AUTOMATIC SUBGROUPING OF MULTITRACK AUDIO

Subgrouping is a mixing technique where the outputs of a subset of audio tracks in a multitrack are summed to a single audio bus. This is done so that the mix engineer can apply signal processing to an entire subgroup, speed up the mix work flow and manipu-late a number of audio tracks at once. In this work, we investigate which audio features from a set of 159 can be used to automati-cally subgroup multitrack audio. We determine a subset of audio features from the original 159 audio features to use for automatic subgrouping, by performing feature selection using a Random For-est classifier on a dataset of 54 individual multitracks. We show that by using agglomerative clustering on 5 test multitracks, the entire set of audio features incorrectly clusters 35.08 % of the audio tracks, while the subset of audio features incorrectly clusters only 7.89 % of the audio tracks. Furthermore, we also show that using the entire set of audio features, ten incorrect subgroups are created. However, when using the subset of audio features, only five incor-rect subgroups are created. This indicates that our reduced set of audio features provides a significant increase in classification ac-curacy for the creation of subgroups automatically. 1

A stochastic resonator to detect BPAM signals ; analysis, PSR designs, and sine-induced SR

A Stochastic Resonator has been considered as an alternative signal processing tool because of its noise-induced performance enhancement ability. Here, the resonator parameters, steady states and transition time of the system are redefined for BPAM signals such that the region in which the resonator benefits from noise can be identified. Simple parameter-induced stochastic resonance (PSR) designs are then built, based on this analysis in order to configure the resonator in the optimum region. Furthermore, Sine-induced SR based on using a periodic signal instead of noise is introduced to enhance the system performance and compared with noise-enhanced SR (NSR). It is shown that Sine-induced SR provides a performance enhancement as it needs less power and does not require an adjustment relevant to the background noise. The results indicate that a resonator improves the receiver performance by eliminating noise if its parameters and BPAM characteristics are set accurately as given in the PSR designs, otherwise the resonator can benefit from either a noise as in NSR, or a sine wave as proposed

Effect of the deposition time on optical and electrical properties of semiconductor ZnS thin films prepared by chemical bath deposition

Semiconductor ZnS thin films have been deposited by a chemical bath deposition (CBD) on a glass substrate at 80 °C with different deposition time (4, 6 and 8 h). The films have been further studied in order to determine the change in optical and electrical properties as a function of deposition time. The film thicknesses have been calculated between 210–1375 nm by using gravimetrical analysis. The optical properties of ZnS thin films have been determined by transmittance (%T) and absorbance (A) measurements by UV-Vis spectroscopy operated wavelength range between 300 and 1100 nm at room temperature. The optical transmittance values of ZnS thin films in the visible region of the electromagnetic spectrum have been found to be between 51–90%. The calculations indicate that the refractive index (n) in the visible region is between 1.40 and 2.45. The optical band gaps (Eg) of thin films have been calculated between 3.61–3.88 eV while the band edge sharpness values (B) are varied between 6.95×109–8.96×1010 eV/cm2. The specific resistivity values (ρ) of the films are found to be between 1.08×105–1.01×106 Ω·cm and exhibit an n-type conductivity by Hall measurement

Towards 3D Retrieval of Exoplanet Atmospheres: Assessing Thermochemical Equilibrium Estimation Methods

Characterizing exoplanetary atmospheres via Bayesian retrievals requires assuming some chemistry model, such as thermochemical equilibrium or parameterized abundances. The higher-resolution data offered by upcoming telescopes enables more complex chemistry models within retrieval frameworks. Yet, many chemistry codes that model more complex processes like photochemistry and vertical transport are computationally expensive, and directly incorporating them into a 1D retrieval model can result in prohibitively long execution times. Additionally, phase-curve observations with upcoming telescopes motivate 2D and 3D retrieval models, further exacerbating the lengthy runtime for retrieval frameworks with complex chemistry models. Here, we compare thermochemical equilibrium approximation methods based on their speed and accuracy with respect to a Gibbs energy-minimization code. We find that, while all methods offer orders of magnitude reductions in computational cost, neural network surrogate models perform more accurately than the other approaches considered, achieving a median absolute dex error <0.03 for the phase space considered. While our results are based on a 1D chemistry model, our study suggests that higher dimensional chemistry models could be incorporated into retrieval models via this surrogate modeling approach.Comment: 22 pages, 14 figures, submitted to PSJ 2022/11/22, revised 2023/3/7, accepted 2023/3/23. Updated to add Zenodo link to Reproducible Research Compendiu

Toward 3D retrieval of exoplanet atmospheres: assessing thermochemical equilibrium estimation methods

Characterizing exoplanetary atmospheres via Bayesian retrievals requires assuming some chemistry model, such as thermochemical equilibrium or parameterized abundances. The higher-resolution data offered by upcoming telescopes enable more complex chemistry models within retrieval frameworks. Yet many chemistry codes that model more complex processes like photochemistry and vertical transport are computationally expensive, and directly incorporating them into a 1D retrieval model can result in prohibitively long execution times. Additionally, phase-curve observations with upcoming telescopes motivate 2D and 3D retrieval models, further exacerbating the lengthy runtime for retrieval frameworks with complex chemistry models. Here we compare thermochemical equilibrium approximation methods based on their speed and accuracy with respect to a Gibbs energy-minimization code. We find that, while all methods offer orders-of-magnitude reductions in computational cost, neural network surrogate models perform more accurately than the other approaches considered, achieving a median absolute dex error of <0.03 for the phase space considered. While our results are based on a 1D chemistry model, our study suggests that higher-dimensional chemistry models could be incorporated into retrieval models via this surrogate modeling approach

Facial Electromyography-based Adaptive Virtual Reality Gaming for Cognitive Training

Cognitive training has shown promising results for delivering improvements in human cognition related to attention, problem solving, reading comprehension and information retrieval. However, two frequently cited problems in cognitive training literature are a lack of user engagement with the training programme, and a failure of developed skills to generalise to daily life. This paper introduces a new cognitive training (CT) paradigm designed to address these two limitations by combining the benefits of gamification, virtual reality (VR), and affective adaptation in the development of an engaging, ecologically valid, CT task. Additionally, it incorporates facial electromyography (EMG) as a means of determining user affect while engaged in the CT task. This information is then utilised to dynamically adjust the game's difficulty in real-time as users play, with the aim of leading them into a state of flow. Affect recognition rates of 64.1% and 76.2%, for valence and arousal respectively, were achieved by classifying a DWT-Haar approximation of the input signal using kNN. The affect-aware VR cognitive training intervention was then evaluated with a control group of older adults. The results obtained substantiate the notion that adaptation techniques can lead to greater feelings of competence and a more appropriate challenge of the user's skills

Matching patient and physician preferences in designing a primary care facility network

Cataloged from PDF version of article.This paper introduces an integer programming model for planning primary care facility networks, which accounts for the interests of different stakeholders while maximizing access to health care. Physician allocation to health-care facilities is explicitly modelled, which allows consideration of physician incentives in the planning phase. An illustrative case study in the Turkish primary care system is presented to show the implications of focusing on patient or physician preferences in the planning phase. A discussion of trade-offs between the different stakeholder preferences and some recommendations for modelling choices to match these preferences are provided. In the context of this case, we found that using an access measure that decays with distance, and incorporating nearest allocation constraints improves performance for all stakeholders. We also show that increasing the number of physicians may have adverse affects on access measures when physician preferences are addressed