Spectator View: Enabling Asymmetric Interaction between HMD Wearers and Spectators with a Large Display

In this paper, we present a system that allows a user with a head-mounted display (HMD) to communicate and collaborate with spectators outside of the headset. We evaluate its impact on task performance, immersion, and collaborative interaction. Our solution targets scenarios like live presentations or multi-user collaborative systems, where it is not convenient to develop a VR multiplayer experience and supply each user (and spectator) with an HMD. The spectator views the virtual world on a large-scale tiled video wall and is given the ability to control the orientation of their own virtual camera. This allows spectators to stay focused on the immersed user's point of view or freely look around the environment. To improve collaboration between users, we implemented a pointing system where a spectator can point at objects on the screen, which maps an indicator directly onto the objects in the virtual world. We conducted a user study to investigate the influence of rotational camera decoupling and pointing gestures in the context of HMD-immersed and non-immersed users utilizing a large-scale display. Our results indicate that camera decoupling and pointing positively impacts collaboration. A decoupled view is preferable in situations where both users need to indicate objects of interest in the scene, such as presentations and joint-task scenarios, as it requires a shared reference space. A coupled view, on the other hand, is preferable in synchronous interactions such as remote-assistant scenarios.

Lipid-lowering and anti-thrombotic therapy in patients with peripheral arterial disease

Summary: Patients with peripheral arterial disease (PAD) are at very high risk of cardiovascular events, but risk factor management is usually suboptimal. This Joint Task Force from the European Atherosclerosis Society and the European Society of Vascular Medicine has updated evidence on the management on dyslipidaemia and thrombotic factors in patients with PAD. Guidelines recommend a low-density lipoprotein cholesterol (LDLC) goal of more than 50% reduction from baseline and <1.4 mmol/L (<55 mg/dL) in PAD patients. As demonstrated by randomized controlled trials, lowering LDL-C not only reduces cardiovascular events but also major adverse limb events (MALE), including amputations, of the order of 25%. Addition of ezetimibe or a PCSK9 inhibitor further decreases the risk of cardiovascular events, and PCSK9 inhibition has also been associated with reduction in the risk of MALE by up to 40%. Furthermore, statin- based treatment improved walking performance, including maximum walking distance, and pain-free walking distance and duration. This Task Force recommends strategies for managing statin-associated muscle symptoms to ensure that PAD patients benefit from lipid-lowering therapy. Antiplatelet therapy, either daily clopidogrel 75 mg or the combination of aspirin 100 mg and rivaroxaban (2×2.5 mg) is also indicated to prevent cardiovascular events. Dual pathway inhibition (aspirin and rivaroxaban) may be considered following revascularization, taking into account bleeding risk. This Joint Task Force believes that adherence with these recommendations for lipid-lowering and antithrombotic therapy will improve the morbidity and mortality in patients with PAD.

Functional connectivity networks of common synaptic inputs to motor neurons reveal neural spinal synergies during a multi-joint task

Movements are reportedly controlled through the combination of synergies that generate specific motor outputs by imposing an activation pattern on a group of muscles. To date, the smallest unit of analysis has been the muscle through the measurement of its activation. However, the muscle is not the lowest neural level of movement control. In this human study, we identified the common synaptic inputs received by motor neurons during an isometric multi-joint task. We decoded the spiking activities of dozens of spinal motor neurons innervating six lower limb muscles in 10 participants. Furthermore, we analyzed these activities by identifying their common low-frequency components, from which networks of common synaptic inputs to the motor neurons were derived. The vast majority of the identified motor neurons shared common inputs with other motor neuron(s). In addition, groups of motor neurons were partly decoupled from their innervated muscle, such that motor neurons innervating the same muscle did not necessarily receive common inputs. Conversely, some motor neurons from different muscles, including distant muscles, received common inputs. Our results provide evidence of a synergistic control of a multi-joint motor task at the spinal motor neuron level. Moreover, we showed that common input to motor neurons is an essential feature of the neural control of movement. We conclude that the central nervous system controls flexible groups of motor neurons by distributing common inputs to substantially reduce the dimensionality of movement control.

Deep Multi-Segmentation Approach for the Joint Classification and Segmentation of the Retinal Arterial and Venous Trees in Color Fundus Images

The analysis of the retinal vasculature represents a crucial stage in the diagnosis of several diseases. An exhaustive analysis involves segmenting the retinal vessels and classifying them into veins and arteries. In this work, we present an accurate approach, based on deep neural networks, for the joint segmentation and classification of the retinal veins and arteries from color fundus images. The presented approach decomposes this joint task into three related subtasks: the segmentation of arteries, veins and the whole vascular tree. The experiments performed show that our method achieves competitive results in the discrimination of arteries and veins, while clearly enhancing the segmentation of the different structures. Moreover, unlike other approaches, our method allows for the straightforward detection of vessel crossings, and preserves the continuity of the arterial and venous vascular trees at these locations.

JOINT DIRECTION AND PROXIMITY CLASSIFICATION OF OVERLAPPING SOUNDEVENTS FROM BINAURAL AUDIO

Sound source proximity and distance estimation are of great interest in many practical applications, since they provide significant information for acoustic scene analysis. As both tasks share complementary qualities, ensuring efficient interaction between these two is crucial for a complete picture of an aural environment. In this paper, we aim to investigate several ways of performing joint proximity and direction estimation from binaural recordings, both defined as coarse classification problems based on Deep Neural Networks (DNNs). Considering the limitations of binaural audio, we propose two methods of splitting the sphere into angular areas in order to obtain a set of directional classes. For each method we study different model types to acquire information about the direction-of-arrival (DoA). Finally, we propose various ways of combining the proximity and direction estimation problems into a joint task providing temporal information about the onsets and offsets of the appearing sources. Experiments are performed for a synthetic reverberant binaural dataset consisting of up to two overlapping sound events.