A non-arbitrage liquidity model with observable parameters for derivatives

We develop a parameterised model for liquidity effects arising from the trading in an asset. Liquidity is defined via a combination of a trader's individual transaction cost and a price slippage impact, which is felt by all market participants. The chosen definition allows liquidity to be observable in a centralised order-book of an asset as is usually provided in most non-specialist exchanges. The discrete-time version of the model is based on the CRR binomial tree and in the appropriate continuous-time limits we derive various nonlinear partial differential equations. Both versions can be directly applied to the pricing and hedging of options; the nonlinear nature of liquidity leads to natural bid-ask spreads that are based on the liquidity of the market for the underlying and the existence of (super-)replication strategies. We test and calibrate our model set-up empirically with high-frequency data of German blue chips and discuss further extensions to the model, including stochastic liquidity

Simulations of Photon Detection in SiPM Number-Resolving Detectors

Number-resolving single photon detectors are essential for the implementation of numerous innovative quantum information schemes. While several number-discriminating techniques have been previously presented, the Silicon Photo-Multiplier (SiPM) detector is a promising candidate due its rather simple integration in optical setups. On the other hand, the photon statistics obtained with the SiPM detector suffer from inaccuracies due to inherent distortions which depend on the geometrical properties of the SiPM. We have simulated the detection process in a SiPM detector and studied these distortions. We use results from the simulation in order to interpret experimental data and study the limits in which available models prevail

Disrupted Sense of Agency as a State Marker of First-Episode Schizophrenia: A Large-Scale Follow-Up Study

Background: Schizophrenia is often characterized by a general disruption of self-processing and self-demarcation. Previous studies have shown that self-monitoring and sense of agency (SoA, i.e., the ability to recognize one's own actions correctly) are altered in schizophrenia patients. However, research findings are inconclusive in regards to how SoA alterations are linked to clinical symptoms and their severity, or cognitive factors. Methods: In a longitudinal study, we examined 161 first-episode schizophrenia patients and 154 controls with a continuous-report SoA task and a control task testing general cognitive/sensorimotor processes. Clinical symptoms were assessed with the Positive and Negative Syndrome Scale (PANSS). Results: In comparison to controls, patients performed worse in terms of recognition of self-produced movements even when controlling for confounding factors. Patients' SoA score correlated with the severity of PANSS-derived “Disorganized” symptoms and with a priori defined symptoms related to self-disturbances. In the follow-up, the changes in the two subscales were significantly associated with the change in SoA performance. Conclusion: We corroborated previous findings of altered SoA already in the early stage of schizophrenia. Decreased ability to recognize self-produced actions was associated with the severity of symptoms in two complementary domains: self-disturbances and disorganization. While the involvement of the former might indicate impairment in self-monitoring, the latter suggests the role of higher cognitive processes such as information updating or cognitive flexibility. The SoA alterations in schizophrenia are associated, at least partially, with the intensity of respective symptoms in a state-dependent manner

A Fuzzy Inference System for Closed-Loop Deep Brain Stimulation in Parkinson’s Disease

Parkinsons disease is a complex neurodegenerative disorder for which patients present many symptoms, tremor being the main one. In advanced stages of the disease, Deep Brain Stimulation is a generalized therapy which can significantly improve the motor symptoms. However despite its beneficial effects on treating the symptomatology, the technique can be improved. One of its main limitations is that the parameters are fixed, and the stimulation is provided uninterruptedly, not taking into account any fluctuation in the patients state. A closed-loop system which provides stimulation by demand would adjust the stimulation to the variations in the state of the patient, stimulating only when it is necessary. It would not only perform a more intelligent stimulation, capable of adapting to the changes in real time, but also extending the devices battery life, thereby avoiding surgical interventions. In this work we design a tool that learns to recognize the principal symptom of Parkinsons disease and particularly the tremor. The goal of the designed system is to detect the moments the patient is suffering from a tremor episode and consequently to decide whether stimulation is needed or not. For that, local field potentials were recorded in the subthalamic nucleus of ten Parkinsonian patients, who were diagnosed with tremor-dominant Parkinsons disease and who underwent surgery for the implantation of a neurostimulator. Electromyographic activity in the forearm was simultaneously recorded, and the relation between both signals was evaluated using two different synchronization measures. The results of evaluating the synchronization indexes on each moment represent the inputs to the designed system. Finally, a fuzzy inference system was applied with the goal of identifying tremor episodes. Results are favourable, reaching accuracies of higher 98.7 % in 70 % of the patients.Centro de Investigación Biomédica en RedDepto. de Psicología Experimental, Procesos Cognitivos y LogopediaDepto. de Radiología, Rehabilitación y FisioterapiaFac. de PsicologíaFac. de MedicinaTRUEpu

The Human Phenotype Ontology in 2024: phenotypes around the world

\ua9 The Author(s) 2023. Published by Oxford University Press on behalf of Nucleic Acids Research. The Human Phenotype Ontology (HPO) is a widely used resource that comprehensively organizes and defines the phenotypic features of human disease, enabling computational inference and supporting genomic and phenotypic analyses through semantic similarity and machine learning algorithms. The HPO has widespread applications in clinical diagnostics and translational research, including genomic diagnostics, gene-disease discovery, and cohort analytics. In recent years, groups around the world have developed translations of the HPO from English to other languages, and the HPO browser has been internationalized, allowing users to view HPO term labels and in many cases synonyms and definitions in ten languages in addition to English. Since our last report, a total of 2239 new HPO terms and 49235 new HPO annotations were developed, many in collaboration with external groups in the fields of psychiatry, arthrogryposis, immunology and cardiology. The Medical Action Ontology (MAxO) is a new effort to model treatments and other measures taken for clinical management. Finally, the HPO consortium is contributing to efforts to integrate the HPO and the GA4GH Phenopacket Schema into electronic health records (EHRs) with the goal of more standardized and computable integration of rare disease data in EHRs

Center For

Several cameras used for panoramic vision or mosaicing cannot be modeled by perspective projections. A summary of existing methods employing these so called non-central cameras is presented in this paper