Prognos (R) in the diagnosis of amalgam hypersensitivity - A diagnostic case-control study

Objective: We aimed to investigate whether the Prognos (R) device might be a useful tool in the diagnosis of disorders suspected to be due to dental amalgam fillings. Participants and Methods: A diagnostic case-control study was performed in 27 patients who complained about health problems attributed to amalgam ( cases), 27 healthy volunteers with amalgam fillings ( controls I), and 27 healthy amalgam-free volunteers ( controls II). All participants were tested before and after application of 300 mg DMPS (2.3-dimercapto-1-propanesulfonic acid) with Prognos, a diagnostic device for the energetic measurement of Traditional Chinese Medicine meridians. In addition, mercury was measured in blood, urine, and saliva, and a lymphocyte transformation test (LTT) was performed. Results: Diagnoses derived from the first and second Prognos testing did not agree above chance (Cohen's Kappa = -0.11, 95% confidence interval -0.33 to 0.10; p = 0.30). Agreement for secondary outcome measures was poor, too. Prognos measurements did not differ between cases and controls. Correlations with measurements in urine, blood and saliva were low. Conclusion: In this study Prognos could not be shown to be a useful tool in the diagnosis of disorders suspected to be due to dental amalgam fillings

A Multi-cut Formulation for Joint Segmentation and Tracking of Multiple Objects

Recently, Minimum Cost Multicut Formulations have been proposed and proven to be successful in both motion trajectory segmentation and multi-target tracking scenarios. Both tasks benefit from decomposing a graphical model into an optimal number of connected components based on attractive and repulsive pairwise terms. The two tasks are formulated on different levels of granularity and, accordingly, leverage mostly local information for motion segmentation and mostly high-level information for multi-target tracking. In this paper we argue that point trajectories and their local relationships can contribute to the high-level task of multi-target tracking and also argue that high-level cues from object detection and tracking are helpful to solve motion segmentation. We propose a joint graphical model for point trajectories and object detections whose Multicuts are solutions to motion segmentation {\it and} multi-target tracking problems at once. Results on the FBMS59 motion segmentation benchmark as well as on pedestrian tracking sequences from the 2D MOT 2015 benchmark demonstrate the promise of this joint approach

Segmentations-Leak: Membership Inference Attacks and Defenses in Semantic Image Segmentation

Today's success of state of the art methods for semantic segmentation is driven by large datasets. Data is considered an important asset that needs to be protected, as the collection and annotation of such datasets comes at significant efforts and associated costs. In addition, visual data might contain private or sensitive information, that makes it equally unsuited for public release. Unfortunately, recent work on membership inference in the broader area of adversarial machine learning and inference attacks on machine learning models has shown that even black box classifiers leak information on the dataset that they were trained on. We show that such membership inference attacks can be successfully carried out on complex, state of the art models for semantic segmentation. In order to mitigate the associated risks, we also study a series of defenses against such membership inference attacks and find effective counter measures against the existing risks with little effect on the utility of the segmentation method. Finally, we extensively evaluate our attacks and defenses on a range of relevant real-world datasets: Cityscapes, BDD100K, and Mapillary Vistas.Comment: Accepted to ECCV 2020. Code at: https://github.com/SSAW14/segmentation_membership_inferenc

A fabrication guide for planar silicon quantum dot heterostructures

We describe important considerations to create top-down fabricated planar quantum dots in silicon, often not discussed in detail in literature. The subtle interplay between intrinsic material properties, interfaces and fabrication processes plays a crucial role in the formation of electrostatically defined quantum dots. Processes such as oxidation, physical vapor deposition and atomic-layer deposition must be tailored in order to prevent unwanted side effects such as defects, disorder and dewetting. In two directly related manuscripts written in parallel we use techniques described in this work to create depletion-mode quantum dots in intrinsic silicon, and low-disorder silicon quantum dots defined with palladium gates. While we discuss three different planar gate structures, the general principles also apply to 0D and 1D systems, such as self-assembled islands and nanowires.Comment: Accepted for publication in Nanotechnology. 31 pages, 12 figure

Antecedents and consequences of effectuation and causation in the international new venture creation process

The selection of the entry mode in an international market is of key importance for the venture. A process-based perspective on entry mode selection can add to the International Business and International Entrepreneurship literature. Framing the international market entry as an entrepreneurial process, this paper analyzes the antecedents and consequences of causation and effectuation in the entry mode selection. For the analysis, regression-based techniques were used on a sample of 65 gazelles. The results indicate that experienced entrepreneurs tend to apply effectuation rather than causation, while uncertainty does not have a systematic influence. Entrepreneurs using causation-based international new venture creation processes tend to engage in export-type entry modes, while effectuation-based international new venture creation processes do not predetermine the entry mod

B-cosification: {T}ransforming Deep Neural Networks to be Inherently Interpretable

B-cos Networks have been shown to be effective for obtaining highly humaninterpretable explanations of model decisions by architecturally enforcingstronger alignment between inputs and weight. B-cos variants of convolutionalnetworks (CNNs) and vision transformers (ViTs), which primarily replace linearlayers with B-cos transformations, perform competitively to their respectivestandard variants while also yielding explanations that are faithful by design.However, it has so far been necessary to train these models from scratch, whichis increasingly infeasible in the era of large, pre-trained foundation models.In this work, inspired by the architectural similarities in standard DNNs andB-cos networks, we propose 'B-cosification', a novel approach to transformexisting pre-trained models to become inherently interpretable. We perform athorough study of design choices to perform this conversion, both forconvolutional neural networks and vision transformers. We find thatB-cosification can yield models that are on par with B-cos models trained fromscratch in terms of interpretability, while often outperforming them in termsof classification performance at a fraction of the training cost. Subsequently,we apply B-cosification to a pretrained CLIP model, and show that, even withlimited data and compute cost, we obtain a B-cosified version that is highlyinterpretable and competitive on zero shot performance across a variety ofdatasets. We release our code and pre-trained model weights athttps://github.com/shrebox/B-cosification.<br