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.

Participatory Design's vision of democratic participation assumes participants' feelings of agency in envisioning a collective future. But this assumption may be leaky when dealing with vulnerable populations. We reflect on the results of a series of activities aimed at supporting agentic-future-envisionment with a group of sex-trafficking survivors in Nepal. We observed a growing sense among the survivors that they could play a role in bringing about change in their families. They also became aware of how they could interact with available institutional resources. Reflecting on the observations, we argue that building participant agency on the small and personal interactions is necessary before demanding larger Political participation. In particular, a value of PD, especially for vulnerable populations, can lie in the process itself if it helps participants position themselves as actors in the larger world.

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.

During VR demos we have performed over last few years, many participants (in the absence of any haptic feedback) have commented on their perceived ability to 'feel' differences between simulated molecular objects. The mechanisms for such 'feeling' are not entirely clear: observing from outside VR, one can see that there is nothing physical for participants to 'feel'. Here we outline exploratory user studies designed to evaluate the extent to which participants can distinguish quantitative differences in the flexibility of VR-simulated molecular objects. The results suggest that an individual's capacity to detect differences in molecular flexibility is enhanced when they can interact with and manipulate the molecules, as opposed to merely observing the same interaction. Building on these results, we intend to carry out further studies investigating humans' ability to sense quantitative properties of VR simulations without haptic technology.

In this paper, we study the problem of fair classification in the presence of prior probability shifts, where the training set distribution differs from the test set. This phenomenon can be observed in the yearly records of several real-world datasets, such as recidivism records and medical expenditure surveys. If unaccounted for, such shifts can cause the predictions of a classifier to become unfair towards specific population subgroups. While the fairness notion called Proportional Equality (PE) accounts for such shifts, a procedure to ensure PE-fairness was unknown.

In this work, we propose a method, called CAPE, which provides a comprehensive solution to the aforementioned problem. CAPE makes novel use of prevalence estimation techniques, sampling and an ensemble of classifiers to ensure fair predictions under prior probability shifts. We introduce a metric, called prevalence difference (PD), which CAPE attempts to minimize in order to ensure PE-fairness. We theoretically establish that this metric exhibits several desirable properties.

We evaluate the efficacy of CAPE via a thorough empirical evaluation on synthetic datasets. We also compare the performance of CAPE with several popular fair classifiers on real-world datasets like COMPAS (criminal risk assessment) and MEPS (medical expenditure panel survey). The results indicate that CAPE ensures PE-fair predictions, while performing well on other performance metrics.

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.

Any media experience must be fully inclusive and accessible to all users regardless of their ability. With the current trend towards immersive experiences, such as Virtual Reality (VR) and 360-degree video, it becomes key that these environments are adapted to be fully accessible. However, until recently the focus has been mostly on adapting the existing techniques to fit immersive displays, rather than considering new approaches for accessibility designed specifically for these increasingly relevant media experiences. This paper surveys a wide range of 360-degree video players and examines the features they include for dealing with accessibility, such as Subtitles, Audio Description, Sign Language, User Interfaces, and other interaction features, like voice control and support for multi-screen scenarios. These features have been chosen based on guidelines from standardization contributions, like in the World Wide Web Consortium (W3C) and the International Communication Union (ITU), and from research contributions for making 360-degree video consumption experiences accessible. The in-depth analysis has been part of a research effort towards the development of a fully inclusive and accessible 360-degree video player. The paper concludes by discussing how the newly developed player has gone above and beyond the existing solutions and guidelines, by providing accessibility features that meet the expectations for a widely used immersive medium, like 360-degree video.

While the exact definition and implementation of accountability depend on the specific context, at its core accountability describes a mechanism that will make decisions transparent and often provides means to sanction "bad" decisions. As such, accountability is specifically relevant for Cyber-Physical Systems, such as robots or drones, that embed themselves into a human society, take decisions and might cause lasting harm. Without a notion of accountability, such systems could behave with impunity and would not fit into society. Despite its relevance, there is currently no agreement on its meaning and, more importantly, no way to express accountability properties for these systems. As a solution we propose to express the accountability properties of systems using Structural Causal Models. They can be represented as human-readable graphical models while also offering mathematical tools to analyze and reason over them. Our central contribution is to show how Structural Causal Models can be used to express and analyze the accountability properties of systems and that this approach allows us to identify accountability patterns. These accountability patterns can be catalogued and used to improve systems and their architectures.

In this paper we analyze a continuous version of the maximal covering location problem, in which the facilities are required to be interconnected by means of a graph structure in which two facilities are allowed to be linked if a given distance is not exceed. We provide a mathematical programming framework for the problem and different resolution strategies. First, we propose a Mixed Integer Non Linear Programming formulation, and derive properties of the problem that allow us to project the continuous variables out avoiding the nonlinear constraints, resulting in an equivalent pure integer programming formulation. Since the number of constraints in the integer programming formulation is large and the constraints are, in general, difficult to handle, we propose two branch-&-cut approaches that avoid the complete enumeration of the constraints resulting in more efficient procedures. We report the results of an extensive battery of computational experiments comparing the performance of the different approaches.

Selecting an appropriate model to forecast product demand is critical to the manufacturing industry. However, due to the data complexity, market uncertainty and users' demanding requirements for the model, it is challenging for demand analysts to select a proper model. Although existing model selection methods can reduce the manual burden to some extent, they often fail to present model performance details on individual products and reveal the potential risk of the selected model. This paper presents DFSeer, an interactive visualization system to conduct reliable model selection for demand forecasting based on the products with similar historical demand. It supports model comparison and selection with different levels of details. Besides, it shows the difference in model performance on similar products to reveal the risk of model selection and increase users' confidence in choosing a forecasting model. Two case studies and interviews with domain experts demonstrate the effectiveness and usability of DFSeer.

A Non-terrestrial Network (NTN) comprising Low Earth Orbit (LEO) satellites can enable connectivity to underserved areas, thus complementing existing telecom networks. The high-speed satellite motion poses several challenges at the physical layer such as large Doppler frequency shifts. In this paper, an analytical framework is developed for statistical characterization of Doppler shift in an NTN where LEO satellites provide communication services to terrestrial users. Using tools from stochastic geometry, the users within a cell are grouped into disjoint clusters to limit the differential Doppler across users. Under some simplifying assumptions, the cumulative distribution function (CDF) and the probability density function are derived for the Doppler shift magnitude at a random user within a cluster. The CDFs are also provided for the minimum and the maximum Doppler shift magnitude within a cluster. Leveraging the analytical results, the interplay between key system parameters such as the cluster size and satellite altitude is examined. Numerical results validate the insights obtained from the analysis.

By using dissipativity approach, we establish the stability condition for the feedback connection of a deterministic dynamical system $Sigma$ and a stochastic memoryless map $Psi$. After that, we extend the result to the class of large scale systems in which: $Sigma$ consists of many sub-systems; and $Psi$ consists of many "stochastic actuators" and "probability controllers" that control the actuator's output events. We will demonstrate the proposed approach by showing the design procedures to globally stabilize the manufacturing systems while locally balance the stock levels in any production process.

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.

We investigate learnability of possibilistic theories from entailments in light of Angluin's exact learning model. We consider cases in which only membership, only equivalence, and both kinds of queries can be posed by the learner. We then show that, for a large class of problems, polynomial time learnability results for classical logic can be transferred to the respective possibilistic extension. In particular, it follows from our results that the possibilistic extension of propositional Horn theories is exactly learnable in polynomial time. As polynomial time learnability in the exact model is transferable to the classical probably approximately correct model extended with membership queries, our work also establishes such results in this model.

We study heterogeneous $k$-facility location games. In this model there are $k$ facilities where each facility serves a different purpose. Thus, the preferences of the agents over the facilities can vary arbitrarily. Our goal is to design strategy proof mechanisms that place the facilities in a way to maximize the minimum utility among the agents. For $k=1$, if the agents' locations are known, we prove that the mechanism that places the facility on an optimal location is strategy proof. For $k geq 2$, we prove that there is no optimal strategy proof mechanism, deterministic or randomized, even when $k=2$ there are only two agents with known locations, and the facilities have to be placed on a line segment. We derive inapproximability bounds for deterministic and randomized strategy proof mechanisms. Finally, we focus on the line segment and provide strategy proof mechanisms that achieve constant approximation. All of our mechanisms are simple and communication efficient. As a byproduct we show that some of our mechanisms can be used to achieve constant factor approximations for other objectives as the social welfare and the happiness.

Recently, the Edge Computing paradigm has gained significant popularity both in industry and academia. Researchers now increasingly target to improve performance and reduce energy consumption of such devices. Some recent efforts focus on using emerging RRAM technologies for improving energy efficiency, thanks to their no leakage property and high integration density. As the complexity and dynamism of applications supported by such devices escalate, it has become difficult to maintain ideal performance by static RRAM controllers. Machine Learning provides a promising solution for this, and hence, this work focuses on extending such controllers to allow dynamic parameter updates. In this work we propose an Adaptive RRAM Variability-Aware Controller, AVAC, which periodically updates Wait Buffer and batch sizes using on-the-fly learning models and gradient ascent. AVAC allows Edge devices to adapt to different applications and their stages, to improve computation performance and reduce energy consumption. Simulations demonstrate that the proposed model can provide up to 29% increase in performance and 19% decrease in energy, compared to static controllers, using traces of real-life healthcare applications on a Raspberry-Pi based Edge deployment.

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.

In this paper, we present a novel MaxSAT-based technique to compute Maximum Probability Minimal Cut Sets (MPMCSs) in fault trees. We model the MPMCS problem as a Weighted Partial MaxSAT problem and solve it using a parallel SAT-solving architecture. The results obtained with our open source tool indicate that the approach is effective and efficient.

By Sci Dev Net Advances in printed solar cell technology promise clean renewable energy, opening possibilities for 1.3 billion people still without electric power in developing countries. The technology, which only requires the use of existing industrial-size printers, can produce … Continue reading →

By Timothy Schoechle Courtesy of Solar Today A Colorado community is developing a community-based clean energy economy. Boulder, Colorado, sits at the foot of the Rocky Mountains a half-hour drive northwest of Denver. It is a city, but still small … Continue reading →

The Cannabis Issue So many of us are stuck inside right now, and that lack of socializing means we're all probably going a little bit stir crazy.…

The CDC designed a flawed test for COVID-19, then took weeks to figure out a fix so state and local labs could use it. New York still doesn’t trust the test’s accuracy By Caroline Chen, Marshall Allen, Lexi Churchill and Isaac Arnsdorf Propublica…

When I first moved to Spokane just over five years ago, I had no idea what kind of literary hotbed I was making my new home.…

The Inlander is a community newspaper covering news, politics, events, happy hour, everything that's happening today, things to do on the weekend, in Spokane, Coeur d'Alene, the greater Inland Northwest and beyond.

Yesterday, Spokane Mayor Nadine Woodward and other local leaders urged Gov. Jay Inslee to allow Spokane County to open on a different schedule than the rest of the state.  Inslee, however, wouldn't budge.…

A modeling framework for evaluating the impact of weather conditions on farming and harvest operations applies real-time, field-level weather data and forecasts of meteorological and climatological conditions together with user-provided and/or observed feedback of a present state of a harvest-related condition to agronomic models and to generate a plurality of harvest advisory outputs for precision agriculture. A harvest advisory model simulates and predicts the impacts of this weather information and user-provided and/or observed feedback in one or more physical, empirical, or artificial intelligence models of precision agriculture to analyze crops, plants, soils, and resulting agricultural commodities, and provides harvest advisory outputs to a diagnostic support tool for users to enhance farming and harvest decision-making, whether by providing pre-, post-, or in situ-harvest operations and crop analyzes.

In one embodiment, a system includes a data collection system configured to collect a data from a control system by using an offline mode of operations. The system further includes a configuration management system configured to manage a hardware configuration and a software configuration for the control system based on the data. The system additionally includes a rule engine configured to use the data as input and to output a health assessment by using a rule database, and a report generator configured to provide a health assessment for the control system.

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.

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.

A glass ceramic as cooktop for induction heating having improved colored display capability and heat shielding is provided. The cooktop includes a transparent, dyed glass ceramic plate having high-quartz mixed crystals as a predominant crystal phase. The glass ceramic contains none of the chemical refining agents arsenic oxide and/or antimony oxide and has a transmittance values greater than 0.4% at at least one wavelength in the blue spectrum between 380 and 500 nm, a transmittance >2% at 630 nm, a transmittance of less than 45% at 1600 nm, and a light transmittance of less than 2.5% in the visible spectrum.

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).

This invention relates to a method for increasing thermal stability of fuel, as well as in reducing nitrogen content and/or enhancing color quality of the fuel. According to the method, a fuel feedstock can be treated with a solid phosphoric acid catalyst under appropriate catalyst conditions, e.g., to increase the thermal stability of the fuel feedstock. Preferably, the fuel feedstock can be treated with the solid phosphoric acid catalyst at a ratio of catalyst mass within a contact zone to a mass flow rate of feedstock through the zone of at least about 18 minutes to increase the thermal stability of the fuel feedstock, along with reducing nitrogen content and/or enhancing color quality.

Provided are a negative active material, a method of preparing the same, and a lithium battery including the negative active material. The negative active material includes a carbonaceous core that has a sulfur content of about 10 ppm to 900 ppm; and an amorphous carbon layer continuously formed on a surface of the carbonaceous core, wherein the carbonaceous core has a crystalloid plate structure, and a crystallite size measured from a full width at half maximum of the peak with respect to the surface (002) of about 10 nm to about 45 nm in an X-ray diffraction spectrum of the carbonaceous core. The lithium battery including a negative electrode including the negative active material has improved capacity characteristics and ring lifetime characteristics.

In a method for fabricating a graphene structure, there is formed on a fabrication substrate a pattern of a plurality of distinct graphene catalyst materials. In one graphene synthesis step, different numbers of graphene layers are formed on the catalyst materials in the formed pattern. In a method for fabricating a graphene transistor, on a fabrication substrate at least one graphene catalyst material is provided at a substrate region specified for synthesizing a graphene transistor channel and at least one graphene catalyst material is provided at a substrate region specified for synthesizing a graphene transistor source, and at a substrate region specified for synthesizing a graphene transistor drain. Then in one graphene synthesis step, at least one layer of graphene is formed at the substrate region for the graphene transistor channel, and at the regions for the transistor source and drain there are formed a plurality of layers of graphene.

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 in particular has as an object detecting incompatibility between equipment items of a on-board system. A logic interface associated with one equipment item comprises at least one input while a logic interface associated with another equipment item comprises at least one output. The input and the output are connected. After a minimal data definition level associated with the input and a data definition level associated with the output have been obtained (505), the said minimal data definition level associated with the input is compared (515) with the said data definition level associated with the output. Following this comparison, if the said minimal data definition level associated with the input is lower than the said data definition level associated with the output, an alarm indicating an incompatibility of these two equipment items is generated (545).

In one embodiment, a processor includes at least one functional block and banking logic. The banking logic may be to determine an average reliability metric associated with the at least one functional block. The banking logic may also be to, if the average reliability metric exceeds a required level, implement a reduced reliability mode in the at least one functional block, where the reduced reliability mode is associated with a reduction in the average reliability metric. Other embodiments are described and claimed.

The invention relates to the treatment of carbonaceous radioactive waste, comprising the delivery of waste to one or more radioactive isotope separation stations isotopes, said isotopes being among at least carbon 14, chlorine 36, and tritium. Advantageously, the delivery to each of the stations occurs in wet form, with water being a common medium for conveying the waste to each of the separation stations.

Containers and methods for disposing unused pharmaceutical product are disclosed. Each container (100, 200, 300) may include a container body (104, 204, 304) with an internal chamber (116, 216, 316) for storing pharmaceutical product, along with a cover (124, 224, 324) for selectively limiting access to the chamber (116, 216, 316). An encapsulation component (128, 228, 328) may be selectively disposable within the chamber (116, 216, 316), and may be operable to encapsulate the pharmaceutical product within the container (100, 200, 300). For instance, the encapsulation component (128, 228, 328) may melt and/or flow into contact with the pharmaceutical product and thereafter solidify to encapsulate the pharmaceutical product. The encapsulation component (128, 228, 328) may melt and thereafter solidify between the cover (124, 224, 324) and shell (104, 204, 304) to limit removal of the cover (124, 224, 324) from the shell (104, 204, 304).

A sine wave generator for a Direct Digital Synthesizer (DDS) converts a digital phase input into a digital sine wave output. Sine values and slopes are stored in read-only memory (ROM) for coarse upper phase bits in a first quadrant. A quadrant folder and phase splitter reflects and inverts values from the first quadrant to generate amplitudes for all four quadrants. Each sine value and slope is stored for a range of lower phase bits. A Delta bit separates upper and lower phase bits. Delta conditionally inverts the lower phase bits, the sine value, and the final polarity. A reduced AND logic array multiplies the slope by the conditionally inverted lower phase bits. A reconstructed ADD logic array then adds the conditionally inverted sine value. The conditionally inverted polarity is added to generate the final sine value. Sine generation logic is streamlined with conditional inversion based on the Delta bit.

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.

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 method, apparatus, and system for switching from an existing target end device to a next target end device in a multi-expander storage topology by using Fast Context Switching. The method enhances Fast Context Switching by allowing Fast Context Switching to reuse or extend part of an existing connection path to an end device directly attached to a remote expander. The method can include reusing or extending at least a partial path of an established connection between an initiator and the existing target end device for a connection between the initiator and the next target end device, whereby the existing target end device and the next target end device are locally attached to different expanders.

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.

An aerosol glitter composition for achieving the “sugar” glitter effect comprises a solvent, binder, square polyester glitter, optionally a rheology modifier, and propellant.

A High Performance Computing (HPC) node comprises a motherboard, a switch comprising eight or more ports integrated on the motherboard, and at least two processors operable to execute an HPC job, with each processor communicably coupled to the integrated switch and integrated on the motherboard.

A data processing system arranged for receiving over a network, according to a data transfer protocol, data directed to any of a plurality of destination identities, the data processing system comprising: data storage for storing data received over the network; and a first processing arrangement for performing processing in accordance with the data transfer protocol on received data in the data storage, for making the received data available to respective destination identities; and a response former arranged for: receiving a message requesting a response indicating the availability of received data to each of a group of destination identities; and forming such a response; wherein the system is arranged to, in dependence on receiving the said message.

A framework for implementing multitenant architecture is provided. The framework comprises a framework services module which is configured to provide framework services that facilitate abstraction of Software-as-a-Service (SaaS) services and crosscutting services for a Greenfield application and a non SaaS based web application. Further the abstraction results in a SaaS based multitenant web application. The framework further comprises a runtime module configured to automatically integrate and consume the framework services and APIs to facilitate monitoring and controlling of features associated with the SaaS based multitenant web application. The framework further comprises a metadata services module configured to provide a plurality of metadata services to facilitate abstraction of storage structure of metadata associated with the framework and act as APIs for managing the metadata. The framework further comprises a role based administration module that facilitates management of the metadata through a tenant administrator and a product administrator.

In one embodiment, a machine-implemented method programs a heterogeneous multi-processor computer system to run a plurality of program modules, wherein each program module is to be run on one of the processors The system includes a plurality of processors of two or more different processor types. According to the recited method, machine-implemented offline processing is performed using a plurality of SIET tools of a scheduling information extracting toolkit (SIET) and a plurality of SBT tools of a schedule building toolkit (SBT). A program module applicability analyzer (PMAA) determines whether a first processor of a first processor type is capable of running a first program module without compiling the first program module. Machine-implemented online processing is performed using realtime data to test the scheduling software and the selected schedule solution.

The present invention relates generally to client-server architectures for allowing generic upload and download functionality between a web application at a server and a client. One exemplary method includes sending a download/upload request to a web application at the server, where the download/upload request specifies at least one file to download/upload; receiving a transmission from the server; parsing the transmission to identify a download/upload command and an associated download/upload manifest, where the download/upload manifest includes executable code that, when executed on the client, will perform the download/upload of the at least one file.