The Landscape Treatment Designer (LTD) is a multicriteria spatial prioritization and optimization system to help design and explore landscape fuel treatment scenarios. The program fills a gap between fire model programs such as FlamMap, and planning systems such as ArcFuels, in the fuel treatment planning process. The LTD uses inputs on spatial treatment objectives, activity constraints, and treatment thresholds, and then identifies optimal fuel treatment locations with respect to the input parameters.

Scientists have had little information about how prescribed fire and cattle grazing—common practices in many Western ponderosa pine forests—affect plant abundance and reproduction in the forest understory. Pacific Northwest Research Station scientists began to explore how these practices affect vegetation in a five-year study of postfire vegetation in eastern Oregon ponderosa pine forests where cattle have been routinely pastured from late June or early July through early to mid August. For this area of eastern Oregon, they found that excluding cattle grazing during peak growing season increased native plant cover and grass flowering capability in ungrazed areas compared to grazed areas. Because vegetation was measured prior to releasing cattle on the land, the study's results tend to reflect lasting grazing impacts rather than simple consumption.

We monitored coarse woody debris dynamics and natural tree regeneration over a 14-year period after the 1991 Warner Creek Fire, a 3631-ha (8,972-ac) mixed severity fire in the western Cascade Range of Oregon. Rates for tree mortality in the fire, postfire mortality, snag fall, and snag fragmentation all showed distinct patterns by tree diameter and species, with Douglas-fir (Pseudotsuga menziesii (Mirb.) Franco) more likely to survive a fire, and to remain standing as a snag, than other common tree species. Natural seedling regeneration was abundant, rapid, and highly variable in space. Densities of seedlings >10 cm height at 14 years postfire ranged from 1,530 to 392,000 per ha. Seedling establishment was not concentrated in a single year, and did not appear to be limited by the abundant growth of shrubs. The simultaneous processes of mortality, snag fall, and tree regeneration increased the variety of many measures of forest structure. The singular event of the fire has increased the structural diversity of the landscape.

The North Cascadia Adaptation Partnership (NCAP) is a science-management partnership consisting of the U.S. Department of Agriculture Forest Service Mount Baker-Snoqualmie and Okanogan-Wenatchee National Forests and Pacific Northwest Research Station; North Cascades National Park Complex; Mount Rainier National Park; and University of Washington Climate Impacts Group.

The Integrated Landscape Assessment Project (ILAP) was a multiyear effort to produce information, maps, and models to help land managers, policymakers, and others conduct mid- to broad-scale (e.g., watersheds to states and larger areas) prioritization of land management actions, perform landscape assessments, and estimate cumulative effects of management actions for planning and other purposes.

This report addresses the need for developing consistent regional guidelines for stand-level management that integrates goals and objectives for dry forest restoration and habitat management for the northern spotted owl.

Over the past decade, the use of wood for thermal energy in Alaska has grown significantly. Since 2000, nearly 30 new thermal wood-energy installations in Alaska have been established.

The Forest Landscape Assessment Tool (FLAT) is a set of procedures and tools used to rapidly determine forest ecological conditions and potential threats.

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.

In dry, open coniferous forests of the montane West, stand-replacing wildfires and land use activities alter the composition and abundance of native grasses and forbs by degrading the habitat and accelerating the invasion of exotic annuals. On these lands, native forbs and grasses delayed or prevented from recovery by natural processes may require intervention through supplementary seeding. However, effective seeding of native plants requires that their seed traits and the potential outcome of the seeding be better understood.

NASCAR's return to racing on MAY 17th at DARLINGTON will kick off radio coverage on MOTOR RACING NETWORK and PERFORMANCE RACING NETWORK, with seven races in 11 days. MRN's return to … more

WASHINGTON, D. C., 3 de abril del 2020— Hoy, más de 200 organizaciones de protección de pacientes, médicas y de salud pública enviaron una carta a altos funcionarios de la administración de Donald Trump, en la cual se apela a la Administración para que...

Researchers are warning that the coronavirus pandemic could inflict long-lasting emotional trauma on an unprecedented global scale. They say it could leave millions wrestling with debilitating psychological disorders while dashing hopes for a swift economic recovery.

Over at All Things Photo, I’ve shared a video detailing a scam targeting photographers selling prints online. Also included in the video are 7 tips to avoid being scammed online. While the video is on the long side it’s worth a listen to protect yourself and learn the limitations of fraud protection with your bank and insurance companies. If you’re driving you can also listen to the podcast recording via the All Things Photo podcast. If you haven’t already I welcome you to follow All Things Photo on YouTube, Twitter and Facebook.

CSS has a habit of creeping up on you. If you’re not careful, your humble stylesheet can go from a few flourishes to a giant maintenance tangle. Before you can say “12-deep nested div”, your in a world of duplication and complexity that prevents you from making timely user-interface updates. [Medium’s](https://medium.com) one organisation that’s been Read the rest...

WordPress makes upgrading very easy . You simply click “Update now”, wait for a minute or two and your system […]

Artwork may be mountains, hills, water bodies such as rivers, lakes, ponds and the sea, living elements of land cover including indigenous vegetation, human elements [...]

The post “Landscapes & Waterscapes” Photography Competition appeared first on Photocompete.

Given spherically symmetric characteristic initial data for the Einstein-scalar field system with a positive cosmological constant, we provide a criterion, in terms of the dimensionless size and dimensionless renormalized mass content of an annular region of the data, for the formation of a future trapped surface. This corresponds to an extension of Christodoulou's classical criterion by the inclusion of the cosmological term.

We construct semiglobal singular spacetimes for the Einstein equations coupled to a massless scalar field. Consistent with the heuristic analysis of Belinskii, Khalatnikov, Lifshitz or BKL for this system, there are no oscillations due to the scalar field. (This is much simpler than the oscillatory BKL heuristics for the Einstein vacuum equations.) Prior results are due to Andersson and Rendall in the real analytic case, and Rodnianski and Speck in the smooth near-spatially-flat-FLRW case. Similar to Andersson and Rendall we give asymptotic data at the singularity, which we refer to as final data, but our construction is not limited to real analytic solutions. This paper is a test application of tools (a graded Lie algebra formulation of the Einstein equations and a filtration) intended for the more subtle vacuum case. We use homological algebra tools to construct a formal series solution, then symmetric hyperbolic energy estimates to construct a true solution well-approximated by truncations of the formal one. We conjecture that the image of the map from final data to initial data is an open set of anisotropic initial data.

We establish the functional convex order results for two scaled McKean-Vlasov processes $X=(X_{t})_{tin[0, T]}$ and $Y=(Y_{t})_{tin[0, T]}$ defined by

[egin{cases} dX_{t}=(alpha X_{t}+eta)dt+sigma(t, X_{t}, mu_{t})dB_{t}, quad X_{0}in L^{p}(mathbb{P}),\ dY_{t}=(alpha Y_{t},+eta)dt+ heta(t, Y_{t}, u_{t})dB_{t}, quad Y_{0}in L^{p}(mathbb{P}). end{cases}] If we make the convexity and monotony assumption (only) on $sigma$ and if $sigmaleq heta$ with respect to the partial matrix order, the convex order for the initial random variable $X_0 leq Y_0$ can be propagated to the whole path of process $X$ and $Y$. That is, if we consider a convex functional $F$ with polynomial growth defined on the path space, we have $mathbb{E}F(X)leqmathbb{E}F(Y)$; for a convex functional $G$ defined on the product space involving the path space and its marginal distribution space, we have $mathbb{E},Gig(X, (mu_t)_{tin[0, T]}ig)leq mathbb{E},Gig(Y, ( u_t)_{tin[0, T]}ig)$ under appropriate conditions. The symmetric setting is also valid, that is, if $ heta leq sigma$ and $Y_0 leq X_0$ with respect to the convex order, then $mathbb{E},F(Y) leq mathbb{E},F(X)$ and $mathbb{E},Gig(Y, ( u_t)_{tin[0, T]}ig)leq mathbb{E},G(X, (mu_t)_{tin[0, T]})$. The proof is based on several forward and backward dynamic programming and the convergence of the Euler scheme of the McKean-Vlasov equation.

In this paper we study manifolds $M_{Sigma}$ with fibered singularities, more specifically, a relevant space $Riem^{psc}(X_{Sigma})$ of Riemannian metrics with positive scalar curvature. Our main goal is to prove that the space $Riem^{psc}(X_{Sigma})$ is homotopy invariant under certain surgeries on $M_{Sigma}$.

Large transformer-based language models have been shown to be very effective in many classification tasks. However, their computational complexity prevents their use in applications requiring the classification of a large set of candidates. While previous works have investigated approaches to reduce model size, relatively little attention has been paid to techniques to improve batch throughput during inference. In this paper, we introduce the Cascade Transformer, a simple yet effective technique to adapt transformer-based models into a cascade of rankers. Each ranker is used to prune a subset of candidates in a batch, thus dramatically increasing throughput at inference time. Partial encodings from the transformer model are shared among rerankers, providing further speed-up. When compared to a state-of-the-art transformer model, our approach reduces computation by 37% with almost no impact on accuracy, as measured on two English Question Answering datasets.

Recent advances in deep learning have facilitated the design of speaker verification systems that directly input raw waveforms. For example, RawNet extracts speaker embeddings from raw waveforms, which simplifies the process pipeline and demonstrates competitive performance. In this study, we improve RawNet by scaling feature maps using various methods. The proposed mechanism utilizes a scale vector that adopts a sigmoid non-linear function. It refers to a vector with dimensionality equal to the number of filters in a given feature map. Using a scale vector, we propose to scale the feature map multiplicatively, additively, or both. In addition, we investigate replacing the first convolution layer with the sinc-convolution layer of SincNet. Experiments performed on the VoxCeleb1 evaluation dataset demonstrate the effectiveness of the proposed methods, and the best performing system reduces the equal error rate by half compared to the original RawNet. Expanded evaluation results obtained using the VoxCeleb1-E and VoxCeleb-H protocols marginally outperform existing state-of-the-art systems.

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.

Traffic signs are essential map features globally in the era of autonomous driving and smart cities. To develop accurate and robust algorithms for traffic sign detection and classification, a large-scale and diverse benchmark dataset is required. In this paper, we introduce a traffic sign benchmark dataset of 100K street-level images around the world that encapsulates diverse scenes, wide coverage of geographical locations, and varying weather and lighting conditions and covers more than 300 manually annotated traffic sign classes. The dataset includes 52K images that are fully annotated and 48K images that are partially annotated. This is the largest and the most diverse traffic sign dataset consisting of images from all over world with fine-grained annotations of traffic sign classes. We have run extensive experiments to establish strong baselines for both the detection and the classification tasks. In addition, we have verified that the diversity of this dataset enables effective transfer learning for existing large-scale benchmark datasets on traffic sign detection and classification. The dataset is freely available for academic research: https://www.mapillary.com/dataset/trafficsign.

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.

Deep learning-based models, such as convolutional neural networks, have advanced various segments of computer vision. However, this technology is rarely applied to seismic shot gather noise localization problem. This letter presents an investigation on the effectiveness of a multi-scale feature-fusion-based network for seismic shot-gather noise localization. Herein, we describe the following: (1) the construction of a real-world dataset of seismic noise localization based on 6,500 seismograms; (2) a multi-scale feature-fusion-based detector that uses the MobileNet combined with the Feature Pyramid Net as the backbone; and (3) the Single Shot multi-box detector for box classification/regression. Additionally, we propose the use of the Focal Loss function that improves the detector's prediction accuracy. The proposed detector achieves an AP@0.5 of 78.67\% in our empirical evaluation.

In this paper, we present three distributed algorithms to solve a class of generalized Nash equilibrium (GNE) seeking problems in strongly monotone games. The first one (SD-GENO) is based on synchronous updates of the agents, while the second and the third (AD-GEED and AD-GENO) represent asynchronous solutions that are robust to communication delays. AD-GENO can be seen as a refinement of AD-GEED, since it only requires node auxiliary variables, enhancing the scalability of the algorithm. Our main contribution is to prove converge to a variational GNE of the game via an operator-theoretic approach. Finally, we apply the algorithms to network Cournot games and show how different activation sequences and delays affect convergence. We also compare the proposed algorithms to the only other in the literature (ADAGNES), and observe that AD-GENO outperforms the alternative.

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the time that patients might convert to the severe stage, for designing effective treatment plan and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time, and if yes, predict the possible conversion time that the patient would spend to convert to the severe stage. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of high-dimensional data and learn the shared information across the classification task and the regression task. To our knowledge, this study is the first work to predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 422 chest computed tomography (CT) scans, where 52 cases were converted to severe on average 5.64 days and 34 cases were severe at admission. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.

Visual localization is a useful alternative to standard localization techniques. It works by utilizing cameras. In a typical scenario, features are extracted from captured images and compared with geo-referenced databases. Location information is then inferred from the matching results. Conventional schemes mainly use low-level visual features. These approaches offer good accuracy but suffer from scalability issues. In order to assist localization in large urban areas, this work explores a different path by utilizing high-level semantic information. It is found that object information in a street view can facilitate localization. A novel descriptor scheme called "semantic signature" is proposed to summarize this information. A semantic signature consists of type and angle information of visible objects at a spatial location. Several metrics and protocols are proposed for signature comparison and retrieval. They illustrate different trade-offs between accuracy and complexity. Extensive simulation results confirm the potential of the proposed scheme in large-scale applications. This paper is an extended version of a conference paper in CBMI'18. A more efficient retrieval protocol is presented with additional experiment results.

By using dissipativity approach, we establish the stability condition for the feedback connection of a deterministic dynamical system $Sigma$ and a stochastic memoryless map $Psi$. After that, we extend the result to the class of large scale systems in which: $Sigma$ consists of many sub-systems; and $Psi$ consists of many "stochastic actuators" and "probability controllers" that control the actuator's output events. We will demonstrate the proposed approach by showing the design procedures to globally stabilize the manufacturing systems while locally balance the stock levels in any production process.

Feature pyramid has been an efficient method to extract features at different scales. Development over this method mainly focuses on aggregating contextual information at different levels while seldom touching the inter-level correlation in the feature pyramid. Early computer vision methods extracted scale-invariant features by locating the feature extrema in both spatial and scale dimension. Inspired by this, a convolution across the pyramid level is proposed in this study, which is termed pyramid convolution and is a modified 3-D convolution. Stacked pyramid convolutions directly extract 3-D (scale and spatial) features and outperforms other meticulously designed feature fusion modules. Based on the viewpoint of 3-D convolution, an integrated batch normalization that collects statistics from the whole feature pyramid is naturally inserted after the pyramid convolution. Furthermore, we also show that the naive pyramid convolution, together with the design of RetinaNet head, actually best applies for extracting features from a Gaussian pyramid, whose properties can hardly be satisfied by a feature pyramid. In order to alleviate this discrepancy, we build a scale-equalizing pyramid convolution (SEPC) that aligns the shared pyramid convolution kernel only at high-level feature maps. Being computationally efficient and compatible with the head design of most single-stage object detectors, the SEPC module brings significant performance improvement ($>4$AP increase on MS-COCO2017 dataset) in state-of-the-art one-stage object detectors, and a light version of SEPC also has $sim3.5$AP gain with only around 7% inference time increase. The pyramid convolution also functions well as a stand-alone module in two-stage object detectors and is able to improve the performance by $sim2$AP. The source code can be found at https://github.com/jshilong/SEPC.

The Cannabis Issue During normal times, I-90 Green House is like a destination resort for marijuana lovers.…

By learning from a MERS outbreak in 2015, South Korea was prepared and acted swiftly to ramp up testing when the new coronavirus appeared there. Meanwhile, the U.S., plagued by delay and dysfunction, wasted its advantage. By Stephen Engelberg, Lisa Song and Lydia DePillis ProPublica…

You shouldn't find yourself rewatching some sitcom for the thousandth time or sitting through a vacuous Hollywood blockbuster just because you're stoned and stuck inside during the age of social distancing.…

Drink Local Life under the COVID-19 pandemic is rough for everyone, individuals and businesses alike.…

Embodiments of the disclosure include a method for partitioning a deterministic finite automaton (DFA) into a plurality of groups. The method includes selecting, with a processing device, a subset of the plurality of states and mapping each state of the subset onto a group of the plurality of groups by assigning one or more transition rules associated with each state to a rule line of the group, wherein each rule line is assigned at most two transition rules and an extended address associated with one of the at most two transition rules. The method also includes iteratively processing each state of the subset mapped onto the group by removing the extended address from each rule line in the group with transition rules referring to a current state if the transition rules in the rule line branch within the group.

This disclosure provides a network security architecture that permits installation of different software security products as virtual machines (VMs). By relying on a standardized data format and communication structure, a general architecture can be created and used to dynamically build and reconfigure interaction between both similar and dissimilar security products. Use of an integration scheme having defined message types and specified query response framework provides for real-time response and easy adaptation for cross-vendor communication. Examples are provided where an intrusion detection system (IDS) can be used to detect network threats based on distributed threat analytics, passing detected threats to other security products (e.g., products with different capabilities from different vendors) to trigger automatic, dynamically configured communication and reaction. A network security provider using this infrastructure can provide hosted or managed boundary security to a diverse set of clients, each on a customized basis.

An illustrative embodiment of a computer-implemented process for scalable group synthesis receives a group definition, applies a sub-set of conditions to the group definition to form a conditioned group definition, receives a set of entities and populates group membership using the received set of entities and the conditioned group definition, wherein each member responds in the affirmative to the sub-set of conditions.

A multi-threaded in-order superscalar processor 2 includes an issue stage 12 including issue circuitry 22, 24 for selecting instructions to be issued to execution units 14, 16 in dependence upon a currently selected issue policy. A plurality of different issue policies are provided by associated different policy circuitry 28, 30, 32 and a selection between which of these instances of the policy circuitry 28, 30, 32 is active is made by policy selecting circuitry 34 in dependence upon detected dynamic behavior of the processor 2.

Described herein is a descriptive framework to facilitate large scale demand responsive transit. In accordance with one aspect of the framework, one or more trip requests from one or more commuter devices are received. A trip request indicates at least one start location and at least one end location. In addition, vehicle information is received from one or more available vehicles. The vehicle information indicates at least one current location of a vehicle. An adaptive route for the vehicle may be planned based on the one or more trip requests and the vehicle information. Update information of the adaptive route may be communicated to the vehicle and the one or more commuter devices.

This disclosure relates generally to systems and methods for simulating physical active semiconductor components using in silico active semiconductor components. To simulate charge degradation effect(s) in a circuit simulation, a simulated defect signal level is produced. More specifically, the simulated defect signal level simulates at least one charge degradation effect in the in silico active semiconductor component as a function of simulation time and a simulated input signal level of a simulated input signal. As such, the charge degradation effect(s) are simulated externally with respect to the in silico active semiconductor component. In this manner, the in silico active semiconductor component does not need to be reprogrammed in order to simulate charge degradation effects.

A leakage power control vector is loaded into existing test scan chain elements for application to circuit elements of a circuit in which the leakage currents are to be controlled. The vector is designed to configure the circuit elements into states in which leakage currents are reduced. A multiplexer selects the power control vector for loading into the scan chain elements, and a clock generator clocks the configuration vector into the scan chain elements. A sleep mode detector may be provided to configure the multiplexer to select the power control vector and to operate the clock generator to clock the power control vector into the scan chain elements when a sleep mode of the circuit is detected.

The invention relates to (among other things) oligomer-foscarnet conjugates and related compounds. A conjugate of the invention, when administered by any of a number of administration routes, exhibits advantages over previously administered un-conjugated foscarnet compounds.

A controller receives a request to perform a release space operation. A determination is made that a new discard scan has to be performed on a cache, in response to the received request to perform the release space operation. A determination is made as to how many task control blocks are to be allocated to the perform the new discard scan, based on how many task control blocks have already been allocated for performing one or more discard scans that are already in progress.