Identifying change points and/or anomalies in dynamic network structures has become increasingly popular across various domains, from neuroscience to telecommunication to finance. One particular objective of anomaly detection from a neuroscience perspective is the reconstruction of the dynamic manner of brain region interactions. However, most statistical methods for detecting anomalies have the following unrealistic limitation for brain studies and beyond: that is, network snapshots at different time points are assumed to be independent. To circumvent this limitation, we propose a distribution-free framework for anomaly detection in dynamic networks. First, we present each network snapshot of the data as a linear object and find its respective univariate characterization via local and global network topological summaries. Second, we adopt a change point detection method for (weakly) dependent time series based on efficient scores, and enhance the finite sample properties of change point method by approximating the asymptotic distribution of the test statistic using the sieve bootstrap. We apply our method to simulated and to real data, particularly, two functional magnetic resonance imaging (fMRI) datasets and the Enron communication graph. We find that our new method delivers impressively accurate and realistic results in terms of identifying locations of true change points compared to the results reported by competing approaches. The new method promises to offer a deeper insight into the large-scale characterizations and functional dynamics of the brain and, more generally, into the intrinsic structure of complex dynamic networks. Supplemental materials for this article are available online.

This dataset is about book series. It has 3 rows and is filtered where the books is All change. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

SCALABLE TIME SERIES CHANGE DETECTION FOR BIOMASS MONITORING USING GAUSSIAN PROCESS VARUN CHANDOLA AND RANGA RAJU VATSAVAI Abstract. Biomass monitoring, specifically, detecting changes in the biomass or vegetation of a geographical region, is vital for studying the carbon cycle of the system and has significant implications in the context of understanding climate change and its impacts. Recently, several time series change detection methods have been proposed to identify land cover changes in temporal profiles (time series) of vegetation collected using remote sensing instruments. In this paper, we adapt Gaussian process regression to detect changes in such time series in an online fashion. While Gaussian process (GP) has been widely used as a kernel based learning method for regression and classification, their applicability to massive spatio-temporal data sets, such as remote sensing data, has been limited owing to the high computational costs involved. In our previous work we proposed an efficient Toeplitz matrix based solution for scalable GP parameter estimation. In this paper we apply these solutions to a GP based change detection algorithm. The proposed change detection algorithm requires a memory footprint which is linear in the length of the input time series and runs in time which is quadratic to the length of the input time series. Experimental results show that both serial and parallel implementations of our proposed method achieve significant speedups over the serial implementation. Finally, we demonstrate the effectiveness of the proposed change detection method in identifying changes in Normalized Difference Vegetation Index (NDVI) data.

This dataset is about book series, has 2 rows and is filtered where the books is The technique of editing 16mm. films. It features 10 columns including book series, number of authors, number of books, earliest publication date, and latest publication date. The preview is ordered by number of books (descending).

BackgroundEnglish editing services are effective for improving manuscript quality as well as providing learning opportunities for non-native English-speaking authors. Herein, we describe the effects of a combined system of in-house and external editing services for handling large volumes of editing requests and providing personalized editing service in academic hospitals.MethodsWe established the Scientific Publications Team (SPT), an in-house editing team in Asan Medical Center in Seoul, Korea. The SPT is composed of two professional editors who manage editing requests sent to external companies while also providing one-on-one in-house editing services. We gathered author satisfaction data from 936 surveys between July 2017 and December 2018 and analyzed the number of editing requests and research publications by segmented regression analysis of interrupted time series data.ResultsThe SPT processed 3931 editing requests in 2017–2018, which was a marked increase compared with prior to its establishment (P = 0.0097). The authors were generally satisfied with the quality of editing services from both in-house and external editors. Upon conducting regular quality control, overall author satisfaction with one external company gradually increased over the course of one year (P for trend = 0.086). Author satisfaction survey results revealed that overall satisfaction of editing service was most strongly correlated with how well the edits conformed to the authors’ intentions (R = 0.796), and was only weakly correlated with quick turnaround time (R = 0.355). We also observed a significant increase in the trend of the number of research publications (P = 0.0007) at one year after the establishment of the SPT.ConclusionProviding a combination of in-house and external editing services resulted in high author satisfaction and subsequent hospital-wide increases in manuscript writing and publication. Our model system may be adapted in academic hospitals to better address the editing needs of non-native English-speaking researchers.

Bu maddede Tek Kameralı Bir Dizide En iyi Kurgu Primetime Emmy ödülü sahipleri ve adaylıkları listelenmektedir 2015 itib

Phenotypic Rates of Change Evolutionary and Ecological Database (PROCEED) is an ongoing compilation of rates of phenotypic change, typically Haldanes and Darwins, published in peer-reviewed manuscripts. This database includes studies that measure the intraspecific change in quantitative (continuous or counting) traits and report the time elapsed from the onset of environmental novelty or refer to a historical or biological event reported in other sources (e.g., a mine opening, a well-documented biological invasion). The maximum elapsed time between the environmental change and the sampling was no longer than 500 years. The included studies followed a single population through time or compared two or more populations, diverging from an originally single population where (at least) one of them was a new condition of known age. About two decades ago, a database of phenotypic rates of change in wild populations was compiled. Since then, researchers have used (and expanded) this database to examine phenotypic responses to specific types of disturbance and according to different features of the species/systems. We compile and add data regularly to the dataset. This dataset is continually being updated as more people ask it to include new variables.

OPTIMUS Dataset

This dataset contains approximately 600K image time series of 40-50 Sentinel-2 satellite images captured between January 2016 and December 2023. It also includes 300 time series that are labeled with binary "change" or "no change" labels. It is used to train and evaluate OPTIMUS (TODO - paper link). The time series are distributed globally, with half of the time series selected at random locations covered by Sentinel-2, and the other half sampled specifically within… See the full description on the dataset page: https://huggingface.co/datasets/optimus-change/optimus-dataset.

TRLE is a fan-based community revolving around the classic Tomb Raider series particularly from Tomb Raider 1 to Tomb Raider 5. Though it does include TR-AOD, TR-Legend, TR-Anniversary, TR-Underworld sometimes too. A tool known as "Tomb Raider Level Editor" is used by fans to create custom levels and they're featured on this site.

The columns extracted are: * nickname * level name * difficulty * rating * size * downloads * date

A validation assessment of Land Cover Monitoring, Assessment, and Projection Collection 1.2 annual land cover products (1985–2018) for the Conterminous United States was conducted with an independently collected reference dataset. Reference data land cover attributes were assigned by trained interpreters for each year of the time series (1984–2018) to a reference sample of 26,971 Landsat resolution (30m x 30m) pixels. The LCMAP and reference dataset labels for each pixel location are displayed here for each year, 1985–2018.

A validation assessment of Land Cover Monitoring, Assessment, and Projection Collection 1.1 annual land cover products (1985–2019) for the Conterminous United States was conducted with an independently collected reference dataset. Reference data land cover attributes were assigned by trained interpreters for each year of the time series (1984–2018) to a reference sample of 24,971 randomly-selected Landsat resolution (30m x 30m) pixels. The LCMAP and reference dataset labels for each pixel location are displayed here for each year, 1985–2018.

Researchers of time series cross sectional (TSCS) data regularly face the change-point problem, which re- quires them to discern between significant parametric shifts that can be deemed structural changes and minor parametric shifts that must be considered noise. In this paper, we develop a general Bayesian method for change-point detection in high dimensional data and present its application in the context of the fixed-effect model. Our proposed method, hidden Markov Bayesian bridge model (HMBB), jointly estimates high dimensional regime-specific parameters and hidden regime transitions in a unified way. We apply our method to Alvarez, Garrett, and Lange (1991)’s study of the relationship between government partisanship and economic growth and Allee and Scalera (2012)’s study of membership effects in international organizations. In both applications, we found that the proposed method successfully identify substantively meaningful temporal heterogeneity in parameters of regression models.

This dataset is about book series. It has 1 row and is filtered where the books is Time and change. It features 10 columns including number of authors, number of books, earliest publication date, and latest publication date.

RetroInstruct Weave Agent Editor Repair Diffs

This component of RetroInstruct trains weave-agent to use the WeaveEditor to fix synthetic corruptions in the vein of the Easy Prose Repair Diffs component. Each row in the dataset provides the pieces you need to make a synthetic episode demonstrating the agent:

Singling out one of three files as corrupted and in need of repair Writing out a patch to the file as either a series of WeaveEditor edit() commands or a unidiff Observing the… See the full description on the dataset page: https://huggingface.co/datasets/jdpressman/retro-weave-agent-editor-repair-diffs-v0.1.

Dataset

This dataset was created by PranjalT

Contents

This paper provides a feasible approach to estimation and forecasting of multiple structural breaks for vector autoregressions and other multivariate models. Owing to conjugate prior assumptions we obtain a very efficient sampler for the regime allocation variable. A new hierarchical prior is introduced to allow for learning over different structural breaks. The model is extended to independent breaks in regression coefficients and the volatility parameters. Two empirical applications show the improvements the model has over benchmarks. In a macro application with seven variables we empirically demonstrate the benefits from moving from a multivariate structural break model to a set of univariate structural break models to account for heterogeneous break patterns across data series.

Data include a collection of annual land cover maps derived from MODIS 250 m spatial resolution remotely sensed imagery for the period 2000 to 2011. Processing of the time series was designed to reduce the occurrence of false change between maps. The method was based on change updating as described in Pouliot et al. (2011, 2013). Change detection accounted for both abrupt changes such as forest harvesting and more gradual changes such as recurrent insect defoliation. To determine the new label for a pixel identified as change, an evidential reasoning approach was used to combine spectral and contextual information. The 2005 MODIS land cover of Canada at 250 m spatial resolution described in Latifovic et al. (2012) was used as the base map. It contains 39 land cover classes, which for time series development was considered too detailed and was reduced to 25 and 19 class versions. The 19 class version corresponds to the North America Land Change Monitoring System (NALCMS) Level 2 legend as described in Latifovic et al. (2012). Accuracy assessment of time series is difficult due to the need to assess many maps. For areas of change in the time series accuracy was found to be 70% based on the 19 class thematic legend. This time series captures the spatial distribution of dominant land cover transitions. It is intended for use in modeling, development of remote sensing products such as leaf area index or land cover based albedo retrievals, and other exploratory analysis. It is not appropriate for use in any rigorous reporting or inventory assessments due to the accuracy of the land cover classification and uncertainty as to the capture of all relevant changes for an application.

Abstract

The electric grid is a key enabling infrastructure for the ambitious transition towards carbon neutrality as we grapple with climate change. With deepening penetration of renewable energy resources and electrified transportation, the reliable and secure operation of the electric grid becomes increasingly challenging. In this paper, we present PSML, a first-of-its-kind open-access multi-scale time-series dataset, to aid in the development of data-driven machine learning (ML) based approaches towards reliable operation of future electric grids. The dataset is generated through a novel transmission + distribution (T+D) co-simulation designed to capture the increasingly important interactions and uncertainties of the grid dynamics, containing electric load, renewable generation, weather, voltage and current measurements at multiple spatio-temporal scales. Using PSML, we provide state-of-the-art ML baselines on three challenging use cases of critical importance to achieve: (i) early detection, accurate classification and localization of dynamic disturbance events; (ii) robust hierarchical forecasting of load and renewable energy with the presence of uncertainties and extreme events; and (iii) realistic synthetic generation of physical-law-constrained measurement time series. We envision that this dataset will enable advances for ML in dynamic systems, while simultaneously allowing ML researchers to contribute towards carbon-neutral electricity and mobility.

Data Navigation

Please download, unzip and put somewhere for later benchmark results reproduction and data loading and performance evaluation for proposed methods.

wget https://zenodo.org/record/5130612/files/PSML.zip?download=1
7z x 'PSML.zip?download=1' -o./

Minute-level Load and Renewable

• File Name
  • ISO_zone_#.csv: `CAISO_zone_1.csv` contains minute-level load, renewable and weather data from 2018 to 2020 in the zone 1 of CAISO.
• - Field Description
  • Field `time`: Time of minute resolution.
  • Field `load_power`: Normalized load power.
  • Field `wind_power`: Normalized wind turbine power.
  • Field `solar_power`: Normalized solar PV power.
  • Field `DHI`: Direct normal irradiance.
  • Field `DNI`: Diffuse horizontal irradiance.
  • Field `GHI`: Global horizontal irradiance.
  • Field `Dew Point`: Dew point in degree Celsius.
  • Field `Solar Zeinth Angle`: The angle between the sun's rays and the vertical direction in degree.
  • Field `Wind Speed`: Wind speed (m/s).
  • Field `Relative Humidity`: Relative humidity (%).
  • Field `Temperature`: Temperature in degree Celsius.

Minute-level PMU Measurements

• File Name
  • case #: The `case 0` folder contains all data of scenario setting #0.
    • pf_input_#.txt: Selected load, renewable and solar generation for the simulation.
    • pf_result_#.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.
• Filed Description
  • Field `time`: Time of minute resolution.
  • Field `Vm_###`: Voltage magnitude (p.u.) at the bus ### in the simulated model.
  • Field `Va_###`: Voltage angle (rad) at the bus ### in the simulated model.
  • Field `P_#_#_#`: `P_3_4_1` means the active power transferring in the #1 branch from the bus 3 to 4.
  • Field `Q_#_#_#`: `Q_5_20_1` means the reactive power transferring in the #1 branch from the bus 5 to 20.

Millisecond-level PMU Measurements

• File Name
  • Forced Oscillation: The folder contains all forced oscillation cases.
    • row_#: The folder contains all data of the disturbance scenario #.
      • dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.
      • info.csv: This file contains the start time, end time, location and type of the disturbance
      • trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.
  • Natural Oscillation: The folder contains all natural oscillation cases.
    • row_#: The folder contains all data of the disturbance scenario #.
      • dist.csv: Three-phased voltage at nodes in the distribution system via T+D simualtion.
      • info.csv: This file contains the start time, end time, location and type of the disturbance.
      • trans.csv: Voltage at nodes and power on branches in the transmission system via T+D simualtion.
• Filed Description
  • trans.csv
    • - Field `Time(s)`: Time of millisecond resolution.
    • - Field `VOLT ###`: Voltage magnitude (p.u.) at the bus ### in the transmission model.
    • - Field `POWR ### TO ### CKT #`: `POWR 151 TO 152 CKT '1 '` means the active power transferring in the #1 branch from the bus 151 to 152.
    • - Field `VARS ### TO ### CKT #`: `VARS 151 TO 152 CKT '1 '` means the reactive power transferring in the #1 branch from the bus 151 to 152.
  • dist.csv
    • Field `Time(s)`: Time of millisecond resolution.
    • Field `####.###.#`: `3005.633.1` means per-unit voltage magnitude of the phase A at the bus 633 of the distribution grid, the one connecting to the bus 3005 in the transmission system.

During a survey among event marketers in the United States released in early 2024, approximately 37 percent reported plans to increase their trade show-related budget for social media, while 60 percent intended to maintain it. Around 30 percent wanted to raise their spending on event-related content creation, whereas 60 percent planned to keep it the same. According to the same study, most U.S. event marketers felt optimistic about the effectiveness of trade shows.

The latest data from show economic growth of 5.37 percent, which is an increase from the rate of growth of 2.9 percent in the previous quarter and
a decrease compared to the growth rate of 6.64 percent in the same quarter last year. The economic growth time series for Taiwan cover the period...