Abstract: Herein disclosed is a surface-enhanced Raman scattering (SERS) chip for generating multiple SERS profiles simultaneously from one or more analytes suspected to be in a sample.

Abstract: An anomaly detection and analysis system generates analysis or summary of the anomalies detected from key performance indicators (KPIs). The system receives anomaly data reporting anomalies detected in key performance indicator (KPI) data. The system classifies the reported anomalies into a plurality of anomaly items, wherein anomalies from KPI data that share a set of features are assigned to one anomaly item. The system computes a ranking score for each anomaly item by assigning predefined weights for different anomaly types that are present in the anomaly item. The system sorts a list of anomaly items from the plurality of anomaly items into a sorted list of anomaly items according to the ranking scores computed for the plurality of anomaly items. The system sends the sorted list of anomaly items to a user device for presentation.

Abstract: Techniques for identifying certain types of network activity are disclosed, including parsing network traffic to automatically recognize anonymous identifiers. Such techniques may be used to identify and eliminate malicious and/or undesirable network traffic, and to identify topics relevant to a user of a particular network device so that communications to such a user are more likely to relate to a topic of interest to the user.

Abstract: An anomaly detection and analysis system detects anomalies in time series data from key performance indicators (KPIs). The system decomposes samples of the time series data received during a first time interval into a trend component, a seasonality component, and a randomness component. The system identifies an upper bound and a lower bound based on the trend component, the seasonality component, and a variance of the randomness component. The system reports a sample received after the first time interval as an anomaly when the sample exceeds the upper bound or the lower bound. The system recalculates the trend component, the seasonality component, and the randomness component when more than a threshold percentage of the samples of the time series data received during a second time interval are reported as being anomalous.

Abstract: Techniques for identifying certain types of network activity are disclosed, including parsing of a Uniform Resource Locator (URL) to identify a plurality of key-value pairs in a query string of the URL. The plurality of key-value pairs may include one or more potential anonymous identifiers. In an example embodiment, a machine learning algorithm is trained on the URL to determine whether the one or more potential anonymous identifiers are actual anonymous identifiers (i.e., advertising identifiers) that provide advertisers a method to identify a user device without using, for example, a permanent device identifier. In this embodiment, a ranking threshold is used to verify the URL. A verified URL associate the one or more potential anonymous identifiers with the user device as actual anonymous identifiers. Such techniques may be used to identify and eliminate malicious and/or undesirable network traffic.

Abstract: Disclosed herein is a composite material suitable for use in surface-enhanced Raman scattering, the material comprising a substrate layer having a surface; a plurality of layers of core-shell particles formed on the surface of the substrate layer, wherein the core is formed from a plasmonic metal nanoparticle, and the shell is formed from a metal-organic framework (MOF), and wherein the plurality of layers of core-shell particles provide a thickness of from 0.5 to 10 um on the surface of the substrate layer. In specific embodiments, the plasmonic metal nanoparticles are silver nanocubes, and the MOF is ZIF-8.

Abstract: An anomaly detection and analysis system generates analysis or summary of the anomalies detected from key performance indicators (KPIs). The system receives anomaly data reporting anomalies detected in key performance indicator (KPI) data. The system classifies the reported anomalies into a plurality of anomaly items, wherein anomalies from KPI data that share a set of features are assigned to one anomaly item. The system computes a ranking score for each anomaly item by assigning predefined weights for different anomaly types that are present in the anomaly item. The system sorts a list of anomaly items from the plurality of anomaly items into a sorted list of anomaly items according to the ranking scores computed for the plurality of anomaly items. The system sends the sorted list of anomaly items to a user device for presentation.

Abstract: An anomaly detection and analysis system generates analysis or summary of the anomalies detected from key performance indicators (KPIs). The system receives anomaly data reporting anomalies detected in key performance indicator (KPI) data. The system classifies the reported anomalies into a plurality of anomaly items, wherein anomalies from KPI data that share a set of features are assigned to one anomaly item. The system computes a ranking score for each anomaly item by assigning predefined weights for different anomaly types that are present in the anomaly item. The system sorts a list of anomaly items from the plurality of anomaly items into a sorted list of anomaly items according to the ranking scores computed for the plurality of anomaly items. The system sends the sorted list of anomaly items to a user device for presentation.

Abstract: An anomaly detection and analysis system detects anomalies in time series data from key performance indicators (KPIs). The system decomposes samples of the time series data received during a first time interval into a trend component, a seasonality component, and a randomness component. The system identifies an upper bound and a lower bound based on the trend component, the seasonality component, and a variance of the randomness component. The system reports a sample received after the first time interval as an anomaly when the sample exceeds the upper bound or the lower bound. The system recalculates the trend component, the seasonality component, and the randomness component when more than a threshold percentage of the samples of the time series data received during a second time interval are reported as being anomalous.

Abstract: An anomaly detection and analysis system generates analysis or summary of the anomalies detected from key performance indicators (KPIs). The system receives anomaly data reporting anomalies detected in key performance indicator (KPI) data. The system classifies the reported anomalies into a plurality of anomaly items, wherein anomalies from KPI data that share a set of features are assigned to one anomaly item. The system computes a ranking score for each anomaly item by assigning predefined weights for different anomaly types that are present in the anomaly item. The system sorts a list of anomaly items from the plurality of anomaly items into a sorted list of anomaly items according to the ranking scores computed for the plurality of anomaly items. The system sends the sorted list of anomaly items to a user device for presentation.

Abstract: Techniques for identifying certain types of network activity are disclosed, including parsing of a Uniform Resource Locator (URL) to identify a plurality of key-value pairs in a query string of the URL. The plurality of key-value pairs may include one or more potential anonymous identifiers. In an example embodiment, a machine learning algorithm is trained on the URL to determine whether the one or more potential anonymous identifiers are actual anonymous identifiers (i.e., advertising identifiers) that provide advertisers a method to identify a user device without using, for example, a permanent device identifier. In this embodiment, a ranking threshold is used to verify the URL. A verified URL associate the one or more potential anonymous identifiers with the user device as actual anonymous identifiers. Such techniques may be used to identify and eliminate malicious and/or undesirable network traffic.

Abstract: Techniques for identifying certain types of network activity are disclosed, including parsing network traffic to automatically recognize anonymous identifiers. Such techniques may be used to identify and eliminate malicious and/or undesirable network traffic, and to identify topics relevant to a user of a particular network device so that communications to such a user are more likely to relate to a topic of interest to the user.

Abstract: Techniques for identifying certain types of network activity are disclosed, including parsing network traffic to automatically recognize anonymous identifiers. Such techniques may be used to identify and eliminate malicious and/or undesirable network traffic, and to identify topics relevant to a user of a particular network device so that communications to such a user are more likely to relate to a topic of interest to the user.

Abstract: Techniques for identifying certain types of network activity are disclosed, including parsing network traffic to automatically recognize anonymous identifiers. Such techniques may be used to identify and eliminate malicious and/or undesirable network traffic, and to identify topics relevant to a user of a particular network device so that communications to such a user are more likely to relate to a topic of interest to the user.

Abstract: An image device includes a plurality of first sub-pixels, a plurality of second sub-pixels, a plurality of third sub-pixels, a plurality of fourth sub-pixels, and a plurality of fifth sub-pixels. The image device further comprises a plurality of basic repeating units. Each of the basic repeating units comprises the first sub-pixels, the second sub-pixels, the third sub-pixels, the fourth sub-pixels, and the fifth sub-pixel. A ratio of the first sub-pixels to the second sub-pixels to the third sub-pixels to the fourth sub-pixels and to the fifth sub-pixel in the basic repeating unit is 3:3:4:1:1.

Abstract: An image device includes a plurality of red sub-pixels, a plurality of green sub-pixels, a plurality of blue sub-pixels, a plurality of white sub-pixels, and a plurality of yellow sub-pixels. A ratio of the total number of red sub-pixels to the total number of green sub-pixels to the total number of blue sub-pixels to the total number of white sub-pixels and to the total number of yellow sub-pixels is about 3:3:3:2:1.

Abstract: An image device includes a plurality of red sub-pixels, a plurality of green sub-pixels, a plurality of blue sub-pixels, a plurality of white sub-pixels, and a plurality of yellow sub-pixels. A ratio of the total number of red sub-pixels to the total number of green sub-pixels to the total number of blue sub-pixels to the total number of white sub-pixels and to the total number of yellow sub-pixels is about 3:3:3:1:2.

Abstract: An image device includes a plurality of first sub-pixels, a plurality of second sub-pixels, a plurality of third sub-pixels, a plurality of fourth sub-pixels, and a plurality of fifth sub-pixels. The image device further comprises a plurality of basic repeating units. Each of the basic repeating units comprises the first sub-pixels, the second sub-pixels, the third sub-pixels, the fourth sub-pixels, and the fifth sub-pixel. A ratio of the first sub-pixels to the second sub-pixels to the third sub-pixels to the fourth sub-pixels and to the fifth sub-pixel in the basic repeating unit is 3:3:4:1:1.

Abstract: An image device includes a plurality of first sub-pixels, a plurality of second sub-pixels, a plurality of third sub-pixels, a plurality of fourth sub-pixels, and a plurality of fifth sub-pixels. The image device further is consisted by a plurality of basic repeating units in a 2×8 matrix. Each of the basic repeating units comprises the first sub-pixels, the second sub-pixels, the third sub-pixels, the fourth sub-pixels, and the fifth sub-pixel. A ratio of the first sub-pixels to the second sub-pixels to the third sub-pixels to the fourth sub-pixels and to the fifth sub-pixel in the basic repeating unit is 4:4:4:3:1.

Abstract: A pixel structure includes a plurality of first pixel groups and a plurality of second pixel groups. The first pixel groups and the second pixel groups are arranged in a matrix manner. The first pixel groups and the second pixel group are staggered with each other along a first direction, and are simultaneously staggered with each other long a second direction. Each of the first pixel groups comprises a first sub-pixel and a second sub-pixel. Each of the second pixel groups comprises a third sub-pixel and a fourth sub-pixel. Three of the first sub-pixel, the second sub-pixel, the third sub-pixel, and the fourth sub-pixel, which are adjacent to each other, cooperate with each other to form a dynamic pixel unit.