229 research outputs found
Stochastically robust personalized ranking for LSH recommendation retrieval
National Research Foundation (NRF) Singapore under NRF Fellowship Programm
Improving Items and Contexts Understanding with Descriptive Graph for Conversational Recommendation
State-of-the-art methods on conversational recommender systems (CRS) leverage
external knowledge to enhance both items' and contextual words' representations
to achieve high quality recommendations and responses generation. However, the
representations of the items and words are usually modeled in two separated
semantic spaces, which leads to misalignment issue between them. Consequently,
this will cause the CRS to only achieve a sub-optimal ranking performance,
especially when there is a lack of sufficient information from the user's
input. To address limitations of previous works, we propose a new CRS framework
KLEVER, which jointly models items and their associated contextual words in the
same semantic space. Particularly, we construct an item descriptive graph from
the rich items' textual features, such as item description and categories.
Based on the constructed descriptive graph, KLEVER jointly learns the
embeddings of the words and items, towards enhancing both recommender and
dialog generation modules. Extensive experiments on benchmarking CRS dataset
demonstrate that KLEVER achieves superior performance, especially when the
information from the users' responses is lacking.Comment: 14 pages, 3 figures, 9 table
Improving Heterogeneous Graph Learning with Weighted Mixed-Curvature Product Manifold
In graph representation learning, it is important that the complex geometric
structure of the input graph, e.g. hidden relations among nodes, is well
captured in embedding space. However, standard Euclidean embedding spaces have
a limited capacity in representing graphs of varying structures. A promising
candidate for the faithful embedding of data with varying structure is product
manifolds of component spaces of different geometries (spherical, hyperbolic,
or euclidean). In this paper, we take a closer look at the structure of product
manifold embedding spaces and argue that each component space in a product
contributes differently to expressing structures in the input graph, hence
should be weighted accordingly. This is different from previous works which
consider the roles of different components equally. We then propose
WEIGHTED-PM, a data-driven method for learning embedding of heterogeneous
graphs in weighted product manifolds. Our method utilizes the topological
information of the input graph to automatically determine the weight of each
component in product spaces. Extensive experiments on synthetic and real-world
graph datasets demonstrate that WEIGHTED-PM is capable of learning better graph
representations with lower geometric distortion from input data, and performs
better on multiple downstream tasks, such as word similarity learning, top-
recommendation, and knowledge graph embedding
Wealth inequalities in physical and cognitive impairments across Japan and Europe: the role of health expenditure and infrastructure
Although prior research has provided insights into the association between country-level factors and health inequalities, key research gaps remain. First, most previous studies examine subjective rather than objective health measures. Second, the wealth dimension in health inequalities is understudied. Third, a handful of studies explicitly focus on older adults. To bridge these research gaps, this study measures wealth-related inequalities in physical and cognitive impairments and examines the extent to which welfare states moderate wealth inequalities in physical and cognitive impairments among older people across Japan and Europe. We utilized harmonized data on non-institutionalized individuals aged 50–75 from the Japanese Study of Aging and Retirement (JSTAR) and the Survey of Health, Ageing and Retirement in Europe (SHARE) (N = 31,969 for physical impairments and 31,348 for cognitive impairments). Our multilevel linear regression analyses examined whether national public health spending and healthcare access resources explained cross-country differences in wealth inequalities in physical and cognitive impairments. We applied a concentration index to quantify the degree of wealth inequalities in impairments. The findings indicate that inequalities in both impairment outcomes favored wealthier individuals in all countries, but the magnitude of inequality varied by country. Furthermore, a higher share of public health spending, lower out-of-pocket expenditure, and higher investment in healthcare resources were associated with lower wealth inequalities, especially for physical impairments. Our findings suggest that different health interventions and policies may be needed to mitigate specific impairment inequalities
Multiperspective graph-theoretic similarity measure
National Research Foundation (NRF) Singapor
Improving Pareto Front Learning via Multi-Sample Hypernetworks
Pareto Front Learning (PFL) was recently introduced as an effective approach
to obtain a mapping function from a given trade-off vector to a solution on the
Pareto front, which solves the multi-objective optimization (MOO) problem. Due
to the inherent trade-off between conflicting objectives, PFL offers a flexible
approach in many scenarios in which the decision makers can not specify the
preference of one Pareto solution over another, and must switch between them
depending on the situation. However, existing PFL methods ignore the
relationship between the solutions during the optimization process, which
hinders the quality of the obtained front. To overcome this issue, we propose a
novel PFL framework namely PHN-HVI, which employs a hypernetwork to generate
multiple solutions from a set of diverse trade-off preferences and enhance the
quality of the Pareto front by maximizing the Hypervolume indicator defined by
these solutions. The experimental results on several MOO machine learning tasks
show that the proposed framework significantly outperforms the baselines in
producing the trade-off Pareto front.Comment: Accepted to AAAI-2
Enhancing Few-shot Image Classification with Cosine Transformer
This paper addresses the few-shot image classification problem, where the
classification task is performed on unlabeled query samples given a small
amount of labeled support samples only. One major challenge of the few-shot
learning problem is the large variety of object visual appearances that
prevents the support samples to represent that object comprehensively. This
might result in a significant difference between support and query samples,
therefore undermining the performance of few-shot algorithms. In this paper, we
tackle the problem by proposing Few-shot Cosine Transformer (FS-CT), where the
relational map between supports and queries is effectively obtained for the
few-shot tasks. The FS-CT consists of two parts, a learnable prototypical
embedding network to obtain categorical representations from support samples
with hard cases, and a transformer encoder to effectively achieve the
relational map from two different support and query samples. We introduce
Cosine Attention, a more robust and stable attention module that enhances the
transformer module significantly and therefore improves FS-CT performance from
5% to over 20% in accuracy compared to the default scaled dot-product
mechanism. Our method performs competitive results in mini-ImageNet, CUB-200,
and CIFAR-FS on 1-shot learning and 5-shot learning tasks across backbones and
few-shot configurations. We also developed a custom few-shot dataset for Yoga
pose recognition to demonstrate the potential of our algorithm for practical
application. Our FS-CT with cosine attention is a lightweight, simple few-shot
algorithm that can be applied for a wide range of applications, such as
healthcare, medical, and security surveillance. The official implementation
code of our Few-shot Cosine Transformer is available at
https://github.com/vinuni-vishc/Few-Shot-Cosine-Transforme
A Framework for Controllable Pareto Front Learning with Completed Scalarization Functions and its Applications
Pareto Front Learning (PFL) was recently introduced as an efficient method
for approximating the entire Pareto front, the set of all optimal solutions to
a Multi-Objective Optimization (MOO) problem. In the previous work, the mapping
between a preference vector and a Pareto optimal solution is still ambiguous,
rendering its results. This study demonstrates the convergence and completion
aspects of solving MOO with pseudoconvex scalarization functions and combines
them into Hypernetwork in order to offer a comprehensive framework for PFL,
called Controllable Pareto Front Learning. Extensive experiments demonstrate
that our approach is highly accurate and significantly less computationally
expensive than prior methods in term of inference time.Comment: Under Review at Neural Networks Journa
Estimation of methane emissions from local and crossbreed beef cattle in Daklak province of Vietnam
Objective: This study was aimed at evaluating effects of cattle breed resources and alternative mixed-feeding practices on meat productivity and emission intensities from household farming systems (HFS) in Daklak Province, Vietnam.
Methods: Records from Local Yellow×Red Sindhi (Bos indicus; Lai Sind) and 1/2 Limousin, 1/2 Drought Master, and 1/2 Red Angus cattle during the growth (0 to 21 months) and fattening (22 to 25 months) periods were used to better understand variations on meat productivity and enteric methane emissions. Parameters were determined by the ruminant model. Four scenarios were developed: (HFS1) grazing from birth to slaughter on native grasses for approximately 10 h plus 1.5 kg dry matter/d (0.8% live weight [LW]) of a mixture of Guinea grass (19%), cassava (43%) powder, cotton (23%) seed, and rice (15%) straw; (HFS2) growth period fed with elephant grass (1% of LW) plus supplementation (1.5% of LW) of rice bran (36%), maize (33%), and cassava (31%) meals; and HFS3 and HFS4 computed elephant grass, but concentrate supplementation reaching 2% and 1% of LW, respectively.
Results: Results show that compared to HFS1, emissions (72.3±0.96 kg CH 4 /animal/life; least squares means± standard error of the mean) were 15%, 6%, and 23% lower (p < 0.01) for the HFS2, HFS3, and HFS4, respectively. The predicted methane efficiencies (CO 2 eq) per kg of LW at slaughter (4.3±0.15), carcass weight (8.8±0.25 kg) and kg of edible protein (44.1±1.29) were also lower (p < 0.05) in the HFS4. In particular, irrespective of the HSF, feed supply and ratio changes had a more positive impact on emission intensities when crossbred 1/2 Red Angus cattle were fed than in their crossbred counterparts.
Conclusion: Modest improvements on feeding practices and integrated modelling frameworks may offer potential trade-offs to respond to climate change in Vietnam
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