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Augmented Reality (AR) is evolving to become the next frontier in social media, merging physical and virtual reality into a living metaverse, a Social MediARverse. With this transition, we must understand how different contexts (public, semi-public, and private) affect user engagement with AR content. We address this gap in current research by conducting an online survey with 110 participants, showcasing 36 AR videos, and polling them about the content's fit and appropriateness. Specifically, we manipulated these three spaces, two forms of dynamism (dynamic vs. static), and two dimensionalities (2D vs. 3D). Our findings reveal that dynamic AR content is generally more favorably received than static content. Additionally, users find sharing and engaging with AR content in private settings more comfortable than in others. By this, the study offers valuable insights for designing and implementing future Social MediARverses and guides industry and academia on content visualization and contextual considerations.

With the proliferation of VR and a metaverse on the horizon, many multi-user activities are migrating to the VR world, calling for effective collaboration support. As one key feature, traditional collaborative systems provide users with undo mechanics to reverse errors and other unwanted changes. While undo has been extensively researched in this domain and is now considered industry standard, it is strikingly absent for VR systems in research and industry. This work addresses this research gap by exploring different undo techniques for basic object manipulation in different collaboration modes in VR. We conducted a study involving 32 participants organized in teams of two. Here, we studied users' performance and preferences in a tower stacking task, varying the available undo techniques and their mode of collaboration. The results suggest that users desire and use undo in VR and that the choice of the undo technique impacts users' performance and social connection., Comment: To appear in Proceedings of the CHI Conference on Human Factors in Computing Systems (CHI '24), May 11-16, 2024, Honolulu, HI, USA

The pleasure that often comes with eating can be further enhanced with intelligent technology, as the field of human-food interaction suggests. However, knowledge on how to design such pleasure-supporting eating systems is limited. To begin filling this knowledge gap, we designed "GustosonicSense", a novel gustosonic eating system that utilizes wireless earbuds for sensing different eating and drinking actions with a machine learning algorithm and trigger playful sounds as a way to facilitate pleasurable eating experiences. We present the findings from our design and a study that revealed how we can support the "stimulation", "hedonism", and "reflexivity" for playful human-food interactions. Ultimately, with our work, we aim to support interaction designers in facilitating playful experiences with food., Comment: To appear at CHI'24: The ACM Conference on Human Factors in Computing Systems (CHI), Honolulu, Hawaii, 2024

The Tribomechadynamics Research Challenge (TRC) was a blind prediction of the vibration behavior of a thin plate clamped on two sides using bolted joints. The first bending mode's natural frequency and damping ratio were requested as function of the amplitude, starting from the linear regime until high levels, where both frictional contact and nonlinear bending-stretching coupling become relevant. The predictions were confronted with experimental results in a companion paper; the present article addresses the experimental analysis of this benchmark system. Amplitude-dependent modal data was obtained from phase resonance and response controlled tests. An original variant of response controlled testing is proposed: Instead of a fixed frequency interval, a fixed phase interval is analyzed. This way, the high excitation levels required outside resonance, which could activate unwanted exciter nonlinearity, are avoided. Consistency of testing methods is carefully analyzed. Overall, these measures have permitted to gain high confidence in the acquired modal data. The different sources of the remaining uncertainty were further analyzed. A low reassembly-variability but a moderate time-variability were identified, where the latter is attributed to some thermal sensitivity of the system. Two nominally identical plates were analyzed, which both have an appreciable initial curvature, and a significant effect on the vibration behavior was found depending on whether the plate is aligned/misaligned with the support structure. Further, a 1:2 nonlinear modal interaction with the first torsion mode was observed, which only occurs in the aligned configurations.

A beam-slider system is considered whose passive self-adaption relies on an intricate locomotion process involving both frictional and unilateral contact. The system also exploits geometric nonlinearity to achieve broadband efficacy. The dynamics of the system take place on three distinct time scales: On the fast time scale of the harmonic base excitation are the vibrations and the locomotion cycle. On the slow time scale, the slider changes its position along the beam, and the overall vibration level varies. Finally, on an intermediate time scale, strong modulations of the vibration amplitude may take place. In the present work, first, an analytical approximation of the beam's response on the slow time scale is derived as function of the slider position, which is a crucial prerequisite for identifying the main drivers of the slider's locomotion. Then, the most important forms of locomotion are described and approximations of their individual contribution to the overall slider transport are estimated. Finally, the theoretical results are compared against numerical results obtained from an experimentally validated model.

Preclinical research and organ-dedicated applications require high-resolution positron emission tomography (PET) detectors to visualize small structures and understand biological processes at a finer level of detail. Current commercial systems often employ finely pixelated or monolithic scintillators, each with its limitations. We present a semi-monolithic detector, tailored for high-resolution PET applications, and merging concepts of monolithic and pixelated crystals. The detector features slabs measuring (24 x 10 x 1) sq. mm, coupled to a 12 x 12 readout channel photosensor with 4 mm pitch. The slabs are grouped in two arrays of 44 slabs each to achieve a higher optical photon density. We employ a fan beam collimator for fast calibration to train machine-learning-based positioning models for all three dimensions, including slab identification and depth-of-interaction (DOI), utilizing gradient tree boosting (GTB). Energy calculation was based on a position-dependent energy calibration. Using an analytical timing calibration, time skews were corrected for coincidence timing resolution (CTR) estimation. Leveraging machine-learning-based calibration in all three dimensions, we achieved high detector spatial resolution: down to 1.18 mm full width at half maximum (FWHM) detector spatial resolution and 0.75 mm mean absolute error (MAE) in the planar-monolithic direction along the slabs, and 2.14 mm FWHM and 1.03 mm MAE for depth-of-interaction (DOI) at an energy window of (435-585) keV. Correct slab interaction identification exceeded 80%, alongside an energy resolution of 13.8% and a CTR of 450 ps FWHM. Therewith, the introduced finely segmented, high-resolution slab detector demonstrates an appealing performance suitable for high-resolution PET applications. The current benchtop-based detector calibration routine allows these detectors to be used in PET systems., Comment: 14 pages, 11 figures, IEEE NSS MIC RTSD 2023

Spectating digital games can be exciting. However, due to its vicarious nature, spectators often wish to engage in the gameplay beyond just watching and cheering. To blur the boundaries between spectators and players, we propose a novel approach called "Fused Spectatorship", where spectators watch their hands play games by loaning bodily control to a computational Electrical Muscle Stimulation (EMS) system. To showcase this concept, we designed three games where spectators loan control over both their hands to the EMS system and watch them play these competitive and collaborative games. A study with 12 participants suggested that participants could not distinguish if they were watching their hands play, or if they were playing the games themselves. We used our results to articulate four spectator experience themes and four fused spectator types, the behaviours they elicited and offer one design consideration to support each of these behaviours. We also discuss the ethical design considerations of our approach to help game designers create future fused spectatorship experiences., Comment: This paper is going to be published at Annual Symposium on Computer-Human Interaction in Play (CHI PLAY) 2023

Most digital bodily games focus on the body as they use movement as input. However, they also draw the player's focus away from the body as the output occurs on visual displays, creating a divide between the physical body and the virtual world. We propose a novel approach - the "Body as a Play Material" - where a player uses their body as both input and output to unify the physical body and the virtual world. To showcase this approach, we designed three games where a player uses one of their hands (input) to play against the other hand (output) by loaning control over its movements to an Electrical Muscle Stimulation (EMS) system. We conducted a thematic analysis on the data obtained from a field study with 12 participants to articulate four player experience themes. We discuss our results about how participants appreciated the engagement with the variety of bodily movements for play and the ambiguity of using their body as a play material. Ultimately, our work aims to unify the physical body and the virtual world., Comment: This paper is published at the Annual Symposium on Computer-Human Interaction in Play (CHI PLAY) 2023

Manufacturing tools like 3D printers have become accessible to the wider society, making the promise of digital fabrication for everyone seemingly reachable. While the actual manufacturing process is largely automated today, users still require knowledge of complex design applications to produce ready-designed objects and adapt them to their needs or design new objects from scratch. To lower the barrier to the design and customization of personalized 3D models, we explored novice mental models in voice-based 3D modeling by conducting a high-fidelity Wizard of Oz study with 22 participants. We performed a thematic analysis of the collected data to understand how the mental model of novices translates into voice-based 3D modeling. We conclude with design implications for voice assistants. For example, they have to: deal with vague, incomplete and wrong commands; provide a set of straightforward commands to shape simple and composite objects; and offer different strategies to select 3D objects., Comment: Presented at INTERACT 2023. This is a self-archival version: the version of record is available via Springer at https://doi.org/10.1007/978-3-031-42293-5_11

Objective. Prompt-gamma imaging encompasses several approaches for online monitoring of beam range or deposited dose distribution in proton therapy. We test one of the imaging techniques - a coded mask approach - both experimentally and via simulations. Approach. Two imaging setups have been investigated experimentally. Each of them comprised a structured tungsten collimator in a form of a MURA mask and a LYSO:Ce scintillation detector of fine granularity. The setups differed in the detector dimensions and the operation mode (1D or 2D imaging). A series of measurements with radioactive sources have been conducted, testing the setups' performance of near-field gamma imaging. Additionally, Monte Carlo simulations of a larger setup of the same type were conducted, investigating its performance with a realistic gamma source distribution occurring during proton therapy. Main results. The images of point-like sources reconstructed from two smallscale prototypes' data using the MLEM algorithm constitute the experimental proof of principle for the near-field coded-mask imaging modality, both in the 1D and the 2D mode. Their precision allowed us to calibrate out certain systematic offsets appearing due to the misalignment of setup elements. The simulation of the full-scale setup yielded a mean distal falloff retrieval precision of 0.72 mm in the studies for beam energy range 89.5-107.9 MeV and with 1x10^8 protons (typical number for single distal spots). The implemented algorithm of image reconstruction is relatively fast - a typical procedure needs several seconds. Significance. Coded-mask imaging appears a valid option for proton therapy monitoring. The results of simulations let us conclude that the proposed fullscale setup is competitive to the knife-edge-shaped and the multiparalell slit cameras investigated by other groups.

When we go for a walk with friends, we can observe an interesting effect: From step lengths to arm movements - our movements unconsciously align; they synchronize. Prior research found that this synchronization is a crucial aspect of human relations that strengthens social cohesion and trust. Generalizing from these findings in synchronization theory, we propose a dynamical approach that can be applied in the design of non-humanoid robots to increase trust. We contribute the results of a controlled experiment with 51 participants exploring our concept in a between-subjects design. For this, we built a prototype of a simple non-humanoid robot that can bend to follow human movements and vary the movement synchronization patterns. We found that synchronized movements lead to significantly higher ratings in an established questionnaire on trust between people and automation but did not influence the willingness to spend money in a trust game., Comment: To appear in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI 23), April 23-28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 14 pages

Recent technological advances have made head-mounted displays (HMDs) smaller and untethered, fostering the vision of ubiquitous interaction with information in a digitally augmented physical world. For interacting with such devices, three main types of input - besides not very intuitive finger gestures - have emerged so far: 1) Touch input on the frame of the devices or 2) on accessories (controller) as well as 3) voice input. While these techniques have both advantages and disadvantages depending on the current situation of the user, they largely ignore the skills and dexterity that we show when interacting with the real world: Throughout our lives, we have trained extensively to use our limbs to interact with and manipulate the physical world around us. This thesis explores how the skills and dexterity of our upper and lower limbs, acquired and trained in interacting with the real world, can be transferred to the interaction with HMDs. Thus, this thesis develops the vision of around-body interaction, in which we use the space around our body, defined by the reach of our limbs, for fast, accurate, and enjoyable interaction with such devices. This work contributes four interaction techniques, two for the upper limbs and two for the lower limbs: The first contribution shows how the proximity between our head and hand can be used to interact with HMDs. The second contribution extends the interaction with the upper limbs to multiple users and illustrates how the registration of augmented information in the real world can support cooperative use cases. The third contribution shifts the focus to the lower limbs and discusses how foot taps can be leveraged as an input modality for HMDs. The fourth contribution presents how lateral shifts of the walking path can be exploited for mobile and hands-free interaction with HMDs while walking., Comment: thesis

From carrying grocery bags to holding onto handles on the bus, there are a variety of situations where one or both hands are busy, hindering the vision of ubiquitous interaction with technology. Voice commands, as a popular hands-free alternative, struggle with ambient noise and privacy issues. As an alternative approach, research explored movements of various body parts (e.g., head, arms) as input modalities, with foot-based techniques proving particularly suitable for hands-free interaction. Whereas previous research only considered the movement of the foot as a whole, in this work, we argue that our toes offer further degrees of freedom that can be leveraged for interaction. To explore the viability of toe-based interaction, we contribute the results of a controlled experiment with 18 participants assessing the impact of five factors on the accuracy, efficiency and user experience of such interfaces. Based on the findings, we provide design recommendations for future toe-based interfaces., Comment: To appear in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI 23), April 23-28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 17 pages

When we get lost in Virtual Reality (VR) or want to return to a previous location, we use the same methods of locomotion for the way back as for the way forward. This is time-consuming and requires additional physical orientation changes, increasing the risk of getting tangled in the headsets' cables. In this paper, we propose the use of undo actions to revert locomotion steps in VR. We explore eight different variations of undo actions as extensions of point\&teleport, based on the possibility to undo position and orientation changes together with two different visualizations of the undo step (discrete and continuous). We contribute the results of a controlled experiment with 24 participants investigating the efficiency and orientation of the undo techniques in a radial maze task. We found that the combination of position and orientation undo together with a discrete visualization resulted in the highest efficiency without increasing orientation errors., Comment: To appear in Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (CHI 23), April 23-28, 2023, Hamburg, Germany. ACM, New York, NY, USA, 15 pages

Subcellular RNA localization is a critical mechanism for the spatial control of gene expression. Its mechanism and precise functional role is not yet very well understood. Single Molecule Fluorescence in Situ Hybridization (smFISH) images allow for the detection of individual RNA molecules with subcellular accuracy. In return, smFISH requires robust methods to quantify and classify RNA spatial distribution. Here, we present PointFISH, a novel computational approach for the recognition of RNA localization patterns. PointFISH is an attention-based network for computing continuous vector representations of RNA point clouds. Trained on simulations only, it can directly process extracted coordinates from experimental smFISH images. The resulting embedding allows scalable and flexible spatial transcriptomics analysis and matches performance of hand-crafted pipelines.

Palm-based tactile displays have the potential to evolve from single motor interfaces (e.g., smartphones) to high-resolution tactile displays (e.g., back-of-device haptic interfaces) enabling richer multi-modal experiences with more information. However, we lack a systematic understanding of vibrotactile perception on the palm and the influence of various factors on the core design decisions of tactile displays (number of actuators, resolution, and intensity). In a first experiment (N=16), we investigated the effect of these factors on the users' ability to localize stationary sensations. In a second experiment (N=20), we explored the influence of resolution on recognition rate for moving tactile sensations.Findings show that for stationary sensations a 9 actuator display offers a good trade-off and a $3\times3$ resolution can be accurately localized. For moving sensations, a $2\times4$ resolution led to the highest recognition accuracy, while $5\times10$ enables higher resolution output with a reasonable accuracy.

Artificial intelligence (AI) is entering medical imaging, mainly enhancing image reconstruction. Nevertheless, improvements throughout the entire processing, from signal detection to computation, potentially offer significant benefits. This work presents a novel and versatile approach to detector optimization using machine learning (ML) and residual physics. We apply the concept to positron emission tomography (PET), intending to improve the coincidence time resolution (CTR). PET visualizes metabolic processes in the body by detecting photons with scintillation detectors. Improved CTR performance offers the advantage of reducing radioactive dose exposure for patients. Modern PET detectors with sophisticated concepts and read-out topologies represent complex physical and electronic systems requiring dedicated calibration techniques. Traditional methods primarily depend on analytical formulations successfully describing the main detector characteristics. However, when accounting for higher-order effects, additional complexities arise matching theoretical models to experimental reality. Our work addresses this challenge by combining traditional calibration with AI and residual physics, presenting a highly promising approach. We present a residual physics-based strategy using gradient tree boosting and physics-guided data generation. The explainable AI framework SHapley Additive exPlanations (SHAP) was used to identify known physical effects with learned patterns. In addition, the models were tested against basic physical laws. We were able to improve the CTR significantly (more than 20%) for clinically relevant detectors of 19 mm height, reaching CTRs of 185 ps (450-550 keV).

The integration of PET into MRI to form a hybrid system requires often compromises for both subsystems. For example, the integration might come at the cost of a reduced PET detector height or a reduced MRI examination volume diameter. Here, we propose a so-called shared-volume concept to use the volume required for both subsystems more efficiently, in which the MR transparency of the scintillator is exploited by integrating the RF shielding into the scintillator of the PET detector. Semi-monolithic scintillator prototypes (PVC slabs) of 7 mm and 12 mm height with integrated copper foil between every and every other slab, respectively, were investigated to evaluate the shielding effectiveness (SE). The SE was measured with probes on a test bench system. In addition, the PET detector performance was evaluated by determining the positioning using gradient tree boosting, energy and timing resolution using digital SiPM arrays (DPC3200, PDPC) in three LYSO scintillator configurations: ESR separation (Slab$\mathrm{_{ESR}}$), ESR with copper foil in between (Slab$\mathrm{_{ESR+Cu}}$), and purely copper foil separation (Slab$\mathrm{_{Cu}}$). The prototype with shielding between each slab and 12 mm height showed the highest SE with 31 dB (mean) in combination with a supporting frame. While Slab$\mathrm{_{Cu}}$ was best for positioning, followed by Slab$\mathrm{_{ESR}}$ and Slab$\mathrm{_{ESR+Cu}}$, the Slab$\mathrm{_{ESR}}$ had the best energy resolution (10.6 %), followed by Slab$\mathrm{_{ESR+Cu}}$ (11.2 %) and Slab$\mathrm{_{Cu}}$ (12.6 %). For the timing resolution, Slab$\mathrm{_{ESR}}$ and Slab$\mathrm{_{Cu}}$ achieved 279 ps and 288 ps, respectively. Slab$\mathrm{_{ESR+Cu}}$ performed worst (293 ps). The scintillator-based RF shielding shows good RF shielding with similar PET performance, demonstrating the potential for more effective integration of PET detectors into MRI systems., Comment: 26 pages, 10 figures, 4 tables

A system consisting of a doubly clamped beam with an attached body (slider) free to move along the beam has been studied recently by multiple research groups. Under harmonic base excitation, the system has the capacity to passively adapt itself (by slowly changing the slider position) to yield either high or low vibrations. The central contributions of this work are the refinement of the recently developed system model with regard to the finite stiffness of the beam's clamping, followed by a thorough validation of this model against experimental results. With the intent to achieve repeatable and robust self-adaption, a new prototype of the system was designed, featuring, in particular, a continuously adjustable gap size and a concave inner contact geometry. The initial beam model is updated based on the results of an Experimental Nonlinear Modal Analysis of the system (without slider). By varying the excitation level and frequency in a wide range, all known types of behavior were reproduced in the experiment. The simulation results of the updated model with slider are in excellent agreement with the measurements, both qualitatively (type of behavior) and quantitatively. Minor deviations are attributed to the system's sensitivity to inevitable uncertainties, in particular with regard to the friction coefficient and the linear natural frequency. It is thus concluded that the proposed model is well-suited for further analysis of its intriguing dynamics and for model-based optimization for technical applications such as energy harvesting., Comment: The final version of this article is available online at https://doi.org/10.1016/j.ymssp.2022.109551

There is a recommended body posture and hand position for playing every musical instrument, allowing efficient and quick movements without blockage. Due to humans' limited cognitive capabilities, they struggle to concentrate on several things simultaneously and thus sometimes lose the correct position while playing their instrument. Incorrect positions when playing an instrument can lead to injuries and movement disorders in the long run. Previous work in HCI mainly focused on developing systems to assist in learning an instrument. However, the design space for posture correction when playing a musical instrument has not yet been explored. In this position paper, we present our vision of providing subtle vibrotactile or thermal feedback to guide the focus of attention back to the correct posture when playing a musical instrument. We discuss our concept with a focus on motion recognition and feedback modalities. Finally, we outline the next steps for future research., Comment: CHI 22, Workshop "Intelligent Music Interfaces: When Interactive Assistance and Augmentation Meet Musical Instruments"

Existing vision based supervised approaches to lateral vehicle control are capable of directly mapping RGB images to the appropriate steering commands. However, they are prone to suffering from inadequate robustness in real world scenarios due to a lack of failure cases in the training data. In this paper, a framework for training a more robust and scalable model for lateral vehicle control is proposed. The framework only requires an unlabeled sequence of RGB images. The trained model takes a point cloud as input and predicts the lateral offset to a subsequent frame from which the steering angle is inferred. The frame poses are in turn obtained from visual odometry. The point cloud is conceived by projecting dense depth maps into 3D. An arbitrary number of additional trajectories from this point cloud can be generated during training. This is to increase the robustness of the model. Online experiments show that the performance of our method is superior to that of the supervised model.

Current clinical PET systems utilize detectors where the scintillator typically contains single elements of 3 - 6 mm width and about 20 mm height. While providing good TOF performance, this design limits the spatial resolution and causes radial astigmatism as the DOI remains unknown. We propose an alternative, aiming to combine the advantages of current detectors with the DOI capabilities shown for monolithic concepts, based on semi-monolithic scintillators (slabs). An array of 8 monolithic LYSO slabs of dimensions 3.9 x 32 x 19 mm3 was read out by a 64-channel photosensor containing digital SiPMs. The position estimation in the detector's monolithic and DOI direction was based on a calibration with a fan beam collimator and the machine learning technique gradient tree boosting (GTB). We achieved a positioning performance in terms of mean absolute error (MAE) of 1.44 mm and 2.12 mm for DOI considering a wide energy window of 300 - 700 keV. The energy resolution was determined to be 11.3%. We established both an analytical and machine-learning-based timing calibration approach and applied them for a first-photon trigger. The analytical timing calibration corrects for electronic and optical time skews leading to 240 ps coincidence resolving time (CRT) for a pair of slab-detectors. The CRT was significantly improved by utilizing GTB to predict the time difference based on specific training data and applied on top of the analytical calibration. We achieved 209 ps for the wide energy window and 198 ps for a narrow selection around the photopeak (411 - 561 keV). To maintain the detector's sensitivity, no filters were applied to the data during processing. Overall, the semi-monolithic detector provides attractive performance characteristics. Especially, a good CRT can be achieved while introducing DOI capabilities to the detector, making the concept suitable for clinical PET scanners., Comment: Accepted by Medical Physics, bibliographic information to follow

Myopia is an eye condition that makes it difficult for people to focus on faraway objects. It has become one of the most serious eye conditions worldwide and negatively impacts the quality of life of those who suffer from it. Although myopia is prevalent, many non-myopic people have misconceptions about it and encounter challenges empathizing with myopia situations and those who suffer from it. In this research, we developed two virtual reality (VR) games, (1) Myopic Bike and (2) Say Hi, to provide a means for the non-myopic population to experience the frustration and difficulties of myopic people. Our two games simulate two inconvenient daily life scenarios (riding a bicycle and greeting someone on the street) that myopic people encounter when not wearing glasses. We evaluated four participants' game experiences through questionnaires and semi-structured interviews. Overall, our two VR games can create an engaging and non-judgmental experience for the non-myopic population to better understand and empathize with those who suffer from myopia., Comment: 7 pages, CHI PLAY 2021

With the popularity of online access in virtual reality (VR) devices, it will become important to investigate exclusive and interactive CAPTCHA (Completely Automated Public Turing test to tell Computers and Humans Apart) designs for VR devices. In this paper, we first present four traditional two-dimensional (2D) CAPTCHAs (i.e., text-based, image-rotated, image-puzzled, and image-selected CAPTCHAs) in VR. Then, based on the three-dimensional (3D) interaction characteristics of VR devices, we propose two vrCAPTCHA design prototypes (i.e., task-driven and bodily motion-based CAPTCHAs). We conducted a user study with six participants for exploring the feasibility of our two vrCAPTCHAs and traditional CAPTCHAs in VR. We believe that our two vrCAPTCHAs can be an inspiration for the further design of CAPTCHAs in VR., Comment: 4 pages, 3 figures, CHI EA' 2021

Deep learning methods have shown extraordinary potential for analyzing very diverse biomedical data, but their dissemination beyond developers is hindered by important computational hurdles. We introduce ImJoy (https://imjoy.io/), a flexible and open-source browser-based platform designed to facilitate widespread reuse of deep learning solutions in biomedical research. We highlight ImJoy's main features and illustrate its functionalities with deep learning plugins for mobile and interactive image analysis and genomics.