6,475 research outputs found
NN-Steiner: A Mixed Neural-algorithmic Approach for the Rectilinear Steiner Minimum Tree Problem
Recent years have witnessed rapid advances in the use of neural networks to
solve combinatorial optimization problems. Nevertheless, designing the "right"
neural model that can effectively handle a given optimization problem can be
challenging, and often there is no theoretical understanding or justification
of the resulting neural model. In this paper, we focus on the rectilinear
Steiner minimum tree (RSMT) problem, which is of critical importance in IC
layout design and as a result has attracted numerous heuristic approaches in
the VLSI literature. Our contributions are two-fold. On the methodology front,
we propose NN-Steiner, which is a novel mixed neural-algorithmic framework for
computing RSMTs that leverages the celebrated PTAS algorithmic framework of
Arora to solve this problem (and other geometric optimization problems). Our
NN-Steiner replaces key algorithmic components within Arora's PTAS by suitable
neural components. In particular, NN-Steiner only needs four neural network
(NN) components that are called repeatedly within an algorithmic framework.
Crucially, each of the four NN components is only of bounded size independent
of input size, and thus easy to train. Furthermore, as the NN component is
learning a generic algorithmic step, once learned, the resulting mixed
neural-algorithmic framework generalizes to much larger instances not seen in
training. Our NN-Steiner, to our best knowledge, is the first neural
architecture of bounded size that has capacity to approximately solve RSMT (and
variants). On the empirical front, we show how NN-Steiner can be implemented
and demonstrate the effectiveness of our resulting approach, especially in
terms of generalization, by comparing with state-of-the-art methods (both
neural and non-neural based).Comment: This paper is the complete version with appendix of the paper
accepted in AAAI'24 with the same titl
Bridging the Semantic Gap with SQL Query Logs in Natural Language Interfaces to Databases
A critical challenge in constructing a natural language interface to database
(NLIDB) is bridging the semantic gap between a natural language query (NLQ) and
the underlying data. Two specific ways this challenge exhibits itself is
through keyword mapping and join path inference. Keyword mapping is the task of
mapping individual keywords in the original NLQ to database elements (such as
relations, attributes or values). It is challenging due to the ambiguity in
mapping the user's mental model and diction to the schema definition and
contents of the underlying database. Join path inference is the process of
selecting the relations and join conditions in the FROM clause of the final SQL
query, and is difficult because NLIDB users lack the knowledge of the database
schema or SQL and therefore cannot explicitly specify the intermediate tables
and joins needed to construct a final SQL query. In this paper, we propose
leveraging information from the SQL query log of a database to enhance the
performance of existing NLIDBs with respect to these challenges. We present a
system Templar that can be used to augment existing NLIDBs. Our extensive
experimental evaluation demonstrates the effectiveness of our approach, leading
up to 138% improvement in top-1 accuracy in existing NLIDBs by leveraging SQL
query log information.Comment: Accepted to IEEE International Conference on Data Engineering (ICDE)
201
A Kind of New Multicast Routing Algorithm for Application of Internet of Things
Wireless Sensor Networks (WSN) is widely used as an effective medium to integrate physical world and information world of Internet of Things (IOT). While keeping energy consumption at a minimal level, WSN requires reliable communication. Multicasting is a general operation performed by the Base Station, where data is to be transmitted to a set of destination nodes. Generally, the packets are routed in a multi-hop approach, where some intermediate nodes are also used for packet forwarding. This problem can be reduced to the well-known Steiner tree problem, which has proven to be NP-complete for deterministic link descriptors and cost functions. In this paper, we propose a novel multicast protocol, named heuristic algorithms for the solution of the Quality of Service (QoS) constrained multicast routing problem, with incomplete information in Wireless Sensor Networks (WSN). As information aggregation or randomly fluctuating traffic loads, link measures are considered to be random variables. Simulation results show that the Hop Neural Networks (HNN) based heuristics with a properly chosen additive measures can yield to a good solution for this traditionally NP complex problem, when compared to the best multicast algorithms known
- …