403 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
An Android-Based Mechanism for Energy Efficient Localization Depending on Indoor/Outdoor Context
Today, there is widespread use of mobile applications that take advantage of a user\u27s location. Popular usages of location information include geotagging on social media websites, driver assistance and navigation, and querying nearby locations of interest. However, the average user may not realize the high energy costs of using location services (namely the GPS) or may not make smart decisions regarding when to enable or disable location services-for example, when indoors. As a result, a mechanism that can make these decisions on the user\u27s behalf can significantly improve a smartphone\u27s battery life. In this paper, we present an energy consumption analysis of the localization methods available on modern Android smartphones and propose the addition of an indoor localization mechanism that can be triggered depending on whether a user is detected to be indoors or outdoors. Based on our energy analysis and implementation of our proposed system, we provide experimental results-monitoring battery life over time-and show that an indoor localization method triggered by indoor or outdoor context can improve smartphone battery life and, potentially, location accuracy
RSA: Byzantine-Robust Stochastic Aggregation Methods for Distributed Learning from Heterogeneous Datasets
In this paper, we propose a class of robust stochastic subgradient methods
for distributed learning from heterogeneous datasets at presence of an unknown
number of Byzantine workers. The Byzantine workers, during the learning
process, may send arbitrary incorrect messages to the master due to data
corruptions, communication failures or malicious attacks, and consequently bias
the learned model. The key to the proposed methods is a regularization term
incorporated with the objective function so as to robustify the learning task
and mitigate the negative effects of Byzantine attacks. The resultant
subgradient-based algorithms are termed Byzantine-Robust Stochastic Aggregation
methods, justifying our acronym RSA used henceforth. In contrast to most of the
existing algorithms, RSA does not rely on the assumption that the data are
independent and identically distributed (i.i.d.) on the workers, and hence fits
for a wider class of applications. Theoretically, we show that: i) RSA
converges to a near-optimal solution with the learning error dependent on the
number of Byzantine workers; ii) the convergence rate of RSA under Byzantine
attacks is the same as that of the stochastic gradient descent method, which is
free of Byzantine attacks. Numerically, experiments on real dataset corroborate
the competitive performance of RSA and a complexity reduction compared to the
state-of-the-art alternatives.Comment: To appear in AAAI 201
Temporal Sentence Grounding in Videos: A Survey and Future Directions
Temporal sentence grounding in videos (TSGV), \aka natural language video
localization (NLVL) or video moment retrieval (VMR), aims to retrieve a
temporal moment that semantically corresponds to a language query from an
untrimmed video. Connecting computer vision and natural language, TSGV has
drawn significant attention from researchers in both communities. This survey
attempts to provide a summary of fundamental concepts in TSGV and current
research status, as well as future research directions. As the background, we
present a common structure of functional components in TSGV, in a tutorial
style: from feature extraction from raw video and language query, to answer
prediction of the target moment. Then we review the techniques for multimodal
understanding and interaction, which is the key focus of TSGV for effective
alignment between the two modalities. We construct a taxonomy of TSGV
techniques and elaborate the methods in different categories with their
strengths and weaknesses. Lastly, we discuss issues with the current TSGV
research and share our insights about promising research directions.Comment: 29 pages, 32 figures, 9 table
SEA: A Scalable Entity Alignment System
Entity alignment (EA) aims to find equivalent entities in different knowledge
graphs (KGs). State-of-the-art EA approaches generally use Graph Neural
Networks (GNNs) to encode entities. However, most of them train the models and
evaluate the results in a fullbatch fashion, which prohibits EA from being
scalable on largescale datasets. To enhance the usability of GNN-based EA
models in real-world applications, we present SEA, a scalable entity alignment
system that enables to (i) train large-scale GNNs for EA, (ii) speed up the
normalization and the evaluation process, and (iii) report clear results for
users to estimate different models and parameter settings. SEA can be run on a
computer with merely one graphic card. Moreover, SEA encompasses six
state-of-the-art EA models and provides access for users to quickly establish
and evaluate their own models. Thus, SEA allows users to perform EA without
being involved in tedious implementations, such as negative sampling and
GPU-accelerated evaluation. With SEA, users can gain a clear view of the model
performance. In the demonstration, we show that SEA is user-friendly and is of
high scalability even on computers with limited computational resources.Comment: SIGIR'23 Demo Trac
An Introduction to hpxMP: A Modern OpenMP Implementation Leveraging HPX, An Asynchronous Many-Task System
Asynchronous Many-task (AMT) runtime systems have gained increasing
acceptance in the HPC community due to the performance improvements offered by
fine-grained tasking runtime systems. At the same time, C++ standardization
efforts are focused on creating higher-level interfaces able to replace OpenMP
or OpenACC in modern C++ codes. These higher level functions have been adopted
in standards conforming runtime systems such as HPX, giving users the ability
to simply utilize fork-join parallelism in their own codes. Despite innovations
in runtime systems and standardization efforts users face enormous challenges
porting legacy applications. Not only must users port their own codes, but
often users rely on highly optimized libraries such as BLAS and LAPACK which
use OpenMP for parallization. Current efforts to create smooth migration paths
have struggled with these challenges, especially as the threading systems of
AMT libraries often compete with the treading system of OpenMP.
To overcome these issues, our team has developed hpxMP, an implementation of
the OpenMP standard, which utilizes the underlying AMT system to schedule and
manage tasks. This approach leverages the C++ interfaces exposed by HPX and
allows users to execute their applications on an AMT system without changing
their code.
In this work, we compare hpxMP with Clang's OpenMP library with four linear
algebra benchmarks of the Blaze C++ library. While hpxMP is often not able to
reach the same performance, we demonstrate viability for providing a smooth
migration for applications but have to be extended to benefit from a more
general task based programming model
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