Subaortic and midventricular obstructive hypertrophic cardiomyopathy with extreme segmental hypertrophy

BACKGROUND: Subaortic and midventricular hypertrophic cardiomyopathy in a patient with extreme segmental hypertrophy exceeding the usual maximum wall thickness reported in the literature is a rare phenomenon. CASE PRESENTATION: A 19-year-old man with recently diagnosed hypertrophic cardiomyopathy (HCM) was referred for sudden death risk assessment. The patient had mild exertional dyspnea (New York Heart Association functional class II), but without syncope or chest pain. There was no family history of HCM or sudden death. A two dimensional echocardiogram revealed an asymmetric type of LV hypertrophy; anterior ventricular septum = 49 mm; posterior ventricular septum = 20 mm; anterolateral free wall = 12 mm; and posterior free wall = 6 mm. The patient had 2 types of obstruction; a LV outflow obstruction due to systolic anterior motion of both mitral leaflets (Doppler-estimated 38 mm Hg gradient at rest); and a midventricular obstruction (Doppler-estimated 43 mm Hg gradient), but without apical aneurysm or dyskinesia. The patient had a normal blood pressure response on exercise test and no episodes of non-sustained ventricular tachycardia in 24-h ECG recording. Cardiac MRI showed a gross late enhancement at the hypertrophied septum. Based on the extreme degree of LV hypertrophy and the myocardial hyperenhancement, an implantation of a cardioverter-defibrillator was recommended prophylactically for primary prevention of sudden death. CONCLUSION: Midventricular HCM is an infrequent phenotype, but may be associated with an apical aneurysm and progression to systolic dysfunction (end-stage HCM)

The step project:societal and political engagement of young people in environmental issues

Decisions on environmental topics taken today are going to have long-term consequences that will affect future generations. Young people will have to live with the consequences of these decisions and undertake special responsibilities. Moreover, as tomorrow’s decision makers, they themselves should learn how to negotiate and debate issues before final decisions are made. Therefore, any participation they can have in environmental decision making processes will prove essential in developing a sustainable future for the community.However, recent data indicate that the young distance themselves from community affairs, mainly because the procedures involved are ‘wooden’, politicians’ discourse alienates the young and the whole experience is too formalized to them. Authorities are aware of this fact and try to establish communication channels to ensure transparency and use a language that speaks to new generations of citizens. This is where STEP project comes in.STEP (www.step4youth.eu) is a digital Platform (web/mobile) enabling youth Societal and Political e-Participation in decision-making procedures concerning environmental issues. STEP is enhanced with web/social media mining, gamification, machine translation, and visualisation features.Six pilots in real contexts are being organised for the deployment of the STEP solution in 4 European Countries: Italy, Spain, Greece, and Turkey. Pilots are implemented with the direct participation of one regional authority, four municipalities, and one association of municipalities, and include decision-making procedures on significant environmental questions.</p

Zero-Knowledge Proofs of Training for Deep Neural Networks

A zero-knowledge proof of training (zkPoT) enables a party to prove that they have correctly trained a committed model based on a committed dataset without revealing any additional information about the model or the dataset. An ideal zkPoT should offer provable security and privacy guarantees, succinct proof size and verifier runtime, and practical prover efficiency. In this work, we present Kaizen, a zkPoT targeted for deep neural networks (DNNs) that achieves the above ideals all at once. In particular, our construction enables a prover to iteratively train their model by the (mini-batch) gradient-descent algorithm where the number of iterations need not be fixed in advance; at the end of each iteration, the prover generates a commitment to the trained model attached with a succinct zkPoT, attesting to the correctness of the entire training process. The proof size and verifier time are independent of the iteration number. Kaizen relies on two essential building blocks to achieve both prover efficiency and verification succinctness. First, we construct an optimized GKR-style (sumcheck-based) proof system for the gradient-descent algorithm with concretely efficient prover cost; this scheme allows the prover to generate a proof for each iteration of the training process. Then, we recursively compose these proofs across multiple iterations to attain succinctness. As of independent interests, we propose a framework for recursive composition of GKR-style proofs and techniques, such as aggregatable polynomial commitment schemes, to minimize the recursion overhead. Benchmarks indicate that Kaizen can handle a large model of VGG-$11$ with $10$ million parameters and batch size $16$. The prover runtime is $22$ minutes (per iteration), which is $\mathbf{43\times}$ faster than generic recursive proofs, while we further achieve at least $\mathbf{224 \times}$ less prover memory overhead. Independent of the number of iterations and, hence, the size of the dataset, the proof size is $1.36$ megabytes, and the verifier runtime is only $103$ milliseconds

First activity and interactions in thalamus and cortex using raw single-trial EEG and MEG elicited by somatosensory stimulation

Introduction: One of the primary motivations for studying the human brain is to comprehend how external sensory input is processed and ultimately perceived by the brain. A good understanding of these processes can promote the identification of biomarkers for the diagnosis of various neurological disorders; it can also provide ways of evaluating therapeutic techniques. In this work, we seek the minimal requirements for identifying key stages of activity in the brain elicited by median nerve stimulation.Methods: We have used a priori knowledge and applied a simple, linear, spatial filter on the electroencephalography and magnetoencephalography signals to identify the early responses in the thalamus and cortex evoked by short electrical stimulation of the median nerve at the wrist. The spatial filter is defined first from the average EEG and MEG signals and then refined using consistency selection rules across ST. The refined spatial filter is then applied to extract the timecourses of each ST in each targeted generator. These ST timecourses are studied through clustering to quantify the ST variability. The nature of ST connectivity between thalamic and cortical generators is then studied within each identified cluster using linear and non-linear algorithms with time delays to extract linked and directional activities. A novel combination of linear and non-linear methods provides in addition discrimination of influences as excitatory or inhibitory.Results: Our method identifies two key aspects of the evoked response. Firstly, the early onset of activity in the thalamus and the somatosensory cortex, known as the P14 and P20 in EEG and the second M20 for MEG. Secondly, good estimates are obtained for the early timecourse of activity from these two areas. The results confirm the existence of variability in ST brain activations and reveal distinct and novel patterns of connectivity in different clusters.Discussion: It has been demonstrated that we can extract new insights into stimulus processing without the use of computationally costly source reconstruction techniques which require assumptions and detailed modeling of the brain. Our methodology, thanks to its simplicity and minimal computational requirements, has the potential for real-time applications such as in neurofeedback systems and brain-computer interfaces

Towards Efficient Decentralized Federated Learning

We focus on the problem of efficiently deploying a federated learning training task in a decentralized setting with multiple aggregators. To that end, we introduce a number of improvements and modifications to the recently proposed IPLS protocol. In particular, we relax its assumption for direct communication across participants, using instead indirect communication over a decentralized storage system, effectively turning it into a partially asynchronous protocol. Moreover, we secure it against malicious aggregators (that drop or alter data) by relying on homomorphic cryptographic commitments for efficient verification of aggregation. We implement the modified IPLS protocol and report on its performance and potential bottlenecks. Finally, we identify important next steps for this line of research