Automatically bridging the semantic gap in machine introspection

Disclosed are various embodiments that facilitate automatically bridging the semantic gap in machine introspection. It may be determined that a program executed by a first virtual machine is requested to introspect a second virtual machine. A system call execution context of the program may be determined in response to determining that the program is requested to introspect the second virtual machine. Redirectable data in a memory of the second virtual machine may be identified based at least in part on the system call execution context of the program. The program may be configured to access the redirectable data. In various embodiments, the program may be able to modify the redirectable data, thereby facilitating configuration, reconfiguration, and recovery operations to be performed on the second virtual machine from within the first virtual machine.Board of Regents, University of Texas Syste

Inhibition of platelet-tumour cell interaction with ibrutinib reduces proliferation, migration and invasion of lung cancer cells

Purpose: To investigate the pharmacological role of the Bruton tyrosine kinase (BTK) inhibitor, ibrutinib, in tumour cell-platelet crosstalk in lung cancer.Methods: Human lung cancer cells A549 were treated with ibrutinib or DMSO. mRNA expression was assessed using reverse transcription-quantitative polymerase chain reaction (RT-PCR), and while western blotting was used to determine protein expression levels. Small interfering RNA (siRNA) transfection was performed to suppress the expression of galectin-3. Colony formation and Transwell® assays were used to determine cell viability, cell invasiveness and migratory ability.Results: Co-culture of A549 cells and platelets induced activation of BTK/PLCγ2 signalling and subsequent release of PDGF, VEGF and TGFβ1 from de-granulated platelets. However, knocking down of galectin-3 inhibited A549-induced platelet activation. Conversely, platelet activation upregulated the expression of galectin-3 via the release of PDGF. Moreover, ibrutinib significantly (p < 0.05) inhibited cell viability, migration, and invasion.Conclusion: These results suggest that ibrutinib may be a novel therapeutic treatment for lung cancer.Keywords: Bruton tyrosine kinase, Ibrutinib, Lung cancer, Platele

Observation of Ultrahigh Mobility Surface States in a Topological Crystalline Insulator by Infrared Spectroscopy

Topological crystalline insulators (TCIs) possess metallic surface states protected by crystalline symmetry, which are a versatile platform for exploring topological phenomena and potential applications. However, progress in this field has been hindered by the challenge to probe optical and transport properties of the surface states owing to the presence of bulk carriers. Here we report infrared (IR) reflectance measurements of a TCI, (001) oriented $Pb_{1-x}Sn_{x}Se$ in zero and high magnetic fields. We demonstrate that the far-IR conductivity is unexpectedly dominated by the surface states as a result of their unique band structure and the consequent small IR penetration depth. Moreover, our experiments yield a surface mobility of 40000 $cm^{2}/(Vs)$, which is one of the highest reported values in topological materials, suggesting the viability of surface-dominated conduction in thin TCI crystals. These findings pave the way for exploring many exotic transport and optical phenomena and applications predicted for TCIs

Study on the fracture regularity of extra thick and hard roof in “short-face” mining and its blasting weakening technology

Based on the 211113 “short face” of Xinji No. 2 Mine of China Coal Group, this study investigated the weakening regularity of blasting vibration and rock bursting of roof through FLAC3D numerical simulation on the basis of the hard roof fracture regularity through theoretical analysis. The results show that the vibration frequency of the hard roof increases linearly with the increase of the distance from the source, and the vibration amplitude decreases exponentially with the increase of the distance from the source within 20 m from the blasting relief hole. Based on the above results, from the three indexes of controlling the amplitude, frequency and duration of pressure relief blasting vibration, it is proposed that the roof on the 211113 “short face” needs to be controlled by the advanced overlying strata weakening technology, and the parameters of advanced deep hole pre-splitting blasting are optimized. After the roof was weakened by the advanced deep-hole pre-splitting blasting, it can basically fall with mining. The blasting method can effectively avoid the phenomenon of large-area “hanging arch” and effectively prevent the occurrence of rock burst

Iterative Soft Shrinkage Learning for Efficient Image Super-Resolution

Image super-resolution (SR) has witnessed extensive neural network designs from CNN to transformer architectures. However, prevailing SR models suffer from prohibitive memory footprint and intensive computations, which limits further deployment on edge devices. This work investigates the potential of network pruning for super-resolution to take advantage of off-the-shelf network designs and reduce the underlying computational overhead. Two main challenges remain in applying pruning methods for SR. First, the widely-used filter pruning technique reflects limited granularity and restricted adaptability to diverse network structures. Second, existing pruning methods generally operate upon a pre-trained network for the sparse structure determination, hard to get rid of dense model training in the traditional SR paradigm. To address these challenges, we adopt unstructured pruning with sparse models directly trained from scratch. Specifically, we propose a novel Iterative Soft Shrinkage-Percentage (ISS-P) method by optimizing the sparse structure of a randomly initialized network at each iteration and tweaking unimportant weights with a small amount proportional to the magnitude scale on-the-fly. We observe that the proposed ISS-P can dynamically learn sparse structures adapting to the optimization process and preserve the sparse model's trainability by yielding a more regularized gradient throughput. Experiments on benchmark datasets demonstrate the effectiveness of the proposed ISS-P over diverse network architectures. Code is available at https://github.com/Jiamian-Wang/Iterative-Soft-Shrinkage-SRComment: Accepted by ICCV 2023, code released at https://github.com/Jiamian-Wang/Iterative-Soft-Shrinkage-S

Correlative Channel-Aware Fusion for Multi-View Time Series Classification

Multi-view time series classification (MVTSC) aims to improve the performance by fusing the distinctive temporal information from multiple views. Existing methods mainly focus on fusing multi-view information at an early stage, e.g., by learning a common feature subspace among multiple views. However, these early fusion methods may not fully exploit the unique temporal patterns of each view in complicated time series. Moreover, the label correlations of multiple views, which are critical to boost-ing, are usually under-explored for the MVTSC problem. To address the aforementioned issues, we propose a Correlative Channel-Aware Fusion (C2AF) network. First, C2AF extracts comprehensive and robust temporal patterns by a two-stream structured encoder for each view, and captures the intra-view and inter-view label correlations with a graph-based correlation matrix. Second, a channel-aware learnable fusion mechanism is implemented through convolutional neural networks to further explore the global correlative patterns. These two steps are trained end-to-end in the proposed C2AF network. Extensive experimental results on three real-world datasets demonstrate the superiority of our approach over the state-of-the-art methods. A detailed ablation study is also provided to show the effectiveness of each model component

Cross-view Graph Contrastive Representation Learning on Partially Aligned Multi-view Data

Multi-view representation learning has developed rapidly over the past decades and has been applied in many fields. However, most previous works assumed that each view is complete and aligned. This leads to an inevitable deterioration in their performance when encountering practical problems such as missing or unaligned views. To address the challenge of representation learning on partially aligned multi-view data, we propose a new cross-view graph contrastive learning framework, which integrates multi-view information to align data and learn latent representations. Compared with current approaches, the proposed method has the following merits: (1) our model is an end-to-end framework that simultaneously performs view-specific representation learning via view-specific autoencoders and cluster-level data aligning by combining multi-view information with the cross-view graph contrastive learning; (2) it is easy to apply our model to explore information from three or more modalities/sources as the cross-view graph contrastive learning is devised. Extensive experiments conducted on several real datasets demonstrate the effectiveness of the proposed method on the clustering and classification tasks