Abstract: Human factors cause most driving accidents; this is why nowadays is common to hear about autonomous driving as an alternative. Autonomous driving will not only increase safety, but also will develop a system of cooperative self-driving cars that will reduce pollution and congestion. Furthermore, it will provide more freedom to handicapped people, elderly or kids.
Autonomous Driving requires perceiving and understanding the vehicle environment (e.g., road, traffic signs, pedestrians, vehicles) using sensors (e.g., cameras, lidars, sonars, and radars), selflocalization (requiring GPS, inertial sensors and visual localization in precise maps), controlling the vehicle and planning the routes. These algorithms require high computation capability, and thanks to NVIDIA GPU acceleration this starts to become feasible.
NVIDIA® is developing a new platform for boosting the Autonomous Driving capabilities that is able of managing the vehicle via CAN-Bus: the Drive™ PX. It has 8 ARM cores with dual accelerated Tegra® X1 chips. It has 12 synchronized camera inputs for 360º vehicle perception, 4G and Wi-Fi capabilities allowing vehicle communications and GPS and inertial sensors inputs for self-localization.
Our research group has been selected for testing Drive™ PX. Accordingly, we are developing a Drive™ PX based autonomous car. Currently, we are porting our previous CPU based algorithms (e.g., Lane Departure Warning, Collision Warning, Automatic Cruise Control, Pedestrian Protection, or Semantic Segmentation) for running in the GPU.

Abstract: Resting-State fMRI (RS-fMRI) is a brain imaging technique useful for exploring functional connectivity. A major point of interest in RS-fMRI analysis is to isolate connectivity patterns characterising disorders such as for instance ADHD. Such characterisation is usually performed in two steps: first, all connectivity patterns in the data are extracted by means of Independent Component Analysis (ICA); second, standard statistical tests are performed over the extracted patterns to find differences between control and clinical groups. In this work we introduce a novel, single-step, approach for this problem termed Spatial Discriminant ICA. The algorithm can efficiently isolate networks of functional connectivity characterising a clinical group by combining ICA and a new variant of the Fisher’s Linear Discriminant also introduced in this work. As the characterisation is carried out in a single step, it potentially provides for a richer characterisation of inter-class differences. The algorithm is tested using synthetic and real fMRI data, showing promising results in both experiments.

Abstract: Transcription of handwritten text in (old) documents is an important, time-consuming task for digital libraries. It might be carried out by first processing all document images off-line, and then manually supervising system transcriptions to edit incorrect parts. However, current techniques for automatic page layout analysis, text line detection and handwriting recognition are still far from perfect, and thus post-editing system output is not clearly better than simply ignoring it.
A more effective approach to transcribe old text documents is to follow an interactive- predictive paradigm in which both, the system is guided by the user, and the user is assisted by the system to complete the transcription task as efficiently as possible. Following this approach, a system prototype called GIDOC (Gimp-based Interactive transcription of old text DOCuments) has been developed to provide user-friendly, integrated support for interactive-predictive layout analysis, line detection and handwriting transcription.
GIDOC is designed to work with (large) collections of homogeneous documents, that is, of similar structure and writing styles. They are annotated sequentially, by (par- tially) supervising hypotheses drawn from statistical models that are constantly updated with an increasing number of available annotated documents. And this is done at different annotation levels. For instance, at the level of page layout analysis, GIDOC uses a novel text block detection method in which conventional, memoryless techniques are improved with a “history” model of text block positions. Similarly, at the level of text line image transcription, GIDOC includes a handwriting recognizer which is steadily improved with a growing number of (partially) supervised transcriptions.

Abstract: Human-machine, human-robot interaction, and collaboration appear in diverse fields, from homecare to Cyber-Physical Systems. Technological development is fast, whereas real-time methods for social communication analysis that can measure small changes in sentiment and personality states, including visual, acoustic and language modalities are lagging, particularly when the goal is to build robust, appearance invariant, and fair methods. We study and compare methods capable of fusing modalities while satisfying real-time and invariant appearance conditions. We compare state-of-the-art transformer architectures in sentiment estimation and introduce them in the much less explored field of personality perception. We show that the architectures perform differently on automatic sentiment and personality perception, suggesting that each task may be better captured/modeled by a particular method. Our work calls attention to the attractive properties of the linear versions of the transformer architectures. In particular, we show that the best results are achieved by fusing the different architectures{’} preprocessing methods. However, they pose extreme conditions in computation power and energy consumption for real-time computations for quadratic transformers due to their memory requirements. In turn, linear transformers pave the way for quantifying small changes in sentiment estimation and personality perception for real-time social communications for machines and robots.