Set Of Templates Research Articles

Identification of New Templates for the Synthesis of BEA, BEC, and ISV Zeolites Using Molecular Topology and Monte Carlo Techniques.

The presence of organic structure directing agents (templates) in the synthesis of zeolites allows the synthesis to be directed, in many cases, toward structures in which there is a large stabilization between the template and the zeolite micropore due to dispersion interactions. Although other factors are also important (temperature, pH, Si/Al ratio, etc.), systems with strong zeolite-template interactions are good candidates for an application of new computational algorithms, for instance those based in molecular topology (MT), that can be used in combination with large databases of organic molecules. Computational design of new templates allows the synthesis of existing and new zeolites to be expanded and refined. Three zeolites with similar 3-D large pore systems, BEA, BEC, and ISV, were selected with the aim of finding new templates for their selective syntheses. Using a training set of active and inactive templates (obtained from the literature) for the synthesis of target zeolites, it was possible to select chemical descriptors related to activity, meaning a good candidate template. With a discriminant function defined upon MT, the screening through a database of organic molecules led to a small subset (preselection) of candidate templates for the synthesis of BEA, BEC, and ISV. As far as we know, this is the first time that topological/topochemical descriptors, which do not consider 3-D information on the molecules, have been used to predict the activity of zeolite structure directing agents (SDAs). Following the prediction of SDAs using MT, an automated approach of sequential template filling of micropores based on a combination of Monte Carlo and lattice energy minimization was applied for all the candidate templates in the three zeolites. Two results can be obtained from this: an evaluation of the quality of the molecular topology QSAR models leading to the preselection of templates, and a final selection of candidate templates for the selective synthesis of BEA, BEC, and ISV. Regarding the latter, a good template will be that which maximizes the zeolite-template dispersion interactions with one, and only one, of the three zeolites. The presented methodology can be used to find alternative (maybe cheaper or perhaps more selective) templates than those already known.

A Robust Structured Tracker Using Local Deep Features

Deep features extracted from convolutional neural networks have been recently utilized in visual tracking to obtain a generic and semantic representation of target candidates. In this paper, we propose a robust structured tracker using local deep features (STLDF). This tracker exploits the deep features of local patches inside target candidates and sparsely represents them by a set of templates in the particle filter framework. The proposed STLDF utilizes a new optimization model, which employs a group-sparsity regularization term to adopt local and spatial information of the target candidates and attain the spatial layout structure among them. To solve the optimization model, we propose an efficient and fast numerical algorithm that consists of two subproblems with the close-form solutions. Different evaluations in terms of success and precision on the benchmarks of challenging image sequences (e.g., OTB50 and OTB100) demonstrate the superior performance of the STLDF against several state-of-the-art trackers.

Pilot of the Life-Sustaining Treatment Decisions Initiative Among Veterans With Serious Illness.

Prior to national spread, the Department of Veterans Affairs implemented a pilot of the life-sustaining treatment decisions initiative (LSTDI) to promote proactive goals of care conversations (GoCC) with seriously ill patients, including policy and practice standards, an electronic documentation template and order set, and implementation support. To describe a 2-year pilot of the LSTDI at 4 demonstration sites. Prospective observational study. A total of 6664 patients who had at least one GoCC. Descriptive statistics characterized patient demographics, goals of care, LST decisions, and risk of hospitalization or mortality among patients with at least one GoCC. Participants were on average 71.4 years old, 93.2% male, 87.1% white, and 64.7% urban; 27.3% died by the end of the pilot period. Fifteen percent lacked decision-making capacity (DMC). Nonmutually exclusive goals included to be cured (7.6%), to prolong life (34%), to improve/maintain quality of life (61.5%), to be comfortable (53%), to obtain support for family/caregiver (8.4%), to achieve life goals (2.1%), and other (10.5%). Many GoCCs resulted in a do not resuscitate (DNR) order (58.8%). Patients without DMC were more likely to have comfort-oriented goals (77.3% vs 48.8%) and a DNR (84% vs 52.6%). Chart abstraction supported content validity of GoCC documentation. The pilot demonstrated that standardizing practices for eliciting and documenting GoCCs resulted in customized documentation of goals of care and LST decisions of a large number of seriously ill patients and established the feasibility of spreading standardized practices throughout a large integrated health care system.

Evolutionary correlation filtering based on pseudo-bacterial genetic algorithm for pose estimation of highly occluded targets

An accurate method based on evolutionary correlation filtering to solve pose estimation of highly occluded targets is presented. The proposed method performs multiple correlation operations between an input scene and a bank of filters designed in frequency-domain. Each filter is computed with statistical parameters of a real-world scene and a template that contains information of the target in a single pose parameter configuration. A vast set of templates is generated from multiple views of a three-dimensional model of the target, which are created synthetically with computer graphics. An evolutionary approach in the bank of filter construction for optimizing the pose estimation parameters is implemented. The evolutionary computation technique based on a pseudo-bacterial genetic algorithm yields high estimation accuracy finding the best filter that produces the highest matching score. The proposed evolutionary correlation filtering yields good convergence of the bank of filter optimization, which produces a reduction of the number of computational operations. Experimental results demonstrate the robustness of the proposed method in terms of detection performance and pose estimation of highly occluded targets compared with state-of-the-art methods.

Evaluation of HMM-Based Network Intrusion Detection System for Multiple Multi-Stage Attacks

With the explosive growth of network security threats, there is a dire need to build secure network systems. In this article, we address the challenges of modeling and detecting advanced network attacks. In particular, we investigate how interleaving multiple multi-stage can exacerbate the stealthiness of the attack and deceive network intrusion detection systems. We design a detection architecture based on a leading statistical machine learning technique, HMM. The proposed architecture deploys a set of HMM templates of recognized multi-stage attacks to detect and track the progress of stealthy attacks. Extensive simulation experiments are conducted to assess the performance of the proposed architecture for multiple multi-stage attack scenarios in the presence of imperfect partitioning of network data streams and false alerts with various rates.

NMR-Derived Conformational Ensemble of State 1 of Activated Ras Reveals Insights into a Druggable Pocket.

The lack of apparent pockets in the ground conformation of Ras has long challenged the rational design of inhibitors against this oncogenic protein. The sparsely populated, transiently formed state 1 of activated Ras, on the other hand, shows appreciable surface roughness and is increasingly recognized as a potential target for drug discovery. State 1, however, is extremely flexible, and a static structure cannot fully unveil its conformational space that can be exploited for drug design. Here, we present a conformational ensemble of state 1 that was derived using chemical shift-based modeling. The ensemble reveals the intrinsic plasticity of a druggable pocket in state 1 and demonstrates the mechanism of conformational selection for inhibitor recognition. The large set of structural templates in the ensemble, providing a comprehensive description of thermally accessible pocket conformations, is expected to significantly aid the rational design of anti-Ras drugs.

Ambiguity and Conflict in Policy Implementation: The Case of the New Care Models (Vanguard) Programme in England

AbstractPolicy driven change is challenging, with a significant gap between theory and practice. A key tension in enacting such change is achieving a balance between bottom-up development of local, context-specific approaches, and top-down, centrally determined policy solutions and their mutual sequencing. Ideal type models of the policy-making process envisage a rational ordered approach, driven by evidence and accompanied by ongoing evaluation of outcomes (Parsons, 1995, p77); however, the reality is far more complex. We examine the implementation and early operation of the New Care Models (NCM) Vanguard programme in England, using Matland’s (1995) ambiguity-conflict model, to explore the aims and expectations of the programme. We consider the relationship between top-down and bottom-up approaches to policy development and draw attention to the pressures coming from what was initially perceived as a permissive policy approach of encouraging experimentation, whilst also requiring rapid learning, scale and spread. We suggest that future programmes for large-scale policy implementation initiatives could be crafted differently to take account of the environment of implementation and render ambitions more realistic. Rather than aiming to create a set of definite products and templates, it may be that a set of principles for design and implementation should be developed and spread.

Supporting smart citizens: Design templates for co-designing data-intensive technologies

The design of interactive technologies for smart cities requires understanding the interactions between diverse data, physical objects, existing services and different stakeholders. Yet there is a lack of supporting methods and tools for modelling the complex interaction processes, especially between humans and data. This can inhibit engagement with smart city applications and services design, particularly for those who are not experts in developing smart technologies. This paper describes the development of a typology and a set of design templates, derived from analysis of both existing and proposed examples of smart apps and services. In the process, it highlights the capability of citizens to contribute to smart city design, if given appropriate support as part of a co-design process. In this regard, we contribute a common language for both professional and non-professional designers to discuss how smart city technologies turn data into services.

A template-based approach for the specification of 3D topological constraints

Several different models have been defined in literature for the definition of 3D scenes that include a geometrical representation of objects together with a semantical classification of them. Such semantical characterization encapsulates important details about the object properties and behavior and often includes spatial relations that are defined only implicitly or through natural language, such as “an external access shall be in touch with the building only when it is classified as a direct access”. The problem of ensuring the coherence between geometric and semantic information is well known in literature. Many attempts exist which try to extent the OCL to allow the representation of spatial integrity constraints in an UML model. However, this approach requires a deep knowledge of the OCL formalism and the implementation of ad-hoc procedures to validate the constraints specified at conceptual level. Therefore, a new approach is needed that helps designers to define complex OCL constraints and at the same time allows the automatic generation of the code to test them on a given dataset. The aim of this paper is to propose a set of predefined templates to express on the classes of an UML data model, a family of 3D spatial integrity constraints based on topological relations; all this without requiring the knowledge of any formal language by domain experts and supporting their automatic translation into validation procedures.

Heat kernel asymptotics on sequences of elliptically degenerating Riemann surfaces

This is the first of two articles in which we define an elliptically degenerating family of hyperbolic Riemann surfaces and study the asymptotic behavior of the associated spectral theory. Our study is motivated by a result which Hejhal attributes to Selberg, proving spectral accumulation for the family of Hecke triangle groups. In this article, we prove various results regarding the asymptotic behavior of heat kernels and traces of heat kernels for both real and complex time. In Garbin et al. (2018) [8], we will use the results from this article and study the asymptotic behavior of numerous spectral functions through elliptic degeneration, including spectral counting functions, Selberg's zeta function, Hurwitz-type zeta functions, determinants of the Laplacian, wave kernels, spectral projections, small eigenfunctions, and small eigenvalues. The method of proof we employ follows the template set in previous articles which study spectral theory on degenerating families of finite volume Riemann surfaces (Huntley et al. (1995) [14] and (1997) [15], Jorgenson et al. (1997) [20] and (1997) [17]) and on degenerating families of finite volume hyperbolic three manifolds (Dodziuk et al. (1998) [4].) Although the types of results developed here and in Garbin et al. (2018) [8], are similar to those in existing articles, it is necessary to thoroughly present all details in the setting of elliptic degeneration in order to uncover all nuances in this setting.

Machine Phraseological Translation of Scientific-Technical Texts Based on the Model of Generalized Syntagmas

This article proposes a new solution to the problem of phraseological machine translation of texts based on the technology of “semantic translation constructor.” This technology is based on the concept of generalized syntagmas, which is based on the proposition that the syntactic structure of a sentence or its fragments (syntactic constructions) can be represented as a sequence of generalized syntagmas that reflect the grammar properties of specific text words. On the basis of this concept the source sentence can be transformed into a set of templates of syntactic constructions accompanied by their “joint assemblies.” Then, the translation process can be performed by “disassembling” the source sentence into templates of syntactic constructions, correlating these source templates with templates of their translated equivalents and final “assembly” of the translated sentence in accordance with “technological instructions” in the “assembly rules.” This article proposes mechanisms for the automatic formation of a template representation of the hierarchy of syntactic constructions of bilingual sentences.

Life-Sustaining Treatment Decisions Initiative: Early Implementation Results of a National Veterans Affairs Program to Honor Veterans' Care Preferences.

On July 1, 2018, the Veterans Health Administration (VA) National Center for Ethics in Health Care implemented the Life-Sustaining Treatment Decisions Initiative (LSTDI). Its goal is to identify, document, and honor LST decisions of seriously ill veterans. Providers document veterans' goals and decisions using a standardized LST template and order set. Evaluate the first 7months of LSTDI implementation and identify predictors of LST template completion. Retrospective observational study of clinical and administrative data. We identified all completed LST templates, defined as completion of four required template fields. Templates also include four non-required fields. Results were stratified by risk of hospitalization or death as estimated by the Care Assessment Need (CAN) score. All veterans with VA utilization between July 1, 2018, and January 31, 2019. Completed LST templates, goals and LST preferences, and predictors of documentation. LST templates were documented for 108,145 veterans, and 85% had one or more of the non-required fields completed in addition to the required fields. Approximately half documented a preference for cardiopulmonary resuscitation. Among those who documented specific goals, half wanted to improve or maintain function, independence, and quality of life while 28% had a goal of life prolongation irrespective of risk of hospitalization/death and 45% expressed a goal of comfort. Only 7% expressed a goal of being cured. Predictors of documentation included VA nursing home residence, older age, frailty, and comorbidity, while non-Caucasian race, rural residence, and receipt of care in a lower complexity medical center were predictive of no documentation. LST decisions were documented for veterans at high risk of hospitalization or death. While few expressed a preference for cure, half desire, cardiopulmonary resuscitation. Predictors of documentation were generally consistent with existing literature. Opportunities to reduce observed disparities exist by leveraging available VA resources and programs.

KLT picker: Particle picking using data-driven optimal templates

Particle picking is currently a critical step in the cryo-EM single particle reconstruction pipeline. Despite extensive work on this problem, for many data sets it is still challenging, especially for low SNR micrographs. We present the KLT (Karhunen Loeve Transform) picker, which is fully automatic and requires as an input only the approximated particle size. In particular, it does not require any manual picking. Our method is designed especially to handle low SNR micrographs. It is based on learning a set of optimal templates through the use of multi-variate statistical analysis via the Karhunen Loeve Transform. We evaluate the KLT picker on publicly available data sets and present high-quality results with minimal manual effort.

An integrated micro-macro approach for high-speed railway energy-efficient timetabling problem

Energy efficiency of train operations is influenced largely by the speed control and the scheduled running time in the train timetable. In practice, the running time of a train is often determined in the train timetabling process at the macroscopic level while the energy-efficient speed control of a train on a segment is often determined at the microscopic level with the given timetable. They are usually optimized separately due to limited computational resources, which however may result in sub-optimal solutions. To address this issue, this paper proposes a novel integrated micro-macro approach for better incorporating train energy-efficient speed control into the railway timetabling process. Firstly, we formulated the integrated train timetabling and speed control optimization problem as a nonlinear mixed-integer programming model. Due to its complexity, we reformulate it on the basis of flow conservation theory in a space–time-speed (STS) network and solve the problem in two steps. In the first step, a set of pre-solved energy-efficient train trajectory templates is generated by a segment-level optimization approach with consideration of train travel time, entry speed and exit speed to save computation time. In the second step, a near-optimum train energy-efficient timetable solution is found by a fast algorithm, which consists of the shortest generalized cost path algorithm, conflict detection and resolution algorithm, and calculation of dynamic headways between two successive trains. The numerical experiments demonstrate that the developed approach provides better outcomes than the benchmark case in terms of both train journey time and energy consumption.

Online signature verification using single-template matching with time-series averaging and gradient boosting

In keeping with recent developments in artificial intelligence in the era of big data, there is a demand for online signature verification systems that operate at high speeds, provide a high level of security, and allow high tolerances while achieving sufficient performance. In response to these needs, the present study proposes a novel, single-template strategy using a mean template set and weighted multiple dynamic time warping (DTW) distances for a function-based approach to online signature verification. Specifically, to obtain an effective mean template for each feature while reflecting intra-user variability between all the reference samples, we adopt a novel time-series averaging method based on Euclidean barycenter-based DTW barycenter averaging. Then, by using the mean template set, we calculate multiple DTW distances from multivariate time series based on dependent and independent warping. Finally, to boost the discriminative power, we apply a weighting scheme using a gradient boosting model to efficiently combine the multiple DTW distances. Experimental results using the common SVC2004 Task1/Task2 and MCYT-100 signature datasets confirm that the proposed method is effective for online signature verification.

Construction of Spatiotemporal Infant Cortical Surface Functional Templates.

Infant cortical surface templates play an essential role in spatial normalization of cortical surfaces across individuals in pediatric neuroimaging analysis. However, existing infant surface templates have two major limitations in functional MRI analysis. First, they are constructed by co-registration of cortical surfaces based on structural attributes, which cannot lead to accurate functional alignment, due to the highly variable relationship between cortical folds and functions. Second, they are constructed by simply averaging co-registered cortical attributes, which is sensitive to registration errors and leads to blurred attribute patterns on templates, thus deteriorating the accuracy in spatial normalization. Therefore, the construction of infant cortical functional templates encoding sharp functional architectures is critical for infant fMRI analysis. To this end, we construct the first set of spatiotemporal infant cortical surface functional templates using Wasserstein barycenter and a state-of-the-art functional feature, namely the gradient density of functional connectivity. To address the first issue, we leverage functional gradient density to drive surface registration to improve inter-individual functional correspondences. To address the second issue, we compute templates based on the Wasserstein barycenter of functional gradient density maps across individuals. The motivation is that Wasserstein barycenter represents a meaningful mean under the Wasserstein distance metric, which takes into account the alignment of local spatial distribution of cortical attributes and thus is robust to registration errors, leading to sharp and detailed patterns on templates. Experiments on a dataset with 207 fMRI scans between 0 and 2 years of age show the validity and accuracy of our constructed infant cortical functional templates.

CSEFuzz: Fuzz Testing Based on Symbolic Execution

Fuzz testing has been successful in finding defects of various software packages. These defects include file parsing, image processing, Internet browsers, and network protocols. However, the quality of the initial seed test cases greatly influences the coverage and defect detection capability of fuzz testing. To address this issue, we propose CSEFuzz, a fuzz testing approach based on symbolic execution for defect detection. First, CSEFuzz generates candidate test cases by symbolic execution and collects coverage information of the test cases. Then, CSEFuzz extracts the test-case templates of the test cases and selects a set of test-case templates according to specific coverage criteria. Finally, CSEFuzz selects test cases according to the selected test-case templates, and the selected test cases are used as initial seed test cases for fuzz testing. Experiments are conducted on 11 open-source programs. The results show that in comparison with afl-cmin, which is the test-case selection command of Kelinci, CSEFuzz with a path coverage criterion reduces the time costs of the initial seed test selection and verification by 94.26%. In addition, compared with afl-cmin, 32 more paths are covered and 16 more defects are detected by CSEFuzz.

Italian Medicines Agency Adopts Template Setting Out Minimum Content Of Commercial Clinical Trial Agreements

Italian Medicines Agency Adopts Template Setting Out Minimum Content Of Commercial Clinical Trial Agreements

Oscillatory Control over Representational States in Working Memory.

In the visual world, attention is guided by perceptual goals activated in visual working memory (VWM). However, planning multiple-task sequences also requires VWM to store representations for future goals. These future goals need to be prevented from interfering with the current perceptual task. Recent findings have implicated neural oscillations as a control mechanism serving the implementation and switching of different states of prioritization of VWM representations. We review recent evidence that posterior alpha-band oscillations underlie the flexible activation and deactivation of VWM representations and that frontal delta-to-theta-band oscillations play a role in the executive control of this process. That is, frontal delta-to-theta appears to orchestrate posterior alpha through long-range oscillatory networks to flexibly set up and change VWM states during multitask sequences.

A novel classification-selection approach for the self updating of template-based face recognition systems

The boosting on the need of security notably increased the amount of possible facial recognition applications, especially due to the success of the Internet of Things (IoT) paradigm. However, although handcrafted and deep learning-inspired facial features reached a significant level of compactness and expressive power, the facial recognition performance still suffers from intra-class variations such as ageing, facial expressions, lighting changes, and pose. These variations cannot be captured in a single acquisition and require multiple acquisitions of long duration, which are expensive and need a high level of collaboration from the users. Among others, self-update algorithms have been proposed in order to mitigate these problems. Self-updating aims to add novel templates to the users’ gallery among the inputs submitted during system operations. Consequently, computational complexity and storage space tend to be among the critical requirements of these algorithms. The present paper deals with the above problems by a novel template-based self-update algorithm, able to keep over time the expressive power of a limited set of templates stored in the system database. The rationale behind the proposed approach is in the working hypothesis that a dominating mode characterises the features’ distribution given the client. Therefore, the key point is to select the best templates around that mode. We propose two methods, which are tested on systems based on handcrafted features and deep-learning-inspired autoencoders at the state-of-the-art. Three benchmark data sets are used. Experimental results confirm that, by effective and compact feature sets which can support our working hypothesis, the proposed classification-selection approaches overcome the problem of manual updating and, in case, stringent computational requirements.