Grand Challenges in Immersive Analytics

The definitive version will be published in CHI 2021, May 8–13, 2021, Yokohama, JapanInternational audienceImmersive Analytics is a quickly evolving field that unites several areas such as visualisation, immersive environments, and humancomputer interaction to support human data analysis with emerging technologies. This research has thrived over the past years with multiple workshops, seminars, and a growing body of publications, spanning several conferences. Given the rapid advancement of interaction technologies and novel application domains, this paper aims toward a broader research agenda to enable widespread adoption. We present 17 key research challenges developed over multiple sessions by a diverse group of 24 international experts, initiated from a virtual scientific workshop at ACM CHI 2020. These challenges aim to coordinate future work by providing a systematic roadmap of current directions and impending hurdles to facilitate productive and effective applications for Immersive Analytics

Multi-modality machine learning predicting Parkinson's disease

Personalized medicine promises individualized disease prediction and treatment. The convergence of machine learning (ML) and available multimodal data is key moving forward. We build upon previous work to deliver multimodal predictions of Parkinson's disease (PD) risk and systematically develop a model using GenoML, an automated ML package, to make improved multi-omic predictions of PD, validated in an external cohort. We investigated top features, constructed hypothesis-free disease-relevant networks, and investigated drug-gene interactions. We performed automated ML on multimodal data from the Parkinson's progression marker initiative (PPMI). After selecting the best performing algorithm, all PPMI data was used to tune the selected model. The model was validated in the Parkinson's Disease Biomarker Program (PDBP) dataset. Our initial model showed an area under the curve (AUC) of 89.72% for the diagnosis of PD. The tuned model was then tested for validation on external data (PDBP, AUC 85.03%). Optimizing thresholds for classification increased the diagnosis prediction accuracy and other metrics. Finally, networks were built to identify gene communities specific to PD. Combining data modalities outperforms the single biomarker paradigm. UPSIT and PRS contributed most to the predictive power of the model, but the accuracy of these are supplemented by many smaller effect transcripts and risk SNPs. Our model is best suited to identifying large groups of individuals to monitor within a health registry or biobank to prioritize for further testing. This approach allows complex predictive models to be reproducible and accessible to the community, with the package, code, and results publicly available

Through Their Eyes and In Their Shoes: Providing Group Awareness During Collaboration Across Virtual Reality and Desktop Platforms

Many collaborative data analysis situations benefit from collaborators utilizing different platforms. However, maintaining group awareness between team members using diverging devices is difficult, not least because common ground diminishes. A person using head-mounted VR cannot physically see a user on a desktop computer even while co-located, and the desktop user cannot easily relate to the VR user’s 3D workspace. To address this, we propose the “eyes-and-shoes” principles for group awareness and abstract them into four levels of techniques. Furthermore, we evaluate these principles with a qualitative user study of 6 participant pairs synchronously collaborating across distributed desktop and VR head-mounted devices. In this study, we vary the group awareness techniques between participants and explore two visualization contexts within participants. The results of this study indicate that the more visual metaphors and views of participants diverge, the greater the level of group awareness is needed.https://doi.org/10.1145/3544548.358109

Convolutional Neural Networks for a Cursor Control Brain Computer Interface

International audienceA Brain-Computer Interface (BCI) platform can be utilized by a patient to control an external device without making any overt movements. This can be beneficial to a variety of patients who suffer from paralysis, loss of limb, or neurodegenerative diseases. We decode brain signals using EEG during imagined body kinematics to control an on-screen cursor. Convolutional neural networks (CNNs) are already a popular choice for image-based learning problems and are useful in EEG applications. The major advantage of CNNs is that they can generate features from the signal automatically and do not require as much domain driven feature engineering as a traditional machine learning approach. We implement a CNN to perform multivariate regression over the EEG signal to predict intended cursor velocity

Convolutional Neural Networks for a Cursor Control Brain Computer Interface

International audienceA Brain-Computer Interface (BCI) platform can be utilized by a patient to control an external device without making any overt movements. This can be beneficial to a variety of patients who suffer from paralysis, loss of limb, or neurodegenerative diseases. We decode brain signals using EEG during imagined body kinematics to control an on-screen cursor. Convolutional neural networks (CNNs) are already a popular choice for image-based learning problems and are useful in EEG applications. The major advantage of CNNs is that they can generate features from the signal automatically and do not require as much domain driven feature engineering as a traditional machine learning approach. We implement a CNN to perform multivariate regression over the EEG signal to predict intended cursor velocity

Effects of livestock exclusion on in-stream habitat and benthic invertebrate assemblages in montane streams

SUMMARY 1. Stream and riparian ecosystems in arid montane areas, like the interior western United States, are often just narrow mesic strands, but support diverse and productive habitats. Meadows along many such streams have long been used for rangeland grazing, and, while impacts to riparian areas are relatively well known, the effect of livestock grazing on aquatic life in streams has received less attention. 2. Attempts to link grazing impacts to disturbance have been hindered by the lack of spatial and temporal replication. In this study, we compared channel features and benthic macroinvertebrate communities (i) between 16 stream reaches on two grazed allotments and between 22 reaches on two allotments where livestock had been completely removed for 4 years, (ii) before and after the 4-year grazing respite at a subset of eight sites and (iii) inside and outside of small-scale fenced grazing exclosures (eight pairings; 10+ year exclosures) in the meadows of the Golden Trout Wilderness, California (U.S.A.). 3. We evaluated grazing disturbance at the reach scale in terms of the effects of livestock trampling on per cent bank erosion and found that macroinvertebrate richness metrics were negatively correlated with bank erosion, while the percentage of tolerant taxa increased. 4. All macroinvertebrate richness metrics were significantly lower in grazed areas. Bank angle, temperature, fine sediment cover and erosion were higher in grazed areas, while riparian cover was lower. Regression models identified riparian cover, in-stream substratum, bank conditions and bankfull width-to-depth ratios as the most important for explaining variability in macroinvertebrate richness metrics. 5. Small-scale grazing exclosures showed no improvements for in-stream communities and only moderate positive effects on riparian vegetation. In contrast, metrics of macroinvertebrate richness increased significantly after a 4-year period of no grazing. 6. The success of grazing removal reported here suggests that short-term removal of livestock at the larger, allotment meadow spatial scale is more effective than long-term, but small-scale, local riparian area fencing, and yields promising results in achieving stream channel, riparian and aquatic biological recovery