Departament de Tecnologies de la Informació i les Comunicacionshttp://hdl.handle.net/10230/59222023-10-02T15:59:06Z2023-10-02T15:59:06ZHigh expectations on phase locking: better quantifying the concentration of circular dataAndrzejak, Ralph GregorEspinoso Palacín, AnaïsGarcía-Portugués, EduardoPewsey, ArthurEpifanio, JacopoLeguia, Marc G.Schindler, Kaspar A.http://hdl.handle.net/10230/579882023-09-29T01:30:20Z2023-01-01T00:00:00ZHigh expectations on phase locking: better quantifying the concentration of circular data
Andrzejak, Ralph Gregor; Espinoso Palacín, Anaïs; García-Portugués, Eduardo; Pewsey, Arthur; Epifanio, Jacopo; Leguia, Marc G.; Schindler, Kaspar A.
The degree to which unimodal circular data are concentrated around the mean direction can be quantified using the mean resultant length, a measure known under many alternative names, such as the phase locking value or the Kuramoto order parameter. For maximal concentration, achieved when all of the data take the same value, the mean resultant length attains its upper bound of one. However, for a random sample drawn from the circular uniform distribution, the expected value of the mean resultant length achieves its lower bound of zero only as the sample size tends to infinity. Moreover, as the expected value of the mean resultant length depends on the sample size, bias is induced when comparing the mean resultant lengths of samples of different sizes. In order to ameliorate this problem, here, we introduce a re-normalized version of the mean resultant length. Regardless of the sample size, the re-normalized measure has an expected value that is essentially zero for a random sample from the circular uniform distribution, takes intermediate values for partially concentrated unimodal data, and attains its upper bound of one for maximal concentration. The re-normalized measure retains the simplicity of the original mean resultant length and is, therefore, easy to implement and compute. We illustrate the relevance and effectiveness of the proposed re-normalized measure for mathematical models and electroencephalographic recordings of an epileptic seizure.
Conté: Suplementary materials
2023-01-01T00:00:00ZPYWDF: an open source library for prototyping and simulating wave filter circuits in PythonAnthon, GustavLizarraga Seijas, XavierFont Corbera, Frederichttp://hdl.handle.net/10230/579032023-09-19T01:30:14Z2023-01-01T00:00:00ZPYWDF: an open source library for prototyping and simulating wave filter circuits in Python
Anthon, Gustav; Lizarraga Seijas, Xavier; Font Corbera, Frederic
This paper introduces a new open-source Python library for the modeling and simulation of wave digital filter (WDF) circuits. The library, called pwydf, allows users to easily create and analyze
WDF circuit models in a high-level, object-oriented manner. The library includes a variety of built-in components, such as voltage sources, capacitors, diodes etc., as well as the ability to create custom components and circuits. Additionally, pywdf includes a variety of analysis tools, such as frequency response and transient
analysis, to aid in the design and optimization of WDF circuits. We demonstrate the library’s efficacy in replicating the nonlinear behavior of an analog diode clipper circuit, and in creating an allpass filter that cannot be realized in the analog world. The library is well-documented and includes several examples to help users get started. Overall, pywdf is a powerful tool for anyone working with WDF circuits, and we hope it can be of great use to researchers and engineers in the field.
Comunicació presentada a 26th International Conference on Digital Audio Effects (DAFx-23), celebrada del 4 al 7 desetembre de 2023 a Copenhaguen, Dinamarca.
2023-01-01T00:00:00ZTowards soccer pass feasibility maps: the role of players’ orientationArbués Sangüesa, AdriàMartin, AdrianFernández, JavierHaro Ortega, GloriaBallester, Colomahttp://hdl.handle.net/10230/578912023-09-16T01:30:18Z2021-01-01T00:00:00ZTowards soccer pass feasibility maps: the role of players’ orientation
Arbués Sangüesa, Adrià; Martin, Adrian; Fernández, Javier; Haro Ortega, Gloria; Ballester, Coloma
Once player tracking has been established as one of the main data sources in soccer, many challenges have emerged for data scientists, who attempt to recognize patterns from 2D trajectories in order to build tools that might help coaches to improve the performance of their teams. For instance, pass models predict where the ball should go next during pass events. However, existing models are mainly fed with players’ location and prior data, hence omitting critical pieces of information such as players’ body orientation. This paper presents a computational model to obtain pass feasibility maps, where player orientation is exploited and analysed. As a matter of fact, orientation proves to be crucial when modelling field-of-view and correct positioning of players, since it limits the potential receiving area of all candidates. Different proposals are given to evaluate the proposed pass feasibility map, reaching 0.46 and 0.79 in Top1 and Top3 accuracy, respectively, with a + 0.2 boost obtained after merging positional data with orientation.
2021-01-01T00:00:00ZDesigning affirmative action policies under uncertaintyHertweck, CorinnaCastillo, CarlosMathioudakis, Michaelhttp://hdl.handle.net/10230/577342023-08-01T01:31:04Z2022-01-01T00:00:00ZDesigning affirmative action policies under uncertainty
Hertweck, Corinna; Castillo, Carlos; Mathioudakis, Michael
We study university admissions under a centralized system that uses grades and standardized test scores to match applicants to university programs. In the context of this system, we explore affirmative action policies that seek to narrow the gap between the admission rates of different socio-demographic groups while still accepting students with high scores. Since there is uncertainty about the score distribution of the students who will apply to each program, it is unclear what policy would have the desired effect on the admission rates of different groups. We address this challenge by using a predictive model trained on historical data to help optimize the parameters of such policies. We find that a learned predictive model does significantly better than relying on the ideal parameters for the last year. At the same time, we also find that a large pool of historical data yields similar results as our predictive approach for our data. Due to the more complex nature of the predictive approach, we conclude that a simpler approach should be preferred if enough data is available (e.g., long-standing, traditional university programs), but not for newer programs and other cases in which our predictive strategy can prove helpful.
2022-01-01T00:00:00Z