An incremental points-to analysis with CFL-reachability

Abstract. Developing scalable and precise points-to analyses is increasingly important for analysing and optimising object-oriented programs where pointers are used pervasively. An incremental analysis for a program updates the existing analysis information after program changes to avoid reanalysing it from scratch. This can be efficiently deployed in software development environments where code changes are often small and frequent. This paper presents an incremental approach for demand-driven context-sensitive points-to analyses based on Context-Free Language (CFL) reachability. By tracing the CFL-reachable paths traversed in computing points-to sets, we can precisely identify and recompute on demand only the points-to sets affected by the program changes made. Combined with a flexible policy for controlling the granularity of traces, our analysis achieves significant speedups with little space overhead over reanalysis from scratch when evaluated with a null dereferencing client using 14 Java benchmarks.

Experimental study on characteristics of self-excited oscillation pulsed water jet

To promote the application of self-excited oscillation pulsed water jet in various fields, various characteristics of self-excited oscillation pulsed water jet were studied experimentally. A test system of self-excited oscillation pulsed water jet characteristics was designed, and it is composed of pulsed jet producer devices, particle image velocity (PIV) measuring system, pressure pulse test device and the confining pressure cavity suitable for a PIV test. The characteristics of flow field, pressure oscillation, pulsed cavitation and acoustic shock of pulsed jet were researched. The results showed that the axis velocity vector at nozzle outlet changes periodically, and gradually becomes smaller away from the nozzle. The peak pressure of self-excited oscillation pulsed water jet is 2.5 times higher than the common continuous jet. The wave crest and the wave trough of pulsed jet are not completely symmetric. As the pump pressure increases, the length of bubble cloud increases, and it first increases and then decreases with confining pressure increasing. The vibration acceleration of sonic boom increases and then decreases as pump pressure increases, and decreases steadily with confining pressure increasing

How to Face COVID-19 in Ophthalmology Practice

Background: The novel coronavirus pneumonia has attracted considerable attention from the international community. With the spread of outbreaks around the world, the WHO characterized COVID-19 as a pandemic. Methods: Relevant studies in PubMed were searched from January 1, 2020 to April 12, 2020, using the following search strategy: (“novel coronavirus pneumonia†OR “severe acute respiratory syndrome coronavirus 2†OR “coronavirus disease 2019†OR “COVID-19†OR “novel coronavirus pneumoniaâ€) AND (“ophthalmology†OR “ophthalmologist†OR “eye†OR “conjunctiva†OR “conjunctivitis†OR “corneal†OR “keratitisâ€). Results: SARS-CoV-2 can spread through aerosol and is detected in tears of patients with COVID-19 infection. Notably, some infected patients had conjunctivitis, and conjunctivitis was the first symptom in some patients later diagnosed to have COVID-19 infection. This would increase the risk for ophthalmologists through inpatient consultations or regular clinical practice. When dealing with seemingly regular ophthalmic patients, the vigilance of ophthalmologists and associated staff tends to be reduced. Conclusion: Ophthalmologists must continuously update their knowledge regarding COVID-19 and take effective measures to prevent COVID-19 transmission

Efficient Temporal Butterfly Counting and Enumeration on Temporal Bipartite Graphs

Bipartite graphs model relationships between two different sets of entities, like actor-movie, user-item, and author-paper. The butterfly, a 4-vertices 4-edges $2\times 2$ bi-clique, is the simplest cohesive motif in a bipartite graph and is the fundamental component of higher-order substructures. Counting and enumerating the butterflies offer significant benefits across various applications, including fraud detection, graph embedding, and community search. While the corresponding motif, the triangle, in the unipartite graphs has been widely studied in both static and temporal settings, the extension of butterfly to temporal bipartite graphs remains unexplored. In this paper, we investigate the temporal butterfly counting and enumeration problem: count and enumerate the butterflies whose edges establish following a certain order within a given duration. Towards efficient computation, we devise a non-trivial baseline rooted in the state-of-the-art butterfly counting algorithm on static graphs, further, explore the intrinsic property of the temporal butterfly, and develop a new optimization framework with a compact data structure and effective priority strategy. The time complexity is proved to be significantly reduced without compromising on space efficiency. In addition, we generalize our algorithms to practical streaming settings and multi-core computing architectures. Our extensive experiments on 11 large-scale real-world datasets demonstrate the efficiency and scalability of our solutions

DIVERSIFY: A General Framework for Time Series Out-of-distribution Detection and Generalization

Time series remains one of the most challenging modalities in machine learning research. The out-of-distribution (OOD) detection and generalization on time series tend to suffer due to its non-stationary property, i.e., the distribution changes over time. The dynamic distributions inside time series pose great challenges to existing algorithms to identify invariant distributions since they mainly focus on the scenario where the domain information is given as prior knowledge. In this paper, we attempt to exploit subdomains within a whole dataset to counteract issues induced by non-stationary for generalized representation learning. We propose DIVERSIFY, a general framework, for OOD detection and generalization on dynamic distributions of time series. DIVERSIFY takes an iterative process: it first obtains the "worst-case" latent distribution scenario via adversarial training, then reduces the gap between these latent distributions. We implement DIVERSIFY via combining existing OOD detection methods according to either extracted features or outputs of models for detection while we also directly utilize outputs for classification. In addition, theoretical insights illustrate that DIVERSIFY is theoretically supported. Extensive experiments are conducted on seven datasets with different OOD settings across gesture recognition, speech commands recognition, wearable stress and affect detection, and sensor-based human activity recognition. Qualitative and quantitative results demonstrate that DIVERSIFY learns more generalized features and significantly outperforms other baselines.Comment: Journal version of arXiv:2209.07027; 17 page

Crack-Free Silicon Monoxide as Anodes for Lithium-Ion Batteries

The volume expansion of Si and SiO particles was investigated using a single-particle battery assembled with a focused ion beam and scanning electron microscopy (FIB-SEM) system. Single Si and SiO particles were galvanostatically charged and discharged as in real batteries. Microstructural changes of the particles were monitored in situ using FIB-SEM from two different angles. The results revealed that the volume expansion of micrometer size particle SiO was not only much smaller than that of Si, but it also kept its original shape with no sign of cracks. This isotropic mechanical property of a SiO particle can be attributed to its microstructure: nanosized Si domains mixed with SiO2 domains. The nanosized Si domains can mitigate the anisotropic swelling caused by the orientation-dependent lithium-ion insertion; the surrounding SiO2 domains can act as a buffer to further constrain the localized anisotropic swelling