Topologically Regularized Multiple Instance Learning to Harness Data Scarcity

In biomedical data analysis, Multiple Instance Learning (MIL) models have emerged as a powerful tool to classify patients' microscopy samples. However, the data-intensive requirement of these models poses a significant challenge in scenarios with scarce data availability, e.g., in rare diseases. We introduce a topological regularization term to MIL to mitigate this challenge. It provides a shape-preserving inductive bias that compels the encoder to maintain the essential geometrical-topological structure of input bags during projection into latent space. This enhances the performance and generalization of the MIL classifier regardless of the aggregation function, particularly for scarce training data. The effectiveness of our method is confirmed through experiments across a range of datasets, showing an average enhancement of 2.8% for MIL benchmarks, 15.3% for synthetic MIL datasets, and 5.5% for real-world biomedical datasets over the current state-of-the-art

A malware instruction set for behavior-based analysis

We introduce a new representation for monitored behavior of malicious software called Malware Instruction Set (MIST). The representation is optimized for effective and efficient analysis of behavior using data mining and machine learning techniques. It can be obtained automatically during analysis of malware with a behavior monitoring tool or by converting existing behavior reports. The representation is not restricted to a particular monitoring tool and thus can also be used as a meta language to unify behavior reports of different sources

VLBI and GPS-based Time-Transfer Using CONT08 Data

One important prerequisite for geodetic Very Long Baseline Interferometry (VLBI) is the use of frequency standards with excellent short term stability. This makes VLBI stations, which are often co-located with Global Navigation Satellite System (GNSS) receiving stations, interesting for studies of time- and frequency-transfer techniques. We present an assessment of VLBI time-transfer based on the data of the two week long consecutive IVS CONT08 VLBI campaign by using GPS Carrier Phase (GPSCP). CONT08 was a 15 day long campaign in August 2008 that involved eleven VLBI stations on five continents. For CONT08 we estimated the worst case VLBI frequency link stability between the stations of Onsala and Wettzell to 1e-15 at one day. Comparisons with GPSCP confirm the VLBI results. We also identify time-transfer related challenges of the VLBI technique as used today

VLBI time-transfer using CONT08 data

One important prerequisite for geodetic Very Long Baseline Interferometry (VLBI) is the use of frequency standards with excellent short term stability, i.e. hydrogen masers. This makes VLBI stations, which are often co-located with Global Navigation Satellite System (GNSS) receiving stations, interesting for studies of time- and frequency-transfer techniques. In this paper we present an assessment of VLBI time-transfer based on the data of the two week long consecutive IVS Cont08 VLBI-campaign by using GPS Carrier Phase (GPSCP). Cont08 was a 15 days long campaign in August 2008 that involved eleven VLBI stations on five continents. For Cont08 we estimated the worst case VLBI time link stability between the station clocks of ONSALA and WETTZELL to about 1.5e-15 at one day. Comparisons with clock differences estimated with GPSCP confirm the VLBI results. The paper also indicates time-transfer related problems of the VLBI technique as used today. \ua9 2010 IEEE

A Diffusion Model Predicts 3D Shapes from 2D Microscopy Images

Diffusion models are a special type of generative model, capable of synthesising new data from a learnt distribution. We introduce DISPR, a diffusion-based model for solving the inverse problem of three-dimensional (3D) cell shape prediction from two-dimensional (2D) single cell microscopy images. Using the 2D microscopy image as a prior, DISPR is conditioned to predict realistic 3D shape reconstructions. To showcase the applicability of DISPR as a data augmentation tool in a feature-based single cell classification task, we extract morphological features from the red blood cells grouped into six highly imbalanced classes. Adding features from the DISPR predictions to the three minority classes improved the macro F1 score from $F1_\text{macro} = 55.2 \pm 4.6\%$ to $F1_\text{macro} = 72.2 \pm 4.9\%$. We thus demonstrate that diffusion models can be successfully applied to inverse biomedical problems, and that they learn to reconstruct 3D shapes with realistic morphological features from 2D microscopy images

Capturing Shape Information with Multi-Scale Topological Loss Terms for 3D Reconstruction

Reconstructing 3D objects from 2D images is both challenging for our brains and machine learning algorithms. To support this spatial reasoning task, contextual information about the overall shape of an object is critical. However, such information is not captured by established loss terms (e.g. Dice loss). We propose to complement geometrical shape information by including multi-scale topological features, such as connected components, cycles, and voids, in the reconstruction loss. Our method uses cubical complexes to calculate topological features of 3D volume data and employs an optimal transport distance to guide the reconstruction process. This topology-aware loss is fully differentiable, computationally efficient, and can be added to any neural network. We demonstrate the utility of our loss by incorporating it into SHAPR, a model for predicting the 3D cell shape of individual cells based on 2D microscopy images. Using a hybrid loss that leverages both geometrical and topological information of single objects to assess their shape, we find that topological information substantially improves the quality of reconstructions, thus highlighting its ability to extract more relevant features from image datasets.Comment: Accepted at the 25th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI

Search for dark matter produced in association with bottom or top quarks in √s = 13 TeV pp collisions with the ATLAS detector

A search for weakly interacting massive particle dark matter produced in association with bottom or top quarks is presented. Final states containing third-generation quarks and miss- ing transverse momentum are considered. The analysis uses 36.1 fb−1 of proton–proton collision data recorded by the ATLAS experiment at √s = 13 TeV in 2015 and 2016. No significant excess of events above the estimated backgrounds is observed. The results are in- terpreted in the framework of simplified models of spin-0 dark-matter mediators. For colour- neutral spin-0 mediators produced in association with top quarks and decaying into a pair of dark-matter particles, mediator masses below 50 GeV are excluded assuming a dark-matter candidate mass of 1 GeV and unitary couplings. For scalar and pseudoscalar mediators produced in association with bottom quarks, the search sets limits on the production cross- section of 300 times the predicted rate for mediators with masses between 10 and 50 GeV and assuming a dark-matter mass of 1 GeV and unitary coupling. Constraints on colour- charged scalar simplified models are also presented. Assuming a dark-matter particle mass of 35 GeV, mediator particles with mass below 1.1 TeV are excluded for couplings yielding a dark-matter relic density consistent with measurements