New customizable and personalized utility customer engagement program creates connections at the individual level reduces customer churn rate.

Haleigh's Hope is proud to announce that they received GMP (Good Manufacturing Practices) certification.

Ambient Edge is one of Kingman's best-rated HVAC services companies

Health and lifestyle experts tackle the challenge of staying healthy during Corona Virus isolation and beyond.

The partnership will provide easier access to high quality video-based training, curricula, lesson plans, and projects for students, teachers, and schools.

Shaboo Prints Zen Beam lamps are great for home use, massage, meditation, yoga centers, and more

Shaboo Prints has just launched a groundbreaking augmented reality tabletop lamp

Harvey Blackwood - With only a year left before Britain leaves the European Union, May seems more willing to compromise on key issues.

Newest Fraternity Member elected into the prestigious Western Region Distinguished Service Society of Phi Beta Sigma Fraternity, Inc.

Noel Tichy, University of Michigan Business School professor and coauthor of "Judgment: How Winning Leaders Make Great Calls."

Paul Krugman, Nobel Prize-winning economist and op-ed columnist for The New York Times.

Amar Bhidé, professor at Tufts University's Fletcher School and author of "A Call for Judgment: Sensible Finance for a Dynamic Economy."

Google has added an Undo Send feature to Gmail. Now you can finally unsend an email, although you have just a short time to unsend it.

Read more on howtoweb.com

The templates enable a holistic approach to analyzing data across marketing tools

Global investors now more confident about India than China: Pacific Paradigm Advisors

On Monday, the court had also said that the order is not sustainable in view of the fact that orders and/or directions were passed interfering with the holding of AGM by the companies that are separate juristic entities without first deciding the issue of jurisdiction.

The issue price for Series I (April 20 to 24, 2020) was Rs 4,639 per gram of gold.

The issue price for Series I (April 20 to 24, 2020) was Rs 4,639 per gram of gold.

Today, a federal district court in Washington, D.C., concluded that the Antitrust Division’s resolution of its challenge to the merger between T-Mobile and Sprint was in the public interest and entered the proposed final judgment following an extensive Tunney Act process. This order gives effect to the settlement that the Department of Justice and numerous states reached with the merging parties and Dish Network Corp. to allow the T-Mobile/Sprint transaction to proceed, subject to substantial divestitures and other remedies.

Changing patterns of land use are at the heart of many environmental concerns regarding U.S. forest lands. Of all the human impacts to forests, development is one of the most significant because of the severity and permanency of the change.

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.

On February 4, 2014, a seven-member expert panel provided objective technical information on the potential effectiveness and feasibility of activities to manage raptors (northern harriers and great horned owls) to aid the recovery of western snowy plovers.

CURB/WORD ENTERTAINMENT four-time GRAMMY award-winning AUSTRALIAN duo FOR KING & COUNTRY will make their debut appearance on ABC-TV's GOOD MORNING AMERICA TODAY (5/1) to premiere … more

https://blog.pixelic.io/pixelic-for-figma/

"After my driver asked me what I did for a living and found out I support the mental health programs at WWP, the discussion moved predictably to the topic of post-traumatic stress disorder (PTSD)...[She said,] 'Let me ask you something. Why can’t they just snap out of it?'"

If extra time on a test or memory aids can make life easier during college, why not use them? Adam talks about moving past the "stigma" of using disability services and getting the help you need to succeed in college.

Let $1 le r < n$ be integers. We give a proof that the group $mathop{mathrm{Aut}}({X_{n}^{mathbb{N}}, sigma_{n}})$ of automorphisms of the one-sided shift on $n$ letters embeds naturally as a subgroup $mathcal{h}_{n}$ of the outer automorphism group $mathop{mathrm{Out}}(G_{n,r})$ of the Higman-Thompson group $G_{n,r}$. From this, we can represent the elements of $mathop{mathrm{Aut}}({X_{n}^{mathbb{N}}, sigma_{n}})$ by finite state non-initial transducers admitting a very strong synchronizing condition.

Let $H in mathcal{H}_{n}$ and write $|H|$ for the number of states of the minimal transducer representing $H$. We show that $H$ can be written as a product of at most $|H|$ torsion elements. This result strengthens a similar result of Boyle, Franks and Kitchens, where the decomposition involves more complex torsion elements and also does not support practical extit{a priori} estimates of the length of the resulting product.

We also give new proofs of some known results about $mathop{mathrm{Aut}}({X_{n}^{mathbb{N}}, sigma_{n}})$.

We relate the $L^p$-mapping properties of the Bergman projections on two domains in $mathbb{C}^n$, one of which is the quotient of the other under the action of a finite group of biholomorphic automorphisms. We use this relation to deduce the sharp ranges of $L^p$-boundedness of the Bergman projection on certain $n$-dimensional model domains generalizing the Hartogs triangle.

The paper proves the Riemann Hypothesis.

Let $(K, u)$ be a valued field, the notions of emph{augmented valuation}, of emph{limit augmented valuation} and of emph{admissible family} of valuations enable to give a description of any valuation $mu$ of $K [x]$ extending $ u$. In the case where the field $K$ is algebraically closed, this description is particularly simple and we can reduce it to the notions of emph{minimal pair} and emph{pseudo-convergent family}. Let $(K, u )$ be a henselian valued field and $ar u$ the unique extension of $ u$ to the algebraic closure $ar K$ of $K$ and let $mu$ be a valuation of $ K [x]$ extending $ u$, we study the extensions $armu$ from $mu$ to $ar K [x]$ and we give a description of the valuations $armu_i$ of $ar K [x]$ which are the extensions of the valuations $mu_i$ belonging to the admissible family associated with $mu$.

Temporal event segmentation of a long video into coherent events requires a high level understanding of activities' temporal features. The event segmentation problem has been tackled by researchers in an offline training scheme, either by providing full, or weak, supervision through manually annotated labels or by self-supervised epoch based training. In this work, we present a continual learning perceptual prediction framework (influenced by cognitive psychology) capable of temporal event segmentation through understanding of the underlying representation of objects within individual frames. Our framework also outputs attention maps which effectively localize and track events-causing objects in each frame. The model is tested on a wildlife monitoring dataset in a continual training manner resulting in $80\%$ recall rate at $20\%$ false positive rate for frame level segmentation. Activity level testing has yielded $80\%$ activity recall rate for one false activity detection every 50 minutes.

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.

For real-time semantic video segmentation, most recent works utilised a dynamic framework with a key scheduler to make online key/non-key decisions. Some works used a fixed key scheduling policy, while others proposed adaptive key scheduling methods based on heuristic strategies, both of which may lead to suboptimal global performance. To overcome this limitation, we model the online key decision process in dynamic video segmentation as a deep reinforcement learning problem and learn an efficient and effective scheduling policy from expert information about decision history and from the process of maximising global return. Moreover, we study the application of dynamic video segmentation on face videos, a field that has not been investigated before. By evaluating on the 300VW dataset, we show that the performance of our reinforcement key scheduler outperforms that of various baselines in terms of both effective key selections and running speed. Further results on the Cityscapes dataset demonstrate that our proposed method can also generalise to other scenarios. To the best of our knowledge, this is the first work to use reinforcement learning for online key-frame decision in dynamic video segmentation, and also the first work on its application on face videos.

Deep learning-based object detection and instance segmentation have achieved unprecedented progress. In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding box regression and Non-Maximum Suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency. In particular, we consider three geometric factors, i.e., overlap area, normalized central point distance and aspect ratio, which are crucial for measuring bounding box regression in object detection and instance segmentation. The three geometric factors are then incorporated into CIoU loss for better distinguishing difficult regression cases. The training of deep models using CIoU loss results in consistent AP and AR improvements in comparison to widely adopted $ell_n$-norm loss and IoU-based loss. Furthermore, we propose Cluster-NMS, where NMS during inference is done by implicitly clustering detected boxes and usually requires less iterations. Cluster-NMS is very efficient due to its pure GPU implementation, , and geometric factors can be incorporated to improve both AP and AR. In the experiments, CIoU loss and Cluster-NMS have been applied to state-of-the-art instance segmentation (e.g., YOLACT), and object detection (e.g., YOLO v3, SSD and Faster R-CNN) models. Taking YOLACT on MS COCO as an example, our method achieves performance gains as +1.7 AP and +6.2 AR$_{100}$ for object detection, and +0.9 AP and +3.5 AR$_{100}$ for instance segmentation, with 27.1 FPS on one NVIDIA GTX 1080Ti GPU. All the source code and trained models are available at https://github.com/Zzh-tju/CIoU

Convolution is an equivariant operation, and image position does not affect its result. A recent study shows that the zero-padding employed in convolutional layers of CNNs provides position information to the CNNs. The study further claims that the position information enables accurate inference for several tasks, such as object recognition, segmentation, etc. However, there is a technical issue with the design of the experiments of the study, and thus the correctness of the claim is yet to be verified. Moreover, the absolute image position may not be essential for the segmentation of natural images, in which target objects will appear at any image position. In this study, we investigate how positional information is and can be utilized for segmentation tasks. Toward this end, we consider {em positional encoding} (PE) that adds channels embedding image position to the input images and compare PE with several padding methods. Considering the above nature of natural images, we choose medical image segmentation tasks, in which the absolute position appears to be relatively important, as the same organs (of different patients) are captured in similar sizes and positions. We draw a mixed conclusion from the experimental results; the positional encoding certainly works in some cases, but the absolute image position may not be so important for segmentation tasks as we think.

Cassava a major food crop in many parts of Africa, has majorly been affected by Cassava Brown Streak Disease (CBSD). The disease affects tuberous roots and presents symptoms that include a yellow/brown, dry, corky necrosis within the starch-bearing tissues. Cassava breeders currently depend on visual inspection to score necrosis in roots based on a qualitative score which is quite subjective. In this paper we present an approach to automate root necrosis scoring using deep convolutional neural networks with semantic segmentation. Our experiments show that the UNet model performs this task with high accuracy achieving a mean Intersection over Union (IoU) of 0.90 on the test set. This method provides a means to use a quantitative measure for necrosis scoring on root cross-sections. This is done by segmentation and classifying the necrotized and non-necrotized pixels of cassava root cross-sections without any additional feature engineering.

This paper proposes a fully automated atlas-based pancreas segmentation method from CT volumes utilizing atlas localization by regression forest and atlas generation using blood vessel information. Previous probabilistic atlas-based pancreas segmentation methods cannot deal with spatial variations that are commonly found in the pancreas well. Also, shape variations are not represented by an averaged atlas. We propose a fully automated pancreas segmentation method that deals with two types of variations mentioned above. The position and size of the pancreas is estimated using a regression forest technique. After localization, a patient-specific probabilistic atlas is generated based on a new image similarity that reflects the blood vessel position and direction information around the pancreas. We segment it using the EM algorithm with the atlas as prior followed by the graph-cut. In evaluation results using 147 CT volumes, the Jaccard index and the Dice overlap of the proposed method were 62.1% and 75.1%, respectively. Although we automated all of the segmentation processes, segmentation results were superior to the other state-of-the-art methods in the Dice overlap.

Spiking neural networks (SNNs) are considered as a potential candidate to overcome current challenges such as the high-power consumption encountered by artificial neural networks (ANNs), however there is still a gap between them with respect to the recognition accuracy on practical tasks. A conversion strategy was thus introduced recently to bridge this gap by mapping a trained ANN to an SNN. However, it is still unclear that to what extent this obtained SNN can benefit both the accuracy advantage from ANN and high efficiency from the spike-based paradigm of computation. In this paper, we propose two new conversion methods, namely TerMapping and AugMapping. The TerMapping is a straightforward extension of a typical threshold-balancing method with a double-threshold scheme, while the AugMapping additionally incorporates a new scheme of augmented spike that employs a spike coefficient to carry the number of typical all-or-nothing spikes occurring at a time step. We examine the performance of our methods based on MNIST, Fashion-MNIST and CIFAR10 datasets. The results show that the proposed double-threshold scheme can effectively improve accuracies of the converted SNNs. More importantly, the proposed AugMapping is more advantageous for constructing accurate, fast and efficient deep SNNs as compared to other state-of-the-art approaches. Our study therefore provides new approaches for further integration of advanced techniques in ANNs to improve the performance of SNNs, which could be of great merit to applied developments with spike-based neuromorphic computing.

Segmentation is a prerequisite yet challenging task for medical image analysis. In this paper, we introduce a novel deeply supervised active learning approach for finger bones segmentation. The proposed architecture is fine-tuned in an iterative and incremental learning manner. In each step, the deep supervision mechanism guides the learning process of hidden layers and selects samples to be labeled. Extensive experiments demonstrated that our method achieves competitive segmentation results using less labeled samples as compared with full annotation.

The temporal segmentation of events is an essential task and a precursor for the automatic recognition of human actions in the video. Several attempts have been made to capture frame-level salient aspects through attention but they lack the capacity to effectively map the temporal relationships in between the frames as they only capture a limited span of temporal dependencies. To this end we propose a complete end-to-end supervised learning approach that can better learn relationships between actions over time, thus improving the overall segmentation performance. The proposed hierarchical recurrent attention framework analyses the input video at multiple temporal scales, to form embeddings at frame level and segment level, and perform fine-grained action segmentation. This generates a simple, lightweight, yet extremely effective architecture for segmenting continuous video streams and has multiple application domains. We evaluate our system on multiple challenging public benchmark datasets, including MERL Shopping, 50 salads, and Georgia Tech Egocentric datasets, and achieves state-of-the-art performance. The evaluated datasets encompass numerous video capture settings which are inclusive of static overhead camera views and dynamic, ego-centric head-mounted camera views, demonstrating the direct applicability of the proposed framework in a variety of settings.

We propose an augmented Lagrangian preconditioner for a three-field stress-velocity-pressure discretization of stationary non-Newtonian incompressible flow with an implicit constitutive relation of power-law type. The discretization employed makes use of the divergence-free Scott-Vogelius pair for the velocity and pressure. The preconditioner builds on the work [P. E. Farrell, L. Mitchell, and F. Wechsung, SIAM J. Sci. Comput., 41 (2019), pp. A3073-A3096], where a Reynolds-robust preconditioner for the three-dimensional Newtonian system was introduced. The preconditioner employs a specialized multigrid method for the stress-velocity block that involves a divergence-capturing space decomposition and a custom prolongation operator. The solver exhibits excellent robustness with respect to the parameters arising in the constitutive relation, allowing for the simulation of a wide range of materials.

Linkage Tree Genetic Algorithm (LTGA) is an effective Evolutionary Algorithm (EA) to solve complex problems using the linkage information between problem variables. LTGA performs well in various kinds of single-task optimization and yields promising results in comparison with the canonical genetic algorithm. However, LTGA is an unsuitable method for dealing with multi-task optimization problems. On the other hand, Multifactorial Optimization (MFO) can simultaneously solve independent optimization problems, which are encoded in a unified representation to take advantage of the process of knowledge transfer. In this paper, we introduce Multifactorial Linkage Tree Genetic Algorithm (MF-LTGA) by combining the main features of both LTGA and MFO. MF-LTGA is able to tackle multiple optimization tasks at the same time, each task learns the dependency between problem variables from the shared representation. This knowledge serves to determine the high-quality partial solutions for supporting other tasks in exploring the search space. Moreover, MF-LTGA speeds up convergence because of knowledge transfer of relevant problems. We demonstrate the effectiveness of the proposed algorithm on two benchmark problems: Clustered Shortest-Path Tree Problem and Deceptive Trap Function. In comparison to LTGA and existing methods, MF-LTGA outperforms in quality of the solution or in computation time.

Hydroswellable, absorbable and non-absorbable, aliphatic, segmented polyurethanes and polyurethane-urea capable of swelling in the biological environment with associated increase in volume of at least 3 percent have more than one type of segments, including those derived from polyethylene glycol and the molecular chains are structurally tailored to allow the use of corresponding formulations and medical devices as carriers for bioactive agents, rheological modifiers of cyanoacrylate-based tissue adhesives, as protective devices for repairing defective or diseased components of articulating joints and their cartilage, and scaffolds for cartilage tissue engineering.

An elongated segmented soap bar is segmented longitudinally into a plurality of soap bodies separate and discrete from one another. Adjacent soap bodies are movable with respect to one another between at least two different configurations including at least an arc configuration with the plurality of soap bodies disposed in an arc. At least one coupler couples the plurality of soap bodies together to allow the adjacent soap bodies to move with respect to one another between the at least two different configurations.

There is described herein a superconducting segment and method of making same comprising one or several layers with very high electrical resistivity, acting as a current flow diverter when the current transfers from the superconductor to the stabilizer. The purpose of this current flow diverter is: i) to increase the contact resistance between the superconductor and the stabilizer, by reducing the contact area, and ii) to force the current to flow along a specific path, so as to increase momentarily the current density in a specific portion of the stabilizer. The consequence of i) and ii) is that heat generated at the extremities of the normal zone is increased and spread over a longer length along the superconducting segment, which increases the NZPV and thus, the uniformity of the quench.

An apparatus for decoding data segments representing a time-domain data stream, a data segment being encoded in the time domain or in the frequency domain, a data segment being encoded in the frequency domain having successive blocks of data representing successive and overlapping blocks of time-domain data samples. The apparatus includes a time-domain decoder for decoding a data segment being encoded in the time domain and a processor for processing the data segment being encoded in the frequency domain and output data of the time-domain decoder to obtain overlapping time-domain data blocks. The apparatus further includes an overlap/add-combiner for combining the overlapping time-domain data blocks to obtain a decoded data segment of the time-domain data stream.