Low-resolution text images are often seen in natural scenes such as documents captured by mobile phones. Recognizing low-resolution text images is challenging because they lose detailed content information, leading to poor recognition accuracy. An intuitive solution is to introduce super-resolution (SR) techniques as pre-processing. However, previous single image super-resolution (SISR) methods are trained on synthetic low-resolution images (e.g.Bicubic down-sampling), which is simple and not suitable for real low-resolution text recognition. To this end, we pro-pose a real scene text SR dataset, termed TextZoom. It contains paired real low-resolution and high-resolution images which are captured by cameras with different focal length in the wild. It is more authentic and challenging than synthetic data, as shown in Fig. 1. We argue improv-ing the recognition accuracy is the ultimate goal for Scene Text SR. In this purpose, a new Text Super-Resolution Network termed TSRN, with three novel modules is developed. (1) A sequential residual block is proposed to extract the sequential information of the text images. (2) A boundary-aware loss is designed to sharpen the character boundaries. (3) A central alignment module is proposed to relieve the misalignment problem in TextZoom. Extensive experiments on TextZoom demonstrate that our TSRN largely improves the recognition accuracy by over 13%of CRNN, and by nearly 9.0% of ASTER and MORAN compared to synthetic SR data. Furthermore, our TSRN clearly outperforms 7 state-of-the-art SR methods in boosting the recognition accuracy of LR images in TextZoom. For example, it outperforms LapSRN by over 5% and 8%on the recognition accuracy of ASTER and CRNN. Our results suggest that low-resolution text recognition in the wild is far from being solved, thus more research effort is needed.

Convolutional Neural Networks (CNNs) are generally prone to noise interruptions, i.e., small image noise can cause drastic changes in the output. To suppress the noise effect to the final predication, we enhance CNNs by replacing max-pooling, strided-convolution, and average-pooling with Discrete Wavelet Transform (DWT). We present general DWT and Inverse DWT (IDWT) layers applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and design wavelet integrated CNNs (WaveCNets) using these layers for image classification. In WaveCNets, feature maps are decomposed into the low-frequency and high-frequency components during the down-sampling. The low-frequency component stores main information including the basic object structures, which is transmitted into the subsequent layers to extract robust high-level features. The high-frequency components, containing most of the data noise, are dropped during inference to improve the noise-robustness of the WaveCNets. Our experimental results on ImageNet and ImageNet-C (the noisy version of ImageNet) show that WaveCNets, the wavelet integrated versions of VGG, ResNets, and DenseNet, achieve higher accuracy and better noise-robustness than their vanilla versions.

Similarity measure, also called information measure, is a concept used to distinguish different objects. It has been studied from different contexts by employing mathematical, psychological, and fuzzy approaches. Image steganography is the art of hiding secret data into an image in such a way that it cannot be detected by an intruder. In image steganography, hiding secret data in the plain or non-edge regions of the image is significant due to the high similarity and redundancy of the pixels in their neighborhood. However, the similarity measure of the neighboring pixels, i.e., their proximity in color space, is perceptual rather than mathematical. This paper proposes an interval type 2 fuzzy logic system (IT2 FLS) to determine the similarity between the neighboring pixels by involving an instinctive human perception through a rule-based approach. The pixels of the image having high similarity values, calculated using the proposed IT2 FLS similarity measure, are selected for embedding via the least significant bit (LSB) method. We term the proposed procedure of steganography as IT2 FLS LSB method. Moreover, we have developed two more methods, namely, type 1 fuzzy logic system based least significant bits (T1FLS LSB) and Euclidean distance based similarity measures for least significant bit (SM LSB) steganographic methods. Experimental simulations were conducted for a collection of images and quality index metrics, such as PSNR, UQI, and SSIM are used. All the three steganographic methods are applied on datasets and the quality metrics are calculated. The obtained stego images and results are shown and thoroughly compared to determine the efficacy of the IT2 FLS LSB method. Finally, we have done a comparative analysis of the proposed approach with the existing well-known steganographic methods to show the effectiveness of our proposed steganographic method.

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.

A method for encoding information in DNA sequences is described. The method is based on the precision-resolution framework, and is aimed to work in conjunction with a recently suggested terminator-free template independent DNA synthesis method. The suggested method optimizes the amount of information bits per synthesis time unit, namely, the writing rate. Additionally, the encoding scheme studied here takes into account the existence of multiple copies of the DNA sequence, which are independently distorted. Finally, quantizers for various run-length distributions are designed.

In the future we can expect that artificial intelligent agents, once deployed, will be required to learn continually from their experience during their operational lifetime. Such agents will also need to communicate with humans and other agents regarding the content of their experience, in the context of passing along their learnings, for the purpose of explaining their actions in specific circumstances or simply to relate more naturally to humans concerning experiences the agent acquires that are not necessarily related to their assigned tasks. We argue that to support these goals, an agent would benefit from an episodic memory; that is, a memory that encodes the agent's experience in such a way that the agent can relive the experience, communicate about it and use its past experience, inclusive of the agents own past actions, to learn more effective models and policies. In this short paper, we propose one potential approach to provide an AI agent with such capabilities. We draw upon the ever-growing body of work examining the function and operation of the Medial Temporal Lobe (MTL) in mammals to guide us in adding an episodic memory capability to an AI agent composed of artificial neural networks (ANNs). Based on that, we highlight important aspects to be considered in the memory organization and we propose an architecture combining ANNs and standard Computer Science techniques for supporting storage and retrieval of episodic memories. Despite being initial work, we hope this short paper can spark discussions around the creation of intelligent agents with memory or, at least, provide a different point of view on the subject.

This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020. Demoireing is a difficult task of removing moire patterns from an image to reveal an underlying clean image. The challenge was divided into two tracks. Track 1 targeted the single image demoireing problem, which seeks to remove moire patterns from a single image. Track 2 focused on the burst demoireing problem, where a set of degraded moire images of the same scene were provided as input, with the goal of producing a single demoired image as output. The methods were ranked in terms of their fidelity, measured using the peak signal-to-noise ratio (PSNR) between the ground truth clean images and the restored images produced by the participants' methods. The tracks had 142 and 99 registered participants, respectively, with a total of 14 and 6 submissions in the final testing stage. The entries span the current state-of-the-art in image and burst image demoireing problems.

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).

With the growing reliance on the ubiquitous availability of IT systems and services, these systems become more global, scaled, and complex to operate. To maintain business viability, IT service providers must put in place reliable and cost efficient operations support. Artificial Intelligence for IT Operations (AIOps) is a promising technology for alleviating operational complexity of IT systems and services. AIOps platforms utilize big data, machine learning and other advanced analytics technologies to enhance IT operations with proactive actionable dynamic insight.

In this paper we share our experience applying the AIOps approach to a production cloud object storage service to get actionable insights into system's behavior and health. We describe a real-life production cloud scale service and its operational data, present the AIOps platform we have created, and show how it has helped us resolving operational pain points.

Linkage Tree Genetic Algorithm (LTGA) is an effective Evolutionary Algorithm (EA) to solve complex problems using the linkage information between problem variables. LTGA performs well in various kinds of single-task optimization and yields promising results in comparison with the canonical genetic algorithm. However, LTGA is an unsuitable method for dealing with multi-task optimization problems. On the other hand, Multifactorial Optimization (MFO) can simultaneously solve independent optimization problems, which are encoded in a unified representation to take advantage of the process of knowledge transfer. In this paper, we introduce Multifactorial Linkage Tree Genetic Algorithm (MF-LTGA) by combining the main features of both LTGA and MFO. MF-LTGA is able to tackle multiple optimization tasks at the same time, each task learns the dependency between problem variables from the shared representation. This knowledge serves to determine the high-quality partial solutions for supporting other tasks in exploring the search space. Moreover, MF-LTGA speeds up convergence because of knowledge transfer of relevant problems. We demonstrate the effectiveness of the proposed algorithm on two benchmark problems: Clustered Shortest-Path Tree Problem and Deceptive Trap Function. In comparison to LTGA and existing methods, MF-LTGA outperforms in quality of the solution or in computation time.

Vision-and-Language Navigation (VLN) requires an agent to follow natural-language instructions, explore the given environments, and reach the desired target locations. These step-by-step navigational instructions are crucial when the agent is navigating new environments about which it has no prior knowledge. Most recent works that study VLN observe a significant performance drop when tested on unseen environments (i.e., environments not used in training), indicating that the neural agent models are highly biased towards training environments. Although this issue is considered as one of the major challenges in VLN research, it is still under-studied and needs a clearer explanation. In this work, we design novel diagnosis experiments via environment re-splitting and feature replacement, looking into possible reasons for this environment bias. We observe that neither the language nor the underlying navigational graph, but the low-level visual appearance conveyed by ResNet features directly affects the agent model and contributes to this environment bias in results. According to this observation, we explore several kinds of semantic representations that contain less low-level visual information, hence the agent learned with these features could be better generalized to unseen testing environments. Without modifying the baseline agent model and its training method, our explored semantic features significantly decrease the performance gaps between seen and unseen on multiple datasets (i.e. R2R, R4R, and CVDN) and achieve competitive unseen results to previous state-of-the-art models. Our code and features are available at: https://github.com/zhangybzbo/EnvBiasVLN

In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients. We formulate this as a non-convex regularisation problem involving a sparsity-enforcing, Cauchy-based penalty function, and the inverse Radon transform. We employ a simple local maxima detection technique in the Radon transform domain, associated with known clinical definitions of line artefacts. Despite being non-convex, the proposed method has guaranteed convergence via a proximal splitting algorithm and accurately identifies both horizontal and vertical line artefacts in LUS images. In order to reduce the number of false and missed detection, our method includes a two-stage validation mechanism, which is performed in both Radon and image domains. We evaluate the performance of the proposed method in comparison to the current state-of-the-art B-line identification method and show a considerable performance gain with 87% correctly detected B-lines in LUS images of nine COVID-19 patients. In addition, owing to its fast convergence, which takes around 12 seconds for a given frame, our proposed method is readily applicable for processing LUS image sequences.

Dialogue engines that incorporate different types of agents to converse with humans are popular.

However, conversations are dynamic in the sense that a selected response will change the conversation on-the-fly, influencing the subsequent utterances in the conversation, which makes the response selection a challenging problem.

We model the problem of selecting the best response from a set of responses generated by a heterogeneous set of dialogue agents by taking into account the conversational history, and propose a emph{Neural Response Selection} method.

The proposed method is trained to predict a coherent set of responses within a single conversation, considering its own predictions via a curriculum training mechanism.

Our experimental results show that the proposed method can accurately select the most appropriate responses, thereby significantly improving the user experience in dialogue systems.

Coronavirus disease 2019 (Covid-19) is highly contagious with limited treatment options. Early and accurate diagnosis of Covid-19 is crucial in reducing the spread of the disease and its accompanied mortality. Currently, detection by reverse transcriptase polymerase chain reaction (RT-PCR) is the gold standard of outpatient and inpatient detection of Covid-19. RT-PCR is a rapid method, however, its accuracy in detection is only ~70-75%. Another approved strategy is computed tomography (CT) imaging. CT imaging has a much higher sensitivity of ~80-98%, but similar accuracy of 70%. To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases. CovidCTNet increases the accuracy of CT imaging detection to 90% compared to radiologists (70%). The model is designed to work with heterogeneous and small sample sizes independent of the CT imaging hardware. In order to facilitate the detection of Covid-19 globally and assist radiologists and physicians in the screening process, we are releasing all algorithms and parametric details in an open-source format. Open-source sharing of our CovidCTNet enables developers to rapidly improve and optimize services, while preserving user privacy and data ownership.

If you don't feel right after a concussion, talk to your parents, your coach, your doctor ... get a second, third, fourth opinion ... Do not accept that you will not get better.

Personalization is one of the most effective marketing techniques to connect with customers online. While the exact methods are different for every business, adding personalized elements to landing pages is a proven method of driving conversions on your site. But why is it so successful? The simple answer is that personalization shows customers that you […]

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Your website serves several important purposes for your company — attracting customers, generating leads, and making sales, just to name a few. And as your home on the Internet, it also needs to explain who you are to the world and why they should choose you over your competitors.   However, creating an “About Us” […]

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Dozens of people find your business when looking for a type of product but aren’t sure which product fits their needs best. With a well-designed and organized category page, you’ll help people browse products easier and find what they want. To help you get inspired, let’s take a look at some excellent category page design […]

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Did you know that 75% of opinions on website credibility comes from design? If you want people to look at your services and find you credible, you must invest in web design for services pages to provide your audience with a positive experience. By looking at some web design examples for service pages, you’ll get […]

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The purpose of this container is to be able to use the Amazon ASK CLI and Amazon AWS CLI in a Docker container in DevOps pipelines.

Note: This is a fork from the martindsouza image with these changes:

[IMAGE-1] Noah Baumbach's Marriage Story begins as its central marriage is coming to an end. Our two protagonists are fiercely independent, articulate, opinionated creative types: Charlie (Adam Driver) is the director of an avant-garde theater troupe in New York City; Nicole (Scarlett Johansson) is an actress and one of his primary collaborators.…

Warm summer nights are on the way.…

The present invention provides a pest control agent, acaricide or fungicide that contains, as the active ingredient thereof, at least one type of compound selected from the aryloxyurea compounds represented by formula (V) (wherein R1 to R5 each independently represents an alkyl group or the like, X is a halogen atom or the like, n is an integer of 0 to 5, and Z is an oxygen atom or sulfur atom) or salts thereof.

Provided are an anti-static and anti-microbial surface treatment agent including a quaternary ammonium salt compound as an active ingredient and a method of preventing a polymer fiber from developing static electricity by using the surface treatment agent. The quaternary ammonium salt compound has excellent anti-static and anti-microbial effects for the prevention or improvement of static electricity in a polymer fiber. Accordingly, the quaternary ammonium salt compound is suitable for use as a fabric softener, or an anti-static agent, and also, provides anti-microbial effects to a polymer fiber.

A system and method assesses and manages risk of an operation of a user. A rules engine of computer executable instructions stored in the storage device determines at least one of a safety risk measurement based on key performance indicators, an operational safety risk measurement for the operation as a function of the operational safety risk measurement information stored in a storage device and/or a conditional safety risk measurement for the operation as a function of the conditional safety risk measurement information stored in the storage device. A processor connected to the storage device executes the rules engine. An output interface connected to the processor indicates the determined safety risk for the operation.

Systems and methods for generating a tuple of structured data files are described herein. In one example, a method includes detecting an expression that describes a structure of a structured image using a constructor. The method can also include using an inference-rule based search strategy to identify a hierarchical arrangement of bounding boxes in the structured image that match the expression. Furthermore, the method can include generating a first tuple of structured data files based on the identified hierarchical arrangement of bounding boxes in the structured image.

A method of identifying one or more particular images from an image collection, includes indexing the image collection to provide image descriptors for each image in the image collection such that each image is described by one or more of the image descriptors; receiving a query from a user specifying at least one keyword for an image search; and using the keyword(s) to search a second collection of tagged images to identify co-occurrence keywords. The method further includes using the identified co-occurrence keywords to provide an expanded list of keywords; using the expanded list of keywords to search the image descriptors to identify a set of candidate images satisfying the keywords; grouping the set of candidate images according to at least one of the image descriptors, and selecting one or more representative images from each grouping; and displaying the representative images to the user.

Systems and methods are disclosed that may be implemented for network management system (NMS) configuration management support for network devices using a learning and natural language processing application to capture the usage and behavior of the Command Line Interface (CLI) of a network device with the aid of a CLI knowledge model, which in one example may be ontology-based.

Provided are processes for the oxidative cleavage of a double bond in an unsaturated carboxylic acid. The process includes contacting the unsaturated carboxylic acid with a mild oxidizing agent and agitating the unsaturated carboxylic acid and the mild oxidizing agent for a time sufficient to cleave a double bond of the unsaturated carboxylic acid and produce a product comprising an aldehyde. The process is typically carried out in a mill, such as a ball, hammer, attrition, or jet mill.

A method for producing a high viscosity, low volatiles blown stripped oil blend is provided. The method may include the steps of: (i) obtaining an oil blend of corn stillage oil and soybean oil having a weight ratio of corn stillage oil to soybean oil of from about 1:2 to 3:1; (ii) heating the oil blend to at least 90° C.; (iii) passing air through the heated oil blend to produce a blown oil having a viscosity of at least 50 cSt at 40° C.; and (iv) stripping the blown oil from step (iii) to reduce an acid value of the blown oil to less than 5.0 mg KOH/gram.

An object of the present invention is to provide a novel trans-2-decenoic acid derivative or a pharmaceutically acceptable salt thereof and to provide a pharmaceutical agent which contains said compound as an active ingredient and has a highly safe neurotrophic factor-like activity or an alleviating action for side effect induced by administration of anti-cancer agents. The trans-2-decenoic acid derivative or a pharmaceutically acceptable salt thereof which is the compound of the present invention is specifically represented by the formula (1): (In the formula, Y is —O—, —NR— or —S—, R is hydrogen atom, alkyl group, dialkylaminoalkyl group or the like and W is a substituent such as dialkylaminoalkyl group) and has a quite high usefulness as a pharmaceutical agent such as a preventive or therapeutic agent for dementia, Alzheimer's disease, Parkinson's disease, depression, etc., a treating or repairing agent for spinal cord injury.

The present invention relates to a cationic lipid having one or more biodegradable groups located in the mid- or distal section of a lipidic moiety (e.g., a hydrophobic chain) of the cationic lipid. These cationic lipids may be incorporated into a lipid particle for delivering an active agent, such as a nucleic acid. The invention also relates to lipid particles comprising a neutral lipid, a lipid capable of reducing aggregation, a cationic lipid of the present invention, and optionally, a sterol. The lipid particle may further include a therapeutic agent such as a nucleic acid.

The invention relates to a polymer product obtained by polymerization of i) at least one monomer selected from N-vinylformamide and vinyl acetate, andii) maleic anhydrideto give a copolymer comprising N-vinylformamide and/or vinyl acetate and maleic anhydride followed by hydrolyzing formamide groups originating from N-vinylformamide to amino groups and/or acetate groups originating from vinyl acetate to hydroxyl groups and acid anhydride to dicarboxylic acid groups to give a water-soluble copolymer comprising amine and/or hydroxyl and carboxyl groups, wherein the molar ratio of the N-vinylformamide and/or vinyl acetate monomer to the maleic anhydride monomer is from 70:30 to 30:70. The polymer product can be used as a dispersing agent or as a scale inhibiting agent.

Formulations comprising an isosorbide-modified unsaturated polyester comprising maleic acid, fumaric acid, itaconic acid or maleic anhydride and isosorbide and one or more low profile additives. The formulations exhibit better shrink control in molding compound formulations with standard low profile additives than their non-isosorbide-modified analogues. These isosorbide-modified unsaturated polyester resins may be applied in molding compounds like sheet molding compounds or bulk molding compounds and may be components of fiber reinforced composites or other composite materials.

Disclosed are polymeric compositions with improved breakdown strength. The polymeric compositions contain a polyolefin and a voltage stabilizing agent. The voltage stabilizing agent contains a triazine. The triazine may include a substituent that enables keto-enol tautomerism, which provides the voltage stabilizing agent with additional energy dissipation capacity. The present polymeric compositions exhibit improved breakdown strength when applied as an insulating layer for power cable.

Herbicidally active 5-amino-1-phenyl-pyrazoles, most of which are new, of the formula ##STR1##

A separation membrane according to the present invention is characterized by having a porous tube containing an alumina as a main component and an attachment member disposed in a connection position of the porous tube, wherein the porous tube and the attachment member are bonded by a ceramic oxide-based bonding agent containing 17 to 48 wt % of SiO2, 2 to 8 wt % of Al2O3, 24 to 60 wt % of BaO, and 0.5 to 5 wt % of ZnO as essential components and containing at least one of La2O3, CaO, and SrO, and a thin zeolite layer is formed on a surface of the porous tube. The attachment member is bonded to the porous tube before the formation of the zeolite layer. Therefore, the bonding agent can have a melting temperature higher than 600° C., which is the upper heatproof temperature limit of the zeolite. Thus, the ceramic oxide material for the bonding agent can be selected from a wider range of compositions such as glass compositions (without limitations on the glass softening temperature).

According to one embodiment, a glass container may include a body formed from a Type I, Class B glass composition according to ASTM Standard E438-92. The body may have an inner surface, an outer surface and a wall thickness extending between the outer surface and the inner surface. The body may also include a compressively stressed layer extending into the wall thickness from at least one of the outer surface and the inner surface. A lubricous coating may be positioned on at least a portion of the outer surface of the body, wherein the outer surface of the body with the lubricous coating has a coefficient of friction less than or equal to 0.7.

Temperatures in microelectronic integrated circuit packages and components may be measured in situ using carbon nanotube networks. An array of carbon nanotubes strung between upstanding structures may be used to measure local temperature. Because of the carbon nanotubes, a highly accurate temperature measurement may be achieved. In some cases, the carbon nanotubes and the upstanding structures may be secured to a substrate that is subsequently attached to a microelectronic package.

The invention relates to a method of neutralizing paints, that includes adding at least one associative neutralizing agent to a formulation such as a pigment concentrate, a white paint base or paint. The associative neutralizing agent includes at least one neutralizing group N and at least one nitrogenous associative group A bonded together by at least one “spacer” group Sp. The invention also relates to pigment concentrates and to paints containing at least one such associative neutralizing agent.

Set of compositions for preparing system-in-package type semiconductor device. The composition set consists of underfill composition for preparing underfill part and encapsulation resin composition for preparing resin encapsulation part. 1) A cured product of the underfill composition has a glass transition temperature, Tg, ≧100° C. and is the same with or differs from a Tg of a cured product of the encapsulation resin composition by ≦20° C. 2) Total linear expansion coefficient of the cured product of the underfill composition at a temperature not higher than (Tg−30)° C. and a linear expansion coefficient of the cured product of the encapsulation resin composition at a temperature not higher than (Tg−30)° C. is ≦42 ppm/° C. 3) A ratio of the linear expansion coefficient of the cured product of the encapsulation resin composition to the linear expansion coefficient of the cured product of the underfill composition ranges from 0.3 to 1.0.

Provided is a thermosetting resin composition including an inorganic filler and a thermosetting resin matrix component, in which the inorganic filler includes secondary sintered particles each formed of primary particles of scaly boron nitride, and at least some of the secondary sintered particles each have a maximum cavity diameter of 5 μm to 80 μm. The thermosetting resin composition can be used for providing a heat conductive sheet in which electrical insulation property is kept by controlling where the defects such as voids and cracks occur and their size, and which has excellent heat conductivity.

A facile synthesis of amphiphilic hyperbranched polymers consisting of poly(amic acid) and polyimide was developed via “A2+B3” approach from difunctional anhydride and trifunctional hydrophilic poly(oxyalkylene)triamine. Various amphiphilic hyperbranched poly(amic acid)s (HBPAAs) with terminal amine functionalities and amic acid structures were prepared through ring-opening polyaddition at room temperature, followed by thermal imidization process for the formation of hyperbranched polyimides (HBPIs), accordingly. The resulting HBPIs were analyzed by GPC, indicating the molecule weights of 5000˜7000 g/mol with a distribution of polydispersity between 2.0 and 3.8. The amine titration for HBPIs indicated the peripheral total-amine contents to be 8.32˜18.32 mequiv/g dependent on compositions.

Disclosed is an epoxy resin composition used for encapsulation of a semiconductor containing an epoxy resin (A), a curing agent (B), an inorganic filler (C) and a mold releasing agent, in which the mold releasing agent contains a compound (D) having a copolymer of an α-olefin having 28 to 60 carbon atoms and a maleic anhydride esterified with a long chain aliphatic alcohol having 10 to 25 carbon atoms.

Embodiments of the present invention relate to an apparatus, method, and/or sequence for a distributed ECC that may be used in a storage system. In another embodiment of the invention, an apparatus for handling distributed error correction code (ECC) operations, includes: a plurality of ECC engines configured to perform ECC operations in parallel on multiple data parts; the plurality of ECC engines distributed in parallel to receive some of the multiple data parts that are read from storage media devices and to receive some of the other multiple data parts that are to be written to the storage media devices; and the plurality of ECC engines configured to use respective ECC bytes corresponding to respective ones of the multiple data parts.

According to one embodiment, a memory controller includes: a first flash encoding unit that performs flash encoding on user data according to a first scheme to generate user data flash codes; an encoding unit that performs an error correction encoding process on the user data flash codes to generate parities; a second flash encoding unit that performs flash encoding on the parities according to a second scheme to generate parity flash codes; and a memory I/F that writes the user data flash codes and the parity flash codes to the nonvolatile memory.

A nonvolatile memory is configured with blocks as deletion units, each block having several pages that are configured as write units. A controller for the nonvolatile memory includes an error correcting circuit, which detects and corrects an error in data read out of a page in one of the blocks of the nonvolatile memory, the page being referenced by a logical address. The controller also determines an error occurrence when the error cannot be corrected. An error block table is provided to store the logical address where the error occurred, and a physical address corresponding to the logical address.

Methods and systems for in-place updating original content stored in a non-volatile storage device and for yielding updated content. Some of the described embodiments illustrate the possibilities for reduction in storage operations, storage blocks, and/or update package size. Some of the described embodiments include the writing of error recovery result(s) such as XOR result(s) which enable the recovery of data in case of an interruption of the update process. In some of the described embodiments, there is re-usage of a protection buffer containing content which is required in the update process.

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.

The present application is directed to a novel composition which acts as a barrier to noxious agents while adding self-detoxifying catalytic treatments to neutralize the noxious and harmful warfare agents when applied for example on a fabric, or other solid support.