Generating distributed entanglement from electron currents

This work is partially supported by a Royal Society University Research FellowshipSeveral recent experiments have demonstrated the viability of a passive device that can generate spin-entangled currents in two separate leads. However, manipulation and measurement of individual flying qubits in a solid state system has yet to be achieved. This is particularly difficult when a macroscopic number of these indistinguishable qubits are present. In order to access such an entangled current resource, we therefore show how to use it to generate distributed, static entanglement. The spatial separation between the entangled static pair can be much higher than that achieved by only exploiting the tunnelling effects between quantum dots. Our device is completely passive, and requires only weak Coulomb interactions between static and flying spins. We show that the entanglement generated is robust to decoherence for large enough currents.Publisher PDFPeer reviewe

Practicality of spin chain 'wiring' in diamond quantum technologies

Coupled spin chains are promising candidates for 'wiring up' qubits in solid-state quantum computing (QC). In particular, two nitrogen-vacancy centers in diamond can be connected by a chain of implanted nitrogen impurities; when driven by a suitable global fields the chain can potentially enable quantum state transfer at room temperature. However, our detailed analysis of error effects suggests that foreseeable systems may fall far short of the fidelities required for QC. Fortunately the chain can function in the more modest role as a mediator of noisy entanglement, enabling QC provided that we use subsequent purification. For instance, a chain of 5 spins with inter-spin distances of 10 nm has finite entangling power as long as the T2 time of the spins exceeds 0.55 ms. Moreover we show that re-purposing the chain this way can remove the restriction to nearest-neighbor interactions, so eliminating the need for complicated dynamical decoupling sequences.Comment: 5 pages (plus 5-page supplement

Light Field Depth Estimation Based on Stitched-EPI

Depth estimation is one of the most essential problems for light field applications. In EPI-based methods, the slope computation usually suffers low accuracy due to the discretization error and low angular resolution. In addition, recent methods work well in most regions but often struggle with blurry edges over occluded regions and ambiguity over texture-less regions. To address these challenging issues, we first propose the stitched-EPI and half-stitched-EPI algorithms for non-occluded and occluded regions, respectively. The algorithms improve slope computation by shifting and concatenating lines in different EPIs but related to the same point in 3D scene, while the half-stitched-EPI only uses non-occluded part of lines. Combined with the joint photo-consistency cost proposed by us, the more accurate and robust depth map can be obtained in both occluded and non-occluded regions. Furthermore, to improve the depth estimation in texture-less regions, we propose a depth propagation strategy that determines their depth from the edge to interior, from accurate regions to coarse regions. Experimental and ablation results demonstrate that the proposed method achieves accurate and robust depth maps in all regions effectively.Comment: 15 page

Measurement-based quantum computing with a spin ensemble coupled to a stripline cavity

Recently a new form of quantum memory has been proposed. The storage medium is an ensemble of electron spins, coupled to a stripline cavity and an ancillary readout system. Theoretical studies suggest that the system should be capable of storing numerous qubits within the ensemble, and an experimental proof-of-concept has already been performed. Here we show that this minimal architecture is not limited to storage but is in fact capable of full quantum processing by employing measurement-based entanglement. The technique appears to be remarkably robust against the anticipated dominant error types. The key enabling component, namely a readout technology that non-destructively determines "are there n photons in the cavity?", has already been realised experimentally.Comment: 17 pages, 8 figure

Metabolomics-based discovery of XHP as a CYP3A4 inhibitor against pancreatic cancer

Background: Xihuang Wan (XHW), a purgative and detoxifying agent, is commonly utilized in modern medicine as a treatment and adjuvant therapy for various malignancies, including breast cancer, liver cancer, and lung cancer. A clinical study demonstrated the potential usefulness of the combination of XHW and gemcitabine as a therapy for pancreatic cancer (PC), indicating that XHW’s broad-spectrum antitumor herbal combination could be beneficial in the treatment of PC. However, the precise therapeutic efficacy of XHW in treating pancreatic cancer remains uncertain.Aim: This study assessed the biological activity of XHW by optimizing the therapeutic concentration of XHW (Xihuang pills, XHP). We performed cell culture and developed an animal test model to determine whether XHP can inhibit pancreatic cancer (PC). We also applied the well-known widely targeted metabolomics analysis and conducted specific experiments to assess the feasibility of our method in PC therapy.Materials and Methods: We used UPLC/Q-TOF-MS to test XHP values to set up therapeutic concentrations for the in vivo test model. SW1990 pancreatic cancer cells were cultured to check the effect the anti-cancer effects of XHP by general in vitro cell analyses including CCK-8, Hoechst 33258, and flow cytometry. To develop the animal model, a solid tumor was subcutaneously formed on a mouse model of PC and assessed by immunohistochemistry and TUNEL apoptosis assay. We also applied the widely targeted metabolomics method following Western blot and RT-PCR to evaluate multiple metabolites to check the therapeutic effect of XHP in our cancer test model.Results: Quantified analysis from UPLC/Q-TOF-MS showed the presence of the following components of XHP: 11-carbonyl-β-acetyl-boswellic acid (AKBA), 11-carbonyl-β-boswellic acid (KBA), 4-methylene-2,8,8-trimethyl-2-vinyl-bicyclo [5.2.0]nonane, and (1S-endo)-2-methyl-3-methylene-2-(4-methyl-3-3-pentenyl)-bicyclo [2.2.1heptane]. The results of the cell culture experiments demonstrated that XHP suppressed the growth of SW1990 PC cells by enhancing apoptosis. The results of the animal model tests also indicated the suppression effect of XHP on tumor growth. Furthermore, the result of the widely targeted metabolomics analysis showed that the steroid hormone biosynthesis metabolic pathway was a critical factor in the anti-PC effect of XHP in the animal model. Moreover, Western blot and RT-PCR analyses revealed XHP downregulated CYP3A4 expression as an applicable targeted therapeutic approach.Conclusion: The results of this study demonstrated the potential of XHP in therapeutic applications in PC. Moreover, the widely targeted metabolomics method revealed CYP3A4 is a potential therapeutic target of XHP in PC control. These findings provide a high level of confidence that XHP significantly acts as a CYP3A4 inhibitor in anti-cancer therapeutic applications

Clinical-radiomics-based treatment decision support for KIT Exon 11 deletion in gastrointestinal stromal tumors: a multi-institutional retrospective study

Objectivegastrointestinal stromal tumors (GISTs) with KIT exon 11 deletions have more malignant clinical outcomes. A radiomics model was constructed for the preoperative prediction of KIT exon 11 deletion in GISTs.MethodsOverall, 126 patients with GISTs who underwent preoperative enhanced CT were included. GISTs were manually segmented using ITK-SNAP in the arterial phase (AP) and portal venous phase (PVP) images of enhanced CT. Features were extracted using Anaconda (version 4.2.0) with PyRadiomics. Radiomics models were constructed by LASSO. The clinical-radiomics model (combined model) was constructed by combining the clinical model with the best diagnostic effective radiomics model. ROC curves were used to compare the diagnostic effectiveness of radiomics model, clinical model, and combined model. Diagnostic effectiveness among radiomics model, clinical model and combine model were analyzed in external cohort (n=57). Statistics were carried out using R 3.6.1.ResultsThe Radscore showed favorable diagnostic efficacy. Among all radiomics models, the AP-PVP radiomics model exhibited excellent performance in the training cohort, with an AUC of 0.787 (95% CI: 0.687-0.866), which was verified in the test cohort (AUC=0.775, 95% CI: 0.608-0.895). Clinical features were also analyzed. Among the radiomics, clinical and combined models, the combined model showed favorable diagnostic efficacy in the training (AUC=0.863) and test cohorts (AUC=0.851). The combined model yielded the largest AUC of 0.829 (95% CI, 0.621–0.950) for the external validation of the combined model. GIST patients could be divided into high or low risk subgroups of recurrence and mortality by the Radscore.ConclusionThe radiomics models based on enhanced CT for predicting KIT exon 11 deletion mutations have good diagnostic performance

OpenLane-V2: A Topology Reasoning Benchmark for Unified 3D HD Mapping

Accurately depicting the complex traffic scene is a vital component for autonomous vehicles to execute correct judgments. However, existing benchmarks tend to oversimplify the scene by solely focusing on lane perception tasks. Observing that human drivers rely on both lanes and traffic signals to operate their vehicles safely, we present OpenLane-V2, the first dataset on topology reasoning for traffic scene structure. The objective of the presented dataset is to advance research in understanding the structure of road scenes by examining the relationship between perceived entities, such as traffic elements and lanes. Leveraging existing datasets, OpenLane-V2 consists of 2,000 annotated road scenes that describe traffic elements and their correlation to the lanes. It comprises three primary sub-tasks, including the 3D lane detection inherited from OpenLane, accompanied by corresponding metrics to evaluate the model's performance. We evaluate various state-of-the-art methods, and present their quantitative and qualitative results on OpenLane-V2 to indicate future avenues for investigating topology reasoning in traffic scenes.Comment: Accepted by NeurIPS 2023 Track on Datasets and Benchmarks | OpenLane-V2 Dataset: https://github.com/OpenDriveLab/OpenLane-V

Topology Reasoning for Driving Scenes

Understanding the road genome is essential to realize autonomous driving. This highly intelligent problem contains two aspects - the connection relationship of lanes, and the assignment relationship between lanes and traffic elements, where a comprehensive topology reasoning method is vacant. On one hand, previous map learning techniques struggle in deriving lane connectivity with segmentation or laneline paradigms; or prior lane topology-oriented approaches focus on centerline detection and neglect the interaction modeling. On the other hand, the traffic element to lane assignment problem is limited in the image domain, leaving how to construct the correspondence from two views an unexplored challenge. To address these issues, we present TopoNet, the first end-to-end framework capable of abstracting traffic knowledge beyond conventional perception tasks. To capture the driving scene topology, we introduce three key designs: (1) an embedding module to incorporate semantic knowledge from 2D elements into a unified feature space; (2) a curated scene graph neural network to model relationships and enable feature interaction inside the network; (3) instead of transmitting messages arbitrarily, a scene knowledge graph is devised to differentiate prior knowledge from various types of the road genome. We evaluate TopoNet on the challenging scene understanding benchmark, OpenLane-V2, where our approach outperforms all previous works by a great margin on all perceptual and topological metrics. The code would be released soon