Ethno-Medicinal Uses and Agro-Biodiversity of Barmana Region in Bilaspur District of Himachal Pradesh, Northwestern Himalaya

India is one of the richest countries in traditional knowledge, because of its ambient biodiversity, variety of habitats and rich ethnic divergence. Thus we have had well established local health tradition still relevant in indigenous healthcare system. The paper provides first hand information on the agro-biodiversity and ethno-medicinal uses of the area. In the present study 50 species belonging to 37 genera and 17 families i.e. Shrub (1 spp.), tree (1 spp.), herb (48 spp.) were recorded under the agro-biodiversity region of the area. The utilization pattern of the species indicated that leaves of 22 species, stem of 1 species and seeds of 23 species, whole part of 11 species, tubers and flowers of 4 species, fruits of 18 species, each are used. 6 species were Indian origins, while others were non-native to Indian Himalayan Region

A Gauss-Newton Approach for Min-Max Optimization in Generative Adversarial Networks

A novel first-order method is proposed for training generative adversarial networks (GANs). It modifies the Gauss-Newton method to approximate the min-max Hessian and uses the Sherman-Morrison inversion formula to calculate the inverse. The method corresponds to a fixed-point method that ensures necessary contraction. To evaluate its effectiveness, numerical experiments are conducted on various datasets commonly used in image generation tasks, such as MNIST, Fashion MNIST, CIFAR10, FFHQ, and LSUN. Our method is capable of generating high-fidelity images with greater diversity across multiple datasets. It also achieves the highest inception score for CIFAR10 among all compared methods, including state-of-the-art second-order methods. Additionally, its execution time is comparable to that of first-order min-max methods.Comment: accepted in IJCNN 2023, 9 page

Spectral Ocean Color (SPOC): Lessons Learned from the University of Georgia Small Satellite Research Laboratory\u27s First Satellite

In October 2020, the University of Georgia Small Satellite Research Laboratory launched its first CubeSat, a 3U Earth-observation mission designed to collect multispectral data from Georgia’s coastal environments for UGA’s Center for Geospatial Research to make recommendations on environmental conservation, care, and use. SPOC successfully detumbled, but after approximately a month in orbit, a coronal mass ejection (we speculate) caused us to lose contact. Despite our disappointment at the loss of SPOC, we are leveraging the lessons learned for our upcoming missions. These lessons can be categorized in four principal areas: software (flight and payload), mission operations, testing, and educational program structure. Specifically, we learned how to carefully design mission controls, how to plan and execute robust batteries of tests, and how to work together to reach our potential as young scientists and engineers. We will show how we implement these lessons on our upcoming missions – the Multi-view Onboard Computational Imager (MOCI), a 6U mission using on-orbit Structure-from-Motion to create 3D terrain maps; and the Mission for Education and Multi-media Engagement Satellite (MEMESat-1), a 2U non-profit-sponsored outreach mission designed to introduce undergraduates to building satellites and K-12 students to the world of satellite and and radio communications. We aim to share what we have learned with other young CubeSat development programs to help them pioneer new space system technology, gain scientific insight from payload data, build strong university space programs, and enrich their surrounding communities

Face editing with GAN -- A Review

In recent years, Generative Adversarial Networks (GANs) have become a hot topic among researchers and engineers that work with deep learning. It has been a ground-breaking technique which can generate new pieces of content of data in a consistent way. The topic of GANs has exploded in popularity due to its applicability in fields like image generation and synthesis, and music production and composition. GANs have two competing neural networks: a generator and a discriminator. The generator is used to produce new samples or pieces of content, while the discriminator is used to recognize whether the piece of content is real or generated. What makes it different from other generative models is its ability to learn unlabeled samples. In this review paper, we will discuss the evolution of GANs, several improvements proposed by the authors and a brief comparison between the different models. Index Terms generative adversarial networks, unsupervised learning, deep learning

The Spectral Ocean Color Imager (SPOC) – An Adjustable Multispectral Imager

SPOC (SPectral Ocean Color) is a 3U small satellite mission that will use an adjustable multispectral imager to map sensitive coastal regions and off coast water quality of Georgia and beyond. SPOC is being developed by the University of Georgia’s (UGA) Small Satellite Research Laboratory (SSRL) through NASA’s Undergraduate Student Instrument Project (USIP). UGA is working with Cloudland Instruments to develop a small scale (\u3c 1000 \u3ecm3) multispectral imager, ranging from 400-850nm, for Earth science applications which will fly as part of the NASA CubeSat Launch Initiative. The project is UGA’s first satellite mission and is built by a team of undergraduates from a wide range of backgrounds and supervised by a multidisciplinary team of graduate students and faculty. Development, assembly, testing, and validation of the multispectral imager, as well integrating it into the satellite are all being done in house. At an orbit of 400 km the resulting images will be 90 km x 100 km in size, with a default spatial resolution and spectral resolution of 130 m and 4 nm, respectively

Data-Driven Techniques for Low-Cost Sensor Selection and Calibration for the Use Case of Air Quality Monitoring

With the emergence of Low-Cost Sensor (LCS) devices, measuring real-time data on a large scale has become a feasible alternative approach to more costly devices. Over the years, sensor technologies have evolved which has provided the opportunity to have diversity in LCS selection for the same task. However, this diversity in sensor types adds complexity to appropriate sensor selection for monitoring tasks. In addition, LCS devices are often associated with low confidence in terms of sensing accuracy because of the complexities in sensing principles and the interpretation of monitored data. From the data analytics point of view, data quality is a major concern as low-quality data more often leads to low confidence in the monitoring systems. Therefore, any applications on building monitoring systems using LCS devices need to focus on two main techniques: sensor selection and calibration to improve data quality. In this paper, data-driven techniques were presented for sensor calibration techniques. To validate our methodology and techniques, an air quality monitoring case study from the Bradford district, UK, as part of two European Union (EU) funded projects was used. For this case study, the candidate sensors were selected based on the literature and market availability. The candidate sensors were narrowed down into the selected sensors after analysing their consistency. To address data quality issues, four different calibration methods were compared to derive the best-suited calibration method for the LCS devices in our use case system. In the calibration, meteorological parameters temperature and humidity were used in addition to the observed readings. Moreover, we uniquely considered Absolute Humidity (AH) and Relative Humidity (RH) as part of the calibration process. To validate the result of experimentation, the Coefficient of Determination (R2), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) were compared for both AH and RH. The experimental results showed that calibration with AH has better performance as compared with RH. The experimental results showed the selection and calibration techniques that can be used in designing similar LCS based monitoring systems

KerasCV and KerasNLP: Vision and Language Power-Ups

We present the Keras domain packages KerasCV and KerasNLP, extensions of the Keras API for Computer Vision and Natural Language Processing workflows, capable of running on either JAX, TensorFlow, or PyTorch. These domain packages are designed to enable fast experimentation, with a focus on ease-of-use and performance. We adopt a modular, layered design: at the library's lowest level of abstraction, we provide building blocks for creating models and data preprocessing pipelines, and at the library's highest level of abstraction, we provide pretrained ``task" models for popular architectures such as Stable Diffusion, YOLOv8, GPT2, BERT, Mistral, CLIP, Gemma, T5, etc. Task models have built-in preprocessing, pretrained weights, and can be fine-tuned on raw inputs. To enable efficient training, we support XLA compilation for all models, and run all preprocessing via a compiled graph of TensorFlow operations using the tf.data API. The libraries are fully open-source (Apache 2.0 license) and available on GitHub.Comment: Submitted to Journal of Machine Learning Open Source Softwar

Guinea pig models for translation of the developmental origins of health and disease hypothesis into the clinic

Over 30 years ago Professor David Barker first proposed the theory that events in early life could explain an individual\u27s risk of non-communicable disease in later life: the developmental origins of health and disease (DOHaD) hypothesis. During the 1990s the validity of the DOHaD hypothesis was extensively tested in a number of human populations and the mechanisms underpinning it characterised in a range of experimental animal models. Over the past decade, researchers have sought to use this mechanistic understanding of DOHaD to develop therapeutic interventions during pregnancy and early life to improve adult health. A variety of animal models have been used to develop and evaluate interventions, each with strengths and limitations. It is becoming apparent that effective translational research requires that the animal paradigm selected mirrors the tempo of human fetal growth and development as closely as possible so that the effect of a perinatal insult and/or therapeutic intervention can be fully assessed. The guinea pig is one such animal model that over the past two decades has demonstrated itself to be a very useful platform for these important reproductive studies. This review highlights similarities in the in utero development between humans and guinea pigs, the strengths and limitations of the guinea pig as an experimental model of DOHaD and the guinea pig\u27s potential to enhance clinical therapeutic innovation to improve human health. (Figure presented.)