Extraordinary focusing of sound above a soda can array without time reversal

Recently, Lemoult et al. [Phys. Rev. Lett. 107, 064301 (2011)] used time reversal to focus sound above an array of soda cans into a spot much smaller than the acoustic wavelength in air. In this study, we show that equally sharp focusing can be achieved without time reversal, by arranging transducers around a nearly circular array of soda cans. The size of the focal spot at the center of the array is made progressively smaller as the frequency approaches the Helmholtz resonance frequency of a can from below, and, near the resonance, becomes smaller than the size of a single can. We show that the locally resonant metamaterial formed by soda cans supports a guided wave at frequencies below the Helmholtz resonance frequency. The small focal spot results from a small wavelength of this guided wave near the resonance in combination with a near field effect making the acoustic field concentrate at the opening of a can. The focusing is achieved with propagating rather than evanescent waves. No sub-diffraction-limited focusing is observed if the diffraction limit is defined with respect to the wavelength of the guided mode in the metamaterial medium rather than the wavelength of the bulk wave in air

Characterization of Soybean Protein Adhesives Modified by Xanthan Gum

The aim of this study was to provide a basis for the preparation of medical adhesives from soybean protein sources. Soybean protein (SP) adhesives mixed with different concentrations of xanthan gum (XG) were prepared. Their adhesive features were evaluated by physicochemical parameters and an in vitro bone adhesion assay. The results showed that the maximal adhesion strength was achieved in 5% SP adhesive with 0.5% XG addition, which was 2.6-fold higher than the SP alone. The addition of XG significantly increased the hydrogen bond and viscosity, as well as increased the β-sheet content but decreased the α-helix content in the second structure of protein. X-ray diffraction data showed significant interactions between SP molecules and XG. Scanning electron microscopy observations showed that the surface of SP adhesive modified by XG was more viscous and compact, which were favorable for the adhesion between the adhesive and bone. In summary, XG modification caused an increase in the hydrogen bonding and zero-shear viscosity of SP adhesives, leading to a significant increase in the bond strength of SP adhesives onto porcine bones

High-Performance Multi-Mode Ptychography Reconstruction on Distributed GPUs

Ptychography is an emerging imaging technique that is able to provide wavelength-limited spatial resolution from specimen with extended lateral dimensions. As a scanning microscopy method, a typical two-dimensional image requires a number of data frames. As a diffraction-based imaging technique, the real-space image has to be recovered through iterative reconstruction algorithms. Due to these two inherent aspects, a ptychographic reconstruction is generally a computation-intensive and time-consuming process, which limits the throughput of this method. We report an accelerated version of the multi-mode difference map algorithm for ptychography reconstruction using multiple distributed GPUs. This approach leverages available scientific computing packages in Python, including mpi4py and PyCUDA, with the core computation functions implemented in CUDA C. We find that interestingly even with MPI collective communications, the weak scaling in the number of GPU nodes can still remain nearly constant. Most importantly, for realistic diffraction measurements, we observe a speedup ranging from a factor of $10$ to $10^3$ depending on the data size, which reduces the reconstruction time remarkably from hours to typically about 1 minute and is thus critical for real-time data processing and visualization.Comment: work presented in NYSDS 201

Lifelong Sequential Modeling with Personalized Memorization for User Response Prediction

User response prediction, which models the user preference w.r.t. the presented items, plays a key role in online services. With two-decade rapid development, nowadays the cumulated user behavior sequences on mature Internet service platforms have become extremely long since the user's first registration. Each user not only has intrinsic tastes, but also keeps changing her personal interests during lifetime. Hence, it is challenging to handle such lifelong sequential modeling for each individual user. Existing methodologies for sequential modeling are only capable of dealing with relatively recent user behaviors, which leaves huge space for modeling long-term especially lifelong sequential patterns to facilitate user modeling. Moreover, one user's behavior may be accounted for various previous behaviors within her whole online activity history, i.e., long-term dependency with multi-scale sequential patterns. In order to tackle these challenges, in this paper, we propose a Hierarchical Periodic Memory Network for lifelong sequential modeling with personalized memorization of sequential patterns for each user. The model also adopts a hierarchical and periodical updating mechanism to capture multi-scale sequential patterns of user interests while supporting the evolving user behavior logs. The experimental results over three large-scale real-world datasets have demonstrated the advantages of our proposed model with significant improvement in user response prediction performance against the state-of-the-arts.Comment: SIGIR 2019. Reproducible codes and datasets: https://github.com/alimamarankgroup/HPM

An Empirical Study of Malicious Code In PyPI Ecosystem

PyPI provides a convenient and accessible package management platform to developers, enabling them to quickly implement specific functions and improve work efficiency. However, the rapid development of the PyPI ecosystem has led to a severe problem of malicious package propagation. Malicious developers disguise malicious packages as normal, posing a significant security risk to end-users. To this end, we conducted an empirical study to understand the characteristics and current state of the malicious code lifecycle in the PyPI ecosystem. We first built an automated data collection framework and collated a multi-source malicious code dataset containing 4,669 malicious package files. We preliminarily classified these malicious code into five categories based on malicious behaviour characteristics. Our research found that over 50% of malicious code exhibits multiple malicious behaviours, with information stealing and command execution being particularly prevalent. In addition, we observed several novel attack vectors and anti-detection techniques. Our analysis revealed that 74.81% of all malicious packages successfully entered end-user projects through source code installation, thereby increasing security risks. A real-world investigation showed that many reported malicious packages persist in PyPI mirror servers globally, with over 72% remaining for an extended period after being discovered. Finally, we sketched a portrait of the malicious code lifecycle in the PyPI ecosystem, effectively reflecting the characteristics of malicious code at different stages. We also present some suggested mitigations to improve the security of the Python open-source ecosystem.Comment: Accepted by the 38th IEEE/ACM International Conference on Automated Software Engineering (ASE2023