Abstract: This is an early warning method for a shallow soil landslide based on a digital topographic map, belonging to the field of landslide prevention and control engineering. It has following steps: a. connecting a straight line along an upward bulged intermediate point of a contour line of the topographic map as an intermediate line; b. determining an intermediate point; c. constituting a three-point group of a plane curvature; d. taking an arithmetic average of slopes as a slope ? of a landslide mass. The shallow soil landslide can be early warned without a lot of historical observation data of landslide occurrence, and the dangerous landslide mass can be determined in advance, which greatly improves the applicability of disaster prevention and the early warning efficiency.

Abstract: The present disclosure discloses an optical engine and an AR device. The optical engine includes a self-luminous display chip and a lens assembly provided on a light outlet side of the self-luminous display chip, wherein the lens assembly includes at least three lenses. The above solution solves the technical problem of large volume of optical engine.

Abstract: The projection optical path comprises a triple light source system emitting a first converged light and a single light source system emitting a light with a monochromatic wavelength. The triple light source system comprises a second converged light converged with a light with a first wavelength to form the first converged light. The single light source system comprises a third light source. The light with the first wavelength, the light with the second wavelength, and the light with the third wavelength are respectively one of red light, green light, blue light, a wavelength range of a light with a fourth wavelength is within a wavelength range of the red light. The wavelength of the light with the fourth wavelength is defined as ?1; the wavelength of the red light among the lights with the three wavelengths is ?2, and ?1??2.

Abstract: Disclosed are a projection optical path and a projection device. The projection optical path includes a first converged light of a first dual-light source system and a second converged light of a second dual-light source system; a first wavelength light, a second wavelength light, and a third wavelength light are respectively one of red light, green light, and blue light; a fourth wavelength light is within a wavelength range of the red light; the wavelength of the fourth wavelength light is defined as ?1; the wavelength of the one set as the red light among the first wavelength light, the second wavelength light, and the third wavelength light is ?2; ?1 is unequal to ?2; an excitation light source emits an excitation light; and the excitation light is emitted to the first dual-light source system or the second dual-light source system.

Abstract: The projection optical path comprises: a main optical path; and an auxiliary optical path, a third light source, and a fourth light source, wherein the light with the second wavelength, the light with the third wavelength and the light with the fourth wavelength are sequentially converged into the light with the first wavelength, wherein the light with the first wavelength, the light with the second wavelength and the light with the third wavelength are one of red light, green light, and blue light, respectively, wherein a wavelength range of the light with the fourth wavelength is within a wavelength range of the red light, and a wavelength of the light with the fourth wavelength is defined as ?1, and a wavelength of the red light among the light with the first wavelength, the light with the second wavelength and the light with the third wavelength is defined as ?2, and ?1??2.

Abstract: Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

Abstract: A query is received that requests issues relevant to a user. Thereafter, a plurality of issues responsive to the query are retrieved. The retrieved issues are ranked using a first machine learning model to result in a first subset of the retrieved issues. The first subset of the retrieved issues are then ranked using a second, different machine learning model to result in a second subset of the retrieved issues which are a subset of the first subset of the retrieved issues. Data can then be provided which is responsive the query and includes at least a portion of the second subset of the retrieved issues. Related apparatus, systems, techniques and articles are also described.

Abstract: Requests are received for handling by a cloud computing environment which are then executed by the cloud computing environment. While each request is executing, performance metrics associated with the request are monitored. A vector is subsequently generated that encapsulates information associated with the request including the text within the request and the corresponding monitored performance metrics. Each request is then assigned (after it has been executed) to either a normal request cluster or an abnormal request cluster based on which cluster has a nearest mean relative to the corresponding vector. In addition, data can be provided that characterizes requests assigned to the abnormal request cluster. Related apparatus, systems, techniques and articles are also described.

Abstract: Supervised learning creates and trains a model to predict resource consumption by a remote system. Historical time-series data (e.g., monitor logs of CPU consumption, memory consumption) are collected from systems called upon to perform a task. This raw data is transformed into a labeled data set ready for supervised learning. Using the labeled data set, a model is constructed to correlate the input data with a resulting load. The constructed model may be a Sequence to Sequence (Seq2Seq) model based upon Gated Recurrent Units of a Recurrent Neural Network. After training, the model is saved for re-use to predict future load based upon an existing input. For example, the existing input may be data from a most recent 24 hour period (hour0-hour23), and the output of the model may be the load predicted for the next 24 hour period (hour24-hour47). This prediction promotes efficient reservation remote server resources.

Abstract: A query is received that requests issues relevant to a user. Thereafter, a plurality of issues responsive to the query are retrieved. The retrieved issues are ranked using a first machine learning model to result in a first subset of the retrieved issues. The first subset of the retrieved issues are then ranked using a second, different machine learning model to result in a second subset of the retrieved issues which are a subset of the first subset of the retrieved issues. Data can then be provided which is responsive the query and includes at least a portion of the second subset of the retrieved issues. Related apparatus, systems, techniques and articles are also described.