11,464 research outputs found
Adaptive Matching for Expert Systems with Uncertain Task Types
A matching in a two-sided market often incurs an externality: a matched
resource may become unavailable to the other side of the market, at least for a
while. This is especially an issue in online platforms involving human experts
as the expert resources are often scarce. The efficient utilization of experts
in these platforms is made challenging by the fact that the information
available about the parties involved is usually limited.
To address this challenge, we develop a model of a task-expert matching
system where a task is matched to an expert using not only the prior
information about the task but also the feedback obtained from the past
matches. In our model the tasks arrive online while the experts are fixed and
constrained by a finite service capacity. For this model, we characterize the
maximum task resolution throughput a platform can achieve. We show that the
natural greedy approaches where each expert is assigned a task most suitable to
her skill is suboptimal, as it does not internalize the above externality. We
develop a throughput optimal backpressure algorithm which does so by accounting
for the `congestion' among different task types. Finally, we validate our model
and confirm our theoretical findings with data-driven simulations via logs of
Math.StackExchange, a StackOverflow forum dedicated to mathematics.Comment: A part of it presented at Allerton Conference 2017, 18 page
Evaluating Resilience of Electricity Distribution Networks via A Modification of Generalized Benders Decomposition Method
This paper presents a computational approach to evaluate the resilience of
electricity Distribution Networks (DNs) to cyber-physical failures. In our
model, we consider an attacker who targets multiple DN components to maximize
the loss of the DN operator. We consider two types of operator response: (i)
Coordinated emergency response; (ii) Uncoordinated autonomous disconnects,
which may lead to cascading failures. To evaluate resilience under response
(i), we solve a Bilevel Mixed-Integer Second-Order Cone Program which is
computationally challenging due to mixed-integer variables in the inner problem
and non-convex constraints. Our solution approach is based on the Generalized
Benders Decomposition method, which achieves a reasonable tradeoff between
computational time and solution accuracy. Our approach involves modifying the
Benders cut based on structural insights on power flow over radial DNs. We
evaluate DN resilience under response (ii) by sequentially computing autonomous
component disconnects due to operating bound violations resulting from the
initial attack and the potential cascading failures. Our approach helps
estimate the gain in resilience under response (i), relative to (ii)
Quasi-Dynamic Frame Coordination For Ultra- Reliability and Low-Latency in 5G TDD Systems
The fifth generation (5G) mobile technology features the ultra-reliable and
low-latency communications (URLLC) as a major service class. URLLC applications
demand a tight radio latency with extreme link reliability. In 5G dynamic time
division duplexing (TDD) systems, URLLC requirements become further challenging
to achieve due to the severe and fast-varying cross link interference (CLI) and
the switching time of the radio frame configurations (RFCs). In this work, we
propose a quasi-dynamic inter-cell frame coordination algorithm using hybrid
frame design and a cyclic-offset-based RFC code-book. The proposed solution
adaptively updates the RFCs in time such that both the average CLI and the
user-centric radio latency are minimized. Compared to state-of-the-art dynamic
TDD studies, the proposed scheme shows a significant improvement in the URLLC
outage latency, i.e., 92% reduction gain, while boosting the cell-edge capacity
by 189% and with a greatly reduced coordination overhead space, limited to
B-bit
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