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¿Qué es el complejo Buenaventura en Río Hato?

Buenaventura es uno de los complejos residenciales y turísticos más exclusivos de Panamá, ubicado en Río Hato, en la Riviera Pacífica. Este desarrollo de primer nivel integra viviendas de lujo, un resort cinco estrellas, un campo de golf de clase mundial y una amplia variedad de amenidades, todo dentro de un entorno natural cuidadosamente preservado.

Diseñado para ofrecer una experiencia integral, Buenaventura combina arquitectura sofisticada con paisajismo tropical, generando una comunidad única que promueve el descanso, el bienestar y la conexión con la naturaleza.

Entre sus principales atractivos se encuentra un campo de golf de 18 hoyos diseñado por Jack Nicklaus, el Spa Corotú, siete restaurantes, un club ecuestre, marina, club de playa, gimnasio, centro de actividades, club infantil, zoológico, senderos y múltiples espacios para deportes y recreación familiar.

Las residencias que incluyen villas, casas, apartamentos y proyectos como Alamar  se caracterizan por sus altos estándares de diseño, acabados de lujo y ubicación privilegiada frente al mar, lagunas o áreas verdes. Cada espacio está concebido para quienes valoran la privacidad, el confort y el estilo de vida sofisticado.

Buenaventura es más que un destino: es una comunidad pensada para quienes buscan calidad de vida, inversión inteligente y una conexión auténtica con lo natural y lo excepcional.

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Lanzamiento de Alamar: Un momento memorable

El lanzamiento de Alamar marcó un hito para Valor Development, PAAR y Buenaventura, presentando un exclusivo proyecto frente al mar en la Riviera Pacífica. Desde el evento de presentación, quedó claro que Alamar representa el estándar más alto en diseño, bienestar y acabados residenciales.

Los apartamentos destacan por sus acabados de lujo: pisos de gran formato, grifería europea, cocinas integradas de diseño contemporáneo y ventanales de piso a techo que ofrecen vistas panorámicas al mar. Cada detalle ha sido pensado para ofrecer una experiencia de vida sofisticada, cómoda y duradera.

Compuesto por siete edificios con acceso directo a la playa, Alamar ofrece apartamentos de 2, 3 y 4 recámaras, amplias terrazas y una gama de amenidades como beach club, piscinas, gimnasio y seguridad 24/7.

Más que un desarrollo inmobiliario, Alamar es una declaración de elegancia costera y compromiso con la excelencia.

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Alamar: Excelencia en acabados, frente al mar

[:es]apartamentos frente al mar[:]

Cuando hablamos de Valor Development, hablamos de un compromiso absoluto con la calidad en cada detalle. Sus proyectos se distinguen por acabados que no solo embellecen, sino que elevan la experiencia de vivir. En Alamar, su nuevo desarrollo en Buenaventura, esto se percibe desde el primer paso dentro de cada residencia.

Los apartamentos de Alamar destacan por su diseño contemporáneo y acabados de alto nivel: pisos de gran formato importados, grifería de lujo, carpintería de precisión, cocinas integradas de diseño europeo y ventanales de piso a techo que permiten que la luz y la brisa marina formen parte del día a día. Cada elección responde a un propósito: crear espacios duraderos, elegantes y funcionales.

Este proyecto frente al mar está compuesto por siete edificios con acceso directo a la playa y opciones de 2, 3 y 4 recámaras. Además, ofrece terrazas amplias que se convierten en una extensión natural del entorno costero.

Alamar también incluye amenidades de primer nivel: beach club privado, piscinas, gimnasio, áreas verdes, salón de eventos, y seguridad con concierge. Todo pensado para quienes buscan una vida de tranquilidad, sofisticación y conexión con la naturaleza.

En Alamar, cada acabado cuenta una historia de cuidado, experiencia y visión. Porque para Valor Development, construir hogares no es solo levantar estructuras: es diseñar espacios que se convierten en legado.

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La Riviera Pacífica de Panamá

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La Riviera pacífica

La Riviera Pacífica de Panamá es un destino exclusivo donde el lujo y la naturaleza se encuentran. A solo unas horas de la ciudad de Panamá, esta franja costera es conocida por sus playas de arena dorada, aguas cristalinas y una oferta residencial de primer nivel.
Cuenta con desarrollos de lujo que ofrecen residencias de alta gama, rodeadas de paisajes impresionantes. Además, dispone de amenidades de primer nivel, como campos de golf, marinas privadas y una oferta gastronómica y de entretenimiento de clase mundial.
La zona continúa su crecimiento y aumento de plusvalía, convirtiéndose en uno de los destinos más atractivos para inversores y compradores que buscan lo mejor en calidad de vida y rentabilidad.
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AI Revolutionizing Industries and Reshaping Tomorrow

Computational models that mimic the structure and function of the human auditory system could help researchers design better hearing aids, cochlear implants, and brain-machine interfaces. A new study from MIT has found that modern computational models derived from machine learning are moving closer to this goal.

Models of hearing

Deep neural networks are computational models that consists of many layers of information-processing units that can be trained on huge volumes of data to perform specific tasks. This type of model has become widely used in many applications, and neuroscientists have begun to explore the possibility that these systems can also be used to describe how the human brain performs certain tasks.

When a neural network is performing a task, its processing units generate activation patterns in response to each audio input it receives. Those model representations of the input can be compared to the activation patterns seen in fMRI brain scans of people listening to the same input.

Hierarchical processing

The new study also supports the idea that the human auditory cortex has some degree of hierarchical organization, in which processing is divided into stages that support distinct computational functions. As in the 2018 study, the researchers found that representations generated in earlier stages of the model most closely resemble those seen in the primary auditory cortex, while representations generated in later model stages more closely resemble those generated in brain regions beyond the primary cortex.

Additionally, the researchers found that models that had been trained on different tasks were better at replicating different aspects of audition. For example, models trained on a speech-related task more closely resembled speech-selective areas.

“The study suggests that models that are derived from machine learning are a step in the right direction, and it gives us some clues as to what tends to make them better models of the brain.”

Brendon Peterson

Conclusion of Deep neural networks show promise as models of human hearing

The research was funded by the National Institutes of Health, an Amazon Fellowship from the Science Hub, an International Doctoral Fellowship from the American Association of University Women, an MIT Friends of McGovern Institute Fellowship, a fellowship from the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT, and a Department of Energy Computational Science Graduate Fellowship.

A goal of our field is to end up with a computer model that can predict brain responses and behavior. We think that if we are successful in reaching that goal, it will open a lot of doors.

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Putting the ‘art’ in artificial intelligence

Computational models that mimic the structure and function of the human auditory system could help researchers design better hearing aids, cochlear implants, and brain-machine interfaces. A new study from MIT has found that modern computational models derived from machine learning are moving closer to this goal.

Models of hearing

Deep neural networks are computational models that consists of many layers of information-processing units that can be trained on huge volumes of data to perform specific tasks. This type of model has become widely used in many applications, and neuroscientists have begun to explore the possibility that these systems can also be used to describe how the human brain performs certain tasks.

When a neural network is performing a task, its processing units generate activation patterns in response to each audio input it receives. Those model representations of the input can be compared to the activation patterns seen in fMRI brain scans of people listening to the same input.

Hierarchical processing

The new study also supports the idea that the human auditory cortex has some degree of hierarchical organization, in which processing is divided into stages that support distinct computational functions. As in the 2018 study, the researchers found that representations generated in earlier stages of the model most closely resemble those seen in the primary auditory cortex, while representations generated in later model stages more closely resemble those generated in brain regions beyond the primary cortex.

Additionally, the researchers found that models that had been trained on different tasks were better at replicating different aspects of audition. For example, models trained on a speech-related task more closely resembled speech-selective areas.

“The study suggests that models that are derived from machine learning are a step in the right direction, and it gives us some clues as to what tends to make them better models of the brain.”

Brendon Peterson

Conclusion of Deep neural networks show promise as models of human hearing

The research was funded by the National Institutes of Health, an Amazon Fellowship from the Science Hub, an International Doctoral Fellowship from the American Association of University Women, an MIT Friends of McGovern Institute Fellowship, a fellowship from the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT, and a Department of Energy Computational Science Graduate Fellowship.

A goal of our field is to end up with a computer model that can predict brain responses and behavior. We think that if we are successful in reaching that goal, it will open a lot of doors.

by admin,
Blog

A computer scientist pushes the boundaries of geometry

Computational models that mimic the structure and function of the human auditory system could help researchers design better hearing aids, cochlear implants, and brain-machine interfaces. A new study from MIT has found that modern computational models derived from machine learning are moving closer to this goal.

Models of hearing

Deep neural networks are computational models that consists of many layers of information-processing units that can be trained on huge volumes of data to perform specific tasks. This type of model has become widely used in many applications, and neuroscientists have begun to explore the possibility that these systems can also be used to describe how the human brain performs certain tasks.

When a neural network is performing a task, its processing units generate activation patterns in response to each audio input it receives. Those model representations of the input can be compared to the activation patterns seen in fMRI brain scans of people listening to the same input.

Hierarchical processing

The new study also supports the idea that the human auditory cortex has some degree of hierarchical organization, in which processing is divided into stages that support distinct computational functions. As in the 2018 study, the researchers found that representations generated in earlier stages of the model most closely resemble those seen in the primary auditory cortex, while representations generated in later model stages more closely resemble those generated in brain regions beyond the primary cortex.

Additionally, the researchers found that models that had been trained on different tasks were better at replicating different aspects of audition. For example, models trained on a speech-related task more closely resembled speech-selective areas.

“The study suggests that models that are derived from machine learning are a step in the right direction, and it gives us some clues as to what tends to make them better models of the brain.”

Brendon Peterson

Conclusion of Deep neural networks show promise as models of human hearing

The research was funded by the National Institutes of Health, an Amazon Fellowship from the Science Hub, an International Doctoral Fellowship from the American Association of University Women, an MIT Friends of McGovern Institute Fellowship, a fellowship from the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT, and a Department of Energy Computational Science Graduate Fellowship.

A goal of our field is to end up with a computer model that can predict brain responses and behavior. We think that if we are successful in reaching that goal, it will open a lot of doors.

by admin,
Blog

Closing the design gap for optical devices

Computational models that mimic the structure and function of the human auditory system could help researchers design better hearing aids, cochlear implants, and brain-machine interfaces. A new study from MIT has found that modern computational models derived from machine learning are moving closer to this goal.

Models of hearing

Deep neural networks are computational models that consists of many layers of information-processing units that can be trained on huge volumes of data to perform specific tasks. This type of model has become widely used in many applications, and neuroscientists have begun to explore the possibility that these systems can also be used to describe how the human brain performs certain tasks.

When a neural network is performing a task, its processing units generate activation patterns in response to each audio input it receives. Those model representations of the input can be compared to the activation patterns seen in fMRI brain scans of people listening to the same input.

Hierarchical processing

The new study also supports the idea that the human auditory cortex has some degree of hierarchical organization, in which processing is divided into stages that support distinct computational functions. As in the 2018 study, the researchers found that representations generated in earlier stages of the model most closely resemble those seen in the primary auditory cortex, while representations generated in later model stages more closely resemble those generated in brain regions beyond the primary cortex.

Additionally, the researchers found that models that had been trained on different tasks were better at replicating different aspects of audition. For example, models trained on a speech-related task more closely resembled speech-selective areas.

“The study suggests that models that are derived from machine learning are a step in the right direction, and it gives us some clues as to what tends to make them better models of the brain.”

Brendon Peterson

Conclusion of Deep neural networks show promise as models of human hearing

The research was funded by the National Institutes of Health, an Amazon Fellowship from the Science Hub, an International Doctoral Fellowship from the American Association of University Women, an MIT Friends of McGovern Institute Fellowship, a fellowship from the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT, and a Department of Energy Computational Science Graduate Fellowship.

A goal of our field is to end up with a computer model that can predict brain responses and behavior. We think that if we are successful in reaching that goal, it will open a lot of doors.

by admin,
Blog

The Intersection between Technology and Art

Computational models that mimic the structure and function of the human auditory system could help researchers design better hearing aids, cochlear implants, and brain-machine interfaces. A new study from MIT has found that modern computational models derived from machine learning are moving closer to this goal.

Models of hearing

Deep neural networks are computational models that consists of many layers of information-processing units that can be trained on huge volumes of data to perform specific tasks. This type of model has become widely used in many applications, and neuroscientists have begun to explore the possibility that these systems can also be used to describe how the human brain performs certain tasks.

When a neural network is performing a task, its processing units generate activation patterns in response to each audio input it receives. Those model representations of the input can be compared to the activation patterns seen in fMRI brain scans of people listening to the same input.

Hierarchical processing

The new study also supports the idea that the human auditory cortex has some degree of hierarchical organization, in which processing is divided into stages that support distinct computational functions. As in the 2018 study, the researchers found that representations generated in earlier stages of the model most closely resemble those seen in the primary auditory cortex, while representations generated in later model stages more closely resemble those generated in brain regions beyond the primary cortex.

Additionally, the researchers found that models that had been trained on different tasks were better at replicating different aspects of audition. For example, models trained on a speech-related task more closely resembled speech-selective areas.

“The study suggests that models that are derived from machine learning are a step in the right direction, and it gives us some clues as to what tends to make them better models of the brain.”

Brendon Peterson

Conclusion of Deep neural networks show promise as models of human hearing

The research was funded by the National Institutes of Health, an Amazon Fellowship from the Science Hub, an International Doctoral Fellowship from the American Association of University Women, an MIT Friends of McGovern Institute Fellowship, a fellowship from the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT, and a Department of Energy Computational Science Graduate Fellowship.

A goal of our field is to end up with a computer model that can predict brain responses and behavior. We think that if we are successful in reaching that goal, it will open a lot of doors.

by admin,
Blog

A Journey into the Heart of Innovation

Computational models that mimic the structure and function of the human auditory system could help researchers design better hearing aids, cochlear implants, and brain-machine interfaces. A new study from MIT has found that modern computational models derived from machine learning are moving closer to this goal.

Models of hearing

Deep neural networks are computational models that consists of many layers of information-processing units that can be trained on huge volumes of data to perform specific tasks. This type of model has become widely used in many applications, and neuroscientists have begun to explore the possibility that these systems can also be used to describe how the human brain performs certain tasks.

When a neural network is performing a task, its processing units generate activation patterns in response to each audio input it receives. Those model representations of the input can be compared to the activation patterns seen in fMRI brain scans of people listening to the same input.

Hierarchical processing

The new study also supports the idea that the human auditory cortex has some degree of hierarchical organization, in which processing is divided into stages that support distinct computational functions. As in the 2018 study, the researchers found that representations generated in earlier stages of the model most closely resemble those seen in the primary auditory cortex, while representations generated in later model stages more closely resemble those generated in brain regions beyond the primary cortex.

Additionally, the researchers found that models that had been trained on different tasks were better at replicating different aspects of audition. For example, models trained on a speech-related task more closely resembled speech-selective areas.

“The study suggests that models that are derived from machine learning are a step in the right direction, and it gives us some clues as to what tends to make them better models of the brain.”

Brendon Peterson

Conclusion of Deep neural networks show promise as models of human hearing

The research was funded by the National Institutes of Health, an Amazon Fellowship from the Science Hub, an International Doctoral Fellowship from the American Association of University Women, an MIT Friends of McGovern Institute Fellowship, a fellowship from the K. Lisa Yang Integrative Computational Neuroscience (ICoN) Center at MIT, and a Department of Energy Computational Science Graduate Fellowship.

A goal of our field is to end up with a computer model that can predict brain responses and behavior. We think that if we are successful in reaching that goal, it will open a lot of doors.

by admin,