Abstract: Systems and methods are provided for decoding visual content using a hybrid framework based on convolutional and neural radiance networks. A decoder receives bitstreams of model parameters, a sequence level representation, and a cross-resolution representation for reconstructing a sequence of frames. The model parameters comprise neural radiance network parameters. The decoder decodes the bitstreams of the model parameters, the sequence level representation, and the cross-resolution representation. The decoder generates, via a channel transformer, a combined representation based on the sequence level representation and the cross-resolution representation. The decoder adapts a neural network model based on the neural radiance network parameters. The decoder reconstructs the sequence of frames by determining, via the adapted neural network model based on the combined representation, pixel attribute information for each frame of the reconstructed sequence of frames.

Abstract: A system for recovering information lost during genomic data compression employs a quality-driven approach using neural networks. The system evaluates the importance of genomic regions through a quality analysis engine that assigns quality scores, while a rate control engine determines optimal compression rates based on these scores. A specialized neural network recovers lost information from correlated genomic datasets that have undergone lossy compression, utilizing recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex relationships between data channels. The neural network architecture incorporates a deblocking network that combines these components to effectively reconstruct compressed data. A decoder receives and decompresses the data, then processes it through the neural network to recover information lost during compression. This adaptive system ensures critical genomic information is preserved while maximizing compression efficiency.

Abstract: For compressing data, preprocessing operations are performed on raw input data. A discrete cosine transform is performed on the preprocessed data, and multiple subbands are created, where each subband represents a particular range of frequencies. The subbands are organized into multiple groups, where the multiple groups comprise a first low frequency group, a second low frequency group, and a high frequency group. A latent space representation is generated corresponding to each of the multiple groups of subbands. A first bitstream is created based on the latent space representation, and an alternate representation of the latent space is used for creating a second bitstream, enabling multiple-pass techniques for data compression. The multiple bitstreams may be multiplexed to form a combined bitstream for storage and/or transmission purposes.

Abstract: A system and method for Light Detection and Ranging (LIDAR) image compression integrates AI-based techniques to enhance compression quality. It incorporates a novel AI deblocking network composed of convolutional layers for feature extraction and a channel-wise transformer with attention to capture complex inter-channel dependencies. The convolutional layers extract multi-dimensional features from the LIDAR, while the channel-wise transformer learns global inter-channel relationships. This hybrid approach addresses both local and global features, mitigating compression artifacts and improving image quality. The model's outputs enable effective LIDAR image reconstruction, achieving advanced compression while preserving crucial information for accurate analysis.

Abstract: A system and methods for upsampling of decompressed financial time-series data after lossy compression using a neural network that integrates AI-based techniques to enhance compression quality. It incorporates a novel deep-learning neural network that upsamples decompressed data to restore information lost during lossy compression, taking advantage of cross-correlations between time-series data sets.

Abstract: A system and methods for implementing adversarial-robust compression and reconstruction using a vector quantized variational autoencoder (VQ-VAE) with secure latent space management. The system provides comprehensive protection against adversarial attacks through multi-channel threat detection, adaptive defensive parameters, and coordinated response mechanisms. Input data is continuously monitored for potential threats, and defensive parameters are dynamically adjusted based on detected threat levels. The system implements bounded constraints and hierarchical projections to maintain latent space security while preserving compression efficiency. Multi-stage reconstruction with progressive validation ensures reliable data recovery even under adversarial conditions. The system coordinates defensive responses across all compression and reconstruction processes, implementing various recovery mechanisms when security violations are detected.

Abstract: A system and method for complex-valued radar image compression integrates AI-based techniques to enhance compression quality. It incorporates a novel AI deblocking network composed of convolutional layers for feature extraction and a channel-wise transformer with attention to capture complex inter-channel dependencies. The convolutional layers extract multi-dimensional features from the complex-valued radar image, while the channel-wise transformer learns global inter-channel relationships. This hybrid approach addresses both local and global features, mitigating compression artifacts and improving image quality. The model's outputs enable effective complex-valued radar image reconstruction, achieving advanced compression while preserving crucial information for accurate analysis.

Abstract: A system for adaptive data compression uses content-aware analysis and dynamic feedback to optimize compression quality. An adaptive quantization subsystem analyzes content characteristics of input data and determines appropriate quantization parameters. A bit allocation engine distributes available bits across different portions of the input data based on the analyzed characteristics. A quality assessment subsystem monitors the compressed output and generates parameter adjustment signals based on measured quality metrics. A feedback control subsystem then modifies the quantization parameters in response to these signals. The modified parameters are used to create optimized compressed output data from the input dataset. This dynamic, content-aware approach enables improved compression quality while maintaining efficient data reduction.

Abstract: A system and methods for upsampling of decompressed data after lossy compression using a neural network that integrates AI-based techniques to enhance compression quality. It incorporates a novel AI deblocking network composed of convolutional layers for feature extraction and a channel-wise transformer with attention to capture complex inter-channel dependencies. The convolutional layers extract multi-dimensional features from the two or more correlated datasets, while the channel-wise transformer learns global inter-channel relationships. This hybrid approach addresses both local and global features, mitigating compression artifacts and improving decompressed data quality. The model's outputs enable effective data reconstruction, achieving advanced compression while preserving crucial information for accurate analysis.

Abstract: A system and methods for upsampling of decompressed correlated multichannel data after lossy compression using a neural network that integrates AI-based techniques to enhance compression quality. It incorporates a novel AI deblocking network composed of recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex inter-channel dependencies. The recurrent layers extract multi-dimensional features from the two or more correlated datasets, while the channel-wise transformer learns global inter-channel relationships. This hybrid approach addresses both local and global features, mitigating compression artifacts and improving decompressed data quality. The model's outputs enable effective data reconstruction, achieving advanced compression while preserving crucial information for accurate analysis.

Abstract: Systems and methods are provided for encoding visual content using a hybrid framework based on convolutional and neural radiance networks. An encoder accesses video data having a sequence of frames. The encoder generates a first frame based on averaging pixel attributes of the sequence of frames. The encoder determines a sequence level representation based on the first frame. The encoder trains a neural network model based on the sequence of frames to determine a cross-resolution representation corresponding to the sequence of frames. Training the neural network model comprises generating a plurality of model parameters for reconstructing the sequence of frames based on the sequence level representation and the cross-resolution representation. The plurality of model parameters comprises neural radiance network parameters. The encoder transmits bitstreams of the plurality of model parameters, the sequence level representation, and the cross-resolution representation.

Abstract: A system and methods for upsampling of decompressed transformed time-series data after lossy compression using a neural network that integrates AI-based techniques to enhance compression quality. It incorporates a novel AI deblocking network composed of recurrent layers for feature extraction and a channel-wise transformer with attention to capture complex inter-channel dependencies. The recurrent layers extract multi-dimensional features from the two or more correlated datasets, while the channel-wise transformer learns global inter-channel relationships. This hybrid approach addresses both local and global features, mitigating compression artifacts and improving decompressed data quality. The model's outputs enable effective data reconstruction, achieving advanced compression while preserving crucial information for accurate analysis.

Abstract: Systems and methods are described for generating pixel image data, using a lensless camera, based on light that travels through a mask that with pattern masking the lensless camera. The system applies a transformation function to the pixel image data to generate frequency domain image data. The system inputs the frequency domain image data into a machine learning model, wherein the machine learning model does not have access to data that represents the pattern of the mask. The model is trained using a set of images with the feature that are captured by the flat, lensless camera through the mask. The system processes the frequency domain image data using the machine learning model to determine whether the pixel image data depicts the image feature. The system further performs an action based on determining that the pixel image data depicts the image feature.

Abstract: A system and methods for multi-type data compression or decompression with a virtual management layer in a distributed computing environment, comprising. It incorporates a virtual management layer to organize incoming data types and allocate compression or decompression tasks across multiple computing devices, selecting techniques best suited for particular data types. Associated data sets may be flagged prior to processing, ensuring preservation of relationships even when compressed or decompressed on different devices. This distributed approach allows efficient parallel processing of multiple data types, improving scalability and performance. A load balancing module optimizes task distribution based on available resources and processing requirements. The system enables each data type to be processed using the most efficient technique while maintaining associations between related data sets, effectively handling larger volumes of diverse data.

Abstract: The system trains a machine learning model using a loss function, with a part that penalizes overall signal loss, and a second part of the loss function that penalizes texture loss. The system computes a first neural feature of a first media frame stored by a media server using the trained machine learning model. The system causes a client device to receive a second media frame as a part of a media stream from the media server where the second frame is a modified version of the first media frame. The system causes the client to compute a second neural feature of the second media frame using the trained machine learning model, and compute a QoE metric based on the first neural feature and the second neural feature. The system receives the QoE metric, and uses it to modify at least one parameter of the media stream.

Abstract: A system and methods for upsampling compressed data using a jointly trained Vector Quantized Variational Autoencoder (VQ-VAE) and neural upsampler. The system compresses input data into a discrete latent space using a VQ-VAE encoder, reconstructs the data using a VQ-VAE decoder, and enhances the reconstructed data using a neural upsampler. The VQ-VAE and neural upsampler are jointly trained using a combined loss function, enabling end-to-end optimization. The system allows for efficient compression and high-quality reconstruction of various data types, including financial time-series, images, audio, video, sensor data, and text. The learned discrete latent space can be explored and manipulated using techniques such as interpolation, extrapolation, and vector arithmetic to generate new or modified data samples. The system finds applications in data storage, transmission, analysis, and generation across multiple domains.

Abstract: Systems and methods are provided for encoding a frame of 3D media content. The systems and methods may be configured to access a first frame of 3D media content and generate a data structure for the first frame based on color attributes information of the first frame, wherein each element of the data structure encodes a single color. The systems and methods may be configured to train a machine learning model based on the first frame of 3D media content, wherein the machine learning model is trained to receive as input a coordinate of a voxel of the first frame, and to output an identifier of a particular element in the generated data structure. The systems and methods may be configured to generate encoded data for the first frame based at least in part on weights of the trained machine learning model and the generated data structure.

Abstract: A system and methods for multi-type data compression or decompression with a virtual management layer, comprising. It incorporates a virtual management layer to organize incoming data types and select a compression or decompression system that utilizes a technique best suited for a particular data type. Associated data sets may be flagged prior to compression or decompression so that associated types may be preserved together after the compression or decompression process is complete. This approach allows each data type to be compressed or decompressed using a technique that is the most efficient for a particular data type. Additionally, the approach allows all information associated with a particular data set to be compressed or decompressed in some way.

Abstract: A ventilation method for a high gas working face based on alternating intake and air return in a mine gallery is provided. In this method, an isolated island working face is divided into several sections along a strike direction. A coal pillar is alternately set as a conventional section and a gas-drainage section. Opposite coal pillars in the two mine galleries within the same section are respectively used as the conventional section and the gas-drainage section. Two mine galleries on both sides of the isolated island working face are alternately used as an intake gallery and a return gallery. The coal pillar at the side of the mine gallery as the return gallery is the gas-drainage section. A gas-drainage hole communicating a goaf is provided in the gas-drainage section, so that gas in the goafs at two sides of the isolated island working face can be extracted alternately.

Abstract: A system and methods for upsampling of decompressed genomic data after lossy compression using a neural network integrates AI-based techniques to enhance compression quality. It incorporates a novel deep-learning neural network that upsamples decompressed data to restore information lost during lossy compression, taking advantage of cross-correlations between genomic data sets.