A5102757820- Wireless Signal Modulation Classification
- Generative Adversarial Networks and Image Synthesis
- Gaussian Processes and Bayesian Inference
- Emotion and Mood Recognition
- Human Pose and Action Recognition
- Radar Systems and Signal Processing
- Machine Learning in Healthcare
- RNA Interference and Gene Delivery
- Virus-based gene therapy research
- Monoclonal and Polyclonal Antibodies Research
- Morphological variations and asymmetry
- Microbial infections and disease research
- Animal Disease Management and Epidemiology
- Functional Brain Connectivity Studies
- Neural Networks and Applications
- Mental Health Research Topics
- PAPR reduction in OFDM
- 3D Shape Modeling and Analysis
- Genetic and phenotypic traits in livestock
- Time Series Analysis and Forecasting
- Full-Duplex Wireless Communications
- Advanced SAR Imaging Techniques
- Complement system in diseases
- Bacteriophages and microbial interactions
- Optical measurement and interference techniques
Northeastern University
2020-2023
Universidad del Noreste
2021
Viruses have evolved the ability to bind and enter cells through interactions with a wide variety of cell macromolecules. We engineered peptide-modified adeno-associated virus (AAV) capsids that transduce brain introduction de novo 2 proteins expressed on mouse blood–brain barrier (BBB), LY6A or LY6C1. The in vivo tropisms these are predictable as they dependent cell- strain-specific expression their target protein. This approach generated hundreds dramatically enhanced central nervous...
Abstract Machine learning methods provide powerful tools to map physical measurements scientific categories. But are such suitable for discovering the ground truth about psychological categories? We use science of emotion as a test case explore this question. In studies emotion, researchers supervised classifiers, guided by labels, attempt discover biomarkers in brain or body corresponding This practice relies on assumption that labels refer objective categories can be discovered. Here, we...
Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for communication links in many current and emerging Internet of Things (IoT) applications, including the latest WiFi standards. For such OFDM-based transceivers, core physical layer functions related to channel estimation, demapping, decoding implemented specific choices types modulation schemes, among others. To decouple hard-wired from receiver chain thereby enhance flexibility IoT deployment novel scenarios...
We introduce deep switching auto-regressive factorization (DSARF), a generative model for spatio-temporal data with the capability to unravel recurring patterns in and perform robust short- long-term predictions. Similar other factor analysis methods, DSARF approximates high dimensional by product between time dependent weights spatially factors. These factors are turn represented terms of lower latent variables that inferred using stochastic variational inference. is different from...
Abstract Viruses have evolved the ability to bind and enter cells through interactions with a wide variety of host cell macromolecules. Here, we screened for AAV capsids that two proteins expressed on mouse blood-brain barrier, LY6A or related protein LY6C1. Introducing either target generated hundreds dramatically enhanced central nervous system (CNS) tropisms. In contrast AAV-PHP.B capsid family, which interacts only exhibits its CNS tropism in subset strains, engage LY6C1 maintain their...
Despite the vast success of standard planar convolutional neural networks, they are not most efficient choice for analyzing signals that lie on an arbitrarily curved manifold, such as a cylinder. The problem arises when one performs projection these and inevitably causes them to be distorted or broken where there is valuable information. We propose Circular-symmetric Correlation Layer (CCL) based formalism roto-translation equivariant correlation continuous group $S^1 \times \mathbb{R}$,...
We propose an epidemic analysis framework for the outbreak prediction in livestock industry, focusing on study of most costly and viral infectious disease swine industry – PRRS virus. Using this framework, we can predict all farms a production system by capturing spatio-temporal dynamics infection transmission based intra-farm pig-level virus dynamics, inter-farm pig shipment network. simulate network SEIR model using statistics extracted from real data provided industry. develop...
We propose an epidemic analysis framework for the outbreak prediction in livestock industry, focusing on study of most costly and viral infectious disease swine industry -- PRRS virus. Using this framework, we can predict all farms a production system by capturing spatio-temporal dynamics infection transmission based intra-farm pig-level virus dynamics, inter-farm pig shipment network. simulate network SEIR model using statistics extracted from real data provided industry. develop...
Learning representations through deep generative modeling is a powerful approach for dynamical to discover the most simplified and compressed underlying description of data, then use it other tasks such as prediction. Most learning have intrinsic symmetries, i.e., input transformations leave output unchanged, or undergoes similar transformation. The process is, however, usually uninformed these symmetries. Therefore, learned individually transformed inputs may not be meaningfully related. In...
Orthogonal Frequency Division Multiplexing (OFDM)-based waveforms are used for communication links in many current and emerging Internet of Things (IoT) applications, including the latest WiFi standards. For such OFDM-based transceivers, core physical layer functions related to channel estimation, demapping, decoding implemented specific choices types modulation schemes, among others. To decouple hard-wired from receiver chain thereby enhance flexibility IoT deployment novel scenarios...
An amendment to this paper has been published and can be accessed via a link at the top of paper.