An econometric model of private land-use decisions is used to project land use to 2030 for each county in the continental United States. On a national scale, forest area is projected to increase overall between 0.1 and 0.2 percent per year between now and 2030. However, forest area is projected to decrease in a majority of regions, including the key forestry regions of the South and the Pacific Northwest Westside. Urban area is projected to increase by 68 million acres, and cropland, pasture, rangeland, and Conservation Reserve Program land is projected to decline in area. Regional econometric models are needed to better represent region-specific economic relationships. County-level models of forest fragmentation indices are estimated for the Western United States. The core forest model is found to perform better than the model of like adjacencies for forest land. A spatially detailed analysis of forest fragmentation in Polk County, Oregon, reveals that forests become more fragmented even though forest area increases. By linking the land-use projection and forest fragmentation models, we project increases in the average county shares of core forest in 8 of the 11 Western States. The average like adjacency measure increases in six of the states. The aggregate and spatially detailed fragmentation methods are compared by projecting the fragmentation indices to 2022 for Polk County, Oregon. Considerable differences in the results were produced with the two methods, especially in the case of the like adjacency metric.

The marbled murrelet (Brachyramphus marmoratus) ranges from Alaska to California and is listed under the Endangered Species Act as a threatened species in Washington, Oregon, and California. Marbled murrelet recovery depends, in large part, on conservation and restoration of breeding habitat on federally managed lands. A major objective of the Northwest Forest Plan (the Plan) is to conserve and restore nesting habitat that will sustain a viable marbled murrelet population. Under the Plan, monitoring is an essential component and is designed to help managers understand the degree to which the Plan is meeting this objective. This report describes methods used to assess the status and trend of marbled murrelet populations under the Plan.

Timber availability, aboveground tree biomass, and changes in aboveground carbon pools are important consequences of landscape management.

Lichens are one of several forest health indicators sampled every year for a subset of plots on the permanent grid established by the Forest Inventory and Analysis (FIA) Program of the U.S. Department of Agriculture Forest Service. This report reviews analysis procedures for standard FIA lichen indicator data. Analyses of lichen data contribute to state, regional, and national reports that evaluate spatial pattern and temporal trends in forest biodiversity, air quality, and climate.

This report highlights key findings from the most recent (2004–2008) data collected by the Forest Inventory and Analysis program across all ownerships in southeast and south-central Alaska. We present basic resource information such as forest area, ownership, volume, biomass, carbon sequestration, growth, and mortality; structure and function topics such as vegetation and lichen diversity and forest age distribution; disturbance topics such as insects and diseases, yellow-cedar decline, fire, and invasive plants; and information about the forest products industry in Alaska, the potential of young growth for timber supply, biofuels, and nontimber forest products. The appendixes describe inventory methods and design in detail and provide summary tables of data and statistical error for the forest characteristics sampled.

This document introduces a computer software named SNAP for ArcGIS®, which has been developed to streamline scheduling and transportation planning for timber harvest areas. Using modern optimization techniques, it can be used to spatially schedule timber harvest with consideration of harvesting costs, multiple products, alternative destinations, and transportation systems. SNAP for ArcGIS attempts either to maximize a net present value or minimize discounted costs of harvesting and transportation over the planning horizon while meeting given harvest volume and acreage constraints. SNAP for ArcGIS works in the ArcGIS environment and provides an easy-to-use analytical tool for sophisticated spatial planning of timber harvest.

This report highlights key findings from the most recent (2001–2010) data collected by the Forest Inventory and Analysis program across all forest land in California, updating previously published findings from data collected from 2001 through 2005 (Christensen et al. 2008).

Sitka, Alaska, has substantial hydroelectric resources, limited driving distances, and a conservation-minded community, all suggesting strong opportunities for achieving a low community carbon footprint.

These proceedings report invited presentations and contributions to the 2015 Forest Inventory and Analysis (FIA) Symposium, which was hosted by the Research and Development branch of the U.S. Forest Service.

This research models a wood pellet heating system at the Tlingit-Haida Regional Housing Authority in Juneau, Alaska, used to provide thermal energy to a 929-m2 warehouse, as an alternative to a heating system that burns more costly fossil fuels. Research objectives were to evaluate project economics of the pellet system and to conduct cost:benefit analysis on key variables (initial capital cost, fuel oil cost, and wood pellet cost). Economic results of interest included net present value, payback, internal rate of return, and cost:benefit ratio. Monte Carlo simulations were conducted using RETScreen software with the parameters of heating oil cost, wood pellet cost, fuel price escalation, and heating load. Cost:benefit analysis was conducted for capital cost versus wood fuel cost and also versus alternative fuel cost. This research found that economic performance was favorable over a wide range of normal operating conditions, even when paying a relatively high price for wood fuel. A pellet production facility in southeast Alaska could lead to lower wood fuel costs and even more favorable regional economics.

This report highlights key findings from a comprehensive vegetation survey of all forested land across the state of Oregon. A total of 5,180 forested field plots in Oregon were visited by Forest Inventory and Analysis (FIA) crews over a 10-year period from 2001 to 2010. Oregon has 30 million acres of forest, covering nearly half the state. The structure and composition of Oregon’s forests differ considerably across the state, particularly east versus west of the Cascade Range. Western Oregon forests are dominated by higher productivity classes (85 to 224 cubic feet per acre annual growth) and are composed of Douglas-fir and western hemlock, while forests in the east typically exhibit lower productivity (0 to 84 cubic feet per acre annual growth) and are composed of ponderosa pine, western juniper, and lodgepole pine. The Forest Service and Bureau of Land Management administer the majority of forested land in Oregon; these public forests managed by federal agencies tend to have older, larger trees. Private owners, both corporate and noncorporate, own nearly half of the forested land in western Oregon, particularly in areas of high productivity. Understory vegetation in Oregon forests is more abundant in younger, moist forests. Non-native species are present in many of Oregon’s forests, most notably cheatgrass in the east and Himalayan blackberry in the west. This report includes estimates of forest growth, removals, and mortality for ownership groups across the state. The FIA program will continue to revisit and remeasure all the field plots over 10 years to report on changes in Oregon’s forest resources.

Industry insiders receive ALL ACCESS' exclusive MEDIABASE chart recap analysis in their e-mail box every MONDAY morning. How about you? This week's data from ANTHONY ACAMPORA, Partner … more

Research Highlights: The blood thinner apixaban, which treats and prevents blood clots in some people with irregular heart rhythm, is safe and effective in stroke patients. Apixaban is associated with less bleeding, death and hospitalization than ...

A study to comparatively examine the effects of sports-related concussions (SRC) on athletes in Major League Soccer (MLS) and the English Premier League (EPL) in terms of incidence, return to play (RTP), performance, and career longevity.

Homelessness is a global public health concern, and traumatic brain injury (TBI) could represent an underappreciated factor in the health trajectories of homeless and marginally housed individuals. We aimed to evaluate the lifetime prevalence of TBI in this population, and to summarise findings on TBI incidence and the association between TBI and health-related or functioning-related outcomes.

In this paper we consider the "quasidensity" of a subset of the product of a Banach space and its dual, and give a connection between quasidense sets and sets of "type (NI)". We discuss "coincidence sets" of certain convex functions and prove two sum theorems for coincidence sets. We obtain new results on the Fitzpatrick extension of a closed quasidense monotone multifunction. The analysis in this paper is self-contained, and independent of previous work on "Banach SN spaces". This version differs from the previous version because it is shown that the (well known) equivalence of quasidensity and "type (NI)" for maximally monotone sets is not true without the monotonicity assumption and that the appendix has been moved to the end of Section 10, where it rightfully belongs.

We study SLE$_{kappa}$ theory with elements of Quasi-Sure Stochastic Analysis through Aggregation. Specifically, we show how the latter can be used to construct the SLE$_{kappa}$ traces quasi-surely (i.e. simultaneously for a family of probability measures with certain properties) for $kappa in mathcal{K}cap mathbb{R}_+ setminus ([0, epsilon) cup {8})$, for any $epsilon>0$ with $mathcal{K} subset mathbb{R}_{+}$ a nontrivial compact interval, i.e. for all $kappa$ that are not in a neighborhood of zero and are different from $8$. As a by-product of the analysis, we show in this language a version of the continuity in $kappa$ of the SLE$_{kappa}$ traces for all $kappa$ in compact intervals as above.

This paper describes a dataset and protocols for evaluating continuous speech separation algorithms. Most prior studies on speech separation use pre-segmented signals of artificially mixed speech utterances which are mostly emph{fully} overlapped, and the algorithms are evaluated based on signal-to-distortion ratio or similar performance metrics. However, in natural conversations, a speech signal is continuous, containing both overlapped and overlap-free components. In addition, the signal-based metrics have very weak correlations with automatic speech recognition (ASR) accuracy. We think that not only does this make it hard to assess the practical relevance of the tested algorithms, it also hinders researchers from developing systems that can be readily applied to real scenarios. In this paper, we define continuous speech separation (CSS) as a task of generating a set of non-overlapped speech signals from a extit{continuous} audio stream that contains multiple utterances that are emph{partially} overlapped by a varying degree. A new real recorded dataset, called LibriCSS, is derived from LibriSpeech by concatenating the corpus utterances to simulate a conversation and capturing the audio replays with far-field microphones. A Kaldi-based ASR evaluation protocol is also established by using a well-trained multi-conditional acoustic model. By using this dataset, several aspects of a recently proposed speaker-independent CSS algorithm are investigated. The dataset and evaluation scripts are available to facilitate the research in this direction.

In this paper, we work on intra-variable handwriting, where the writing samples of an individual can vary significantly. Such within-writer variation throws a challenge for automatic writer inspection, where the state-of-the-art methods do not perform well. To deal with intra-variability, we analyze the idiosyncrasy in individual handwriting. We identify/verify the writer from highly idiosyncratic text-patches. Such patches are detected using a deep recurrent reinforcement learning-based architecture. An idiosyncratic score is assigned to every patch, which is predicted by employing deep regression analysis. For writer identification, we propose a deep neural architecture, which makes the final decision by the idiosyncratic score-induced weighted average of patch-based decisions. For writer verification, we propose two algorithms for patch-fed deep feature aggregation, which assist in authentication using a triplet network. The experiments were performed on two databases, where we obtained encouraging results.

Interaction languages such as MSC are often associated with formal semantics by means of translations into distinct behavioral formalisms such as automatas or Petri nets. In contrast to translational approaches we propose an operational approach. Its principle is to identify which elementary communication actions can be immediately executed, and then to compute, for every such action, a new interaction representing the possible continuations to its execution. We also define an algorithm for checking the validity of execution traces (i.e. whether or not they belong to an interaction's semantics). Algorithms for semantic computation and trace validity are analyzed by means of experiments.

For future Internet of Things (IoT)-based Big Data applications (e.g., smart cities/transportation), wireless data collection from ubiquitous massive smart sensors with limited spectrum bandwidth is very challenging. On the other hand, to interpret the meaning behind the collected data, it is also challenging for edge fusion centers running computing tasks over large data sets with limited computation capacity. To tackle these challenges, by exploiting the superposition property of a multiple-access channel and the functional decomposition properties, the recently proposed technique, over-the-air computation (AirComp), enables an effective joint data collection and computation from concurrent sensor transmissions. In this paper, we focus on a single-antenna AirComp system consisting of $K$ sensors and one receiver (i.e., the fusion center). We consider an optimization problem to minimize the computation mean-squared error (MSE) of the $K$ sensors' signals at the receiver by optimizing the transmitting-receiving (Tx-Rx) policy, under the peak power constraint of each sensor. Although the problem is not convex, we derive the computation-optimal policy in closed form. Also, we comprehensively investigate the ergodic performance of AirComp systems in terms of the average computation MSE and the average power consumption under Rayleigh fading channels with different Tx-Rx policies. For the computation-optimal policy, we prove that its average computation MSE has a decay rate of $O(1/sqrt{K})$, and our numerical results illustrate that the policy also has a vanishing average power consumption with the increasing $K$, which jointly show the computation effectiveness and the energy efficiency of the policy with a large number of sensors.

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

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

The Hierarchical Equal Area isoLatitude Pixelation (HEALPix) scheme is used extensively in astrophysics for data collection and analysis on the sphere. The scheme was originally designed for studying the Cosmic Microwave Background (CMB) radiation, which represents the first light to travel during the early stages of the universe's development and gives the strongest evidence for the Big Bang theory to date. Refined analysis of the CMB angular power spectrum can lead to revolutionary developments in understanding the nature of dark matter and dark energy. In this paper, we present a new method for performing spherical harmonic analysis for HEALPix data, which is a central component to computing and analyzing the angular power spectrum of the massive CMB data sets. The method uses a novel combination of a non-uniform fast Fourier transform, the double Fourier sphere method, and Slevinsky's fast spherical harmonic transform (Slevinsky, 2019). For a HEALPix grid with $N$ pixels (points), the computational complexity of the method is $mathcal{O}(Nlog^2 N)$, with an initial set-up cost of $mathcal{O}(N^{3/2}log N)$. This compares favorably with $mathcal{O}(N^{3/2})$ runtime complexity of the current methods available in the HEALPix software when multiple maps need to be analyzed at the same time. Using numerical experiments, we demonstrate that the new method also appears to provide better accuracy over the entire angular power spectrum of synthetic data when compared to the current methods, with a convergence rate at least two times higher.

Compromised accounts on social networks are regular user accounts that have been taken over by an entity with malicious intent. Since the adversary exploits the already established trust of a compromised account, it is crucial to detect these accounts to limit the damage they can cause. We propose a novel general framework for discovering compromised accounts by semantic analysis of text messages coming out from an account. Our framework is built on the observation that normal users will use language that is measurably different from the language that an adversary would use when the account is compromised. We use our framework to develop specific algorithms that use the difference of language models of users and adversaries as features in a supervised learning setup. Evaluation results show that the proposed framework is effective for discovering compromised accounts on social networks and a KL-divergence-based language model feature works best.

Multimodal Sentiment Analysis is an active area of research that leverages multimodal signals for affective understanding of user-generated videos. The predominant approach, addressing this task, has been to develop sophisticated fusion techniques. However, the heterogeneous nature of the signals creates distributional modality gaps that pose significant challenges. In this paper, we aim to learn effective modality representations to aid the process of fusion. We propose a novel framework, MISA, which projects each modality to two distinct subspaces. The first subspace is modality invariant, where the representations across modalities learn their commonalities and reduce the modality gap. The second subspace is modality-specific, which is private to each modality and captures their characteristic features. These representations provide a holistic view of the multimodal data, which is used for fusion that leads to task predictions. Our experiments on popular sentiment analysis benchmarks, MOSI and MOSEI, demonstrate significant gains over state-of-the-art models. We also consider the task of Multimodal Humor Detection and experiment on the recently proposed UR_FUNNY dataset. Here too, our model fares better than strong baselines, establishing MISA as a useful multimodal framework.

The patterns in which the syntax of different languages converges and diverges are often used to inform work on cross-lingual transfer. Nevertheless, little empirical work has been done on quantifying the prevalence of different syntactic divergences across language pairs. We propose a framework for extracting divergence patterns for any language pair from a parallel corpus, building on Universal Dependencies. We show that our framework provides a detailed picture of cross-language divergences, generalizes previous approaches, and lends itself to full automation. We further present a novel dataset, a manually word-aligned subset of the Parallel UD corpus in five languages, and use it to perform a detailed corpus study. We demonstrate the usefulness of the resulting analysis by showing that it can help account for performance patterns of a cross-lingual parser.

Most currently available schemes for performance based ranking of Universities or Research organizations, such as, Quacarelli Symonds (QS), Times Higher Education (THE), Shanghai University based All Research of World Universities (ARWU) use a variety of criteria that include productivity, citations, awards, reputation, etc., while Leiden and Scimago use only bibliometric indicators. The research performance evaluation in the aforesaid cases is based on bibliometric data from Web of Science or Scopus, which are commercially available priced databases. The coverage includes peer reviewed journals and conference proceedings. Google Scholar (GS) on the other hand, provides a free and open alternative to obtaining citations of papers available on the net, (though it is not clear exactly which journals are covered.) Citations are collected automatically from the net and also added to self created individual author profiles under Google Scholar Citations (GSC). This data was used by Webometrics Lab, Spain to create a ranked list of 4000+ institutions in 2016, based on citations from only the top 10 individual GSC profiles in each organization. (GSC excludes the top paper for reasons explained in the text; the simple selection procedure makes the ranked list size-independent as claimed by the Cybermetrics Lab). Using this data (Transparent Ranking TR, 2016), we find the regional and country wise distribution of GS-TR Citations. The size independent ranked list is subdivided into deciles of 400 institutions each and the number of institutions and citations of each country obtained for each decile. We test for correlation between institutional ranks between GS TR and the other ranking schemes for the top 20 institutions.

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.

Data analytics in the cloud has become an integral part of enterprise businesses. Big data analytics systems, however, still lack the ability to take user performance goals and budgetary constraints for a task, collectively referred to as task objectives, and automatically configure an analytic job to achieve these objectives. This paper presents a data analytics optimizer that can automatically determine a cluster configuration with a suitable number of cores as well as other system parameters that best meet the task objectives. At a core of our work is a principled multi-objective optimization (MOO) approach that computes a Pareto optimal set of job configurations to reveal tradeoffs between different user objectives, recommends a new job configuration that best explores such tradeoffs, and employs novel optimizations to enable such recommendations within a few seconds. We present efficient incremental algorithms based on the notion of a Progressive Frontier for realizing our MOO approach and implement them into a Spark-based prototype. Detailed experiments using benchmark workloads show that our MOO techniques provide a 2-50x speedup over existing MOO methods, while offering good coverage of the Pareto frontier. When compared to Ottertune, a state-of-the-art performance tuning system, our approach recommends configurations that yield 26\%-49\% reduction of running time of the TPCx-BB benchmark while adapting to different application preferences on multiple objectives.

Visualization-oriented natural language interfaces (V-NLIs) have been explored and developed in recent years. One challenge faced by V-NLIs is in the formation of effective design decisions that usually requires a deep understanding of user queries. Learning-based approaches have shown potential in V-NLIs and reached state-of-the-art performance in various NLP tasks. However, because of the lack of sufficient training samples that cater to visual data analytics, cutting-edge techniques have rarely been employed to facilitate the development of V-NLIs. We present a new dataset, called Quda, to help V-NLIs understand free-form natural language. Our dataset contains 14;035 diverse user queries annotated with 10 low-level analytic tasks that assist in the deployment of state-of-the-art techniques for parsing complex human language. We achieve this goal by first gathering seed queries with data analysts who are target users of V-NLIs. Then we employ extensive crowd force for paraphrase generation and validation. We demonstrate the usefulness of Quda in building V-NLIs by creating a prototype that makes effective design decisions for free-form user queries. We also show that Quda can be beneficial for a wide range of applications in the visualization community by analyzing the design tasks described in academic publications.

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

Monitoring users on large computing platforms such as high performance computing (HPC) and cloud computing systems is non-trivial. Utilities such as process viewers provide limited insight into what users are running, due to granularity limitation, and other sources of data, such as system call tracing, can impose significant operational overhead. However, despite technical and procedural measures, instances of users abusing valuable HPC resources for personal gains have been documented in the past cite{hpcbitmine}, and systems that are open to large numbers of loosely-verified users from around the world are at risk of abuse. In this paper, we show how electrical power consumption data from an HPC platform can be used to identify what programs are executed. The intuition is that during execution, programs exhibit various patterns of CPU and memory activity. These patterns are reflected in the power consumption of the system and can be used to identify programs running. We test our approach on an HPC rack at Lawrence Berkeley National Laboratory using a variety of scientific benchmarks. Among other interesting observations, our results show that by monitoring the power consumption of an HPC rack, it is possible to identify if particular programs are running with precision up to and recall of 95\% even in noisy scenarios.

In this paper, we analyze the symbol error rate (SER) performance of the simultaneous wireless information and power transfer (SWIPT) enabled three-node differential decode-and-forward (DDF) relay networks, which adopt the power splitting (PS) protocol at the relay. The use of non-coherent differential modulation eliminates the need for sending training symbols to estimate the instantaneous channel state informations (CSIs) at all network nodes, and therefore improves the power efficiency, as compared with the coherent modulation. However, performance analysis results are not yet available for the state-of-the-art detectors such as the approximate maximum-likelihood detector. Existing works rely on Monte-Carlo simulation to show that there exists an optimal PS ratio that minimizes the overall SER. In this work, we propose a near-optimal detector with linear complexity with respect to the modulation size. We derive an accurate approximate SER expression, based on which the optimal PS ratio can be accurately estimated without requiring any Monte-Carlo simulation.

This paper illustrates five different techniques to assess the distinctiveness of topics, key terms and features, speed of information dissemination, and network behaviors for Covid19 tweets. First, we use pattern matching and second, topic modeling through Latent Dirichlet Allocation (LDA) to generate twenty different topics that discuss case spread, healthcare workers, and personal protective equipment (PPE). One topic specific to U.S. cases would start to uptick immediately after live White House Coronavirus Task Force briefings, implying that many Twitter users are paying attention to government announcements. We contribute machine learning methods not previously reported in the Covid19 Twitter literature. This includes our third method, Uniform Manifold Approximation and Projection (UMAP), that identifies unique clustering-behavior of distinct topics to improve our understanding of important themes in the corpus and help assess the quality of generated topics. Fourth, we calculated retweeting times to understand how fast information about Covid19 propagates on Twitter. Our analysis indicates that the median retweeting time of Covid19 for a sample corpus in March 2020 was 2.87 hours, approximately 50 minutes faster than repostings from Chinese social media about H7N9 in March 2013. Lastly, we sought to understand retweet cascades, by visualizing the connections of users over time from fast to slow retweeting. As the time to retweet increases, the density of connections also increase where in our sample, we found distinct users dominating the attention of Covid19 retweeters. One of the simplest highlights of this analysis is that early-stage descriptive methods like regular expressions can successfully identify high-level themes which were consistently verified as important through every subsequent analysis.

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

What Is Real-Time Conversation Analytics?

In contrast to post-call conversation analytics, which provides insights after the fact, real-time call conversation analytics can point them out at present times.

In this blog, I will walk through the essential steps to build a web app that can analyze call conversations in real-time to assist an agent. Once we’re finished, we’ll have an app which will:

An apparatus and method are disclosed for analysing a process. An exemplary method includes: generating a process template; and determining a probabilistic model specifying the process template. The method can include use of task nodes for tasks of the process; observables nodes for observables that may be caused by performance of the tasks; and a background activities node, wherein observables may further be caused by background activities of the background node. The method can include use of task nodes for tasks of the process; observables nodes for observables that may be caused by performance of the tasks; and a background activities node, observables may further be caused by background activities of the background node. The method can include measuring values of an observable corresponding to one of the observables nodes; and updating a probabilistic estimate of the process state using the measured values.

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.

In one embodiment, a plurality of objects associated with a dataset and a specified number of nearest objects to be identified are received. The received objects are sorted in a structured format. Further, a key object and a number of adjacent objects corresponding to the key object are selected from the sorted plurality of objects, wherein the number of adjacent objects is selected based on the specified number of nearest objects to be identified. Furthermore, distances between the key object and the number of adjacent objects are determined to identify the specified number of nearest objects, wherein the distances are determined until the specified number of nearest objects is identified. Based on the determined distances, the specified number of nearest objects in the dataset is identified for data analysis.

Embodiments are described for a system and method of providing a data miner that decouples the analytic flow solution components from the data source. An analytic-flow solution then couples with the target data source through a simple set of data source connector, table and transformation objects, to perform the requisite analytic flow function. As a result, the analytic-flow solution needs to be designed only once and can be re-used across multiple target data sources. The analytic flow can be modified and updated at one place and then deployed for use on various different target data sources.

The present invention relates to compounds of formula (I) wherein X1 to X5, Y, Z1 to Z3, and R have the meaning as cited in the description and the claims. Said compounds are useful as JAK inhibitors for the treatment or prophylaxis of immunological, inflammatory, autoimmune, allergic disorders, and immunologically-mediated diseases. The invention also relates to pharmaceutical compositions including said compounds, the preparation of such compounds as well as the use as medicaments.

The invention provides a process for monitoring and/or adjusting a dispersion polymerization of an olefin-based polymer, the process comprising monitoring the concentration of the carbon-carbon unsaturations in the dispersion using Raman Spectroscopy. The invention also provides a process for polymerizing an olefin-based polymer, the process comprising polymerizing one or more monomer types, in the presence of at least one catalyst and at least one solvent, to form the polymer as a dispersed phase in the solvent; and monitoring the concentration of the carbon-carbon unsaturations in the dispersion using Raman Spectroscopy.

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

Computer-implemented methods for analyzing computer programs written in semi-structured languages are disclosed. The method is based on unification of the two classic forms of program flow analysis, control flow and data flow analysis. As such, it is capable of substantially increased precision, which increases the effectiveness of applications such as automated parallelization and software testing. Certain implementations of the method are based on a process of converting source code to a decision graph and transforming that into one or more alpha graphs which support various applications in software development. The method is designed for a wide variety of digital processing platforms, including highly parallel machines. The method may also be adapted to the analysis of (semi-structured) flows in other contexts including water systems and electrical grids.

One implementation of the disclosure provides a crosstalk analysis method executed by a computer. The method includes steps of: executing a layout program; executing a crosstalk analysis program; acquiring, by the crosstalk analysis program, a plurality of parameters from a layout result generated by the layout program; estimating a crosstalk value according to the parameters; determining whether the crosstalk value is larger than a predetermined value; providing a layout suggestion table when the crosstalk value is larger than the predetermined value.

A method and apparatus for partitioning of the input design into repeating patterns called template cores for the application of reticle enhancement methods, design verification for manufacturability and design corrections for optical and process effects is accomplished by hierarchy analysis to extract cell overlap information. Also hierarchy analysis is performed to extract hierarchy statistics. Finally template core candidates are identified. This allows to the design to be made amenable for design corrections or other analyses or modifications that are able to leverage the hierarchy of the design since the cell hierarchy could otherwise be very deep or cells could have significant overlap with each other.

A mechanism for generating gate-level activity data for use in clock gating efficiency analysis of an integrated circuit (IC) design is provided. Generating the gate-level activity data includes generating a signal behaviour description for inter-register signals, generating a gate-level netlist for the IC design, generating gate-level stimuli based at least partly on the generated signal behaviour description, and performing gate-level simulation using the generated gate-level stimuli to generate gate-level activity data for the IC design. In one embodiment, generating the signal behaviour description includes performing Register Transfer Level (RTL) simulation of the IC design, and generating the gate-level netlist includes performing RTL synthesis. The RTL simulation and RTL synthesis are performed on RTL data for the IC design.

A system, method, and computer program product for containing analog verification IP for circuit simulation. Embodiments introduce analog verification units (“vunits”), and corresponding analog verification files to contain them. Vunits allow circuit design verification requirement specification via text file. No editing of netlist files containing design IP is required to implement static and dynamic circuit checks, PSL assertions, clock statements, or legacy assertions. Vunits reference a top-level circuit or subcircuits (by name or by specific instance), and the simulator automatically binds vunit contents appropriately during circuit hierarchy expansion. Vunits may be re-used for other design cells, and may be easily processed by text-based design tools. Vunits may be provided via vunit_include statements in a control netlist file, command line arguments, or by directly placing a vunit block into a netlist. Vunits may also contain instance statements to monitor or process signals, such as those needed by assertions.

An embodiment of an analysis filterbank for filtering a plurality of time domain input frames, wherein an input frame comprises a number of ordered input samples, comprises a windower configured to generate a plurality of windowed frames, wherein a windowed frame comprises a plurality of windowed samples, wherein the windower is configured to process the plurality of input frames in an overlapping manner using a sample advance value, wherein the sample advance value is less than the number of ordered input samples of an input frame divided by two, and a time/frequency converter configured to provide an output frame comprising a number of output values, wherein an output frame is a spectral representation of a windowed frame.

A method for configuring an interactive platform for monitoring the performance and the quality of a computer network, the monitoring data being suitable to be displayed on a dynamic page of type webpage in a form of graphic components called “displets”; including providing, on the interactive platform a configuration interface in which are defined, for at least one given user, filtering criteria for displaying displets, the criteria being defined in the form of parameters for configuring the rights of the at least one user.