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Varying density of point clouds increases the difficulty of 3D detection. In this paper, we present a context-aware dynamic network (CADNet) to capture the variance of density by considering both point context and semantic context. Point-level contexts are generated from original point clouds to enlarge the effective receptive filed. They are extracted around the voxelized pillars based on our extended voxelization method and processed with the context encoder in parallel with the pillar features. With a large perception range, we are able to capture the variance of features for potential objects and generate attentive spatial guidance to help adjust the strengths for different regions. In the region proposal network, considering the limited representation ability of traditional convolution where same kernels are shared among different samples and positions, we propose a decomposable dynamic convolutional layer to adapt to the variance of input features by learning from the local semantic context. It adaptively generates the position-dependent coefficients for multiple fixed kernels and combines them to convolve with local features. Based on our dynamic convolution, we design a dual-path convolution block to further improve the representation ability. We conduct experiments on KITTI dataset and the proposed CADNet has achieved superior performance of 3D detection outperforming SECOND and PointPillars by a large margin at the speed of 30 FPS.

Programmed death-1 (PD-1) and programmed death ligand-1 (PD-L1), the research focus in immune checkpoint regulation, play an important role in tumor immunotherapy. Inhibitors of this pathway are also the focus of tumor immunotherapy research. The PD-1/PD-L1 pathway can be blocked by selective binding to PD-L1. Clinical trials have been conducted in a variety of patients with advanced solid tumors. CS1001 is a high-affinity humanized full-length anti-PD-L1 monoclonal antibody with great clinical significance. We constructed a PD-L1-targeted radioactive molecular probe

Carrier-selective contacts, especially in electron-selective contact (ESC), between the metal electrodes and the crystalline silicon (c-Si) substrates play a pivotal section in promoting the power ...

Electron-transport layer (ETL) that promotes charge carrier separation and electron extraction is a key component of perovskite solar cells (PSCs). Here, we report a simple approach for improving c...

Irrespective of the success on reduction of contact resistivity, lack of chemical passivation of evaporated metal oxides heavily hinders their applications as passivating contacts, such contacts can be an alternative route for high efficiency and cost effective silicon solar cells. Here, we demonstrate that electron beam evaporated magnesium oxide (MgOx) thin film can work as a promising electron-selective passivating contact for n-Si solar cells after a post-annealing treatment and an alumina-initiated atomic hydrogenation. 10 nm MgOx on n-Si provided a surface recombination velocity down to 14.9 cm/s while 1 nm MgOx showed a low contact resistivity of 14 mΩ cm2. Comprehensive characterizations revealed the formation of Si–O–Mg bonds and the activation of atomic hydrogens were the main reasons for such high-level passivation. A PERC-like dopant-free rear contact was formed by using the 1 nm-MgOx as electron-collector and the 10 nm-MgOx as passivating layer, the resultant solar cells achieved 27% increment in efficiency and 51 mV increase in open-circuit voltage in comparison with reference devices. The ways of improving passivation quality of MgOx and novel design of contact structure open up the possibility of using evaporation-processed metal oxides as effective and low-cost carrier-selective passivating contacts for n-Si photovoltaic devices.

Feedback is a fundamental mechanism existing in the human visual system, but has not been explored deeply in designing computer vision algorithms. In this paper, we claim that feedback plays a critical role in understanding convolutional neural networks (CNNs), e.g., how a neuron in CNNs describes an object's pattern, and how a collection of neurons form comprehensive perception to an object. To model the feedback in CNNs, we propose a novel model named Feedback CNN and develop two new processing algorithms, i.e., neural pathway pruning and pattern recovering. We mathematically prove that the proposed method can reach local optimum. Note that Feedback CNN belongs to weakly supervised methods and can be trained only using category-level labels. But it possesses a powerful capability to accurately localize and segment category-specific objects. We conduct extensive visualization analysis, and the results reveal the close relationship between neurons and object parts in Feedback CNN. Finally, we evaluate the proposed Feedback CNN over the tasks of weakly supervised object localization and segmentation, and the experimental results on ImageNet and Pascal VOC show that our method remarkably outperforms the state-of-the-art ones.

At present, it is a significant challenge to design electrode materials with both desirable electrochemical performance and high mass loading for supercapacitors by using a simple, efficient and safe method. With the introduction of Ni2+ ions, the hollow Ni-decorated Co9S8 nanospheres meeting the above requirements have been successfully synthesized by one-step electrodeposition method under a two-electrode system. As electrodes for supercapacitors, such Ni-decorated Co9S8 electrodes deliver an ultrahigh areal and volumetric specific capacitance of 5.64 F cm−2 and 171.85 F cm−3 at a current density of 1 mA cm−2, superior rate capability (88.9% retention at a current density of 20 mA cm−2) and remarkable cycling stability with a capacitance retention of 89% after 8500 cycles. Also, the as-fabricated flexible quasi-solid-state asymmetric supercapacitors based on Ni-decorated Co9S8 nanospheres and active carbon electrodes present high energy density. The practicability and operability of the device were successfully demonstrated by lighting up the LEDs with five different colors.

Black silicon (b-Si) with nanotextures is a promising light-trapping scheme for potentially achieving high conversion efficiency at reduced manufacturing cost in crystalline-silicon solar cells. However, the inherently high aspect-ratio and tiny feature size of the nanostructures are subject to severe surface (large surface areas) and Auger recombination (worse doping profile). These will abate the cost values of b-Si since one has to adopt a comprise strategy of applying shallow nanotextures with antireflection and passivation layers. Here, we show that silicon microwire solar cells featuring well-defined radial junctions can extensively suppress both surface and Auger recombination by providing excellent all-around electrical field. The radially doped silicon micropillar devices even show an internal quantum efficiency as good as that of planar substrate and their measured minority carrier lifetimes become nearly independent of total surface area. A great reduction in short-circuit current density loss was further identified as the junction abruptly changed from a fully diffused to a core-shell configuration, manifesting the powerful effectiveness of radial p-n+ junction on the suppression of Auger recombination. Furthermore, silicon microwire solar cell with a radial junction demonstrates 37% increase in efficiency compared with the reference cell, suggesting a feasible strategy towards high-efficiency solar devices.

Explosive growth in video traffic volumes incurs a high percentage of redundancy in today's Internet, following the 80–20 rule. Fortunately, the advanced in-network cache is considered as an effective scheme for eliminating the repetitive traffic by caching the popular content in network nodes. Besides, the emerging software-defined networking (SDN) enables centralized control and management, as well as the collaboration between network devices and upper applications. Moreover, the Network Functions Virtualization is also developed to support for customized network functions, including caching and streaming. This inspires us to design an SDN-assisted multimedia streaming Video-on-Demand system, integrating in-network cache, to improve the quality of service. The designed architecture is capable of reducing the redundant traffic via the reusable duplications. In particular, it can achieve greater performance gains by deploying specific scheduling policy. We further propose a variable-length interval cache strategy for RTP streaming, which can realize the self-adaptive adjustment of the size of cached video segments based on their access patterns. Our goal is to efficiently utilize the limited storage resources and increase the cache hit ratio. We present the theoretical analysis to demonstrate the attainable performance of the proposed algorithm; furthermore, the integrated system design is implemented as a prototype to show its feasibility and applicability. Ultimately, emulation experiments are conducted to evaluate the achievable performance improvement more comprehensively.

Person re-identification aims at identifying a certain pedestrian across non-overlapping multi-camera networks in different time and places. Existing person re-identification approaches mainly focus on matching pedestrians on images; however, little attention has been paid to re-identify pedestrians in videos. Compared to images, video clips contain motion patterns of pedestrians, which is crucial to person re-identification. Moreover, consecutive video frames present pedestrian appearance with different body poses and from different viewpoints, providing valuable information toward addressing the challenge of pose variation, occlusion, and viewpoint change, and so on. In this article, we propose a Dense 3D-Convolutional Network (D3DNet) to jointly learn spatio-temporal and appearance representation for person re-identification in videos. The D3DNet consists of multiple three-dimensional (3D) dense blocks and transition layers. The 3D dense blocks enlarge the receptive fields of visual neurons in both spatial and temporal dimensions, leading to discriminative appearance representation as well as short-term and long-term motion patterns of pedestrians without the requirement of an additional motion estimation module. Moreover, we formulate a loss function consisting of an identification loss and a center loss to minimize intra-class variance and maximize inter-class variance simultaneously, toward addressing the challenge of large intra-class variance and small inter-class variance. Extensive experiments on two real-world video datasets of person identification, i.e., MARS and iLIDS-VID, have shown the effectiveness of the proposed approach.

Due to the advantages of safe, cost-effective and environment friendly, aqueous high-voltage carbon/carbon supercapacitors attract much attention but the poor cycling stability hinders their practical applications. In this study, by electrodepositing a Zn/Zn4SO4(OH)6·4H2O (ATDS) composite on a microporous carbon cloth modified with basic functional groups (OCC) as a negative electrode, we developed an aqueous carbon/carbon supercapacitor operating steadily at a voltage as high as 2.5 V. The ATDS composite expands the negative potential window of carbon to −1.7 V vs. SCE and thus makes the positive carbon cloth (CC) operate in the stable potential range to inhibit the capacity degradation of the supercapacitor. A 2.1 V beaker-shaped supercapacitor of ATDS@OCC//CC presents almost 100% capacity retention after 19 000 cycles at 18 mA cm−2. Moreover, the cycled 2.1 V ATDS@OCC//CC supercapacitor exhibits the improved energy density of 9 mW h cm−3 and better retention of the energy density. A 2.5 V ATDS@OCC//CC supercapacitor after 4900 cycles exhibits about 100% capacity retention, an ultrahigh volumetric energy density of 20 mW h cm−3 and a mass energy density of 29 W h kg−1. The novel strategy may pave the way for developing safe, environment friendly and high-energy carbon/carbon supercapacitors.

Counting on-road vehicles in the highway is fundamental for intelligent transportation management. This paper presents the first highway vehicle counting method in compressed domain, aiming at achieving comparable estimation performance with the pixel-domain methods. Counting in compressed domain is rather challenging due to limited information about vehicles and large variance in vehicle numbers. To address this problem, we develop new low-level features to mitigate the challenge from insufficient information in compressed videos. The new proposed features can be easily extracted from the coding-related metadata. Then, we propose a hierarchical classification-based regression (HCR) model to estimate the number of vehicles from the compressed-domain low-level features for individual frame. HCR hierarchically divides the traffic scenes into different cases according to the density of vehicles such that the large variance of traffic scenes can be effectively captured. Beside the spatial regression in each frame, we propose a locally temporal regression model to further refine the counting results, which exploits the continuous variation characteristics of the traffic flow. We extensively evaluate the proposed method on real highway surveillance videos. The experimental results consistently show that the proposed method is very competitive compared with the pixel-domain methods, which can reach similar performance with much lower computational cost.

Cobalt-based composites are generally investigated as cathode for supercapacitor due to its excellent electrochemical properties. Here, we explore its application as anode with the potential window of −0.3–0 V in 6 M KOH aqueous electrolyte. Interconnected Co3O4/CoO/Co(OH)2 nanosheets array is one-step electrodeposited on the surface of active carbon cloth, but showing low capacitance. Surprisingly, after in-situ compositing with MnO2 (ratio of Mn4+ is ∼23%), the hybrid MnO2/cobalt composites nanosheets array anode delivers ∼705 F g−1 at 0.68 A g−1, much higher than that of ∼161 F g−1 for pristine Co3O4/CoO/Co(OH)2. Besides, using NiCo2S4 nanosheet array@ nickel foam as cathode, the asymmetric supercapacitor exhibits high specific capacitance of 198 F g−1 at the current density of 0.5 A g−1. This work manifests a promising strategy to explore novel anode material and improve their properties, which holds great promise in energy storage/conversion devices.

For crystalline-silicon (c-Si) solar cells, the interdigitated back contact (IBC) structure has been long known as an efficient way to approach the theoretical limit of efficiency. However, the complexity of fabricating this kind of devices as well as the high dependence on expensive vacuum systems pose concerns about their commercial potential. Here, we demonstrate a novel c-Si IBC solar cell featuring dopant-free heterocontacts for both polarities, i.e. a solution-proceeded PEDOT:PSS film as hole-transporting layer (HTL) and an evaporated magnesium-oxide film as electron-transporting layer (ETL). Our innovatively buried ETL method provides substantial simplification on the architecture and fabrication of the IBC cells and makes it possible to adapt solution-proceeded HTLs while keeping good passivation in gap regions. The IBC solar cell shows an efficiency of 16.3%, with a promising short-circuit current density (Jsc) up to 38.4 mA/cm2. A thorough simulation concerning the influence of pitch size, surface recombination rate (at ETL and gap regions) was conducted, revealing a readily achievable Jsc of 41 mA/ cm 2 and a PCE beyond 22%. Our findings demonstrated a feasibility of using solution method to fabricate high efficiency dopant-free IBC solar cells.

Nanostructured silicon (Si) can provide improved light trapping capacity in Si/organic hybrid solar cells (HSCs) due to its low light reflectance compared with planar Si. However, the poor contact of nanostructured Si/organic interface and serious recombination on the uncovered Si surface can result in an inferior open-circuit voltage ( V OC ) and fill factor ( FF ) for HSCs. Here, we have designed and fabricated a novel cone-shaped Si nanoholes (SiNHs) structures to form a superior interface contact between SiNHs and organic layer by using an advanced metal assisted chemical etching method. In addition, a Cs 2 CO 3 layer was also introduced on the Si back surface to reduce the contact resistance as well as to suppress the surface recombination for the HSCs. With such improvements, a power conversion efficiency (PCE) up to 13.5% was achieved for Si/organic hybrid solar cell with the back passivation layer. This work provided a new strategy to improve the junction quality and performance of nanostructured Si/PEDOT:PSS HSCs.

The optical and electrical properties of PEDOT:PSS organic layer play a very important role in determining the power conversion efficiency (PCE) of Si–organic hybrid solar cells (HSCs). In the present study, properties of PEDOT:PSS thin films with reduced graphene oxide (rGO) and their impacts on the performances of the resultant Si–organic HSCs have been systematically investigated. The electrical conductivity of PEDOT:PSS improved 35% when rGO was added to PEDOT:PSS, and the fabricated HSCs with 2 mg/ml rGO addition yielded an PCE of 11.95% with a J sc of 31.94 mA cm −2 , a V oc of 579 mV and a FF of 0.648 . However, excess rGO would deteriorate the solar cells performances and it might create additional defects and prevent carriers being collected. The Raman spectroscopy, sheet resistance and EQE analyses with rGO suggested that the interaction between the conductive rGO flakes and the aromatic PEDOT most probably not only provide additional charge transport pathways in hole transport layer to improve carrier mobility leading to a higher carrier collection efficiency, but also suppress the electron recombination at the junction interface. In addition, the rGO serve as an antireflection coating to reduce the reflectance of PEDOT:PSS thin film leading to further enhanced performances of solar cells.

The background information is a significant prior for salient object detection, especially when images contain cluttered background and diverse object parts. In this paper, we propose a background-driven salient object detection (BD-SOD) method to more comprehensively exploit the background prior, aiming at generating more accurate and robust salient maps. To be specific, we first exploit the background prior to conduct the saliency estimation, i.e., computing the regional saliency values. In this stage, the background prior is utilized in threefold: restricting the reference regions to only the background regions, weighting the contribution of reference regions, and leveraging the importance of different features. Benefiting from such an explicit utilization, the proposed model can greatly mitigate the negative interference of the cluttered background and diverse object parts. We then embed the background prior into the optimization graph for saliency refinement. Specifically, two virtual supernodes (representing the background and foreground, respectively) are introduced with extra connections, and the nonlocal feature connections between similar regions are also set up. These connections enhance the power of optimization graph to alleviate the perturbations from diverse parts, and thus help to achieve the uniformity of saliency values. Finally, we provide systematical studies to investigate the effectiveness of the proposed BD-SOD in exploiting the valuable background prior. Experimental results on multiple public benchmark datasets, including MSRA-1000, THUS-10000, PASCAL-S, and ECSSD, clearly show that BD-SOD consistently outperforms the well-established baselines and achieves state-of-the-art performance.

The increasing market growth of hybrid vehicles requires not only improvement on supercapacitor performance, but also cheap, abundant and sustainable material for supercapacitor fabrication. The present study demonstrated a novel strategy to convert used disposable bamboo chopsticks into uniform carbon fibers, and subsequently manganese dioxide (MnO 2 ) was conformably grown on each fiber to form a carbon fiber/MnO 2 composite. When it used as a self-supported binder-free electrode for supercapacitors, it delivered a superior mechanical stability, excellent rate capability, high specific capacitance of 375 F g −1 at the current density of 1 A g −1 and an increase specific capacitance was found after 5000 cycles interestingly. Moreover, the symmetric supercapacitor comprised of CFS/MnO 2 electrodes presents a maximum energy density of 11 Wh kg −1 and a maximum power density of 17.4 kW kg −1 . In addition, this approach presents a scalable, low-cost and sustainable route to transform chopsticks waste into carbon fibers to make electrodes for supercapacitors and lithium-ion capacitors.

The problem of adaptive multi-user scalable layered video transmission is considered in energy harvesting (EH) aided wireless communication systems. With the goal of improving the quality of video services while providing fairness amongst the users despite the random nature of both energy harvesting and the channel quality, we formulate our Scalable Video Coding (SVC) design as a Constrained Utility Function Maximization (CUFM) problem. The proportional fairness and playback smoothness of our design is guaranteed by maximizing the log-sum of the users’ video qualities, while satisfying the battery fullness constraint and video layer (quality) fluctuation constraint. By invoking the classical Lyapunov drift based optimization technique, we further decompose the CUFM problem into two parallel subproblems, i.e., a dynamic transmission power allocation problem and a dynamic layer selection problem. By solving these two subproblems, we derive a joint power allocation and video layer selection strategy for multi-user SVC video transmission. The theoretical performance bound of the proposed solution is also presented. Numerical simulations are conducted with real H.264 SVC video traces and the experimental results demonstrate the reduced playback interruption rate and layer switching rate compared to a heuristic algorithm ProNTO. The results also illustrate a tradeoff between the system’s utility function and the playback smoothness experienced by the users.

3D petal-like NiCo2S4 nanostructures have been fabricated via a simple, mild and efficient hydrothermal strategy and the growth mechanism of NiCo2S4 nano-petals has been investigated. Such NiCo2S4 nano-petal electrodes can deliver an ultrahigh specific capacitance of 2036.5 F g−1 at a current density of 1 A g−1, superior rate capability and remarkable cycle stability (94.3% of capacitance retention after 5000 cycles). The as-fabricated asymmetric supercapacitors based on NiCo2S4 nano-petals//active carbon electrodes demonstrate a high energy density of 35.6 W h kg−1 at a power density of 819.5 W kg−1, with both long-term cycling and high rate stabilities. Such supercapacitors have been tested to power ten LEDs (2.03 V, 20 mA) in series for around 60 minutes, indicating their great potential for practical application.

3D flexible O/N co-doped graphene foams (GF) have been designed and fabricated successfully via combining the compression/hot curing strategy with chemical reduction and hydrothermal treatment, in which melamine foams were used not only as the source of N/O functional groups for the introduction of pseudocapacitance but also as the sacrificial template to inhibit the agglomeration of graphene. Also, the mechanism for method of the compressing/hot curing has been investigated systematically. And the resultant GF demonstrates excellent mechanical strength and flexibility. When the compacting GF is used as free-standing electrodes for supercapacitor, it exhibits more excellent ability of charge storage than that of pristine graphene foams. And 10 MPa-GF electrodes delivers high areal capacitance of 375 mu F cm(-2) at a current density of 1 A g(-1), excellent rate capabilities and superior cycling stability of above 100%. According to the analysis of capacitance contribution for 10 MPa-GF electrode, the pseudocapacitance originated from N/O functional groups is up to-65% of the contribution of total capacitance. Moreover, the symmetric supercapacitor comprised of 10 MPa-GF electrodes presents a maximum energy density of 16 Wh kg(-1) (8 Wh L-1) and a maximum power density of 17 kW kg(-1) (8.6 kW L-1). Published by Elsevier B.V.

Wireless Multimedia service faces the difficulty of QoS decrease caused by the cache underflow, due to both the unreliable and time-varying nature of the wireless channel. In order to reduce the probability of the cache outage, a statistical model is established to estimate the underflow time of the cache. Based on the estimation of the underflow time, an adaptive playout algorithm with dual-threshold to adjust the frame rate is presented. This approach adjusts the threshold dynamically according to the channel conditions and the status of the cache, and thus regulates the frame rate timely. Moreover, the algorithm possesses scene-aware characteristics, since it adjusts the playout rate according to the motion intensity of the playout scenes to reduce the underflow probability of the cache and provide better QoS. Simulation results show that as compared to other adaptive media playout algorithms, the proposed algorithm efficiently reduces the probability of the cache underflow, especially in low rate channel and timevarying rate environment, and provides better visual quality.

Minimizing the number of bandwidth changes of individual stream and enabling the transmission schedule flexible can benefit increasing the number of concurrent multiplexed streams in periodic network without degrading quality of video. However, minimum change bandwidth allocation (MCBA) only provides a fixed transmission schedule for given video and client buffer although it achieves the minimum number of rate changes and the minimum peak rate. So the MCBA schedule cannot be adjusted when multiple streams are multiplexed over a limited bandwidth shared link. In this paper, we propose a relaxing transmission schedule that provides a range of rates for each interval and preserves the minimum number of rate changes without increasing any rate. As a result, the individual schedule can be adjusted by network service to try to satisfy bandwidth constraint when the total required bandwidth of multiplexed streams exceeds the available network capacity. First, we underline that the key of MCBA is to segment video with making each linear trajectory as long as possible. Then we use linear separability and support vector machine under the classification model to compute the longest interval and the required rate, respectively. Due to our proposed decoupled form between segmentation and rate computation, finally, we construct the relaxing schedule bounded by the original MCBA rate and the minimum rate. The simulation using real MPEG4 and H.264 video traces confirms the philosophy and evaluates the relaxing performance of our proposed algorithm.

As a trade-off technique, bandwidth smoothing can reduce the client buffer requirements and simultaneously keep transmission scheme as smooth as possible. In this paper, bandwidth smoothing is formulated into a binary classification problem of the underflow and overflow points. We propose a novel method to solve that problem based on support vector machine (SVM). Our method is proven to be able to achieve the minimum buffer requirements of constant rate transmission and transport. Furthermore, it directly computes the transmission rate without exhaustively searching buffer size and startup delay. Besides this method, this paper provides a systematic video segmentation algorithm, which can intelligently partition the playback curve into some unequal segments to naturally track the trends of playback curve. The smoothing results with the playback curve of y = x n demonstrate that this video systematic segmentation requires smaller than half of the buffer of the equal segmentation algorithm. Finally, we construct a generalized piecewise constant rate transmission and transport algorithm with SVM and the systematic video segmentation method. The experiments of some real MPEG4 and H.264 video data confirmed the efficiency of our proposed algorithm.