This paper presents a novel approach for multi-lingual sentiment classification in short texts. This is a challenging task as the amount of training data in languages other than English is very limited. Previously proposed multi-lingual approaches typically require to establish a correspondence to English for which powerful classifiers are already available. In contrast, our method does not require such supervision. We leverage large amounts of weaklysupervised data in various languages to train a multi-layer convolutional network and demonstrate the importance of using pretraining of such networks. We thoroughly evaluate our approach on various multi-lingual datasets, including the recent SemEval-2016 sentiment prediction benchmark (Task 4), where we achieved stateof-the-art performance. We also compare the performance of our model trained individually for each language to a variant trained for all languages at once. We show that the latter model reaches slightly worse -but still acceptable -performance when compared to the single language model, while benefiting from better generalization properties across languages.

In this paper, we propose a classifier for predicting message-level sentiments of English micro-blog messages from Twitter. Our method builds upon the convolutional sentence embedding approach proposed by (Severyn and Moschitti, 2015a; Severyn and Moschitti, 2015b). We leverage large amounts of data with distant supervision to train an ensemble of 2-layer convolutional neural networks whose predictions are combined using a random forest classifier. Our approach was evaluated on the datasets of the SemEval-2016 competition (Task 4) outperforming all other approaches for the Message Polarity Classification task.

The Challenge on Liver Ultrasound Tracking (CLUST) was held in conjunction with the MICCAI 2014 conference to enable direct comparison of tracking methods for this application. This paper reports the outcome of this challenge, including setup, methods, results and experiences. The database included 54 2D and 3D sequences of the liver of healthy volunteers and tumor patients under free breathing. Participants had to provide the tracking results of 90% of the data (test set) for pre-defined point-landmarks (healthy volunteers) or for tumor segmentations (patient data). In this paper we compare the best six methods which participated in the challenge. Quantitative evaluation was performed by the organizers with respect to manual annotations. Results of all methods showed a mean tracking error ranging between 1.4 mm and 2.1 mm for 2D points, and between 2.6 mm and 4.6 mm for 3D points. Fusing all automatic results by considering the median tracking results, improved the mean error to 1.2 mm (2D) and 2.5 mm (3D). For all methods, the performance is still not comparable to human inter-rater variability, with a mean tracking error of 0.5–0.6 mm (2D) and 1.2–1.8 mm (3D). The segmentation task was fulfilled only by one participant, resulting in a Dice coefficient ranging from 76.7% to 92.3%. The CLUST database continues to be available and the online leader-board will be updated as an ongoing challenge.

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Liver motion estimation and prediction during free-breathing from 2D ultrasound images can substantially reduce the in-plane motion uncertainty and hence treatment margins. Employing an accurate tracking method while avoiding non-linear temporal prediction would be favorable. This approach has the potential to shorten treatment time compared to breath-hold and gated approaches, and increase treatment efficiency and safety.

We present the modeling efforts on antenna design, frequency selection and receiver sensitivity estimation to detect vesicoureteral reflux (VUR) using microwave (MW) radiometry as the warm urine from the bladder maintained at fever range temperature using a MW hyperthermia device reflows into the kidneys. Radiometer center frequency (f c ), frequency band (Δf), and aperture radius (r a ) of the physical antenna for kidney temperature monitoring are determined using a simplified universal antenna model with circular aperture. Anatomical information extracted from computed tomography (CT) images of children age 4-6 years is used to construct a layered 3D tissue model. Radiometric antenna efficiency is evaluated in terms of the ratio between the power collected from the target at depth and the total power received by the antenna (η). Power ratio of the theoretical antenna is used to design a microstrip log spiral antenna with directional radiation pattern over f c ± Δf/2. Power received by the log spiral from the deep target is enhanced using a thin low-loss dielectric matching layer. A cylindrical metal cup is proposed to shield the antenna from electromagnetic interference (EMI). Transient thermal simulations are carried out to determine the minimum detectable change in antenna brightness temperature (δT B ) for 15-25 mL urine refluxes at 40-42°C located 35 mm from the skin surface. Theoretical antenna simulations indicate maximum η over 1.1-1.6 GHz for r a = 30-40 mm. Simulations of the 35 mm radius tapered log spiral yielded higher power ratio over f c ± Δf/2 for the 35-40 mm deep targets in the presence of an optimal matching layer. Radiometric temperature calculations indicate δT B ≥ 0.1 K for the 15 mL urine at 40°C and 35 mm depth. Higher η and δT B were observed for the antenna and matching layer inside the metal cup. Reflection measurements of the log spiral in saline phantom are in agreement with the simulation data. Numerical study suggests a radiometer with f c =1.35 GHz, Δf = 500 MHz and detector sensitivity better than 0.1 K would be the appropriate tool to noninvasively detect VUR using the log spiral antenna.

<b><i>Background:</i></b> Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts patient health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are severely limited. <b><i>Methods:</i></b> After using a recurrent neural network-based algorithm to impute missing time series data form a multisensor wearable device, we compared supervised and unsupervised machine learning approaches to gain insights on the relationship between self-reported non-pathological fatigue and multimodal sensor data. <b><i>Results:</i></b> A total of 27 healthy subjects and 405 recording days were analyzed. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters, and daily questionnaires on fatigue. The best results were obtained when using the causal convolutional neural network model for unsupervised representation learning of multivariate sensor data, and random forest as a classifier trained on subject-reported physical fatigue labels (weighted precision of 0.70 ± 0.03 and recall of 0.73 ± 0.03). When using manually engineered features on sensor data to train our random forest (weighted precision of 0.70 ± 0.05 and recall of 0.72 ± 0.01), both physical activity (energy expenditure, activity counts, and steps) and vital signs (heart rate, heart rate variability, and respiratory rate) were important parameters to measure. Furthermore, vital signs contributed the most as top features for predicting mental fatigue compared to physical ones. These results support the idea that fatigue is a highly multimodal concept. Analysis of clusters from sensor data highlighted a digital phenotype indicating the presence of fatigue (95% of observations) characterized by a high intensity of physical activity. Mental fatigue followed similar trends but was less predictable. Potential future directions could focus on anomaly detection assuming longer individual monitoring periods. <b><i>Conclusion:</i></b> Taken together, these results are the first demonstration that multimodal digital data can be used to inform, quantify, and augment subjectively captured non-pathological fatigue measures.

This study demonstrates that, truly, simultaneous US/MR dynamic acquisition in the abdomen is achievable using clinical instruments. A potential application is the US/MR hybrid guidance of high-intensity focused US therapy in the liver.