related to polythermal glacier systems, and glacier and ice sheet beds composed of cold and warm based mosaics. Process observations at the ice–bed interface led to the discovery of the third glacier flow mechanism, substrate deformation, which provided the impetus to reconstruct the genesis of subglacial bedforms such as drumlins and to evaluate the origins and potential flow law for till. Numerical evaluations of glacial erosion led to a better understanding of abrasion and quarrying as well as the erection of genetic models and erosion rates for larger-scale features such as U-shaped valleys and cirques. Linkages were made between debris transport pathways and moraine construction in supraglacial environments, with the role of glacier structure being linked to specific landforms, such as medial, lateral, hummocky and ice-cored moraines as well as rock glaciers. Our appreciation of the erosional and depositional impacts of glacifluvial systems was enhanced significantly with the advent of process observations on the hydrology of modern glaciers as well as the final vindication of J.H. Bretz and his proposed jökulhlaup origins of the Channelled Scablands and the Missoula Floods. In addition to the increasing numbers of studies at modern glacier snouts, the embracing of sedimentology by glacial geomorphologists was to result in significant developments in understanding the process-form regimes of subglacial, marginal and proglacial landforms, particularly the recognition of landform continua and hybrids. Advances resulting from this included the recognition of different modes of moraine and glacitectonic thrust mass development, lithofacies models of the varied glacifluvial depositional environments, and the initial expansion of work on the sediments and depocentres of glacimarine settings, the latter being the result of glacial research taking to submersibles and ice-strengthened ships for the first time. A similarly new frontier was the expansion of research on the increasingly higher resolution images returning from Mars, where extraterrestrial glaciations were recognized based on comparisons with Earth analogues. Holistic appreciations of glaciation signatures using landform assemblages were developed, initially as process-form models and later as glacial landsystems, providing an ever-expanding set of templates for reconstructing palaeoglaciology in the wide variety of topographic and environmental settings, which also acknowledge spatial and temporal change in glacier and ice sheet systems.

Detection of Text in Videos Using Discrete Wavelet Transform and Gradient Difference

Text detection in video frames provide highly condensed information about the content of the video and it is useful for video seeking, browsing, retrieval and understanding video text in large video databases. In this paper, we propose a hybrid method that it automatically detects segments and recognizes the text present in the video. Detection is done by using laplacian method based on wavelet and color features. Segmentation of detected text is divided into two modules Line segmentation and Character segmentation. Line segmentation is done by using mathematical statistical method based on projection profile analysis. In line segmentation, multiple lines of text in video frame obtained from text detection are segmented into single line. Character segmentation is done by using Connected Component. Analysis (CCA) and Vertical Projection Profile Analysis. The input for character segmentation is the line of text obtained from line segmentation, in which all the characters in the line are segmented separately for recognition. Optical character recognition is Processed by using template matching and correlation technique. Template matching is performed by comparing an input character with a set of templates, each comparison results in a similarity measure between the input characters with a set of templates. After all templates have been compared with the observed character image, the character’s identity is assigned with the most similar template based on correlation. Eventually, the text in video frame is detected, segmented, and processed to OCR for recognition.

Algorithm for an automatic treatment planning system using a single-arc VMAT for prostate cancer.

Optimization process in treatment planning for intensity‐modulated radiation therapy varies with the treatment planner. Therefore, a large variation in the quality of dose distribution is usually observed. To reduce variation, an automatic optimizing toolkit was developed for the Monaco treatment planning system (Elekta AB, Stockholm, Sweden) for prostate cancer using volumetric‐modulated arc therapy (VMAT). This toolkit was able to create plans automatically. However, most plans needed two arcs per treatment to ensure the dose coverage for targets. For prostate cancer, providing a plan with a single arc was advisable in clinical practice because intrafraction motion management must be considered to irradiate accurately. The purpose of this work was to develop an automatic treatment planning system with a single arc per treatment for prostate cancer using VMAT. We designed the new algorithm for the automatic treatment planning system to use one arc per treatment for prostate cancer in Monaco. We constructed the system in two main steps: (1) Determine suitable cost function parameters for each case before optimization, and (2) repeat the calculation and optimization until the conditions for dose indices are fulfilled. To evaluate clinical suitability, the plan quality between manual planning and the automatic planning system was compared. Our system created the plans automatically in all patients within a few iterations. Statistical differences between the plans were not observed for the target and organ at risk. It created the plans with no human input other than the initial template setting and system initiation. This system offers improved efficiency in running the treatment planning system and human resources while ensuring high‐quality outputs.

The miniJPAS survey: Photometric redshift catalogue

MiniJPAS is a ∼1 deg2 imaging survey of the AEGIS field in 60 bands, performed to demonstrate the scientific potential of the upcoming Javalambre-Physics of the Accelerating Universe Astrophysical Survey (J-PAS). Full coverage of the 3800–9100 Å range with 54 narrow-band filters, in combination with 6 optical broad-band filters, allows for extremely accurate photometric redshifts (photo-z), which, applied over areas of thousands of square degrees, will enable new applications of the photo-z technique, such as measurement of baryonic acoustic oscillations. In this paper we describe the method we used to obtain the photo-z that is included in the publicly available miniJPAS catalogue, and characterise the photo-z performance. We built photo-spectra with 100 Å resolution based on forced-aperture photometry corrected for point spread function. Systematic offsets in the photometry were corrected by applying magnitude shifts obtained through iterative fitting with stellar population synthesis models. We computed photo-z with a customised version of LEPHARE, using a set of templates that is optimised for the J-PAS filter-set. We analysed the accuracy of miniJPAS photo-z and their dependence on multiple quantities using a subsample of 5266 galaxies with spectroscopic redshifts from SDSS and DEEP, which we find to be representative of the whole r < 23 miniJPAS sample. Formal 1σ uncertainties for the photo-z that are calculated with the Δχ2 method underestimate the actual redshift errors. The odds parameter has a stronger correlation with |Δz| and accurately reproduces the probability of a redshift outlier (|Δz| > 0.03), regardless of the magnitude, redshift, or spectral type of the sources. We show that the two main summary statistics characterising the photo-z accuracy for a population of galaxies (σNMAD and η) can be predicted by the distribution of odds in this population, and we use this to estimate the statistics for the whole miniJPAS sample. At r < 23, there are ∼17 500 galaxies per deg2 with valid photo-z estimates, ∼4200 of which are expected to have |Δz| < 0.003. The typical error is σNMAD = 0.013 with an outlier rate η = 0.39. The target photo-z accuracy σNMAD = 0.003 is achieved for odds > 0.82 with η = 0.05, at the cost of decreasing the density of selected galaxies to n ∼ 5200 deg−2 (∼2600 of which have |Δz| < 0.003).

A high-resolution seismic catalog for the 2021 MS6.4/MW6.1 Yangbi earthquake sequence, Yunnan, China: Application of AI picker and matched filter

We present a high-resolution seismic catalog for the 2021 MS6.4/MW6.1 Yangbi sequence. The catalog has a time range of 2021-05-01 to 2021-05-28, and contains ~8,000 well located events. It captures the features of the whole foreshock sequence and the early aftershocks. We designed a detection strategy incorporating both an artificial intelligent (AI) picker and a matched filter algorithm. Here, we adopt a hybrid AI method incorporating convolutional and recurrent neural network (CNN & RNN) for event detection and phase picking respectively (i.e. CERP), a light-weight AI picker that can be trained with small volume of data. CERP is first trained with detections from a STA/LTA and Kurtosis-based method called PAL, and then construct a rather complete template set of ~4,000 events. Finally, the matched filter algorithm MESS augments the initial detections and measures differential travel times with cross-correlation, which finally results in precise relocation. This process gives 9,026 detections, among which 7,943 events can be well relocated. The catalog shows as expected power-law distribution of frequency magnitude and reveals detailed pattern of seismicity evolution. The main features are: (1) the foreshock sequence images simple fault geometry with consistent strike, but also show a variable event depth along strike; (2) the mainshock ruptures the same fault of the foreshock sequence and activate conjugate faults further to the southeast; (3) complex seismicity are developed in the post-seismic period, indicating complex triggering mechanisms. Thus, our catalog provides a reliable basis for further investigations, such as b-value studies, rupture process, and triggering relations.

Towards threshold‐agnostic heavy‐hitter classification

SummaryA heavy‐hitter (HH) network traffic flow consumes considerably more network resources than other flows combined. The classification of HHs is critical to provide, among others, the required level of Quality of Service and reliability in both conventional and data center networks. HH classification is typically threshold‐based. However, there is no consistent and accepted threshold or set of thresholds that would reliably classify flows. Furthermore, existing threshold‐driven approaches use counters (e.g., duration, packets, and bytes); thus, their accuracy depends on how complete the flow information is. This paper paves the way to threshold‐agnostic HH identification by proposing an approach that performs HH classification based on per‐flow packet size distribution (PSD) and template matching (TM). PSD allows capturing the behavior and dynamism of network traffic flows (even from their first few packets). TM enables to classify HHs by measuring the similarity between the PSD of observed flows and a set of master templates representing the flow size behavior of HH classes. We evaluated the PSD‐ and TM‐based approach using flows extracted from real traffic traces. Results show that our approach classifies HHs accurately and timely, corroborating that the threshold‐less perspective is feasible for HH identification.