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Solar radiation is made up of three components of electromagnetic waves: infrared, visible and ultraviolet. The infrared component is the cause of thermal energy, the visible spectrum allows to see through the eyes and the ultraviolet component is the most energetic and damaging. Solar radiation has several benefits, such as helping to synthesize vitamin D in the skin, favors blood circulation, among others benefits for the human body. In the Earth, it is the main source of energy for agriculture, also used as an alternative source of energy to hydrocarbons, through solar cells. The solar irradiance represents the surface power density with units W/m$^2$ in SI. Too much exposure can cause damage and an increase in value over the time can be can be also damaging. In this work it was used an open data base provided by Secretar\'ia del Medio Ambiente, from which a statistical analysis was performed of the solar irradiance values measured at various meteorological stations in Mexico City and the so-called metropolitan area, from 2018 to 2021. This analysis was carried out per years, months and days. From the solar irradiance values distributions, it was obtained the averages, maximums and means were it was found there was no variation in the solar irradiance values over this period of years.

Automatic music transcription (AMT) for musical performances is a long standing problem in the field of Music Information Retrieval (MIR). Visual piano transcription (VPT) is a multimodal subproblem of AMT which focuses on extracting a symbolic representation of a piano performance from visual information only (e.g., from a top-down video of the piano keyboard). Inspired by the success of Transformers for audio-based AMT, as well as their recent successes in other computer vision tasks, in this paper we present a Transformer based architecture for VPT. The proposed VPT system combines a piano bounding box detection model with an onset and pitch detection model, allowing our system to perform well in more naturalistic conditions like imperfect image crops around the piano and slightly tilted images., Comment: 9 pages, 2 figures

We present three models concerning Tukey types of ultrafilters on $\omega$. The first model is built via a countable support iteration, and we show there is no basically generated ultrafilter in such model. The second and third models are built upon different and novel techniques, and in such models all ultrafilters are Tukey top, thus providing an answer to the Isbell problem. In all models there is no $\mathsf{nwd}$-ultrafilter.

In the pursuit of developing expressive music performance models using artificial intelligence, this paper introduces DExter, a new approach leveraging diffusion probabilistic models to render Western classical piano performances. In this approach, performance parameters are represented in a continuous expression space and a diffusion model is trained to predict these continuous parameters while being conditioned on the musical score. Furthermore, DExter also enables the generation of interpretations (expressive variations of a performance) guided by perceptually meaningful features by conditioning jointly on score and perceptual feature representations. Consequently, we find that our model is useful for learning expressive performance, generating perceptually steered performances, and transferring performance styles. We assess the model through quantitative and qualitative analyses, focusing on specific performance metrics regarding dimensions like asynchrony and articulation, as well as through listening tests comparing generated performances with different human interpretations. Results show that DExter is able to capture the time-varying correlation of the expressive parameters, and compares well to existing rendering models in subjectively evaluated ratings. The perceptual-feature-conditioned generation and transferring capabilities of DExter are verified by a proxy model predicting perceptual characteristics of differently steered performances., Comment: in submission to appsci special session

We present the first on-sky results from an ultra-low-readout-noise Skipper CCD focal plane prototype for the SOAR Integral Field Spectrograph (SIFS). The Skipper CCD focal plane consists of four 6k x 1k, 15 $\mu$m pixel, fully-depleted, p-channel devices that have been thinned to ~250 $\mu$m, backside processed, and treated with an anti-reflective coating. These Skipper CCDs were configured for astronomical spectroscopy, i.e., single-sample readout noise < 4.3 e- rms/pixel, the ability to achieve multi-sample readout noise $\ll$ 1 e- rms/pixel, full-well capacities ~40,000-65,000 e-, low dark current and charge transfer inefficiency (~2 x 10$^{-4}$ e-/pixel/s and 3.44 x 10$^{-7}$, respectively), and an absolute quantum efficiency of $\gtrsim$ 80% between 450 nm and 980 nm ($\gtrsim$ 90% between 600 nm and 900 nm). We optimized the readout sequence timing to achieve sub-electron noise (~0.5 e- rms/pixel) in a region of 2k x 4k pixels and photon-counting noise (~0.22 e- rms/pixel) in a region of 220 x 4k pixels, each with a readout time of $\lesssim$ 17 min. We observed two quasars (HB89 1159+123 and QSO J1621-0042) at redshift z ~ 3.5, two high-redshift galaxy clusters (CL J1001+0220 and SPT-CL J2040-4451), an emission line galaxy at z = 0.3239, a candidate member star of the Bo\"{o}tes II ultra-faint dwarf galaxy, and five CALSPEC spectrophotometric standard stars (HD074000, HD60753, HD106252, HD101452, HD200654). We present charge-quantized, photon-counting observations of the quasar HB89 1159+123 and show the detector sensitivity increase for faint spectral features. We demonstrate signal-to-noise performance improvements for SIFS observations in the low-background, readout-noise-dominated regime. We outline scientific studies that will leverage the SIFS-Skipper CCD data and new detector architectures that utilize the Skipper floating gate amplifier with faster readout times., Comment: 20 pages, 13 figures, 1 table; Proc. SPIE

Automatic piano transcription models are typically evaluated using simple frame- or note-wise information retrieval (IR) metrics. Such benchmark metrics do not provide insights into the transcription quality of specific musical aspects such as articulation, dynamics, or rhythmic precision of the output, which are essential in the context of expressive performance analysis. Furthermore, in recent years, MAESTRO has become the de-facto training and evaluation dataset for such models. However, inference performance has been observed to deteriorate substantially when applied on out-of-distribution data, thereby questioning the suitability and reliability of transcribed outputs from such models for specific MIR tasks. In this work, we investigate the performance of three state-of-the-art piano transcription models in two experiments. In the first one, we propose a variety of musically informed evaluation metrics which, in contrast to the IR metrics, offer more detailed insight into the musical quality of the transcriptions. In the second experiment, we compare inference performance on real-world and perturbed audio recordings, and highlight musical dimensions which our metrics can help explain. Our experimental results highlight the weaknesses of existing piano transcription metrics and contribute to a more musically sound error analysis of transcription outputs., Comment: Accepted at the 25th International Society for Music Information Retrieval Conference (ISMIR 2024)

Machine learning (ML) algorithms are predictively competitive algorithms with many human-impact applications. However, the issue of long execution time remains unsolved in the literature for high-dimensional spaces. This study proposes combining ML algorithms with an efficient methodology known as the barycentric correction procedure (BCP) to address this issue. This study uses synthetic data and an educational dataset from a private university to show the benefits of the proposed method. It was found that this combination provides significant benefits related to time in synthetic and real data without losing accuracy when the number of instances and dimensions increases. Additionally, for high-dimensional spaces, it was proved that BCP and linear support vector classification (LinearSVC), after an estimated feature map for the gaussian radial basis function (RBF) kernel, were unfeasible in terms of computational time and accuracy.

The intricate relationship between human decision-making and emotions, particularly guilt and regret, has significant implications on behavior and well-being. Yet, these emotions subtle distinctions and interplay are often overlooked in computational models. This paper introduces a dataset tailored to dissect the relationship between guilt and regret and their unique textual markers, filling a notable gap in affective computing research. Our approach treats guilt and regret recognition as a binary classification task and employs three machine learning and six transformer-based deep learning techniques to benchmark the newly created dataset. The study further implements innovative reasoning methods like chain-of-thought and tree-of-thought to assess the models interpretive logic. The results indicate a clear performance edge for transformer-based models, achieving a 90.4% macro F1 score compared to the 85.3% scored by the best machine learning classifier, demonstrating their superior capability in distinguishing complex emotional states.

We answer in negative the problem if the existence of a P-measure implies the existence of a P-point. Namely, we show that if we add random reals to a certain unique P-point model, then in the resulting model we will have a P-measure but not P-points. Also, we investigate the question if there is a P-measure in the Silver model. We show that rapid filters cannot be extended to a P-measure in the extension by $\omega$ product of Silver forcings and that in the model obtained by the product of $\omega_2$ many Silver forcings there are no P-measures of countable Maharam type

Semantic embeddings play a crucial role in natural language-based information retrieval. Embedding models represent words and contexts as vectors whose spatial configuration is derived from the distribution of words in large text corpora. While such representations are generally very powerful, they might fail to account for fine-grained domain-specific nuances. In this article, we investigate this uncertainty for the domain of characterizations of expressive piano performance. Using a music research dataset of free text performance characterizations and a follow-up study sorting the annotations into clusters, we derive a ground truth for a domain-specific semantic similarity structure. We test five embedding models and their similarity structure for correspondence with the ground truth. We further assess the effects of contextualizing prompts, hubness reduction, cross-modal similarity, and k-means clustering. The quality of embedding models shows great variability with respect to this task; more general models perform better than domain-adapted ones and the best model configurations reach human-level agreement.

Generative models of expressive piano performance are usually assessed by comparing their predictions to a reference human performance. A generative algorithm is taken to be better than competing ones if it produces performances that are closer to a human reference performance. However, expert human performers can (and do) interpret music in different ways, making for different possible references, and quantitative closeness is not necessarily aligned with perceptual similarity, raising concerns about the validity of this evaluation approach. In this work, we present a number of experiments that shed light on this problem. Using precisely measured high-quality performances of classical piano music, we carry out a listening test indicating that listeners can sometimes perceive subtle performance difference that go unnoticed under quantitative evaluation. We further present tests that indicate that such evaluation frameworks show a lot of variability in reliability and validity across different reference performances and pieces. We discuss these results and their implications for quantitative evaluation, and hope to foster a critical appreciation of the uncertainties involved in quantitative assessments of such performances within the wider music information retrieval (MIR) community.

We present results from the characterization and optimization of six Skipper CCDs for use in a prototype focal plane for the SOAR Integral Field Spectrograph (SIFS). We tested eight Skipper CCDs and selected six for SIFS based on performance results. The Skipper CCDs are 6k $\times$ 1k, 15 $\mu$m pixels, thick, fully-depleted, $p$-channel devices that have been thinned to $\sim 250 \mu$m, backside processed, and treated with an antireflective coating. We optimize readout time to achieve $<4.3$ e$^-$ rms/pixel in a single non-destructive readout and $0.5$ e$^-$ rms/pixel in $5 \%$ of the detector. We demonstrate single-photon counting with $N_{\rm samp}$ = 400 ($\sigma_{\rm 0e^-} \sim$ 0.18 e$^-$ rms/pixel) for all 24 amplifiers (four amplifiers per detector). We also perform conventional CCD characterization measurements such as cosmetic defects ($ <0.45 \%$ ``bad" pixels), dark current ($\sim 2 \times 10^{-4}$ e$^-$/pixel/sec.), charge transfer inefficiency ($3.44 \times 10^{-7}$ on average), and charge diffusion (PSF $< 7.5 \mu$m). We report on characterization and optimization measurements that are only enabled by photon-counting. Such results include voltage optimization to achieve full-well capacities $\sim 40,000-63,000$ e$^-$ while maintaining photon-counting capabilities, clock induced charge optimization, non-linearity measurements at low signals (few tens of electrons). Furthermore, we perform measurements of the brighter-fatter effect and absolute quantum efficiency ($\gtrsim\, 80 \%$ between 450 nm and 980 nm; $\gtrsim\,90 \%$ between 600 nm and 900 nm) using Skipper CCDs.

Music Information Retrieval (MIR) has seen a recent surge in deep learning-based approaches, which often involve encoding symbolic music (i.e., music represented in terms of discrete note events) in an image-like or language like fashion. However, symbolic music is neither an image nor a sentence, and research in the symbolic domain lacks a comprehensive overview of the different available representations. In this paper, we investigate matrix (piano roll), sequence, and graph representations and their corresponding neural architectures, in combination with symbolic scores and performances on three piece-level classification tasks. We also introduce a novel graph representation for symbolic performances and explore the capability of graph representations in global classification tasks. Our systematic evaluation shows advantages and limitations of each input representation. Our results suggest that the graph representation, as the newest and least explored among the three approaches, exhibits promising performance, while being more light-weight in training., Comment: To be published in the Proceedings of the 24th International Society for Music Information Retrieval Conference (ISMIR 2023), Milan, Italy

This paper introduces the ACCompanion, an expressive accompaniment system. Similarly to a musician who accompanies a soloist playing a given musical piece, our system can produce a human-like rendition of the accompaniment part that follows the soloist's choices in terms of tempo, dynamics, and articulation. The ACCompanion works in the symbolic domain, i.e., it needs a musical instrument capable of producing and playing MIDI data, with explicitly encoded onset, offset, and pitch for each played note. We describe the components that go into such a system, from real-time score following and prediction to expressive performance generation and online adaptation to the expressive choices of the human player. Based on our experience with repeated live demonstrations in front of various audiences, we offer an analysis of the challenges of combining these components into a system that is highly reactive and precise, while still a reliable musical partner, robust to possible performance errors and responsive to expressive variations., Comment: In Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI-23), Macao, China. The differences/extensions with the previous version include a technical appendix, added missing links, and minor text updates. 10 pages, 4 figures

A family $\mathcal{A} \subseteq [\omega]^\omega$ such that for all finite $\{X_i\}_{i\in n}\subseteq \mathcal A$ and $A \in \mathcal{A} \setminus \{X_i\}_{i\in n}$, the set $A \setminus \bigcup_{i \in n} X_i$ is infinite, is said to be ideal independent. We prove that an ideal independent family $\mathcal{A}$ is maximal if and only if $\mathcal A$ is $\mathcal J$-completely separable and maximal $\mathcal J$-almost disjoint for a particular ideal $\mathcal J$ on $\omega$. We show that $\mathfrak{u}\leq\mathfrak{s}_{mm}$, where $\mathfrak{s}_{mm}$ is the minimal cardinality of maximal ideal independent family. This, in particular, establishes the independence of $\mathfrak{s}_{mm}$ and $\mathfrak{i}$. Given an arbitrary set $C$ of uncountable cardinals, we show how to simultaneously adjoin via forcing maximal ideal independent families of cardinality $\lambda$ for each $\lambda\in C$, thus establishing the consistency of $C\subseteq \hbox{spec}(\mathfrak{s}_{mm})$. Assuming $\mathsf{CH}$, we construct a maximal ideal independent family, which remains maximal after forcing with any proper, $^\omega\omega$-bounding, $p$-point preserving forcing notion and evaluate $\mathfrak{s}_{mm}$ in several well studied forcing extensions. We also study natural filters associated with ideal independence and introduce an analog of Mr\'owka spaces for ideal independent families., Comment: 18 Pages, subsumes arXiv:2206.14019

Credit scoring models are the primary instrument used by financial institutions to manage credit risk. The scarcity of research on behavioral scoring is due to the difficult data access. Financial institutions have to maintain the privacy and security of borrowers' information refrain them from collaborating in research initiatives. In this work, we present a methodology that allows us to evaluate the performance of models trained with synthetic data when they are applied to real-world data. Our results show that synthetic data quality is increasingly poor when the number of attributes increases. However, creditworthiness assessment models trained with synthetic data show a reduction of 3\% of AUC and 6\% of KS when compared with models trained with real data. These results have a significant impact since they encourage credit risk investigation from synthetic data, making it possible to maintain borrowers' privacy and to address problems that until now have been hampered by the availability of information.

We present the development of a Skipper Charge-Coupled Device (CCD) focal plane prototype for the SOAR Telescope Integral Field Spectrograph (SIFS). This mosaic focal plane consists of four 6k $\times$ 1k, 15 $\mu$m pixel Skipper CCDs mounted inside a vacuum dewar. We describe the process of packaging the CCDs so that they can be easily tested, transported, and installed in a mosaic focal plane. We characterize the performance of $\sim 650 \mu$m thick, fully-depleted engineering-grade Skipper CCDs in preparation for performing similar characterization tests on science-grade Skipper CCDs which will be thinned to 250$\mu$m and backside processed with an antireflective coating. We achieve a single-sample readout noise of $4.5 e^{-} rms/pix$ for the best performing amplifiers and sub-electron resolution (photon counting capabilities) with readout noise $\sigma \sim 0.16 e^{-} rms/pix$ from 800 measurements of the charge in each pixel. We describe the design and construction of the Skipper CCD focal plane and provide details about the synchronized readout electronics system that will be implemented to simultaneously read 16 amplifiers from the four Skipper CCDs (4-amplifiers per detector). Finally, we outline future plans for laboratory testing, installation, commissioning, and science verification of our Skipper CCD focal plane.

A family $\mathscr{I} \subseteq [\omega]^\omega$ such that for all finite $\{X_i\}_{i\in n}\subseteq \mathcal I$ and $A \in \mathscr{I} \setminus \{X_i\}_{i\in n}$, the set $A \setminus \bigcup_{i < n} X_i$ is infinite, is said to be ideal independent. An ideal independent family which is maximal under inclusion is said to be a maximal ideal independent family and the least cardinality of such family is denoted $\mathfrak{s}_{mm}$. We show that $\mathfrak{u}\leq\mathfrak{s}_{mm}$, which in particular establishes the independence of $\mathfrak{s}_{mm}$ and $\mathfrak{i}$. Given an arbitrary set $C$ of uncountable cardinals, we show how to simultaneously adjoin via forcing maximal ideal independent families of cardinality $\lambda$ for each $\lambda\in C$, thus establishing the consistency of $C\subseteq \hbox{spec}(\mathfrak{s}_{mm})$. Assuming $\mathsf{CH}$, we construct a maximal ideal independent family, which remains maximal after forcing with any proper, $^\omega\omega$-bounding, $p$-point preserving forcing notion and evaluate $\mathfrak{s}_{mm}$ in several well studied forcing extensions., Comment: 12 pages, submitted

This paper presents the specifications of match: a file format that extends a MIDI human performance with note-, beat-, and downbeat-level alignments to a corresponding musical score. This enables advanced analyses of the performance that are relevant for various tasks, such as expressive performance modeling, score following, music transcription, and performer classification. The match file includes a set of score-related descriptors that makes it usable also as a bare-bones score representation. For applications that require the use of structural score elements (e.g., voices, parts, beams, slurs), the match file can be easily combined with the symbolic score. To support the practical application of our work, we release a corrected and upgraded version of the Vienna4x22 dataset of scores and performances aligned with match files.