381 research outputs found
Mathematical software for gas transmission networks
This thesis is concerned with the development of
numerical software for the simulation of gas transmission
networks. This involves developing software for the solution
of a large system of stiff differential/algebraic equations
(DAE) containing frequent severe disturbances. The disturbances
arise due to the varying consumer demands and the operation
of network controlling devices such as the compressors.
Special strategies are developed to solve the DAE system
efficiently using a variable-step integrator. Two sets of
strategies are devised; one for the implicit methods such as
the semi-implicit Runge-Kutta method, and the other for the
linearly implicit Rosenbrock-type method. Four integrators,
based on different numerical methods, have been implemented
and the performance of each one is compared with the British
Gas network analysis program PAN, using a number of large,
realistic transmission networks. The results demonstrate that
the variable-step integrators are reliable and efficient.
An efficient sparse matrix decomposition scheme is
developed to solve the large, sparse system of equations that
arise during the integration of the DAE system. The decomposition
scheme fully exploits the special structure of the
coefficient matrix.
Lastly, for certain networks, the existing simulation
programs fail to compute a feasible solution because of the
interactions of the controlling devices in the network. To
overcome this difficulty, the problem is formulated as a
variational inequality model and solved numerically using an
optimization routine from the NAG library (NAGFLIB(l982)).
The reliability of the model is illustrated using three test networks
Discovering Gender Differences in Facial Emotion Recognition via Implicit Behavioral Cues
We examine the utility of implicit behavioral cues in the form of EEG brain
signals and eye movements for gender recognition (GR) and emotion recognition
(ER). Specifically, the examined cues are acquired via low-cost, off-the-shelf
sensors. We asked 28 viewers (14 female) to recognize emotions from unoccluded
(no mask) as well as partially occluded (eye and mouth masked) emotive faces.
Obtained experimental results reveal that (a) reliable GR and ER is achievable
with EEG and eye features, (b) differential cognitive processing especially for
negative emotions is observed for males and females and (c) some of these
cognitive differences manifest under partial face occlusion, as typified by the
eye and mouth mask conditions.Comment: To be published in the Proceedings of Seventh International
Conference on Affective Computing and Intelligent Interaction.201
Neural Graph Collaborative Filtering
Learning vector representations (aka. embeddings) of users and items lies at
the core of modern recommender systems. Ranging from early matrix factorization
to recently emerged deep learning based methods, existing efforts typically
obtain a user's (or an item's) embedding by mapping from pre-existing features
that describe the user (or the item), such as ID and attributes. We argue that
an inherent drawback of such methods is that, the collaborative signal, which
is latent in user-item interactions, is not encoded in the embedding process.
As such, the resultant embeddings may not be sufficient to capture the
collaborative filtering effect.
In this work, we propose to integrate the user-item interactions -- more
specifically the bipartite graph structure -- into the embedding process. We
develop a new recommendation framework Neural Graph Collaborative Filtering
(NGCF), which exploits the user-item graph structure by propagating embeddings
on it. This leads to the expressive modeling of high-order connectivity in
user-item graph, effectively injecting the collaborative signal into the
embedding process in an explicit manner. We conduct extensive experiments on
three public benchmarks, demonstrating significant improvements over several
state-of-the-art models like HOP-Rec and Collaborative Memory Network. Further
analysis verifies the importance of embedding propagation for learning better
user and item representations, justifying the rationality and effectiveness of
NGCF. Codes are available at
https://github.com/xiangwang1223/neural_graph_collaborative_filtering.Comment: SIGIR 2019; the latest version of NGCF paper, which is distinct from
the version published in ACM Digital Librar
TransNFCM: Translation-Based Neural Fashion Compatibility Modeling
Identifying mix-and-match relationships between fashion items is an urgent
task in a fashion e-commerce recommender system. It will significantly enhance
user experience and satisfaction. However, due to the challenges of inferring
the rich yet complicated set of compatibility patterns in a large e-commerce
corpus of fashion items, this task is still underexplored. Inspired by the
recent advances in multi-relational knowledge representation learning and deep
neural networks, this paper proposes a novel Translation-based Neural Fashion
Compatibility Modeling (TransNFCM) framework, which jointly optimizes fashion
item embeddings and category-specific complementary relations in a unified
space via an end-to-end learning manner. TransNFCM places items in a unified
embedding space where a category-specific relation (category-comp-category) is
modeled as a vector translation operating on the embeddings of compatible items
from the corresponding categories. By this way, we not only capture the
specific notion of compatibility conditioned on a specific pair of
complementary categories, but also preserve the global notion of compatibility.
We also design a deep fashion item encoder which exploits the complementary
characteristic of visual and textual features to represent the fashion
products. To the best of our knowledge, this is the first work that uses
category-specific complementary relations to model the category-aware
compatibility between items in a translation-based embedding space. Extensive
experiments demonstrate the effectiveness of TransNFCM over the
state-of-the-arts on two real-world datasets.Comment: Accepted in AAAI 2019 conferenc
KGAT: Knowledge Graph Attention Network for Recommendation
To provide more accurate, diverse, and explainable recommendation, it is
compulsory to go beyond modeling user-item interactions and take side
information into account. Traditional methods like factorization machine (FM)
cast it as a supervised learning problem, which assumes each interaction as an
independent instance with side information encoded. Due to the overlook of the
relations among instances or items (e.g., the director of a movie is also an
actor of another movie), these methods are insufficient to distill the
collaborative signal from the collective behaviors of users. In this work, we
investigate the utility of knowledge graph (KG), which breaks down the
independent interaction assumption by linking items with their attributes. We
argue that in such a hybrid structure of KG and user-item graph, high-order
relations --- which connect two items with one or multiple linked attributes
--- are an essential factor for successful recommendation. We propose a new
method named Knowledge Graph Attention Network (KGAT) which explicitly models
the high-order connectivities in KG in an end-to-end fashion. It recursively
propagates the embeddings from a node's neighbors (which can be users, items,
or attributes) to refine the node's embedding, and employs an attention
mechanism to discriminate the importance of the neighbors. Our KGAT is
conceptually advantageous to existing KG-based recommendation methods, which
either exploit high-order relations by extracting paths or implicitly modeling
them with regularization. Empirical results on three public benchmarks show
that KGAT significantly outperforms state-of-the-art methods like Neural FM and
RippleNet. Further studies verify the efficacy of embedding propagation for
high-order relation modeling and the interpretability benefits brought by the
attention mechanism.Comment: KDD 2019 research trac
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