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

Self-assembling fluorescent hydrogel for highly efficient water purification and photothermal conversion

Employing fluorescent hydrogels for hazardous Hg(II) detection and removal is an efficient method for water purification. However, it remains challenging to establish a fluorescent system with low detection limit and high adsorption capacity that can readily be upcycled into a valuable material resource. Herein, we report on a fluorescent hydrogel with 0D sulfydryl-based carbon dots that are self-assembled with a 3D hydrogel network. The cellulose-based hydrogel exhibited good sensitivity for the detection of Hg(II) over a range from 0 to 40 µM with a limit detection of 3.0 × 10-6 M. The adsorption experiments confirmed that the cellulose-based hydrogel exhibits good Hg(II) extraction capacity of over 662.25 mg g−1 at room temperature, and can effectively reduce the Hg concentration to attain acceptable levels that comply with industrial water standards (0.05 mg L-1). Subsequently, we used a facile strategy to convert the exhausted waste adsorbent by in-situ sulfurization into a suitable material for solar steam generation. The as-prepared upcycled aerogel evaporators exhibited excellent evaporation rates of ∼ 1.30 kg m−2 h−1 under one sun irradiation. These results not only provide a strategy for heavy metal ion recognition and adsorption, but also provide a route to recycle hazardous waste for seawater desalination.</p

Semiconducting transport in Pb10−x_{10-x}Cux_x(PO4_4)6_6O sintered from Pb2_2SO5_5 and Cu3_3P

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 (Cu$_3$P). To verify this exciting claim, we have successfully synthesized Pb$_2$SO$_5$, Cu$_3$P, and finally the modified lead-apatite Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O. Detailed electrical transport and magnetic properties of these compounds were systematically analyzed. It turns out that Pb$_2$SO$_5$ is a highly insulating diamagnet with a room-temperature resistivity of ~7.18x10$^9$ Ohm.cm and Cu$_3$P is a paramagnetic metal with a room-temperature resistivity of ~5.22x10$^{-4}$ Ohm.cm. In contrast to the claimed superconductivity, the resulting Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O compound sintered from Pb$_2$SO$_5$ and Cu$_3$P exhibits semiconductor-like transport behavior with a large room-temperature resistivity of ~1.94x10$^4$ Ohm.cm although our compound shows greatly consistent x-ray diffraction spectrum with the previously reported structure data. In addition, when a pressed Pb$_{10-x}$Cu$_x$(PO$_4$)$_6$O pellet is located on top of a commercial Nd$_2$Fe$_{14}$B 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

Engineering a ratiometric fluorescent sensor membrane containing carbon dots for efficient fluoride detection and removal

Fluoride anion pollution is one of the main problems that needs to be addressed in contaminated water. Herein, we have developed a novel sensing platform using a pyrene boronic acid and carbon dots (CDs) for the selective detection and removal of fluoride (F−) ion at environmentally relevant levels. The probe consists of pyrene-boronic acid (PyB) moieties immobilized on to the surface of water-soluble CDs. The pyrene-boronic acid-based CDs (CDs-PyB) result in a sensor whose response is linear for F− concentrations over a range from 0 to 200 µM (R2 = 0.996) with a detection limit of 5.9 × 10−5 M and display high selectivity for F− over other anions. In addition, an amino-modified cellulose membrane containing CDs-PyB has been prepared for practical sensing and removal of F−. The cellulose membrane-based sensor shows great potential for the detection of F− with a high sensitivity, and excellent F− adsorption and removal efficiency of 90.2%. Moreover, an MTT assay for the membrane demonstrates high cell proliferation ca 400% after 5 days culture, indicating excellent cytocompatibility. Our approach offers a promising direction for the construction of other sensors by simply swapping the current probe with suitable replacements for a variety of relevant applications using biocompatible and abundant naturally based materials.</p

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