185 research outputs found
Energy-Efficient Transmission Schedule for Delay-Limited Bursty Data Arrivals under Non-Ideal Circuit Power Consumption
This paper develops a novel approach to obtaining energy-efficient
transmission schedules for delay-limited bursty data arrivals under non-ideal
circuit power consumption. Assuming a-prior knowledge of packet arrivals,
deadlines and channel realizations, we show that the problem can be formulated
as a convex program. For both time-invariant and time-varying fading channels,
it is revealed that the optimal transmission between any two consecutive
channel or data state changing instants, termed epoch, can only take one of the
three strategies: (i) no transmission, (ii) transmission with an
energy-efficiency (EE) maximizing rate over part of the epoch, or (iii)
transmission with a rate greater than the EE-maximizing rate over the whole
epoch. Based on this specific structure, efficient algorithms are then
developed to find the optimal policies that minimize the total energy
consumption with a low computational complexity. The proposed approach can
provide the optimal benchmarks for practical schemes designed for transmissions
of delay-limited data arrivals, and can be employed to develop efficient online
scheduling schemes which require only causal knowledge of data arrivals and
deadline requirements.Comment: 30 pages, 7 figure
Learning and Management for Internet-of-Things: Accounting for Adaptivity and Scalability
Internet-of-Things (IoT) envisions an intelligent infrastructure of networked
smart devices offering task-specific monitoring and control services. The
unique features of IoT include extreme heterogeneity, massive number of
devices, and unpredictable dynamics partially due to human interaction. These
call for foundational innovations in network design and management. Ideally, it
should allow efficient adaptation to changing environments, and low-cost
implementation scalable to massive number of devices, subject to stringent
latency constraints. To this end, the overarching goal of this paper is to
outline a unified framework for online learning and management policies in IoT
through joint advances in communication, networking, learning, and
optimization. From the network architecture vantage point, the unified
framework leverages a promising fog architecture that enables smart devices to
have proximity access to cloud functionalities at the network edge, along the
cloud-to-things continuum. From the algorithmic perspective, key innovations
target online approaches adaptive to different degrees of nonstationarity in
IoT dynamics, and their scalable model-free implementation under limited
feedback that motivates blind or bandit approaches. The proposed framework
aspires to offer a stepping stone that leads to systematic designs and analysis
of task-specific learning and management schemes for IoT, along with a host of
new research directions to build on.Comment: Submitted on June 15 to Proceeding of IEEE Special Issue on Adaptive
and Scalable Communication Network
FreeCOS: Self-Supervised Learning from Fractals and Unlabeled Images for Curvilinear Object Segmentation
Curvilinear object segmentation is critical for many applications. However,
manually annotating curvilinear objects is very time-consuming and error-prone,
yielding insufficiently available annotated datasets for existing supervised
methods and domain adaptation methods. This paper proposes a self-supervised
curvilinear object segmentation method that learns robust and distinctive
features from fractals and unlabeled images (FreeCOS). The key contributions
include a novel Fractal-FDA synthesis (FFS) module and a geometric information
alignment (GIA) approach. FFS generates curvilinear structures based on the
parametric Fractal L-system and integrates the generated structures into
unlabeled images to obtain synthetic training images via Fourier Domain
Adaptation. GIA reduces the intensity differences between the synthetic and
unlabeled images by comparing the intensity order of a given pixel to the
values of its nearby neighbors. Such image alignment can explicitly remove the
dependency on absolute intensity values and enhance the inherent geometric
characteristics which are common in both synthetic and real images. In
addition, GIA aligns features of synthetic and real images via the prediction
space adaptation loss (PSAL) and the curvilinear mask contrastive loss (CMCL).
Extensive experimental results on four public datasets, i.e., XCAD, DRIVE,
STARE and CrackTree demonstrate that our method outperforms the
state-of-the-art unsupervised methods, self-supervised methods and traditional
methods by a large margin. The source code of this work is available at
https://github.com/TY-Shi/FreeCOS.Comment: Accepted by ICCV 202
CTP-Net: Character Texture Perception Network for Document Image Forgery Localization
Due to the progression of information technology in recent years, document
images have been widely disseminated in social networks. With the help of
powerful image editing tools, document images are easily forged without leaving
visible manipulation traces, which leads to severe issues if significant
information is falsified for malicious use. Therefore, the research of document
image forensics is worth further exploring. In a document image, the character
with specific semantic information is most vulnerable to tampering, for which
capturing the forgery traces of the character is the key to localizing the
forged region in document images. Considering both character and image
textures, in this paper, we propose a Character Texture Perception Network
(CTP-Net) to localize the forgery of document images. Based on optical
character recognition, a Character Texture Stream (CTS) is designed to capture
features of text areas that are essential components of a document image.
Meanwhile, texture features of the whole document image are exploited by an
Image Texture Stream (ITS). Combining the features extracted from the CTS and
the ITS, the CTP-Net can reveal more subtle forgery traces from document
images. To overcome the challenge caused by the lack of fake document images,
we design a data generation strategy that is utilized to construct a Fake
Chinese Trademark dataset (FCTM). Through a series of experiments, we show that
the proposed CTP-Net is able to capture tampering traces in document images,
especially in text regions. Experimental results demonstrate that CTP-Net can
localize multi-scale forged areas in document images and outperform the
state-of-the-art forgery localization methods
MVP: Multi-task Supervised Pre-training for Natural Language Generation
Pre-trained language models (PLMs) have achieved remarkable success in
natural language generation (NLG) tasks. Up to now, most NLG-oriented PLMs are
pre-trained in an unsupervised manner using the large-scale general corpus. In
the meanwhile, an increasing number of models pre-trained with labeled data
(i.e. "supervised pre-training") showcase superior performance compared to
unsupervised pre-trained models. Motivated by the success of supervised
pre-training, we propose Multi-task superVised Pre-training (MVP) for natural
language generation. We collect a large-scale natural language generation
corpus, MVPCorpus, from datasets over diverse NLG tasks. Then we
unify these examples into a general text-to-text format to pre-train the text
generation model MVP in a supervised manner. For each task, we further
pre-train specific soft prompts to stimulate the model's capacity to perform a
specific task. Our MVP model can be seen as a practice that utilizes recent
instruction tuning on relatively small PLMs. Extensive experiments have
demonstrated the effectiveness and generality of our MVP model in a number of
NLG tasks, which achieves state-of-the-art performance on out of
datasets, outperforming BART by and Flan-T5 by .Comment: Accepted by ACL 202
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