Abstract: Disclosed are a voice coil motor a zoom lens and a photography device. The voice coil motor includes a machine shell, a magnet structure, a mover structure and a drive structure. The magnet structure is provided on the machine shell and includes an annular magnetic yoke. The mover structure includes a plurality of mover coils, and the plurality of mover coils are movably sleeved on a yoke section of the annular magnetic yoke. The drive structure includes a plurality of drive chips and the plurality of drive chips are electrically connected to the plurality of mover coils respectively.

Abstract: Disclosed are a voice coil motor, a zoom lens and a photography device. The voice coil motor includes a machine shell, an annular magnetic yoke, a plurality of magnets and a mover structure. The annular magnetic yoke is provided on the machine shell. The plurality of magnets are provided in the annular magnetic yoke and are provided at interval along a length direction of the annular magnetic yoke. The mover structure includes a mover coil movably sleeved on a yoke section of the annular magnetic yoke.

Abstract: An ablation system (3300) includes a support (3310), a delivery device (3320) configured to deliver the support (3310) to a target tissue, and an ablation element (3330) including a first conductive portion (3331) and a second conductive portion (3332). The first conductive portion (3331) is provided on the support (3310). The second conductive portion (3332) is provided on the support (3310) or the delivery device (3320) or independently from the support (3310) and the delivery device (3320). Both the first conductive portion (3331) and the second conductive portion (3332) are electrically connected to an external pulse ablation source, and the first conductive portion (3331) and the second conductive portion (3332) are configured to transmit pulse ablation energy with different polarities to the target tissue.

Abstract: A device receives historical data associated with multiple cloud computing environments, trains one or more machine learning models, with the historical data, to generate trained machine learning models that generate outputs, and trains a model with the outputs to generate a trained model. The device receives particular data, associated with a cloud computing environment, that includes data identifying usage of resources associated with the cloud computing environment, and processes the particular data, with the trained machine learning models, to generate anomaly scores indicating anomalous usage of the resources associated with the cloud computing environment. The device processes the one or more anomaly scores, with the trained model, to generate a final anomaly score indicating anomalous usage of at least one of the resources associated with the cloud computing environment, and performs one or more actions based on the final anomaly score.

Abstract: A logistics conveying line for conveying a workpiece includes a frame including at least two stations and a driving mechanism including a driving portion and a connecting member. The driving portion is movably mounted at the frame. The connecting member is movably mounted at the driving portion. The connecting member is configured to switch between a connected state and a disconnected state with respect to a carrier. The carrier is configured to bear the workpiece. The driving portion is configured to, when the connecting member is in the connected state with respect to the carrier, drive the carrier to move from one of the stations to another one of the stations.

Abstract: Examples described herein relate to a programmable packet processing pipeline configured to: access a data corresponding to multiple bins, respective bins associated with multiple packet flows and for respective bins: identify a single flow associated with a bin of the multiple bins as a candidate heavy hitter flow and determine a different packet flow as the candidate heavy hitter flow for the bin of the multiple bins.

Abstract: A device may receive data that is related to historical reports associated with an organization, historical audits of the historical reports, and individuals associated with the historical reports. The device may determine a multi-entity profile for the data. The multi-entity profile may include a set of groupings of the data by a set of attributes included in the data. The device may determine, using the multi-entity profile, a set of supervised model features for the historical reports. The device may determine, using the multi-entity profile, a set of unsupervised model features for the historical reports independent of the historical audits. The device may determine, utilizing a model, a score for a report. The device may perform one or more actions.

Abstract: A vehicle power supply device is provided. The vehicle power supply device includes: a fixed base; a scissor-fork lifting mechanism, where the scissor-fork lifting mechanism is configured to be driven by a drive device to ascend or descend; and the scissor-fork lifting mechanism includes at least one scissor-fork swing rod set, the scissor-fork swing rod set includes at least one pair of swing rods, each pair of swing rods includes two swing rods that are cross-arranged and are hinged with each other, and two swing rods in a pair of swing rods that is closest to the fixed base are separately mounted on the fixed base; and a power supply head, where the power supply head is mounted on two swing rods in a pair of swing rods that is farthest from the fixed base, and the power supply head is driven by the scissor-fork lifting mechanism to ascend or descend.

Abstract: Methods and apparatus for a flexible Deterministic Finite Automata (DFA) tokenizer for AI-based malicious traffic detection. A DFA compiler is used to process profiles, such as SQLi, HTML5 and XSS profiles, as well as user-defined profiles, to generate corresponding DFA transition tables. The DFA tokenizer includes a DFA engine that employs the DFA transition table(s) to generate token sequences derived from input strings. The token sequences are converted into feature vectors using a feature extraction engine, and the feature vectors are used for training a machine learning/Artificial Intelligence (AI) model configured to perform binary classification (benign or malicious). During run-time, strings are extracted from input received via a network and tokenized with the DFA tokenizer to generate token sequences that are converted into feature vectors. The feature vectors are then classified using the AI model to determine whether the input is benign or malicious.

Abstract: A device may receive historical cloud data associated with resources of a cloud computing environment, and may receive historical customer data associated with requested resource usage by customers of the cloud computing environment. The device may determine a usage growth profile based on the historical cloud data and the historical customer data, and may determine, based on the historical cloud data and the historical customer data, usage deviation data indicating deviations between actual and planned resource usage. The device may train a model, with the usage growth profile and the usage deviation data, to generate a trained model, and may receive a request for new resource usage by a customer associated with the cloud computing environment. The device may process the request for the new resource usage, with the trained model, to generate projected resource usage data, and may perform actions based on the projected resource usage data.

Abstract: Examples include techniques to encode and decode for character class matching. A character class including a plurality of characters may be encoded in a mask table. A sampled chunk of input data is used to perform a decode operation to check, in a parallel manner, each character included in the input data with the encoded mask table. The checking of each character in the input data to determine whether at least one character in the input data matches a character included in the character class.

Abstract: A key distribution method based on broadband physical random sources includes: utilizing a driving semiconductor laser to generate an optical signal, passing the optical signal through a phase modulator driven by a random signal and then equally dividing the phase-modulated optical signal into two identical paths, injecting the two identical paths into slave semiconductor lasers at both communication parties Alice and Bob's sides, respectively, to generate initial synchronized signals, using the generated initial synchronized signals as driving signals to phase-modulate optical signals generated by continuous-wave (CW) light sources, and inputting the modulated optical signals to dispersion modules; wherein after the modulated CW optical signals pass through the dispersion modules, two synchronized broadband noise-like random signals are generated, and then high-speed synchronized keys are generated by a post-processing method.

Abstract: The disclosure is directed to systems, methods, and apparatus, including non-transitory computer-readable media, for performing quota resolution on a cloud computing platform. A system can receive user account data from one or more user accounts representing a first user. The system can generate a plurality of features from the user account data characterizing interactions between the first user and the computing platform. From at least the plurality of features, the system can generate a score at least partially representing a predicted likelihood that the additional computing resources allocated to the first user account will be used in violation of one or more predetermined abusive usage parameters during a predetermined future time period.

Abstract: An ultrasound-based handheld animal and plant tissue ablation instrument includes a housing internally provided with a control board and a drive board, and an ultrasonic vibrator fixedly connected to a front end of the housing. The ultrasonic vibrator includes an ultrasonic transducer and an amplitude transformer. The amplitude transformer is fixedly connected to a front end of the ultrasonic transducer, changes an amplitude of vibration input by the ultrasonic transducer and transmits out the vibration. The control board is provided with a power supply, a DCDC conversion unit, a main control unit, a DCDC power adjustment unit and a sampling unit. The drive board is provided with a drive unit, a transformer unit and a resonance unit. The housing includes a first section and a second section. The end of the first section away from the second section is the front end of the housing.

Abstract: Examples include techniques four use of a large scale multi-literal matching algorithm. Implementation of the large scale multi-literal matching algorithm includes processing a chunk of input data via performance of a SHIFT-OR operation using the chunk of input data to identify a match candidate for a target literal character pattern. A single input multiple data (SIMD) instruction may be utilized by a processor to perform the SHIFT-OR operation as a parallel table lookup of rows of SHIFT-OR mask table for the chunk of input data to facilitate identification of the match candidate.

Abstract: A key distribution method based on broadband physical random sources includes: utilizing a driving semiconductor laser to generate an optical signal, passing the optical signal through a phase modulator driven by a random signal and then equally dividing the phase-modulated optical signal into two identical paths, injecting the two identical paths into slave semiconductor lasers at both communication parties Alice and Bob's sides, respectively, to generate initial synchronized signals, using the generated initial synchronized signals as driving signals to phase-modulate optical signals generated by continuous-wave (CW) light sources, and inputting the modulated optical signals to dispersion modules; wherein after the modulated CW optical signals pass through the dispersion modules, two synchronized broadband noise-like random signals are generated, and then high-speed synchronized keys are generated by a post-processing method.

Abstract: A device receives historical data associated with multiple cloud computing environments, trains one or more machine learning models, with the historical data, to generate trained machine learning models that generate outputs, and trains a model with the outputs to generate a trained model. The device receives particular data, associated with a cloud computing environment, that includes data identifying usage of resources associated with the cloud computing environment, and processes the particular data, with the trained machine learning models, to generate anomaly scores indicating anomalous usage of the resources associated with the cloud computing environment. The device processes the one or more anomaly scores, with the trained model, to generate a final anomaly score indicating anomalous usage of at least one of the resources associated with the cloud computing environment, and performs one or more actions based on the final anomaly score.

Abstract: A device may receive historical cloud data associated with resources of a cloud computing environment, and may receive historical customer data associated with requested resource usage by customers of the cloud computing environment. The device may determine a usage growth profile based on the historical cloud data and the historical customer data, and may determine, based on the historical cloud data and the historical customer data, usage deviation data indicating deviations between actual and planned resource usage. The device may train a model, with the usage growth profile and the usage deviation data, to generate a trained model, and may receive a request for new resource usage by a customer associated with the cloud computing environment. The device may process the request for the new resource usage, with the trained model, to generate projected resource usage data, and may perform actions based on the projected resource usage data.

Abstract: A device may receive data related to operations of a plurality of managed devices. The device may determine, after receiving the data, a multi-entity profile for the data. The device may determine, using the multi-entity profile, a set of sub-models for the data after determining the multi-entity profile. The set of sub-models may be associated with processing the data in a contextualized manner. The device may generate a model based on the set of sub-models. The device may perform one or more actions related to the plurality of managed devices or the at least one alert based on respective scores associated with the plurality of managed devices after generating the model.

Abstract: A device may receive utility usage data for multiple buildings across multiple locations. The device may process the utility usage data using a first set of models associated with performing at least one of: an intra-building anomaly detection for the utility usage data, a first grouping of the utility usage data based on characteristics of the utility usage data, or a second grouping of the utility usage data based on the multiple locations. The device may process first output from the first set of models using a second set of models associated with pre-processing the first output in association with identifying anomalies in the first grouping or in the second grouping. The device may process the first output and second output from the second set of models using a super model associated with identifying the anomalies. The device may perform, based on the score, one or more actions.