The problem of sampling from the stationary distribution of a Markov chain finds widespread applications in a variety of fields. The time required for a Markov chain to converge to its stationary distribution is known as the classical mixing time. In this article, we deal with analog quantum algorithms for mixing. First, we provide an analog quantum algorithm that given a Markov chain, allows us to sample from its stationary distribution in a time that scales as the sum of the square root of the classical mixing time and the square root of the classical hitting time. Our algorithm makes use of the framework of interpolated quantum walks and relies on Hamiltonian evolution in conjunction with von Neumann measurements.

There also exists a different notion for quantum mixing: the problem of sampling from the limiting distribution of quantum walks, defined in a time-averaged sense. In this scenario, the quantum mixing time is defined as the time required to sample from a distribution that is close to this limiting distribution. Recently we provided an upper bound on the quantum mixing time for Erd"os-Renyi random graphs [Phys. Rev. Lett. 124, 050501 (2020)]. Here, we also extend and expand upon our findings therein. Namely, we provide an intuitive understanding of the state-of-the-art random matrix theory tools used to derive our results. In particular, for our analysis we require information about macroscopic, mesoscopic and microscopic statistics of eigenvalues of random matrices which we highlight here. Furthermore, we provide numerical simulations that corroborate our analytical findings and extend this notion of mixing from simple graphs to any ergodic, reversible, Markov chain.

In this paper we revisit the deterministic version of the Sparse Fourier Transform problem, which asks to read only a few entries of $x in mathbb{C}^n$ and design a recovery algorithm such that the output of the algorithm approximates $hat x$, the Discrete Fourier Transform (DFT) of $x$. The randomized case has been well-understood, while the main work in the deterministic case is that of Merhi et al.@ (J Fourier Anal Appl 2018), which obtains $O(k^2 log^{-1}k cdot log^{5.5}n)$ samples and a similar runtime with the $ell_2/ell_1$ guarantee. We focus on the stronger $ell_{infty}/ell_1$ guarantee and the closely related problem of incoherent matrices. We list our contributions as follows.

1. We find a deterministic collection of $O(k^2 log n)$ samples for the $ell_infty/ell_1$ recovery in time $O(nk log^2 n)$, and a deterministic collection of $O(k^2 log^2 n)$ samples for the $ell_infty/ell_1$ sparse recovery in time $O(k^2 log^3n)$.

2. We give new deterministic constructions of incoherent matrices that are row-sampled submatrices of the DFT matrix, via a derandomization of Bernstein's inequality and bounds on exponential sums considered in analytic number theory. Our first construction matches a previous randomized construction of Nelson, Nguyen and Woodruff (RANDOM'12), where there was no constraint on the form of the incoherent matrix.

Our algorithms are nearly sample-optimal, since a lower bound of $Omega(k^2 + k log n)$ is known, even for the case where the sensing matrix can be arbitrarily designed. A similar lower bound of $Omega(k^2 log n/ log k)$ is known for incoherent matrices.

We consider the problem of efficiently simulating population protocols. In the population model, we are given a distributed system of $n$ agents modeled as identical finite-state machines. In each time step, a pair of agents is selected uniformly at random to interact. In an interaction, agents update their states according to a common transition function. We empirically and analytically analyze two classes of simulators for this model.

First, we consider sequential simulators executing one interaction after the other. Key to the performance of these simulators is the data structure storing the agents' states. For our analysis, we consider plain arrays, binary search trees, and a novel Dynamic Alias Table data structure.

Secondly, we consider batch processing to efficiently update the states of multiple independent agents in one step. For many protocols considered in literature, our simulator requires amortized sub-constant time per interaction and is fast in practice: given a fixed time budget, the implementation of our batched simulator is able to simulate population protocols several orders of magnitude larger compared to the sequential competitors, and can carry out $2^{50}$ interactions among the same number of agents in less than 400s.

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.

Quantum dialogue is a process of two way secure and simultaneous communication using a single channel. Recently, a Measurement Device Independent Quantum Dialogue (MDI-QD) protocol has been proposed (Quantum Information Processing 16.12 (2017): 305). To make the protocol secure against information leakage, the authors have discarded almost half of the qubits remaining after the error estimation phase. In this paper, we propose two modified versions of the MDI-QD protocol such that the number of discarded qubits is reduced to almost one-fourth of the remaining qubits after the error estimation phase. We use almost half of their discarded qubits along with their used qubits to make our protocol more efficient in qubits count. We show that both of our protocols are secure under the same adversarial model given in MDI-QD protocol.

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.

Cassava a major food crop in many parts of Africa, has majorly been affected by Cassava Brown Streak Disease (CBSD). The disease affects tuberous roots and presents symptoms that include a yellow/brown, dry, corky necrosis within the starch-bearing tissues. Cassava breeders currently depend on visual inspection to score necrosis in roots based on a qualitative score which is quite subjective. In this paper we present an approach to automate root necrosis scoring using deep convolutional neural networks with semantic segmentation. Our experiments show that the UNet model performs this task with high accuracy achieving a mean Intersection over Union (IoU) of 0.90 on the test set. This method provides a means to use a quantitative measure for necrosis scoring on root cross-sections. This is done by segmentation and classifying the necrotized and non-necrotized pixels of cassava root cross-sections without any additional feature engineering.

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.

We show that the traveling salesman problem (TSP) and its many variants may be modeled as functional optimization problems over a graph. In this formulation, all vertices and arcs of the graph are functionals; i.e., a mapping from a space of measurable functions to the field of real numbers. Many variants of the TSP, such as those with neighborhoods, with forbidden neighborhoods, with time-windows and with profits, can all be framed under this construct. In sharp contrast to their discrete-optimization counterparts, the modeling constructs presented in this paper represent a fundamentally new domain of analysis and computation for TSPs and their variants. Beyond its apparent mathematical unification of a class of problems in graph theory, the main advantage of the new approach is that it facilitates the modeling of certain application-specific problems in their home space of measurable functions. Consequently, certain elements of economic system theory such as dynamical models and continuous-time cost/profit functionals can be directly incorporated in the new optimization problem formulation. Furthermore, subtour elimination constraints, prevalent in discrete optimization formulations, are naturally enforced through continuity requirements. The price for the new modeling framework is nonsmooth functionals. Although a number of theoretical issues remain open in the proposed mathematical framework, we demonstrate the computational viability of the new modeling constructs over a sample set of problems to illustrate the rapid production of end-to-end TSP solutions to extensively-constrained practical problems.

Randomized Numerical Linear Algebra (RandNLA) uses randomness to develop improved algorithms for matrix problems that arise in scientific computing, data science, machine learning, etc. Determinantal Point Processes (DPPs), a seemingly unrelated topic in pure and applied mathematics, is a class of stochastic point processes with probability distribution characterized by sub-determinants of a kernel matrix. Recent work has uncovered deep and fruitful connections between DPPs and RandNLA which lead to new guarantees and improved algorithms that are of interest to both areas. We provide an overview of this exciting new line of research, including brief introductions to RandNLA and DPPs, as well as applications of DPPs to classical linear algebra tasks such as least squares regression, low-rank approximation and the Nystr"om method. For example, random sampling with a DPP leads to new kinds of unbiased estimators for least squares, enabling more refined statistical and inferential understanding of these algorithms; a DPP is, in some sense, an optimal randomized algorithm for the Nystr"om method; and a RandNLA technique called leverage score sampling can be derived as the marginal distribution of a DPP. We also discuss recent algorithmic developments, illustrating that, while not quite as efficient as standard RandNLA techniques, DPP-based algorithms are only moderately more expensive.

We study the problem of jointly designing a sparse sensor and actuator schedule for linear dynamical systems while guaranteeing a control/estimation performance that approximates the fully sensed/actuated setting. We further prove a separation principle, showing that the problem can be decomposed into finding sensor and actuator schedules separately. However, it is shown that this problem cannot be efficiently solved or approximated in polynomial, or even quasi-polynomial time for time-invariant sensor/actuator schedules; instead, we develop deterministic polynomial-time algorithms for a time-varying sensor/actuator schedule with guaranteed approximation bounds. Our main result is to provide a polynomial-time joint actuator and sensor schedule that on average selects only a constant number of sensors and actuators at each time step, irrespective of the dimension of the system. The key idea is to sparsify the controllability and observability Gramians while providing approximation guarantees for Hankel singular values. This idea is inspired by recent results in theoretical computer science literature on sparsification.

In this technical report we give an elementary introduction to Quantum Computing for non-physicists. In this introduction we describe in detail some of the foundational Quantum Algorithms including: the Deutsch-Jozsa Algorithm, Shor's Algorithm, Grocer Search, and Quantum Counting Algorithm and briefly the Harrow-Lloyd Algorithm. Additionally we give an introduction to Solomonoff Induction, a theoretically optimal method for prediction. We then attempt to use Quantum computing to find better algorithms for the approximation of Solomonoff Induction. This is done by using techniques from other Quantum computing algorithms to achieve a speedup in computing the speed prior, which is an approximation of Solomonoff's prior, a key part of Solomonoff Induction. The major limiting factors are that the probabilities being computed are often so small that without a sufficient (often large) amount of trials, the error may be larger than the result. If a substantial speedup in the computation of an approximation of Solomonoff Induction can be achieved through quantum computing, then this can be applied to the field of intelligent agents as a key part of an approximation of the agent AIXI.

The interoperability of proof assistants and the integration of their libraries is a highly valued but elusive goal in the field of theorem proving. As a preparatory step, in previous work, we translated the libraries of multiple proof assistants, specifically the ones of Coq, HOL Light, IMPS, Isabelle, Mizar, and PVS into a universal format: OMDoc/MMT.

Each translation presented tremendous theoretical, technical, and social challenges, some universal and some system-specific, some solvable and some still open. In this paper, we survey these challenges and compare and evaluate the solutions we chose.

We believe similar library translations will be an essential part of any future system interoperability solution and our experiences will prove valuable to others undertaking such efforts.

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.

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.

Did you know that breaking the Americans with Disabilities Act (ADA) can result in a six-figure fine? For every violation, companies can receive a $150,000 fine — and if you have a WordPress site, you could be liable. While WordPress aims to ensure website accessibility, it cannot guarantee it since every site owner customizes the […]

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What do Amazon, Hershey’s, and The Wall Street Journal have in common? They’ve all gotten named in lawsuits related to website accessibility and the Americans with Disabilities Act (ADA). They aren’t alone, either. In 2018, more than 2000 website accessibility lawsuits (a 177% increase from 2017) got filed, emphasizing the increased importance and focus on […]

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By Lauren McCauley Common Dreams In landmark agreement, California’s last remaining nuclear plant will be replaced by greenhouse-gas-free energy sources A plan to shutter the last remaining nuclear power plant in California and replace it with renewable energy is being … Continue reading →

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Agencies, local authorities and national governments do not agree on who should be quarantined or what that should actually look like. Here’s what we do know. By Maya Miller, Caroline Chen and Joshua Kaplan ProPublica People who have been exposed to the coronavirus are being given incomplete or misleading information about whether they should quarantine themselves, exposing major gaps in the public health response to the pandemic and illuminating disagreement among officials about how useful the tactic even is at this point in the disease’s spread.…

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Thirty years into his distinguished career as one of the world's great guitar players, Joe Bonamassa is still finding new ways to showcase his skills, explore new sounds and stretch his artistic horizons.…

As soon as state Rep. Marcus Riccelli returned home from Olympia, he jumpstarted a community-wide effort to feed Spokane constituents deeply affected by the COVID-19 crisis.…

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 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.

A surfactant composition for agricultural chemicals, containing fatty acid polyoxyalkylene alkyl ether expressed by the following general formula (I), R1CO(EO)m(PO)nOR2 (I) wherein the fatty acid polyoxyalkylene alkyl ether has a narrow ratio of 55% by mass or more, where the narrow ratio is expressed by the following formula (A): Narrow ratio=Σi=nMAX−2i=nMAX+2Yi (A).

Disclosed are oil-extended olefin block copolymer compositions with precipitated silica. The precipitated silica reduces oil-bleed while maintaining composition softness.

A composition comprising particulate solid (typically a pigment or filler), an organic medium (typically the organic medium may be a plastics material or an organic liquid) and a polyacrylic copolymer.

Disclosed are oil-extended olefin block copolymer compositions with microcrystalline wax. The microcrystalline wax reduces oil-bleed while maintaining composition softness.

The present invention pertains to an additive combination comprising at least two sterically hindered amines, at least one further stabilizer, a dispersing agent and a plasticizer. The present invention also pertains to a composition comprising an organic material susceptible to degradation by light, oxygen and/or heat, and the additive combination and to the use and the process for stabilizing organic material against degradation by light, oxygen and/or heat by the additive combination.

Novel surfactants and drying, drycleaning and soil repellency compositions containing such surfactants which utilize such surfactants. The surfactants are fluorine containing quaternary ammonium salts, and the drying, drycleaning and soil repellency compositions contain at least one halocarbon component and at least one of the fluorine containing surfactants. These compositions have the ability to remove water or aqueous films from the surfaces of a broad range of substrates and impart soil repellency to fabrics.

Compounds of the formula (I) or (I'), where R1 is a hydrogen atom or C1-C4-alkyl and R'1 is C1-C4-alkyl; X1 and X2 are each, independently of one another, a secondary phosphine group; R2 is hydrogen, R01R02R03Si—, C1-C18.acyl substituted by halogen, hydroxy, C1-C8-alkoxy or R04R05N—, -or R06—X01—C(O)—; R01, R02 and R03 are each, independently of one another, C1-C12-alkyl, unsubstituted or C1-C4-alkyl or C1-C4-alkoxy-substituted C6-C10-aryl or C7-C12-aralkyl; R04 and R05 are each, independently of one another, hydrogen, C1-C12-alkyl, C3-C8-cycloalkyl, C6-C10-aryl or C7-C12-aralkyl, or R04 and R05 together are trimethylene, tetramethylene, pentamethylene or 3-oxapcntylene; R06 is C1-C18-alkyl, unsubstituted or C1-C4-alkyl- or C1-C4-alkoxy-substituted C3-C8-cycloalkyl, C6-C10-aryl or C7-C12-aralkyl; X01 is —O— or —NH—; T is C6-C20-arylene; v is 0 or an integer from 1 to 4; and * denotes a mixture of racemic or enantiomerically pure diastereomers or pure racemic or enantiomerically diastereomers, are excellent chiral ligands for metal complexes as enantioselective catalysts for the hydrogenation of prochiral organic compounds.

A flame retardant suitable for manufacturing a polymer composition is provided. The polymer composition is used for forming a cured film in which a balance among flame retardancy, adhesion, chemical resistance, heat resistance, and elasticity, and so on, is provided. A flame-retardant polymer composition with an excellent balance among the above properties is also provided. The flame retardant of the invention has a structure of Formula (1), (2), or (3): (in which, R1 is hydrogen or methyl, R2 is C2-20 alkylene or C2-20 alkylene in which any —CH2— is replaced by —O—, R3 and R4 are C1-20 alkyl, phenyl, and phenyl substituted by C1-5 alkyl or phenyl, R3 and R4 may also be an integrally-formed cyclic group, and p and q are 0 or 1).

Embodiments are directed to glass frits containing phosphors that can be used in LED lighting devices and for methods associated therewith for making the phosphor containing glass frit and their use in glass articles, for example, LED devices.

A lubricating oil composition for an internal combustion engine contains: a base oil including a component (A) of a polyalphaolefin having a kinematic viscosity at 100 degrees C. of 5.5 mm2/s or less, a CCS viscosity at −35 degrees C. of 3000 mPa·s or less and a NOACK of 12 mass % or less and a component (B) of a mineral oil having a viscosity index of 120 or more; and polyisobutylene having a mass average molecular weight of 500,000 or more. A content of the composition (A) is 25 mass % or more of a total amount of a lubricating oil.

A lubricating oil composition for an internal combustion engine contains a component (A) of a polyalphaolefin having a kinematic viscosity at 100 degrees C. of 5.5 mm2/s or less, a CCS viscosity at −35 degrees C. of 3000 mPA·s or less and a NOACK of 12 mass % or less, and a component (B) of a mineral oil having a viscosity index of 120 or more. The component (A) is contained at a content of 10 mass % or more of a total amount of the composition.

Materials and Methods are disclosed for producing nanoparticles linked to antibacterial ligands, including antibiotics and/or molecules which bind to bacterial markers, and for the use of the nanoparticles for the treatment of conditions treatable by the antibiotic ligands.

Methods for the synthesis of metal quantum clusters within the framework of a porous gel matrix are described. For example, Ag25(glutathione)18 quantum clusters are synthesized in a cross-linked polyacrylamide gel matrix. The methods can be performed on large-scale and yields monodispersed metal quantum clusters.

A coating/sealant system that includes a coating and a sealant deposited over at least a portion of the coating, in which the coating includes a reaction product formed from reactants comprising a phosphated epoxy resin and a curing agent, and the sealant includes a sulfur-containing polymer.

Provided are a thermosetting adhesive composition excellent in storage stability, reliability, and low-temperature adhesion properties; and a curl-resistant heat-resistant film and a wiring film obtained using the composition. The thermosetting adhesive composition includes 100 parts by weight of a phenoxy resin having a bisphenol S skeleton in the structure thereof; 5 to 30 parts by weight of a maleimide compound containing a plurality of maleimide groups in the structure thereof; and 3 to 20 vol % of an inorganic needle-like filler. The heat resistant adhesive film is obtained by applying the thermosetting adhesive composition onto a polyimide film, followed by drying. The wiring film is obtained by placing a conductor wiring layer on the heat resistant adhesive film.

The present invention relates to a method for the degradation of pollutants in water and/or soil. More specific, the present invention relates to a method for the on-site decontamination or re-mediation of water and/or soil which are contaminated with organic compounds. Moreover, the invention relates to a method for forming a barrier against the spreading of a contamination with pollutants within the water and/or soil, especially within groundwater (aquifer). Further, the invention relates to means for use in these methods, and to the production of such means.

A treatment process of persistent organic pollutants contained in particles is provided. Said process includes reacting persistent organic pollutant in particles under hydrothermal conditions in the presence of Fe2+ and Fe3+. Several beneficial effects can be achieved, including 1) no other additive is needed during the reaction process; 2) Fe2+ and Fe3+ are safe, cheap and extensive sources; 3) because Fe2+ and Fe3+ are dissolved, they can fully disperse into particles, and fully contact can be achieved, thus obtaining a decomposition rate no less than 70% of the persistent organic pollutants is under subcritical conditions.

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

The present invention relates to methods for making oxidation resistant homogenized polymeric materials and medical implants that comprise polymeric materials, for example, ultra-high molecular weight polyethylene (UHMWPE). The invention also provides methods of making antioxidant-doped medical implants, for example, doping of medical devices containing cross-linked UHMWPE with vitamin E by diffusion and annealing the anti-oxidant doped UHMWPE in a super critical fluid, and materials used therein.

The present disclosure relates to a thermally resistant optical siloxane resin composition including siloxane containing photo-cationically polymerizable epoxy group, a photo initiator, and an antioxidant.

3-Esters and 3-amides of thioxanthone bearing alkyl chains of appropriate length and can be used as photoinitiators or sensitizers in photopolymerizable systems, in particular for the preparation of coatings compatible with the food use.

A method of processing an electronic file by identifying portions of content data in the electronic file and determining if each portion of content data is passive content data having a fixed purpose or active content data having an associated function. If a portion is passive content data, then a determination is made as to whether the portion of passive content data is to be re-generated. If a portion is active content data, then the portion is analyzed to determine whether the portion of active content data is to be re-generated. A re-generated electronic file is then created from the portions of content data which are determined to be re-generated.

Emulsions, and processes for making the emulsions, useful for imparting water resistance to gypsum products are disclosed. Process for making the emulsion and gypsum products made from the emulsion are also disclosed. The emulsions of the invention include at least one paraffin wax and a hydrophilic metallic salt. The emulsions of the invention may further include a saponifiable wax substitute for montan wax. The emulsions of the invention may further include a biocide.

The invention relates to polymers that can be obtained by polymerizing the monomers (A), (B), and (D), and optionally (C), where (A) is a monomer of formula (I), wherein A stands for C2 to C4 alkylene, B stands for a C2 to C4 alkylene different from A, R stands for hydrogen or methyl, m stands for a number from 1 to 500, n stands for a number from 1 to 500, (B) is an ethylenically unsaturated monomer that contains at least one carboxylic acid function, (C) is optionally a further ethylenically unsaturated monomer different from (A) and (B), (D) is a monomer of formula (II), wherein D stands for C2 to C4 alkylene, E stands for a C2 to C4 alkylene group different from D, F stands for a C2 to C4 alkylene group different from E, R stands for hydrogen or methyl, o stands for a number from 1 to 500, p stands for a number from 1 to 500, q stands for a number from 1 to 500, and wherein the weight fraction of the monomers is 35 to 99% for the macromonomer (A), 0.5 to 45% for the monomer (B), 0 to 20% for the monomer (C), and 1 to 20% for the monomer (D), and to the use of said polymers as defoamers for inorganic solid suspensions.