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Magnetometers based on the extraordinary magnetoresistance (EMR) effect are promising for applications which demand high sensitivity combined with room temperature operation but their application for magnetic field sensing requires further optimization. A key challenge is to obtain Ohmic metal/semiconductor contacts with low contact resistances in EMR devices comprising semiconductors with low carrier densities and high electron mobilities, yet, this topic remains scarcely investigated experimentally. By annealing high-mobility InSb in argon with systematically increasing temperatures, we experimentally demonstrate how the contact resistance to InSb films can be improved by two orders of magnitude by annealing to the micro-Ohm cm2 range without degrading the high mobility. We further show that lowering the contact resistance monotonously increases the room temperature magnetoresistance at 2 T from 700% to 65,000%. Lastly, we explore the origin of intrinsic magnetoresistance in high-mobility InSb thin films and suggest that it can best be explained by multiple band conduction., Comment: 13 pages + 2 pages of supplemental information. 5 Figues + 3 supplemental figures

Authorship Verification (AV) is the process of analyzing a set of documents to determine whether they were written by a specific author. This problem often arises in forensic scenarios, e.g., in cases where the documents in question constitute evidence for a crime. Existing state-of-the-art AV methods use computational solutions that are not supported by a plausible scientific explanation for their functioning and that are often difficult for analysts to interpret. To address this, we propose a method relying on calculating a quantity we call $\lambda_G$ (LambdaG): the ratio between the likelihood of a document given a model of the Grammar for the candidate author and the likelihood of the same document given a model of the Grammar for a reference population. These Grammar Models are estimated using $n$-gram language models that are trained solely on grammatical features. Despite not needing large amounts of data for training, LambdaG still outperforms other established AV methods with higher computational complexity, including a fine-tuned Siamese Transformer network. Our empirical evaluation based on four baseline methods applied to twelve datasets shows that LambdaG leads to better results in terms of both accuracy and AUC in eleven cases and in all twelve cases if considering only topic-agnostic methods. The algorithm is also highly robust to important variations in the genre of the reference population in many cross-genre comparisons. In addition to these properties, we demonstrate how LambdaG is easier to interpret than the current state-of-the-art. We argue that the advantage of LambdaG over other methods is due to fact that it is compatible with Cognitive Linguistic theories of language processing.

Oxygen vacancies are often attributed to changes in the electronic transport for perovskite oxide materials (ABO$_3$). Here, we use density functional theory (DFT) coupled with non-equilibrium Green's functions (NEGF) to systematically investigate the influence of O vacancies and also A and B-site vacancies, on the electronic transport as characterised by a scattering cross-section. We consider SrNbO$_3$ and n-type SrTiO$_3$ and contrast results for bulk and thin film (slab) geometries. By varying the electron doping in SrTiO$_3$ we get insight into how the electron-vacancy scattering vary for different experimental conditions. We observe a significant increase in the scattering cross-section (in units of square-lattice parameter, $a^2$) from ca. $0.5-2.5a^2$ per vacancy in SrNbO$_3$ and heavily doped SrTiO$_3$ to more than $9a^2$ in SrTiO$_3$ with 0.02 free carriers per unit cell. Furthermore, the scattering strength of O vacancies is enhanced in TiO$_2$ terminated surfaces by more than 6 times in lowly doped SrTiO$_3$ compared to other locations in slabs and bulk systems. Interestingly, we also find that Sr vacancies go from being negligible scattering centers in SrNbO$_3$ and heavily doped SrTiO$_3$, to having a large scattering cross-section in weakly doped SrTiO$_3$. We therefore conclude that the electron-vacancy scattering in these systems is sensitive to the combination of electron concentration and vacancy location.

3D object detection in Bird's-Eye-View (BEV) space has recently emerged as a prevalent approach in the field of autonomous driving. Despite the demonstrated improvements in accuracy and velocity estimation compared to perspective view methods, the deployment of BEV-based techniques in real-world autonomous vehicles remains challenging. This is primarily due to their reliance on vision-transformer (ViT) based architectures, which introduce quadratic complexity with respect to the input resolution. To address this issue, we propose an efficient BEV-based 3D detection framework called BEVENet, which leverages a convolutional-only architectural design to circumvent the limitations of ViT models while maintaining the effectiveness of BEV-based methods. Our experiments show that BEVENet is 3$\times$ faster than contemporary state-of-the-art (SOTA) approaches on the NuScenes challenge, achieving a mean average precision (mAP) of 0.456 and a nuScenes detection score (NDS) of 0.555 on the NuScenes validation dataset, with an inference speed of 47.6 frames per second. To the best of our knowledge, this study stands as the first to achieve such significant efficiency improvements for BEV-based methods, highlighting their enhanced feasibility for real-world autonomous driving applications.

Transition metal oxides with a wide variety of electronic and magnetic properties offer an extraordinary possibility to be a platform for developing future electronics based on unconventional quantum phenomena, for instance, the topology. The formation of topologically non-trivial states is related to crystalline symmetry, spin-orbit coupling, and magnetic ordering. Here, we demonstrate how lattice distortions and octahedral rotation in SrNbO$_3$ films induce the band topology. By employing angle-resolved photoemission spectroscopy (ARPES) and density functional theory (DFT) calculations, we verify the presence of in-phase $a^0a^0c^+$ octahedral rotation in ultra-thin SrNbO$_3$ films, which causes the formation of topologically-protected Dirac band crossings. Our study illustrates that octahedral engineering can be effectively exploited for implanting and controlling quantum topological phases in transition metal oxides., Comment: 6 pages, 4 figures

Accurate segmentation of lesions is crucial for diagnosis and treatment of early esophageal cancer (EEC). However, neither traditional nor deep learning-based methods up to today can meet the clinical requirements, with the mean Dice score - the most important metric in medical image analysis - hardly exceeding 0.75. In this paper, we present a novel deep learning approach for segmenting EEC lesions. Our approach stands out for its uniqueness, as it relies solely on a single image coming from one patient, forming the so-called "You-Only-Have-One" (YOHO) framework. On one hand, this "one-image-one-network" learning ensures complete patient privacy as it does not use any images from other patients as the training data. On the other hand, it avoids nearly all generalization-related problems since each trained network is applied only to the input image itself. In particular, we can push the training to "over-fitting" as much as possible to increase the segmentation accuracy. Our technical details include an interaction with clinical physicians to utilize their expertise, a geometry-based rendering of a single lesion image to generate the training set (the \emph{biggest} novelty), and an edge-enhanced UNet. We have evaluated YOHO over an EEC data-set created by ourselves and achieved a mean Dice score of 0.888, which represents a significant advance toward clinical applications.