Accurate prediction of public transit ridership is vital for efficient planning and management of transit in rapidly growing urban areas in Canada. Unexpected increases in passengers can cause overcrowded vehicles, longer boarding times, and service disruptions. Traditional time series models like ARIMA and SARIMA face limitations, particularly in short-term predictions and integration of spatial and temporal features. These models struggle with the dynamic nature of ridership patterns and often ignore spatial correlations between nearby stops. Deep Learning (DL) models present a promising alternative, demonstrating superior performance in short-term prediction tasks by effectively capturing both spatial and temporal features. However, challenges such as dynamic spatial feature extraction, balancing accuracy with computational efficiency, and ensuring scalability remain. This paper introduces DST-TransitNet, a hybrid DL model for system-wide station-level ridership prediction. This proposed model uses graph neural networks (GNN) and recurrent neural networks (RNN) to dynamically integrate the changing temporal and spatial correlations within the stations. The model also employs a precise time series decomposition framework to enhance accuracy and interpretability. Tested on Bogota's BRT system data, with three distinct social scenarios, DST-TransitNet outperformed state-of-the-art models in precision, efficiency and robustness. Meanwhile, it maintains stability over long prediction intervals, demonstrating practical applicability.

Fox News Flash top headlines are here. Check out what's clicking on Foxnews.com. BOGOTA, Colombia (AP) -- Colombia's government on Friday announced plans for a deep-water expedition to explore the mythical galleon San José, sunk in the 18th century in the country's northern Caribbean and believed to contain cargo valued at billions of dollars. It is the first phase of a scientific research into deep waters that aims at collecting information to determine which pieces are suitable and possible to extract. The wreckage is 600 meters deep in the sea.

This paper presents the Holistic and Multi-Granular Surgical Scene Understanding of Prostatectomies (GraSP) dataset, a curated benchmark that models surgical scene understanding as a hierarchy of complementary tasks with varying levels of granularity. Our approach enables a multi-level comprehension of surgical activities, encompassing long-term tasks such as surgical phases and steps recognition and short-term tasks including surgical instrument segmentation and atomic visual actions detection. To exploit our proposed benchmark, we introduce the Transformers for Actions, Phases, Steps, and Instrument Segmentation (TAPIS) model, a general architecture that combines a global video feature extractor with localized region proposals from an instrument segmentation model to tackle the multi-granularity of our benchmark. Through extensive experimentation, we demonstrate the impact of including segmentation annotations in short-term recognition tasks, highlight the varying granularity requirements of each task, and establish TAPIS's superiority over previously proposed baselines and conventional CNN-based models. Additionally, we validate the robustness of our method across multiple public benchmarks, confirming the reliability and applicability of our dataset. This work represents a significant step forward in Endoscopic Vision, offering a novel and comprehensive framework for future research towards a holistic understanding of surgical procedures.

Three people have been jailed in the kidnapping and killing of a Hmong American comedian and activist who was found dead near Medellín after going out to meet a woman he reportedly met on social media, Colombian officials announced Thursday. The Prosecutor's Office said in a statement that two men and a woman were charged with the crimes of aggravated kidnapping for extortion and aggravated homicide in the death last month of Tou Ger Xiong, 50. The suspects denied the charges at a hearing, the statement said. A minor who presented himself to the Public Prosecutor's Office admitting to having participated in the crime also was charged in the case and transferred to a special detention center for minors, it added. The U.S. Embassy in Bogota warned a week ago about Colombian criminals who use dating apps to lure victims and then assault and rob them.

Rep. Cory Mills, R-Fla., sits down with'FOX & Friends Weekend' to discuss Ukraine funding, Biden's border policies and attacks on U.S. bases in the Middle East. The U.S. Embassy in Bogota, Colombia, is warning Americans traveling to the country not to use dating apps after eight "suspicious deaths" of private U.S. citizens. According to the embassy, the deaths -- potentially involuntary drug overdoes or suspected homicides -- took place in Medellin between November 1 and December 31, 2023. "Over the last year, the Embassy has seen an increase in reports of incidents involving the use of online dating applications to lure victims, typically foreigners, for robbery by force or using sedatives to drug and rob individuals," the embassy said. The Embassy said it regularly receives reports of such incidents occurring in major cities, like Medellin, Cartagena, and Bogota.

Voice actor Armando Plata does not recall promoting a shopping mall in Bogota, narrating a porn movie or advertizing a big bank. Yet his voice comes over loud and clear: schmoozing, sighing and selling with neither permission nor payment. It was the mild, robotic twang -- rather than worry over any memory lapse -- that alerted Plata to the fact his voice had been quietly cloned via artificial intelligence, robbing the veteran actor of his key asset, artistic choice and vocal rights. "I believe that the most cloned and artificially used voice in Spanish is mine," said Plata, owner of a deep and lilting voice, 50-year audio career and president of the Colombian Association of Voice Actors.

Real-time demand prediction is a critical input for dynamic bus routing. While many researchers have developed numerous complex methods to predict short-term transit demand, the applications have been limited to short, stable time frames and a few stations. How these methods perform in highly dynamic environments has not been studied, nor has their performance been systematically compared. We built an open-source infrastructure with five common methodologies, including econometric and deep learning approaches, and assessed their performance under stable and highly dynamic conditions. We used a time series from smartcard data to predict demand for the following day for the BRT system in Bogota, Colombia. The dynamic conditions in the time series include a month-long protest and the COVID-19 pandemic. Both conditions triggered drastic shifts in demand. The results reveal that most tested models perform similarly in stable conditions, with MAAPE varying from 0.08 to 0.12. The benchmark demonstrated that all models performed significantly worse in both dynamic conditions compared to the stable conditions. In the month-long protest, the increased MAAPE ranged from 0.14 to 0.24. Similarly, during the COVID-19 pandemic, the increased MAAPE ranged from 0.12 to 0.82. Notably, in the COVID-19 pandemic condition, an LSTM model with adaptive training and a multi-output design outperformed other models, adapting faster to disruptions. The prediction error stabilized within approximately 1.5 months, whereas other models continued to exhibit higher error rates even a year after the start of the pandemic. The aim of this open-source codebase infrastructure is to lower the barrier for other researchers to replicate and reproduce models, facilitate a collective effort within the research community to improve the benchmarking process and accelerate the advancement of short-term ridership prediction models.

The Department of Geosciences at the Universidad de Los Andes in Bogotá, Colombia, invites applications for one tenure-track faculty position in Geophysics, with particular emphasis in the areas of Seismology, Seismic Hazards or Seismic Exploration. We encourage candidates whose research integrates numerical modeling or Artificial Intelligence techniques with data analysis and processing. Applicants must hold a Ph.D. degree, ideally a relevant postdoctoral experience, and should have a significant record of research experience documented by peer-reviewed publications. Candidates with relevant experience in seismological observatories/networks or industry are also encouraged to apply. Fluency in Spanish language is preferred but not compulsory.

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The career path of Colombian physicist Luisa Fernanda Chaparro Sierra took her from studying the Higgs Boson at CERN, to using similar machine learning techniques to gauge perceptions of crime in the Colombian capital of Bogota. Chaparro, currently a Research Professor at Tecnológico de Monterrey in Monterrey, México, says that after finishing her Phd, she had the opportunity to be part of the DataLab (Laboratorio de Datos) of the Universidad Nacional de Colombia where she used the techniques of handling large databases to help understand the problem of the perception of security in Bogota via machine learning methods. "At CERN, we handled large amounts of data and to differentiate between signal and background; we used supervised machine learning techniques, so I used similar methods and adapted others for the case of perception of security," she says, adding that DataLab was composed of mathematicians, physicists, and engineers with knowledge in programming and statistics. "We used Twitter as our data source and reviewed tweets that talked about security in the city for a year," Chaparro says, "The goal was to design a model that would allow us to quantify something as subjective as perception." The researchers were also hoping to find a relationship between it and real crimes by comparing the results with the databases provided by the National Police.