49 research outputs found
Real-time Sampling and Estimation on Random Access Channels: Age of Information and Beyond
Efficient sampling and remote estimation are critical for a plethora of
wireless-empowered applications in the Internet of Things and cyber-physical
systems. Motivated by such applications, this work proposes decentralized
policies for the real-time monitoring and estimation of autoregressive
processes over random access channels. Two classes of policies are
investigated: (i) oblivious schemes in which sampling and transmission policies
are independent of the processes that are monitored, and (ii) non-oblivious
schemes in which transmitters causally observe their corresponding processes
for decision making. In the class of oblivious policies, we show that
minimizing the expected time-average estimation error is equivalent to
minimizing the expected age of information. Consequently, we prove lower and
upper bounds on the minimum achievable estimation error in this class. Next, we
consider non-oblivious policies and design a threshold policy, called
error-based thinning, in which each source node becomes active if its
instantaneous error has crossed a fixed threshold (which we optimize). Active
nodes then transmit stochastically following a slotted ALOHA policy. A
closed-form, approximately optimal, solution is found for the threshold as well
as the resulting estimation error. It is shown that non-oblivious policies
offer a multiplicative gain close to compared to oblivious policies.
Moreover, it is shown that oblivious policies that use the age of information
for decision making improve the state-of-the-art at least by the multiplicative
factor . The performance of all discussed policies is compared using
simulations. The numerical comparison shows that the performance of the
proposed decentralized policy is very close to that of centralized greedy
scheduling
MotifRetro: Exploring the Combinability-Consistency Trade-offs in retrosynthesis via Dynamic Motif Editing
Is there a unified framework for graph-based retrosynthesis prediction?
Through analysis of full-, semi-, and non-template retrosynthesis methods, we
discovered that they strive to strike an optimal balance between combinability
and consistency: \textit{Should atoms be combined as motifs to simplify the
molecular editing process, or should motifs be broken down into atoms to reduce
the vocabulary and improve predictive consistency?}
Recent works have studied several specific cases, while none of them explores
different combinability-consistency trade-offs. Therefore, we propose
MotifRetro, a dynamic motif editing framework for retrosynthesis prediction
that can explore the entire trade-off space and unify graph-based models.
MotifRetro comprises two components: RetroBPE, which controls the
combinability-consistency trade-off, and a motif editing model, where we
introduce a novel LG-EGAT module to dynamiclly add motifs to the molecule. We
conduct extensive experiments on USPTO-50K to explore how the trade-off affects
the model performance and finally achieve state-of-the-art performance
Age of Information in Random Access Channels
In applications of remote sensing, estimation, and control, timely
communication is not always ensured by high-rate communication. This work
proposes distributed age-efficient transmission policies for random access
channels with transmitters. In the first part of this work, we analyze the
age performance of stationary randomized policies by relating the problem of
finding age to the absorption time of a related Markov chain. In the second
part of this work, we propose the notion of \emph{age-gain} of a packet to
quantify how much the packet will reduce the instantaneous age of information
at the receiver side upon successful delivery. We then utilize this notion to
propose a transmission policy in which transmitters act in a distributed manner
based on the age-gain of their available packets. In particular, each
transmitter sends its latest packet only if its corresponding age-gain is
beyond a certain threshold which could be computed adaptively using the
collision feedback or found as a fixed value analytically in advance. Both
methods improve age of information significantly compared to the state of the
art. In the limit of large , we prove that when the arrival rate is small
(below ), slotted ALOHA-type algorithms are asymptotically
optimal. As the arrival rate increases beyond , while age
increases under slotted ALOHA, it decreases significantly under the proposed
age-based policies. For arrival rates , , the
proposed algorithms provide a multiplicative factor of at least two compared to
the minimum age under slotted ALOHA (minimum over all arrival rates). We
conclude that, as opposed to the common practice, it is beneficial to increase
the sampling rate (and hence the arrival rate) and transmit packets selectively
based on their age-gain
1Cademy @ Causal News Corpus 2022: Leveraging Self-Training in Causality Classification of Socio-Political Event Data
This paper details our participation in the Challenges and Applications of
Automated Extraction of Socio-political Events from Text (CASE) workshop @
EMNLP 2022, where we take part in Subtask 1 of Shared Task 3. We approach the
given task of event causality detection by proposing a self-training pipeline
that follows a teacher-student classifier method. More specifically, we
initially train a teacher model on the true, original task data, and use that
teacher model to self-label data to be used in the training of a separate
student model for the final task prediction. We test how restricting the number
of positive or negative self-labeled examples in the self-training process
affects classification performance. Our final results show that using
self-training produces a comprehensive performance improvement across all
models and self-labeled training sets tested within the task of event causality
sequence classification. On top of that, we find that self-training performance
did not diminish even when restricting either positive/negative examples used
in training. Our code is be publicly available at
https://github.com/Gzhang-umich/1CademyTeamOfCASE.Comment: Paper from CASE workshop at EMNLP 202
DeepWSD: Projecting Degradations in Perceptual Space to Wasserstein Distance in Deep Feature Space
Existing deep learning-based full-reference IQA (FR-IQA) models usually
predict the image quality in a deterministic way by explicitly comparing the
features, gauging how severely distorted an image is by how far the
corresponding feature lies from the space of the reference images. Herein, we
look at this problem from a different viewpoint and propose to model the
quality degradation in perceptual space from a statistical distribution
perspective. As such, the quality is measured based upon the Wasserstein
distance in the deep feature domain. More specifically, the 1DWasserstein
distance at each stage of the pre-trained VGG network is measured, based on
which the final quality score is performed. The deep Wasserstein distance
(DeepWSD) performed on features from neural networks enjoys better
interpretability of the quality contamination caused by various types of
distortions and presents an advanced quality prediction capability. Extensive
experiments and theoretical analysis show the superiority of the proposed
DeepWSD in terms of both quality prediction and optimization.Comment: ACM Multimedia 2022 accepted thesi