Abstract: A refrigerant leak detection (RLD) and/or mitigation/containment (RLM/RLC) system/method for use in heating, ventilation, and air conditioning (HVAC) systems that incorporates a plurality of temperature and/or humidity sensors (THS), alarm status indicator (ASI), and digital control processor (DCP) is disclosed. The THS are positioned within the HVAC system (condenser coils (HCC), evaporator coils (HEC), air intake fans (AIF), air supply plenums (ASP), air return plenums (ARP), and/or temperature controlled environment (TCE)) and reports THS temperature readings to the DCP via a common temperature sensor bus (TSB) and/or a wireless temperature interface (WTI). The DCP interprets THS temperature readings in a closed control loop (CCL) to dynamically determine if a refrigerant leak has occurred within the HVAC system. If a leak is detected, the DCP activates the ASI and optionally one or more refrigerant control valves (RCV) is closed to limit refrigerant leakage in the HVAC system.

Abstract: A refrigerant metering system/method incorporating a manual expansion valve (MEV), condenser isolation valve (CIV), flow isolation valve (FIV), and evaporator isolation valve (EIV) is disclosed. The MEV is configured to replace a conventional automated expansion valve (AEV) that controls a refrigerant flow valve (RFV) that is positioned in a heating, ventilation, and air conditioning (HVAC) system between a refrigerant condenser coil (RCC) and a refrigerant evaporator coil (REC) and permits manual metering of refrigerant by the RFV from the RCC to the REC and also allows complete shutoff of refrigerant flow by the RFV from the RCC to the REC. The MEV allows rapid HVAC repair and restoration of service where a replacement AEV is not readily available. The CIV/FIV/EIV are positioned in the refrigerant flow lines to permit the AEV and/or REC to be isolated from HVAC refrigerant flow for repairs to the AEV and/or REC.

Abstract: A refrigerant metering system/method incorporating a manual expansion valve (MEV), condenser isolation valve (CIV), flow isolation valve (FIV), and evaporator isolation valve (EIV) is disclosed. The MEV is configured to replace a conventional automated expansion valve (AEV) that controls a refrigerant flow valve (RFV) that is positioned in a heating, ventilation, and air conditioning (HVAC) system between a refrigerant condenser coil (RCC) and a refrigerant evaporator coil (REC) and permits manual metering of refrigerant by the RFV from the RCC to the REC and also allows complete shutoff of refrigerant flow by the RFV from the RCC to the REC. The MEV allows rapid HVAC repair and restoration of service where a replacement AEV is not readily available. The CIV/FIV/EIV are positioned in the refrigerant flow lines to permit the AEV and/or REC to be isolated from HVAC refrigerant flow for repairs to the AEV and/or REC.

Abstract: Systems, methods, and non-transitory computer-readable media can be configured to determine a page embedding for each page in a sequence of pages visited by a user. A pooled page embedding can be determined based on the page embeddings for the sequence of pages visited by the user. One or more page recommendations for the user can be determined based at least in part on the pooled page embedding.

Abstract: Systems and methods of the present disclosure are directed to a method for training a machine-learned visual attention model. The method can include obtaining image data that depicts a head of a person and an additional entity. The method can include processing the image data with an encoder portion of the visual attention model to obtain latent head and entity encodings. The method can include processing the latent encodings with the visual attention model to obtain a visual attention value and processing the latent encodings with a machine-learned visual location model to obtain a visual location estimation. The method can include training the models by evaluating a loss function that evaluates differences between the visual location estimation and a pseudo visual location label derived from the image data and between the visual attention value and a ground truth visual attention label.

Abstract: In one embodiment, a method involves accessing training data, where the training data contains an ordered sequence of data associated with a plurality of entities, training one or more deep learning models to determine, from the ordered sequence of data, a first set of embeddings for each entity of the plurality of entities, where each entity has a plurality of entity attributes, determining, for each of the plurality of entity attributes, a corresponding initial embedding, training the one or more deep-learning models to refine the initial embeddings according to one or more criterion, generating one or more updated embeddings for each of the plurality of entities based on the refined initial embeddings of the plurality of entity attributes, and modifying the first set of embeddings based on the one or more updated embeddings.

Abstract: Systems and methods for training a restoration model can leverage training for two sub-tasks to train the restoration model to generate realistic and identity-preserved outputs. The systems and methods can balance the training of the generation task and the reconstruction task to ensure the generated outputs preserve the identity of the original subject while generating realistic outputs. The systems and methods can further leverage a feature quantization model and skip connections to improve the model output and overall training.

Abstract: At training time, a base neural network can be trained to perform each of a plurality of basis subtasks included in a total set of basis subtasks (e.g., individually or some combination thereof). Next, a description of a desired combined subtask can be obtained. Based on the description of the combined subtask, a mask generator can produce a pruning mask which is used to prune the base neural network into a smaller combined-subtask-specific network that performs only the two or more basis subtasks included in the combined subtask.

Abstract: Systems, methods, and non-transitory computer readable media are configured to apply a spectral clustering technique to at least a portion of a similarity graph to generate clusters of geographic sub-regions constituting geographic regions. A tf-idf technique is performed to determine pages of a social networking system associated with a geographic region as potential local suggestions for a user associated with a geographic sub-region in the geographic region. References to at least a portion of the pages are presented as local suggestions to the user.

Abstract: Systems, methods, and non-transitory computer readable media are configured to train a machine learning model. The training can be based on a training set of embeddings of a first type and a training set of embeddings of a second type. The machine learning model can be trained to receive an embedding of a second type and to output a corresponding embedding of the first type. A given embedding of the second type can be provided as input to the machine learning model. An embedding of the first type can be obtained from the machine learning model. The embedding of the first type can correspond to the given embedding of the second type.

Abstract: A refrigerant metering system/method incorporating a manual expansion valve (MEV), condenser isolation valve (CIV), flow isolation valve (FIV), and evaporator isolation valve (EIV) is disclosed. The MEV is configured to replace a conventional automated expansion valve (AEV) that controls a refrigerant flow valve (RFV) that is positioned in a heating, ventilation, and air conditioning (HVAC) system between a refrigerant condenser coil (RCC) and a refrigerant evaporator coil (REC) and permits manual metering of refrigerant by the RFV from the RCC to the REC and also allows complete shutoff of refrigerant flow by the RFV from the RCC to the REC. The MEV allows rapid HVAC repair and restoration of service where a replacement AEV is not readily available. The CIV/FIV/EIV are positioned in the refrigerant flow lines to permit the AEV and/or REC to be isolated from HVAC refrigerant flow for repairs to the AEV and/or REC.

Abstract: Systems, methods, and non-transitory computer readable media are configured to determine an interaction between a first entity and a first item. A second entity can be determined. The first entity can have formed a connection with the second entity on a social networking system. A belief that the second entity will interact with the first item can then be generated.

Abstract: A refrigerant metering system/method incorporating a manual expansion valve (MEV), condenser isolation valve (CIV), flow isolation valve (FIV), and evaporator isolation valve (EIV) is disclosed. The MEV is configured to replace a conventional automated expansion valve (AEV) that controls a refrigerant flow valve (RFV) that is positioned in a heating, ventilation, and air conditioning (HVAC) system between a refrigerant condenser coil (RCC) and a refrigerant evaporator coil (REC) and permits manual metering of refrigerant by the RFV from the RCC to the REC and also allows complete shutoff of refrigerant flow by the RFV from the RCC to the REC. The MEV allows rapid HVAC repair and restoration of service where a replacement AEV is not readily available. The CIV/FIV/EIV are positioned in the refrigerant flow lines to permit the AEV and/or REC to be isolated from HVAC refrigerant flow for repairs to the AEV and/or REC.

Abstract: A refrigerant metering system/method incorporating a manual expansion valve (MEV), condenser isolation valve (CIV), flow isolation valve (FIV), and evaporator isolation valve (EIV) is disclosed. The MEV is configured to replace a conventional automated expansion valve (AEV) that controls a refrigerant flow valve (RFV) that is positioned in a heating, ventilation, and air conditioning (HVAC) system between a refrigerant condenser coil (RCC) and a refrigerant evaporator coil (REC) and permits manual metering of refrigerant by the RFV from the RCC to the REC and also allows complete shutoff of refrigerant flow by the RFV from the RCC to the REC. The MEV allows rapid HVAC repair and restoration of service where a replacement AEV is not readily available. The CIV/FIV/EIV are positioned in the refrigerant flow lines to permit the AEV and/or REC to be isolated from HVAC refrigerant flow for repairs to the AEV and/or REC.

Abstract: A refrigerant metering system/method incorporating a manual expansion valve (MEV), condenser isolation valve (CIV), flow isolation valve (FIV), and evaporator isolation valve (EIV) is disclosed. The MEV is configured to replace a conventional automated expansion valve (AEV) that controls a refrigerant flow valve (RFV) that is positioned in a heating, ventilation, and air conditioning (HVAC) system between a refrigerant condenser coil (RCC) and a refrigerant evaporator coil (REC) and permits manual metering of refrigerant by the RFV from the RCC to the REC and also allows complete shutoff of refrigerant flow by the RFV from the RCC to the REC. The MEV allows rapid HVAC repair and restoration of service where a replacement AEV is not readily available. The CIV/FIV/EIV are positioned in the refrigerant flow lines to permit the AEV and/or REC to be isolated from HVAC refrigerant flow for repairs to the AEV and/or REC.

Abstract: The present disclosure provides for the generation of embeddings within a machine learning framework, such as, for example, a federated learning framework in which a high-quality centralized model is trained on training data distributed over a large number of clients each with unreliable network connections and low computational power. In an example federated learning setting, in each of a plurality of rounds, each client independently updates the model based on its local data and communicates the updated model back to the server, where all the client-side updates are used to update a global model. The present disclosure provides systems and methods that may generate embeddings with local training data while preserving the privacy of a user of the client device.

Abstract: Embodiments relate to systems and methods for producing magnetic material. The method includes providing a mixture of alloys. The composition of alloy are not particularly limited. The method includes melting the mixture of alloys to arrive at a molten mixture of alloys. The method includes performing a melt-spinning process to rapidly solidify the molten mixture of alloys via a rotatable wheel to arrive at a preliminary metallic ribbon. The preliminary metallic ribbon having an elongated flat body with a bottom side and a top side, the top side opposite to the bottom side. The method includes performing a grain size refinement and uniformity process, the grain size refinement and uniformity process including delivering a first coolant directly to at least a central region of the top side and/or bottom side of the preliminary metallic ribbon to arrive at a final metallic ribbon.

Abstract: Systems, methods, and non-transitory computer readable media are configured to determine an interaction between a first entity and a first item. A second entity can be determined. The first entity can have formed a connection with the second entity on a social networking system. A belief that the second entity will interact with the first item can then be generated.

Abstract: In one embodiment, a method involves accessing training data, where the training data contains an ordered sequence of data associated with a plurality of entities, training one or more deep learning models to determine, from the ordered sequence of data, a first set of embeddings for each entity of the plurality of entities, where each entity has a plurality of entity attributes, determining, for each of the plurality of entity attributes, a corresponding initial embedding, training the one or more deep-learning models to refine the initial embeddings according to one or more criterion, generating one or more updated embeddings for each of the plurality of entities based on the refined initial embeddings of the plurality of entity attributes, and modifying the first set of embeddings based on the one or more updated embeddings.

Abstract: Systems, methods, and non-transitory computer readable media can determine one or more actions that a user is likely to take on a page associated with a social networking system, based on one or more first machine learning models. One or more card types that correspond to the one or more actions can be ranked based on a second machine learning model. One or more cards can be generated based on the ranked card types, and each card can include a recommended action associated with the page.