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1. Poster: Video Fingerprinting in Tor

   https://doi.org/10.1145/3319535.3363273  

   Rahman, Mohammad Saidur; Matthews, Nate; Wright, Matthew (November 2019, CCS '19: Proceedings of the 2019 ACM SIGSAC Conference on Computer and Communications Security)

   Over 8 million users rely on the Tor network each day to protect their anonymity online. Unfortunately, Tor has been shown to be vulnerable to the website fingerprinting attack, which allows an attacker to deduce the website a user is visiting based on patterns in their traffic. The state-of-the-art attacks leverage deep learning to achieve high classification accuracy using raw packet information. Work thus far, however, has examined only one type of media delivered over the Tor network: web pages, and mostly just home pages of sites. In this work, we instead investigate the fingerprintability of video content served over Tor. We collected a large new dataset of network traces for 50 YouTube videos of similar length. Our preliminary experiments utilizing a convolutional neural network model proposed in prior works has yielded promising classification results, achieving up to 55% accuracy. This shows the potential to unmask the individual videos that users are viewing over Tor, creating further privacy challenges to consider when defending against website fingerprinting attacks. 

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2. A neural network-assisted open boundary molecular dynamics simulation method

   https://doi.org/10.1063/5.0083198  

   Floyd, J. E.; Lukes, J. R. (May 2022, The Journal of Chemical Physics)

   A neural network-assisted molecular dynamics method is developed to reduce the computational cost of open boundary simulations. Particle influxes and neural network-derived forces are applied at the boundaries of an open domain consisting of explicitly modeled Lennard-Jones atoms in order to represent the effects of the unmodeled surrounding fluid. Canonical ensemble simulations with periodic boundaries are used to train the neural network and to sample boundary fluxes. The method, as implemented in the LAMMPS, yields temperature, kinetic energy, potential energy, and pressure values within 2.5% of those calculated using periodic molecular dynamics and runs two orders of magnitude faster than a comparable grand canonical molecular dynamics system. 

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3. Deep transfer learning for star cluster classification: I. application to the PHANGS–HST survey

   https://doi.org/10.1093/mnras/staa325  

   Wei, Wei; Huerta, E A; Whitmore, Bradley C; Lee, Janice C; Hannon, Stephen; Chandar, Rupali; Dale, Daniel A; Larson, Kirsten L; Thilker, David A; Ubeda, Leonardo; et al (April 2020, Monthly Notices of the Royal Astronomical Society)

   ABSTRACT We present the results of a proof-of-concept experiment that demonstrates that deep learning can successfully be used for production-scale classification of compact star clusters detected in Hubble Space Telescope(HST) ultraviolet-optical imaging of nearby spiral galaxies ($$D\lesssim 20\, \textrm{Mpc}$$) in the Physics at High Angular Resolution in Nearby GalaxieS (PHANGS)–HST survey. Given the relatively small nature of existing, human-labelled star cluster samples, we transfer the knowledge of state-of-the-art neural network models for real-object recognition to classify star clusters candidates into four morphological classes. We perform a series of experiments to determine the dependence of classification performance on neural network architecture (ResNet18 and VGG19-BN), training data sets curated by either a single expert or three astronomers, and the size of the images used for training. We find that the overall classification accuracies are not significantly affected by these choices. The networks are used to classify star cluster candidates in the PHANGS–HST galaxy NGC 1559, which was not included in the training samples. The resulting prediction accuracies are 70 per cent, 40 per cent, 40–50 per cent, and 50–70 per cent for class 1, 2, 3 star clusters, and class 4 non-clusters, respectively. This performance is competitive with consistency achieved in previously published human and automated quantitative classification of star cluster candidate samples (70–80 per cent, 40–50 per cent, 40–50 per cent, and 60–70 per cent). The methods introduced herein lay the foundations to automate classification for star clusters at scale, and exhibit the need to prepare a standardized data set of human-labelled star cluster classifications, agreed upon by a full range of experts in the field, to further improve the performance of the networks introduced in this study. 

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4. Separation of particles of different surface energies through control of humidity

   Sansao, B.; Kellar, J.; Cross, W.; Romkes, A. (October 2020, Minerals engineering)

   null (Ed.)

   Many of the methods to classify and concentrate minerals and the subsequent extraction of metals takes place in water-based environments (aqueous solutions). Sustainable processing through the reduction of water consumption will become a key factor to make mining operations viable in the long term. In humid environments, capillary condensation of water can occur between the particle and substrate. The objective herein is to identify separation windows in which control of relative air humidity (RH) yields different substrate adhesion for hydrophilic and hydrophobic particles of different values of interfacial energy. Plasma cleaned glass beads, and trichloro(octadecyl)silane (TCOD) treated beads were poured on a plasma cleaned glass disk and an impact caused the detachment of particles. Impact tests performed under a range of RH showed that separation of plasma cleaned and TCOD treated particles can be achieved in 80% of the tests at humidity levels between 45% and 55%. The recovery of plasma cleaned particles was five times greater than TCOD treated particles at humidity levels between 50% and 55%. 

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5. Graph convolutional neural networks with global attention for improved materials property prediction

   https://doi.org/10.1039/D0CP01474E  

   Louis, Steph-Yves; Zhao, Yong; Nasiri, Alireza; Wang, Xiran; Song, Yuqi; Liu, Fei; Hu, Jianjun (August 2020, Physical Chemistry Chemical Physics)

   null (Ed.)

   The development of an efficient and powerful machine learning (ML) model for materials property prediction (MPP) remains an important challenge in materials science. While various techniques have been proposed to extract physicochemical features in MPP, graph neural networks (GNN) have also shown very strong capability in capturing effective features for high-performance MPP. Nevertheless, current GNN models do not effectively differentiate the contributions from different atoms. In this paper we develop a novel graph neural network model called GATGNN for predicting properties of inorganic materials. GATGNN is characterized by its composition of augmented graph-attention layers (AGAT) and a global attention layer. The application of AGAT layers and global attention layers respectively learn the local relationship among neighboring atoms and overall contribution of the atoms to the material's property; together making our framework achieve considerably better prediction performance on various tested properties. Through extensive experiments, we show that our method is able to outperform existing state-of-the-art GNN models while it can also provide a measurable insight into the correlation between the atoms and their material property. Our code can found on – https://github.com/superlouis/GATGNN. 

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