A smooth basis for atomistic machine learning

Machine learning frameworks based on correlations of interatomic positions begin with a discretized description of the density of other atoms in the neighbourhood of each atom in the system. Symmetry considerations support the use of spherical harmonics to expand the angular dependence of this density, but there is as yet no clear rationale to choose one radial basis over another. Here we investigate the basis that results from the solution of the Laplacian eigenvalue problem within a sphere around the atom of interest. We show that this generates the smoothest possible basis of a given size within the sphere, and that a tensor product of Laplacian eigenstates also provides the smoothest possible basis for expanding any higher-order correlation of the atomic density within the appropriate hypersphere. We consider several unsupervised metrics of the quality of a basis for a given dataset, and show that the Laplacian eigenstate basis has a performance that is much better than some widely used basis sets and is competitive with data-driven bases that numerically optimize each metric. In supervised machine learning tests, we find that the optimal function smoothness of the Laplacian eigenstates leads to comparable or better performance than can be obtained from a data-driven basis of a similar size that has been optimized to describe the atom-density correlation for the specific dataset. We conclude that the smoothness of the basis functions is a key and hitherto largely overlooked aspect of successful atomic density representations

HideMyApp : Hiding the Presence of Sensitive Apps on Android

Millions of users rely on mobile health (mHealth) apps to manage their wellness and medical conditions. Although the popularity of such apps continues to grow, several privacy and security challenges can hinder their potential. In particular, the simple fact that an mHealth app is installed on a user’s phone can reveal sensitive information about the user’s health. Due to Android’s open design, any app, even without per- missions, can easily check for the presence of a specific app or collect the entire list of installed apps on the phone. Our analysis shows that Android apps expose a significant amount of metadata, which facilitates fingerprinting them. Many third parties are interested in such information: Our survey of 2917 popular apps in the Google Play Store shows that around 57% of these apps explicitly query for the list of installed apps. Therefore, we designed and implemented HideMyApp (HMA), an effective and practical solution for hiding the presence of sensitive apps from other apps. HMA does not require any changes to the Android operating system or to apps yet still supports their key functionalities. By using a diverse dataset of both free and paid mHealth apps, our experimental eval- uation shows that HMA supports the main functionalities in most apps and introduces acceptable overheads at runtime (i.e., several milliseconds); these findings were validated by our user-study (N = 30). In short, we show that the practice of collecting information about installed apps is widespread and that our solution, HMA, provides a robust protection against such a threat

Interest modeling in games: the case of dead reckoning

In games, the goals and interests of players are key factors in their behavior. However, techniques used by networked games to cope with infrequent updates and message loss, such as dead reckoning, estimate a player's movements based mainly on previous observations. The estimations are typically made by using dynamics of motion, taking only inertia and some external factors (e.g., gravity, wind) into account while completely ignoring the player's goals (e.g., chasing other players or collecting objects). This paper proposes AntReckoning: a dead reckoning algorithm, inspired from ant colonies, which models the players' interests to predict their movements. AntReckoning incorporates a player's interest in specific locations, objects, and avatars in the equations of motion in the form of attraction forces. In practice, these points of interest generate pheromones, which spread and fade in the game world, and are a source of attraction. To motivate and validate our approach we collected traces from Quake III. We conducted specific experiments that demonstrate the effect of game-related goals, map features, objects, and other players on the mobility of avatars. Our simulations using traces from Quake III and World of Warcraft show that AntReckoning improves the accuracy by up to 44 % over traditional dead reckoning techniques and can decrease the upload bandwidth by up to 32 %

Completeness of atomic structure representations

In this paper, we address the challenge of obtaining a comprehensive and symmetric representation of point particle groups, such as atoms in a molecule, which is crucial in physics and theoretical chemistry. The problem has become even more important with the widespread adoption of machine-learning techniques in science, as it underpins the capacity of models to accurately reproduce physical relationships while being consistent with fundamental symmetries and conservation laws. However, some of the descriptors that are commonly used to represent point clouds— notably those based on discretized correlations of the neighbor density that power most of the existing ML models of matter at the atomic scale—are unable to distinguish between special arrangements of particles in three dimensions. This makes it impossible to machine learn their properties. Atom-density correlations are provably complete in the limit in which they simultaneously describe the mutual relationship between all atoms, which is impractical. We present a novel approach to construct descriptors of finite correlations based on the relative arrangement of particle triplets, which can be employed to create symmetry-adapted models with universal approximation capabilities, and have the resolution of the neighbor discretization as the sole convergence parameter. Our strategy is demonstrated on a class of atomic arrangements that are specifically built to defy a broad class of conventional symmetric descriptors, showing its potential for addressing their limitations

2D foam film coating of antimicrobial lysozyme amyloid fibrils onto cellulose nanopapers

Amyloid fibrils made from inexpensive hen egg white lysozyme (HEWL) are bio-based, bio-degradable and bio-compatible colloids with broad-spectrum antimicrobial activity, making them an attractive alternative to existing small-molecule antibiotics. Their surface activity leads to the formation of 2D foam films within a loop, similar to soap films when blowing bubbles. The stability of the foam was optimized by screening concentration and pH, which also revealed that the HEWL amyloid foams were actually stabilized by unconverted peptides unable to undergo amyloid self-assembly rather than the fibrils themselves. The 2D foam film was successfully deposited on different substrates to produce a homogenous coating layer with a thickness of roughly 30 nm. This was thick enough to shield the negative charge of dry cellulose nanopaper substrates, leading to a positively charged HEWL amyloid coating. The coating exhibited a broad-spectrum antimicrobial effect based on the interactions with the negatively charged cell walls and membranes of clinically relevant pathogens (Staphylococcus aureus, Escherichia coli and Candida albicans). The coating method presented here offers an alternative to existing techniques, such as dip and spray coating, in particular when optimized for continuous production. Based on the facile preparation and broad spectrum antimicrobial performance, we anticipate that these biohybrid materials could potentially be used in the biomedical sector as wound dressings.ISSN:2516-023