Pediatric Spinal Hemangioblastomas: Clinical Features and Surgical Outcomes of 39 Cases

Objective Spinal hemangioblastomas (HBs) are a rare pathology, especially in the pediatric population. The natural history and long-term outcomes of pediatric patients with spinal HBs remain unclear due to their scarcity. Methods A retrospective review of the clinical data and treatment outcomes of children with spinal HBs in our institution from 2012 to 2021 was conducted. Results Thirty-nine pediatric patients were included, with an average age of 15.9 ± 2.9 years (range, 8–18 years), and 51.3% were female. Children were more likely to have von Hippel-Lindau (VHL) disease (p < 0.001), a family history of VHL (p < 0.001), multiple symptoms (p = 0.006), a shorter duration of symptoms (p < 0.001), and a larger lesion size (p = 0.004) and volume (p = 0.008) than their adult counterparts. The VHL-associated group of patients was more likely to present with multiple symptoms (p = 0.026), have a family history of VHL (p < 0.001), have multiple HBs (p < 0.001) and have synchronous intracranial lesions (p < 0.001) than the sporadic group. After surgery, 15 patients (38.5%) showed improved clinical outcomes, 17 patients (43.6%) remained unchanged, 4 patients (10.2%) worsened, and 3 patients (7.7%) died of tumor progression. During follow-up, there was a high rate of recurrence and repeated surgery, especially for children in the VHL-associated group. Conclusion Pediatric patients with spinal HBs appear to have a higher relapse risk than their adult counterparts. Therefore, life-long follow-up of these patients is necessary, especially for VHL-associated cases. Surgery can benefit children with HBs and should be considered early to avoid irreversible neurological deterioration

Directional Spin Wave in Spin-Torque Oscillators Induced by Interfacial Dzyaloshinskii–Moriya Interaction

Spin torque oscillators (STOs) are currently of great interest due to its wide tunable frequencies, low energy consumption and high quality factors compared with traditional oscillators. Here, we report the characteristics of the nanocontact-(NC-)STO in the presence of interfacial Dzyaloshinskii-Moriya interaction (DMI), using micromagnetic simulations. We find that the DMI can decrease the STO frequency by around 2 GHz. More importantly, the DMI is able to break the isotropy of the spin-wave spectrum and turn the emitted microwave into directional spin-wave beams potentially facilitating the synchronization of multiple STOs

DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment

Cross-modal garment synthesis and manipulation will significantly benefit the way fashion designers generate garments and modify their designs via flexible linguistic interfaces. However, despite the significant progress that has been made in generic image synthesis using diffusion models, producing garment images with garment part level semantics that are well aligned with input text prompts and then flexibly manipulating the generated results still remains a problem. Current approaches follow the general text-to-image paradigm and mine cross-modal relations via simple cross-attention modules, neglecting the structural correspondence between visual and textual representations in the fashion design domain. In this work, we instead introduce DiffCloth, a diffusion-based pipeline for cross-modal garment synthesis and manipulation, which empowers diffusion models with flexible compositionality in the fashion domain by structurally aligning the cross-modal semantics. Specifically, we formulate the part-level cross-modal alignment as a bipartite matching problem between the linguistic Attribute-Phrases (AP) and the visual garment parts which are obtained via constituency parsing and semantic segmentation, respectively. To mitigate the issue of attribute confusion, we further propose a semantic-bundled cross-attention to preserve the spatial structure similarities between the attention maps of attribute adjectives and part nouns in each AP. Moreover, DiffCloth allows for manipulation of the generated results by simply replacing APs in the text prompts. The manipulation-irrelevant regions are recognized by blended masks obtained from the bundled attention maps of the APs and kept unchanged. Extensive experiments on the CM-Fashion benchmark demonstrate that DiffCloth both yields state-of-the-art garment synthesis results by leveraging the inherent structural information and supports flexible manipulation with region consistency

Quantum Oscillations from Nontrivial States in Quasi-Two-Dimensional Dirac Semimetal ZrTe 5 Nanowires

Recently discovered Dirac semimetal ZrTe 5 bulk crystal, exhibits nontrivial conducting states in each individual layer, holding great potential for novel spintronic applications. Here, to reveal the transport properties of ZrTe 5 , we fabricated ZrTe 5 nanowires (NWs) devices, with much larger surface-to-volume ratio than bulk materials. Quantum oscillations induced by the two-dimensional (2D) nontrivial conducting states have been observed from these NWs and a finite Berry phase of ~π is obtained by the analysis of Landau-level fan diagram. More importantly, the absence of the Aharonov-Bohm (A-B) oscillations, along with the SdH oscillations, suggests that the electrons only conduct inside each layer. And the intralayer conducting is suppressed because of the weak connection between adjacent layers. Our results demonstrate that ZrTe 5 NWs can serve as a suitable quasi-2D Dirac semimetal with high mobility (~85000 cm 2 V −1 s −1 ) and large nontrivial conductance contribution (up to 8.68%)

DESCN: Deep Entire Space Cross Networks for Individual Treatment Effect Estimation

Causal Inference has wide applications in various areas such as E-commerce and precision medicine, and its performance heavily relies on the accurate estimation of the Individual Treatment Effect (ITE). Conventionally, ITE is predicted by modeling the treated and control response functions separately in their individual sample spaces. However, such an approach usually encounters two issues in practice, i.e. divergent distribution between treated and control groups due to treatment bias, and significant sample imbalance of their population sizes. This paper proposes Deep Entire Space Cross Networks (DESCN) to model treatment effects from an end-to-end perspective. DESCN captures the integrated information of the treatment propensity, the response, and the hidden treatment effect through a cross network in a multi-task learning manner. Our method jointly learns the treatment and response functions in the entire sample space to avoid treatment bias and employs an intermediate pseudo treatment effect prediction network to relieve sample imbalance. Extensive experiments are conducted on a synthetic dataset and a large-scaled production dataset from the E-commerce voucher distribution business. The results indicate that DESCN can successfully enhance the accuracy of ITE estimation and improve the uplift ranking performance. A sample of the production dataset and the source code are released to facilitate future research in the community, which is, to the best of our knowledge, the first large-scale public biased treatment dataset for causal inference.Comment: Accepted by SIGKDD 2022 Applied Data Science Trac

DiffCloth: Diffusion Based Garment Synthesis and Manipulation via Structural Cross-modal Semantic Alignment

Cross-modal garment synthesis and manipulation will significantly benefit the way fashion designers generate garments and modify their designs via flexible linguistic interfaces.Current approaches follow the general text-to-image paradigm and mine cross-modal relations via simple cross-attention modules, neglecting the structural correspondence between visual and textual representations in the fashion design domain. In this work, we instead introduce DiffCloth, a diffusion-based pipeline for cross-modal garment synthesis and manipulation, which empowers diffusion models with flexible compositionality in the fashion domain by structurally aligning the cross-modal semantics. Specifically, we formulate the part-level cross-modal alignment as a bipartite matching problem between the linguistic Attribute-Phrases (AP) and the visual garment parts which are obtained via constituency parsing and semantic segmentation, respectively. To mitigate the issue of attribute confusion, we further propose a semantic-bundled cross-attention to preserve the spatial structure similarities between the attention maps of attribute adjectives and part nouns in each AP. Moreover, DiffCloth allows for manipulation of the generated results by simply replacing APs in the text prompts. The manipulation-irrelevant regions are recognized by blended masks obtained from the bundled attention maps of the APs and kept unchanged. Extensive experiments on the CM-Fashion benchmark demonstrate that DiffCloth both yields state-of-the-art garment synthesis results by leveraging the inherent structural information and supports flexible manipulation with region consistency.Comment: accepted by ICCV202

Experimental Observation of Dual Magnetic States in Topological Insulators

The recently discovered topological phase offers new possibilities for spintronics and condensed matter. Even insulating material exhibits conductivity at the edges of certain systems, giving rise to an anomalous quantum Hall effect and other coherent spin transport phenomena, in which heat dissipation is minimized, with potential uses for next-generation energy-efficient electronics. While the metallic surface states of topological insulators (TIs) have been extensively studied, direct comparison of the surface and bulk magnetic properties of TIs has been little explored. We report unambiguous evidence for distinctly enhanced surface magnetism in a prototype magnetic TI, Cr-doped Bi 2 Se 3 . Using synchrotron-based x-ray techniques, we demonstrate a “three-step transition” model, with a temperature window of ~15 K, where the TI surface is magnetically ordered while the bulk is not. Understanding the dual magnetization process has strong implications for defining a physical model of magnetic TIs and lays the foundation for applications to information technology