Traffic signs are essential map features globally in the era of autonomous driving and smart cities. To develop accurate and robust algorithms for traffic sign detection and classification, a large-scale and diverse benchmark dataset is required. In this paper, we introduce a traffic sign benchmark dataset of 100K street-level images around the world that encapsulates diverse scenes, wide coverage of geographical locations, and varying weather and lighting conditions and covers more than 300 manually annotated traffic sign classes. The dataset includes 52K images that are fully annotated and 48K images that are partially annotated. This is the largest and the most diverse traffic sign dataset consisting of images from all over world with fine-grained annotations of traffic sign classes. We have run extensive experiments to establish strong baselines for both the detection and the classification tasks. In addition, we have verified that the diversity of this dataset enables effective transfer learning for existing large-scale benchmark datasets on traffic sign detection and classification. The dataset is freely available for academic research: https://www.mapillary.com/dataset/trafficsign.

Our starting point are register automata for data words, in the style of Kaminski and Francez. We study the effects of the single-use restriction, which says that a register is emptied immediately after being used. We show that under the single-use restriction, the theory of automata for data words becomes much more robust. The main results are: (a) five different machine models are equivalent as language acceptors, including one-way and two-way single-use register automata; (b) one can recover some of the algebraic theory of languages over finite alphabets, including a version of the Krohn-Rhodes Theorem; (c) there is also a robust theory of transducers, with four equivalent models, including two-way single use transducers and a variant of streaming string transducers for data words. These results are in contrast with automata for data words without the single-use restriction, where essentially all models are pairwise non-equivalent.

We argue that "Question Answering with Knowledge Base" and "Question Answering over Linked Data" are currently two instances of the same problem, despite one explicitly declares to deal with Linked Data. We point out the lack of existing methods to evaluate question answering on datasets which exploit external links to the rest of the cloud or share common schema. To this end, we propose the creation of new evaluation settings to leverage the advantages of the Semantic Web to achieve AI-complete question answering.

Passive optical networks (PON) can play a vital role in data centres and access fog solutions by providing scalable, cost and energy efficient architectures. This paper proposes a Mixed Integer Linear Programming (MILP) model to optimize the placement of virtual machines (VMs) over an energy efficient WDM-TDM AWGR PON based data centre architecture. In this optimization, the use of VMs and their requirements affect the optimum number of servers utilized in the data centre when minimizing the power consumption and enabling more efficient utilization of servers is considered. Two power consumption minimization objectives were examined for up to 20 VMs with different computing and networking requirements. The results indicate that considering the minimization of the processing and networking power consumption in the allocation of VMs in the WDM-TDM AWGR PON can reduce the networking power consumption by up to 70% compared to the minimization of the processing power consumption.

Image-based tracking of medical instruments is an integral part of many surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the methods proposed still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on robustness and generalization capabilities of the methods. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all frames in the videos as well as instance-wise segmentation masks for surgical instruments in more than 10,000 individual frames. The data has successfully been used to organize international competitions in the scope of the Endoscopic Vision Challenges (EndoVis) 2017 and 2019.

XSS is a security vulnerability that permits injecting malicious code into the client side of a web application. In the simplest situations, XSS vulnerabilities arise when a web application includes the user input in the web output without due sanitization. Such simple XSS vulnerabilities can be detected fairly reliably with blackbox scanners, which inject malicious payload into sensitive parts of HTTP requests and look for the reflected values in the web output.

Contemporary blackbox scanners are not effective against stored XSS vulnerabilities, where the malicious payload in an HTTP response originates from the database storage of the web application, rather than from the associated HTTP request. Similarly, many blackbox scanners do not systematically handle context-sensitive XSS vulnerabilities, where the user input is included in the web output after a transformation that prevents the scanner from recognizing the original value, but does not sanitize the value sufficiently. Among the combination of two basic data sources (stored vs reflected) and two basic vulnerability patterns (context sensitive vs not so), only one is therefore tested systematically by state-of-the-art blackbox scanners.

Our work focuses on systematic coverage of the three remaining combinations. We present a graybox mechanism that extends a general purpose database to cooperate with our XSS scanner, reporting and injecting the test inputs at the boundary between the database and the web application. Furthermore, we design a mechanism for identifying the injected inputs in the web output even after encoding by the web application, and check whether the encoding sanitizes the injected inputs correctly in the respective browser context. We evaluate our approach on eight mature and technologically diverse web applications, discovering previously unknown and exploitable XSS flaws in each of those applications.

Data analytics in the cloud has become an integral part of enterprise businesses. Big data analytics systems, however, still lack the ability to take user performance goals and budgetary constraints for a task, collectively referred to as task objectives, and automatically configure an analytic job to achieve these objectives. This paper presents a data analytics optimizer that can automatically determine a cluster configuration with a suitable number of cores as well as other system parameters that best meet the task objectives. At a core of our work is a principled multi-objective optimization (MOO) approach that computes a Pareto optimal set of job configurations to reveal tradeoffs between different user objectives, recommends a new job configuration that best explores such tradeoffs, and employs novel optimizations to enable such recommendations within a few seconds. We present efficient incremental algorithms based on the notion of a Progressive Frontier for realizing our MOO approach and implement them into a Spark-based prototype. Detailed experiments using benchmark workloads show that our MOO techniques provide a 2-50x speedup over existing MOO methods, while offering good coverage of the Pareto frontier. When compared to Ottertune, a state-of-the-art performance tuning system, our approach recommends configurations that yield 26\%-49\% reduction of running time of the TPCx-BB benchmark while adapting to different application preferences on multiple objectives.

This work addresses the problem of traffic management at and near an isolated un-signalized intersection for autonomous and networked vehicles through coordinated optimization of their trajectories. We decompose the trajectory of each vehicle into two phases: the provisional phase and the coordinated phase. A vehicle, upon entering the region of interest, initially operates in the provisional phase, in which the vehicle is allowed to optimize its trajectory but is constrained to guarantee in-lane safety and to not enter the intersection. Periodically, all the vehicles in their provisional phase switch to their coordinated phase, which is obtained by coordinated optimization of the schedule of the vehicles' intersection usage as well as their trajectories. For the coordinated phase, we propose a data-driven solution, in which the intersection usage order is obtained through a data-driven online "classification" and the trajectories are computed sequentially. This approach is computationally very efficient and does not compromise much on optimality. Moreover, it also allows for incorporation of "macro" information such as traffic arrival rates into the solution. We also discuss a distributed implementation of this proposed data-driven sequential algorithm. Finally, we compare the proposed algorithm and its two variants against traditional methods of intersection management and against some existing results in the literature by micro-simulations.

We propose Cotatron, a transcription-guided speech encoder for speaker-independent linguistic representation. Cotatron is based on the multispeaker TTS architecture and can be trained with conventional TTS datasets. We train a voice conversion system to reconstruct speech with Cotatron features, which is similar to the previous methods based on Phonetic Posteriorgram (PPG). By training and evaluating our system with 108 speakers from the VCTK dataset, we outperform the previous method in terms of both naturalness and speaker similarity. Our system can also convert speech from speakers that are unseen during training, and utilize ASR to automate the transcription with minimal reduction of the performance. Audio samples are available at https://mindslab-ai.github.io/cotatron, and the code with a pre-trained model will be made available soon.

Multi-view data capture permits free-viewpoint video (FVV) content creation. To this end, several users must capture video streams, calibrated in both time and pose, framing the same object/scene, from different viewpoints. New-generation network architectures (e.g. 5G) promise lower latency and larger bandwidth connections supported by powerful edge computing, properties that seem ideal for reliable FVV capture. We have explored this possibility, aiming to remove the need for bespoke synchronisation hardware when capturing a scene from multiple viewpoints, making it possible through off-the-shelf mobiles. We propose a novel and scalable data capture architecture that exploits edge resources to synchronise and harvest frame captures. We have designed an edge computing unit that supervises the relaying of timing triggers to and from multiple mobiles, in addition to synchronising frame harvesting. We empirically show the benefits of our edge computing unit by analysing latencies and show the quality of 3D reconstruction outputs against an alternative and popular centralised solution based on Unity3D.

Learning-based techniques are increasingly effective at controlling complex systems using data-driven models. However, most work done so far has focused on learning individual tasks or control laws. Hence, it is still a largely unaddressed research question how multiple tasks can be learned efficiently and simultaneously on the same system. In particular, no efficient state space exploration schemes have been designed for multi-task control settings. Using this research gap as our main motivation, we present an algorithm that approximates the smallest data set that needs to be collected in order to achieve high control performance for multiple learning-based control laws. We describe system uncertainty using a probabilistic Gaussian process model, which allows us to quantify the impact of potentially collected data on each learning-based controller. We then determine the optimal measurement locations by solving a stochastic optimization problem approximately. We show that, under reasonable assumptions, the approximate solution converges towards that of the exact problem. Additionally, we provide a numerical illustration of the proposed algorithm.

Visualization-oriented natural language interfaces (V-NLIs) have been explored and developed in recent years. One challenge faced by V-NLIs is in the formation of effective design decisions that usually requires a deep understanding of user queries. Learning-based approaches have shown potential in V-NLIs and reached state-of-the-art performance in various NLP tasks. However, because of the lack of sufficient training samples that cater to visual data analytics, cutting-edge techniques have rarely been employed to facilitate the development of V-NLIs. We present a new dataset, called Quda, to help V-NLIs understand free-form natural language. Our dataset contains 14;035 diverse user queries annotated with 10 low-level analytic tasks that assist in the deployment of state-of-the-art techniques for parsing complex human language. We achieve this goal by first gathering seed queries with data analysts who are target users of V-NLIs. Then we employ extensive crowd force for paraphrase generation and validation. We demonstrate the usefulness of Quda in building V-NLIs by creating a prototype that makes effective design decisions for free-form user queries. We also show that Quda can be beneficial for a wide range of applications in the visualization community by analyzing the design tasks described in academic publications.

Smart meters enable remote and automatic electricity, water and gas consumption reading and are being widely deployed in developed countries. Nonetheless, there is still a huge number of non-smart meters in operation. Image-based Automatic Meter Reading (AMR) focuses on dealing with this type of meter readings. We estimate that the Energy Company of Paran'a (Copel), in Brazil, performs more than 850,000 readings of dial meters per month. Those meters are the focus of this work. Our main contributions are: (i) a public real-world dial meter dataset (shared upon request) called UFPR-ADMR; (ii) a deep learning-based recognition baseline on the proposed dataset; and (iii) a detailed error analysis of the main issues present in AMR for dial meters. To the best of our knowledge, this is the first work to introduce deep learning approaches to multi-dial meter reading, and perform experiments on unconstrained images. We achieved a 100.0% F1-score on the dial detection stage with both Faster R-CNN and YOLO, while the recognition rates reached 93.6% for dials and 75.25% for meters using Faster R-CNN (ResNext-101).

Welcome back! In the first part of this series, we looked at a very "low-level" way to interact with a relational database by sending it raw SQL strings and retrieving the results. We created a very simple Express application that we can use as an example and deployed it on Heroku with a Postgres database.

In this part, we're going to examine a few libraries which build on top of that foundation, adding layers of abstraction that let you read and manipulate database data in a more "JavaScript-like" way.

Processing metadata includes storing, in a data storage system, a specification for each of multiple sources, each specification including information identifying one or more data elements of the corresponding source; and processing, in a data processing system coupled to the data storage system, data elements from the sources, including generating a set of rules for each source based on a corresponding one of the stored specifications, and matching data elements of different sources and determining a quality metric characterizing a given match between a first data element of a first source and a second data element of a second source according to the set of rules generated for the first source and the set of rules generated for the second source.

A computer-implemented method is provided for statistical data learning under privacy constraints. The method includes: receiving, by a processor, a plurality of pieces of statistical information relating to a statistical object and aggregating, by the processor, the plurality of pieces of statistical information so as to provide an estimation of the statistical object. Each piece of statistical information includes an uncertainty variable, the uncertainty variable being a value determined from a function having a predetermined mean. The number of pieces of statistical information aggregated is proportional to the reliability of the estimation of the statistical object.

The present disclosure involves systems, software, and computer-implemented methods for providing process intelligence by correlating events from multiple business process systems to a single business scenario using configurable correlation strategies. An example method includes identifying a raw event associated with a sending business process and a receiving business process, identifying a sending business process attribute associated with the sending business process and a receiving business process attribute associated with the receiving business process, determining a correlation strategy for associating the raw event with a business scenario instance, the determination based at least in part on the sending business process attribute and the receiving business process attribute, and generating a visibility scenario event from the raw event according to the correlation strategy, the visibility scenario event associated with the business scenario instance.

Embodiments of the invention provide systems and methods for analyzing sequential data. The sequential data can comprise a sequence of data points arranged in a particular order and thus representing a sequence. A number of such sequences can be analyzed, for example, to identify patterns or commonalities within the sequences or portions of sequences represented by the data. According to one embodiment, a method of identifying patterns in sequences of data points can comprise reading a set of sequential data. The sequential data can comprises a plurality of sequences and each of the plurality of sequences can represent an ordered sequence of tokens. A dotplot representing matches between each sequence of the plurality sequences can be generated. One or more patterns within the sequential data can then be identified based on the dotplot.

A computer-implemented method for classifying documents for data loss prevention may include 1) identifying training documents for a machine learning classifier configured for data loss prevention, 2) performing a semantic analysis on training documents to identify topics within the set training documents, 3) applying a similarity metric to the topics to identify at least one unrelated topic with a similarity to the other topics within the plurality of topics, as determined by the similarity metric, that falls below a similarity threshold, 4) identifying, based on the semantic analysis, at least one irrelevant training document within the set of training documents in which a predominance of the unrelated topic is above a predominance threshold, and 5) excluding the irrelevant training document from the set of training documents based on the predominance of the unrelated topic within the irrelevant training document. Various other methods, systems, and computer-readable media are also disclosed.

Methods and arrangements for conducting a search using query logs. A query log is consulted and query rewrite rules are learned automatically based on data in the query log. The learning includes obtaining click-through data present in the query log.

In one embodiment, a plurality of objects associated with a dataset and a specified number of nearest objects to be identified are received. The received objects are sorted in a structured format. Further, a key object and a number of adjacent objects corresponding to the key object are selected from the sorted plurality of objects, wherein the number of adjacent objects is selected based on the specified number of nearest objects to be identified. Furthermore, distances between the key object and the number of adjacent objects are determined to identify the specified number of nearest objects, wherein the distances are determined until the specified number of nearest objects is identified. Based on the determined distances, the specified number of nearest objects in the dataset is identified for data analysis.

Embodiments of the disclosure include a method for partitioning a deterministic finite automaton (DFA) into a plurality of groups. The method includes selecting, with a processing device, a subset of the plurality of states and mapping each state of the subset onto a group of the plurality of groups by assigning one or more transition rules associated with each state to a rule line of the group, wherein each rule line is assigned at most two transition rules and an extended address associated with one of the at most two transition rules. The method also includes iteratively processing each state of the subset mapped onto the group by removing the extended address from each rule line in the group with transition rules referring to a current state if the transition rules in the rule line branch within the group.

A data warehouse is constructed using the relational mapping of a transactional database without reconstructing the data relationships of the transactional database. First, an application programmer analyzes an object model in order to describe facts and dimensions using the objects, attributes, and paths of the object model. Each of the dimensions has an identifier that correlates an item in the transactional database to a dimension record in the data warehouse. The fact and dimension descriptions are saved to a description file. Second, a Data Warehouse Engine (DWE) then access the description file and uses the object model, fact and dimension descriptions, and object-relational mapping to map transactional data to the data warehouse.

Embodiments are described for a system and method of providing a data miner that decouples the analytic flow solution components from the data source. An analytic-flow solution then couples with the target data source through a simple set of data source connector, table and transformation objects, to perform the requisite analytic flow function. As a result, the analytic-flow solution needs to be designed only once and can be re-used across multiple target data sources. The analytic flow can be modified and updated at one place and then deployed for use on various different target data sources.

A system and method is described for use with a data grid cluster, which uses cluster quorum to prevent split brain scenario. The data grid cluster includes a plurality of cluster nodes, each of which runs a cluster service. Each cluster service collects and maintains statistics regarding communication flow between its cluster node and the other cluster nodes in the data grid cluster. The statistics are used to determine a status associated with other cluster nodes in the data grid cluster whenever a disconnect event happens. The data grid cluster is associated with a quorum policy, which is defined in a cache configuration file, and which specifies a time period that a cluster node will wait before making a decision on whether or not to evict one or more cluster nodes from the data grid cluster.

Methods and apparatus for output of high-bandwidth debug data/traces in electronic devices using embedded high-speed debug port(s). Debug data is received from multiple blocks and buffered in a buffer. The buffer's output is operatively coupled to one or more high-speed serial I/O interfaces via muxing logic during debug test operations. The buffered data is encoded as serialized data and sent over the one or more high-speed serial I/O interfaces to a logic device that receives serialized data and de-serializes it to generate parallel debug data that is provided to a debugger. The buffer may be configured as a bandwidth-adapting buffer that facilitates transfer of debug data that is received at a variable combined data rate outbound via the one or more high-speed serial I/O interfaces at a data rate corresponding to the bandwidth of the serial I/O interfaces.

Embodiments relate to a computer implemented information processing system, method and program product for data access. The information processing system includes a data store having a top tier store and at least another tier store with the top tier store including a counter for each entry of a symbol and another tier store including a representative frequency value defined for the another tier store. A sorter is also provided configured to sort the symbol in the top tier store and the another tier stores according to a value generated in the counter for the assessed symbol. The said sorter is also configured to restore entry of the symbol in the top tier store, in response to a symbol having moved from said top tier store to another tier store, by using the representative frequency value defined for said another store to which said symbol was moved.

A double data rate memory physical interface having self checking loopback logic on-chip is disclosed. Disposed on the chip is a first linear feedback shift register, which is capable of generating a set of test data values that comprise at least two data bits. Also disposed on the chip is a second linear feedback shift register. The second linear feedback shift register is capable of generating a set of expected data values that match the test data values. Further, an internal loopback error check element is disposed on the chip. The internal loopback error check element is used to compare the set of expected data values with the set of test data values.

Method and apparatus for managing data in a memory. In accordance with some embodiments, a first data object and an associated first ECC data set are generated and stored in a non-volatile (NV) main memory responsive to a first set of data blocks having a selected logical address. A second data object and an associated second ECC data set are generated responsive to receipt of a second set of data blocks having the selected logical address. The second data object and the second ECC data set are subsequently stored in the in the NV main memory responsive to a mismatch between the first ECC data set and the second ECC data set.

Within a radio communication network infrastructure transmitting data organized into a sequence of symbols to a receiving device over a plurality of radio links, data to be transmitted is encoded according to an error correction coding scheme in order to produce a set of systematic symbols and a set of corresponding redundancy symbols; the systematic symbols and a first subset of the corresponding redundancy symbols are transmitted, over a first radio link among said plurality of radio links, in broadcast mode, and a second subset of the corresponding redundancy symbols, distinct from the first one, is transmitted over a second radio link among said plurality of radio links.

A method of transmitting data according to a data transmission protocol wherein the data is transmitted as a plurality of data frames and each data frame includes an error checking field comprising at least two sub-fields, the data of the first sub-field being formed by a first error checking method performed on data of the frame and the data of the second sub-field being formed by a second error checking method performed on the said data of the frame, the first and second methods being such that the data of the first sub-field has different error checking properties from those of the data of the second sub-field.

A method includes determining a read threshold voltage corresponding to a group of storage elements in a non-volatile memory that includes a three-dimensional (3D) memory of a data storage device. The method also includes determining an error metric corresponding to data read from the group of storage elements using the read threshold voltage. The method includes comparing the read threshold voltage and the error metric to one or more criteria corresponding to a corrupting event.

The present inventions are related to systems and methods for data processing, and more particularly to systems and methods for variable rate coding in a data processing system.

Generating error data associated with decoding data is disclosed, including: processing an input sequence of samples associated with data stored on media using a detector and a decoder during a global iteration; and generating one or more error values based at least in part on one or more decision bits output by the detector or the decoder and the input sequence of samples.

A method begins by a processing module obtaining common storage name information regarding data that is stored in storage units of a distributed storage network (DSN) as a set of data slices. Each data slice of the set of data slices has a unique storage name, where each of the unique storage names for the set of data slices has common naming information regarding the data. The method continues where the processing module interprets the common storage name information to determine whether a difference exists between the common naming information of a data slice of the set of data slices and the common naming information of other data slices of the set of data slices. When the difference exists, the method continues where the processing module indicates a potential storage error of the data slice and implements a storage error process regarding the potential storage error of the data slice.

It is provided a computer comprising a nonvolatile memory for storing data, a control processor for controlling the saving of data into the nonvolatile memory, and a battery for supplying power to the computer in case of a failure of an external power supply, wherein the control processor checks a charge amount stored in the battery, calculates an amount of data which can be saved in the nonvolatile memory by the battery in case of a failure of the external power supply based on the checked charge amount, and saves data excluding the amount of data that can be saved, out of data which should be saved into the nonvolatile memory, into the nonvolatile memory in advance.

The present disclosure includes apparatus (e.g., computing systems, memory systems, controllers, etc.) and methods for providing data integrity. One or more methods can include, for example: receiving a number of sectors of data to be written to a number of memory devices; appending first metadata corresponding to the number of sectors and including first integrity data to the number of sectors, the first metadata has a particular format; generating second integrity data to be provided in second metadata, the second integrity data corresponding to at least one of the number of sectors (wherein the second metadata has a second format); and generating third integrity data to be provided in the second metadata, the third integrity data including error data corresponding to the second integrity data and the at least one of the number of sectors.

A data collection unit obtains physiological data from a subject interface on a subject. The subject interface can be connected to the data collection unit. When the subject interface is connected to the data collection unit, subject interface contacts on the subject interface make contact with data collection unit contacts on the data collection unit. Some of the data collection unit contacts are for communicating physiological data from the subject interface to the data collection unit. Some of the contacts are for powering the data collection unit upon the subject interface being connected to the data collection unit and for powering down the data collection unit upon the subject interface being disconnected from the data collection unit.

Memory system operations are extended for a data processor by DMA, cache, or memory controller to include a DMA descriptor, including a set of operations and parameters for the operations, which provides for data compression and decompression during or in conjunction with processes for moving data between memory elements of the memory system. The set of operations can be configured to use the parameters and perform the operations of the DMA, cache, or memory controller. The DMA, cache, or memory controller can support moves between memory having a first access latency, such as memory integrated on the same chip as a processor core, and memory having a second access latency that is longer than the first access latency, such as memory on a different integrated circuit than the processor core.

Method, apparatus, and program means for performing a conversion. In one embodiment, a disclosed apparatus includes a destination storage location corresponding to a first architectural register. A functional unit operates responsive to a control signal, to convert a first packed first format value selected from a set of packed first format values into a plurality of second format values. Each of the first format values has a plurality of sub elements having a first number of bits The second format values have a greater number of bits. The functional unit stores the plurality of second format values into an architectural register.

Method, apparatus, and program means for performing a conversion. In one embodiment, a disclosed apparatus includes a destination storage location corresponding to a first architectural register. A functional unit operates responsive to a control signal, to convert a first packed first format value selected from a set of packed first format values into a plurality of second format values. Each of the first format values has a plurality of sub elements having a first number of bits The second format values have a greater number of bits. The functional unit stores the plurality of second format values into an architectural register.

Various embodiments of the present invention provide systems and methods for data processing. As an example, a data processing circuit is disclosed that includes a data detector circuit. The data detector circuit includes an anti-causal noise predictive filter circuit and a data detection circuit. In some cases, the anti-causal noise predictive filter circuit is operable to apply noise predictive filtering to a detector input to yield a filtered output, and the data detection circuit is operable to apply a data detection algorithm to the filtered output derived from the anti-causal noise predictive filter circuit.

Method, apparatus, and program means for performing a conversion. In one embodiment, a disclosed apparatus includes a destination storage location corresponding to a first architectural register. A functional unit operates responsive to a control signal, to convert a first packed first format value selected from a set of packed first format values into a plurality of second format values. Each of the first format values has a plurality of sub elements having a first number of bits The second format values have a greater number of bits. The functional unit stores the plurality of second format values into an architectural register.

Method, apparatus, and program means for performing a conversion. In one embodiment, a disclosed apparatus includes a destination storage location corresponding to a first architectural register. A functional unit operates responsive to a control signal, to convert a first packed first format value selected from a set of packed first format values into a plurality of second format values. Each of the first format values has a plurality of sub elements having a first number of bits The second format values have a greater number of bits. The functional unit stores the plurality of second format values into an architectural register.

A communication device includes a storage unit to store quotients and remainders associated with multiplication values obtained by multiplying a specified integer number, which is expressed in a form of (2β+α) where β is a positive integer number and α is a positive integer number other than integral multiples of 2, respectively, the quotients and the remainders being obtained by dividing the multiplication values by 2β, respectively, a first unit to divide a dividend by 2βand calculate a quotient and a remainder, a second unit to obtain a quotient, which corresponds to the remainder from the storage unit, and a third unit to determine that the data length of the packet data is normal, when a combination of the quotient and the remainder calculated by the first unit is in the storage unit.

A system (200) and a method (100) of operating a computing device to perform memoization are disclosed. The method includes determining whether a result of a function is stored in a cache and, if so, retrieving the result from the cache and, if not, calculating the result and storing it in the cache. The method (100) includes transforming (104) by the computing device at least one selected from the input parameters and the output parameters of the function, the transforming being based on an analysis of the function and its input arguments to establish whether or not there is a possible relationship reflecting redundancy among the input parameters and output parameters of the function. The transforming may include at least one of: use of symmetry, scaling, linear shift, interchanging of variables, inversion, polynomial and/or trigonometric transformations, spectral or logical transformations, fuzzy transformations, and systematic arrangement of parameters.

A method includes configuring a processing circuit to perform: receiving a control word for an I/O operation, forwarding a transport command control block (TCCB) from the channel subsystem to a control unit, gathering data associated with the I/O operation, and transmitting the gathered data to the control unit in the I/O processing system. Gathering the data includes accessing entries of a list of storage addresses that collectively specifying the data. Based on an entry of the list comprising a not-set first flag and a corresponding first storage address, gathering data from a corresponding storage location, and based on an entry of the list comprising a set first flag and a corresponding second storage address, obtaining a next entry of the list from a second storage location.

A dispersed storage network memory includes a pool of storage nodes, where the pool of storage nodes stores a multitude of encoded data files. A storage node obtains and analyzes data access response performance data for each of the storage nodes to produce a modified data access response plan that includes identity of an undesired performing storage node and an alternative data access response for the undesired performing storage node. The storage nodes receive corresponding portions of a data access request for at least a portion of one of the multitude of encoded data files. The undesired performing storage node or another storage node processes one of the corresponding portions of the data access request in accordance with the alternative data access response.

A method of interacting with data at a wireless communication device is provided. The wireless communication device has access to a first set of capabilities. Data is received at the wireless communication device via a wireless transmission. The data represents visual content that is viewable via a display device. A graphical user interface, including a delayed action selector, is provided via the display device. An input is received within a limited period of time after displaying the delayed action selector. The input is associated with a command to delay execution of an action with respect to the data until the wireless communication device has access to a second set of capabilities. The action is not supported by the first set of capabilities but is supported by the second set of capabilities. An indication of receipt of the input is provided at the wireless communication device.

Provided are a storage device, controller, and method for using host transfer rates to select a recording medium transfer rate for transferring data to a recording medium. A host transfer rate of data with respect to a buffer is measured. Provided are a plurality of recording medium transfer rates at which data is transferred between the buffer and the recording medium. A determination is made of an amount of decrease in the host transfer rate. The recording medium transfer rate is selected based on the amount of decrease in the host transfer rate. A transfer rate at which the storage device transfers data is set to the selected recording medium transfer rate.