Abstract: The present disclosure discloses a sound device comprising: a frame; a vibration system supported on the frame; a magnetic system driving the vibration system for sound generation supported on the frame; the magnetic system comprises a main magnet, a sub-magnet and a magnetic gap formed by spacing of the main magnet and the sub-magnet; the vibration system includes a diaphragm fixed to the frame and a coil driving the diaphragm for sound generation, the coil is at least partially inserted in the magnetic gap; the coil includes a first coil and a second coil both in a shape of ring, one end of the first coil is fixed to the side of the diaphragm near the magnetic system, one end of the second coil is fixed to the end of the first coil near the magnetic system, and the first coil and the second coil are insulated from each other. The present disclosure provides a sound device with adjustable voice coil position and good acoustic performance.

Abstract: A speaker includes: a frame, a vibration system, including a diaphragm, a voice coil, and a flexible circuit board, the flexible circuit board including a first fixing portion, a second fixing portion, and an elastic arm, and a magnetic circuit system, including a yoke with a through hole and a main magnet and a secondary magnet fixed to the yoke, the secondary magnet surrounding and spaced from the main magnet for forming a magnetic gap, the voice coil located in the magnetic gap, wherein a projection of the elastic arm along a vibration direction of the diaphragm is at least partially located in the through hole, the speaker further comprises a connecting piece connected with an outer surface of the yoke, the connecting piece covers the through hole. Compared with the related art, the speaker disclosed by the present disclosure has better acoustic performance.

Abstract: Provided herein are methods of treating cancer using LSD1 inhibitors in combination with immunotherapy.

Abstract: Disclosed is an amorphous nanocrystalline alloy strip. When the amorphous nanocrystalline alloy strip is cut into a plurality of narrow strips having a same width of less than or equal to 10 mm, a relative length difference among the plurality of narrow strips is not greater than 0.50%. Further disclosed is a method for manufacturing the amorphous nanocrystalline alloy strip. The amorphous nanocrystalline alloy strip has good surface flatness. The transverse temperature non-uniformity of molten steel inside a melting pool and the surface temperature non-uniformity of a cooling roller within a strip manufacturing position range are controlled in a process of manufacturing the amorphous nanocrystalline alloy strip, so that the surface flatness of the manufactured strip is improved. The manufacturing process is simple and convenient.

Abstract: The present disclosure provides a speaker unit, which includes a frame, and a vibration system and a magnetic circuit system fixed on the frame. The vibration system includes a diaphragm and a voice coil that drives the diaphragm to vibrate and sound. The speaker unit further includes a magnetic front cover fixed on the frame for improving the BL value of the voice coil. The speaker unit of the present disclosure improves the BL value of the voice coil.

Abstract: The present disclosure provides a chitosan-modified opto-hydrodynamic micromotor and a preparation method and use thereof, and belongs to the technical field of micromotors. The chitosan-modified opto-hydrodynamic micromotor provided by the present disclosure is composed of Phaeodactylum tricornutum Bohlin and a chitosan solution. The chitosan-modified opto-hydrodynamic micromotor provided by the present disclosure has high biocompatibility, can non-invasively remove biological threats in a microenvironment containing cells with a sterilization rate reaching about 98%, and can achieve high-efficiency sterilization without affecting cell viability.

Abstract: The invention relates to a polymeric micelle comprising a block copolymer comprising a polyethylene glycol (PEG) hydrophilic block and a hydrophobic block, and a compound according to formula (I) or formula (III) encapsulated within said polymeric micelle and to uses thereof.

Abstract: A large-flux in-situ filtration device for marine microorganism based on a multi-channel circulation distributor is provided, comprising an energy and control module, a circulation distribution module, a filtration module, a pump module, a filtration volume measurement module, a pipeline connection, and a main body support frame. The circulation distribution module may include a multi-channel circulation distributor, a deep-sea servo motor, etc. The filtration module may include a plurality of filter film holders. The pump module may include a stainless steel gear pump, a deep-sea drive motor, etc. The filtration volume measurement module may include a water meter, a camera, and an LED light.

Abstract: Provided herein are methods of treating cancer using LSD1 inhibitors in combination with immunotherapy.

Abstract: A method of manufacturing a battery includes introducing a first material to the battery, providing an anode, a cathode and a separator of the battery; and assembling the anode, the separator and the cathode. The first material is configured and arranged to increase the internal impedance of the battery upon mechanical or thermal loading.

Abstract: The present disclosure relates to a new method for cross-modal retrieval via deep binary hashing and quantization. In a training phase, the system simultaneously learns to generate feature vectors, binary codes, and quantization codes for data across two or more modalities that preserves the semantic similarity correlations in the original data. In a prediction phase, the system retrieve a data item in a database that is semantically similar to a query item of a different modality. To identify the database item closest in semantic meaning to the query item, the system first narrows the database search space based on binary hash code distances between each of the database items and the query item. The system then measures the quantization distances between the query items and the database items in the smaller search space. The system identifies database item have the closest quantization distance to the query item as the closest semantic match to the query item.

Abstract: A multiple scenario-oriented item retrieval method and system. The method includes the steps of extracting, by Hashing learning, image features from an image training set to train a pre-built item retrieval model; when an image is in a scenario of hard samples, introducing an adaptive similarity matrix, optimizing the similarity matrix by an image transfer matrix, constructing an adaptive similarity matrix objective function in combination with an image category label; constructing a loss quantization objective function between the image and a Hash code according to the image transfer matrix; when the image is in a scenario of zero samples, introducing an asymmetric similarity matrix, constructing an objective function by taking the image category label as supervisory information in combination with equilibrium and decorrelation constraints of the Hash code; and training the item retrieval model based on the above objective function to obtain a retrieved result of a target item image.

Abstract: A method and system for rapid retrieval of target images based on artificial intelligence, obtaining a template image and a plurality of known labels corresponding to the template image; extracting an image to be detected from a target image database; inputting both the image to be detected and the template image into a trained convolutional neural network, and outputting a hash code of the image to be detected and a hash code of the template image; obtaining a similarity between the images based on a Hamming distance between the hash codes, then selecting one or more images to be detected with the similarity higher than a set threshold as a retrieval result to output. Accordingly, the method and system is able to better cope with the retrieval of items in complex scenarios.

Abstract: A interconnect system, including a plurality of sockets and a first interconnect interface. Any two of the sockets are accessible to each other's hardware resources by transmitting a first packet and a second packet through the first interconnect interface. The first packet includes first interconnect information, used for establishing communication between the two sockets. The second packet includes a first data payload, loaded from one of the two sockets. The sockets include a first socket and a second socket, configured to be interconnected with each other through the first interconnect interface.

Abstract: An electronic device comprises a first processor, a second processor and a communication interface. The first processor operates according to a first clock, and comprises a first time-stamp counter to count the first clock to obtain a first count value. The second processor operates according to a second clock, and comprises a second time-stamp counter to count the second clock to obtain a second count value. The communication interface is coupled between the first processor and the second processor. The first processor periodically sends the first count value to the second processor through the communication interface. When the second processor receives the first count value, the second processor adds a preset deviation value to the first count value to obtain a synchronization value, resets the second count value, and the sum of the synchronization value and the second count value is read by the second processor.

Abstract: The present disclosure relates to improving recommendations for small shops on an ecommerce platform while maintaining accuracy for larger shops. The improvement is achieved by retraining a machine-learning recommendation model to reduce sample selection bias using a meta-learning process. The retraining process comprises identifying a sample subset of shops on the ecommerce platform, and then creating shop-specific versions of the recommendation model for each of the shops in the subset. Each shop-specific model is created by optimizing the baseline model to predict user-item interactions in a first training dataset for the applicable shop. Each of the shop-specific models is then tested using a second training dataset for the shop. A loss is calculated for each shop-specific model based on the model's predicted user-item interactions and the actual user-item interactions in the second training dataset for the shop.

Abstract: The present disclosure relates to improving recommendations for small shops on an ecommerce platform while maintaining accuracy for larger shops. The improvement is achieved by retraining a machine-learning recommendation model to reduce sample selection bias using a meta-learning process. The retraining process comprises identifying a sample subset of shops on the ecommerce platform, where the sample batch includes shops in both a small size category and shops in a large size category. The machine-learning model is then used to make shop-specific user-item interaction predictions for each shop in the sample batch. A shop-specific loss is calculated for each shop based on the model's predicted user-item interactions for the shop-specific training dataset and the actual user-item interactions in the shop-specific training dataset. A global loss is calculated based on each of the shop-specific losses, and the baseline model is updated to minimize the global loss.

Abstract: The present disclosure relates to improving recommendations for small shops on an ecommerce platform while maintaining accuracy for larger shops. The improvement is achieved by retraining a machine-learning recommendation model to reduce sample selection bias using a meta-learning process. The retraining process comprises identifying a sample subset of shops on the ecommerce platform, and then creating shop-specific versions of the recommendation model for each of the shops in the subset. A global parameter adjustment is calculated for the global model based on minimizing losses associated with the shop-specific models and increasing the probability of items being recommended from small shops. The latter is achieved by introducing regularizer terms for small shops during the meta-learning process. The regularizer terms serve to increase the probability that an item from a small shop will be recommended, thereby countering the sample selection bias faced by small-shop items.

Abstract: The present disclosure relates to improving recommendations for small shops on an ecommerce platform while maintaining accuracy for larger shops. The improvement is achieved by retraining a machine-learning recommendation model to reduce sample selection bias using a meta-learning process. The retraining process comprises identifying a sample subset of shops on the ecommerce platform, and then creating shop-specific versions of the recommendation model for each of the shops in the subset. Each shop-specific model is created by optimizing the baseline model to predict user-item interactions in a first training dataset for the applicable shop. Each of the shop-specific models is then tested using a second training dataset for the shop. A loss is calculated for each shop-specific model based on the model's predicted user-item interactions and the actual user-item interactions in the second training dataset for the shop.

Abstract: The image restoration device has an initialization block that initializes the luminance value of each pixel coordinate to an intermediate value in the luminance array list that stores one of a pair of polarity values and intermediate values as the luminance value for each pixel coordinate. The image restoration device also has an update block that updates the initialized luminance array list according to the pixel coordinates and polarity values for each event, and an output block that outputs the luminance array list updated by the update block over the shooting period as a binary image. By the update performed in the update block, the luminance values of the firing coordinates where the event fired in the luminance array list are overwritten by the polarity values of the event. In addition, the update preserves the luminance values of the non-firing coordinates in the luminance array list, excluding the firing coordinates.