Relational Gating for ''What If'' Reasoning

Proceedings of the Thirtieth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2021/553 ◽

2021 ◽

Author(s):

Chen Zheng ◽

Parisa Kordjamshidi

Keyword(s):

State Of The Art ◽

Higher Order ◽

The State ◽

Order Relations ◽

Reasoning Problem ◽

Gating Network

This paper addresses the challenge of learning to do procedural reasoning over text to answer "What if..." questions. We propose a novel relational gating network that learns to filter the key entities and relationships and learns contextual and cross representations of both procedure and question for finding the answer. Our relational gating network contains an entity gating module, relation gating module, and contextual interaction module. These modules help in solving the "What if..." reasoning problem. We show that modeling pairwise relationships helps to capture higher-order relations and find the line of reasoning for causes and effects in the procedural descriptions. Our proposed approach achieves the state-of-the-art results on the WIQA dataset.

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C3MM: Clique-Closure based Hyperlink Prediction

Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2020/465 ◽

2020 ◽

Author(s):

Govind Sharma ◽

Prasanna Patil ◽

M. Narasimha Murty

Keyword(s):

Link Prediction ◽

Recent Literature ◽

State Of The Art ◽

Hypothesis Test ◽

Higher Order ◽

The State ◽

Complex Structures ◽

Prediction Problem ◽

Order Relations

Usual networks lossily (if not incorrectly) represent higher-order relations, i.e. those between multiple entities instead of a pair. This calls for complex structures such as hypergraphs to be used instead. Akin to the link prediction problem in graphs, we deal with hyperlink (higher-order link) prediction in hypergraphs. With a handful of solutions in the literature that seem to have merely scratched the surface, we provide improvements for the same. Motivated by observations in recent literature, we first formulate a "clique-closure" hypothesis (viz., hyperlinks are more likely to be formed from near-cliques rather than from non-cliques), test it on real hypergraphs, and then exploit it for our very problem. In the process, we generalize hyperlink prediction on two fronts: (1) from small-sized to arbitrary-sized hyperlinks, and (2) from a couple of domains to a handful. We perform experiments (both the hypothesis-test as well as the hyperlink prediction) on multiple real datasets, report results, and provide both quantitative and qualitative arguments favoring better performances w.r.t. the state-of-the-art.

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PR_22_0642603_Chipscale_NLO

10.31219/osf.io/drw74 ◽

2022 ◽

Author(s):

Shayan Mookherjee

Keyword(s):

Signal Processing ◽

Nonlinear Optics ◽

Quantum Optics ◽

Conversion Efficiency ◽

Optical Signal Processing ◽

State Of The Art ◽

Optical Signal ◽

Higher Order ◽

The State ◽

Disorder Effects

Multi-microresonator photonic circuits can improve the conversion efficiency of nonlinear optics, realize higher-order, implement programmable filters, and other advances in optical signal processing. However, such structures are challenging to realize in practice. Through a deeper understanding of disorder effects in photonics, we have greatly advanced the state-of-the-art in CROW structures and their applications in linear, nonlinear and quantum optics.

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Learning Non-Uniform Hypergraph for Multi-Object Tracking

Proceedings of the AAAI Conference on Artificial Intelligence ◽

10.1609/aaai.v33i01.33018981 ◽

2019 ◽

Vol 33 ◽

pp. 8981-8988 ◽

Cited By ~ 7

Author(s):

Longyin Wen ◽

Dawei Du ◽

Shengkun Li ◽

Xiao Bian ◽

Siwei Lyu

Keyword(s):

Support Vector Machine ◽

Object Tracking ◽

State Of The Art ◽

Higher Order ◽

The State ◽

Support Vector ◽

Support Vector Machine Algorithm ◽

Uniform Hypergraph ◽

Detection Scheme ◽

Structural Support

The majority of Multi-Object Tracking (MOT) algorithms based on the tracking-by-detection scheme do not use higher order dependencies among objects or tracklets, which makes them less effective in handling complex scenarios. In this work, we present a new near-online MOT algorithm based on non-uniform hypergraph, which can model different degrees of dependencies among tracklets in a unified objective. The nodes in the hypergraph correspond to the tracklets and the hyperedges with different degrees encode various kinds of dependencies among them. Specifically, instead of setting the weights of hyperedges with different degrees empirically, they are learned automatically using the structural support vector machine algorithm (SSVM). Several experiments are carried out on various challenging datasets (i.e., PETS09, ParkingLot sequence, SubwayFace, and MOT16 benchmark), to demonstrate that our method achieves favorable performance against the state-of-the-art MOT methods.

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RWNE: A Scalable Random-Walk based Network Embedding Framework with Personalized Higher-order Proximity Preserved

Journal of Artificial Intelligence Research ◽

10.1613/jair.1.12567 ◽

2021 ◽

Vol 71 ◽

pp. 237-263

Author(s):

Jianxin Li ◽

Cheng Ji ◽

Hao Peng ◽

Yu He ◽

Yangqiu Song ◽

...

Keyword(s):

Random Walk ◽

Random Walks ◽

Real World ◽

State Of The Art ◽

Higher Order ◽

The State ◽

Random Walk With Restart ◽

Network Embedding ◽

Network Proximity ◽

Embedding Methods

Higher-order proximity preserved network embedding has attracted increasing attention. In particular, due to the superior scalability, random-walk-based network embedding has also been well developed, which could efficiently explore higher-order neighborhoods via multi-hop random walks. However, despite the success of current random-walk-based methods, most of them are usually not expressive enough to preserve the personalized higher-order proximity and lack a straightforward objective to theoretically articulate what and how network proximity is preserved. In this paper, to address the above issues, we present a general scalable random-walk-based network embedding framework, in which random walk is explicitly incorporated into a sound objective designed theoretically to preserve arbitrary higher-order proximity. Further, we introduce the random walk with restart process into the framework to naturally and effectively achieve personalized-weighted preservation of proximities of different orders. We conduct extensive experiments on several real-world networks and demonstrate that our proposed method consistently and substantially outperforms the state-of-the-art network embedding methods.

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Extending a brainiac prover to lambda-free higher-order logic

International Journal on Software Tools for Technology Transfer ◽

10.1007/s10009-021-00639-7 ◽

2021 ◽

Author(s):

Petar Vukmirović ◽

Jasmin Blanchette ◽

Simon Cruanes ◽

Stephan Schulz

Keyword(s):

Data Structures ◽

Theorem Proving ◽

State Of The Art ◽

Higher Order ◽

The State ◽

Order Logic ◽

Automatic Theorem Proving ◽

Higher Order Logic ◽

First Order ◽

Automatic Theorem

AbstractDecades of work have gone into developing efficient proof calculi, data structures, algorithms, and heuristics for first-order automatic theorem proving. Higher-order provers lag behind in terms of efficiency. Instead of developing a new higher-order prover from the ground up, we propose to start with the state-of-the-art superposition prover E and gradually enrich it with higher-order features. We explain how to extend the prover’s data structures, algorithms, and heuristics to $$\lambda $$ λ -free higher-order logic, a formalism that supports partial application and applied variables. Our extension outperforms the traditional encoding and appears promising as a stepping stone toward full higher-order logic.

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Superposition with First-class Booleans and Inprocessing Clausification

Automated Deduction – CADE 28 - Lecture Notes in Computer Science ◽

10.1007/978-3-030-79876-5_22 ◽

2021 ◽

pp. 378-395

Author(s):

Visa Nummelin ◽

Alexander Bentkamp ◽

Sophie Tourret ◽

Petar Vukmirović

Keyword(s):

State Of The Art ◽

Higher Order ◽

The State ◽

Order Logic ◽

Theorem Prover ◽

First Order Logic ◽

Higher Order Logic ◽

First Order ◽

Superposition Calculus

AbstractWe present a complete superposition calculus for first-order logic with an interpreted Boolean type. Our motivation is to lay the foundation for refutationally complete calculi in more expressive logics with Booleans, such as higher-order logic, and to make superposition work efficiently on problems that would be obfuscated when using clausification as preprocessing. Working directly on formulas, our calculus avoids the costly axiomatic encoding of the theory of Booleans into first-order logic and offers various ways to interleave clausification with other derivation steps. We evaluate our calculus using the Zipperposition theorem prover, and observe that, with no tuning of parameters, our approach is on a par with the state-of-the-art approach.

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Practical Picture Processing

Proceedings, annual meeting, Electron Microscopy Society of America ◽

10.1017/s0424820100051700 ◽

1974 ◽

Vol 32 ◽

pp. 338-339

Author(s):

T. A. Welton

Keyword(s):

Radiation Damage ◽

Coherence Length ◽

Spatial Information ◽

State Of The Art ◽

Coherent Radiation ◽

The State ◽

Energy Spread ◽

Electron Micrograph ◽

Picture Processing ◽

Molecular Skeleton

Various authors have emphasized the spatial information resident in an electron micrograph taken with adequately coherent radiation. In view of the completion of at least one such instrument, this opportunity is taken to summarize the state of the art of processing such micrographs. We use the usual symbols for the aberration coefficients, and supplement these with £ and 6 for the transverse coherence length and the fractional energy spread respectively. He also assume a weak, biologically interesting sample, with principal interest lying in the molecular skeleton remaining after obvious hydrogen loss and other radiation damage has occurred.

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