Abstract: Disclosed herein are various approaches for sharing knowledge within and between organizations while protecting sensitive data. A machine learning model may be trained using training prompts querying a vector store to prevent unauthorized user disclosure of data derived from the vector store. A prompt may be received and a response to the prompt may be generated using the machine learning model based at least in part on the vector store.

Abstract: The present disclosure relates to LLM orchestration with vector store generation. An embeddings model may be selected to generate an embedding for a digital artifact. Metadata for the digital artifact may also be generated and stored in a vector store in association with the embedding. A user query may be received and categorized. One of a plurality of machine learning models may be selected based on the categorization of the user query. A prompt may be generated based at least in part on the user query, and the selected machine learning model may generate a response to the user query based at least in part on the prompt.

Abstract: System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data for a condition of each of the plurality of Wi-Fi networks; determining one or more resolutions for the condition of a Wi-Fi network of the plurality of Wi-Fi networks; and initiating a customer outreach workflow to provide the one or more resolutions to the customer associated with the Wi-Fi network.

Abstract: System and methods include obtaining Wi-Fi network data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers and obtaining customer data for each customer associated with the plurality of Wi-Fi networks, the customer data including call-ins made by customers; aggregating and filtering the data; analyzing the aggregated and filtered data including correlating the call-ins made by customers to the Wi-Fi network data; predicting customer call-ins based on correlations made between the call-ins made by customers and the Wi-Fi network data; and initiating a customer outreach workflow prior to a predicted customer call-in.

Abstract: System and methods include obtaining data, over the Internet, associated with a plurality of Wi-Fi networks each Wi-Fi network having one or more access points and each Wi-Fi network being associated with a customer of one or more service providers; aggregating and filtering the data; analyzing the aggregated and filtered data for Wi-Fi metric based alarms, each Wi-Fi metric based alarm being associated with detection of one of an offline Wi-Fi network of the plurality of Wi-Fi networks, an offline node of the Wi-Fi network, instability of the Wi-Fi network, congestion in the Wi-Fi network, and interference in the Wi-Fi network; determining the Wi-Fi metric based alarms based on the analyzing; and performing one or more actions based on the determined Wi-Fi metric based alarms.

Abstract: Techniques are described herein for generating block extender model. An example method includes a system accessing a base model trained for identifying a base class. The system can access an extender comprising block extenders, the extender class distinct from the base class. The system can connect the extender with the base model to generate an augmented model. The system can input training data to the augmented model, the training data is provided to the base model and the extender, the training data comprising data points associated with the extender class. The system can train the extender model to identify the extender class based at least in part on the training data and the signal received from the base machine learning model. The system can generate a trained extender based at least in part on the training, the extender trained to identify an object associated with the extender class.

Abstract: The innovations disclosed herein concern improvements for a hosted gaming environment to provide improved flexibility, scalability, and game deployment/maintenance. In particular, the multi-form gaming platforms disclosed herein may host so-called “iGaming” sessions (i.e., online gaming with real money wagering), mobile Class-II gaming sessions (e.g., Bingo games) and/or mobile Class-III gaming sessions (e.g., traditional casino-style games), as well as “Social” or “Demo” gaming sessions (i.e., online gaming with non-real money wagering).

Abstract: Methods and systems which use a load controller with a smart relay module are provided herein. For example, an apparatus for controlling one or more loads in an energy management system comprises a load controller configured to connect to a system controller and a storage system of the energy management system and a smart relay module comprising single phase relays and configured to control at least one of a single phase circuit, a split phase circuit, or a three phase circuit and configured to measure at least one of current. voltage, power, or energy for each relay load.

Abstract: Methods, apparatus and systems for robust and accurate detection of anomalies in medical images and electrocardiograms are disclosed. One example system for training a neural network engine includes a processor that is configured to receive a set of training electrocardiogram signals. At least one electrocardiogram signal in the set of training electrocardiogram signals is associated with metadata identifying a region of interest that includes a heart anomaly. The processor is configured to input the set of training electrocardiogram signals into the neural network engine. The neural network engine is trained using an objective function having a first regularization parameter and a second regularization parameter. The processor is also configured to operate the neural network engine to identify the heart anomaly by classifying the set of training electrocardiogram signals and adjust the neural network engine based on the identified heart anomaly and the metadata.

Abstract: The method may comprise scanning the configuration of cloud resources and identifying where the cloud resources may have drifted from a desired state. The method may comprise identifying a creation of a cloud resource or a change to a configuration of the cloud resource; scanning, in real-time, the configuration of the cloud resource, in response to the identifying; analyzing the configuration of the cloud resource for deviations from a desired state; determining a type of the cloud resource; determining a deployment of the cloud resource; obtaining a desired state for the configuration of the cloud resource, based on the type of cloud resource and the deployment of the cloud resource; obtaining rule sets for the desired state; identifying the deviations of the configuration of the cloud resource from the desired state and rule sets; and automatically remediating the deviations based on remediation policies.

Abstract: Compositions containing MiniVectors and gene therapy uses, including long term repeated gene therapy uses, to treat liver fibrosis or liver cancer.

Abstract: Methods, non-transitory computer readable media, network traffic management apparatuses, and network traffic management systems that identify when a domain name identifier in a received request matches one of a plurality of domain names stored in a whitelist domain name storage. When the identification indicates the received domain name identifier fails to match one of the plurality of domain names stored in the whitelist domain name storage, then a determination is made on whether the received request is a suspicious request. Another storage is updated when the determination indicates the received request is the suspicious request or otherwise updating the received request as a valid request.

Abstract: A bedslide assembly for use on a deck bed of a pickup truck is provided. An illustrative example of the bedslide assembly includes a bottom frame assembly, middle frame assembly, and top frame assembly. The bottom frame assembly includes a first latch bracket and a second latch bracket. The middle frame assembly includes a first latch assembly and a second latch assembly. The first latch assembly includes a hook. The second latch assembly also includes a hook. The top frame assembly includes an end plate. The hook of the first latch assembly is selectively engageable with the first latch bracket of the bottom frame assembly. The hook of the second latch assembly is selectively engageable with the end plate of the top frame assembly.

Abstract: Methods, non-transitory computer readable media, network traffic manager apparatuses, and systems that assist with mitigating DDoS attack using a hardware device includes determining when a received network packet in an established connection between a client and a destination server includes a connection identifier cookie. A connection validation cookie is generated based on at least data in the received network packet, when the determination indicates the received network packet includes the connection identifier cookie. The connection identifier cookie is compared against the generated connection validation cookie. The received network packet is dropped when the comparison indicates the connection validation cookie fails to match the connection identifier cookie.

Abstract: A bedslide assembly for use on a deck bed of a pickup truck is provided. An illustrative example of the bedslide assembly includes a bottom frame assembly, middle frame assembly, and top frame assembly. The bottom frame assembly includes a first latch bracket and a second latch bracket. The middle frame assembly includes a first latch assembly and a second latch assembly. The first latch assembly includes a hook. The second latch assembly also includes a hook. The top frame assembly includes an end plate. The hook of the first latch assembly is selectively engageable with the first latch bracket of the bottom frame assembly. The hook of the second latch assembly is selectively engageable with the end plate of the top frame assembly.

Abstract: A bedslide assembly for use on a deck bed of a pickup truck is provided. An illustrative example of the bedslide assembly includes a bottom frame assembly, middle frame assembly, and top frame assembly. The bottom frame assembly includes a first latch bracket and a second latch bracket. The middle frame assembly includes a first latch assembly and a second latch assembly. The first latch assembly includes a hook. The second latch assembly also includes a hook. The top frame assembly includes an end plate. The hook of the first latch assembly is selectively engageable with the first latch bracket of the bottom frame assembly. The hook of the second latch assembly is selectively engageable with the end plate of the top frame assembly.

Abstract: A multi-source energy storage system (ESS) includes a master controller and a plurality of ESSs. A first ESS includes an ESS controller and a plurality of micro-ESSs. A first micro-ESS includes a plurality of nano-ESSs, a micro-ESS controller, and a pulse width modulator. A first nano-ESS includes a plurality of cells, a plurality of cell balancing converters, a bidirectional DC-DC converter and a nano-ESS controller. The multi-source ESS operates in a charging mode and a discharging mode. The multi-source ESS is connected to one of a load, an electric grid or a micro-grid, and provides power thereto when it operates in the discharging mode. The multi-source ESS receives power when it operates in the charging mode.

Abstract: A system and a method for transforming a cold fusion technology environment to an open source environment utilizes a comprehensive library that identifies and maps a source code to their corresponding open source equivalent using a collaboration of tokenizers and syntax analyzers. The system and the method significantly reduce overall total cost and time of conversion. The system and the method facilitate intrinsic bug-free code conversion. The system provides solutions that are easily utilized by any application irrespective of domain or industry.

Abstract: Systems and methods provide a storage controller with write-back caching capabilities that may be used during scenarios where the storage controller is required to provide write-through caching, and thus unable to utilize internal cache memory for write-back caching. The storage controller utilizes an allocation of persistent memory that is made available by the host IHS (Information Handling System), to which the storage controller is coupled. In scenarios where the storage controller is required to provide write-through caching, the storage controller may be configured to route received write data to the allocated host memory. In this manner, the data integrity provided by write-through operations is maintained, while also providing the host IHS with the speed of write-back operations. When ready to store the write data, the storage controller may request the flushing of write data from the allocated host memory.

Abstract: In accordance with embodiments of the present disclosure, a method may be implemented to respond to receiving data A to be stored in a storage system that includes a plurality of N spans, each including a plurality of M drives. The method may include performing data storing operations. The data storing operations may include storing the data A in N portions across the plurality of spans, generating a Za parity based on an XOR operation applied to the N portions of the data A, storing the Za parity in N?1 portions across a subset of the plurality of spans, determining a Zap parity based on an XOR operation applied to the N?1 portions of the Za parity, and storing the Zap parity.