1. OnePlus slipped to third position in India's premium smartphone segment in Q1 2020, iPhone 11 was the top selling device  gizmochina
2. Samsung releases One UI 2.0 update for Galaxy A20s  NewsBytes
3. Features we want and don’t want from the Galaxy Note 20  SamMobile
4. Leaked Samsung Galaxy A21s render shows a familiar design - GSMArena.com news  GSMArena.com
5. Samsung is Now the Top Brand for Premium Smartphones in India  Gizchina.com
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The Hero Maestro Electric Proto concept electric scooter has been spotted ahead of its India launch. The eMaestro electric scooter will be the brand's first product offering to the EV segment in India.

Mycobacterium tuberculosis produces glycogen (also known as α-glucan) to help evade human immunity. This pathogen uses the GlgE pathway to generate glycogen rather than the more well known glycogen synthase GlgA pathway, which is absent in this bacterium. Thus, the building block for this glucose polymer is α-maltose-1-phosphate rather than an NDP-glucose donor. One of the routes to α-maltose-1-phosphate is now known to involve the GlgA homologue GlgM, which uses ADP-glucose as a donor and α-glucose-1-phosphate as an acceptor. To help compare GlgA (a GT5 family member) with GlgM enzymes (GT4 family members), the X-ray crystal structure of GlgM from Mycobacterium smegmatis was solved to 1.9 Å resolution. While the enzymes shared a GT-B fold and several residues responsible for binding the donor substrate, they differed in some secondary-structural details, particularly in the N-terminal domain, which would be expected to be largely responsible for their different acceptor-substrate specificities.

Construction is scheduled to begin Sunday, August 12, weather permitting, on the third of three segments to widen approximately 21 miles total on Interstate 64 in Newport News, James City County and York County.

Egmont Key National Wildlife Refuge is rich with wildlife and history — and it's under siege from the waters that surround it. And it's only to get worse.

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

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.

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.

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.

For a plurality of input/output (I/O) operations waiting to assemble complete data tracks from data segments, a process, separate from a process responsible for the data assembly into the complete data tracks, is initiated for waking a predetermined number of the waiting I/O operations.

An example method transmits a path message to an egress node, via intermediate nodes, that configures OAM monitoring of an entire connection between an ingress node and the egress node and also configures OAM monitoring of at least one segment of the connection shorter than the entire connection by at least one intermediate node. The path message comprises a LSP attributes object and an ERO. The LSP object comprises technology-specific attributes defining descriptors for configuring the egress node in accordance with a communication protocol utilized for the connection; and technology-independent OAM parameters that, irrespective of the communication protocol, define an OAM type to be used by the egress node, and define desired monitoring actions for the egress node to perform. The ERO comprises a respective HOP Attributes sub-object for configuring each of the at least one intermediate nodes. Each HOP Attributes sub-object also comprises technology-specific attributes and technology-independent OAM parameters.

A multiple-segment transmission line in a hard disk drive enables a wider optimization range of the slope, duration and amplitude of the transmission line overshoot (TLO) wave shape. There is a first segment with two traces for connection to the write driver circuitry, an end segment with two traces for connection to the write head and at least two intermediate segments. The number of traces in a segment is different from the number of traces in the segments to which the segment is immediately connected. There is an even number of traces in each segment and the traces in each segment are interleaved. The number of segments and the number of traces in each segment can be selected to achieve the desired impedance levels for the different segments to achieve the desired wave shape for the TLO. All of the traces on the transmission line are preferably coplanar.

A bearing cage assembly (100) comprises a plurality of discrete bridge elements (206) disposed between adjacent rolling elements (112) and coupled between first and second axially spaced cage support wire rings (102, 104) which are appropriately tensioned. Spacers (110) are disposed between adjacent bridge elements and engage the bridge elements in a piloted engagement. The bridge elements maintain a separation between rolling elements, retain the rolling elements within the bearing assembly, and function as a lubrication reservoir for grease lubricated bearings. Profiled surfaces on the bridge elements position the bearing cage assembly on at least one axial end of the rolling elements.

An image forming device includes a photoreceptor drum including a target surface that is scanned in a main scanning direction and a sub-scanning direction, an exposure head including a plurality of light emitting segments aligned in parallel to the main scanning direction, an exposure driving unit which selectively drives the plural light emitting segments, a storing unit which stores a profile where the respective positions of the plural light emitting segments correspond to a correction amount from the main scanning direction toward the sub-scanning direction at every position, and a correcting unit which smoothes a local change of the correction amount in the profile.

The present invention relates to shaped bodies made of fiber-reinforced ceramic composites and comprising a core zone and at least one covering layer which has a coefficient of thermal expansion which is higher than that of the core zone. The covering layer is an SiC-rich covering layer and is divided into segments which are separated from the adjacent segments by gaps or bridging zones of a material which is different from the material of the segments. The invention also relates to a process for producing such shaped bodies by infiltration of an intermediate body with molten silicon and their use for friction disks, in vehicle construction or as protective plates.

There is provided a transport vehicle for transporting wind power installation rotor blades and/or pylon segments. The transport vehicle has a transport support structure having a main frame, a receiving frame fixedly connected to the main frame at a first angle, and a rotary displacement unit which is fixed with one end to the receiving frame and which at its second end has a blade adaptor for receiving a rotor blade or a pylon segment. The main frame spans a main plane. The rotary displacement unit has at least one first rotary mounting, wherein there is provided a second angle between the second rotary plane of the second rotary mounting.

A blade for a rotor of a wind turbine is manufactured with a root region with a substantially circular or elliptical profile closest to the hub, an airfoil region with a lift generating profile furthest away from the hub and a transition region having a profile gradually changing the root region to the airfoil region. A first blade design is used for the first base part on a first longitudinal section of an airfoil region of a second blade, so that an induction factor of the first base part on the second blade deviates from a target induction factor. The first longitudinal section of the second blade is provided with flow altering devices so as to adjust the aerodynamic properties of the first longitudinal segment to substantially meet the target induction factor at the design point on the second blade.

Disclosed is a system for transferring electric energy to a vehicle, in particular to a track bound vehicle such as a light rail vehicle. The system includes an electric conductor arrangement for producing an alternating electromagnetic field and for thereby transferring the energy to the vehicle. The electric conductor arrangement includes at least one alternating current line. Each alternating current line carries one phase of an alternating electric current. The conductor arrangement includes a plurality of consecutive segments. The segments extend along the path of travel of the, vehicle. Each segment includes one section of each of the at least one alternating current line.