1,620 research outputs found
Efficient iterative method for solving the Dirac-Kohn-Sham density functional theory
We present for the first time an efficient iterative method to directly solve
the four-component Dirac-Kohn-Sham (DKS) density functional theory. Due to the
existence of the negative energy continuum in the DKS operator, the existing
iterative techniques for solving the Kohn-Sham systems cannot be efficiently
applied to solve the DKS systems. The key component of our method is a novel
filtering step (F) which acts as a preconditioner in the framework of the
locally optimal block preconditioned conjugate gradient (LOBPCG) method. The
resulting method, dubbed the LOBPCG-F method, is able to compute the desired
eigenvalues and eigenvectors in the positive energy band without computing any
state in the negative energy band. The LOBPCG-F method introduces mild extra
cost compared to the standard LOBPCG method and can be easily implemented. We
demonstrate our method in the pseudopotential framework with a planewave basis
set which naturally satisfies the kinetic balance prescription. Numerical
results for Pt, Au, TlF, and BiSe indicate that the
LOBPCG-F method is a robust and efficient method for investigating the
relativistic effect in systems containing heavy elements.Comment: 31 pages, 5 figure
Deep Recurrent Survival Analysis
Survival analysis is a hotspot in statistical research for modeling
time-to-event information with data censorship handling, which has been widely
used in many applications such as clinical research, information system and
other fields with survivorship bias. Many works have been proposed for survival
analysis ranging from traditional statistic methods to machine learning models.
However, the existing methodologies either utilize counting-based statistics on
the segmented data, or have a pre-assumption on the event probability
distribution w.r.t. time. Moreover, few works consider sequential patterns
within the feature space. In this paper, we propose a Deep Recurrent Survival
Analysis model which combines deep learning for conditional probability
prediction at fine-grained level of the data, and survival analysis for
tackling the censorship. By capturing the time dependency through modeling the
conditional probability of the event for each sample, our method predicts the
likelihood of the true event occurrence and estimates the survival rate over
time, i.e., the probability of the non-occurrence of the event, for the
censored data. Meanwhile, without assuming any specific form of the event
probability distribution, our model shows great advantages over the previous
works on fitting various sophisticated data distributions. In the experiments
on the three real-world tasks from different fields, our model significantly
outperforms the state-of-the-art solutions under various metrics.Comment: AAAI 2019. Supplemental material, slides, code:
https://github.com/rk2900/drs
86 PFLOPS Deep Potential Molecular Dynamics simulation of 100 million atoms with ab initio accuracy
We present the GPU version of DeePMD-kit, which, upon training a deep neural
network model using ab initio data, can drive extremely large-scale molecular
dynamics (MD) simulation with ab initio accuracy. Our tests show that the GPU
version is 7 times faster than the CPU version with the same power consumption.
The code can scale up to the entire Summit supercomputer. For a copper system
of 113, 246, 208 atoms, the code can perform one nanosecond MD simulation per
day, reaching a peak performance of 86 PFLOPS (43% of the peak). Such
unprecedented ability to perform MD simulation with ab initio accuracy opens up
the possibility of studying many important issues in materials and molecules,
such as heterogeneous catalysis, electrochemical cells, irradiation damage,
crack propagation, and biochemical reactions.Comment: 29 pages, 11 figure
Sampled in Pairs and Driven by Text: A New Graph Embedding Framework
In graphs with rich texts, incorporating textual information with structural
information would benefit constructing expressive graph embeddings. Among
various graph embedding models, random walk (RW)-based is one of the most
popular and successful groups. However, it is challenged by two issues when
applied on graphs with rich texts: (i) sampling efficiency: deriving from the
training objective of RW-based models (e.g., DeepWalk and node2vec), we show
that RW-based models are likely to generate large amounts of redundant training
samples due to three main drawbacks. (ii) text utilization: these models have
difficulty in dealing with zero-shot scenarios where graph embedding models
have to infer graph structures directly from texts. To solve these problems, we
propose a novel framework, namely Text-driven Graph Embedding with Pairs
Sampling (TGE-PS). TGE-PS uses Pairs Sampling (PS) to improve the sampling
strategy of RW, being able to reduce ~99% training samples while preserving
competitive performance. TGE-PS uses Text-driven Graph Embedding (TGE), an
inductive graph embedding approach, to generate node embeddings from texts.
Since each node contains rich texts, TGE is able to generate high-quality
embeddings and provide reasonable predictions on existence of links to unseen
nodes. We evaluate TGE-PS on several real-world datasets, and experiment
results demonstrate that TGE-PS produces state-of-the-art results on both
traditional and zero-shot link prediction tasks.Comment: Accepted by WWW 2019 (The World Wide Web Conference. ACM, 2019
Machine-Learned Invertible Coarse Graining for Multiscale Molecular Modeling
Multiscale molecular modeling is widely applied in scientific research of
molecular properties over large time and length scales. Two specific challenges
are commonly present in multiscale modeling, provided that information between
the coarse and fine representations of molecules needs to be properly
exchanged: One is to construct coarse grained (CG) models by passing
information from the fine to coarse levels; the other is to restore finer
molecular details given CG configurations. Although these two problems are
commonly addressed independently, in this work, we present a theory connecting
them, and develop a methodology called Cycle Coarse Graining (CCG) to solve
both problems in a unified manner. In CCG, reconstruction can be achieved via a
tractable optimization process, leading to a general method to retrieve fine
details from CG simulations, which in turn, delivers a new solution to the CG
problem, yielding an efficient way to calculate free energies in a
rare-event-free manner. CCG thus provides a systematic way for multiscale
molecular modeling, where the finer details of CG simulations can be
efficiently retrieved, and the CG models can be improved consistently.Comment: 10 pages, 5 figures, plus S
Class Incremental Learning via Likelihood Ratio Based Task Prediction
Class incremental learning (CIL) is a challenging setting of continual
learning, which learns a series of tasks sequentially. Each task consists of a
set of unique classes. The key feature of CIL is that no task identifier (or
task-id) is provided at test time. Predicting the task-id for each test sample
is a challenging problem. An emerging theory-guided approach (called TIL+OOD)
is to train a task-specific model for each task in a shared network for all
tasks based on a task-incremental learning (TIL) method to deal with
catastrophic forgetting. The model for each task is an out-of-distribution
(OOD) detector rather than a conventional classifier. The OOD detector can
perform both within-task (in-distribution (IND)) class prediction and OOD
detection. The OOD detection capability is the key to task-id prediction during
inference. However, this paper argues that using a traditional OOD detector for
task-id prediction is sub-optimal because additional information (e.g., the
replay data and the learned tasks) available in CIL can be exploited to design
a better and principled method for task-id prediction. We call the new method
TPL (Task-id Prediction based on Likelihood Ratio). TPL markedly outperforms
strong CIL baselines and has negligible catastrophic forgetting. The code of
TPL is publicly available at https://github.com/linhaowei1/TPL
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