367 research outputs found
WeaveNet for Approximating Two-sided Matching Problems
Matching, a task to optimally assign limited resources under constraints, is
a fundamental technology for society. The task potentially has various
objectives, conditions, and constraints; however, the efficient neural network
architecture for matching is underexplored. This paper proposes a novel graph
neural network (GNN), \textit{WeaveNet}, designed for bipartite graphs. Since a
bipartite graph is generally dense, general GNN architectures lose node-wise
information by over-smoothing when deeply stacked. Such a phenomenon is
undesirable for solving matching problems. WeaveNet avoids it by preserving
edge-wise information while passing messages densely to reach a better
solution. To evaluate the model, we approximated one of the \textit{strongly
NP-hard} problems, \textit{fair stable matching}. Despite its inherent
difficulties and the network's general purpose design, our model reached a
comparative performance with state-of-the-art algorithms specially designed for
stable matching for small numbers of agents
Silicene Nanomesh
Similar to graphene, zero band gap limits the application of silicene in
nanoelectronics despite of its high carrier mobility. By using first-principles
calculations, we reveal that a band gap is opened in silicene nanomesh (SNM)
when the width W of the wall between the neighboring holes is even. The size of
the band gap increases with the reduced W and has a simple relation with the
ratio of the removed Si atom and the total Si atom numbers of silicene. Quantum
transport simulation reveals that the sub-10 nm single-gated SNM field effect
transistors show excellent performance at zero temperature but such a
performance is greatly degraded at room temperature
An Intent Taxonomy of Legal Case Retrieval
Legal case retrieval is a special Information Retrieval~(IR) task focusing on
legal case documents. Depending on the downstream tasks of the retrieved case
documents, users' information needs in legal case retrieval could be
significantly different from those in Web search and traditional ad-hoc
retrieval tasks. While there are several studies that retrieve legal cases
based on text similarity, the underlying search intents of legal retrieval
users, as shown in this paper, are more complicated than that yet mostly
unexplored. To this end, we present a novel hierarchical intent taxonomy of
legal case retrieval. It consists of five intent types categorized by three
criteria, i.e., search for Particular Case(s), Characterization, Penalty,
Procedure, and Interest. The taxonomy was constructed transparently and
evaluated extensively through interviews, editorial user studies, and query log
analysis. Through a laboratory user study, we reveal significant differences in
user behavior and satisfaction under different search intents in legal case
retrieval. Furthermore, we apply the proposed taxonomy to various downstream
legal retrieval tasks, e.g., result ranking and satisfaction prediction, and
demonstrate its effectiveness. Our work provides important insights into the
understanding of user intents in legal case retrieval and potentially leads to
better retrieval techniques in the legal domain, such as intent-aware ranking
strategies and evaluation methodologies.Comment: 28 pages, work in proces
Constructing an Interaction Behavior Model for Web Image Search
User interaction behavior is a valuable source of implicit relevance
feedback. In Web image search a different type of search result presentation is
used than in general Web search, which leads to different interaction
mechanisms and user behavior. For example, image search results are
self-contained, so that users do not need to click the results to view the
landing page as in general Web search, which generates sparse click data. Also,
two-dimensional result placement instead of a linear result list makes browsing
behaviors more complex. Thus, it is hard to apply standard user behavior models
(e.g., click models) developed for general Web search to Web image search.
In this paper, we conduct a comprehensive image search user behavior analysis
using data from a lab-based user study as well as data from a commercial search
log. We then propose a novel interaction behavior model, called grid-based user
browsing model (GUBM), whose design is motivated by observations from our data
analysis. GUBM can both capture users' interaction behavior, including cursor
hovering, and alleviate position bias. The advantages of GUBM are two-fold: (1)
It is based on an unsupervised learning method and does not need manually
annotated data for training. (2) It is based on user interaction features on
search engine result pages (SERPs) and is easily transferable to other
scenarios that have a grid-based interface such as video search engines. We
conduct extensive experiments to test the performance of our model using a
large-scale commercial image search log. Experimental results show that in
terms of behavior prediction (perplexity), and topical relevance and image
quality (normalized discounted cumulative gain (NDCG)), GUBM outperforms
state-of-the-art baseline models as well as the original ranking. We make the
implementation of GUBM and related datasets publicly available for future
studies.Comment: 10 page
A proposal for detecting the spin of a single electron in superfluid helium
The electron bubble in superfluid helium has two degrees of freedom that may
offer exceptionally low dissipation: the electron's spin and the bubble's
motion. If these degrees of freedom can be read out and controlled with
sufficient sensitivity, they would provide a novel platform for realizing a
range of quantum technologies and for exploring open questions in the physics
of superfluid helium. Here we propose a practical scheme for accomplishing this
by trapping an electron bubble inside a superfluid-filled opto-acoustic cavity.Comment: Main text: 5 pages, 5 figures. Supplement: 11 pages, 2 figures, 1
tabl
Evaluating Interpolation and Extrapolation Performance of Neural Retrieval Models
A retrieval model should not only interpolate the training data but also
extrapolate well to the queries that are different from the training data.
While neural retrieval models have demonstrated impressive performance on
ad-hoc search benchmarks, we still know little about how they perform in terms
of interpolation and extrapolation. In this paper, we demonstrate the
importance of separately evaluating the two capabilities of neural retrieval
models. Firstly, we examine existing ad-hoc search benchmarks from the two
perspectives. We investigate the distribution of training and test data and
find a considerable overlap in query entities, query intent, and relevance
labels. This finding implies that the evaluation on these test sets is biased
toward interpolation and cannot accurately reflect the extrapolation capacity.
Secondly, we propose a novel evaluation protocol to separately evaluate the
interpolation and extrapolation performance on existing benchmark datasets. It
resamples the training and test data based on query similarity and utilizes the
resampled dataset for training and evaluation. Finally, we leverage the
proposed evaluation protocol to comprehensively revisit a number of
widely-adopted neural retrieval models. Results show models perform differently
when moving from interpolation to extrapolation. For example,
representation-based retrieval models perform almost as well as
interaction-based retrieval models in terms of interpolation but not
extrapolation. Therefore, it is necessary to separately evaluate both
interpolation and extrapolation performance and the proposed resampling method
serves as a simple yet effective evaluation tool for future IR studies.Comment: CIKM 2022 Full Pape
SEA: A Combined Model for Heat Demand Prediction
Heat demand prediction is a prominent research topic in the area of
intelligent energy networks. It has been well recognized that periodicity is
one of the important characteristics of heat demand. Seasonal-trend
decomposition based on LOESS (STL) algorithm can analyze the periodicity of a
heat demand series, and decompose the series into seasonal and trend
components. Then, predicting the seasonal and trend components respectively,
and combining their predictions together as the heat demand prediction is a
possible way to predict heat demand. In this paper, STL-ENN-ARIMA (SEA), a
combined model, was proposed based on the combination of the Elman neural
network (ENN) and the autoregressive integrated moving average (ARIMA) model,
which are commonly applied to heat demand prediction. ENN and ARIMA are used to
predict seasonal and trend components, respectively. Experimental results
demonstrate that the proposed SEA model has a promising performance
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