136 research outputs found
Support Vector Machine for Behavior-Based Driver Identification System
We present an intelligent driver
identification system to handle vehicle theft based on modeling
dynamic human behaviors. We propose to recognize illegitimate
drivers through their driving behaviors. Since human driving
behaviors belong to a dynamic biometrical feature which is
complex and difficult to imitate compared with static features
such as passwords and fingerprints, we find that this novel
idea of utilizing human dynamic features for enhanced security
application is more effective. In this paper, we first describe
our experimental platform for collecting and modeling human
driving behaviors. Then we compare fast Fourier transform
(FFT), principal component analysis (PCA), and independent
component analysis (ICA) for data preprocessing. Using machine
learning method of support vector machine (SVM), we derive the individual
driving behavior model and we then demonstrate
the procedure for recognizing different drivers by analyzing
the corresponding models. The experimental results of learning
algorithms and evaluation are described
QH9: A Quantum Hamiltonian Prediction Benchmark for QM9 Molecules
Supervised machine learning approaches have been increasingly used in
accelerating electronic structure prediction as surrogates of first-principle
computational methods, such as density functional theory (DFT). While numerous
quantum chemistry datasets focus on chemical properties and atomic forces, the
ability to achieve accurate and efficient prediction of the Hamiltonian matrix
is highly desired, as it is the most important and fundamental physical
quantity that determines the quantum states of physical systems and chemical
properties. In this work, we generate a new Quantum Hamiltonian dataset, named
as QH9, to provide precise Hamiltonian matrices for 2,399 molecular dynamics
trajectories and 130,831 stable molecular geometries, based on the QM9 dataset.
By designing benchmark tasks with various molecules, we show that current
machine learning models have the capacity to predict Hamiltonian matrices for
arbitrary molecules. Both the QH9 dataset and the baseline models are provided
to the community through an open-source benchmark, which can be highly valuable
for developing machine learning methods and accelerating molecular and
materials design for scientific and technological applications. Our benchmark
is publicly available at
https://github.com/divelab/AIRS/tree/main/OpenDFT/QHBench.Comment: Accepted by NeurIPS 2023, Track on Datasets and Benchmark
Semiconducting transport in PbCu(PO)O sintered from PbSO and CuP
The very recent claim on the discovery of ambient-pressure room-temperature
superconductivity in modified lead-apatite has immediately excited sensational
attention in the entire society, which is fabricated by sintering lanarkite
(Pb2SO5) and copper(I) phosphide (CuP). To verify this exciting claim, we
have successfully synthesized PbSO, CuP, and finally the modified
lead-apatite PbCu(PO)O. Detailed electrical transport and
magnetic properties of these compounds were systematically analyzed. It turns
out that PbSO is a highly insulating diamagnet with a room-temperature
resistivity of ~7.18x10 Ohm.cm and CuP is a paramagnetic metal with a
room-temperature resistivity of ~5.22x10 Ohm.cm. In contrast to the
claimed superconductivity, the resulting PbCu(PO)O
compound sintered from PbSO and CuP exhibits semiconductor-like
transport behavior with a large room-temperature resistivity of ~1.94x10
Ohm.cm although our compound shows greatly consistent x-ray diffraction
spectrum with the previously reported structure data. In addition, when a
pressed PbCu(PO)O pellet is located on top of a commercial
NdFeB magnet at room temperature, no repulsion could be felt and no
magnetic levitation was observed either. These results imply that the claim of
a room-temperature superconductor in modified lead-apatite may need more
careful re-examination, especially for the electrical transport properties.Comment: 12 pages, 13 figure
Villain Stardom in Socialist China: Chen Qiang and the Cultural Politics of Affect
Despite playing various kinds of roles across genres from 1949 to 1965, Chen Qiang acquired stardom mainly due to his remarkable screen performance as villainous landlords in socialist China. His villain stardom is an aberrant case, compared to the majority of film stars in Chinese socialist cinema who encouraged identification and emulation and helped propagate socialist ideology to reform Chinese citizens. Paying special attention to socio-historically specific film exhibition practices and the actor's own reflections on his villain performance, this article argues that Chen's stardom functioned as an important affective technology within a wider and complex Communist propaganda enterprise in that it helped cultivate class hatred necessary for the Communist revolution and socialist land reform campaigns. Through this case study, the article suggests that close engagement with both cultural–historical specificities of cinema and recent critical theories of affect open up a space for researching the diversified star phenomena in contemporary China
Publisher Correction: An anomalous Hall effect in altermagnetic ruthenium dioxide
In the version of this article initially published, square brackets and parentheses were incorrect in Fig. 1g and throughout Fig. 2 (excepting lower labels in Fig. 2d–f). Further, in the second paragraph of the “Consistency with theoretical prediction” subsection of the main article, in the text now reading “the reorientation-field scale, namely, HC = (H2 AE − H2 d) /Hd,” the term “H2 AE” wasn’t shown as squared. The changes have been made in the HTML and PDF versions of the article
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems
Advances in artificial intelligence (AI) are fueling a new paradigm of
discoveries in natural sciences. Today, AI has started to advance natural
sciences by improving, accelerating, and enabling our understanding of natural
phenomena at a wide range of spatial and temporal scales, giving rise to a new
area of research known as AI for science (AI4Science). Being an emerging
research paradigm, AI4Science is unique in that it is an enormous and highly
interdisciplinary area. Thus, a unified and technical treatment of this field
is needed yet challenging. This work aims to provide a technically thorough
account of a subarea of AI4Science; namely, AI for quantum, atomistic, and
continuum systems. These areas aim at understanding the physical world from the
subatomic (wavefunctions and electron density), atomic (molecules, proteins,
materials, and interactions), to macro (fluids, climate, and subsurface) scales
and form an important subarea of AI4Science. A unique advantage of focusing on
these areas is that they largely share a common set of challenges, thereby
allowing a unified and foundational treatment. A key common challenge is how to
capture physics first principles, especially symmetries, in natural systems by
deep learning methods. We provide an in-depth yet intuitive account of
techniques to achieve equivariance to symmetry transformations. We also discuss
other common technical challenges, including explainability,
out-of-distribution generalization, knowledge transfer with foundation and
large language models, and uncertainty quantification. To facilitate learning
and education, we provide categorized lists of resources that we found to be
useful. We strive to be thorough and unified and hope this initial effort may
trigger more community interests and efforts to further advance AI4Science
Finishing the euchromatic sequence of the human genome
The sequence of the human genome encodes the genetic instructions for human physiology, as well as rich information about human evolution. In 2001, the International Human Genome Sequencing Consortium reported a draft sequence of the euchromatic portion of the human genome. Since then, the international collaboration has worked to convert this draft into a genome sequence with high accuracy and nearly complete coverage. Here, we report the result of this finishing process. The current genome sequence (Build 35) contains 2.85 billion nucleotides interrupted by only 341 gaps. It covers ∼99% of the euchromatic genome and is accurate to an error rate of ∼1 event per 100,000 bases. Many of the remaining euchromatic gaps are associated with segmental duplications and will require focused work with new methods. The near-complete sequence, the first for a vertebrate, greatly improves the precision of biological analyses of the human genome including studies of gene number, birth and death. Notably, the human enome seems to encode only 20,000-25,000 protein-coding genes. The genome sequence reported here should serve as a firm foundation for biomedical research in the decades ahead
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