We prove that there are no irreducible representations of $B_n$ of dimension $n+1$ for $ngeq 10.$

Large-scale pretrained language models are the major driving force behind recent improvements in performance on the Winograd Schema Challenge, a widely employed test of common sense reasoning ability. We show, however, with a new diagnostic dataset, that these models are sensitive to linguistic perturbations of the Winograd examples that minimally affect human understanding. Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones. Overall, humans are correct more often than out-of-the-box models, and the models are sometimes right for the wrong reasons. Finally, we show that fine-tuning on a large, task-specific dataset can offer a solution to these issues.

Device fingerprints like sensor pattern noise (SPN) are widely used for provenance analysis and image authentication. Over the past few years, the rapid advancement in digital photography has greatly reshaped the pipeline of image capturing process on consumer-level mobile devices. The flexibility of camera parameter settings and the emergence of multi-frame photography algorithms, especially high dynamic range (HDR) imaging, bring new challenges to device fingerprinting. The subsequent study on these topics requires a new purposefully built image dataset. In this paper, we present the Warwick Image Forensics Dataset, an image dataset of more than 58,600 images captured using 14 digital cameras with various exposure settings. Special attention to the exposure settings allows the images to be adopted by different multi-frame computational photography algorithms and for subsequent device fingerprinting. The dataset is released as an open-source, free for use for the digital forensic community.

The global health threat from COVID-19 has been controlled in a number of instances by large-scale testing and contact tracing efforts. We created this document to suggest three functionalities on how we might best harness computing technologies to supporting the goals of public health organizations in minimizing morbidity and mortality associated with the spread of COVID-19, while protecting the civil liberties of individuals. In particular, this work advocates for a third-party free approach to assisted mobile contact tracing, because such an approach mitigates the security and privacy risks of requiring a trusted third party. We also explicitly consider the inferential risks involved in any contract tracing system, where any alert to a user could itself give rise to de-anonymizing information.

More generally, we hope to participate in bringing together colleagues in industry, academia, and civil society to discuss and converge on ideas around a critical issue rising with attempts to mitigate the COVID-19 pandemic.

Given a fixed graph $H$ that embeds in a surface $Sigma$, what is the maximum number of copies of $H$ in an $n$-vertex graph $G$ that embeds in $Sigma$? We show that the answer is $Theta(n^{f(H)})$, where $f(H)$ is a graph invariant called the `flap-number' of $H$, which is independent of $Sigma$. This simultaneously answers two open problems posed by Eppstein (1993). When $H$ is a complete graph we give more precise answers.

High-resolution remote sensing images (HRRSIs) contain substantial ground object information, such as texture, shape, and spatial location. Semantic segmentation, which is an important task for element extraction, has been widely used in processing mass HRRSIs. However, HRRSIs often exhibit large intraclass variance and small interclass variance due to the diversity and complexity of ground objects, thereby bringing great challenges to a semantic segmentation task. In this paper, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively. We compare our method with several classic methods on the ISPRS Vaihingen and Potsdam datasets. Experimental results show that our method can achieve better semantic segmentation results. The source codes are available at https://github.com/lehaifeng/SCAttNet.

Reading comprehension (RC) has been studied in a variety of datasets with the boosted performance brought by deep neural networks. However, the generalization capability of these models across different domains remains unclear. To alleviate this issue, we are going to investigate unsupervised domain adaptation on RC, wherein a model is trained on labeled source domain and to be applied to the target domain with only unlabeled samples. We first show that even with the powerful BERT contextual representation, the performance is still unsatisfactory when the model trained on one dataset is directly applied to another target dataset. To solve this, we provide a novel conditional adversarial self-training method (CASe). Specifically, our approach leverages a BERT model fine-tuned on the source dataset along with the confidence filtering to generate reliable pseudo-labeled samples in the target domain for self-training. On the other hand, it further reduces domain distribution discrepancy through conditional adversarial learning across domains. Extensive experiments show our approach achieves comparable accuracy to supervised models on multiple large-scale benchmark datasets.

Within the field of multilinear algebra, inverses and generalized inverses of tensors based on the Einstein product have been investigated over the past few years. In this paper, we explore the singular value decomposition and full-rank decomposition of arbitrary-order tensors using {it reshape} operation. Applying range and null space of tensors along with the reshape operation; we further study the Moore-Penrose inverse of tensors and their cancellation properties via the Einstein product. Then we discuss weighted Moore-Penrose inverses of arbitrary-order tensors using such product. Following a specific algebraic approach, a few characterizations and representations of these inverses are explored. In addition to this, we obtain a few necessary and sufficient conditions for the reverse-order law to hold for weighted Moore-Penrose inverses of arbitrary-order tensors.

Dense kernel matrices $Theta in mathbb{R}^{N imes N}$ obtained from point evaluations of a covariance function $G$ at locations ${ x_{i} }_{1 leq i leq N} subset mathbb{R}^{d}$ arise in statistics, machine learning, and numerical analysis. For covariance functions that are Green's functions of elliptic boundary value problems and homogeneously-distributed sampling points, we show how to identify a subset $S subset { 1 , dots , N }^2$, with $# S = O ( N log (N) log^{d} ( N /epsilon ) )$, such that the zero fill-in incomplete Cholesky factorisation of the sparse matrix $Theta_{ij} 1_{( i, j ) in S}$ is an $epsilon$-approximation of $Theta$. This factorisation can provably be obtained in complexity $O ( N log( N ) log^{d}( N /epsilon) )$ in space and $O ( N log^{2}( N ) log^{2d}( N /epsilon) )$ in time, improving upon the state of the art for general elliptic operators; we further present numerical evidence that $d$ can be taken to be the intrinsic dimension of the data set rather than that of the ambient space. The algorithm only needs to know the spatial configuration of the $x_{i}$ and does not require an analytic representation of $G$. Furthermore, this factorization straightforwardly provides an approximate sparse PCA with optimal rate of convergence in the operator norm. Hence, by using only subsampling and the incomplete Cholesky factorization, we obtain, at nearly linear complexity, the compression, inversion and approximate PCA of a large class of covariance matrices. By inverting the order of the Cholesky factorization we also obtain a solver for elliptic PDE with complexity $O ( N log^{d}( N /epsilon) )$ in space and $O ( N log^{2d}( N /epsilon) )$ in time, improving upon the state of the art for general elliptic operators.

In recent years there has been a burgeoning interest in the use of computational methods to distinguish between elicited speech samples produced by patients with dementia, and those from healthy controls. The difference between perplexity estimates from two neural language models (LMs) - one trained on transcripts of speech produced by healthy participants and the other trained on transcripts from patients with dementia - as a single feature for diagnostic classification of unseen transcripts has been shown to produce state-of-the-art performance. However, little is known about why this approach is effective, and on account of the lack of case/control matching in the most widely-used evaluation set of transcripts (DementiaBank), it is unclear if these approaches are truly diagnostic, or are sensitive to other variables. In this paper, we interrogate neural LMs trained on participants with and without dementia using synthetic narratives previously developed to simulate progressive semantic dementia by manipulating lexical frequency. We find that perplexity of neural LMs is strongly and differentially associated with lexical frequency, and that a mixture model resulting from interpolating control and dementia LMs improves upon the current state-of-the-art for models trained on transcript text exclusively.

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.

Image-based tracking of medical instruments is an integral part of many surgical data science applications. Previous research has addressed the tasks of detecting, segmenting and tracking medical instruments based on laparoscopic video data. However, the methods proposed still tend to fail when applied to challenging images and do not generalize well to data they have not been trained on. This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on robustness and generalization capabilities of the methods. Our data set comprises 30 laparoscopic videos and corresponding sensor data from medical devices in the operating room for three different types of laparoscopic surgery. Annotations include surgical phase labels for all frames in the videos as well as instance-wise segmentation masks for surgical instruments in more than 10,000 individual frames. The data has successfully been used to organize international competitions in the scope of the Endoscopic Vision Challenges (EndoVis) 2017 and 2019.

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.

Wireless sensor networks have attracted much attention because of many applications in the fields of industry, military, medicine, agriculture, and education. In addition, the vast majority of researches has been done to expand its applications and improve its efficiency. However, there are still many challenges for increasing the efficiency in different parts of this network. One of the most important parts is to improve the network lifetime in the wireless sensor network. Since the sensor nodes are generally powered by batteries, the most important issue to consider in these types of networks is to reduce the power consumption of the nodes in such a way as to increase the network lifetime to an acceptable level. The contribution of this paper is using topology control, the threshold for the remaining energy in nodes, and two of the meta-algorithms include SA (Simulated annealing) and VNS (Variable Neighbourhood Search) to increase the energy remaining in the sensors. Moreover, using a low-cost spanning tree, an appropriate connectivity control among nodes is created in the network in order to increase the network lifetime. The results of simulations show that the proposed method improves the sensor lifetime and reduces the energy consumed.

XSS is a security vulnerability that permits injecting malicious code into the client side of a web application. In the simplest situations, XSS vulnerabilities arise when a web application includes the user input in the web output without due sanitization. Such simple XSS vulnerabilities can be detected fairly reliably with blackbox scanners, which inject malicious payload into sensitive parts of HTTP requests and look for the reflected values in the web output.

Contemporary blackbox scanners are not effective against stored XSS vulnerabilities, where the malicious payload in an HTTP response originates from the database storage of the web application, rather than from the associated HTTP request. Similarly, many blackbox scanners do not systematically handle context-sensitive XSS vulnerabilities, where the user input is included in the web output after a transformation that prevents the scanner from recognizing the original value, but does not sanitize the value sufficiently. Among the combination of two basic data sources (stored vs reflected) and two basic vulnerability patterns (context sensitive vs not so), only one is therefore tested systematically by state-of-the-art blackbox scanners.

Our work focuses on systematic coverage of the three remaining combinations. We present a graybox mechanism that extends a general purpose database to cooperate with our XSS scanner, reporting and injecting the test inputs at the boundary between the database and the web application. Furthermore, we design a mechanism for identifying the injected inputs in the web output even after encoding by the web application, and check whether the encoding sanitizes the injected inputs correctly in the respective browser context. We evaluate our approach on eight mature and technologically diverse web applications, discovering previously unknown and exploitable XSS flaws in each of those applications.

Software development has been changing rapidly. This development process can be influenced through changing developer friendly approaches. We can save time consumption and accelerate the development process if we can automatically guide programmer during software development. There are some approaches that recommended relevant code snippets and APIitems to the developer. Some approaches apply general code, searching techniques and some approaches use an online based repository mining strategies. But it gets quite difficult to help programmers when they need particular type conversion problems. More specifically when they want to adapt existing interfaces according to their expectation. One of the familiar triumph to guide developers in such situation is adapting collections and arrays through automated adaptation of object ensembles. But how does it help to a novice developer in real time software development that is not explicitly specified? In this paper, we have developed a system that works as a plugin-tool integrated with a particular Data Mining Integrated environment (DMIE) to recommend relevant interface while they seek for a type conversion situation. We have a mined repository of respective adapter classes and related APIs from where developer, search their query and get their result using the relevant transformer classes. The system that recommends developers titled automated objective ensembles (AOE plugin).From the investigation as we have ever made, we can see that our approach much better than some of the existing approaches.

Avikainen proved the estimate $mathbb{E}[|f(X)-f(widehat{X})|^{q}] leq C(p,q) mathbb{E}[|X-widehat{X}|^{p}]^{frac{1}{p+1}} $ for $p,q in [1,infty)$, one-dimensional random variables $X$ with the bounded density function and $widehat{X}$, and a function $f$ of bounded variation in $mathbb{R}$. In this article, we will provide multi-dimensional analogues of this estimate for functions of bounded variation in $mathbb{R}^{d}$, Orlicz-Sobolev spaces, Sobolev spaces with variable exponents and fractional Sobolev spaces. The main idea of our arguments is to use Hardy-Littlewood maximal estimates and pointwise characterizations of these function spaces. We will apply main statements to numerical analysis on irregular functionals of a solution to stochastic differential equations based on the Euler-Maruyama scheme and the multilevel Monte Carlo method, and to estimates of the $L^{2}$-time regularity of decoupled forward-backward stochastic differential equations with irregular terminal conditions.

Pandemic measures such as social distancing and contact tracing can be enhanced by rapidly integrating dynamic location data and demographic data. Projecting billions of longitude and latitude locations onto hundreds of thousands of highly irregular demographic census block polygons is computationally challenging in both research and deployment contexts. This paper describes two approaches labeled "simple" and "fast". The simple approach can be implemented in any scripting language (Matlab/Octave, Python, Julia, R) and is easily integrated and customized to a variety of research goals. This simple approach uses a novel combination of hierarchy, sparse bounding boxes, polygon crossing-number, vectorization, and parallel processing to achieve 100,000,000+ projections per second on 100 servers. The simple approach is compact, does not increase data storage requirements, and is applicable to any country or region. The fast approach exploits the thread, vector, and memory optimizations that are possible using a low-level language (C++) and achieves similar performance on a single server. This paper details these approaches with the goal of enabling the broader community to quickly integrate location and demographic data.

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.

We consider generic optimal Bayesian inference, namely, models of signal reconstruction where the posterior distribution and all hyperparameters are known. Under a standard assumption on the concentration of the free energy, we show how replica symmetry in the strong sense of concentration of all multioverlaps can be established as a consequence of the Franz-de Sanctis identities; the identities themselves in the current setting are obtained via a novel perturbation of the prior distribution of the signal. Concentration of multioverlaps means that asymptotically the posterior distribution has a particularly simple structure encoded by a random probability measure (or, in the case of binary signal, a non-random probability measure). We believe that such strong control of the model should be key in the study of inference problems with underlying sparse graphical structure (error correcting codes, block models, etc) and, in particular, in the derivation of replica symmetric formulas for the free energy and mutual information in this context.

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

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

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

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

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

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

After Governor Jay Inslee announced a prohibition on gatherings of 250 people or more in three Washington counties (Snohomish, King, Pierce) on Wednesday, and with public health concerns growing over the COVID-19 pandemic, many organizations in Spokane are following suit. The Inlander will be frequently updating its online calendar of events to reflect local cancelations as we hear of them.…

Forty years ago, Mount St. Helens in southwestern Washington was just days away from a devastating eruption. When the mountain finally did blow, after weeks of frequent quakes and ash belching, on the morning of May 18, 1980, it caused the deadliest and most economically destructive volcanic event in U.S. history.…

Northern Quest Resort & Casino is set to reopen Tuesday, albeit with strict social-distancing and other safety protocols in place, becoming the second regional casino to reopen after closures caused by the coronavirus. Resort officials expect a crowd due to pent-up interest in the community for getting out of the house (not to mention Cinco de Mayo).…

In times of trouble, escaping into the art that calms you is key to keeping your sanity.…

A glycerol derivative which is effective to reduce blood pressure and has the formula: ##STR1## wherein R1 is an alkyl group having 10-22 carbon atoms, R2 is a lower acyl group or benzoyl, each of R3 and R4 independently is hydrogen or a straight or branched chain alkyl group having 1-6 carbon atoms; each of R5, R6 and R7 independently is hydrogen, a straight or branched chain alkyl group having 1-6 carbon atoms, an aryl group or an aralkyl group; and each of m and n independently is 0 or a positive integer under the condition of m+n=2-8.

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

A sensor for biomolecules includes a silicon fin comprising undoped silicon; a source region adjacent to the silicon fin, the source region comprising heavily doped silicon; a drain region adjacent to the silicon fin, the drain region comprising heavily doped silicon of a doping type that is the same doping type as that of the source region; and a layer of a gate dielectric covering an exterior portion of the silicon fin between the source region and the drain region, the gate dielectric comprising a plurality of antibodies, the plurality of antibodies configured to bind with the biomolecules, such that a drain current flowing between the source region and the drain region varies when the biomolecules bind with the antibodies.

Disclosed are a biomolecular sensor and a method of fabricating the same having high sensitivity and resolution by using a plurality of metal plates that change electrical properties of a plurality of nanostructures according to the attachment of biomolecules. The biomolecular sensor includes a substrate, first and second electrodes disposed to be spaced apart from each other on the substrate, a plurality of nanostructures disposed on the substrate to connect the first and second electrodes to each other, and a plurality of metal plates that change electrical properties of the plurality of nanostructures according to the attachment of biomolecules.

A molding composition formulation includes a thermoset cross-linkable 12 to 45 micron polymeric resin. Hollow glass microspheroids are present from 2 to 12 total weight percent. An article formed from such a composition is further strengthened by the addition of a surface activating agent bonded to the surface of the glass microspheroids. Conventional particulate fillers when added to an inventive formulation provide enhanced performance when the filler particle has a size sufficiently small to insert within adjacent microspheroid interstitial voids. An unsaturated polyester resin so formed is particularly well suited for the formation of sheet molding compound formulations.

The present invention relates to a process for the production of a ready-to-use epoxy composition having a filler content of at least 55 vol.-%, relative to the complete ready-to-use epoxy composition, which comprises: providing a liquid A, which comprises at least one epoxy resin,providing a liquid B, which comprises at least one curing agent,providing a solid component C, which comprises at least one filler,wherein in a first step one of the liquids A or B is filled in a mixing container,in a second step the solid component C is deposited on top of the liquid in the mixing container,in a third step the remaining liquid A or B is deposited on top of the solid component C, andin a fourth step the components are mixed to obtain the ready-to-use epoxy composition.

Provided is a polymer material having superior water wettability and lubricity, and enabling persistence of the same by allowing the surfactant to be retained by the polymer material so as not to be gradually released. The present invention is directed to a polymer material including: [I] a polymer having a constitutional unit derived from (A) a polymerizable compound having an acryloyloxy group and not having a silicon atom; and [II] a surfactant. The content of the surfactant [II] is preferably 0.05% by mass or greater and 1% by mass or less. The surfactant [II] is preferably a nonionic surfactant having a polyoxyethylene group.

Generally, compositions and methods of producing dimensionally stable three dimensional objects using an additive build up process. Specifically, materials combinable in an additive build up process using a materials printer for the production of stable three dimensional molds useful in the production of molded or formed parts.

Method of stabilizing radium present in radium-containing effluent, in which the effluent and a metal chloride are mixed, then the previously obtained mixture is reacted with a sulfate ion to obtain effluent containing stabilized radium. The chloride can be a barium, strontium or lead chloride. The sulfate ion can be supplied by the addition of sulfuric acid, sulfuric anhydride, soluable sulfate or soluble sulfate salt. The method applies in particular to the treatment of solid radium-containing effluents or effluents containing substances in suspension coming from chemistry or metallurgy of zirconium or treatment of uranium-containing minerals.

In this application, a set of orthogonal functions is introduced whose power spectral densities are all rectangular shape. To find the orthogonal function set, it was considered that their spectrums (Fourier transforms of the functions) are either real-valued or imaginary-valued, which are corresponding to even and odd real-valued time domain signals, respectively. The time domain functions are all considered real-valued because they are actually physical signals. The shape of the power spectral densities of the signals are rectangular thus, the Haar orthogonal function set can be employed in the frequency domain to decompose them to several orthogonal functions. Based on the inverse Fourier transform of the Haar orthogonal functions, the time domain functions with rectangular power spectral densities can be determined. This is equivalent to finding the time-domain functions by taking the inverse Fourier transform of the frequency domain Walsh functions. The obtained functions are sampled and truncated to generate finite-length discrete signals. Truncation destroys the orthogonality of the signals. The Singular Value Decomposition method is used to restore the orthogonality of the truncated discrete signals.

Provided is a photosensitive composition which can be cured with low energy consumption, even when a substance (such as a colorant) that attenuates or shades an illumination light is contained in a high concentration or even when the photosensitive composition is in the form of a thick film. Specifically provided is a photosensitive composition which comprises the following four components: (1) a radical initiator (A); (2) an acid generator (B) or a base generator (C); (3) a polymerizable substance (D); and (4) a colorant (E), a metal oxide powder (F), or a metal powder (G). Further, the photosensitive composition is characterized in that the radical initiator (A), the acid generator (B), and/or the base generator (C) generates an active species (H) through irradiation with an active ray of light; the active species (H) reacts the radical initiator (A), the acid generator (B), or the base generator (C) to form another species (I); and thus the polymerization of the polymerizable substance (D) by means of the active species (I) proceeds, said active species (H) or (I) being an acid or a base.

The present disclosure provides a method of providing an adhesive composition comprising the steps of combining crosslinkable composition including: a) a (meth)acryloyl monomer mixture with the b) photocrosslinking agent mixture, and irradiating with UVC radiation to polymerize and crosslink the composition.

Systems, mediums, and methods are provided for scheduling input/output requests to a storage system. The input output requests may be received, categorized based on their priority, and scheduled for retrieval from the storage system. Lower priority requests may be divided into smaller sub-requests, and the sub-requests may be scheduled for retrieval only when there are no pending higher priority requests, and/or when higher priority requests are not predicted to arrive for a certain period of time. By servicing the small sub-requests rather than the entire lower priority request, the retrieval of the lower priority request may be paused in the event that a high priority request arrives while the lower priority request is being serviced.

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.

A method of extending advanced process control (APC) models includes constructing an APC model table including APC model parameters of a plurality of products and a plurality of work stations. The APC model table includes empty cells and cells filled with existing APC model parameters. Average APC model parameters of the existing APC model parameters are calculated, and filled into the empty cells as initial values. An iterative calculation is performed to update the empty cells with updated values.

A system and method is disclosed for communicating with sensors/loggers in integrated radio frequency identification (RFID) tags. An RFID reader uses a Communicate With Data Logger Command to communicate with a data logger in an RFID tag. The RFID reader performs data access processes using an Index Register and a Data Register of the RFID tag. The RFID reader selects one of (1) Index Read access (2) Index Write access (3) Data Write access (4) Data Read access with parity and (5) Data Read access with cyclic redundancy check (CRC). The RFID tag performs the requested data access and then performs an error detection process.

A cancelation registry provides a cancelation interface whose implementation registers cancelable items such as synchronous operations, asynchronous operations, type instances, and transactions. Items may be implicitly or explicitly registered with the cancelation registry. A consistent cancelation interface unifies cancelation management for heterogeneous items, and allows cancelation of a group of items with a single invocation of a cancel-registered-items procedure.

A film formed from a water-soluble polymer matrix within which is contained at least one fragrance is provided. The film is water-sensitive (e.g., water-soluble, water-dispersible, etc.) so that upon contact with a sufficient amount of water, the polymer matrix loses its integrity over time to increasingly expose the fragrance to the ambient environment for releasing its odor. The ability to incorporate a fragrance into the polymer matrix is achieved in the present invention by controlling a variety of aspects of the film construction, including the nature of the fragrance, the nature of the water-soluble polymer, the manner in which the polymer matrix and fragrance are melt processed, etc. For example, the fragrance may be injected directly into the extruder and melt blended with the water-soluble polymer. In this manner, the costly and time-consuming steps of pre-encapsulation or pre-compounding of the fragrance into a masterbatch are not required. Furthermore, to obtain a balance between the ability of the fragrance to release the desired odor during use and likewise to minimize the premature exhaustion of the odor during melt processing, the fragrance is selected to have a boiling point (at atmospheric pressure) within a certain range, such as from about 125° C. to about 350° C.

Various methods, apparatuses and/or articles of manufacture are provided which may be implemented to estimate a trajectory of a mobile device within an indoor environment. In some embodiments, the trajectory may be estimated without the use of any signal-based positioning information. For example, a mobile device may estimate such a trajectory based, at least in part, on one or more sensor measurements obtained at the mobile device, and further affect the estimated trajectory based, at least in part, on one or more objects identified in an electronic map of the indoor environment.

A method includes providing a design of a semiconductor device such as a stacked CMOS device comprising a plurality of circuit elements to be assigned into a layout of a plurality of tiers, and identifying at least one first type of circuit element within the plurality of circuit elements based on at least one predetermined criterion. Each respective one of the at least one first type of circuit element is to be assigned to a respective designated one of the plurality of tiers. The method further includes dividing the remainder of the plurality of circuit elements into at least two groups of circuit elements based on circuit density, and assigning the at least one first type of circuit element and the at least two groups of circuit elements to respectively different ones of the plurality of tiers of the semiconductor device.

A method for mask data preparation or mask process correction is disclosed in which a set of charged particle beam shots is determined which is capable of forming a pattern on a surface, wherein critical dimension uniformity (CDU) of the pattern is optimized. In some embodiments the CDU is optimized by varying at least two factors. In other embodiments, model-based techniques are used. In yet other embodiments, the surface is a reticle to be used in an optical lithographic process to form a pattern on a wafer, and CDU on the wafer is optimized.

Routing methods for an integrated circuit design layout are disclosed. The layout can include design netlists and library cells. A multiple-level global routing can generate topological wire for each net. An area oriented graph-based detail routing on the design can be performed. A post route optimization after the detail routing can be performed to further improve the routing quality. Some methods can be single threaded all or some of the time, and/or multi-threaded some or all of the time.

The disclosed technology is related to adjusting an integrated circuit design while accounting for a local density of the design. In particular exemplary embodiments, a local density value for a layout design that defines a plurality of geometric shapes is derived. Subsequently, one or more of the geometric shapes are adjusted such that the local density value is preserved. With some implementations, the local density value is preserved if the adjusted local density value is within a threshold amount of the derived local density value.