Context. With the most recent Gaia data release, the number of sources with complete 6D phase space information (position and velocity) has increased to well over 33 million stars, while stellar astrophysical parameters are provided for more than 470 million sources, and more than 11 million variable stars are identified. Aims. Using the astrophysical parameters and variability classifications provided in Gaia DR3, we selected various stellar populations to explore and identify non-axisymmetric features in the disc of the Milky Way in configuration and velocity space. Methods. Using more about 580 000 sources identified as hot OB stars, together with 988 known open clusters younger than 100 Myr, we mapped the spiral structure associated with star formation 4'5 kpc from the Sun. We selected over 2800 Classical Cepheids younger than 200 Myr that show spiral features extending as far as 10 kpc from the Sun in the outer disc. We also identified more than 8.7 million sources on the red giant branch (RGB), of which 5.7 million have line-of-sight velocities. This later sample allows the velocity field of the Milky Way to be mapped as far as 8 kpc from the Sun, including the inner disc. Results. The spiral structure revealed by the young populations is consistent with recent results using Gaia EDR3 astrometry and source lists based on near-infrared photometry, showing the Local (Orion) Arm to be at least 8 kpc long, and an outer arm consistent with what is seen in HI surveys, which seems to be a continuation of the Perseus arm into the third quadrant. The subset of RGB stars with velocities clearly reveals the large-scale kinematic signature of the bar in the inner disc, as well as evidence of streaming motions in the outer disc that might be associated with spiral arms or bar resonances. A local comparison of the velocity field of the OB stars reveals similarities and differences with the RGB sample. Conclusions. This cursory study of Gaia DR3 data shows there is a rich bounty of kinematic information to be explored more deeply, which will undoubtedly lead us to a clearer understanding of the dynamical nature of the non-axisymmetric structures of the Milky Way.@en

Deep learning has enabled personal and IoT devices to rethink microphones as a multi-purpose sensor for understanding conversation and the surrounding environment. This resulted in a proliferation of Voice Controllable Systems (VCS) around us. The increasing popularity of such systems is also prone to attracting miscreants, who often want to take advantage of the VCS without the knowledge of the user. Consequently, understanding the robustness of VCS, especially under adversarial attacks, has become an important research topic. Although there exists some previous work on audio adversarial attacks, their scopes are limited to embedding the attacks onto pre-recorded music clips, which when played through speakers cause VCS to misbehave. As an attack-audio needs to be played, the occurrence of this type of attacks can be suspected by a human listener. In this paper, we focus on audio-based Denial-of-Service (DoS) attack, which is unexplored in the literature. Contrary to previous work, we show that adversarial audio attacks in real-time and overthe-air are possible, while a user interacts with VCS. We show that the attacks are effective regardless of the user's command and interaction timings. In this paper, we present a first-of-itskind imperceptible and always-on universal audio perturbation technique that enables such DoS attack to be successful. We thoroughly evaluate the performance of the attacking scheme across (i) two learning tasks, (ii) two model architectures and (iii) three datasets. We demonstrate that the attack can introduce as high as 78% error rate in audio recognition tasks.@en

We report results from a pilot study that focuses mainly on understanding the everyday life quality of patients suffering from multiple sclerosis through the lens of connected Nokia Health devices. Our dataset comprises of 198 individuals (184 females and 14 males) and the study lasted over six months. By analyzing carefully crafted user-studies and correlating with personal sensor data collected with Nokia devices, we found that the level of fatigue is one of the main sources of discomfort across the majority of the patients. We further perform an exploratory analysis, which provides an early indication that by actively monitoring and perturbing users' daily activity levels, such as increasing daily step-counts, sleep duration and decreasing body weight, patients can potentially reduce their daily fatigue level.@en

We report results from a pilot study that focuses mainly on understanding the everyday life quality of patients suffering from multiple sclerosis through the lens of connected Nokia Health devices. Our dataset comprises of 198 individuals (184 females and 14 males) and the study lasted over six months. By analyzing carefully crafted user-studies and correlating with personal sensor data collected with Nokia devices, we found that the level of fatigue is one of the main sources of discomfort across the majority of the patients. We further perform an exploratory analysis, which provides an early indication that by actively monitoring and perturbing users' daily activity levels, such as increasing daily step-counts, sleep duration and decreasing body weight, patients can potentially reduce their daily fatigue level.@en