GIS tax parcels determined to be owned by the State of New York in all 62 counties. This designation was based on the tax assessment owner name, Internet research, and work with individual State agencies. The State agency that owns and/or operates the tax parcel is also identified. Refinement of this dataset is on-going.
Parcel centroid data for all 62 New York State counties. Parcel centroids were generated using the NYS Office of Information Technology Services GIS Program Office's (GPO) Statewide Parcel Map program data. Attribute values were populated using Assessment Roll tabular data the GPO obtained from the NYS Department of Tax and Finance's Office of Real Property Tax Services (ORPTS).
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As far back as 2004, the NYS Geospatial Advisory Council identified tax parcel boundary and land ownership information as one of three framework data sets necessary for governments to effectively use and benefit from GIS technology.
In 2011, the Office of Cyber Security utilized another CAP grant to develop the Business Plan for Centralized Access to Consistent Cadastral Data which identified that at least a dozen NYS agencies undertake independent efforts to gather, standardize and utilize tax parcel data. It also identified "enormous barriers" to gaining universal acceptance of sharing and standardization of the data.
In 2014, the NYS GIS Program Office created the Statewide Parcel Map Program and began the work of collecting GIS tax parcel data from counties and incorporating it into a statewide dataset under a common data schema for release to the public. This work is on-going.
141, 084702 (2014); ... Further evidence for intermixing is provided by the core-level XPS data shown in Fig. ... The circular inset is a magnified view of one triplet of spots. ... T. Koch, G. Krausch, J. Marien, A. Plewnia, B.-U. Runge, G. Schatz, A. Siber, and P. Ziemann, Phys. DeusExHumanRevolutionv126330UpdateSKIDROW
Several hate speech which spread in social media activity aroused because of freedom of speech euphoria in a democratic nation. Those hate speech had effectively used as a negative campaign during election. This study aims to analyze hate speech and cyber war in social media. The method used is qualitative with data retrieval through in-depth interviews of netizens as opinion makers in social media, document studies and literature studies relevant to the research. The study found that cyber warfare in social media has formed two netizen polarizations. The polarization can be identified as conservative and liberal groups. Both groups are actively producing discourse, opinion, information, issues and rumors through social media. This study has implications for the change or shift of opinion leader concept on the theory of two step communication. The concept of opinion maker in the new media tradition that emerges today allows anyone anonymously to become opinion leaders.
Overview: The group's focus on the telecommunications and travel industries suggests intent to perform monitoring, tracking, or surveillance operations against specific individuals, collect proprietary or customer data for commercial or operational purposes that serve strategic requirements related to national priorities, or create additional accesses and vectors to facilitate future campaigns. Government entities targeting suggests a potential secondary intent to collect geopolitical data that may benefit nation-state decision making.
Overview: Mandiant Intelligence believes that APT40's operations are a cyber counterpart to China's efforts to modernize its naval capabilities; this is also manifested in targeting wide-scale research projects at universities and obtaining designs for marine equipment and vehicles. The group's operations tend to target government-sponsored projects and take large amounts of information specific to such projects, including proposals, meetings, financial data, shipping information, plans and drawings, and raw data.
Attack vectors: APT30 uses a suite of tools that includes downloaders, backdoors, a central controller and several components designed to infect removable drives and cross air-gapped networks to steal data. APT30 frequently registers its own DNS domains for malware CnC activities.
Attack vectors: APT24 has used phishing emails that use military, renewable energy, or business strategy themes as lures. Further, APT24 engages in cyber operations where the goal is intellectual property theft, usually focusing on the data and projects that make a particular organization competitive within its field.
Overview: APT23 has stolen information that has political and military significance, rather than intellectual property. This suggests that APT23 may perform data theft in support of more traditional espionage operations.
Overview: APT20 engages in cyber operations where the goal is data theft. APT20 conducts intellectual property theft but also appears interested in stealing data from or monitoring the activities of individuals with particular political interests. Based on available data, we assess that this is a freelancer group with some nation state sponsorship located in China.
Attack vectors: Frequently developed or adapted zero-day exploits for operations, which were likely planned in advance. Used data from Hacking Team leak, which demonstrated how the group can shift resources (i.e. selecting targets, preparing infrastructure, crafting messages, updating tools) to take advantage of unexpected opportunities like newly exposed exploits.
Overview: APT14 engages in cyber operations where the goal is data theft, with a possible focus on military and maritime equipment, operations, and policies. We believe that the stolen data, especially encryption and satellite communication equipment specifications, could be used to enhance military operations, such as intercepting signals or otherwise interfering with military satellite communication networks.
Overview: APT10 is a Chinese cyber espionage group that Mandiant has tracked since 2009. They have historically targeted construction and engineering, aerospace, and telecom firms, and governments in the United States, Europe, and Japan. We believe that the targeting of these industries has been in support of Chinese national security goals, including acquiring valuable military and intelligence information as well as the theft of confidential business data to support Chinese corporations.
Overview: APT8 engages in cyber operations where the goal is intellectual property theft, usually focusing on the data and projects that make an organization competitive within its field. We assess that this is a freelancer group located in China with some nation-state sponsorship. APT8 has targeted organizations headquartered in multiple countries, including the U.S., Germany, the U.K., India, and Japan.
Overview: APT7 engages in cyber operations where the goal is intellectual property theft, usually focusing on data and projects that make an organization competitive within its field. This group is known to have targeted organizations headquartered in the U.S. and U.K.
Overview: APT6 engages in cyber operations where the goal is data theft, most likely data and projects that make an organization competitive within its field. APT6 targeted organizations headquartered in the U.S and U.K.
Overview: This group was first observed in 2010. APT2 engages in cyber operations where the goal is intellectual property theft, usually focusing on the data and projects that make an organization competitive within its field
Associated malware: This large and prolific group uses a variety of custom malware families, including backdoors, tunnelers, dataminers, and destructive malware to steal millions of dollars from financial institutions and render victim networks inoperable.
Results: We present SIBER (systematic identification of bimodally expressed genes using RNAseq data) for effectively identifying bimodally expressed genes from next-generation RNAseq data. We evaluate several candidate methods for modelling RNAseq count data and compare their performance in identifying bimodal genes through both simulation and real data analysis. We show that the lognormal mixture model performs best in terms of power and robustness under various scenarios. We also compare our method with alternative approaches, including profile analysis using clustering and kurtosis (PACK) and cancer outlier profile analysis (COPA). Our method is robust, powerful, invariant to shifting and scaling, has no blind spots and has a sample-size-free interpretation.
Negative binomial mixture: Our first model is motivated by the negative binomial (NB) distribution, which is widely used to model RNAseq data in differential gene expression analysis (Anders and Huber, 2010; Di et al., 2011; Hardcastle and Kelly, 2010; Robinson and Smyth, 2007). Of note, we prefer NB rather than the Poisson distribution to account for the overdispersion observed in RNAseq data. Specifically, the probability of observing count can be formulated as:
Generalized Poisson mixture:The generalized Poisson (GP) distribution is another model used to describe RNAseq count data (Srivastava and Chen, 2010). Under the two-component mixture framework, it can be formulated similarly as:
where is the effect size that measures the distance between the two-components. The coefficient is maximized at ; hence, it penalizes unbalanced allocation into the two components. A limitation of the original BI is that it is defined based on a normal mixture with equal variance. It does not apply to normal mixtures with unequal variance or to genes whose expressions do not follow normal distributions (e.g. discrete distributions as in RNAseq data). To deal with these situations, here we generalize the original BI.
Bimodal genes: For the bimodally expressed genes, we choose different combinations of parameters to represent a wide range of bimodal shapes. Specifically, takes values between 0.1 and 0.5 with a step of 0.1 ( are omitted by symmetry). In practice, or leads to an unbalanced mixture, whereas or leads to more balanced mixture distribution. We also use a range of effect sizes, . To simulate genes that have different expression levels, we set for LN model (corresponding mean at exponential scale is 244.7), for NB model and for GP model. For LN, we set because of the equal variance assumption (corresponding variance at exponential scale is 34.5). We assume equal dispersion between the two groups for both NB and GP models. As a result, we set the dispersion parameter for NB model and for GP model. This implies that in both NB and GP models, the variance is a quadratic function of the mean, as typically seen in RNAseq experiments (see mean-variance relationship in Supplementary Figure S15). We use Equation (6) to solve for . All possible combinations of and are considered in each model, which results in 20 (5 1 4) settings in LN datasets and 80 (5 4 4) settings in NB and GP datasets. The parameters were chosen to mimic real data. For each setting, we simulate 100 genes, which lead to 2000 bimodal genes in LN datasets and 8000 genes in NB or GP datasets. We choose four different sample size settings (N = 50, 100, 200 and 300) for each dataset. Data generated from the LN model is continuous, but is rounded to the nearest integer. 2ff7e9595c
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