Abstract: Systems and methods generate reasonably secure hash values at relatively few CPU cycles per byte. An example method includes, for each of a plurality of packets, injecting the packet into an internal state that represents an internal hash sum, mixing the internal state using multiplication, and shuffling the result of the multiplication so that bytes with highest quality are moved to locations that will propagate most widely in a next multiplication operation. Each of the plurality of packets include data from an input to be hashed. In some implementation, a last packet for the input is padded. The method may also include further mixing the internal state using multiplication after processing the plurality of packets and providing, to a requesting process, a portion of the final internal state as a hash of the input.

Abstract: A fast cryptographic hash of an input file using multiplication and permutation operations in a parallel processing environment. An example method includes updating an internal state for each of a plurality of packets, the packets being read from an input file. Updating the state for a packet can include injecting the packet into an internal state, mixing the bits of the internal state using multiplication, and shuffling the result of the multiplication so that bits with highest quality are permuted to locations that will propagate most widely in a next multiplication operation. The method also includes performing a reduction on the internal state and repeating the update of the internal state, the reduction, and the injecting a second time. The method may further include finalizing the internal state and storing a portion of the final internal state as a cryptographic hash of the input file.

Abstract: An improved color space (YHB model) for compressing image files is provided. An example method includes storing a sum of an unweighted first color value and an unweighted second color value for each pixel in a plurality of pixels of an image as a first channel, sub sampling, among the plurality of pixels, a difference between the first color value and the second color value as a second channel, sub sampling, among the plurality of pixels, a third color value as a third channel, and storing the first channel, the second channel, and the third channel as the compressed image. In some implementations, the original image may be split into a low frequency version and a high frequency version. The system may apply the YHB model to the high frequency version and apply a conventional model or a second variation of the YHB model to the low frequency version.

Abstract: A non-transitory computer-readable storage medium may include instructions stored thereon. When executed by at least one processor, the instructions may be configured to cause a computing system to compress data by storing positions of strings that hash to a same hash value in a ring buffer, and retrieving the ring buffer in a single memory operation to determine a longest matching string that hashes to the same hash value.

Abstract: An improved color space (YHB model) for compressing image files is provided. An example method includes storing a sum of an unweighted first color value and an unweighted second color value for each pixel in a plurality of pixels of an image as a first channel, subsampling, among the plurality of pixels, a difference between the first color value and the second color value as a second channel, subsampling, among the plurality of pixels, a third color value as a third channel, and storing the first channel, the second channel, and the third channel as the compressed image. In some implementations, the original image may be split into a low frequency version and a high frequency version. The system may apply the YHB model to the high frequency version and apply a conventional model or a second variation of the YHB model to the low frequency version.

Abstract: An improved color space (YHB model) for compressing image files is provided. An example method includes storing a sum of an unweighted first color value and an unweighted second color value for each pixel in a plurality of pixels of an image as a first channel, subsampling, among the plurality of pixels, a difference between the first color value and the second color value as a second channel, subsampling, among the plurality of pixels, a third color value as a third channel, and storing the first channel, the second channel, and the third channel as the compressed image. In some implementations, the original image may be split into a low frequency version and a high frequency version. The system may apply the YHB model to the high frequency version and apply a conventional model or a second variation of the YHB model to the low frequency version.

Abstract: Data may be decompressed by receiving a compressed sequence of characters, the compressed sequence of characters being represented by at least a first received number, dividing the first received number by a number of words in a corpus of words to determine a quotient and a remainder, retrieving a word from the corpus of words based on the remainder, retrieving a transformation from a transformation index based on the quotient, and performing the retrieved transformation on the retrieved word. The representations of characters included in the transformed word may be a decompressed version of the received compressed sequence of characters.

Abstract: Systems and methods for iterative near lossless image compression are provided. An exemplary computer-implemented method of compressing image data can include performing a plurality of compression iterations. Each compression iteration can include at least one decision regarding a loss of image data. The method can also include updating an entropy model following each compression iteration. The entropy model can describe an entropy associated with selected of a plurality of data blocks. The at least one decision regarding the loss of image data can be decided, for each compression iteration, based on the entropy model as updated following the previous compression iteration. Further, a total loss of image data from each data block can remain within an acceptable loss bound associated with the pixel described by such data block. An exemplary system can include a loss determination module, an entropy modeling module, a compression module, and an entropy coding module.

Abstract: A non-transitory computer-readable storage medium comprising instructions stored thereon. When executed by at least one processor, the instructions may be configured to cause a computing system to at least determine, for each of a plurality of current symbols in a data block, frequencies of succeeding symbols within the data block, generate a plurality of clusters, each of the plurality of clusters including a subset of the plurality of current symbols, generate, for each of the clusters, a code, the code including variable length codewords for each of the succeeding symbols of the current symbols included in the respective cluster, and encode each of the succeeding symbols in the data block based on the code for the cluster that includes the succeeding symbol's respective current symbol.

Abstract: Methods, systems, and articles of manufacture for image compression and decompression using sub-resolution images are disclosed. Compressing of images includes, determining a plurality of entropy code sets based upon statistically similar regions in the input image, generating one or more sub-resolution images including a first sub-resolution image comprising the plurality of entropy code sets, encoding the input image using the generated one or more sub-resolution images to control one or more compression parameters including an entropy code, and outputting the encoded input image and the generated one or more sub-resolution images as a compressed entropy coded image file.

Abstract: A system detects a message from a sender to a recipient, the message including a reference to external content. The system accesses a user model comprising interest information about interests of the sender or the recipient. The system identifies interest content from the external content as relevant to an interest from the interest information, generates a summarized content from the external content and based on the interest content and containing only a subset of information in the external content, and modifies the message to include the summarized content in the message.

Abstract: Methods, systems, and articles of manufacture for image compression and decompression using sub-resolution images are disclosed. Compressing of images includes, determining a plurality of entropy code sets based upon statistically similar regions in the input image, generating one or more sub-resolution images including a first sub-resolution image comprising the plurality of entropy code sets, encoding the input image using the generated one or more sub-resolution images to control one or more compression parameters including an entropy code, and outputting the encoded input image and the generated one or more sub-resolution images as a compressed entropy coded image file.

Abstract: Systems and methods for optimizing the compression of data using a dictionary coding compression system are described. A system performs an initial compression operation, such as by using a dictionary or other coder. The system then refines the compression by performing an iterative backward reference search for an optimized compressed file.

Abstract: Methods, systems, and articles of manufacture for image compression and decompression using sub-resolution images are disclosed. Compressing of images includes, determining a plurality of entropy code sets based upon statistically similar regions in the input image, generating one or more sub-resolution images including a first sub-resolution image comprising the plurality of entropy code sets, encoding the input image using the generated one or more sub-resolution images to control one or more compression parameters including an entropy code, and outputting the encoded input image and the generated one or more sub-resolution images as a compressed entropy coded image file.

Abstract: Methods, systems, and articles of manufacture for image compression and decompression using sub-resolution images are disclosed. Compressing of images includes, determining a plurality of entropy code sets based upon statistically similar regions in the input image, generating one or more sub-resolution images including a first sub-resolution image comprising the plurality of entropy code sets, encoding the input image using the generated one or more sub-resolution images to control one or more compression parameters including an entropy code, and outputting the encoded input image and the generated one or more sub-resolution images as a compressed entropy coded image file.

Abstract: A system for use to determine or evaluate a radiation treatment plan includes a processor configured for determining a spatially variable constraint, and imposing the spatially variable constraint on a healthy tissue. A system for use to determine or evaluate a radiation treatment plan includes a processor configured for determining a first position of a first healthy tissue, imposing a first constraint on the first healthy tissue based on the determined first position, determining a second position of a second healthy tissue, and imposing a second constraint on the second healthy tissue based on the determined second position, wherein the first constraint and the second constraint have different values. A radiation system includes a processor configured for determining a treatment plan using a spatially variable constraint on healthy tissue, and a radiation machine for performing a radiation procedure based on the determined treatment plan.