Feature Selective Networks for Object Detection

Objects for detection usually have distinct characteristics in different sub-regions and different aspect ratios. However, in prevalent two-stage object detection methods, Region-of-Interest (RoI) features are extracted by RoI pooling with little emphasis on these translation-variant feature components. We present feature selective networks to reform the feature representations of RoIs by exploiting their disparities among sub-regions and aspect ratios. Our network produces the sub-region attention bank and aspect ratio attention bank for the whole image. The RoI-based sub-region attention map and aspect ratio attention map are selectively pooled from the banks, and then used to refine the original RoI features for RoI classification. Equipped with a light-weight detection subnetwork, our network gets a consistent boost in detection performance based on general ConvNet backbones (ResNet-101, GoogLeNet and VGG-16). Without bells and whistles, our detectors equipped with ResNet-101 achieve more than 3% mAP improvement compared to counterparts on PASCAL VOC 2007, PASCAL VOC 2012 and MS COCO datasets

Evaluating the Impacts of Parking App Services on Travellers\u27 Choice Behaviour and Traffic Dynamics

As the products of intelligent transportation systems, parking apps have become convenient platforms for implementing parking policies, which can be provided as parking app services. This paper proposes a traffic simulation model for evaluating the impacts of parking app services on the travellers’ choice behaviour and traffic dynamics. Travellers are assumed to use three types of parking app services: the provision of information on real-time parking lot occupancies, parking reservation, and the display of dynamic parking fees. The behaviour of travellers, such as travellers’ mode choices, departure time choices, and learning behaviour, are considered in this model. Numerical experiments show that providing information on real-time parking lot occupancies can be helpful in reducing the use ratio of commercial parking lots, but the effect will ultimately be smoothed during the evolution of traffic dynamics. Moreover, parking reservation is an effective way to reduce travel costs and encourage travellers to choose park-and-ride. Furthermore, dynamic parking fees usually lead to the oscillation of traffic dynamics and travellers’ choices, in addition to an increase in travel costs. This model is a useful tool for analysing the impacts of other parking management policies that can be implemented as parking app services and can be a reference for evaluating the impacts of other parking polices.</p

Absence of a transport signature of spin-orbit coupling in graphene with indium adatoms

Enhancement of the spin-orbit coupling in graphene may lead to various topological phenomena and also find applications in spintronics. Adatom absorption has been proposed as an effective way to achieve the goal. In particular, great hope has been held for indium in strengthening the spin-orbit coupling and realizing the quantum spin Hall effect. To search for evidence of the spin-orbit coupling in graphene absorbed with indium adatoms, we carry out extensive transport measurements, i.e., weak localization magnetoresistance, quantum Hall effect and non-local spin Hall effect. No signature of the spin-orbit coupling is found. Possible explanations are discussed.Comment: 5 pages, 4 figures, with supplementary material

Parameters Design for Logarithmic Quantizer Based on Zoom Strategy

This paper is concerned with the problem of designing suitable parameters for logarithmic quantizer such that the closed-loop system is asymptotic convergent. Based on zoom strategy, we propose two methods for quantizer parameters design, under which it ensures that the state of the closed-loop system can load in the invariant sets after some certain moments. Then we obtain that the quantizer is unsaturated, and thus the quantization errors are bounded under the time-varying logarithm quantization strategy. On that basis, we obtain that the closed-loop system is asymptotic convergent. A benchmark example is given to show the usefulness of the proposed methods, and the comparison results are illustrated

Event-Driven Control for NCSs with Logarithmic Quantization and Packet Losses

The stabilization problem of the networked control systems (NCSs) affected by data quantization, packet losses, and event-driven communication is studied in this paper. By proposing two event-driven schemes and the extended forms of them relying on quantized states, zoom strategy is adopted here to study the system stability with time-varying logarithmic quantization and independent identically distributed (IID) packet losses process. On the basis of that, some sufficient conditions ensuring the mean square stability of the system are obtained here. Although zoom strategy has been utilized by many literatures to study the quantized stabilization problem of NCSs, it has not been adopted to analyze the stability of NCSs with data quantization, IID packet losses, and event-driven communication. Furthermore, the existing literatures relating to zoom strategy employ the quantizer with quantization regions holding arbitrary shapes, but here we use the logarithmic quantizer which holds better performance near the origin. In addition, the detailed comparisons of the system performance under different event-driven schemes are given here, which can guide the strategy selection according to the different design goals. The above three points are the main innovations of this paper. At last, the effectiveness of the proposed methods is illustrated by a benchmark example

A Semipersistent Plant Virus Differentially Manipulates Feeding Behaviors of Different Sexes and Biotypes of Its Whitefly Vector.

It is known that plant viruses can change the performance of their vectors. However, there have been no reports on whether or how a semipersistent plant virus manipulates the feeding behaviors of its whitefly vectors. Cucurbit chlorotic yellows virus (CCYV) (genus Crinivirus, family Closteroviridae) is an emergent plant virus in many Asian countries and is transmitted specifically by B and Q biotypes of tobacco whitefly, Bemisia tabaci (Gennadius), in a semipersistent manner. In the present study, we used electrical penetration graph (EPG) technique to investigate the effect of CCYV on the feeding behaviors of B. tabaci. The results showed that CCYV altered feeding behaviors of both biotypes and sexes of B. tabaci with different degrees. CCYV had stronger effects on feeding behaviors of Q biotype than those of B biotype, by increasing duration of phloem salivation and sap ingestion, and could differentially manipulate feeding behaviors of males and females in both biotype whiteflies, with more phloem ingestion in Q biotype males and more non-phloem probing in B biotype males than their respective females. With regard to feeding behaviors related to virus transmission, these results indicated that, when carrying CCYV, B. tabaci Q biotype plays more roles than B biotype, and males make greater contribution than females

Indoor pedestrian dead reckoning calibration by visual tracking and map information

Currently, Pedestrian Dead Reckoning (PDR) systems are becoming more attractive in market of indoor positioning. This is mainly due to the development of cheap and light Micro Electro-Mechanical Systems (MEMS) on smartphones and less requirement of additional infrastructures in indoor areas. However, it still faces the problem of drift accumulation and needs the support from external positioning systems. Vision-aided inertial navigation, as one possible solution to that problem, has become very popular in indoor localization with satisfied performance than individual PDR system. In the literature however, previous studies use fixed platform and the visual tracking uses feature-extraction-based methods. This paper instead contributes a distributed implementation of positioning system and uses deep learning for visual tracking. Meanwhile, as both inertial navigation and optical system can only provide relative positioning information, this paper contributes a method to integrate digital map with real geographical coordinates to supply absolute location. This hybrid system has been tested on two common operation systems of smartphones as iOS and Android, based on corresponded data collection apps respectively, in order to test the robustness of method. It also uses two different ways for calibration, by time synchronization of positions and heading calibration based on time steps. According to the results, localization information collected from both operation systems has been significantly improved after integrating with visual tracking data

FAM222A encodes a protein which accumulates in plaques in Alzheimer's disease.

Alzheimer's disease (AD) is characterized by amyloid plaques and progressive cerebral atrophy. Here, we report FAM222A as a putative brain atrophy susceptibility gene. Our cross-phenotype association analysis of imaging genetics indicates a potential link between FAM222A and AD-related regional brain atrophy. The protein encoded by FAM222A is predominantly expressed in the CNS and is increased in brains of patients with AD and in an AD mouse model. It accumulates within amyloid deposits, physically interacts with amyloid-β (Aβ) via its N-terminal Aβ binding domain, and facilitates Aβ aggregation. Intracerebroventricular infusion or forced expression of this protein exacerbates neuroinflammation and cognitive dysfunction in an AD mouse model whereas ablation of this protein suppresses the formation of amyloid deposits, neuroinflammation and cognitive deficits in the AD mouse model. Our data support the pathological relevance of protein encoded by FAM222A in AD