SwinCross: Cross-modal Swin Transformer for Head-and-Neck Tumor Segmentation in PET/CT Images

Radiotherapy (RT) combined with cetuximab is the standard treatment for patients with inoperable head and neck cancers. Segmentation of head and neck (H&N) tumors is a prerequisite for radiotherapy planning but a time-consuming process. In recent years, deep convolutional neural networks have become the de facto standard for automated image segmentation. However, due to the expensive computational cost associated with enlarging the field of view in DCNNs, their ability to model long-range dependency is still limited, and this can result in sub-optimal segmentation performance for objects with background context spanning over long distances. On the other hand, Transformer models have demonstrated excellent capabilities in capturing such long-range information in several semantic segmentation tasks performed on medical images. Inspired by the recent success of Vision Transformers and advances in multi-modal image analysis, we propose a novel segmentation model, debuted, Cross-Modal Swin Transformer (SwinCross), with cross-modal attention (CMA) module to incorporate cross-modal feature extraction at multiple resolutions.To validate the effectiveness of the proposed method, we performed experiments on the HECKTOR 2021 challenge dataset and compared it with the nnU-Net (the backbone of the top-5 methods in HECKTOR 2021) and other state-of-the-art transformer-based methods such as UNETR, and Swin UNETR. The proposed method is experimentally shown to outperform these comparing methods thanks to the ability of the CMA module to capture better inter-modality complimentary feature representations between PET and CT, for the task of head-and-neck tumor segmentation.Comment: 9 pages, 3 figures. Med Phys. 202

Detection of promoter hypermethylation of the CpG island of E-cadherin in gastric cardiac adenocarcinoma

<p>Abstract</p> <p>Aim</p> <p>Abnormal hypermethylation of CpG islands associated with tumor suppressor genes can lead to transcriptional silencing in neoplasia. The aim of this study was to investigate the promoter methylation and expression of E-cadherin gene in gastric cardiac adenocarcinoma (GCA).</p> <p>Methods</p> <p>A nested MSP approach, immunohistochemistry method and RT-PCR were used respectively to examine the methylation status of the 5' CpG island of E-cadherin, its protein expression and mRNA expression in tumors and corresponding normal tissues.</p> <p>Results</p> <p>E-cadherin was methylated in 63 of 92 (68.5%) tumor specimens, which was significantly higher than that in corresponding normal tissues (P < 0.001). Methylation frequencies of stage III and IV tumor tissues was significantly higher than that in stage I and II tumor tissues (P = 0.01). Methylation status of poor differentiation group was significantly higher than moderate and poor-moderate differentiation groups (P < 0.01). By immunostaining 51 of 92 tumor tisssues demonstrated heterogeneous, positive immunostaining of tumor tissues (44.6%), significantly different from matched normal tissues (P < 0.001). Positive immunostaining of stage III and IV tumor tissues was significantly lower than stage I and II tumor tissues (P < 0.01). Poor differentiation group was also significantly lower than moderate and poor-moderate differentiation groups (P < 0.05). 80 percent of tumor tissues with E-cadherin gene methylated showed inactivated mRNA expression.</p> <p>Conclusions</p> <p>High methylation status of the 5' CpG island of E-cadherin gene may be one of the mechanisms in the development of gastric cardiac adenocarcinoma.</p

Discovery From Non-Parties (Third-Party Discovery) in International Arbitration

International arbitration rules and many arbitration laws usually provide procedures that permit tribunals to order parties to disclose documents and other materials to the other parties.1 More complex are the rules that determine opportunities to obtain discovery from persons that are not party to the arbitration (third-party discovery). This article will review third-party discovery under the Federal Arbitration Act (FAA) and the provisions of the US Code s.1782 that authorise US courts to act in aid of actions before foreign tribunals. Section 1782 has unique interest at this time because it figured prominently in the EU antitrust investigation of Intel that was initiated on request from Advanced Micro Devices (AMD). Early in that investigation, AMD filed a s.1782 request in the US District Court to obtain evidence from US sources for submission to the DG-Competition of the European Commission (EC). This request ultimately led to the Supreme Court’s decision in Intel Corp v Advanced Micro Devices Inc2 which appeared to significantly expand the scope of s.1782. Ironically, after AMD won on key legal issues in the Supreme Court, the District Court on remand exercised its discretion and denied the request for judicial assistance. This paper first describes the FAA non-party discovery rules and the split among the federal appellate courts concerning the authority of arbitrators to order prehearing discovery from non-parties. Next, it provides an analysis of the meaning of the terms “interested party” and “tribunal”—terms that were controversially interpreted by the Supreme Court in Intel and are essential to the application of s.1782. Finally, it discusses the “discretionary” factors used by the federal courts in deciding whether to grant a s.1782 request even when the statutory criteria are met. The opportunity to exercise this discretion seems to rebut the argument that the Supreme Court’s interpretation of s.1782 gives participants before foreign tribunals more discovery rights in the United States than are available to the parties in arbitrations covered by the FAA

Azimuth sidelobes suppression using multi-azimuth angle synthetic aperture radar images

A novel method is proposed for azimuth sidelobes suppression using multi-pass squinted (MPS) synthetic aperture radar (SAR) data. For MPS SAR, the radar observes the scene with different squint angles and heights on each pass. The MPS SAR mode acquisition geometry is given first. Then, 2D signals are focused and the images are registered to the master image. Based on the new signal model, elevation processing and incoherent addition are introduced in detail, which are the main parts for azimuth sidelobes suppression. Moreover, parameter design criteria in incoherent addition are derived for the best performance. With the proposed parameter optimization step, the new method has a prominent azimuth sidelobes suppression effect with a slightly better azimuth resolution, as verified by experimental results on both simulated point targets and TerraSAR-X data

Parity-time-symmetric two-qubit system: entanglement and sensing

In this paper we study exceptional-point (EP) effects and quantum sensing in a parity-time (PT)-symmetric two-qubit system with the Ising-type interaction. We explore EP properties of the system by analyzing degeneracy of energy eigenvalues or entanglement of eigenstates. We investigate entanglement dynamics of the two qubits in detail. In particular, we demonstrate that the system can create the steady-state entanglement in the PT-broken phase and collapse-revival phenomenon of entanglement in the PT-symmetric phase during the long-time evolution. We show that entanglement can be generated more quickly than the corresponding Hermitian system. Finally, we prove that the sensitivity of eigenstate quantum sensing for the parameters exhibits the remarkable enharncement at EPs, and propose a quantum-coherence measurement to witness the existence of EPs.Comment: 11 pages, 9 figure

Towards Impartial Multi-task Learning

Multi-task learning (MTL) has been widely used in representation learning. However, naively training all tasks simultaneously may lead to the partial training issue, where specific tasks are trained more adequately than others. In this paper, we propose to learn multiple tasks impartially. Specifically, for the task-shared parameters, we optimize the scaling factors via a closed-form solution, such that the aggregated gradient (sum of raw gradients weighted by the scaling factors) has equal projections onto individual tasks. For the task-specific parameters, we dynamically weigh the task losses so that all of them are kept at a comparable scale. Further, we find the above gradient balance and loss balance are complementary and thus propose a hybrid balance method to further improve the performance. Our impartial multi-task learning (IMTL) can be end-to-end trained without any heuristic hyper-parameter tuning, and is general to be applied on all kinds of losses without any distribution assumption. Moreover, our IMTL can converge to similar results even when the task losses are designed to have different scales, and thus it is scale-invariant. We extensively evaluate our IMTL on the standard MTL benchmarks including Cityscapes, NYUv2 and CelebA. It outperforms existing loss weighting methods under the same experimental settings

Probing onset of strong localization and electron-electron interactions with the presence of direct insulator-quantum Hall transition

We have performed low-temperature transport measurements on a disordered two-dimensional electron system (2DES). Features of the strong localization leading to the quantum Hall effect are observed after the 2DES undergoes a direct insulator-quantum Hall transition with increasing the perpendicular magnetic field. However, such a transition does not correspond to the onset of strong localization. The temperature dependences of the Hall resistivity and Hall conductivity reveal the importance of the electron-electron interaction effects to the observed transition in our study.Comment: 9 pages, 4 figure

GenDet: Meta Learning to Generate Detectors From Few Shots

Object detection has made enormous progress and has been widely used in many applications. However, it performs poorly when only limited training data is available for novel classes that the model has never seen before. Most existing approaches solve few-shot detection tasks implicitly without directly modeling the detectors for novel classes. In this article, we propose GenDet, a new meta-learning-based framework that can effectively generate object detectors for novel classes from few shots and, thus, conducts few-shot detection tasks explicitly. The detector generator is trained by numerous few-shot detection tasks sampled from base classes each with sufficient samples, and thus, it is expected to generalize well on novel classes. An adaptive pooling module is further introduced to suppress distracting samples and aggregate the detectors generated from multiple shots. Moreover, we propose to train a reference detector for each base class in the conventional way, with which to guide the training of the detector generator. The reference detectors and the detector generator can be trained simultaneously. Finally, the generated detectors of different classes are encouraged to be orthogonal to each other for better generalization. The proposed approach is extensively evaluated on the ImageNet, VOC, and COCO data sets under various few-shot detection settings, and it achieves new state-of-the-art results

New Insights into Traffic Dynamics: A Weighted Probabilistic Cellular Automaton Model

From the macroscopic viewpoint for describing the acceleration behavior of drivers, this letter presents a weighted probabilistic cellular automaton model (the WP model, for short) by introducing a kind of random acceleration probabilistic distribution function. The fundamental diagrams, the spatio-temporal pattern are analyzed in detail. It is shown that the presented model leads to the results consistent with the empirical data rather well, nonlinear velocity-density relationship exists in lower density region, and a new kind of traffic phenomenon called neo-synchronized flow is resulted. Furthermore, we give the criterion for distinguishing the high-speed and low-speed neo-synchronized flows and clarify the mechanism of this kind of traffic phenomena. In addition, the result that the time evolution of distribution of headways is displayed as a normal distribution further validates the reasonability of the neo-synchronized flow. These findings suggest that the diversity and randomicity of drivers and vehicles has indeed remarkable effect on traffic dynamics.Comment: 12 pages, 5 figures, submitted to Europhysics Letter

Dynamic modelling of heart rate response under different exercise intensity.

Heart rate is one of the major indications of human cardiovascular response to exercises. This study investigates human heart rate response dynamics to moderate exercise. A healthy male subject has been asked to walk on a motorised treadmill under a predefined exercise protocol. ECG, body movements, and oxygen saturation (SpO2) have been reliably monitored and recorded by using non-invasive portable sensors. To reduce heart rate variation caused by the influence of various internal or external factors, the designed step response protocol has been repeated three times. Experimental results show that both steady state gain and time constant of heart rate response are not invariant when walking speed is faster than 3 miles/hour, and time constant of offset exercise is noticeably longer than that of onset exercise