This paper examines several widespread assumptions about artificial intelligence, particularly machine learning, that are often taken as factual premises in discussions on the future of patent law in the wake of '...
We explore the class of trilevel equilibrium problems with a focus on energy-environmental applications and present a novel single-level reformulation for such problems, based on strong duality. To the best of our kno...
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Deep Learning has been successfully applied in diverse fields, and its impact on deepfake detection is no exception. Deepfakes are fake yet realistic synthetic content that can be used deceitfully for political impers...
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The emergence of contemporary deepfakes has attracted significant attention in machine learning research, as artificial intelligence (AI) generated synthetic media increases the incidence of misinterpretation and is d...
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Anterior cruciate ligament (ACL) is one of the most common injuries associated with sports. Knee osseous morphology can play a role in increased knee instability. Our hypothesis is that the morphological features of t...
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For expressive music, the tempo may change over time, posing challenges to tracking the beats by an automatic model. The model may first tap to the correct tempo, but then may fail to adapt to a tempo change, or switc...
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作者:
Juan ZuluagaMichael CastilloDivya SyalAndres CalleNavid ShaghaghiDepartment of Bioengineering (BIOE)
Computer Science & Engineering (CSEN) Ethical Pragmatic & Intelligent Computing (EPIC) Laboratory in collaboration with the Healthcare Innovation & Design (HID) Program Information Systems & Analytics (ISA) and Mathematics & Computer Science (MCS) Santa Clara University Santa Clara California USA
Humanity has battled tuberculosis for all of recorded history. Some studies estimate that Mycobacterium tuberculosis may have been around as long as 3 million years but it was only in 1834 when Johann Schonlein offici...
Humanity has battled tuberculosis for all of recorded history. Some studies estimate that Mycobacterium tuberculosis may have been around as long as 3 million years but it was only in 1834 when Johann Schonlein officially presented the characteristics of it. Even though the TB epidemic has touched all corners of the world, Africa and Asia are the regions that currently suffer the worst consequences. The purpose of this study is to construct a model within the eVision forecasting environment, capable of forecasting the trend of Tuberculosis cases in India, as India is the country that accounts for the largest percentage of TB cases and deaths worldwide. And being able to make predictions for India may also lead to successful results for other regions in Asia and Africa. In order to do so, this study presents different test cases that show the effectiveness of the model, varying the number of steps for each one of the data sets created. It's important to note, that these data sets are combinations of data gathered from the states with the most TB cases in India in the last years, as well as the total data for India, and supplemental data from Google Trends, as a way to facilitate the machine learning model. Even though the final results were respectable compared to past research done on India and other countries, the model nevertheless has a limitation on the number of weeks ahead which the predictions are still considered to be good; with 7 weeks being the optimal result.
Forged content shared widely on social media platforms is a major social problem that requires increased regulation and poses new challenges to the research community. The recent proliferation of hyper-realistic deepf...
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Recent theoretical and experimental work in neuroscience has focused on the representational and dynamical character of neural manifolds –subspaces in neural activity space wherein many neurons coactivate. Importantl...
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Recent theoretical and experimental work in neuroscience has focused on the representational and dynamical character of neural manifolds –subspaces in neural activity space wherein many neurons coactivate. Importantly, neural populations studied under this "neural manifold hypothesis" are continuous and not cleanly divided into separate neural populations. This perspective clashes with the "modular hypothesis" of brain organization, wherein neural elements maintain an "all- or-nothing" affiliation with modules. In line with this modular hypothesis, recent research on recurrent neural networks suggests that multi-task networks become modular across training, such that different modules specialize for task-general dynamical motifs [1, 2]. If the modular hypothesis is true, then it would be important to use a dimensionality reduction technique that captures modular structure. Here, we investigate the features of such a method. We leverage RNNs as a model system to study the character of modular neural populations, using a community detection method from network science known as modularity maximization to partition neurons into distinct modules. These partitions allow us to ask the following question: do these modular boundaries matter to the system? We find evidence that they do. First, we find that these boundaries neatly divide the representational content and role of neurons. Next, we find that these boundaries can be directly inferred from features of the weight matrix in feed-forward neural networks, and are related to clustering of the Jacobian matrix in recurrent neural networks. We also find that the weights of input neurons to recurrent neurons partially inform their modular structure, an observation that we corroborated using structural and functional imaging data from mice and humans. Finally, we find that the dynamics of these RNNs reflected the boundaries between modules. Collectively, our results suggest that neural populations in RNNs sometimes form modu
In this paper, an approach to aid in introductory programming courses is presented, focused on students that had never had any contact with computer programming. The approach, that was introduced in a previous paper [...
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ISBN:
(数字)9781665462808
ISBN:
(纸本)9781665462815
In this paper, an approach to aid in introductory programming courses is presented, focused on students that had never had any contact with computer programming. The approach, that was introduced in a previous paper [3], presents programming concepts using icons and “flowcharts”, and a 3D robotics simulator (CoppeliaSim), that has free educational use. This time, a real version of the simulated robot, built using Arduino and cheap, easily obtainable electronic components, is also presented. The programs to control the robot are developed by graphically constructing flowcharts on the visual editor, and then following the execution of programs using the simulated (or real) robot, step by step. This graphic program editor is named “FluxProg”, and its 2nd version is now available online, with new features comprising variables, arithmetic and logic expressions, thus providing a complete programming environment.
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