A Parallel Genetic Algorithm For Tuning Neural Networks

One challenge in using artificial neural networks is how to determine appropriate parameters for network structure and learning. Often parameters such as learning rate or number of hidden units are set arbitrarily or with a general intuition as to what would be most effective. The goal of this project is to use a genetic algorithm to tune a population of neural networks to determine the best structure and parameters. This paper considers a genetic algorithm to tune the number of hidden units, learning rate, momentum, and number of examples viewed per weight update. Experiments and results are discussed for two domains with distinct properties, demonstrating the importance of careful tuning of network parameters and structure for best performance

Python bindings for the open source electromagnetic simulator Meep

Meep is a broadly used open source package for finite-difference time-domain electromagnetic simulations. Python bindings for Meep make it easier to use for researchers and open promising opportunities for integration with other packages in the Python ecosystem. As this project shows, implementing Python-Meep offers benefits for specific disciplines and for the wider research community

A Highly-Parameterized Ensemble To Play Gin Rummy

In this work we describe the development and tuning of a computer Gin Rummy player. The system includes three main components to make decisions about drawing cards, discarding, and ending the game, with numerous hyperparameters controlling behavior. After the components are described, three sets of hyperparameter tuning and performance experiments are analyzed

Glucose sensing by means of silicon photonics

Diabetes is a fast growing metabolic disease, where the patients suffer from disordered glucose blood levels. Monitoring the blood glucose values in combination with extra insulin injection is currently the only therapy to keep the glucose concentration in diabetic patients under control, minimizing the long- term effects of elevated glucose concentrations and improving quality of life of the diabetic patients. Implantable sensors allow continuous glucose monitoring, offering the most reliable data to control the glucose levels. Infrared absorption spectrometers offer a non-chemical measurement method to determine the small glucose concentrations in blood serum. In this work, a spectrometer platform based on silicon photonics is presented, allowing the realization of very small glucose sensors suitable for building implantable sensors. A proof-of-concept of a spectrometer with integrated evanescent sample interface is presented, and the route towards a fully implantable spectrometer is discussed

Monte Carlo Approaches to Parameterized Poker Squares

The paper summarized a variety of Monte Carlo approaches employed in the top three performing entries to the Parameterized Poker Squares NSG Challenge competition. In all cases AI players benefited from real-time machine learning and various Monte Carlo game-tree search techniques

Emotion-Aware Music Recommendation

People often listen to songs that match their mood. Thus, an AI music recommendation system that is aware of the user’s emotions is likely to provide a superior user experience to one that is unaware. In this paper, we present an emotion-aware music recommendation system. Multiple models are discussed and evaluated for affect identification from a live image of the user. We propose two models: DRViT, which applies dynamic routing to vision transformers, and InvNet50, which uses involution. All considered models are trained and evaluated on the AffectNet dataset. Each model outputs the user’s estimated valence and arousal under the circumplex model of affect. These values are compared to the valence and arousal values for songs in a Spotify dataset, and the top-five closest-matching songs are presented to the user. Experimental results of the models and user testing are presented

A Highly-Parameterized Ensemble to Play Gin Rummy

This paper describes the design and training of a computer Gin Rummy player. The system includes three main components to make decisions about drawing cards, discarding, and ending the game, with numerous parameters controlling behavior. In particular, an ensemble approach is explored in the discard decision. Finally, three sets of parameter tuning and performance experiments are analyzed

Dual-Mode Electro-Optical Techniques for Biosensing Applications : A Review

The monitoring of biomolecular interactions is a key requirement for the study of complex biological processes and the diagnosis of disease. Technologies that are capable of providing label-free, real-time insight into these interactions are of great value for the scientific and clinical communities. Greater understanding of biomolecular interactions alongside increased detection accuracy can be achieved using technology that can provide parallel information about multiple parameters of a single biomolecular process. For example, electro-optical techniques combine optical and electrochemical information to provide more accurate and detailed measurements that provide unique insights into molecular structure and function. Here, we present a comparison of the main methods for electro-optical biosensing, namely, electrochemical surface plasmon resonance (EC-SPR), electrochemical optical waveguide lightmode spectroscopy (EC-OWLS), and the recently reported silicon-based electrophotonic approach. The comparison considers different application spaces, such as the detection of low concentrations of biomolecules, integration, the tailoring of light-matter interaction for the understanding of biomolecular processes, and 2D imaging of biointeractions on a surface