377 research outputs found
Project scheduling with modular project completion on a bottleneck resource.
In this paper, we model a research-and-development project as consisting of several modules, with each module containing one or more activities. We examine how to schedule the activities of such a project in order to maximize the expected profit when the activities have a probability of failure and when an activity’s failure can cause its module and thereby the overall project to fail. A module succeeds when at least one of its constituent activities is successfully executed. All activities are scheduled on a scarce resource that is modeled as a single machine. We describe various policy classes, establish the relationship between the classes, develop exact algorithms to optimize over two different classes (one dynamic program and one branch-and-bound algorithm), and examine the computational performance of the algorithms on two randomly generated instance sets.Scheduling; Uncertainty; Research and development; Activity failures; Modular precedence network;
PanoVOS: Bridging Non-panoramic and Panoramic Views with Transformer for Video Segmentation
Panoramic videos contain richer spatial information and have attracted
tremendous amounts of attention due to their exceptional experience in some
fields such as autonomous driving and virtual reality. However, existing
datasets for video segmentation only focus on conventional planar images. To
address the challenge, in this paper, we present a panoramic video dataset,
PanoVOS. The dataset provides 150 videos with high video resolutions and
diverse motions. To quantify the domain gap between 2D planar videos and
panoramic videos, we evaluate 15 off-the-shelf video object segmentation (VOS)
models on PanoVOS. Through error analysis, we found that all of them fail to
tackle pixel-level content discontinues of panoramic videos. Thus, we present a
Panoramic Space Consistency Transformer (PSCFormer), which can effectively
utilize the semantic boundary information of the previous frame for pixel-level
matching with the current frame. Extensive experiments demonstrate that
compared with the previous SOTA models, our PSCFormer network exhibits a great
advantage in terms of segmentation results under the panoramic setting. Our
dataset poses new challenges in panoramic VOS and we hope that our PanoVOS can
advance the development of panoramic segmentation/tracking
Dynamic Prompting: A Unified Framework for Prompt Tuning
It has been demonstrated that the art of prompt tuning is highly effective in
efficiently extracting knowledge from pretrained foundation models,
encompassing pretrained language models (PLMs), vision pretrained models, and
vision-language (V-L) models. However, the efficacy of employing fixed soft
prompts with a predetermined position for concatenation with inputs for all
instances, irrespective of their inherent disparities, remains uncertain.
Variables such as the position, length, and representations of prompts across
diverse instances and tasks can substantially influence the performance of
prompt tuning. In this context, we provide a theoretical analysis, which
reveals that optimizing the position of the prompt to encompass the input can
capture additional semantic information that traditional prefix or postfix
prompt tuning methods fail to capture. Building upon our analysis, we present a
unified dynamic prompt (DP) tuning strategy that dynamically determines
different factors of prompts based on specific tasks and instances. To
accomplish this, we employ a lightweight learning network with Gumble-Softmax,
allowing us to learn instance-dependent guidance. Experimental results
underscore the significant performance improvement achieved by dynamic prompt
tuning across a wide range of tasks, including NLP tasks, vision recognition
tasks, and vision-language tasks. Furthermore, we establish the universal
applicability of our approach under full-data, few-shot, and multitask
scenarios. Codes are available at https://github.com/Xianjun-Yang/DPT.Comment: updat
Trust-Aware Resilient Control and Coordination of Connected and Automated Vehicles
We address the security of a network of Connected and Automated Vehicles
(CAVs) cooperating to navigate through a conflict area. Adversarial attacks
such as Sybil attacks can cause safety violations resulting in collisions and
traffic jams. In addition, uncooperative (but not necessarily adversarial) CAVs
can also induce similar adversarial effects on the traffic network. We propose
a decentralized resilient control and coordination scheme that mitigates the
effects of adversarial attacks and uncooperative CAVs by utilizing a trust
framework. Our trust-aware scheme can guarantee safe collision free
coordination and mitigate traffic jams. Simulation results validate the
theoretical guarantee of our proposed scheme, and demonstrate that it can
effectively mitigate adversarial effects across different traffic scenarios.Comment: Keywords: Resilient control and coordination, Cybersecurity, Safety
guaranteed coordination, Connected And Autonomous Vehicle
Optimal Control of Connected Automated Vehicles with Event-Triggered Control Barrier Functions: a Test Bed for Safe Optimal Merging
We address the problem of controlling Connected and Automated Vehicles (CAVs)
in conflict areas of a traffic network subject to hard safety constraints. It
has been shown that such problems can be solved through a combination of
tractable optimal control problems and Control Barrier Functions (CBFs) that
guarantee the satisfaction of all constraints. These solutions can be reduced
to a sequence of Quadratic Programs (QPs) which are efficiently solved on line
over discrete time steps. However, guaranteeing the feasibility of the
CBF-based QP method within each discretized time interval requires the careful
selection of time steps which need to be sufficiently small. This creates
computational requirements and communication rates between agents which may
hinder the controller's application to real CAVs. In this paper, we overcome
this limitation by adopting an event-triggered approach for CAVs in a conflict
area such that the next QP is triggered by properly defined events with a
safety guarantee. We present a laboratory-scale test bed we have developed to
emulate merging roadways using mobile robots as CAVs which can be used to
demonstrate how the event-triggered scheme is computationally efficient and can
handle measurement uncertainties and noise compared to time-driven control
while guaranteeing safety.Comment: arXiv admin note: substantial text overlap with arXiv:2203.12089,
arXiv:2209.1305
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