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This article describes a procedure for measuring and evaluating radio impulsive noise (IN) from a specific source. A good knowledge of the noise caused by different sources is essential to plan radio services and to ensure good quality of service. Moreover, it is necessary to harmonize noise measurement methods to achieve results that can be mutually compared. This article not only provides steps that should be followed to make proper measurements, but also specifies appropriate parameters to characterize the IN when it is generated by a principal source. A detailed description of parameter calculation is presented, based on the recommendations of the International Telecommunication Union (ITU). This answers the request made in an ITU-R 214-4/3 question, which suggests determining the appropriate parameters to describe the noise when it has an impulsive characteristic.

The NAHU$^2$ project is a Franco-Mexican collaboration aimed at building the $\pi$-YALLI corpus adapted to machine learning, which will subsequently be used to develop computer resources for the Nahuatl language. Nahuatl is a language with few computational resources, even though it is a living language spoken by around 2 million people. We have decided to build $\pi$-YALLI, a corpus that will enable to carry out research on Nahuatl in order to develop Language Models (LM), whether dynamic or not, which will make it possible to in turn enable the development of Natural Language Processing (NLP) tools such as: a) a grapheme unifier, b) a word segmenter, c) a POS grammatical analyser, d) a content-based Automatic Text Summarization; and possibly, e) a translator translator (probabilistic or learning-based)., Comment: 9 pages, in French language, 2 figures

Embedding high-dimensional data into a low-dimensional space is an indispensable component of data analysis. In numerous applications, it is necessary to align and jointly embed multiple datasets from different studies or experimental conditions. Such datasets may share underlying structures of interest but exhibit individual distortions, resulting in misaligned embeddings using traditional techniques. In this work, we propose \textit{Entropic Optimal Transport (EOT) eigenmaps}, a principled approach for aligning and jointly embedding a pair of datasets with theoretical guarantees. Our approach leverages the leading singular vectors of the EOT plan matrix between two datasets to extract their shared underlying structure and align the datasets accordingly in a common embedding space. We interpret our approach as an inter-data variant of the classical Laplacian eigenmaps and diffusion maps embeddings, showing that it enjoys many favorable analogous properties. We then analyze a data-generative model where two observed high-dimensional datasets share latent variables on a common low-dimensional manifold, but each dataset is subject to data-specific translation, scaling, nuisance structures, and noise. We show that in a high-dimensional asymptotic regime, the EOT plan recovers the shared manifold structure by approximating a kernel function evaluated at the locations of the latent variables. Subsequently, we provide a geometric interpretation of our embedding by relating it to the eigenfunctions of population-level operators encoding the density and geometry of the shared manifold. Finally, we showcase the performance of our approach for data integration and embedding through simulations and analyses of real-world biological data, demonstrating its advantages over alternative methods in challenging scenarios.

Political leaning can be defined as the inclination of an individual towards certain political orientations that align with their personal beliefs. Political leaning inference has traditionally been framed as a binary classification problem, namely, to distinguish between left vs. right or conservative vs liberal. Furthermore, although some recent work considers political leaning inference in a multi-party multi-region framework, their study is limited to the application of social interaction data. In order to address these shortcomings, in this study we propose Hybrid Text-Interaction Modeling (HTIM), a framework that enables hybrid modeling fusioning text and interactions from Social Media to accurately identify the political leaning of users in a multi-party multi-region framework. Access to textual and interaction-based data not only allows us to compare these data sources but also avoids reliance on specific data types. We show that, while state-of-the-art text-based representations on their own are not able to improve over interaction-based representations, a combination of text-based and interaction-based modeling using HTIM considerably improves the performance across the three regions, an improvement that is more prominent when we focus on the most challenging cases involving users who are less engaged in politics.

Social media users express their political preferences via interaction with other users, by spontaneous declarations or by participation in communities within the network. This makes a social network such as Twitter a valuable data source to study computational science approaches to political learning inference. In this work we focus on three diverse regions in Spain (Basque Country, Catalonia and Galicia) to explore various methods for multi-party categorization, required to analyze evolving and complex political landscapes, and compare it with binary left-right approaches. We use a two-step method involving unsupervised user representations obtained from the retweets and their subsequent use for political leaning detection. Comprehensive experimentation on a newly collected and curated dataset comprising labeled users and their interactions demonstrate the effectiveness of using Relational Embeddings as representation method for political ideology detection in both binary and multi-party frameworks, even with limited training data. Finally, data visualization illustrates the ability of the Relational Embeddings to capture intricate intra-group and inter-group political affinities.

We analyze the performance of a quantum error correction code subject to physically-motivated noise modeled by a Lindblad master equation. Working within the code-capacity framework, we consider dissipative and coherent single-qubit terms and two-qubit crosstalk, studying how different approximations of the noise capture the performance of the five-qubit code. A composite-channel approximation where every noise term is considered separately, captures the behavior in many physical cases up to considerably-long timescales, and we analyze its eventual failure due to the effect of noncommuting terms. In contrast, we find that single-qubit approximations do not properly capture the error correction dynamics with two-qubit noise, even for short times. A Pauli approximation going beyond a single-qubit channel, is sensitive to the details of the noise, state, and decoder, and succeeds in many cases at short timescales relative to the noise strength, beyond which it fails. We calculate the code pseudo-threshold emerging within this model, and demonstrate how knowledge of the qubit parameters and connectivity can be used to design better decoders. These results shed light on the performance of error correction codes in the presence of realistic noise and can advance the ongoing efforts toward useful quantum error correction.

The therapeutic working alliance is a critical factor in predicting the success of psychotherapy treatment. Traditionally, working alliance assessment relies on questionnaires completed by both therapists and patients. In this paper, we present COMPASS, a novel framework to directly infer the therapeutic working alliance from the natural language used in psychotherapy sessions. Our approach utilizes advanced large language models (LLMs) to analyze transcripts of psychotherapy sessions and compare them with distributed representations of statements in the working alliance inventory. Analyzing a dataset of over 950 sessions covering diverse psychiatric conditions including anxiety, depression, schizophrenia, and suicidal tendencies, we demonstrate the effectiveness of our method in providing fine-grained mapping of patient-therapist alignment trajectories and offering interpretability for clinical psychiatry and in identifying emerging patterns related to the condition being treated. By employing various deep learning-based topic modeling techniques in combination with prompting generative language models, we analyze the topical characteristics of different psychiatric conditions and their evolution at a turn-level resolution. This combined framework enhances the understanding of therapeutic interactions, enabling timely feedback for therapists regarding the quality of therapeutic relationships and providing interpretable insights to improve the effectiveness of psychotherapy., Comment: This work extends our research series in computational psychiatry (e.g auto annotation in arXiv:2204.05522, topic extraction in arXiv:2204.10189, and diagnosis in arXiv:2210.15603) with the introduction of LLMs to complete the full cycle of interpreting and understanding psychotherapy strategies as a comprehensive analytical framework

Function-as-a-Service (FaaS) allows to directly submit function code to a cloud provider without the burden of managing infrastructure resources. Each cloud provider establishes execution time limits to their FaaS offerings, which impose the risk of spending computation time without achieving partial results. In this work, a framework that enables limitless execution time in FaaS, with little to no modifications to the user-provided function code, is presented. After a thorough literature and theoretical framework review, Apache OpenWhisk Actions and the DMCTP checkpoint-and-restore (CR) tool were selected. With these, dependent successive serverless same-function invocations that exploit the persistence of partial results were implemented. The solution was submitted to the FaaSDom benchmark and time metrics were collected. Additionally, the solution was characterized in terms of the Serverless Trilemma. The resultant system, even at this proof-of-concept state, offers a lot of value to companies that rely heavily on serverless architecture., Comment: 7 pages, 7 figures

The cross-domain capability of wireless sensing is currently one of the major challenges on human activity recognition (HAR) based on the channel state information (CSI) of wireless signals. The difficulty of labeling samples from new domains has encouraged the use of few and zero shot strategies. In this context, prototype networks have attracted attention due to their reasonable cross-domain transferability. This paper presents a novel zero-shot prototype recurrent convolutional network that implements a zero-shot learning strategy for HAR via CSI. This method extracts the prototypes from an available source domain to classify unseen and unlabeled data from the target domain for the same or similar classes. The experiments have been developed using three datasets with real measurements, and the results include an inter-datasets evaluation. Overall, the results improve the state of the art and make it a promising solution for cross-domain HAR., Comment: The authors have identified a significant error in the neural network configuration, specifically related to the addition of the LSTM layer after the CNN blocks and the method used to input data into the network. As a result, we have verified that the outcomes are inconsistent with what would be expected from a correctly configured neural network

An ability to infer the political leaning of social media users can help in gathering opinion polls thereby leading to a better understanding of public opinion. While there has been a body of research attempting to infer the political leaning of social media users, this has been typically simplified as a binary classification problem (e.g. left vs right) and has been limited to a single location, leading to a dearth of investigation into more complex, multiclass classification and its generalizability to different locations, particularly those with multi-party systems. Our work performs the first such effort by studying political leaning inference in three of the UK's nations (Scotland, Wales and Northern Ireland), each of which has a different political landscape composed of multiple parties. To do so, we collect and release a dataset comprising users labelled by their political leaning as well as interactions with one another. We investigate the ability to predict the political leaning of users by leveraging these interactions in challenging scenarios such as few-shot learning, where training data is scarce, as well as assessing the applicability to users with different levels of political engagement. We show that interactions in the form of retweets between users can be a very powerful feature to enable political leaning inference, leading to consistent and robust results across different regions with multi-party systems. However, we also see that there is room for improvement in predicting the political leaning of users who are less engaged in politics.

Gaussian potentials serve as a valuable tool for the comprehensive modeling of short-range interactions, spanning applications from nuclear physics to the artificial confinement of electrons within quantum dots. This study focuses on examining the lowest states within Gaussian wells, with particular emphasis on the weakly bound regime. The analysis delves into the asymptotic behavior of the exact wave function at both small and large distances, motivating the development of a few-parametric Ansatz which is locally accurate and yields to a fast convergent basis set. To validate its efficacy, we assess its convergence rate using a toy model of Nuclear Physics, specifically for Deuteron. Furthermore, we employ the expansion of the energy close to the threshold to derive an analytical formula for the binding energy of the deuteron whose accuracy improves as the effective parameter approaches the critical. In concluding our investigation, we evaluate the performance of our Ansatz as an orbital in the exploration of the electronic structure of a two-electron quantum dot.