SEGA: Structural Entropy Guided Anchor View for Graph Contrastive Learning

In contrastive learning, the choice of ``view'' controls the information that the representation captures and influences the performance of the model. However, leading graph contrastive learning methods generally produce views via random corruption or learning, which could lead to the loss of essential information and alteration of semantic information. An anchor view that maintains the essential information of input graphs for contrastive learning has been hardly investigated. In this paper, based on the theory of graph information bottleneck, we deduce the definition of this anchor view; put differently, \textit{the anchor view with essential information of input graph is supposed to have the minimal structural uncertainty}. Furthermore, guided by structural entropy, we implement the anchor view, termed \textbf{SEGA}, for graph contrastive learning. We extensively validate the proposed anchor view on various benchmarks regarding graph classification under unsupervised, semi-supervised, and transfer learning and achieve significant performance boosts compared to the state-of-the-art methods.Comment: ICML'2

HiTIN: Hierarchy-aware Tree Isomorphism Network for Hierarchical Text Classification

Hierarchical text classification (HTC) is a challenging subtask of multi-label classification as the labels form a complex hierarchical structure. Existing dual-encoder methods in HTC achieve weak performance gains with huge memory overheads and their structure encoders heavily rely on domain knowledge. Under such observation, we tend to investigate the feasibility of a memory-friendly model with strong generalization capability that could boost the performance of HTC without prior statistics or label semantics. In this paper, we propose Hierarchy-aware Tree Isomorphism Network (HiTIN) to enhance the text representations with only syntactic information of the label hierarchy. Specifically, we convert the label hierarchy into an unweighted tree structure, termed coding tree, with the guidance of structural entropy. Then we design a structure encoder to incorporate hierarchy-aware information in the coding tree into text representations. Besides the text encoder, HiTIN only contains a few multi-layer perceptions and linear transformations, which greatly saves memory. We conduct experiments on three commonly used datasets and the results demonstrate that HiTIN could achieve better test performance and less memory consumption than state-of-the-art (SOTA) methods.Comment: Accepted by ACL'2

A low frequency mechanical transmitter based on magnetoelectric heterostructures operated at their resonance frequency

Magneto-elasto-electric (ME) coupling heterostructures, consisting of piezoelectric layers bonded to magnetostrictive ones, provide for a new class of electromagnetic emitter materials on which a portable (area ~ 16 cm 2 ) very low frequency (VLF) transmitter technology could be developed. The proposed ME transmitter functions as follows: (a) a piezoelectric layer is first driven by alternating current AC electric voltage at its electromechanical resonance (EMR) frequency, (b) subsequently, this EMR excites the magnetostrictive layers, giving rise to magnetization change, (c) in turn, the magnetization oscillations result in oscillating magnetic fields. By Maxwell’s equations, a corresponding electric field, is also generated, leading to electromagnetic field propagation. Our hybrid piezoelectric-magnetostrictive transformer can take an input electric voltage that may include modulation-signal over a carrier frequency and transmit via oscillating magnetic field or flux change. The prototype measurements reveal a magnetic dipole like near field, demonstrating its transmission capabilities. Furthermore, the developed prototype showed a 10 4 times higher efficiency over a small-circular loop of the same area, exhibiting its superiority over the class of traditional small antennas

Brain Functional Networks in Type 2 Diabetes Mellitus Patients: A Resting-State Functional MRI Study

BackgroundPrevious diabetes mellitus studies of cognitive impairments in the early stages have focused on changes in brain structure and function, and more recently the focus has shifted to the relationships between encephalic regions and diversification of network topology. However, studies examining network topology in diabetic brain function are still limited.MethodsThe study included 102 subjects; 55 type 2 diabetes mellitus (T2DM) patients plus 47 healthy controls. All subjects were examined by resting-state functional magnetic resonance imaging (rs-fMRI) scan. According to Automated Anatomical Labeling, the brain was divided into 90 anatomical regions, and every region corresponds to a brain network analysis node. The whole brain functional network was constructed by thresholding the correlation matrices of the 90 brain regions, and the topological properties of the network were computed based on graph theory. Then, the topological properties of the network were compared between different groups by using a non-parametric test. Finally, the associations between differences in topological properties and the clinical indicators were analyzed.ResultsThe brain functional networks of both T2DM patients and healthy controls were found to possess small-world characteristics, i.e., normalized clustering coefficient (γ) > 1, and normalized characteristic path length (λ) close to 1. No significant differences were found in the small-world characteristics (σ). Second, the T2DM patient group displayed significant differences in node properties in certain brain regions. Correlative analytic results showed that the node degree of the right inferior temporal gyrus (ITG) and the node efficiencies of the right ITG and superior temporal gyrus of T2DM patients were positively correlated with body mass index.ConclusionThe brain network of T2DM patients has the same small-world characteristics as normal people, but the normalized clustering coefficient is higher and the normalized characteristic path length is lower than that of the normal control group, indicating that the brain function network of the T2DM patients has changed. The changes of node properties were mostly concentrated in frontal lobe, temporal lobe and posterior cingulate gyrus. The abnormal changes in these indices in T2DM patients might be explained as a compensatory behavior to reduce cognitive impairments, which is achieved by mobilizing additional neural resources, such as the excessive activation of the network and the efficient networking of multiple brain regions

Interface magnetic and electrical properties of CoFeB /InAs heterostructures

Amorphous magnetic CoFeB ultrathin films have been synthesized on the narrow band gap semiconductor InAs(100) surface, and the nature of the interface magnetic anisotropy and electrical contact has been studied. Angle-dependent hysteresis loops reveal that the films have an in-plane uniaxial magnetic anisotropy (UMA) with the easy axis along the InAs [0-11] crystal direction. The UMA was found to be dependent on the annealing temperatures of the substrates, which indicates the significant role of the Fe, Co-As bonding at the interface related to the surface condition of the InAs(100). I-V measurements show an ohmic contact interface between the CoFeB films and the InAs substrates, which is not affected by the surface condition of the InAs (100)

OWL: A Large Language Model for IT Operations

With the rapid development of IT operations, it has become increasingly crucial to efficiently manage and analyze large volumes of data for practical applications. The techniques of Natural Language Processing (NLP) have shown remarkable capabilities for various tasks, including named entity recognition, machine translation and dialogue systems. Recently, Large Language Models (LLMs) have achieved significant improvements across various NLP downstream tasks. However, there is a lack of specialized LLMs for IT operations. In this paper, we introduce the OWL, a large language model trained on our collected OWL-Instruct dataset with a wide range of IT-related information, where the mixture-of-adapter strategy is proposed to improve the parameter-efficient tuning across different domains or tasks. Furthermore, we evaluate the performance of our OWL on the OWL-Bench established by us and open IT-related benchmarks. OWL demonstrates superior performance results on IT tasks, which outperforms existing models by significant margins. Moreover, we hope that the findings of our work will provide more insights to revolutionize the techniques of IT operations with specialized LLMs.Comment: 31 page

Targeting Radioresistant Breast Cancer Cells by Single Agent CHK1 Inhibitor via Enhancing Replication Stress

Radiotherapy (RT) remains a standard therapeutic modality for breast cancer patients. However, intrinsic or acquired resistance limits the efficacy of RT. Here, we demonstrate that CHK1 inhibitor AZD7762 alone significantly inhibited the growth of radioresistant breast cancer cells (RBCC). Given the critical role of ATR/CHK1 signaling in suppressing oncogene-induced replication stress (RS), we hypothesize that CHK1 inhibition leads to the specific killing for RBCC due to its abrogation in the suppression of RS induced by oncogenes. In agreement, the expression of oncogenes c-Myc/CDC25A/c-Src/H-ras/E2F1 and DNA damage response (DDR) proteins ATR/CHK1/BRCA1/CtIP were elevated in RBCC. AZD7762 exposure led to significantly higher levels of RS in RBCC, compared to the parental cells. The mechanisms by which CHK1 inhibition led to specific increase of RS in RBCC were related to the interruptions in the replication fork dynamics and the homologous recombination (HR). In summary, RBCC activate oncogenic pathways and thus depend upon mechanisms controlled by CHK1 signaling to maintain RS under control for survival. Our study provided the first example where upregulating RS by CHK1 inhibitor contributes to the specific killing of RBCC, and highlight the importance of the CHK1 as a potential target for treatment of radioresistant cancer cells

Targeting Radioresistant Breast Cancer Cells by Single Agent CHK1 Inhibitor via Enhancing Replication Stress

Radiotherapy (RT) remains a standard therapeutic modality for breast cancer patients. However, intrinsic or acquired resistance limits the efficacy of RT. Here, we demonstrate that CHK1 inhibitor AZD7762 alone significantly inhibited the growth of radioresistant breast cancer cells (RBCC). Given the critical role of ATR/CHK1 signaling in suppressing oncogene-induced replication stress (RS), we hypothesize that CHK1 inhibition leads to the specific killing for RBCC due to its abrogation in the suppression of RS induced by oncogenes. In agreement, the expression of oncogenes c-Myc/CDC25A/c-Src/H-ras/E2F1 and DNA damage response (DDR) proteins ATR/CHK1/BRCA1/CtIP were elevated in RBCC. AZD7762 exposure led to significantly higher levels of RS in RBCC, compared to the parental cells. The mechanisms by which CHK1 inhibition led to specific increase of RS in RBCC were related to the interruptions in the replication fork dynamics and the homologous recombination (HR). In summary, RBCC activate oncogenic pathways and thus depend upon mechanisms controlled by CHK1 signaling to maintain RS under control for survival. Our study provided the first example where upregulating RS by CHK1 inhibitor contributes to the specific killing of RBCC, and highlight the importance of the CHK1 as a potential target for treatment of radioresistant cancer cells