In this paper, we focus on the task of small-footprint keyword spotting under the far-field scenario. Far-field environments are commonly encountered in real-life speech applications, and it causes serve degradation of performance due to room reverberation and various kinds of noises. Our baseline system is built on the convolutional neural network trained with pooled data of both far-field and close-talking speech. To cope with the distortions, we adopt the multi-task learning scheme with alignment loss to reduce the mismatch between the embedding features learned from different domains of data. Experimental results show that our proposed method maintains the performance on close-talking speech and achieves significant improvement on the far-field test set.

Deep learning-based models, such as convolutional neural networks, have advanced various segments of computer vision. However, this technology is rarely applied to seismic shot gather noise localization problem. This letter presents an investigation on the effectiveness of a multi-scale feature-fusion-based network for seismic shot-gather noise localization. Herein, we describe the following: (1) the construction of a real-world dataset of seismic noise localization based on 6,500 seismograms; (2) a multi-scale feature-fusion-based detector that uses the MobileNet combined with the Feature Pyramid Net as the backbone; and (3) the Single Shot multi-box detector for box classification/regression. Additionally, we propose the use of the Focal Loss function that improves the detector's prediction accuracy. The proposed detector achieves an AP@0.5 of 78.67\% in our empirical evaluation.

We present a sound and complete method for the verification of qualitative liveness properties of replicated systems under stochastic scheduling. These are systems consisting of a finite-state program, executed by an unknown number of indistinguishable agents, where the next agent to make a move is determined by the result of a random experiment. We show that if a property of such a system holds, then there is always a witness in the shape of a Presburger stage graph: a finite graph whose nodes are Presburger-definable sets of configurations. Due to the high complexity of the verification problem (non-elementary), we introduce an incomplete procedure for the construction of Presburger stage graphs, and implement it on top of an SMT solver. The procedure makes extensive use of the theory of well-quasi-orders, and of the structural theory of Petri nets and vector addition systems. We apply our results to a set of benchmarks, in particular to a large collection of population protocols, a model of distributed computation extensively studied by the distributed computing community.

Multimodal Sentiment Analysis is an active area of research that leverages multimodal signals for affective understanding of user-generated videos. The predominant approach, addressing this task, has been to develop sophisticated fusion techniques. However, the heterogeneous nature of the signals creates distributional modality gaps that pose significant challenges. In this paper, we aim to learn effective modality representations to aid the process of fusion. We propose a novel framework, MISA, which projects each modality to two distinct subspaces. The first subspace is modality invariant, where the representations across modalities learn their commonalities and reduce the modality gap. The second subspace is modality-specific, which is private to each modality and captures their characteristic features. These representations provide a holistic view of the multimodal data, which is used for fusion that leads to task predictions. Our experiments on popular sentiment analysis benchmarks, MOSI and MOSEI, demonstrate significant gains over state-of-the-art models. We also consider the task of Multimodal Humor Detection and experiment on the recently proposed UR_FUNNY dataset. Here too, our model fares better than strong baselines, establishing MISA as a useful multimodal framework.

Sentence level quality estimation (QE) for machine translation (MT) attempts to predict the translation edit rate (TER) cost of post-editing work required to correct MT output. We describe our view on sentence-level QE as dictated by several practical setups encountered in the industry. We find consumers of MT output---whether human or algorithmic ones---to be primarily interested in a binary quality metric: is the translated sentence adequate as-is or does it need post-editing? Motivated by this we propose a quality classification (QC) view on sentence-level QE whereby we focus on maximizing recall at precision above a given threshold. We demonstrate that, while classical QE regression models fare poorly on this task, they can be re-purposed by replacing the output regression layer with a binary classification one, achieving 50-60\% recall at 90\% precision. For a high-quality MT system producing 75-80\% correct translations, this promises a significant reduction in post-editing work indeed.

In this paper, we present three distributed algorithms to solve a class of generalized Nash equilibrium (GNE) seeking problems in strongly monotone games. The first one (SD-GENO) is based on synchronous updates of the agents, while the second and the third (AD-GEED and AD-GENO) represent asynchronous solutions that are robust to communication delays. AD-GENO can be seen as a refinement of AD-GEED, since it only requires node auxiliary variables, enhancing the scalability of the algorithm. Our main contribution is to prove converge to a variational GNE of the game via an operator-theoretic approach. Finally, we apply the algorithms to network Cournot games and show how different activation sequences and delays affect convergence. We also compare the proposed algorithms to the only other in the literature (ADAGNES), and observe that AD-GENO outperforms the alternative.

We study the language universality problem for One-Counter Nets, also known as 1-dimensional Vector Addition Systems with States (1-VASS), parameterized either with an initial counter value, or with an upper bound on the allowed counter value during runs. The language accepted by an OCN (defined by reaching a final control state) is monotone in both parameters. This yields two natural questions: 1) Does there exist an initial counter value that makes the language universal? 2) Does there exist a sufficiently high ceiling so that the bounded language is universal? Despite the fact that unparameterized universality is Ackermann-complete and that these problems seem to reduce to checking basic structural properties of the underlying automaton, we show that in fact both problems are undecidable. We also look into the complexities of the problems for several decidable subclasses, namely for unambiguous, and deterministic systems, and for those over a single-letter alphabet.

Visual localization is a useful alternative to standard localization techniques. It works by utilizing cameras. In a typical scenario, features are extracted from captured images and compared with geo-referenced databases. Location information is then inferred from the matching results. Conventional schemes mainly use low-level visual features. These approaches offer good accuracy but suffer from scalability issues. In order to assist localization in large urban areas, this work explores a different path by utilizing high-level semantic information. It is found that object information in a street view can facilitate localization. A novel descriptor scheme called "semantic signature" is proposed to summarize this information. A semantic signature consists of type and angle information of visible objects at a spatial location. Several metrics and protocols are proposed for signature comparison and retrieval. They illustrate different trade-offs between accuracy and complexity. Extensive simulation results confirm the potential of the proposed scheme in large-scale applications. This paper is an extended version of a conference paper in CBMI'18. A more efficient retrieval protocol is presented with additional experiment results.

Correlation alignment (CORAL), a representative domain adaptation (DA) algorithm, decorrelates and aligns a labelled source domain dataset to an unlabelled target domain dataset to minimize the domain shift such that a classifier can be applied to predict the target domain labels. In this paper, we implement the CORAL on quantum devices by two different methods. One method utilizes quantum basic linear algebra subroutines (QBLAS) to implement the CORAL with exponential speedup in the number and dimension of the given data samples. The other method is achieved through a variational hybrid quantum-classical procedure. In addition, the numerical experiments of the CORAL with three different types of data sets, namely the synthetic data, the synthetic-Iris data, the handwritten digit data, are presented to evaluate the performance of our work. The simulation results prove that the variational quantum correlation alignment algorithm (VQCORAL) can achieve competitive performance compared with the classical CORAL.

This paper proposes a fully automated atlas-based pancreas segmentation method from CT volumes utilizing atlas localization by regression forest and atlas generation using blood vessel information. Previous probabilistic atlas-based pancreas segmentation methods cannot deal with spatial variations that are commonly found in the pancreas well. Also, shape variations are not represented by an averaged atlas. We propose a fully automated pancreas segmentation method that deals with two types of variations mentioned above. The position and size of the pancreas is estimated using a regression forest technique. After localization, a patient-specific probabilistic atlas is generated based on a new image similarity that reflects the blood vessel position and direction information around the pancreas. We segment it using the EM algorithm with the atlas as prior followed by the graph-cut. In evaluation results using 147 CT volumes, the Jaccard index and the Dice overlap of the proposed method were 62.1% and 75.1%, respectively. Although we automated all of the segmentation processes, segmentation results were superior to the other state-of-the-art methods in the Dice overlap.

In this paper, some useful necessary and sufficient conditions for the unique solution of the generalized absolute value equation (GAVE) $Ax-B|x|=b$ with $A, Bin mathbb{R}^{n imes n}$ from the optimization field are first presented, which cover the fundamental theorem for the unique solution of the linear system $Ax=b$ with $Ain mathbb{R}^{n imes n}$. Not only that, some new sufficient conditions for the unique solution of the GAVE are obtained, which are weaker than the previous published works.

In recent years, all-neural end-to-end approaches have obtained state-of-the-art results on several challenging automatic speech recognition (ASR) tasks. However, most existing works focus on building ASR models where train and test data are drawn from the same domain. This results in poor generalization characteristics on mismatched-domains: e.g., end-to-end models trained on short segments perform poorly when evaluated on longer utterances. In this work, we analyze the generalization properties of streaming and non-streaming recurrent neural network transducer (RNN-T) based end-to-end models in order to identify model components that negatively affect generalization performance. We propose two solutions: combining multiple regularization techniques during training, and using dynamic overlapping inference. On a long-form YouTube test set, when the non-streaming RNN-T model is trained with shorter segments of data, the proposed combination improves word error rate (WER) from 22.3% to 14.8%; when the streaming RNN-T model trained on short Search queries, the proposed techniques improve WER on the YouTube set from 67.0% to 25.3%. Finally, when trained on Librispeech, we find that dynamic overlapping inference improves WER on YouTube from 99.8% to 33.0%.

Inspired by Bitcoin, many different kinds of cryptocurrencies based on blockchain technology have turned up on the market. Due to the special structure of the blockchain, it has been deemed impossible to directly trade between traditional currencies and cryptocurrencies or between different types of cryptocurrencies. Generally, trading between different currencies is conducted through a centralized third-party platform. However, it has the problem of a single point of failure, which is vulnerable to attacks and thus affects the security of the transactions. In this paper, we propose a distributed cryptocurrency trading scheme to solve the problem of centralized exchanges, which can achieve trading between different types of cryptocurrencies. Our scheme is implemented with smart contracts on the Ethereum blockchain and deployed on the Ethereum test network. We not only implement transactions between individual users, but also allow transactions between multiple users. The experimental result proves that the cost of our scheme is acceptable.

Public key encryption with equality test (PKEET) allows testing whether two ciphertexts are generated by the same message or not. PKEET is a potential candidate for many practical applications like efficient data management on encrypted databases. Potential applicability of PKEET leads to intensive research from its first instantiation by Yang et al. (CT-RSA 2010). Most of the followup constructions are secure in the random oracle model. Moreover, the security of all the concrete constructions is based on number-theoretic hardness assumptions which are vulnerable in the post-quantum era. Recently, Lee et al. (ePrint 2016) proposed a generic construction of PKEET schemes in the standard model and hence it is possible to yield the first instantiation of PKEET schemes based on lattices. Their method is to use a $2$-level hierarchical identity-based encryption (HIBE) scheme together with a one-time signature scheme. In this paper, we propose, for the first time, a direct construction of a PKEET scheme based on the hardness assumption of lattices in the standard model. More specifically, the security of the proposed scheme is reduces to the hardness of the Learning With Errors problem.

In this work, we consider a group of robots working together to manipulate a rigid object to track a desired trajectory in $SE(3)$. The robots have no explicit communication network among them, and they do no know the mass or friction properties of the object, or where they are attached to the object. However, we assume they share data from a common IMU placed arbitrarily on the object. To solve this problem, we propose a decentralized adaptive control scheme wherein each agent maintains and adapts its own estimate of the object parameters in order to track a reference trajectory. We present an analysis of the controller's behavior, and show that all closed-loop signals remain bounded, and that the system trajectory will almost always (except for initial conditions on a set of measure zero) converge to the desired trajectory. We study the proposed controller's performance using numerical simulations of a manipulation task in 3D, and with hardware experiments which demonstrate our algorithm on a planar manipulation task. These studies, taken together, demonstrate the effectiveness of the proposed controller even in the presence of numerous unmodelled effects, such as discretization errors and complex frictional interactions.

Coordinating the operation of combined heat and power plants (CHPs) and heat pumps (HPs) at the interface between heat and power systems is essential to achieve a cost-effective and efficient operation of the overall energy system. Indeed, in the current sequential market practice, the heat market has no insight into the impacts of heat dispatch on the electricity market. While preserving this sequential practice, this paper introduces an electricity-aware heat unit commitment model. Coordination is achieved through bid validity constraints, which embed the techno-economic linkage between heat and electricity outputs and costs of CHPs and HPs. This approach constitutes a novel market mechanism for the coordination of heat and power systems, defining heat bids conditionally on electricity market prices. The resulting model is a trilevel optimization problem, which we recast as a mixed-integer linear program using a lexicographic function. We use a realistic case study based on the Danish power and heat system, and show that the proposed model yields a 4.5% reduction in total operating cost of heat and power systems compared to a traditional decoupled unit commitment model, while reducing the financial losses of each CHP and HP due to invalid bids by up-to 20.3 million euros.

Feature pyramid has been an efficient method to extract features at different scales. Development over this method mainly focuses on aggregating contextual information at different levels while seldom touching the inter-level correlation in the feature pyramid. Early computer vision methods extracted scale-invariant features by locating the feature extrema in both spatial and scale dimension. Inspired by this, a convolution across the pyramid level is proposed in this study, which is termed pyramid convolution and is a modified 3-D convolution. Stacked pyramid convolutions directly extract 3-D (scale and spatial) features and outperforms other meticulously designed feature fusion modules. Based on the viewpoint of 3-D convolution, an integrated batch normalization that collects statistics from the whole feature pyramid is naturally inserted after the pyramid convolution. Furthermore, we also show that the naive pyramid convolution, together with the design of RetinaNet head, actually best applies for extracting features from a Gaussian pyramid, whose properties can hardly be satisfied by a feature pyramid. In order to alleviate this discrepancy, we build a scale-equalizing pyramid convolution (SEPC) that aligns the shared pyramid convolution kernel only at high-level feature maps. Being computationally efficient and compatible with the head design of most single-stage object detectors, the SEPC module brings significant performance improvement ($>4$AP increase on MS-COCO2017 dataset) in state-of-the-art one-stage object detectors, and a light version of SEPC also has $sim3.5$AP gain with only around 7% inference time increase. The pyramid convolution also functions well as a stand-alone module in two-stage object detectors and is able to improve the performance by $sim2$AP. The source code can be found at https://github.com/jshilong/SEPC.

We develop a categorical compositional distributional semantics for Lambek Calculus with a Relevant Modality !L*, which has a limited edition of the contraction and permutation rules. The categorical part of the semantics is a monoidal biclosed category with a coalgebra modality, very similar to the structure of a Differential Category. We instantiate this category to finite dimensional vector spaces and linear maps via "quantisation" functors and work with three concrete interpretations of the coalgebra modality. We apply the model to construct categorical and concrete semantic interpretations for the motivating example of !L*: the derivation of a phrase with a parasitic gap. The effectiveness of the concrete interpretations are evaluated via a disambiguation task, on an extension of a sentence disambiguation dataset to parasitic gap phrase one, using BERT, Word2Vec, and FastText vectors and Relational tensors.

The increasing ubiquity of low-cost wireless sensors in smart homes and buildings has enabled users to easily deploy systems to remotely monitor and control their environments. However, this raises privacy concerns for third-party occupants, such as a hotel room guest who may be unaware of deployed clandestine sensors. Previous methods focused on specific modalities such as detecting cameras but do not provide a generalizable and comprehensive method to capture arbitrary sensors which may be "spying" on a user. In this work, we seek to determine whether one can walk in a room and detect any wireless sensor monitoring an individual. As such, we propose SnoopDog, a framework to not only detect wireless sensors that are actively monitoring a user, but also classify and localize each device. SnoopDog works by establishing causality between patterns in observable wireless traffic and a trusted sensor in the same space, e.g., an inertial measurement unit (IMU) that captures a user's movement. Once causality is established, SnoopDog performs packet inspection to inform the user about the monitoring device. Finally, SnoopDog localizes the clandestine device in a 2D plane using a novel trial-based localization technique. We evaluated SnoopDog across several devices and various modalities and were able to detect causality 96.6% percent of the time, classify suspicious devices with 100% accuracy, and localize devices to a sufficiently reduced sub-space.

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.

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 […]

The post How Personalized Landing Pages Can Make Your Site More Profitable appeared first on WebFX Blog.

Handling user input with forms is the cornerstone of many common applications.

Applications use forms to enable users to log in, to update a profile, to enter sensitive information, and to perform many other data-entry tasks

The apps and devices you use are conducting surveillance with your every move Sure, you lock your home, and you probably don't share your deepest secrets with random strangers.…

Our staff of reporters and photographers at the Inlander has been working tirelessly to cover the coronavirus pandemic and all of its implications for the Inland Northwest — on jobs, schools, employment, the restaurant industry, arts organizations, hospitals and much, much more. However, we’ve also tapped into a boundless resource that is our region’s community of writers, and in recent days they’ve shared with Inlander readers an awe-inspiring series of essays and stories that has left us inspired, hopeful, heartbroken and more than a little grateful.…

By Johnny Diaz The New York Times Company…

If you go to check how many people are hospitalized with COVID-19 in Spokane, the Spokane County Regional Health District website will give you an answer. Right now, it lists eight people as currently hospitalized with COVID-19, and that number has been trending downward.…

Designology Interiors' Deanna Goguen has three bathrooms in her South Hill-area home, each with its own personality.…

Disclosed is a method for producing graphene functionalized at its edge positions of graphite. Organic material having one or more functional groups is reacted with graphite in reaction medium comprising methanesulfonic acid and phosphorus pentoxide, or in reaction medium comprising trifluoromethanesulfonic acid, to produce graphene having organic material fuctionalized at edges. And then, high purity and large scaled graphene and film can be obtained by dispersing, centrifugal separating the functionalized graphene in a solvent and reducing, in particular heat treating the graphene. According to the present invention graphene can be produced inexpensively in a large amount with a minimum loss of graphite. (FIG. 1)

A process for reductive amination of aliphatic cyanoaldehydes to aliphatic diamines comprising (1) providing a mixture of 1,3-cyanocyclohexane carboxaldehyde and/or 1,4-cyanocyclohexane carboxaldehyde; (2) contacting said mixture with a metal carbonate based solid bed or a weak base anion exchange resin bed at a temperature from 15 to 40 ° C. for a period of at least 1 minute; (3) thereby treating said mixture, wherein said treated mixture has a pH in the range of 6 to 9; (4) feeding said treated mixture, hydrogen, and ammonia into a continuous reductive amination reactor system; (6) contacting said treated mixture, hydrogen, and ammonia with each other in the presence of one or more heterogeneous metal based catalyst systems at a temperature from 80 ° C. to 160 ° C. and a pressure from 700 to 3500 psig; (7) thereby producing one or more cycloaliphatic diamines is provided.

A process for conversion of aliphatic bicyclic amines to aliphatic diamines including contacting one or more bicyclic amines selected from the group consisting of 3-azabicyclo[3.3.1]nonane and azabicyclo[3.3.1]non-2-ene with ammonia and hydrogen, and alcohols in the presence of heterogeneous metal based catalyst systems, a metal selected from the group consisting of Co, Ni, Ru, Fe, Cu, Re, Pd, and their oxides at a temperature from 140° C. to 200° C. and a pressure from 1540 to 1735 psig for at least one hour reactor systems; forming a product mixture comprising aliphatic diamine(s), bicyclic amine(s), ammonia, hydrogen, and alcohol(s); removing said product mixture from the reactor system; removing at least some of the ammonia, hydrogen, water, alcohols, bicyclic amines from said product mixture; thereby separating the aliphatic diamines from said product mixture.

Bioassays for detecting the ability of one sample of a food substance, nutritional supplement, therapeutic agent and/or disease preventive agent relative to that of a second sample of such a substance, supplement and/or agent to inhibit, upregulate or otherwise modulate translation initiation, and thereby demonstrate a disease curative and/or preventive effect in a human and/or animal that consumes a such substance, supplement and/or agent or to whom a such substance, supplement and/or agent is administered are provided.

The invention discloses quinoxaline derivatives or salts thereof having PDE9-inhibiting activity and being useful as treating agent of dysuria and the like, which are represented by the formula (I) in the formula, R1 and R2 each independently stands for hydrogen, halogen, alkyl, alkoxy, acyl, amino and the like,R3 stands for alkyl, aryl, saturated carbocyclic group, saturated heterocyclic group, acyl and the like,R4 stands for hydrogen, hydroxy, alkyl or amino,R5 and R8 each independently stands for hydrogen, halogen, alkyl, alkenyl, alkoxy, cyano or nitro,R6 and R7 each independently stands for hydrogen, halogen, alkyl, alkenyl, alkynyl, alkoxy, cyano, amino, carbocyclic group, heterocyclic group, COR9 or SO2R9,R9 stands for hydrogen, hydroxy, alkyl, amino, pyrrolidin-1-yl, piperidin-1-yl, pyperazin-1-yl or the like,X stands for S or O, andA1, A2 and A3 each independently stands for N or C.

A process for producing an alkali metal cyclopentadienylide is disclosed which comprises reacting in a solvent an alkali metal hydride with a disubstituted or trisubstituted 1,3-cyclopentadiene. Further, a process for producing a dihalobis(η-substituted-cyclopentadienyl)zirconium is disclosed which comprises reacting a zirconium halide with the above alkali metal cyclopentadienylide. The former process enables performing the reaction between the disubstituted or trisubstituted 1,3-cyclopentadiene and the alkali metal hydride at an easily controllable temperature of room temperature to about 150° C. and also enables obtaining the alkali metal cyclopentadienylide in high yield. The latter process enables obtaining the dihalobis(η-substituted-cyclopentadienyl)zirconium in high yield.

Provided is an alkali-free glass, which is substantially free of alkali metal oxides, and has a strain point of more than 680° C., an average coefficient of thermal expansion in the temperature range of 30 to 380° C. of 40 to 55×10−7/° C., and a liquidus temperature of less than 1,200° C. Further, the alkali-free glass comprises, as a glass composition in terms of mass %, 55 to 70% of SiO2, 10 to 20% of Al2O3, 0.1 to 4.5% of B2O3, 0 to 1% of MgO, 5 to 15% of CaO, 0.5 to 5% of SrO, and 5 to 15% of BaO.

Methods using a sequence of externally generated magnetic fields to initialize the magnetization directions of each of the layers in perpendicular MTJ MRAM elements for data and reference bits when the required magnetization directions are anti-parallel are described. The coercivity of the fixed pinned and reference layers can be made unequal so that one of them can be switched by a magnetic field that will reliably leave the other one unswitched. Embodiments of the invention utilize the different effective coercivity fields of the pinned, reference and free layers to selectively switch the magnetization directions using a sequence of magnetic fields of decreasing strength. Optionally the chip or wafer can be heated to reduce the required field magnitude. It is possible that the first magnetic field in the sequence can be applied during an annealing step in the MRAM manufacture process.

In certain embodiments novel nanoparticles (nanowontons) are provided that are suitable for multimodal imaging and/or therapy. In one embodiment, the nanoparticles include a first biocompatible (e.g., gold) layer, an inner core layer (e.g., a non-biocompatible material), and a biocompatible (e.g., gold) layer. The first gold layer includes a concave surface that forms a first outer surface of the layered nanoparticle. The second gold layer includes a convex surface that forms a second outer surface of the layered nanoparticle. The first and second gold layers encapsulate the inner core material layer. Methods of fabricating such nanoparticles are also provided.

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.

A composite material and method of making and using the same is described. In particular a dental (and preferably photocurable) composite material, and the use of a composite material according to the invention as a dental material and a method for preparation of a composite material according to the invention. Also disclosed are novel radically polymerizable monomers comprising at least one polyalicyclic structure element and certain ethylenic structure elements, which are particularly suitable for use in a composite material according to the invention.

Hydroswellable, absorbable and non-absorbable, aliphatic, segmented polyurethanes and polyurethane-urea capable of swelling in the biological environment with associated increase in volume of at least 3 percent have more than one type of segments, including those derived from polyethylene glycol and the molecular chains are structurally tailored to allow the use of corresponding formulations and medical devices as carriers for bioactive agents, rheological modifiers of cyanoacrylate-based tissue adhesives, as protective devices for repairing defective or diseased components of articulating joints and their cartilage, and scaffolds for cartilage tissue engineering.

In a wireless communication system, a compact control signaling scheme is provided for signaling the selected retransmission mode and codeword identifier for a codeword retransmission when one of a plurality of codewords being transmitted over two codeword pipes to a receiver fails the transmission and when the base station/transmitter switches from a higher order channel rank to a lower order channel rank, either by including one or more additional signaling bits in the control signal to identify the retransmitted codeword, or by re-using existing control signal information in a way that can be recognized by the subscriber station/receiver to identify the retransmitted codeword. With the compact control signal, the receiver is able to determine which codeword is being retransmitted and to determine the corresponding time-frequency resource allocation for the retransmitted codeword.

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.

Examples disclose a computing system comprising a computing device with a display surface to detect a selection of functionality from a list of functionalities to be disabled on a calculator. Further, the computing device creates a custom configuration based on the selected functionality. Additionally, the examples also disclose a calculator with a processor to integrate the custom configuration, the custom configuration restricts the selected functionality on the calculator.

A photocurable material for sealing including (A) an oligomer having a weight average molecular weight of 10,000 to 30,000 and having (meth)acryloyl group(s), (B) a (meth)acrylate monomer, (C) a polythiol compound, and optionally (D) a carbodiimide compound enables the provision of a sealing material that has high compression recovery performance, high tensile strength and excellent flexibility, can have low hardness if required, and therefore has excellent sealing properties including air-tightness performance and water-proof performance and undergoes the formation of little surface tacks and the like.

The present invention is directed to a coating composition including a (meth)acrylate binder resin, a UV initiator, an organic solvent, and silica particles surface-treated with a (meth)acrylate compound, a coating film including a cured product of the coating composition, and a method of producing the coating film. The present invention makes it possible to provide a coating material having high transmittance and a low level of haze, and excellent scratch resistance and self-healing capabilities.

A virtual machine (VM) migration from a source virtual machine monitor (VMM) to a destination VMM on a computer system. Each of the VMMs includes virtualization software, and one or more VMs are executed in each of the VMMs. The virtualization software allocates hardware resources in a form of virtual resources for the concurrent execution of one or more VMs and the virtualization software. A portion of a memory of the hardware resources includes hardware memory segments. A first portion of the memory segments is assigned to a source logical partition and a second portion is assigned to a destination logical partition. The source VMM operates in the source logical partition and the destination VMM operates in the destination logical partition. The first portion of the memory segments is mapped into a source VMM memory, and the second portion of the memory segments is mapped into a destination VMM memory.

Apparatuses and methods of calibrating a memory interface are described. Calibrating a memory interface can include loading and outputting units of a first data pattern into and from at least a portion of a register to generate a first read capture window. Units of a second data pattern can be loaded into and output from at least the portion of the register to generate a second read capture window. One of the first read capture window and the second read capture window can be selected and a data capture point for the memory interface can be calibrated according to the selected read capture window.

Various embodiments include apparatuses, stacked devices and methods of forming dice stacks on an interface die. In one such apparatus, a dice stack includes at least a first die and a second die, and conductive paths coupling the first die and the second die to the common control die. In some embodiments, the conductive paths may be arranged to connect with circuitry on alternating dice of the stack. In other embodiments, a plurality of dice stacks may be arranged on a single interface die, and some or all of the dice may have interleaving conductive paths.

Systems, methods and computer program products may provide managing utilization of one or more physical processors in a shared processor pool. A method of managing utilization of one or more physical processors in a shared processor pool may include determining a current amount of utilization of the one or more physical processors and generating an instruction message. The instruction message may be at least partially determined by the current amount of utilization. The method may further include sending the instruction message to a guest operating system, the guest operating system having a number of enabled virtual processors.

Methods and systems for statistically eliding fences in a work stealing algorithm are disclosed. A data structure comprising a head pointer, tail pointer, barrier pointer and an advertising flag allows for dynamic load-balancing across processing resources in computer applications.

Methods and systems for managing a removal of a passthrough device from a guest managed by a hypervisor in virtualized computing environment. A hypervisor receives a request from the guest for access to a passthrough device. The hypervisor sets, in a memory, a last accessed state associated with a virtual machine executing the guest. The hypervisor forwards the request to the passthrough device and configures the host CPU to send a subsequent access request directly to the passthrough device. In response to a virtual machine reset, the hypervisor clears the last accessed state and instructs the host CPU to send a post-reset access request to the hypervisor.