An elliptic expansion of the potential field source surface model

Context. The potential field source surface model is frequently used as a basis for further scientific investigations where a comprehensive coronal magnetic field is of importance. Its parameters, especially the position and shape of the source surface, are crucial for the interpretation of the state of the interplanetary medium. Improvements have been suggested that introduce one or more additional free parameters to the model, for example, the current sheet source surface (CSSS) model. Aims. Relaxing the spherical constraint of the source surface and allowing it to be elliptical gives modelers the option of deforming it to more accurately match the physical environment of the specific period or location to be analyzed. Methods. A numerical solver is presented that solves Laplace's equation on a three-dimensional grid using finite differences. The solver is capable of working on structured spherical grids that can be deformed to create elliptical source surfaces. Results. The configurations of the coronal magnetic field are presented using this new solver. Three-dimensional renderings are complemented by Carrington-like synoptic maps of the magnetic configuration at different heights in the solar corona. Differences in the magnetic configuration computed by the spherical and elliptical models are illustrated.Comment: 11 pages, 7 figure

The TREC2001 video track: information retrieval on digital video information

The development of techniques to support content-based access to archives of digital video information has recently started to receive much attention from the research community. During 2001, the annual TREC activity, which has been benchmarking the performance of information retrieval techniques on a range of media for 10 years, included a ”track“ or activity which allowed investigation into approaches to support searching through a video library. This paper is not intended to provide a comprehensive picture of the different approaches taken by the TREC2001 video track participants but instead we give an overview of the TREC video search task and a thumbnail sketch of the approaches taken by different groups. The reason for writing this paper is to highlight the message from the TREC video track that there are now a variety of approaches available for searching and browsing through digital video archives, that these approaches do work, are scalable to larger archives and can yield useful retrieval performance for users. This has important implications in making digital libraries of video information attainable

PANEL: Challenges for multimedia/multimodal research in the next decade

The multimedia and multimodal community is witnessing an explosive transformation in the recent years with major societal impact. With the unprecedented deployment of multimedia devices and systems, multimedia research is critical to our abilities and prospects in advancing state-of-theart technologies and solving real-world challenges facing the society and the nation. To respond to these challenges and further advance the frontiers of the field of multimedia, this panel will discuss the challenges and visions that may guide future research in the next ten years

Supporting High-Uncertainty Decisions through AI and Logic-Style Explanations

A common criteria for Explainable AI (XAI) is to support users in establishing appropriate trust in the AI - rejecting advice when it is incorrect, and accepting advice when it is correct. Previous findings suggest that explanations can cause an over-reliance on AI (overly accepting advice). Explanations that evoke appropriate trust are even more challenging for decision-making tasks that are difficult for humans and AI. For this reason, we study decision-making by non-experts in the high-uncertainty domain of stock trading. We compare the effectiveness of three different explanation styles (influenced by inductive, abductive, and deductive reasoning) and the role of AI confidence in terms of a) the users' reliance on the XAI interface elements (charts with indicators, AI prediction, explanation), b) the correctness of the decision (task performance), and c) the agreement with the AI's prediction. In contrast to previous work, we look at interactions between different aspects of decision-making, including AI correctness, and the combined effects of AI confidence and explanations styles. Our results show that specific explanation styles (abductive and deductive) improve the user's task performance in the case of high AI confidence compared to inductive explanations. In other words, these styles of explanations were able to invoke correct decisions (for both positive and negative decisions) when the system was certain. In such a condition, the agreement between the user's decision and the AI prediction confirms this finding, highlighting a significant agreement increase when the AI is correct. This suggests that both explanation styles are suitable for evoking appropriate trust in a confident AI. Our findings further indicate a need to consider AI confidence as a criterion for including or excluding explanations from AI interfaces. In addition, this paper highlights the importance of carefully selecting an explanation style according to the characteristics of the task and data

Draft genome sequence of the psychrophilic and alkaliphilic <em>Rhodonellum psychrophilum</em> strain GCM71^T

Rhodonellum psychrophilum GCM71(T), isolated from the cold and alkaline submarine ikaite columns in the Ikka Fjord in Greenland, displays optimal growth at 5 to 10°C and pH 10. Here, we report the draft genome sequence of this strain, which may provide insight into the mechanisms of adaptation to these extreme conditions

Convolutional Neural Network for Material Decomposition in Spectral CT Scans

Spectral computed tomography acquires energy-resolved data that allows recovery of densities of constituents of an object. This can be achieved by decomposing the measured spectral projection into material projections, and passing these decomposed projections through a tomographic reconstruction algorithm, to get the volumetric mass density of each material. Material decomposition is a nonlinear inverse problem that has been traditionally solved using model-based material decomposition algorithms. However, the forward model is difficult to estimate in real prototypes. Moreover, the traditional regularizers used to stabilized inversions are not fully relevant in the projection domain.In this study, we propose a deep-learning method for material decomposition in the projection domain. We validate our methodology with numerical phantoms of human knees that are created from synchrotron CT scans. We consider four different scans for training, and one for validation. The measurements are corrupted by Poisson noise, assuming that at most 10 5 photons hit the detector. Compared to a regularized Gauss-Newton algorithm, the proposed deep-learning approach provides a compromise between noise and resolution, which reduces the computation time by a factor of 100

Interference of a first-order transition with the formation of a spin-Peierls state in alpha'-NaV2O5?

We present results of high-resolution thermal-expansion and specific-heat measurements on single crystalline alpha'-NaV2O5. We find clear evidence for two almost degenerate phase transitions associated with the formation of the dimerized state around 33K: A sharp first-order transition at T1=(33+-0.1)K slightly below the onset of a second-order transition at T2onset around (34+-0.1)K. The latter is accompanied by pronounced spontaneous strains. Our results are consistent with a structural transformation at T1 induced by the incipient spin-Peierls (SP) order parameter above T2=TSP.Comment: 5 pages, 7 figure

Application of Proximal Alternating Linearized Minimization (PALM) and inertial PALM to dynamic 3D CT

The foot and ankle is a complex structure consisting of 28 bones and 30 joints that changes from being completely mobile when positioning the foot on the floor to a rigid closed pack position during propulsion such as when running or jumping. An understanding of this complex structure has largely been derived from cadaveric studies. In vivo studies have largely relied on skin surface markers and multi-camera systems that are unable to differentiate small motions between the bones of the foot. MRI and CT based studies have struggled to interpret functional weight bearing motion as imaging is largely static and non-load bearing. Arthritic diseases of the foot and ankle are treated either by fusion of the joints to remove motion, or joint replacement to retain motion. Until a better understanding of the biomechanics of these joints can be achieved

Material Decomposition in Spectral CT using deep learning: A Sim2Real transfer approach

The state-of-the art for solving the nonlinear material decomposition problem in spectral computed tomography is based on variational methods, but these are computationally slow and critically depend on the particular choice of the regularization functional. Convolutional neural networks have been proposed for addressing these issues. However, learning algorithms require large amounts of experimental data sets. We propose a deep learning strategy for solving the material decomposition problem based on a U-Net architecture and a Sim2Real transfer learning approach where the knowledge that we learn from synthetic data is transferred to a real-world scenario. In order for this approach to work, synthetic data must be realistic and representative of the experimental data. For this purpose, numerical phantoms are generated from human CT volumes of the KiTS19 Challenge dataset, segmented into specific materials (soft tissue and bone). These volumes are projected into sinogram space in order to simulate photon counting data, taking into account the energy response of the scanner. We compared projection- and image-based decomposition approaches where the network is trained to decompose the materials either in the projection or in the image domain. The proposed Sim2Real transfer strategies are compared to a regularized Gauss-Newton (RGN) method on synthetic data, experimental phantom data and human thorax data

Real-time Cardiovascular MR with Spatio-temporal De-aliasing using Deep Learning - Proof of Concept in Congenital Heart Disease

PURPOSE: Real-time assessment of ventricular volumes requires high acceleration factors. Residual convolutional neural networks (CNN) have shown potential for removing artifacts caused by data undersampling. In this study we investigated the effect of different radial sampling patterns on the accuracy of a CNN. We also acquired actual real-time undersampled radial data in patients with congenital heart disease (CHD), and compare CNN reconstruction to Compressed Sensing (CS). METHODS: A 3D (2D plus time) CNN architecture was developed, and trained using 2276 gold-standard paired 3D data sets, with 14x radial undersampling. Four sampling schemes were tested, using 169 previously unseen 3D 'synthetic' test data sets. Actual real-time tiny Golden Angle (tGA) radial SSFP data was acquired in 10 new patients (122 3D data sets), and reconstructed using the 3D CNN as well as a CS algorithm; GRASP. RESULTS: Sampling pattern was shown to be important for image quality, and accurate visualisation of cardiac structures. For actual real-time data, overall reconstruction time with CNN (including creation of aliased images) was shown to be more than 5x faster than GRASP. Additionally, CNN image quality and accuracy of biventricular volumes was observed to be superior to GRASP for the same raw data. CONCLUSION: This paper has demonstrated the potential for the use of a 3D CNN for deep de-aliasing of real-time radial data, within the clinical setting. Clinical measures of ventricular volumes using real-time data with CNN reconstruction are not statistically significantly different from the gold-standard, cardiac gated, BH techniques