Multiresolution Feature Guidance Based Transformer for Anomaly Detection

Anomaly detection is represented as an unsupervised learning to identify deviated images from normal images. In general, there are two main challenges of anomaly detection tasks, i.e., the class imbalance and the unexpectedness of anomalies. In this paper, we propose a multiresolution feature guidance method based on Transformer named GTrans for unsupervised anomaly detection and localization. In GTrans, an Anomaly Guided Network (AGN) pre-trained on ImageNet is developed to provide surrogate labels for features and tokens. Under the tacit knowledge guidance of the AGN, the anomaly detection network named Trans utilizes Transformer to effectively establish a relationship between features with multiresolution, enhancing the ability of the Trans in fitting the normal data manifold. Due to the strong generalization ability of AGN, GTrans locates anomalies by comparing the differences in spatial distance and direction of multi-scale features extracted from the AGN and the Trans. Our experiments demonstrate that the proposed GTrans achieves state-of-the-art performance in both detection and localization on the MVTec AD dataset. GTrans achieves image-level and pixel-level anomaly detection AUROC scores of 99.0% and 97.9% on the MVTec AD dataset, respectively

Genome-wide cloning and sequence analysis of leucine-rich repeat receptor-like protein kinase genes in Arabidopsis thaliana

Abstract Background Transmembrane receptor kinases play critical roles in both animal and plant signaling pathways regulating growth, development, differentiation, cell death, and pathogenic defense responses. In Arabidopsis thaliana, there are at least 223 Leucine-rich repeat receptor-like kinases (LRR-RLKs), representing one of the largest protein families. Although functional roles for a handful of LRR-RLKs have been revealed, the functions of the majority of members in this protein family have not been elucidated. Results As a resource for the in-depth analysis of this important protein family, the complementary DNA sequences (cDNAs) of 194 LRR-RLKs were cloned into the GatewayR donor vector pDONR/ZeoR and analyzed by DNA sequencing. Among them, 157 clones showed sequences identical to the predictions in the Arabidopsis sequence resource, TAIR8. The other 37 cDNAs showed gene structures distinct from the predictions of TAIR8, which was mainly caused by alternative splicing of pre-mRNA. Most of the genes have been further cloned into GatewayR destination vectors with GFP or FLAG epitope tags and have been transformed into Arabidopsis for in planta functional analysis. All clones from this study have been submitted to the Arabidopsis Biological Resource Center (ABRC) at Ohio State University for full accessibility by the Arabidopsis research community. Conclusions Most of the Arabidopsis LRR-RLK genes have been isolated and the sequence analysis showed a number of alternatively spliced variants. The generated resources, including cDNA entry clones, expression constructs and transgenic plants, will facilitate further functional analysis of the members of this important gene family

Effects of activation energy on the instability of oblique detonation surfaces with a one-step chemistry model

A numerical study was performed to investigate the detailed effects of activation energy Ea on the oblique detonation wave surface instability. Numerical simulations were performed using an ideal reactive flow model given by the inviscid Euler equations with one-step irreversible Arrhenius reaction kinetics. The numerical results demonstrate two types of unstable structures following the initial smooth surface after detonation initiation. One exhibits by a “saw-tooth” reactive front and the other exhibits by a “keystone” feature. To quantify the destabilization processes, two characteristic length scales, L1 and L2, are defined statistically to be the length of the smooth detonation surface before the appearance of instabilities and the length of the unstable surface before the first cellular structure with the onset of right-running transverse waves, respectively. Their dependence on Ea was simulated and analyzed. In general, both lengths decrease with increasing Ea, making the surface more unstable. However, with increasing Ea, the high temperature sensitivity of the mixture causes an abrupt explosion in the initiation region, introducing a high overdriven surface and suppressing the instability. With the balance between the destabilizing effect of Ea and the stabilizing effect of increasing overdrive factor, both L1 and L2 are found to approach a near-constant value in the high Ea limit

The association between type 2 diabetes and attention- deficit/hyperactivity disorder: A systematic review, meta-analysis, and population-based sibling study

Attention deficit hyperactivity disorder; Cardiovascular risk factors; Type 2 diabetesTrastorno por déficit de atención con hiperactividad; Factores de riesgo cardiovascular; Diabetes tipo 2Trastorn per dèficit d'atenció amb hiperactivitat; Factors de risc cardiovascular; Diabetis tipus 2We conducted a systematic review and a meta-analysis to quantitatively summarize evidence on the association between attention-deficit/hyperactivity disorder (ADHD) and type 2 diabetes (T2D). Moreover, a register-based sibling study was conducted to simultaneously control for confounding factors. A systematic search identified four eligible observational studies (N = 5738,287). The meta-analysis showed that individuals with ADHD have a more than doubled risk of T2D when considering adjusted estimates (OR=2.29 [1.48–3.55], d=0.46). Results from the register-based Swedish data showed a significant association between ADHD and T2D (HR=2.35 [2.14–2.58]), with substance use disorder, depression, and anxiety being the main drivers of the association, and cardiovascular and familiar risk playing a smaller role. While results from the meta-analysis provide evidence for an increased risk of T2D in individuals with ADHD, the register-based analyses show that the association between ADHD and T2D is largely explained by psychiatric comorbidities. Pending further evidence of causal association, our findings suggest that early identification and treatment of ADHD comorbidities might greatly reduce the risk of developing T2D in individuals with ADHD.The project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 965381. This report reflects only the author’s view, and the European Union is not responsible for any use that may be made of the information it contains. Henrik Larsson acknowledges financial support from the Swedish Research Council (2018-02599) and the Swedish Brain Foundation (FO2021-0115). Zheng Chang acknowledges financial support from the Swedish Council for Health, Working Life and Welfare (2019-00176). Ebba Du Rietz was supported by grant PD20-0036 from the Swedish Society for Medical Research (SSMF), Funds from the Strategic Research Program in Epidemiology at Karolinska Institutet, Fredrik & Ingrid Thurings Stiftelse and Fonden för Psykisk Hälsa

TensorMD: Scalable Tensor-Diagram based Machine Learning Interatomic Potential on Heterogeneous Many-Core Processors

Molecular dynamics simulations have emerged as a potent tool for investigating the physical properties and kinetic behaviors of materials at the atomic scale, particularly in extreme conditions. Ab initio accuracy is now achievable with machine learning based interatomic potentials. With recent advancements in high-performance computing, highly accurate and large-scale simulations become feasible. This study introduces TensorMD, a new machine learning interatomic potential (MLIP) model that integrates physical principles and tensor diagrams. The tensor formalism provides a more efficient computation and greater flexibility for use with other scientific codes. Additionally, we proposed several portable optimization strategies and developed a highly optimized version for the new Sunway supercomputer. Our optimized TensorMD can achieve unprecedented performance on the new Sunway, enabling simulations of up to 52 billion atoms with a time-to-solution of 31 ps/step/atom, setting new records for HPC + AI + MD

Multiple firing coherence resonances in excitatory and inhibitory coupled neurons

The impact of inhibitory and excitatory synapses in delay-coupled Hodgkin--Huxley neurons that are driven by noise is studied. If both synaptic types are used for coupling, appropriately tuned delays in the inhibition feedback induce multiple firing coherence resonances at sufficiently strong coupling strengths, thus giving rise to tongues of coherency in the corresponding delay-strength parameter plane. If only inhibitory synapses are used, however, appropriately tuned delays also give rise to multiresonant responses, yet the successive delays warranting an optimal coherence of excitations obey different relations with regards to the inherent time scales of neuronal dynamics. This leads to denser coherence resonance patterns in the delay-strength parameter plane. The robustness of these findings to the introduction of delay in the excitatory feedback, to noise, and to the number of coupled neurons is determined. Mechanisms underlying our observations are revealed, and it is suggested that the regularity of spiking across neuronal networks can be optimized in an unexpectedly rich variety of ways, depending on the type of coupling and the duration of delays.Comment: 7 two-column pages, 6 figures; accepted for publication in Communications in Nonlinear Science and Numerical Simulatio

TPX2-LIKE PROTEIN 3 is the primary activator of α-Aurora kinases and is essential for embryogenesis

Aurora kinases are key regulators of mitosis. Multicellular eukaryotes generally possess two functionally-diverged types of Aurora kinases. In plants, including Arabidopsis thaliana, these are termed α and β Auroras. As the functional specification of Aurora kinases is determined by their specific interaction partners, we initiated interactomics analyses using both Arabidopsis α Aurora kinases (AUR1 and AUR2). Proteomics results revealed that TPX2-LIKE PROTEINS 2 and 3 (TPXL2/3) prominently associated with α Auroras, as did the conserved TPX2 to a lower degree. Like TPX2, TPXL2 and TPXL3 strongly activated the AUR1 kinase but exhibited cell cycle-dependent localization differences on microtubule arrays. The separate functions of TPX2 and TPXL2/3 were also suggested by their different influences on AUR1 localization upon ectopic expressions. Furthermore, genetic analyses showed that TPXL3, but not TPX2 and TPXL2, acts non-redundantly to enable proper embryo development. In contrast to vertebrates, plants have an expanded TPX2 family and these family members have both redundant and unique functions. Moreover, as neither TPXL2 nor TPXL3 contains the C-terminal Kinesin-5 binding domain present in the canonical TPX2, the targeting and activity of this kinesin must be organized differently in plants