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Volume Modeling for Rapid Prototyping
The expanding workspace of Rapid Prototyping will draw on the new developments
in geometric modeling. Volume modeling has substantial advantages over other modeling
schemes to meet the emerging requirements of Rapid Prototyping technology. It provides us with
a new approach to design complex geometry and topology. The integration of the volume
modeling and Rapid Prototyping technology will help us to fully exploit RP's ability to fabricate
objects with complex structures. This paper addresses our research and practice in a volume
modeling system toward Rapid Prototyping. Novel techniques in volumetric data manipulation,
NURBS volume models and triangular facet generation over solid models are presented.
Computer models designed by this system and their corresponding DTM products are also
shown atthe end of this paper.Mechanical Engineerin
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
Using Data Analytics to predict students score
Education is very important to Singapore, and the government has continued to
invest heavily in our education system to become one of the world-class systems
today. A strong foundation of Science, Technology, Engineering, and Mathematics
(STEM) was what underpinned Singapore's development over the past 50 years.
PISA is a triennial international survey that evaluates education systems
worldwide by testing the skills and knowledge of 15-year-old students who are
nearing the end of compulsory education. In this paper, the authors used the
PISA data from 2012 and 2015 and developed machine learning techniques to
predictive the students' scores and understand the inter-relationships among
social, economic, and education factors. The insights gained would be useful to
have fresh perspectives on education, useful for policy formulation
Diffusion Variational Autoencoder for Tackling Stochasticity in Multi-Step Regression Stock Price Prediction
Multi-step stock price prediction over a long-term horizon is crucial for
forecasting its volatility, allowing financial institutions to price and hedge
derivatives, and banks to quantify the risk in their trading books.
Additionally, most financial regulators also require a liquidity horizon of
several days for institutional investors to exit their risky assets, in order
to not materially affect market prices. However, the task of multi-step stock
price prediction is challenging, given the highly stochastic nature of stock
data. Current solutions to tackle this problem are mostly designed for
single-step, classification-based predictions, and are limited to low
representation expressiveness. The problem also gets progressively harder with
the introduction of the target price sequence, which also contains stochastic
noise and reduces generalizability at test-time. To tackle these issues, we
combine a deep hierarchical variational-autoencoder (VAE) and diffusion
probabilistic techniques to do seq2seq stock prediction through a stochastic
generative process. The hierarchical VAE allows us to learn the complex and
low-level latent variables for stock prediction, while the diffusion
probabilistic model trains the predictor to handle stock price stochasticity by
progressively adding random noise to the stock data. Our Diffusion-VAE (D-Va)
model is shown to outperform state-of-the-art solutions in terms of its
prediction accuracy and variance. More importantly, the multi-step outputs can
also allow us to form a stock portfolio over the prediction length. We
demonstrate the effectiveness of our model outputs in the portfolio investment
task through the Sharpe ratio metric and highlight the importance of dealing
with different types of prediction uncertainties.Comment: CIKM 202
Sex differences in exercise-induced diaphragmatic fatigue in endurance-trained athletes
There is evidence that female athletes may be more susceptible to exercise-induced arterial hypoxemia and expiratory flow limitation and have greater increases in operational lung volumes during exercise relative to men. These pulmonary limitations may ultimately lead to greater levels of diaphragmatic fatigue in women. Accordingly, the purpose of this study was to determine whether there are sex differences in the prevalence and severity of exercise-induced diaphragmatic fatigue in 38 healthy endurance-trained men (n = 19; maximal aerobic capacity = 64.0 ± 1.9 ml·kg–1·min–1) and women (n = 19; maximal aerobic capacity = 57.1 ± 1.5 ml·kg–1·min–1). Transdiaphragmatic pressure (Pdi) was calculated as the difference between gastric and esophageal pressures. Inspiratory pressure-time products of the diaphragm and esophagus were calculated as the product of breathing frequency and the Pdi and esophageal pressure time integrals, respectively. Cervical magnetic stimulation was used to measure potentiated Pdi twitches (Pdi,tw) before and 10, 30, and 60 min after a constant-load cycling test performed at 90% of peak work rate until exhaustion. Diaphragm fatigue was considered present if there was a 15% reduction in Pdi,tw after exercise. Diaphragm fatigue occurred in 11 of 19 men (58%) and 8 of 19 women (42%). The percent drop in Pdi,tw at 10, 30, and 60 min after exercise in men (n = 11) was 30.6 ± 2.3, 20.7 ± 3.2, and 13.3 ± 4.5%, respectively, whereas results in women (n = 8) were 21.0 ± 2.1, 11.6 ± 2.9, and 9.7 ± 4.2%, respectively, with sex differences occurring at 10 and 30 min (P < 0.05). Men continued to have a reduced contribution of the diaphragm to total inspiratory force output (pressure-time product of the diaphragm/pressure-time product of the esophagus) during exercise, whereas diaphragmatic contribution in women changed very little over time. The findings from this study point to a female diaphragm that is more resistant to fatigue relative to their male counterparts
Context-aware Event Forecasting via Graph Disentanglement
Event forecasting has been a demanding and challenging task throughout the
entire human history. It plays a pivotal role in crisis alarming and disaster
prevention in various aspects of the whole society. The task of event
forecasting aims to model the relational and temporal patterns based on
historical events and makes forecasting to what will happen in the future. Most
existing studies on event forecasting formulate it as a problem of link
prediction on temporal event graphs. However, such pure structured formulation
suffers from two main limitations: 1) most events fall into general and
high-level types in the event ontology, and therefore they tend to be
coarse-grained and offers little utility which inevitably harms the forecasting
accuracy; and 2) the events defined by a fixed ontology are unable to retain
the out-of-ontology contextual information. To address these limitations, we
propose a novel task of context-aware event forecasting which incorporates
auxiliary contextual information. First, the categorical context provides
supplementary fine-grained information to the coarse-grained events. Second and
more importantly, the context provides additional information towards specific
situation and condition, which is crucial or even determinant to what will
happen next. However, it is challenging to properly integrate context into the
event forecasting framework, considering the complex patterns in the
multi-context scenario. Towards this end, we design a novel framework named
Separation and Collaboration Graph Disentanglement (short as SeCoGD) for
context-aware event forecasting. Since there is no available dataset for this
novel task, we construct three large-scale datasets based on GDELT.
Experimental results demonstrate that our model outperforms a list of SOTA
methods.Comment: KDD 2023, 9 pages, 7 figures, 4 table
Leveraging Multimodal Features and Item-level User Feedback for Bundle Construction
Automatic bundle construction is a crucial prerequisite step in various
bundle-aware online services. Previous approaches are mostly designed to model
the bundling strategy of existing bundles. However, it is hard to acquire
large-scale well-curated bundle dataset, especially for those platforms that
have not offered bundle services before. Even for platforms with mature bundle
services, there are still many items that are included in few or even zero
bundles, which give rise to sparsity and cold-start challenges in the bundle
construction models. To tackle these issues, we target at leveraging multimodal
features, item-level user feedback signals, and the bundle composition
information, to achieve a comprehensive formulation of bundle construction.
Nevertheless, such formulation poses two new technical challenges: 1) how to
learn effective representations by optimally unifying multiple features, and 2)
how to address the problems of modality missing, noise, and sparsity problems
induced by the incomplete query bundles. In this work, to address these
technical challenges, we propose a Contrastive Learning-enhanced Hierarchical
Encoder method (CLHE). Specifically, we use self-attention modules to combine
the multimodal and multi-item features, and then leverage both item- and
bundle-level contrastive learning to enhance the representation learning, thus
to counter the modality missing, noise, and sparsity problems. Extensive
experiments on four datasets in two application domains demonstrate that our
method outperforms a list of SOTA methods. The code and dataset are available
at https://github.com/Xiaohao-Liu/CLHE
Discovering Dynamic Causal Space for DAG Structure Learning
Discovering causal structure from purely observational data (i.e., causal
discovery), aiming to identify causal relationships among variables, is a
fundamental task in machine learning. The recent invention of differentiable
score-based DAG learners is a crucial enabler, which reframes the combinatorial
optimization problem into a differentiable optimization with a DAG constraint
over directed graph space. Despite their great success, these cutting-edge DAG
learners incorporate DAG-ness independent score functions to evaluate the
directed graph candidates, lacking in considering graph structure. As a result,
measuring the data fitness alone regardless of DAG-ness inevitably leads to
discovering suboptimal DAGs and model vulnerabilities. Towards this end, we
propose a dynamic causal space for DAG structure learning, coined CASPER, that
integrates the graph structure into the score function as a new measure in the
causal space to faithfully reflect the causal distance between estimated and
ground truth DAG. CASPER revises the learning process as well as enhances the
DAG structure learning via adaptive attention to DAG-ness. Grounded by
empirical visualization, CASPER, as a space, satisfies a series of desired
properties, such as structure awareness and noise robustness. Extensive
experiments on both synthetic and real-world datasets clearly validate the
superiority of our CASPER over the state-of-the-art causal discovery methods in
terms of accuracy and robustness.Comment: Accepted by KDD 2023. Our codes are available at
https://github.com/liuff19/CASPE
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