Abstract: A computing system, for example, a multi-tenant system configures a datacenter in a cloud platform using a cloud platform infrastructure language that is cloud platform independent. The system destroys the datacenter or a set of datacenter entities of the datacenter. The system determines dependencies between datacenter entities based on the declarative specification. The system generates an aggregate destroy pipeline comprising a hierarchy of pipelines. The system generates an aggregate destruction version map associating datacenter entities of the datacenter with versions of software artifacts, wherein a software artifact stores instructions for destroying resources of a datacenter entity. The system collects a set of software artifacts according to the aggregate destruction version map. The system executes the aggregate destroy pipeline in conjunction with the aggregate destruction version map to destroy the set of datacenter entities of the datacenter.

Abstract: A perforating gun used in oil and gas completions operations. A gun string including the perforating gun and one or more additional perforating guns substantially identical to the perforating gun.

Abstract: Electrical Tomography (ET) Images of an object can be generated based on features extracted during an excitation cycle. For example, a method described herein can include selecting, from a plurality of electrodes attached to the object, a first pair of electrodes. The method can include exciting the first pair of electrodes during a first excitation cycle. Additionally, the method can include generating an analog signal by performing measurements across remaining pairs of electrodes of the plurality of electrodes during the excitation cycle. The method can include generating the digital signal based on an analog to digital conversion of the analog signal. Further, the method can include extracting, during the excitation cycle, features based on the digital signal. The method can include generating an ET image associated with the object based on the features.

Abstract: In one aspect, a delta prefetcher disclosed herein bases its predictions on both past memory accesses and predictively prefetched memory accesses. More specifically, the delta prefetcher disclosed herein bases its prediction on both the difference or “delta” between memory addresses of data previously fetched from memory and the difference or “delta” between addresses of data predictively fetched from memory. The delta prefetcher tracks the delta memory accesses by utilizing two distinctive tables. The fetch table tracks the memory deltas for each memory operation, such as a LOAD or STORE instruction, that the CPU has executed. The delta table predicts the next memory address to prefetch based on the last prefetched memory accesses.

Abstract: Techniques and systems are provided for processing sensor data. For instance a process can include obtaining first sensor data of an environment, wherein the first sensor data includes a representation of a first object occluding a second object, obtaining second sensor data of the environment, wherein the second sensor data includes points associated with the first object and points associated with the second object, generating estimated segment data from the first sensor data, wherein the estimated segment data includes a first segment corresponding to the first object; matching points associated with the first object to the first segment, and deemphasizing points associated with the second object based on matching the points associated with the first object to the first segment.

Abstract: A system for retrieving multimodal assets using domain-specific knowledge includes receiving a search query for searching for multimodal assets; encoding the search query into a first query representation via a first trained query representation machine-learning (ML) model and a second query representation via a second trained query representation ML model; comparing the first query representation to a plurality of multimodal representations to calculate a first similarity score, each of the plurality of multimodal representations being a representation of one of the plurality of candidate multimodal assets; comparing the second query representation to a plurality of domain-specific representations to calculate a second similarity score, the domain-specific representations being representations of domain-specific data associated with one or more of the plurality of the multimodal representations; calculating a third similarity score based on keyword matching between the domain-specific data and the one or mor

Abstract: A method includes determining, based on an image having an initial viewpoint, a depth image, and determining a foreground visibility map including visibility values that are inversely proportional to a depth gradient of the depth image. The method also includes determining, based on the depth image, a background disocclusion mask indicating a likelihood that pixel of the image will be disoccluded by a viewpoint adjustment. The method additionally includes generating, based on the image, the depth image, and the background disocclusion mask, an inpainted image and an inpainted depth image. The method further includes generating, based on the depth image and the inpainted depth image, respectively, a first three-dimensional (3D) representation of the image and a second 3D representation of the inpainted image, and generating a modified image having an adjusted viewpoint by combining the first and second 3D representation based on the foreground visibility map.

Abstract: This disclosure provides systems, methods, and devices for vehicle driving assistance systems that support image processing. In a first aspect, a method is provided that includes receiving sensor data from a plurality of sensors on a vehicle and determining a three-dimensional representation of an area surrounding the vehicle by mapping the sensor data onto a three-dimensional surface. The plurality of sensors may include at least one perspective view sensor and at least one top view sensor, and the three-dimensional surface may include sensor data from the at least one perspective view sensor and sensor data from the at least one top view sensor. The method may further include determining, with a machine learning model, one or more characteristics of the area surrounding the vehicle based on the three-dimensional representation. Other aspects and features are also claimed and described.

Abstract: A semiconductor package includes: a substrate having a metallic surface; a semiconductor die metallurgically bonded to the metallic surface of the substrate by a first solder joint; and a solder wetting structure metallurgically welded to the metallic surface of the substrate outside a perimeter of the semiconductor die and adjacent to one or more side faces of the semiconductor die. Excess solder squeezed out from under the semiconductor die is metallurgically bonded to the solder wetting structure. Methods of producing the semiconductor package and solder wetting structure are also described.

Abstract: The present teachings include a flow coating solution, a fuser member and an image forming apparatus. The fuser member includes a substrate and flow coating solution applied onto the substrate. The outer layer includes a flow coating solution including a fluoroelastomer, a nucleophilic crosslinking agent and a non-functional polydimethylsiloxane having the formula: wherein n is from 100 to 20,000. The flow coating solution includes an effective solvent selected from the group consisting of N-methyl 2-pyrrolidone, dimethyl formamide, and dimethyl sulfoxide.

Abstract: A system and method relate to excluding biasing data from talent profiles, including identifying one or more classes of bias, obtaining a first talent profile, wherein the first talent profile comprises an identifier of a person, and a plurality of values characterizing aspects of the person, determining, from the plurality of values, a first value that is indicative of influences over at least one of the one or more classes of bias, and removing or substituting the first value in the first talent profile to generate a second talent profile.

Abstract: Systems and methods for encoding data in a machine-readable image (e.g., an artificial fingerprint) are disclosed. The methods include segmenting a plurality of bits included in a bit stream to be encoded into a plurality of blocks such that each block includes two or more of the plurality of bits. The plurality of blocks are used to generate a minutiae template comprising a plurality of minutiae points each of which is representative of a local ridge discontinuity in a fingerprint, and is associated with a location and an orientation. An artificial fingerprint is generated from the minutiae template, and stored in association with the bit stream. The artificial fingerprint encodes the bit stream and comprises a plurality of ridges and a plurality of valleys.

Abstract: This disclosure relates, at least in part, to a method of treatment. In one embodiment, the method of treatment comprises administering to a subject in need of such treatment at least a first therapeutic agent including ONC-206, 7-benzyl-4-(2,4-difluorobenzyl)-2,4,6,7,8,9-hexahydroimidazo[1,2-a]pyrido[3,4-e]pyrimidin-5(1H)-one, for the treatment of one or more CNS neoplasms.

Abstract: Systems and methods of extracting information encoded in a machine-readable image (e.g., an artificial fingerprint) are disclosed. The methods include receiving a machine-readable image and generating a minutiae template from the machine-readable image. The minutiae template includes a plurality of minutiae points, each being representative of a local ridge discontinuity in a human fingerprint. The methods further include identifying, for each of the plurality of minutiae points, a minutia point orientation and a minutia point type, and retrieving, from a data store, a plurality of blocks associated with the identified minutiae point orientations and minutiae point types where each block includes two or more bit values. The retrieved plurality of blocks are concatenated to generate a bit stream encoded in the machine-readable image.

Abstract: An example operation includes determining a driving style of a vehicle, wherein the driving style is associated with a greatest amount of carbon emissions over a period; and notifying a device associated with the vehicle, wherein the notifying includes a modification of the driving style, an environmental benefit, and a value, when an amount of carbon emissions for the modification of the driving style falls below the greatest amount in a future period equal to the period.

Abstract: Attachment devices that can secure a phone or other electronic device in place in a vehicle or other structure. One example can provide an attachment device that can include a stalk portion to attach to a surface or structure in a vehicle, such as a vent cover, dashboard, monitor, cup holder or other surface or structure. The attachment device can further include an attachment feature to provide for an attachment to the electronic device. Further examples can provide power for a phone or other electronic device.

Abstract: Methods, systems, and media for identifying abusive user accounts based on playlists are provided. In accordance with some embodiments of the disclosed subject matter, a method for identifying abusive content is provided, the method comprising: determining at least one feature associated with a playlist created by a user-generated channel; calculating a playlist score associated with the playlist based on a playlist classifier, wherein the playlist classifier comprises a function that maps the at least one feature to the playlist score; calculating a channel score associated with the user-generated channel based at least on the calculated playlist score; determining that one or more content items associated with the user-generated channel is to be demoted based on the calculated channel score, wherein the one or more content items comprises the playlist; and causing the one or more content items to be demoted.

Abstract: Machine classifiers in accordance with embodiments of the invention capture long-term temporal dependencies in particular tasks, such as turn-based dialogues. Machine classifiers may be used to help users to perform tasks indicated by the user. When a user utterance is received, natural language processing techniques may be used to understand the user's intent. Templates may be determined based on the user's intent in the generation of responses to solicit information from the user. A variety of persona attributes may be determined for a user. The persona attributes may be determined based on the user's utterances and/or provided as metadata included with the user's utterances. A response persona may be used to generate responses to the user's utterances such that the generated responses match a tone appropriate to the task. A response persona may be used to generate templates to solicit additional information and/or generate responses appropriate to the task.

Abstract: Atomic layer etching (ALE) processes are disclosed. In some embodiments, the methods comprise at least one etch cycle in which a substrate comprising a metal, metal oxide, metal nitride or metal oxynitride layer is contacted with an etch reactant comprising an vapor-phase N-substituted derivative of amine compound. In some embodiments the etch reactant reacts with the substrate surface to form volatile species including metal atoms from the substrate surface. In some embodiments a metal or metal nitride surface is oxidized as part of the ALE cycle. In some embodiments a substrate surface is contacted with a halide as part of the ALE cycle. In some embodiments a substrate surface is contacted with a plasma reactant as part of the ALE cycle.

Abstract: A system generates alerts based on sensor data obtained from equipment, for example, refrigeration equipment. Examples of equipment instances include refrigeration equipment, heating equipment, air conditioning equipment, and so on. The system accesses a model, for example, a machine learning model trained to predict an alert threshold for generation of alerts. The system modifies the alert threshold value for an equipment instance based on the output of the machine learning model. The system uses the modified alert threshold value for generating alerts. The system may track the number of times the alert threshold was adjusted. If the number of times the alert threshold value is modified exceeds a predetermined fault threshold value, the system determines that the equipment is faulty.