184 research outputs found
GumDrop at the DISRPT2019 Shared Task: A Model Stacking Approach to Discourse Unit Segmentation and Connective Detection
In this paper we present GumDrop, Georgetown University's entry at the DISRPT
2019 Shared Task on automatic discourse unit segmentation and connective
detection. Our approach relies on model stacking, creating a heterogeneous
ensemble of classifiers, which feed into a metalearner for each final task. The
system encompasses three trainable component stacks: one for sentence
splitting, one for discourse unit segmentation and one for connective
detection. The flexibility of each ensemble allows the system to generalize
well to datasets of different sizes and with varying levels of homogeneity.Comment: Proceedings of Discourse Relation Parsing and Treebanking
(DISRPT2019
Classical algorithms for many-body quantum systems at finite energies
We investigate quantum inspired algorithms to compute physical observables of
quantum many-body systems at finite energies. They are based on the quantum
algorithms proposed in [Lu et al. PRX Quantum 2, 020321 (2021)], which use the
quantum simulation of the dynamics of such systems, as well as classical
filtering and sampling techniques. Here, we replace the quantum simulation by
standard classical methods based on matrix product states and operators. As a
result, we can address significantly larger systems than those reachable by
exact diagonalization or by other algorithms. We demonstrate the performance
with spin chains up to 80 sites
Efficient Climate Simulation via Machine Learning Method
Hybrid modeling combining data-driven techniques and numerical methods is an
emerging and promising research direction for efficient climate simulation.
However, previous works lack practical platforms, making developing hybrid
modeling a challenging programming problem. Furthermore, the lack of standard
data sets and evaluation metrics may hamper researchers from comprehensively
comparing various algorithms under a uniform condition. To address these
problems, we propose a framework called NeuroClim for hybrid modeling under the
real-world scenario, a basic setting to simulate the real climate that we live
in. NeuroClim consists of three parts: (1) Platform. We develop a user-friendly
platform NeuroGCM for efficiently developing hybrid modeling in climate
simulation. (2) Dataset. We provide an open-source dataset for data-driven
methods in hybrid modeling. We investigate the characteristics of the data,
i.e., heterogeneity and stiffness, which reveals the difficulty of regressing
climate simulation data; (3) Metrics. We propose a methodology for
quantitatively evaluating hybrid modeling, including the approximation ability
of machine learning models and the stability during simulation. We believe that
NeuroClim allows researchers to work without high level of climate-related
expertise and focus only on machine learning algorithm design, which will
accelerate hybrid modeling research in the AI-Climate intersection. The codes
and data are released at https://github.com/x-w19/NeuroClim.Comment: Work in progres
Multi-task super resolution method for vector field critical points enhancement
It is a challenging task to handle the vector field visualization at local critical points. Generally, topological based methods firstly divide critical regions into different categories, and then process the different types of critical regions to improve the effect, which pipeline is complex. In the paper, a learning based multi-task super resolution (SR) method is proposed to improve the refinement of vector field, and enhance the visualization effect, especially at the critical region. In detail, the multi-task model consists of two important designs on task branches: one task is to simulate the interpolation of discrete vector fields based on an improved super-resolution network; and the other is a classification task to identify the types of critical vector fields. It is an efficient end-to-end architecture for both training and inferencing stages, which simplifies the pipeline of critical vector field visualization and improves the visualization effect. In experiment, we compare our method with both traditional interpolation and pure SR network on both simulation data and real data, and the reported results indicate our method lower the error and improve PSNR significantly
Probing Thermalization through Spectral Analysis with Matrix Product Operators
We combine matrix product operator techniques with Chebyshev polynomial expansions and present a method that is able to explore spectral properties of quantum many-body Hamiltonians. In particular, we show how this method can be used to probe thermalization of large spin chains without explicitly simulating their time evolution, as well as to compute full and local densities of states. The performance is illustrated with the examples of the Ising and PXP spin chains. For the nonintegrable Ising chain, our findings corroborate the presence of thermalization for several initial states, well beyond what direct timedependent simulations have been able to achieve so far
VERTICES: Efficient Two-Party Vertical Federated Linear Model with TTP-aided Secret Sharing
Vertical Federated Learning (VFL) has emerged as one of the most predominant
approaches for secure collaborative machine learning where the training data is
partitioned by features among multiple parties. Most VFL algorithms primarily
rely on two fundamental privacy-preserving techniques: Homomorphic Encryption
(HE) and secure Multi-Party Computation (MPC). Though generally considered with
stronger privacy guarantees, existing general-purpose MPC frameworks suffer
from expensive computation and communication overhead and are inefficient
especially under VFL settings. This study centers around MPC-based VFL
algorithms and presents a novel approach for two-party vertical federated
linear models via an efficient secret sharing (SS) scheme with a trusted
coordinator. Our approach can achieve significant acceleration of the training
procedure in vertical federated linear models of between 2.5x and 6.6x than
other existing MPC frameworks under the same security setting
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