Christian Böhm
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View article: Remote consultations in sexual and reproductive health services: a systematic review of evidence on effectiveness, cost-effectiveness, experiences, access and equity
Remote consultations in sexual and reproductive health services: a systematic review of evidence on effectiveness, cost-effectiveness, experiences, access and equity Open
Objectives Timely access to sexual health screening and contraception is an important public health issue. Substantial funding reductions for sexual and reproductive health services (SRHS) and COVID-19 have led to significant changes in se…
View article: What guidance exists to support remote consultations in sexual and reproductive health services? A review of the policy and practice literature
What guidance exists to support remote consultations in sexual and reproductive health services? A review of the policy and practice literature Open
Introduction The use of remote consultations, such as appointments via telephone, video, online or text in sexual and reproductive health services (SRHS) across the UK, has expanded in recent years. This review synthesises grey literature …
View article: Engineering NIR-Sighted Bacteria
Engineering NIR-Sighted Bacteria Open
Spatially and temporally orchestrated gene expression underpins organismal development, physiology, and adaptation. In bacteria, two-component systems (TCS) translate environmental cues into inducible expression outputs. Inducible expressi…
View article: An Introductory Survey to Autoencoder-based Deep Clustering -- Sandboxes for Combining Clustering with Deep Learning
An Introductory Survey to Autoencoder-based Deep Clustering -- Sandboxes for Combining Clustering with Deep Learning Open
Autoencoders offer a general way of learning low-dimensional, non-linear representations from data without labels. This is achieved without making any particular assumptions about the data type or other domain knowledge. The generality and…
View article: Deep Learning-Adjusted Monitoring of In-Hospital Mortality after Liver Transplantation
Deep Learning-Adjusted Monitoring of In-Hospital Mortality after Liver Transplantation Open
Background: Surgeries represent a mainstay of medical care globally. Patterns of complications are frequently recognized late and place a considerable burden on health care systems. The aim was to develop and test the first deep learning-a…
View article: Adjoint computation of Berry phase gradients
Adjoint computation of Berry phase gradients Open
Berry phases offer a geometric perspective on wave propagation and are key to designing materials with topological wave transport. However, controlling Berry phases is challenging due to their dependence on global integrals over the Brillo…
View article: SHADE: Deep Density-based Clustering
SHADE: Deep Density-based Clustering Open
Detecting arbitrarily shaped clusters in high-dimensional noisy data is challenging for current clustering methods. We introduce SHADE (Structure-preserving High-dimensional Analysis with Density-based Exploration), the first deep clusteri…
View article: Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores
Adversarial Anomaly Detection using Gaussian Priors and Nonlinear Anomaly Scores Open
Anomaly detection in imbalanced datasets is a frequent and crucial problem, especially in the medical domain where retrieving and labeling irregularities is often expensive. By combining the generative stability of a $β$-variational autoen…
View article: Application of Deep Clustering Algorithms
Application of Deep Clustering Algorithms Open
Deep clustering algorithms have gained popularity for clustering complex, large-scale data sets, but getting started is difficult because of numerous decisions regarding architecture, optimizer, and other hyperparameters. Theoretical found…
View article: Data for Non-Redundant Image Clustering of Early Medieval Glass Beads (DSAA 2023)
Data for Non-Redundant Image Clustering of Early Medieval Glass Beads (DSAA 2023) Open
Archeological glass beads images with measurements (width, height, weight), pretrained models, resampled data (with less color bias) and clustering results from the paper Non-Redundant Image Clustering of Early Medieval Glass Beads (Accept…
View article: Data for Non-Redundant Image Clustering of Early Medieval Glass Beads (DSAA 2023)
Data for Non-Redundant Image Clustering of Early Medieval Glass Beads (DSAA 2023) Open
Archeological glass beads images with measurements (width, height, weight), pretrained models, resampled data (with less color bias) and clustering results from the paper Non-Redundant Image Clustering of Early Medieval Glass Beads (Accept…
View article: Archean ultrahigh temperature event in the northwestern Superior Province, Canada: Metamorphic evolution and tectonic implications of the Pikwitonei Granulite Domain
Archean ultrahigh temperature event in the northwestern Superior Province, Canada: Metamorphic evolution and tectonic implications of the Pikwitonei Granulite Domain Open
The Pikwitonei Granulite Domain (PGD), located in the northwestern Superior Province, is one of the largest Neoarchean high‐grade metamorphic domains in the world, and is a key to understanding the Neoarchean crustal evolution of the Super…
View article: A Novel Deep Learning Model as a Donor–Recipient Matching Tool to Predict Survival after Liver Transplantation
A Novel Deep Learning Model as a Donor–Recipient Matching Tool to Predict Survival after Liver Transplantation Open
Background: The “digital era” in the field of medicine is the new “here and now”. Artificial intelligence has entered many fields of medicine and is recently emerging in the field of organ transplantation. Solid organs remain a scarce reso…
View article: The DipEncoder: Enforcing Multimodality in Autoencoders
The DipEncoder: Enforcing Multimodality in Autoencoders Open
Hartigan's Dip-test of unimodality gained increasing interest in unsupervised learning over the past few years. It is free from complex parameterization and does not require a distribution assumed a priori. A useful property is that the re…
View article: Network Embedding via Deep Prediction Model
Network Embedding via Deep Prediction Model Open
Network-structured data becomes ubiquitous in daily life and is growing at a rapid pace. It presents great challenges to feature engineering due to the high non-linearity and sparsity of the data. The local and global structure of the real…
View article: Dip-based Deep Embedded Clustering with k-Estimation
Dip-based Deep Embedded Clustering with k-Estimation Open
The combination of clustering with Deep Learning has gained much attention in recent years. Unsupervised neural networks like autoencoders can autonomously learn the essential structures in a data set. This idea can be combined with cluste…
View article: Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces
Details (Don't) Matter: Isolating Cluster Information in Deep Embedded Spaces Open
Deep clustering techniques combine representation learning with clustering objectives to improve their performance. Among existing deep clustering techniques, autoencoder-based methods are the most prevalent ones. While they achieve promis…
View article: Massively Parallel Graph Drawing and Representation Learning
Massively Parallel Graph Drawing and Representation Learning Open
To fully exploit the performance potential of modern multi-core processors, machine learning and data mining algorithms for big data must be parallelized in multiple ways. Today's CPUs consist of multiple cores, each following an independe…
View article: Data Compression as a Comprehensive Framework for Graph Drawing and Representation Learning
Data Compression as a Comprehensive Framework for Graph Drawing and Representation Learning Open
Embedding a graph into feature space is a promising approach to understand its structure. Embedding into 2D or 3D space enables visualization; representation in higher-dimensional vector space (typically >100D) enables the application of d…
View article: Space-filling Curves for High-performance Data Mining
Space-filling Curves for High-performance Data Mining Open
Space-filling curves like the Hilbert-curve, Peano-curve and Z-order map natural or real numbers from a two or higher dimensional space to a one dimensional space preserving locality. They have numerous applications like search structures,…
View article: DeepECT: The Deep Embedded Cluster Tree
DeepECT: The Deep Embedded Cluster Tree Open
The idea of combining the high representational power of deep learning techniques with clustering methods has gained much attention in recent years. Optimizing a clustering objective and the dataset representation simultaneously has been s…
View article: Online Semi-supervised Multi-label Classification with Label Compression and Local Smooth Regression
Online Semi-supervised Multi-label Classification with Label Compression and Local Smooth Regression Open
Online semi-supervised multi-label classification serves a practical yet challenging task since only a small number of labeled instances are available in real streaming environments. However, the mainstream of existing online classificatio…
View article: Clustering of mixed-type data considering concept hierarchies: problem specification and algorithm
Clustering of mixed-type data considering concept hierarchies: problem specification and algorithm Open
Most clustering algorithms have been designed only for pure numerical or pure categorical data sets, while nowadays many applications generate mixed data. It raises the question how to integrate various types of attributes so that one coul…
View article: Deep Embedded Non-Redundant Clustering
Deep Embedded Non-Redundant Clustering Open
Complex data types like images can be clustered in multiple valid ways. Non-redundant clustering aims at extracting those meaningful groupings by discouraging redundancy between clusterings. Unfortunately, clustering images in pixel space …
View article: 3-D seismic full-waveform inversion of the Taurus-Zagros region in Iran and Turkey
3-D seismic full-waveform inversion of the Taurus-Zagros region in Iran and Turkey Open
<p><span>We present an interpretation of a 3-D velocity model resulting from a regional analysis of earthquake waveforms. This model contains 3-D structure of the crust and upper mantle beneath the Arabian-Eurasian collision zo…
View article: Network Structure and Transfer Behaviors Embedding via Deep Prediction Model
Network Structure and Transfer Behaviors Embedding via Deep Prediction Model Open
Network-structured data is becoming increasingly popular in many applications. However, these data present great challenges to feature engineering due to its high non-linearity and sparsity. The issue on how to transfer the link-connected …
View article: Multi-core K-means
Multi-core K-means Open
Today's microprocessors consist of multiple cores each of which can perform multiple additions, multiplications, or other operations simultaneously in one clock cycle. To maximize performance, two types of parallelism must be applied in a …