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The pepenaactivity is part of the informal sector, characterized by precariousness and social invisibility; however, it is a key element in the recycling production chain, providing an economic income for many people. In the city of Chihuahua, this activity is carried out on a significant scale. Therefore, this study analyzes and dignifies the recycling of urban solid waste from the perspective of scavengers, also explaining their relationship with other social actors at the city's final disposal site. The study starts from the premise that waste picking is, in the current era of global environmental crisis, an effective way to conserve resources and reduce environmental impacts. Methodologically, participatory action research was used as a strategy to raise awareness among scavengers about the importance of their work and to present their community organization as an example to other urban waste picking groups in different locations. Finally, we found issues in these groups such as informality, lack of legal support,specific health risks, and, more broadly, the absence of public policies to recognize this activity as valuable in addressing the global environmental crisis, Comment: in Spanish language

Neural information retrieval requires costly annotated data for each target domain to be competitive. Synthetic annotation by query generation using Large Language Models or rule-based string manipulation has been proposed as an alternative, but their relative merits have not been analysed. In this paper, we compare both methods head-to-head using the same neural IR architecture. We focus on the BEIR benchmark, which includes test datasets from several domains with no training data, and explore two scenarios: zero-shot, where the supervised system is trained in a large out-of-domain dataset (MS-MARCO); and unsupervised domain adaptation, where, in addition to MS-MARCO, the system is fine-tuned in synthetic data from the target domain. Our results indicate that Large Language Models outperform rule-based methods in all scenarios by a large margin, and, more importantly, that unsupervised domain adaptation is effective compared to applying a supervised IR system in a zero-shot fashion. In addition we explore several sizes of open Large Language Models to generate synthetic data and find that a medium-sized model suffices. Code and models are publicly available for reproducibility.

On-chip super-resolution optical microscopy is an emerging field relying on waveguide excitation with visible light. Here, we investigate two commonly used high-refractive index waveguide platforms, tantalum pentoxide (Ta$_2$O$_5$) and silicon nitride (Si$_3$N$_4$), with respect to their background with excitation in the range 488-640 nm. The background strength from these waveguides were estimated by imaging fluorescent beads. The spectral dependence of the background from these waveguide platforms was also measured. For 640 nm wavelength excitation both the materials had a weak background, but the background increases progressively for shorter wavelengths for Si3N4. We further explored the effect of the waveguide background on localization precision of single molecule localization for direct stochastic optical reconstruction microscopy (dSTORM). An increase in background for Si$_3$N$_4$ at 488 nm is shown to reduce the localization precision and thus the resolution of the reconstructed images. The localization precision at 640 nm was very similar for both the materials. Thus, for shorter wavelength applications Ta$_2$O$_5$ is preferable. Reducing the background from Si$_3$N$_4$ at shorter wavelengths via improved fabrication will be worth pursuing.

More than 47 years have passed without any new example of nonsingular bilinear maps appearing in literature. The purpose of this paper is to construct a new family of nonsingular bilinear maps., Comment: This version has a new title and a joint authorship

In this paper, the design trade-offs for the implementation of small footprint thermal tuners on silicon nitride are presented, and explored through measurements and supporting simulations of a photonic chip based on Mach-Zehnder Interferometers. Firstly, the electrical properties of the tuners are assessed, showing a compromise between compactness and deterioration. Secondly, the different variables involved in the thermal efficiency, switching power and heater dimensions, are analysed. Finally, with focus on exploring the limits of this compact tuners with regards to on chip component density, the thermal-cross talk is also investigated. Tuners with footprint of 270x5 {\mu}m 2 and switching power of 350 mW are reported, with thermal-cross talk, in terms of induced phase change in adjacent devices of less than one order of magnitude at distances over 20 {\mu}m. Paths for the improvement of thermal efficiency, power consumption and resilience of the devices are also outlined