Difference between revisions of "Resource:Seminar"

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{{SemNote
{{SemNote
|time='''2023-04-20 9:30'''
|time='''Friday 10:30-12:00'''
|addr=4th Research Building A527-B
|addr=4th Research Building A518
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
|note=Useful links: [[Resource:Reading_List|Readling list]]; [[Resource:Seminar_schedules|Schedules]]; [[Resource:Previous_Seminars|Previous seminars]].
}}
}}
Line 7: Line 7:
===Latest===
===Latest===
{{Latest_seminar
{{Latest_seminar
|abstract =Low-power wireless networks have the potential to enable applications that are of great importance to industry and society. However, existing network protocols do not meet the dependability requirements of many scenarios as the failure of a single node or link can completely disrupt communication and take significant time and energy to recover. This paper presents Hydra, a low-power wireless protocol that guarantees robust communication despite arbitrary node and link failures. Unlike most existing deterministic protocols, Hydra steers clear of centralized coordination to avoid a single point of failure. Instead, all nodes are equivalent in terms of protocol logic and configuration, performing coordination tasks such as synchronization and scheduling concurrently. This concept of concurrent coordination relies on a novel distributed consensus algorithm that yields provably unique decisions with low delay and energy overhead. In addition to a theoretical analysis, we evaluate Hydra in a multi-hop network of 23 nodes. Our experiments demonstrate that Hydra withstands random node failures without increasing coordination overhead and that it re-establishes efficient and reliable data exchange within seconds after a major disruption.
|abstract=We present NeuriCam, a novel deep learning-based system to achieve video capture from low-power dual-mode IoT camera systems. Our idea is to design a dual-mode camera system where the first mode is low power (1.1 mW) but only outputs grey-scale, low resolution and noisy video and the second mode consumes much higher power (100 mW) but outputs color and higher resolution images. To reduce total energy consumption, we heavily duty cycle the high power mode to output an image only once every second. The data for this camera system is then wirelessly sent to a nearby plugged-in gateway, where we run our real-time neural network decoder to reconstruct a higher-resolution color video. To achieve this, we introduce an attention feature filter mechanism that assigns different weights to different features, based on the correlation between the feature map and the contents of the input frame at each spatial location. We design a wireless hardware prototype using off-the-shelf cameras and address practical issues including packet loss and perspective mismatch. Our evaluations show that our dual-camera approach reduces energy consumption by 7x compared to existing systems. Further, our model achieves an average greyscale PSNR gain of 3.7 dB over prior single and dual-camera video super-resolution methods and 5.6 dB RGB gain over prior color propagation methods.
|confname=IPSN 2023
|confname=MobiCom 2023
|link=https://www.research-collection.ethz.ch/bitstream/handle/20.500.11850/602741/ipsn23-22.pdf?sequence=1&isAllowed=y
|link=https://dl.acm.org/doi/10.1145/3570361.3592523
|title=Hydra: Concurrent Coordination for Fault-tolerant Networking
|title=NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras
|speaker=Pengfei}}
|speaker=Jiyi
|date=2024-04-12}}
{{Latest_seminar
{{Latest_seminar
|abstract = We report our experiences of developing, deploying, and evaluating MLoc, a smartphone-based indoor localization system for malls. MLoc uses Bluetooth Low Energy RSSI and geomagnetic field strength as fingerprints. We develop efficient approaches for large-scale, outsourced training data collection. We also design robust online algorithms for localizing and tracking users' positions in complex malls. Since 2018, MLoc has been deployed in 7 cities in China, and used by more than 1 million customers. We conduct extensive evaluations at 35 malls in 7 cities, covering 152K m2 mall areas with a total walking distance of 215 km (1,100 km training data). MLoc yields a median location tracking error of 2.4m. We further characterize the behaviors of MLoc's customers (472K users visiting 12 malls), and demonstrate that MLoc is a promising marketing platform through a promotion event. The e-coupons delivered through MLoc yield an overall conversion rate of 22%. To facilitate future research on mobile sensing and indoor localization, we have released a large dataset (43 GB at the time when this paper was published) that contains IMU, BLE, GMF readings, and the localization ground truth collected by trained testers from 37 shopping malls.
|abstract=The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.
|confname=MobiCom 2022
|confname=Neurips 2017
|link=https://dl.acm.org/doi/pdf/10.1145/3495243.3517021
|link=https://proceedings.neurips.cc/paper_files/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
|title=Experience: practical indoor localization for malls
|title=Attention Is All You Need
|speaker=Zhuoliu}}
|speaker=Qinyong
{{Latest_seminar
|date=2024-04-12}}
|abstract = Low-earth-orbit (LEO) satellite mega-constellations promise broadband, low-latency network infrastructure from space for terrestrial users in remote areas. However, they face new QoS bottlenecks from infrastructure mobility due to the fast-moving LEO satellites and earth’s rotations. Both cause frequent space-ground link churns and challenge the network latency, bandwidth, and availability at the global scale. Today’s LEO networks mask infrastructure mobility with fixed anchors (ground stations) but cause single-point bandwidth/latency bottlenecks. Instead, we design LBP to remove the LEO network’s QoS bottlenecks from infrastructure mobility. LBP removes remote terrestrial fixed anchors via geographic addressing for shorter latencies and more bandwidth. It adopts local, orbit direction-aware geographic routing to avoid global routing updates for high network availability. LBP further shortens the routing paths by refining handover policies by satellites’ orbital directions. Our experiments in controlled testbeds and trace-driven emulations validate LBP’s 1.64× network latency reduction, 9.66× more bandwidth, and improve network availability to 100%.
|confname=IWQoS 2022
|link=https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9796680
|title=Geographic Low-Earth-Orbit Networking without QoS Bottlenecks from Infrastructure Mobility
|speaker=Kun}}
 
 
 
=== History ===
 
{{Resource:Previous_Seminars}}
{{Resource:Previous_Seminars}}

Revision as of 15:10, 9 April 2024

Time: Friday 10:30-12:00
Address: 4th Research Building A518
Useful links: Readling list; Schedules; Previous seminars.

Latest

  1. [MobiCom 2023] NeuriCam: Key-Frame Video Super-Resolution and Colorization for IoT Cameras, Jiyi
    Abstract: We present NeuriCam, a novel deep learning-based system to achieve video capture from low-power dual-mode IoT camera systems. Our idea is to design a dual-mode camera system where the first mode is low power (1.1 mW) but only outputs grey-scale, low resolution and noisy video and the second mode consumes much higher power (100 mW) but outputs color and higher resolution images. To reduce total energy consumption, we heavily duty cycle the high power mode to output an image only once every second. The data for this camera system is then wirelessly sent to a nearby plugged-in gateway, where we run our real-time neural network decoder to reconstruct a higher-resolution color video. To achieve this, we introduce an attention feature filter mechanism that assigns different weights to different features, based on the correlation between the feature map and the contents of the input frame at each spatial location. We design a wireless hardware prototype using off-the-shelf cameras and address practical issues including packet loss and perspective mismatch. Our evaluations show that our dual-camera approach reduces energy consumption by 7x compared to existing systems. Further, our model achieves an average greyscale PSNR gain of 3.7 dB over prior single and dual-camera video super-resolution methods and 5.6 dB RGB gain over prior color propagation methods.
  2. [Neurips 2017] Attention Is All You Need, Qinyong
    Abstract: The dominant sequence transduction models are based on complex recurrent or convolutional neural networks in an encoder-decoder configuration. The best performing models also connect the encoder and decoder through an attention mechanism. We propose a new simple network architecture, the Transformer, based solely on attention mechanisms, dispensing with recurrence and convolutions entirely. Experiments on two machine translation tasks show these models to be superior in quality while being more parallelizable and requiring significantly less time to train. Our model achieves 28.4 BLEU on the WMT 2014 English-to-German translation task, improving over the existing best results, including ensembles by over 2 BLEU. On the WMT 2014 English-to-French translation task, our model establishes a new single-model state-of-the-art BLEU score of 41.8 after training for 3.5 days on eight GPUs, a small fraction of the training costs of the best models from the literature. We show that the Transformer generalizes well to other tasks by applying it successfully to English constituency parsing both with large and limited training data.

History

2024

2023

2022

2021

2020

  • [Topic] [ The path planning algorithm for multiple mobile edge servers in EdgeGO], Rong Cong, 2020-11-18

2019

2018

2017

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