While object part segmentation is useful for many applications, typical approaches require a large amount of labeled data to train a model for good performance. To reduce the labeling effort, weak supervision cues such as object keypoints have been used to generate pseudo-part annotations which can subsequently be used to train larger models. However, previous weakly-supervised part segmentation methods require the same object classes during both training and testing. We propose a new model to use key-point guidance for segmenting parts of novel object classes given that they have similar structures as seen objects - different types of four-legged animals, for example. We show that a non-parametric template matching approach is more effective than pixel classification for part segmentation, especially for small or less frequent parts. To evaluate the generalizability of our approach, we introduce two new datasets that contain 200 quadrupeds in total with both key-point and part segmentation annotations. We show that our approach can outperform existing models by a large margin on the novel object part segmentation task using limited part segmentation labels during training.

BACKGROUND: The biological network is highly dynamic. Functional relations between genes can be activated or deactivated depending on the biological conditions. On the genome-scale network, subnetworks that gain or lose local expression consistency may shed light on the regulatory mechanisms related to the changing biological conditions, such as disease status or tissue developmental stages. RESULTS: In this study, we develop a new method to select genes and modules on the existing biological network, in which local expression consistency changes significantly between clinical conditions. The method is called DNLC: Differential Network Local Consistency. In simulations, our algorithm detected artificially created local consistency changes effectively. We applied the method on two publicly available datasets, and the method detected novel genes and network modules that were biologically plausible. CONCLUSIONS: The new method is effective in finding modules in which the gene expression consistency change between clinical conditions. It is a useful tool that complements traditional differential expression analyses to make discoveries from gene expression data. The R package is available at https://cran.r-project.org/web/packages/DNLC.

Flare is a practical system for streaming 360-degree videos on commodity mobile devices. It takes a viewport-adaptive approach, which fetches only portions of a panoramic scene that cover what a viewer is about to perceive. We conduct an IRB-approved user study where we collect head movement traces from 130 diverse users to gain insights on how to design the viewport prediction mechanism for Flare. We then develop novel online algorithms that determine which spatial portions to fetch and their corresponding qualities. We also innovate other components in the streaming pipeline such as decoding and server-side transmission. Through extensive evaluations (~400 hours' playback on WiFi and ~100 hours over LTE), we show that Flare significantly improves the QoE in real-world settings. Compared to non-viewport-adaptive approaches, Flare yields up to 18x quality level improvement on WiFi, and achieves high bandwidth reduction (up to 35%) and video quality enhancement (up to 4.9x) on LTE.

Flare is a practical system for streaming 360-degree videos on smartphones. It takes a viewport-adaptive approach, which fetches only portions of a panoramic scene that cover what a viewer is about to perceive. Flare consists of a novel framework for the end-to-end streaming pipeline, introduces innovative streaming algorithms, and brings numerous system-level optimizations. In our demo, we will show that Flare substantially outperforms traditional viewport-agnostic streaming algorithms in terms of the video quality. We will also invite the audience to use Flare to watch attractive 360-degree videos.

360-degree videos are becoming increasingly popular on commercial platforms. In this position paper, we propose a holistic research agenda aiming at improving the performance, resource utilization efficiency, and users' quality of experience (QoE) for 360° video streaming on commodity mobile devices. Based on a Field-of-View (FoV) guided approach that fetches only portions of a scene that users will see, our proposed research includes the following: robust video rate adaptation with incremental chunk upgrading, big-data-assisted head movement prediction and rate adaptation, novel support for multipath streaming, and enhancements to live 360° video broadcast. We also show preliminary results demonstrating promising performance of our proof-of-concept 360° video streaming system on which our proposed research are being prototyped, integrated, and evaluated.

While TCP has been extensively studied in static and low speed mobility situations, it has not yet been well explored in high speed mobility scenarios. Given the increasing deployment of high speed transport systems (such as high speed rails), there is an urgent need to understand the performance and behavior of TCP in such high speed mobility environments. In this paper, we conduct a comprehensive study to investigate the performance and behavior of TCP in a high speed environment with a peak speed of 310 km/h. Over a 16-month period spanning four years, we collect 500 GB of performance data on 3/4G networks in high speed trains in China, covering a distance of 108,490 km. We start by analyzing performance metrics, such as RTT, packet loss rate, and throughput. We then evaluate the challenges posed on the main TCP operations (establishment, transmission, congestion control, flow control, and termination) by such high speed mobility. This paper shows that RTT and packet loss rate increase significantly and throughput drops considerably in high speed situations. Moreover, TCP fails to adapt well to such extremely high speed leading to abnormal behavior, such as high spurious retransmission time out rate, aggressive congestion window reduction, long delays during connection establishment and closure, and transmission interruption. As we prepare to move into the era of 5G, and as the need for high speed travel continues to increase, our findings indicate a critical need for efforts to develop more adaptive transport protocols for such high speed environments.

The rapid growth of high-speed transit systems, such as High Speed Rail (HSR), is putting considerable pressure on TCP-based data transmission. It is well known that TCP is suffering from severe throughput degradation in high-speed mobility scenarios. The root cause at the transport layer however remains unclear and largely undetermined to date. In this paper, we aim to pinpoint the throughput bottlenecks and develop a throughput model to understand TCP in high-speed mobility environments. Based on the analysis of real-world HSR traces, we find that high-speed mobility will introduce significant challenges to the packet retransmission process after timeouts. And ACKs are more likely to trigger spurious retransmission timeouts in TCP flows in high-speed mobile environments. Such problems are not yet considered in the existing TCP models because classic timeouts can easily be recovered by retransmission in stationary scenarios. We therefore propose an enhanced TCP throughput model to integrate the above features. Our model analysis indicates that the optimization of TCP ACK latency is critical to obtain better throughput. Moreover, reliable retransmission mechanisms, e.g., multi-path TCP (MPTCP), can also bring notable benefits in high-speed mobility environments.

The explosion of mobile content and usage imposes enormous pressures on mobile communication networks. To reduce content delivery latency and to ease the burden on network bottlenecks (e.g., backhaul networks), besides upgrading the infrastructures, it is promising to cache popular contents at the edge-storage on BSs (Base Stations), APs (Access Points) or other third-party devices associated with BSs and APs, which have been widely deployed in mobile networks. Then it is a challenge here to effectively match such demands of CPs (Content Providers) and the supplies of edge-storage owners because of the complexity caused by the two-fold matching requirements on both coverage and quantity combined with the multi-buyer multi-seller scenario and the divisibility of heterogeneous edge-storages. In this paper, we propose ESTRA (Edge Storage TRading Auction) mechanism to tackle such a challenge. By proposing a region-based model for edge-storage trading, we design a demand cover mechanism to transfer subscriber coverage demands into edge-storage bundle demands and design a truthful, weakly budget balanced and individually rational auction mechanism under the constraints of enabling multi-unit asks and bundle bids. Our theoretical analysis proves the economic robustness, and the simulation result shows that ESTRA achieves 74%-91% of the maximum social welfare and maintains the sustainability of the trading platform through a proper distribution of social welfare.

TCP has been the dominant transport protocol for mobile internet since its origin. Its behaviors play an essential role in determining quality of service/experience (QoS and QoE) for mobile apps. While TCP has been extensively studied in a static, walking, or driving mobility, it has not been well explored in highspeed (> 200 km/h) mobility cases. With increasing investment and deployment of high speed rails (HSRs), a critical demand of understanding TCP performance under extremely high-speed mobility arises. In this paper, we conduct an in-depth study to investigate TCP behaviors on HSR. We collect 90 GB of measurement data on HSPA+ networks in Chinese high-speed trains with a peak speed of 310 km/h, along various routes (covering 5,000 km) during an 8-month period. We analyze the impacts of high-speed mobility and handoff on performance metrics including RTT, packet loss and network disconnection. Then we demystify the grand challenges posed on TCP operations (TCP establishment, transmission, congestion control and termination). Our study shows that performance greatly declines in HSR, where RTT spikes, packet drops and network disconnections are more significant and occur more frequently, compared with static, slowly moving or driving mobility cases. Moreover, TCP fails to adapt well to such extremely high-speed and yields severely abnormal behaviors, such as high spurious RTO rate, aggressive congestion window reduction, long delay of connection establishment and closure, and transmission interruption. All these findings indicate that extremely high-speed indeed poses a big threat to today's TCP and it calls for urgent efforts to develop HSR-friendly protocols and wireless networks to address even more complicated challenges raised by faster trains/aircrafts in the foreseeable future.

Recently, the performance of mobile data networks has been evaluated from many aspects, e.g., TCP/IP protocols, comparison with WiFi or even satellite communication, under different movements within a metropolis area. Nevertheless, the result is still unknown in high-speed mobility scenarios and in a scale that crosses different metropolis and geographic areas. To fill in this blank, we carry out a comprehensive measurement study on the performance of mobile data networks under high-speed mobility, i.e., 300 km/h or above. Such speed is the current de facto standard of the China Railway High speed (CRH) network, the largest commercial high-speed railway network in the world so far. We first present an overview on the TCP performance over LTE networks. We observe that decent throughput may exist under high-speed mobility. However, comparing to the stationary and driving (100 km/h) scenarios, the throughput and RTT not only are worse, but also have a large variance. We then take an in-depth investigation into two key factors affecting the performance, i.e., The wireless channel and handoff. We believe our study on these factors is useful not only for TCP, but also for other upper-layer protocols.