Modelling conditional probabilities with Riemann-Theta Boltzmann Machines

The probability density function for the visible sector of a Riemann-Theta Boltzmann machine can be taken conditional on a subset of the visible units. We derive that the corresponding conditional density function is given by a reparameterization of the Riemann-Theta Boltzmann machine modelling the original probability density function. Therefore the conditional densities can be directly inferred from the Riemann-Theta Boltzmann machine.Comment: 7 pages, 3 figures, in proceedings of the 19th International Workshop on Advanced Computing and Analysis Techniques in Physics Research (ACAT 2019

Real-time error mitigation for variational optimization on quantum hardware

In this work we put forward the inclusion of error mitigation routines in the process of training Variational Quantum Circuit (VQC) models. In detail, we define a Real Time Quantum Error Mitigation (RTQEM) algorithm to assist in fitting functions on quantum chips with VQCs. While state-of-the-art QEM methods cannot address the exponential loss concentration induced by noise in current devices, we demonstrate that our RTQEM routine can enhance VQCs' trainability by reducing the corruption of the loss function. We tested the algorithm by simulating and deploying the fit of a monodimensional $\textit{u}$-Quark Parton Distribution Function (PDF) on a superconducting single-qubit device, and we further analyzed the scalability of the proposed technique by simulating a multidimensional fit with up to 8 qubits.Comment: 12 pages, 9 figure

Precision Medicine in Non-Communicable Diseases

The increase in life expectancy during the 20th century ranks as one of society's greatest achievements, with massive growth in the numbers and proportion of the elderly, virtually occurring in every country of the world. The burden of chronic diseases is one of the main consequences of this phenomenon, severely hampering the quality of life of elderly people and challenging the efficiency and sustainability of healthcare systems. Non-communicable diseases (NCDs) are considered a global emergency responsible for over 70% of deaths worldwide. NCDs are also the basis for complex and multifactorial diseases such as hypertension, diabetes, and obesity. The epidemics of NCDs are a consequence of a complex interaction between health, economic growth, and development. This interaction includes the individual genome, the microbiome, the metabolome, the immune status, and environmental factors such as nutritional and chemical exposure. To counteract NCDs, it is therefore essential to develop an innovative, personalized, preventative, early care model through the integration of different molecular profiles of individuals to identify both the critical biomarkers of NCD susceptibility and to discover novel therapeutic targets

Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics: Student Research Workshop

In this work we put forward to combine pre-trained knowledge base graph embeddings with transformer based language models to improve performance on the sentential Relation Extraction task in natural language processing. Our proposed model is based on a simple variation of existing models to incorporate off-task pre-trained graph embeddings with an on-task finetuned BERT encoder. We perform a detailed statistical evaluation of the model on standard datasets. We provide evidence that the added graph embeddings improve the performance, making such a simple approach competitive with the state-of-the-art models that perform explicit on-task training of the graph embeddings. Furthermore, we observe for the underlying BERT model an interesting power-law scaling behavior between the variance of the F1 score obtained for a relation class and its support in terms of training examples

Real-time error mitigation for variational optimization on quantum hardware

In this work we put forward the inclusion of error mitigation routines in the process of training Variational Quantum Circuit (VQC) models. In detail, we define a Real Time Quantum Error Mitigation (RTQEM) algorithm to assist in fitting functions on quantum chips with VQCs. While state-of-the-art QEM methods cannot address the exponential loss concentration induced by noise in current devices, we demonstrate that our RTQEM routine can enhance VQCs' trainability by reducing the corruption of the loss function. We tested the algorithm by simulating and deploying the fit of a monodimensional $\textit{u}$-Quark Parton Distribution Function (PDF) on a superconducting single-qubit device, and we further analyzed the scalability of the proposed technique by simulating a multidimensional fit with up to 8 qubits

Changes and determination of dosing recommendations for medicinal products recently authorised in the European Union

Introduction: The quantity and quality of data for determining the dose and treatment schedule of medicinal products is directly related to how safe and efficacious these medicines are and how successful they can be used to treat patients. Areas covered: This review provides an analysis of dose-related label modifications of recently approved drugs. It shows which areas could benefit from a better dose-exposure-response understanding, both during initial assessment and after marketing authorisation. This analysis highlights regulators' considerations in dosage evaluations and provides reflections for drug developers on how to ensure best possible dose selection in the interest of the patients. Expert opinion: Using modelling and simulation, pharmacogenomics, population pharmacokinetics, physiologically based pharmacokinetic models and drug-drug interaction studies in conjunction with well-designed clinical trials will improve the understanding of the pharmacology of medicines, of the physiology of the disease and of the dose-exposure-response relationship during drug development. More focus should be given to the investigation of dose and regimens for special populations before applying for marketing authorisation. Consequently, regulators could review dose-exposure-response data with more certainty and better define dose recommendations in the label

Forensic genetic value of a 27 Y-STR loci multiplex (Yfiler® Plus kit) in an Italian population sample

The analysis of Y chromosome short tandem repeat (Y-STR) haplotypes provides important information that can be used for investigative purposes and in population studies. The Yfiler® Plus PCR Amplification kit (Yfiler® Plus, Thermo Fisher Scientific, Waltham, MA, USA) allows the multiplex amplification of 27 Y-STRs, including 7 rapidly mutating markers (RM Y-STRs). In this study, 203 unrelated males from Italy, which were subdivided into 4 different geographical groups (North, Center, South and Sardinia) were analyzed. Several intra-population diversity indexes were computed and compared to those obtained using only loci either from the minimal haplotype or the 17-plex (Yfiler®, Thermo Fisher Scientific, Waltham, MA, USA). In addition, inter-population diversity analysis (RST) among the four Italian samples was performed. The same analysis was also used to compare the Italian sub-sets to other European populations where the Yfiler® Plus haplotype frequency data were available. The Sardinians were significantly differentiated from the other three Italian groups, thus requiring a specific sub-national Y-STR haplotype database. The Yfiler® Plus kit showed a high power of discrimination which is useful for criminal investigations, principally due to the inclusion of RM Y-STRs