We have measured the nondipolar contribution to the Ar ls photoelectron angular distribution over the 30 -2000 eV electron-energy range. The nondipolar interaction results in a forward or backward asymmetry with respect to the photon beam. The asymmetry is directed backward near threshold, is symmetric near 230 eV, and becomes increasingly forward directed at higher energies. The measured asymmetries are in excellent agreement with theoretical calculations, which include interference between the electric-dipole and electric-quadrupole photoionization amplitudes.PACS numbers: 32.80.Fb Current understanding of atomic photoionization phenomena is largely based on the dipole approximation [1][2][3][4]. Within this approximation, the standard transition matrix element used to describe photoionization between initial and final states, M;f = (f~e xp(ik r)c p~i), is simplified. In this expression, exp(ik . r)E describes the photon field (k is the photon propagation vector, r is the electron position vector, and c the photon polarization vector), and p is the electron momentum operator. In the dipole approximation, only the first term of the expan-0031-9007/95/75(26)/4736(4) $06.00

In a tokamak plasma, sawtooth oscillations in the central temperature, caused by a magnetohydrodynamic instability, can be partially stabilized by fast ions. The resulting less frequent sawtooth crashes can trigger unwanted magnetohydrodynamic activity. This Letter reports on experiments showing that modest electron-cyclotron current drive power, with the deposition positioned by feedback control of the injection angle, can reliably shorten the sawtooth period in the presence of ions with energies >or=0.5 MeV. Certain surprising elements of the results are evaluated qualitatively in terms of existing theory.

On page 1026, the last sentence of the second paragraph should read, "However, Lablanquie et al.[5] could show from their ͑g, 2e͒ data that at E exc 4 eV the ratio K 0 (which is proportional to the a u term in their notation) to K 1 is small, typically down to 1͞19."

B one tumors include benign, intermediate, and malignant lesions, according to the classification system of the World Health Organization (1). Malignant neoplasms can be further divided into primary and secondary bone tumors or metastases (2,3). Radiography is the suggested primary imaging modality for the diagnosis of bone tumors because it can enable visualization of the location, destruction pattern, and periosteal reaction pattern of bone lesions (4,5). These destruction patterns reflect the biologic activity of bone lesions, through which they can be categorized as aggressive or nonaggressive (6). As demonstrated by Lodwick's well-established grading system, the destruction patterns of bone tumors observed on radiographs allow for evaluation of biologic activity and subsequently allow for risk assessment of malignancy (7,8). Primary bone tumors are uncommon. Thus, many radiologists may not be able to develop sufficient expertise to reliably identify and assess these lesions on radiographs (9). However, early detection and correct diagnosis are crucial for adequate and successful treatment (10). To improve the rates of early detection and correct assessment, an artificial intelligence model that could detect and accurately categorize bone lesions into malignant or benign bone lesions on radiographs may be beneficial. Recently, studies have shown that deep learning (DL) models reliably assess and detect a variety of diseases based on medical imaging data (11,12). Clinical implementation of these models may improve the reliability and accuracy of radiologic assessment, thus potentially leading to improved diagnostics and better patient outcomes (13). Recently, a preliminary study used DL to classify primary bone tumors on radiographs ( 14), but Background: An artificial intelligence model that assesses primary bone tumors on radiographs may assist in the diagnostic workflow.Purpose: To develop a multitask deep learning (DL) model for simultaneous bounding box placement, segmentation, and classification of primary bone tumors on radiographs. Materials and Methods:This retrospective study analyzed bone tumors on radiographs acquired prior to treatment and obtained from patient data from January 2000 to June 2020. Benign or malignant bone tumors were diagnosed in all patients by using the histopathologic findings as the reference standard. By using split-sample validation, 70% of the patients were assigned to the training set, 15% were assigned to the validation set, and 15% were assigned to the test set. The final performance was evaluated on an external test set by using geographic validation, with accuracy, sensitivity, specificity, and 95% CIs being used for classification, the intersection over union (IoU) being used for bounding box placements, and the Dice score being used for segmentations. Results:Radiographs from 934 patients (mean age, 33 years 6 19 [standard deviation]; 419 women) were evaluated in the internal data set, which included 667 benign bone tumors and 267 malignant bone tumors. Six hundred f...