722 research outputs found
Prompt Tuning on Graph-augmented Low-resource Text Classification
Text classification is a fundamental problem in information retrieval with
many real-world applications, such as predicting the topics of online articles
and the categories of e-commerce product descriptions. However, low-resource
text classification, with no or few labeled samples, presents a serious concern
for supervised learning. Meanwhile, many text data are inherently grounded on a
network structure, such as a hyperlink/citation network for online articles,
and a user-item purchase network for e-commerce products. These graph
structures capture rich semantic relationships, which can potentially augment
low-resource text classification. In this paper, we propose a novel model
called Graph-Grounded Pre-training and Prompting (G2P2) to address low-resource
text classification in a two-pronged approach. During pre-training, we propose
three graph interaction-based contrastive strategies to jointly pre-train a
graph-text model; during downstream classification, we explore handcrafted
discrete prompts and continuous prompt tuning for the jointly pre-trained model
to achieve zero- and few-shot classification, respectively. Besides, for
generalizing continuous prompts to unseen classes, we propose conditional
prompt tuning on graphs (G2P2). Extensive experiments on four real-world
datasets demonstrate the strength of G2P2 in zero- and few-shot low-resource
text classification tasks, and illustrate the advantage of G2P2 in dealing
with unseen classes.Comment: 14 pages, journal under review. arXiv admin note: substantial text
overlap with arXiv:2305.0332
Sciunits: Reusable Research Objects
Science is conducted collaboratively, often requiring knowledge sharing about
computational experiments. When experiments include only datasets, they can be
shared using Uniform Resource Identifiers (URIs) or Digital Object Identifiers
(DOIs). An experiment, however, seldom includes only datasets, but more often
includes software, its past execution, provenance, and associated
documentation. The Research Object has recently emerged as a comprehensive and
systematic method for aggregation and identification of diverse elements of
computational experiments. While a necessary method, mere aggregation is not
sufficient for the sharing of computational experiments. Other users must be
able to easily recompute on these shared research objects. In this paper, we
present the sciunit, a reusable research object in which aggregated content is
recomputable. We describe a Git-like client that efficiently creates, stores,
and repeats sciunits. We show through analysis that sciunits repeat
computational experiments with minimal storage and processing overhead.
Finally, we provide an overview of sharing and reproducible cyberinfrastructure
based on sciunits gaining adoption in the domain of geosciences
Destruction of valence-bond order in a sawtooth chain with a Dzyaloshinskii-Moriya term
A small value of the spin gap in quantum antiferromagnets with strong
frustration makes them susceptible to nominally small deviations from the ideal
Heisenberg model. One of such perturbations, the anisotropic
Dzyaloshinskii-Moriya interaction, is an important perturbation for the
kagome antiferromagnet, one of the current candidates for a quantum-disordered
ground state. We study the influence of the DM term in a related
one-dimensional system, the sawtooth chain that has valence-bond order in its
ground state. Through a combination of analytical and numerical methods, we
show that a relatively weak DM coupling, , is sufficient to destroy the
valence-bond order, close the spin gap, and turn the system into a Luttinger
liquid with algebraic spin correlations. A similar mechanism may be at work in
the kagome antiferromagnet.Comment: 11 pages. References added. Revisions made as requested by referee
GNSS-IR Model of Sea Level Height Estimation Combining Variational Mode Decomposition
The Global Navigation Satellite System-Reflections (GNSS-R) signal has been confirmed to be used to retrieve sea level height. At present, the GNSS-Interferometric Reflectometry (GNSS-IR) technology based on the least square method to process signal-to-noise ratio (SNR) data is restricted by the satellite elevation angle in terms of accuracy and stability. This paper proposes a new GNSS-IR model combining variational mode decomposition (VMD) for sea level height estimation. VMD is used to decompose the SNR data into intrinsic mode functions (IMF) of layers with different frequencies, remove the IMF representing the trend item of the SNR data, and reconstruct the remaining IMF components to obtain the SNR oscillation item. In order to verify the validity of the new GNSS-IR model, the measurement data provided by the Onsala Space Observatory in Sweden is used to evaluate the performance of the algorithm and its stability in high elevation range. The experimental results show that the VMD method has good results in terms of accuracy and stability, and has advantages compared to other methods. For the half-year GNSS SNR data, the root mean square error (RMSE) and correlation coefficient of the new model based on the VMD method are 4.86 cm and 0.97, respectively
Voucher Abuse Detection with Prompt-based Fine-tuning on Graph Neural Networks
Voucher abuse detection is an important anomaly detection problem in
E-commerce. While many GNN-based solutions have emerged, the supervised
paradigm depends on a large quantity of labeled data. A popular alternative is
to adopt self-supervised pre-training using label-free data, and further
fine-tune on a downstream task with limited labels. Nevertheless, the
"pre-train, fine-tune" paradigm is often plagued by the objective gap between
pre-training and downstream tasks. Hence, we propose VPGNN, a prompt-based
fine-tuning framework on GNNs for voucher abuse detection. We design a novel
graph prompting function to reformulate the downstream task into a similar
template as the pretext task in pre-training, thereby narrowing the objective
gap. Extensive experiments on both proprietary and public datasets demonstrate
the strength of VPGNN in both few-shot and semi-supervised scenarios. Moreover,
an online deployment of VPGNN in a production environment shows a 23.4%
improvement over two existing deployed models.Comment: 7 pages, Accepted by CIKM23 Applied Research Trac
Utilizing Provenance in Reusable Research Objects
Science is conducted collaboratively, often requiring the sharing of
knowledge about computational experiments. When experiments include only
datasets, they can be shared using Uniform Resource Identifiers (URIs) or
Digital Object Identifiers (DOIs). An experiment, however, seldom includes only
datasets, but more often includes software, its past execution, provenance, and
associated documentation. The Research Object has recently emerged as a
comprehensive and systematic method for aggregation and identification of
diverse elements of computational experiments. While a necessary method, mere
aggregation is not sufficient for the sharing of computational experiments.
Other users must be able to easily recompute on these shared research objects.
Computational provenance is often the key to enable such reuse. In this paper,
we show how reusable research objects can utilize provenance to correctly
repeat a previous reference execution, to construct a subset of a research
object for partial reuse, and to reuse existing contents of a research object
for modified reuse. We describe two methods to summarize provenance that aid in
understanding the contents and past executions of a research object. The first
method obtains a process-view by collapsing low-level system information, and
the second method obtains a summary graph by grouping related nodes and edges
with the goal to obtain a graph view similar to application workflow. Through
detailed experiments, we show the efficacy and efficiency of our algorithms.Comment: 25 page
An Iterative Learning Based Compensation in Model Predictive Control for DC/DC Boost Converter
Attributed to the increased processing power of modern microprocessors, model predictive control (MPC) for power converters is gaining more attention. However, the non-minimum phase behavior in DC/DC boost converters complicates the design of model predictive control. When controlling the output voltage directly, it fails to track the reference with short prediction horizons, nevertheless, long prediction horizons cause a heavy computational burden. Although controlling the inductor current is a feasible option with a short prediction horizon, the control accuracy of the output voltage cannot be guaranteed. To address this issue, this work introduces a compensation term into the difference equation of the inductor current. Then the proportion of the compensation term is designed with an iterative learning method to improve the control accuracy. Finally, the results indicate the proposed method can ensure a good control performance with different load occasions
Realization of corner and helical edge states in topologically trivial band gap by twig edge
The twig edge states in graphene-like structures are viewed as the fourth
states complementary to their zigzag, bearded, and armchair counterparts. In
this work, we study a rod-in-plasma system in honeycomb lattice with twig edges
under external magnetic fields and lattice scaling and show that twig edge
states can exist in different phases of the system, such as quantum Hall phase,
quantum spin Hall phase and insulating phase. The twig edge states in the
quantum Hall phase exhibit robust one-way transmission property immune to
backscattering and thus provide a novel avenue for solving the plasma
communication blackout problem. Moreover, we demonstrate that corner and edge
states can exist within the trivial band gap of the insulating phase by
modulating the on-site potential of the twig edges. Especially, helical edge
states with the unique feature of pseudospin-momentum locking that could be
exited by chiral sources are demonstrated at the twig edges within the trivial
band gap. Our results show that many topological-like behaviors of
electromagnetic waves are not necessarily tied to the exact topology of the
systems and the twig edges and interface engineering can bring new
opportunities for more flexible manipulation of electromagnetic waves
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