In this paper we design suboptimal control laws for an unknown linear system on the basis of measured data. We focus on the suboptimal linear quadratic regulator problem and the suboptimal H2 control problem. For both problems, we establish conditions under which a given data set contains sufficient information for controller design. We follow up by providing a data-driven parameterization of all suboptimal controllers. We will illustrate our results by numerical simulations, which will reveal an interesting trade-off between the number of collected data samples and the achieved controller performance.

We consider the most general class of linear inhomogeneous boundary-value problems for systems of ordinary differential equations of an arbitrary order whose solutions and right-hand sides belong to appropriate Sobolev spaces. For parameter-dependent problems from this class, we prove a constructive criterion for their solutions to be continuous in the Sobolev space with respect to the parameter. We also prove a two-sided estimate for the degree of convergence of these solutions to the solution of the nonperturbed problem.

We define local indices for projective umbilics and godrons (also called cusps of Gauss) on generic smooth surfaces in projective 3-space. By means of these indices, we provide formulas that relate the algebraic numbers of those characteristic points on a surface (and on domains of the surface) with the Euler characteristic of that surface (resp. of those domains). These relations determine the possible coexistences of projective umbilics and godrons on the surface. Our study is based on a "fundamental cubic form" for which we provide a closed simple expression.

Recent studies of optimization methods and GNC of spacecraft near small bodies focusing on descent, landing, rendezvous, etc., with key safety constraints such as line-of-sight conic zones and soft landings have shown promising results; this paper considers descent missions to an asteroid surface with a constraint that consists of an onboard camera and asteroid surface markers while using a stochastic convex MPC law. An undermodeled asteroid gravity and spacecraft technology inspired measurement model is established to develop the constraint. Then a computationally light stochastic Linear Quadratic MPC strategy is presented to keep the spacecraft in satisfactory field of view of the surface markers while trajectory tracking, employing chance based constraints and up-to-date estimation uncertainty from navigation. The estimation uncertainty giving rise to the tightened constraints is particularly addressed. Results suggest robust tracking performance across a variety of trajectories.

We study the two-parameter quadratic sieve for a general test function. We prove, under some very general assumptions, that the function considered by Barban and Vehov [BV68] and Graham [Gra78] for this problem is optimal up to the second-order term. We determine that second-order term explicitly.

High-performance implementations of graph algorithms are challenging to implement on new parallel hardware such as GPUs, because of three challenges: (1) difficulty of coming up with graph building blocks, (2) load imbalance on parallel hardware, and (3) graph problems having low arithmetic intensity. To address these challenges, GraphBLAS is an innovative, on-going effort by the graph analytics community to propose building blocks based in sparse linear algebra, which will allow graph algorithms to be expressed in a performant, succinct, composable and portable manner. In this paper, we examine the performance challenges of a linear algebra-based approach to building graph frameworks and describe new design principles for overcoming these bottlenecks. Among the new design principles is exploiting input sparsity, which allows users to write graph algorithms without specifying push and pull direction. Exploiting output sparsity allows users to tell the backend which values of the output in a single vectorized computation they do not want computed. Load-balancing is an important feature for balancing work amongst parallel workers. We describe the important load-balancing features for handling graphs with different characteristics. The design principles described in this paper have been implemented in "GraphBLAST", the first open-source linear algebra-based graph framework on GPU targeting high-performance computing. The results show that on a single GPU, GraphBLAST has on average at least an order of magnitude speedup over previous GraphBLAS implementations SuiteSparse and GBTL, comparable performance to the fastest GPU hardwired primitives and shared-memory graph frameworks Ligra and Gunrock, and better performance than any other GPU graph framework, while offering a simpler and more concise programming model.

The connection between visual input and tactile sensing is critical for object manipulation tasks such as grasping and pushing. In this work, we introduce the challenging task of estimating a set of tactile physical properties from visual information. We aim to build a model that learns the complex mapping between visual information and tactile physical properties. We construct a first of its kind image-tactile dataset with over 400 multiview image sequences and the corresponding tactile properties. A total of fifteen tactile physical properties across categories including friction, compliance, adhesion, texture, and thermal conductance are measured and then estimated by our models. We develop a cross-modal framework comprised of an adversarial objective and a novel visuo-tactile joint classification loss. Additionally, we develop a neural architecture search framework capable of selecting optimal combinations of viewing angles for estimating a given physical property.

How to generate testing scenario libraries for connected and automated vehicles (CAVs) is a major challenge faced by the industry. In previous studies, to evaluate maneuver challenge of a scenario, surrogate models (SMs) are often used without explicit knowledge of the CAV under test. However, performance dissimilarities between the SM and the CAV under test usually exist, and it can lead to the generation of suboptimal scenario libraries. In this paper, an adaptive testing scenario library generation (ATSLG) method is proposed to solve this problem. A customized testing scenario library for a specific CAV model is generated through an adaptive process. To compensate the performance dissimilarities and leverage each test of the CAV, Bayesian optimization techniques are applied with classification-based Gaussian Process Regression and a new-designed acquisition function. Comparing with a pre-determined library, a CAV can be tested and evaluated in a more efficient manner with the customized library. To validate the proposed method, a cut-in case study was performed and the results demonstrate that the proposed method can further accelerate the evaluation process by a few orders of magnitude.

For decades, two-player (antagonistic) games on graphs have been a framework of choice for many important problems in theoretical computer science. A notorious one is controller synthesis, which can be rephrased through the game-theoretic metaphor as the quest for a winning strategy of the system in a game against its antagonistic environment. Depending on the specification, optimal strategies might be simple or quite complex, for example having to use (possibly infinite) memory. Hence, research strives to understand which settings allow for simple strategies.

In 2005, Gimbert and Zielonka provided a complete characterization of preference relations (a formal framework to model specifications and game objectives) that admit memoryless optimal strategies for both players. In the last fifteen years however, practical applications have driven the community toward games with complex or multiple objectives, where memory -- finite or infinite -- is almost always required. Despite much effort, the exact frontiers of the class of preference relations that admit finite-memory optimal strategies still elude us.

In this work, we establish a complete characterization of preference relations that admit optimal strategies using arena-independent finite memory, generalizing the work of Gimbert and Zielonka to the finite-memory case. We also prove an equivalent to their celebrated corollary of great practical interest: if both players have optimal (arena-independent-)finite-memory strategies in all one-player games, then it is also the case in all two-player games. Finally, we pinpoint the boundaries of our results with regard to the literature: our work completely covers the case of arena-independent memory (e.g., multiple parity objectives, lower- and upper-bounded energy objectives), and paves the way to the arena-dependent case (e.g., multiple lower-bounded energy objectives).

Introduction of spectrum-sharing in 5G and subsequent generation networks demand base-station(s) with the capability to characterize the wideband spectrum spanned over licensed, shared and unlicensed non-contiguous frequency bands. Spectrum characterization involves the identification of vacant bands along with center frequency and parameters (energy, modulation, etc.) of occupied bands. Such characterization at Nyquist sampling is area and power-hungry due to the need for high-speed digitization. Though sub-Nyquist sampling (SNS) offers an excellent alternative when the spectrum is sparse, it suffers from poor performance at low signal to noise ratio (SNR) and demands careful design and integration of digital reconstruction, tunable channelizer and characterization algorithms. In this paper, we propose a novel deep-learning framework via a single unified pipeline to accomplish two tasks: 1)~Reconstruct the signal directly from sub-Nyquist samples, and 2)~Wideband spectrum characterization. The proposed approach eliminates the need for complex signal conditioning between reconstruction and characterization and does not need complex tunable channelizers. We extensively compare the performance of our framework for a wide range of modulation schemes, SNR and channel conditions. We show that the proposed framework outperforms existing SNS based approaches and characterization performance approaches to Nyquist sampling-based framework with an increase in SNR. Easy to design and integrate along with a single unified deep learning framework make the proposed architecture a good candidate for reconfigurable platforms.

Stockhusen and Tantau (IPEC 2013) defined the operators paraW and paraBeta for parameterised space complexity classes by allowing bounded nondeterminism with multiple read and read-once access, respectively. Using these operators, they obtained characterisations for the complexity of many parameterisations of natural problems on graphs.

In this article, we study the counting versions of such operators and introduce variants based on tail-nondeterminism, paraW[1] and paraBetaTail, in the setting of parameterised logarithmic space. We examine closure properties of the new classes under the central reductions and arithmetic operations. We also identify a wide range of natural complete problems for our classes in the areas of walk counting in digraphs, first-order model-checking and graph-homomorphisms. In doing so, we also see that the closure of #paraBetaTail-L under parameterised logspace parsimonious reductions coincides with #paraBeta-L. We show that the complexity of a parameterised variant of the determinant function is #paraBetaTail-L-hard and can be written as the difference of two functions in #paraBetaTail-L for (0,1)-matrices. Finally, we characterise the new complexity classes in terms of branching programs.

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.

Thin structures, such as wire-frame sculptures, fences, cables, power lines, and tree branches, are common in the real world.

It is extremely challenging to acquire their 3D digital models using traditional image-based or depth-based reconstruction methods because thin structures often lack distinct point features and have severe self-occlusion.

We propose the first approach that simultaneously estimates camera motion and reconstructs the geometry of complex 3D thin structures in high quality from a color video captured by a handheld camera.

Specifically, we present a new curve-based approach to estimate accurate camera poses by establishing correspondences between featureless thin objects in the foreground in consecutive video frames, without requiring visual texture in the background scene to lock on.

Enabled by this effective curve-based camera pose estimation strategy, we develop an iterative optimization method with tailored measures on geometry, topology as well as self-occlusion handling for reconstructing 3D thin structures.

Extensive validations on a variety of thin structures show that our method achieves accurate camera pose estimation and faithful reconstruction of 3D thin structures with complex shape and topology at a level that has not been attained by other existing reconstruction methods.

Web services often impose inter-parameter dependencies that restrict the way in which two or more input parameters can be combined to form valid calls to the service. Unfortunately, current specification languages for web services like the OpenAPI Specification (OAS) provide no support for the formal description of such dependencies, which makes it hardly possible to automatically discover and interact with services without human intervention. In this article, we present an approach for the specification and automated analysis of inter-parameter dependencies in web APIs. We first present a domain-specific language, called Inter-parameter Dependency Language (IDL), for the specification of dependencies among input parameters in web services. Then, we propose a mapping to translate an IDL document into a constraint satisfaction problem (CSP), enabling the automated analysis of IDL specifications using standard CSP-based reasoning operations. Specifically, we present a catalogue of nine analysis operations on IDL documents allowing to compute, for example, whether a given request satisfies all the dependencies of the service. Finally, we present a tool suite including an editor, a parser, an OAS extension, a constraint programming-aided library, and a test suite supporting IDL specifications and their analyses. Together, these contributions pave the way for a new range of specification-driven applications in areas such as code generation and testing.

Bayesian predictive coding is a putative neuromorphic method for acquiring higher-level neural representations to account for sensory input. Although originating in the neuroscience community, there are also efforts in the machine learning community to study these models. This paper reviews some of the more well known models. Our review analyzes module connectivity and patterns of information transfer, seeking to find general principles used across the models. We also survey some recent attempts to cast these models within a deep learning framework. A defining feature of Bayesian predictive coding is that it uses top-down, reconstructive mechanisms to predict incoming sensory inputs or their lower-level representations. Discrepancies between the predicted and the actual inputs, known as prediction errors, then give rise to future learning that refines and improves the predictive accuracy of learned higher-level representations. Predictive coding models intended to describe computations in the neocortex emerged prior to the development of deep learning and used a communication structure between modules that we name the Rao-Ballard protocol. This protocol was derived from a Bayesian generative model with some rather strong statistical assumptions. The RB protocol provides a rubric to assess the fidelity of deep learning models that claim to implement predictive coding.

We establish versions of Conley's (i) fundamental theorem and (ii) decomposition theorem for a broad class of hybrid dynamical systems. The hybrid version of (i) asserts that a globally-defined "hybrid complete Lyapunov function" exists for every hybrid system in this class. Motivated by mechanics and control settings where physical or engineered events cause abrupt changes in a system's governing dynamics, our results apply to a large class of Lagrangian hybrid systems (with impacts) studied extensively in the robotics literature. Viewed formally, these results generalize those of Conley and Franks for continuous-time and discrete-time dynamical systems, respectively, on metric spaces. However, we furnish specific examples illustrating how our statement of sufficient conditions represents merely an early step in the longer project of establishing what formal assumptions can and cannot endow hybrid systems models with the topologically well characterized partitions of limit behavior that make Conley's theory so valuable in those classical settings.

We analyze the computational complexity of motion planning through local "input/output" gadgets with separate entrances and exits, and a subset of allowed traversals from entrances to exits, each of which changes the state of the gadget and thereby the allowed traversals. We study such gadgets in the 0-, 1-, and 2-player settings, in particular extending past motion-planning-through-gadgets work to 0-player games for the first time, by considering "branchless" connections between gadgets that route every gadget's exit to a unique gadget's entrance. Our complexity results include containment in L, NL, P, NP, and PSPACE; as well as hardness for NL, P, NP, and PSPACE. We apply these results to show PSPACE-completeness for certain mechanics in Factorio, [the Sequence], and a restricted version of Trainyard, improving prior results. This work strengthens prior results on switching graphs and reachability switching games.

We consider the problem of protecting and manipulating elections by recounting and changing ballots, respectively. Our setting involves a plurality-based election held across multiple districts, and the problem formulations are based on the model proposed recently by~[Elkind et al, IJCAI 2019]. It turns out that both of the manipulation and protection problems are NP-complete even in fairly simple settings. We study these problems from a parameterized perspective with the goal of establishing a more detailed complexity landscape. The parameters we consider include the number of voters, and the budgets of the attacker and the defender. While we observe fixed-parameter tractability when parameterizing by number of voters, our main contribution is a demonstration of parameterized hardness when working with the budgets of the attacker and the defender.

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.

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.

Retired soccer star Briana Scurry talks about how frustrating and complicated it is trying to explain what it feels like to have symptoms from a concussion and why bouncing back is not always an option.

What happens when the drive to play outweighs the potential risk of injury? Some high school athletes are finding ways around the precautions coaching and medical staff take to ensure their safety.

[IMAGE-1] I honestly don't know how people like Bryan Stevenson keep up the fight. Just Mercy is the true origin story of a literal social justice warrior, a Harvard-educated lawyer who, in the late 1980s, launched the Equal Justice Initiative in Montgomery, Alabama, to take on the neediest, most desperate cases.…

[IMAGE-1] First of all: No. Why would a suddenly single dad to traumatized young children leave said shocked and distressed kids with his new fiancée, who is also the sole survivor of her own massive childhood trauma?…

Back in November, we wrote about local singer-songwriter Eliza Johnson's musical project Eliza Catastrophe and her new album You, which she released on pre-loaded MP3 players. One thing we weren't able to mention in our interview — for contractual reasons — is that she had only a couple months prior auditioned for American Idol, and her performance finally aired on the ABC reality competition show Sunday night.…

As COVID-19 fears grew, public officials and sports execs contemplated health risks — and debated a PR message — but let 33,000 fans into a Seattle Sounders soccer match, emails show. By Ken Armstrong, ProPublica, and David Gutman and Lewis Kamb, The Seattle Times On March 6, at 2:43 p.m., the health officer for Public Health — Seattle & King County, the hardest-hit region in the first state to be slammed by COVID-19, sent an email to a half-dozen colleagues, saying, “I want to cancel large group gatherings now.”…

Gonzaga Athletic Director Mike Roth took to the Zoom online meeting app Wednesday for a lengthy chat with members of the school community, fans and media to answer questions about college sports in the era of COVID-19. Like so many things regarding the coronavirus, there are a lot of hopes for a rapid return to normalcy — all of them couched in the reality that none of us really know how the pandemic is going to affect our lives three months from now, or six months down the line.…

You’re out of a job. You’ve been stuck inside for weeks.…

At every level of America's food system, mandated closures and outbreaks of COVID-19 have interrupted the finely tuned network that normally gets food from farmers and food processors to restaurants, grocery stores and food banks.…

As this issue goes to press, our city, our state, our country and our world are coping with the spread of a virus that's deadly to some, and not even noticed by others who have no symptoms but may spread it.…

Growing up, Luke Parker played sports.…

AMC River Park Square…

Tool's music is not only not for everyone, it's such a challenging polyglot of oft-derided musical styles that it risks not being for anyone. And yet, the quartet's blend of prog-rock, art-rock, metal and performance art has become a genre unto itself over the course of 30 years, and it's a genre that has proved remarkably commercial.…

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.…

In one embodiment, the invention relates to processes for producing acesulfame potassium. In one embodiment, the process comprises the step of reacting a first reaction mixture to form an amidosulfamic acid salt such as a trialkyl ammonium amidosulfamic acid salt. The first reaction mixture comprises sulfamic acid, an amine, and smaller amounts, if any, acetic acid, e.g., less than 1 wt % (10000 wppm). In terms of ranges, the first reaction mixture may comprise from 1 wppm to 1 wt % acetic acid. The process further comprises the step of reacting the amidosulfamic acid salt with diketene to form an acetoacetamide salt. In preferred embodiments, the amidosulfamic acid salt formation reaction is conducted at pH levels from 5.5 to 7.0. The process further comprises the step of deriving the acesulfame-K from the acetoacetamide salt.

A compound represented by Formula 1 below and an organic light-emitting device including an organic layer containing the compound of Formula 1: wherein R1 to R4, X and Y, a and b, and m and n are defined as in the specification.

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.

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

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

Described herein are dicarboxylate-capped estolide compound and methods of making the same. Exemplary dicarboxylate-capped estolide compounds include those of the formula x is, independently for each occurrence, an integer selected from 0 to 20; y is, independently for each occurrence, an integer selected from 0 to 20; W is, independently for each occurrence, selected from —CH2— and —CH═CH—; z is an integer selected from 1 to 40; n is an integer equal to or greater than 0; R5 is selected from hydrogen, optionally substituted alkyl that is saturated or unsaturated, and branched or unbranched, and an estolide residue; and R2 is selected from hydrogen and optionally substituted alkyl that is saturated or unsaturated, and branched or unbranched, wherein each fatty acid chain residue of said at least one compound is independently optionally substituted.

The present invention provides a compound useful as an agent for the prevention or treatment of a sex hormone-dependent disease or the like. That is, the present invention provides a fused heterocyclic derivative represented by the following general formula (I), a pharmaceutical composition containing the same, a medicinal use thereof and the like. In the formula (I), ring A represents 5-membered cyclic unsaturated hydrocarbon or 5-membered heteroaryl; RA represents halogen, alkyl, alkenyl, alkynyl, carboxy, alkoxy, carbamoyl, alkylcarbamoyl or the like ; ring B represents aryl or heteroaryl; RB represents halogen, alkyl, carboxy, alkoxy, carbamoyl, alkylcarbamoyl or the like; E1 and E2 represent an oxygen atom or the like; U represents a single bond or alkylene; X represents a group represented by Y, —SO2—Y, —O—(alkylene)—Y, —O—Z in which Y represents Z, amino or the like; Z represents cycloalkyl, heterocycloalkyl, aryl, heteroaryl or the like; or the like.

A composite sheet of the present invention comprises an oxetane-epoxy-based compound, represented by chemical formula 1, as a binder.

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

Disclosed is an adhesive for a polarizing plate that comprises a polyvinyl alcohol-based resin with an acetoacetyl group and an amine-based metal compound crosslinking agent, and a method of manufacturing the same.

A 1,3-diketoamide functional monomer represented by the following formula (1): wherein R and Y are independently selected from the group consisting of hydrogen, an alkyl group having from 1 to 10 carbon atoms, an aryl group having from 6 to 20 carbon atoms, and an aralkyl group having from 7 to 20 carbon atoms; and wherein X and Z are independently selected from the group consisting of an alkyl group having from 1 to 10 carbon atoms, an aryl group having from 6 to 20 carbon atoms, and an aralkyl group having from 7 to 20 carbon atoms. Also disclosed are emulsion, suspension, and solution polymers comprising residues from the 1,3-diketoamide functional monomer of formula 1 and, optionally, one or more additional ethylenically unsaturated monomers. Both latex and self-curing coating compositions described herein exhibit excellent hydrolytic stability, including increased retention of 1,3-diketo functionality.

The present invention has an object to provide an ink for inkjet textile printing which has excellent fastnesses and causes less bleeding on a textile and to provide a low cost method for inkjet textile printing by using said ink; and relates to an ink for inkjet textile printing wherein the ink contains at least one kind disperse dye, a dispersing agent, water and at least one kind (referred to as A compound) of the compounds represented by the following formula (1): (wherein, n is an integer number of 1 to 12) and at least one kind compound (referred to as B' compound) selected from the group consisting of alkanediols having 3 to 5 carbon atoms and polypropylene glycols, as organic solvents, the total content of A compound and B' compound is 12 to 50% by weight based on the total amount of the ink, and the ratio by weight of A compound to B' compound is in the range of 5:1 to 0.7:1.

The present invention is directed at a binder for a battery electrode comprising an ethylene oxide-containing copolymer including a first monomer of ethylene oxide (EO) and at least one additional monomer selected from an alkylene-oxide that is different from the first monomer of EO, an alkyl glycidyl ether, or a combination thereof; wherein the ethylene oxide-containing copolymer has a weight average molecular weight less than about 200,000 g/mole (e.g., from about 10,000 to about 100,000), the molar fraction of the first monomer of EO (XEo) in the ethylene oxide-containing copolymer is greater than 0.80 (e.g., from about 0.80 to about 0.995), and the ethylene oxide-containing copolymer has a peak melting temperature (Tp), in ° C., for a selected XEO in the range of about 0.80 to about 0.995, which is below a maximum value of Tpmax, at the selected XEO, which is calculated using the equation Tpmax=(60−150 (1−XEO)).

Disclosed are a ferrocene-containing conductive polymer, an organic memory device using the conductive polymer and a method for fabricating the organic memory device. The conductive polymer may include a fluorenyl repeating unit, a thienyl repeating unit and a diarylferrocenyl repeating unit. The organic memory device may possess the advantages of rapid switching time, decreased operating voltage, decreased fabrication costs and increased reliability. Based on these advantages, the organic memory device may be used as a highly integrated, large-capacity memory device.

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).