Poética dialéctica de la narrativa de Ignacio Martínez de Pisón

Programa de Doctorado en Humanidades por la Universidad Carlos III de MadridPresidenta: Carmen Luna Sellés.- Secretaria: Sonia Pérez Castro.- Vocal: Cristina Oñoro Oter

Application of the red-shifted channelrhodopsin Chrimson for the Caenorhabditis elegans cGAL bipartite system

Channelrhodopsins are light-gated ion channels that serve as photoreceptors in photosynthetic microbes and have been applied as crucial optogenetic tools in genetic model organisms. When expressed in animals, they enable light-inducible control of ionic membrane permeability, which directly manipulates the activity of neurons expressing the protein. The application of channelrhodopsin-based optogenetics is particularly powerful when used in conjugation with the cGAL (GAL4-UAS) bipartite system (Wang, 2017). The mating of neuron-specific GAL4 driver lines to new channelrhodopsin effector lines could expand the genetic toolkit to perturb and manipulate neural circuits in the organism. Blue light-gated channelrhodopsins have been widely used in C. elegans neurobiology but often have to be performed in lite-1(ce314) mutant backgrounds because short-wavelength blue light is an aversive cue in wild-type animals and directly affects C. elegans neuronal physiology. Previously, a red light-gated variant of channelrhodopsin, termed Chrimson, has been successfully applied in Drosophila and mice, and has recently been codon-optimized for use in C. elegans (Klapoetke, 2014; Schild, 2015). Here, we constructed a Chrimson (15xUAS::chrimson::gfp) cGAL effector line. We introduced the UAS::chrimson::gfp effector DNA construct as an extrachromosomal array into a previously published cGAL pan-neuronal driver line (PS6961 syIs334) and generated integrants on chromosome II (PS8023, syIs503) and chromosome V (PS8024, syIs504) (Table 1) via standard X-ray irradiation. We showed Chrimson-GFP expression in the C. elegans head and tail neurons (Fig. 1A-1D). We also showed that red light could induce a seizure-like motility phenotype in C. elegans expressing Chrimson-GFP in a pan-neuronal manner (videos), while the negative controls expressing only the effector, or without light induction showed regular motility as expected (Table 2). The body curvature maps from normal and seizure-like motilities showed distinct patterns (Fig. 1E and 1F). We report the effector construct of red-light-gated channelrhodopsin Chrimson as an addition to our cGAL toolkit, which could be widely used in future research to overcome the technical restrictions of blue light-gated channelrhodopsins in C. elegans

Fourier Features for Identifying Differential Equations (FourierIdent)

We investigate the benefits and challenges of utilizing the frequency information in differential equation identification. Solving differential equations and Fourier analysis are closely related, yet there is limited work in exploring this connection in the identification of differential equations. Given a single realization of the differential equation perturbed by noise, we aim to identify the underlying differential equation governed by a linear combination of linear and nonlinear differential and polynomial terms in the frequency domain. This is challenging due to large magnitudes and sensitivity to noise. We introduce a Fourier feature denoising, and define the meaningful data region and the core regions of features to reduce the effect of noise in the frequency domain. We use Subspace Pursuit on the core region of the time derivative feature, and introduce a group trimming step to refine the support. We further introduce a new energy based on the core regions of features for coefficient identification. Utilizing the core regions of features serves two critical purposes: eliminating the low-response regions dominated by noise, and enhancing the accuracy in coefficient identification. The proposed method is tested on various differential equations with linear, nonlinear, and high-order derivative feature terms. Our results demonstrate the advantages of the proposed method, particularly on complex and highly corrupted datasets

Modiff: Action-Conditioned 3D Motion Generation with Denoising Diffusion Probabilistic Models

Diffusion-based generative models have recently emerged as powerful solutions for high-quality synthesis in multiple domains. Leveraging the bidirectional Markov chains, diffusion probabilistic models generate samples by inferring the reversed Markov chain based on the learned distribution mapping at the forward diffusion process. In this work, we propose Modiff, a conditional paradigm that benefits from the denoising diffusion probabilistic model (DDPM) to tackle the problem of realistic and diverse action-conditioned 3D skeleton-based motion generation. We are a pioneering attempt that uses DDPM to synthesize a variable number of motion sequences conditioned on a categorical action. We evaluate our approach on the large-scale NTU RGB+D dataset and show improvements over state-of-the-art motion generation methods

COVID-19 and Employee Job Performance Trajectories: The Moderating Effect of Different Sources of Status

This study investigates the impact of the COVID-19 pandemic on employee job performance trajectories, and further examines the moderating effects of different sources of status. Drawing from event system theory (EST), we propose that employee job performance decreases upon COVID-19 onset, but gradually increases during the postonset period. Furthermore, we argue that status from society, occupation, and workplace functions to moderate such performance trajectories. We test our hypotheses with a unique dataset of 708 employees that combines survey responses and job performance archival data over 21 consecutive months (10,808 observations) spanning the preonset, onset, and postonset periods of the initial encounter with COVID-19 in China. Utilizing discontinuous growth modeling (DGM), our findings indicate that the onset of COVID-19 created an immediate decrease in job performance, but such decrease was weakened by higher occupation and/or workplace status. However, the postonset period resulted in a positive employee job performance trajectory, which was strengthened for employees with lower occupational status. These findings enrich our understanding of COVID-19\u27s impact on employee job performance trajectories, highlight the role of status in moderating such changes over time, and also provide practical implications to understand employee performance when facing such a crisis

FedEdge AI-TC: A Semi-supervised Traffic Classification Method based on Trusted Federated Deep Learning for Mobile Edge Computing

As a typical entity of MEC (Mobile Edge Computing), 5G CPE (Customer Premise Equipment)/HGU (Home Gateway Unit) has proven to be a promising alternative to traditional Smart Home Gateway. Network TC (Traffic Classification) is a vital service quality assurance and security management method for communication networks, which has become a crucial functional entity in 5G CPE/HGU. In recent years, many researchers have applied Machine Learning or Deep Learning (DL) to TC, namely AI-TC, to improve its performance. However, AI-TC faces challenges, including data dependency, resource-intensive traffic labeling, and user privacy concerns. The limited computing resources of 5G CPE further complicate efficient classification. Moreover, the "black box" nature of AI-TC models raises transparency and credibility issues. The paper proposes the FedEdge AI-TC framework, leveraging Federated Learning (FL) for reliable Network TC in 5G CPE. FL ensures privacy by employing local training, model parameter iteration, and centralized training. A semi-supervised TC algorithm based on Variational Auto-Encoder (VAE) and convolutional neural network (CNN) reduces data dependency while maintaining accuracy. To optimize model light-weight deployment, the paper introduces XAI-Pruning, an AI model compression method combined with DL model interpretability. Experimental evaluation demonstrates FedEdge AI-TC's superiority over benchmarks in terms of accuracy and efficient TC performance. The framework enhances user privacy and model credibility, offering a comprehensive solution for dependable and transparent Network TC in 5G CPE, thus enhancing service quality and security.Comment: 13 pages, 13 figure