In this paper we analyze different Mixed Integer Linear Programming (MILP) models in order to produce 1D and 3D Spread-Out Bragg peaks (SOBP) for protons in water. Our techniques do not use much computational resources; in particular, all our experiments have been performed by a standard personal computer. As main result we give the proof of concept that the techniques that we use to create parameterized uniform SOBP can be fruitfully used in Treatment Planning Systems (TPS) for Intensity Modulated Proton Therapy (IMPT). As technical result we show, for the first time to our best knowledge, that there is a trade-off between the minimum number of energies (or layers) to be used to have a SOBP peak within a uniformity tolerance parameter Dtand the same parameter Dt. Minimizing the number of energies also has the advantage of reducing the delivery time using the facilities in operation nowadays.

Hand tracking and gesture recognition are fundamental in a multitude of applications. Various sensors have been used for this purpose, however, all monocular vision systems face limitations caused by occlusions. Wearable equipment overcome said limitations, although deemed impractical in some cases. Using more than one sensor provides a way to overcome this problem, but necessitates more complicated designs. In this work, we aim to highlight contemporary methods used for hand tracking and gesture recognition by collecting publications of systems developed in the last decade, that employ contactless devices as RGB cameras, IR, and depth sensors, along with some preceding pillar works. Additionally, we briefly present common steps, techniques, and basic algorithms used during the process of developing modern hand tracking and gesture recognition systems and, finally, we derive the trend for the next future.

Proton therapy is a cancer therapy that is more expensive than classical radiotherapy but that is considered the gold standard in several situations. Since there is also a limited amount of delivering facilities for this techniques, it is fundamental to increase the number of treated patients over time. The objective of this work is to offer an insight on the problem of the optimization of the part of the delivery time of a treatment plan that relates to the movements of the system. We denote it as the Nozzle Travel Time Problem (NTTP), in analogy with the Leaf Travel Time Problem (LTTP) in classical radiotherapy. In particular this work: (i) describes a mathematical model for the delivery system and formalize the optimization problem for finding the optimal sequence of movements of the system (nozzle and bed) that satisfies the covering of the prescribed irradiation directions; (ii) provides an optimization pipeline that solves the problem for instances with an amount of irradiation directions much greater than those usually employed in the clinical practice; (iii) reports preliminary results about the effects of employing two different resolution strategies within the aforementioned pipeline, that rely on an exact Traveling Salesman Problem (TSP) solver, Concorde, and an efficient Vehicle Routing Problem (VRP) heuristic, VROOM.

Proton therapy is nowadays a major clinical modality in the fight against cancer due to the advantages offered by its peculiar depth dose profile, that allows to improve its efficacy on tumors while reducing damages to healthy tissues. The number of worldwide facilities and of treated patients is increasing every year. A challenge of proton therapy is that treatment planning systems require accurate dose computation, but the golden standard in accuracy are Monte Carlo algorithms that are slow. For this reason, accurate and faster dose calculation algorithms are needed. In this paper we use Deep Learning to achieve both speed and accuracy in dose calculation for proton therapy. The results positively compare with previous existing literature in the thorax cases, that are usually the most difficult to calculate by fast algorithms.

In this paper we advocate the use of Inductive Logic Programming as a device for explaining black-box models, e.g. Support Vector Machines (SVMs), when they are used to learn user preferences. We present a case study where we use the ILP system ILASP to explain the output of SVM classifiers trained on preference datasets. Explanations are produced in terms of weak constraints, which can be easily understood by humans. We use ILASP both as a global and a local approximator for SVMs, score its fidelity, and discuss how its output can prove useful e.g. for interactive learning tasks and for identifying unwanted biases when the original dataset is not available. Finally, we highlight directions for further work and discuss relevant application areas.

Abstract Emotions represent a key aspect of human life and behavior. In recent years, automatic recognition of emotions has become an important component in the fields of affective computing and human-machine interaction. Among many physiological and kinematic signals that could be used to recognize emotions, acquiring facial expression images is one of the most natural and inexpensive approaches. The creation of a generalized, inter-subject, model for emotion recognition from facial expression is still a challenge, due to anatomical, cultural and environmental differences. On the other hand, using traditional machine learning approaches to create a subject-customized, personal, model would require a large dataset of labelled samples. For these reasons, in this work, we propose the use of transfer learning to produce subject-specific models for extracting the emotional content of facial images in the valence/arousal dimensions. Transfer learning allows us to reuse the knowledge assimilated from a large multi-subject dataset by a deep-convolutional neural network and employ the feature extraction capability in the single subject scenario. In this way, it is possible to reduce the amount of labelled data necessary to train a personalized model, with respect to relying just on subjective data. Our results suggest that generalized transferred knowledge, in conjunction with a small amount of personal data, is sufficient to obtain high recognition performances and improvement with respect to both a generalized model and personal models. For both valence and arousal dimensions, quite good performances were obtained (RMSE = 0.09 and RMSE = 0.1 for valence and arousal, respectively). Overall results suggested that both the transferred knowledge and the personal data helped in achieving this improvement, even though they alternated in providing the main contribution. Moreover, in this task, we observed that the benefits of transferring knowledge are so remarkable that no specific active or passive sampling techniques are needed for selecting images to be labelled.

Many critical aspects affect the correct operation of a Brain Computer Interface. The term ‘BCI-illiteracy’ describes the impossibility of using a BCI paradigm. At present, a universal solution does not exist and seeking innovative protocols to drive a BCI is mandatory. This work presents a meta-analytic review on recent advances in emotions recognition with the perspective of using emotions as voluntary, stimulus-independent, commands for BCIs. 60 papers, based on electroencephalography measurements, were selected to evaluate what emotions have been most recognised and what brain regions were activated by them. It was found that happiness, sadness, anger and calm were the most recognised emotions. Relevant discriminant locations for emotions recognition and for the particular case of discrete emotions recognition were identified in the temporal, frontal and parietal areas. The meta-analysis was mainly performed on stimulus-elicited emotions, due to the limited amount of literature about self-induced emotions. The obtained results represent a good starting point for the development of BCI driven by emotions and allow to: (1) ascertain that emotions are measurable and recognisable one from another (2) select a subset of most recognisable emotions and the corresponding active brain regions.

Hand rehabilitation therapy is fundamental in the recovery process for patients suffering from post-stroke or post-surgery impairments.Traditional approaches require the presence of therapist during the sessions, involving high costs and subjective measurements of the patients' abilities and progresses.Recently, several alternative approaches have been proposed.Mechanical devices are often expensive, cumbersome and patient specific, while virtual devices are not subject to this limitations, but, especially if based on a single sensor, could suffer from occlusions.In this paper a novel multi-sensor approach, based on the simultaneous use of two LEAP motion controllers, is proposed.The hardware and software design is illustrated and the measurements error induced by the mutual infrared interference is discussed.Finally, a calibration procedure, a tracking model prototype based on the sensors turnover and preliminary experimental results are presented.

The aim of this paper is to propose a real-time classification algorithm for the low-amplitude electroencephalography (EEG) signals, such as those produced by remembering an unpleasant odor, to drive a brain-computer interface. The peculiarity of these EEG signals is that they require ad hoc signals preprocessing by wavelet decomposition, and the definition of a set of features able to characterize the signals and to discriminate among different conditions. The proposed method is completely parameterized, aiming at a multiclass classification and it might be considered in the framework of machine learning. It is a two stages algorithm. The first stage is offline and it is devoted to the determination of a suitable set of features and to the training of a classifier. The second stage, the real-time one, is to test the proposed method on new data. In order to avoid redundancy in the set of features, the principal components analysis is adapted to the specific EEG signal characteristics and it is applied; the classification is performed through the support vector machine. Experimental tests on ten subjects, demonstrating the good performance of the algorithm in terms of both accuracy and efficiency, are also reported and discussed.