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Spin-triplet topological superconductors should exhibit many unprecedented electronic properties, including fractionalized electronic states relevant to quantum information processing. Although UTe2 may embody such bulk topological superconductivity1-11, its superconductive order parameter Δ(k) remains unknown12. Many diverse forms for Δ(k) are physically possible12 in such heavy fermion materials13. Moreover, intertwined14,15 density waves of spin (SDW), charge (CDW) and pair (PDW) may interpose, with the latter exhibiting spatially modulating14,15 superconductive order parameter Δ(r), electron-pair density16-19 and pairing energy gap17,20-23. Hence, the newly discovered CDW state24 in UTe2 motivates the prospect that a PDW state may exist in this material24,25. To search for it, we visualize the pairing energy gap with µeV-scale energy resolution using superconductive scanning tunnelling microscopy (STM) tips26-31. We detect three PDWs, each with peak-to-peak gap modulations of around 10 µeV and at incommensurate wavevectors Pi=1,2,3 that are indistinguishable from the wavevectors Qi=1,2,3 of the prevenient24 CDW. Concurrent visualization of the UTe2 superconductive PDWs and the non-superconductive CDWs shows that every Pi:Qi pair exhibits a relative spatial phase Î´Ï ≈ π. From these observations, and given UTe2 as a spin-triplet superconductor12, this PDW state should be a spin-triplet PDW24,25. Although such states do exist32 in superfluid 3He, for superconductors, they are unprecedented.
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In cuprate superconductors, due to strong electronic correlations, there are multiple intertwined orders which either coexist or compete with superconductivity. Among them, the antiferromagnetic (AF) order is the most prominent one. In the region where superconductivity sets in, the long-range AF order is destroyed. Yet the residual short-range AF spin fluctuations are present up to a much higher doping, and their role in the emergence of the superconducting phase is still highly debated. Here, by using a spin-polarized scanning tunneling microscope, we directly visualize an emergent incommensurate AF order in the nearby region of Fe impurities embedded in the optimally doped Bi2Sr2CaCu2O8+δ (Bi2212). Remarkably, the Fe impurities suppress the superconducting coherence peaks with the gapped feature intact, but pin down the ubiquitous short-range incommensurate AF order. Our work shows an intimate relation between antiferromagnetism and superconductivity.
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Topological Weyl semimetals (TWSs) with pairs of Weyl points and topologically protected Fermi arc states have broadened the classification of topological phases and provide superior platform for study of topological superconductivity. Here we report the nontrivial superconductivity and topological features of sulfur-doped Td -phase MoTe2 with enhanced Tc compared with type-II TWS MoTe2 It is found that Td -phase S-doped MoTe2 (MoTe2-x S x , x â¼ 0.2) is a two-band s-wave bulk superconductor (â¼0.13 meV and 0.26 meV), where the superconducting behavior can be explained by the s+- pairing model. Further, measurements of the quasi-particle interference (QPI) patterns and a comparison with band-structure calculations reveal the existence of Fermi arcs in MoTe2-x S x More interestingly, a relatively large superconducting gap (â¼1.7 meV) is detected by scanning tunneling spectroscopy on the sample surface, showing a hint of topological nontrivial superconductivity based on the pairing of Fermi arc surface states. Our work demonstrates that the Td -phase MoTe2-x S x is not only a promising topological superconductor candidate but also a unique material for study of s+- superconductivity.
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The experimental manifestation of topological effects in bulk materials is attracting enormous research interest. However, direct experimental evidence of the effective k-space monopole of the Weyl nodes has so far been lacking. Here, signatures of the singular topology of the type-II Weyl semimetal TaIrTe4 are revealed in the photoresponses, which are related to divergence of the Berry curvature. TaIrTe4 exhibits a large photoresponsivity of 130.2 mA W-1-with 4 µm excitation in an unbiased field-effect transistor at room temperature-arising from the third-order nonlinear optical response, approaching the performance of commercial low-temperature detectors. In addition, the circularly polarized galvanic response is enhanced at 4 µm, possibly due to the same Berry curvature singularity enhancement. Considering the optical selection rule of chiral Weyl cones, this may open the door for studying and controlling the chiral polarization of Weyl fermions with an electric field in addition to the optical helicities.
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Simulating electronic behavior in materials and devices with realistic large system sizes remains a formidable task within the ab initio framework due to its computational intensity. Here we show DeePTB, an efficient deep learning-based tight-binding approach with ab initio accuracy to address this issue. By training on structural data and corresponding ab initio eigenvalues, the DeePTB model can efficiently predict tight-binding Hamiltonians for unseen structures, enabling efficient simulations of large-size systems under external perturbations such as finite temperatures and strain. This capability is vital for semiconductor band gap engineering and materials design. When combined with molecular dynamics, DeePTB facilitates efficient and accurate finite-temperature simulations of both atomic and electronic behavior simultaneously. This is demonstrated by computing the temperature-dependent electronic properties of a gallium phosphide system with 106 atoms. The availability of DeePTB bridges the gap between accuracy and scalability in electronic simulations, potentially advancing materials science and related fields by enabling large-scale electronic structure calculations.
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CONTEXT.: Computational pathology combines clinical pathology with computational analysis, aiming to enhance diagnostic capabilities and improve clinical productivity. However, communication barriers between pathologists and developers often hinder the full realization of this potential. OBJECTIVE.: To propose a standardized framework that improves mutual understanding of clinical objectives and computational methodologies. The goal is to enhance the development and application of computer-aided diagnostic (CAD) tools. DESIGN.: The article suggests pivotal roles for pathologists and computer scientists in the CAD development process. It calls for increased understanding of computational terminologies, processes, and limitations among pathologists. Similarly, it argues that computer scientists should better comprehend the true use cases of the developed algorithms to avoid clinically meaningless metrics. RESULTS.: CAD tools improve pathology practice significantly. Some tools have even received US Food and Drug Administration approval. However, improved understanding of machine learning models among pathologists is essential to prevent misuse and misinterpretation. There is also a need for a more accurate representation of the algorithms' performance compared to that of pathologists. CONCLUSIONS.: A comprehensive understanding of computational and clinical paradigms is crucial for overcoming the translational gap in computational pathology. This mutual comprehension will improve patient care through more accurate and efficient disease diagnosis.
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Colorectal cancer (CRC) is the second most commonly diagnosed cancer in the United States. Genetic testing is critical in assisting in the early detection of CRC and selection of individualized treatment plans, which have shown to improve the survival rate of CRC patients. The tissue slide review (TSR), a tumor tissue macro-dissection procedure, is a required pre-analytical step to perform genetic testing. Due to the subjective nature of the process, major discrepancies in CRC diagnostics by pathologists are reported, and metrics for quality are often only qualitative. Progressive context encoder anomaly detection (P-CEAD) is an anomaly detection approach to detect tumor tissue from whole slide images (WSIs), since tumor tissue is by its nature, an anomaly. P-CEAD-based CRC tumor segmentation achieves a 71% 26% sensitivity, 92% 7% specificity, and 63% 23% F1 score. The proposed approach provides an automated CRC tumor segmentation pipeline with a quantitatively reproducible quality compared with the conventional manual tumor segmentation procedure.
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INTRODUCTION: Aneurysmal subarachnoid hemorrhage (SAH) is a subtype of hemorrhagic stroke with thirty-day mortality as high as 40%. Given the expansion of Machine Learning (ML) and Artificial intelligence (AI) methods in health care, SAH patients desperately need an integrated AI system that detects, segments, and supports clinical decisions based on presentation and severity. OBJECTIVES: This review aims to synthesize the current state of the art of AI and ML tools for the management of SAH patients alongside providing an up-to-date account of future horizons in patient care. METHODS: We performed a systematic review through various databases such as Cochrane Central Register of Controlled Trials, MEDLINE, Scopus, Cochrane Database of Systematic Reviews, and Embase. RESULTS: A total of 507 articles were identified. Following extensive revision, only 21 articles were relevant. Two studies reported improved mortality prediction using Glasgow Coma Scale and biomarkers such as Neutrophil to Lymphocyte Ratio and glucose. One study reported that ffANN is equal to the SAHIT and VASOGRADE scores. One study reported that metabolic biomarkers Ornithine, Symmetric Dimethylarginine, and Dimethylguanidine Valeric acid were associated with poor outcomes. Nine studies reported improved prediction of complications and reduction in latency until intervention using clinical scores and imaging. Four studies reported accurate prediction of aneurysmal rupture based on size, shape, and CNN. One study reported AI-assisted Robotic Transcranial Doppler as a substitute for clinicians. CONCLUSION: AI/ML technologies possess tremendous potential in accelerating SAH systems-of-care. Keeping abreast of developments is vital in advancing timely interventions for critical diseases.
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Hemorragia Subaracnoidea , Humanos , Hemorragia Subaracnoidea/diagnóstico por imagen , Hemorragia Subaracnoidea/terapia , Inteligencia Artificial , Revisiones Sistemáticas como Asunto , Biomarcadores , Aprendizaje AutomáticoRESUMEN
Superconductivity has been discovered recently in infinite-layer nickel-based 112 thin films R 1-x A xNiO2 (R = La, Nd, Pr and A = Sr, Ca). They are isostructural to the infinite-layer cuprate (Ca,Sr)CuO2 and are supposed to have a formal Ni 3d 9 valence, thus providing a new platform to study the unconventional pairing mechanism of high-temperature superconductors. This important discovery immediately triggers a huge amount of innovative scientific curiosity in the field. In this paper, we try to give an overview of the recent research progress on the newly found superconducting nickelate systems, both from experimental and theoretical aspects. We mainly focus on the electronic structures, magnetic excitations, phase diagrams and superconducting gaps, and finally make some open discussions for possible pairing symmetries in Ni-based 112 systems.
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Despite their rich information content, electronic structure data amassed at high volumes in ab initio molecular dynamics simulations are generally under-utilized. We introduce a transferable high-fidelity neural network representation of such data in the form of tight-binding Hamiltonians for crystalline materials. This predictive representation of ab initio electronic structure, combined with machine-learning boosted molecular dynamics, enables efficient and accurate electronic evolution and sampling. When it is applied to a one-dimension charge-density wave material, carbyne, we are able to compute the spectral function and optical conductivity in the canonical ensemble. The spectral functions evaluated during soliton-antisoliton pair annihilation process reveal significant renormalization of low-energy edge modes due to retarded electron-lattice coupling beyond the Born-Oppenheimer limit. The availability of an efficient and reusable surrogate model for the electronic structure dynamical system will enable calculating many interesting physical properties, paving the way to previously inaccessible or challenging avenues in materials modeling.
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Simulación de Dinámica Molecular , Teoría Cuántica , Redes Neurales de la Computación , Electrónica , ElectronesRESUMEN
Fermi arcs on Weyl semimetals exhibit many exotic quantum phenomena. Usually found on atomically flat surfaces with approximate translation symmetry, Fermi arcs are rooted in the peculiar topology of bulk Bloch bands of 3D crystals. The fundamental question of whether a 1D Fermi arc can be probed remains unanswered. Such an answer could significantly broaden potential applications of Weyl semimetals. Here, we report a direct observation of robust edge states on atomic-scale ledges in TaAs using low-temperature scanning tunneling microscopy/spectroscopy. Spectroscopic signatures and theoretical calculations reveal that the 1D Fermi arcs arise from the chiral Weyl points of bulk crystals. The crossover from 2D Fermi arcs to eventual complete localization on 1D edges was arrested experimentally on a sequence of surfaces. Our results demonstrate extreme robustness of the bulk-boundary correspondence, which offers topological protection for Fermi arcs, even in cases in which the boundaries are at the atomic-scale. The persistent 1D Fermi arcs can be profitably exploited in miniaturized quantum devices.
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BACKGROUND: Adoption of the Digital Imaging and Communications in Medicine (DICOM) standard for whole slide images (WSIs) has been slow, despite significant time and effort by standards curators. One reason for the lack of adoption is that there are few tools which exist that can meet the requirements of WSIs, given an evolving ecosystem of best practices for implementation. Eventually, vendors will conform to the specification to ensure enterprise interoperability, but what about archived slides? Millions of slides have been scanned in various proprietary formats, many with examples of rare histologies. Our hypothesis is that if users and developers had access to easy to use tools for migrating proprietary formats to the open DICOM standard, then more tools would be developed as DICOM first implementations. METHODS: The technology we present here is dicom_wsi, a Python based toolkit for converting any slide capable of being read by the OpenSlide library into DICOM conformant and validated implementations. Moreover, additional postprocessing such as background removal, digital transformations (e.g., ink removal), and annotation storage are also described. dicom_wsi is a free and open source implementation that anyone can use or modify to meet their specific purposes. RESULTS: We compare the output of dicom_wsi to two other existing implementations of WSI to DICOM converters and also validate the images using DICOM capable image viewers. CONCLUSION: dicom_wsi represents the first step in a long process of DICOM adoption for WSI. It is the first open source implementation released in the developer friendly Python programming language and can be freely downloaded at .
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Purpose: Deep learning models are showing promise in digital pathology to aid diagnoses. Training complex models requires a significant amount and diversity of well-annotated data, typically housed in institutional archives. These slides often contain clinically meaningful markings to indicate regions of interest. If slides are scanned with the ink present, then the downstream model may end up looking for regions with ink before making a classification. If scanned without the markings, the information regarding where the relevant regions are located is lost. A compromise solution is to scan the slide with the annotations present but digitally remove them. Approach: We proposed a straightforward framework to digitally remove ink markings from whole slide images using a conditional generative adversarial network based on Pix2Pix. Results: The peak signal-to-noise ratio increased 30%, structural similarity index increased 20%, and visual information fidelity increased 200% relative to previous methods. Conclusions: When comparing our digital removal of marked images with rescans of clean slides, our method qualitatively and quantitatively exceeds current benchmarks, opening the possibility of using archived clinical samples as resources to fuel the next generation of deep learning models for digital pathology.
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The pairing mechanism in cuprates remains as one of the most challenging issues in condensed matter physics. Recently, superconductivity was discovered in thin films of the infinite-layer nickelate Nd1-xSrxNiO2 (x = 0.12-0.25) which is believed to have the similar 3d9 orbital electrons as that in cuprates. Here we report single-particle tunneling measurements on the superconducting nickelate thin films. We find predominantly two types of tunneling spectra, one shows a V-shape feature which can be fitted well by a d-wave gap function with gap maximum of about 3.9 meV, another one exhibits a full gap of about 2.35 meV. Some spectra demonstrate mixed contributions of these two components. Combining with theoretical calculations, we attribute the d-wave gap to the pairing potential of the [Formula: see text] orbital. Several possible reasons are given for explaining the full gap feature. Our results indicate both similarities and distinctions between the newly found Ni-based superconductors and cuprates.
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The superconducting state is formed by the condensation of Cooper pairs and protected by the superconducting gap. The pairing interaction between the two electrons of a Cooper pair determines the gap function. Thus, it is pivotal to detect the gap structure for understanding the mechanism of superconductivity. In cuprate superconductors, it has been well established that the gap may have a d-wave function. This gap function has an alternative sign change in the momentum space. It is however hard to visualize this sign change. Here we report the measurements of scanning tunneling spectroscopy in Bi2Sr2CaCu2O8+δ and conduct the analysis of phase-referenced quasiparticle interference (QPI). We see the seven basic scattering vectors that connect the octet ends of the banana-shaped contour of Fermi surface. The phase-referenced QPI clearly visualizes the sign change of the d-wave gap. Our results illustrate an effective way for determining the sign change of unconventional superconductors.
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The layered ternary compound TaIrTe4 is an important candidate to host the recently predicted type-II Weyl fermions. However, a direct and definitive proof of the absence of inversion symmetry in this material, a prerequisite for the existence of Weyl Fermions, has so far remained evasive. Herein, an unambiguous identification of the broken inversion symmetry in TaIrTe4 is established using angle-resolved polarized Raman spectroscopy. Combining with high-resolution transmission electron microscopy, an efficient and nondestructive recipe to determine the exact crystallographic orientation of TaIrTe4 crystals is demonstrated. Such technique could be extended to the fast identification and characterization of other type-II Weyl fermions candidates. A surprisingly strong in-plane electrical anisotropy in TaIrTe4 thin flakes is also revealed, up to 200% at 10 K, which is the strongest known electrical anisotropy for materials with comparable carrier density, notably in such good metals as copper and silver.
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Photodetectors based on Weyl semimetal promise extreme performance in terms of highly sensitive, broadband and self-powered operation owing to its extraordinary material properties. Layered Type-II Weyl semimetal that break Lorentz invariance can be further integrated with other two-dimensional materials to form van der Waals heterostructures and realize multiple functionalities inheriting the advantages of other two-dimensional materials. Herein, we report the realization of a broadband self-powered photodetector based on Type-II Weyl semimetal Td -MoTe2 . The prototype metal-MoTe2 -metal photodetector exhibits a responsivity of 0.40 mA W-1 and specific directivity of 1.07 × 108 Jones with 43 µs response time at 532 nm. Broadband responses from 532 nm to 10.6 µm are experimentally tested with a potential detection range extendable to far-infrared and terahertz. Furthermore, we identify the response of the detector is polarization angle sensitive due to the anisotropic response of MoTe2 . The anisotropy is found to be wavelength dependent, and the degree of anisotropy increases as the excitation wavelength gets closer to the Weyl nodes. In addition, with power and temperature dependent photoresponse measurements, the photocurrent generation mechanisms are investigated. Our results suggest this emerging class of materials can be harnessed for broadband angle sensitive, self-powered photodetection with decent responsivities.
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Transition metal dichalcogenide MoTe2 is an important candidate for realizing the newly predicted type-II Weyl fermions, for which the breaking of the inversion symmetry is a prerequisite. Here we present direct spectroscopic evidence for the inversion symmetry breaking in the low-temperature phase of MoTe2 by systematic Raman experiments and first-principles calculations. We identify five lattice vibrational modes that are Raman-active only in the low-temperature noncentrosymmetric structure. A hysteresis is also observed in the peak intensity of inversion symmetry-activated Raman modes, confirming a temperature-induced structural phase transition with a concomitant change in the inversion symmetry. Our results provide definitive evidence for the low-temperature noncentrosymmetric Td phase from vibrational spectroscopy, and suggest MoTe2 as an ideal candidate for investigating the temperature-induced topological phase transition.