Utilization of non-conventional systems for conversion of biomass to food components: Potential for utilization of algae in engineered foods

The major nutritional components of the green algae (Scenedesmus obliquus) grown in a Constant Cell density Apparatus were determined. Suitable methodology to prepare proteins from which three major undesirable components of these cells (i.e., cell walls, nucleic acids, and pigments) were either removed or substantially reduced was developed. Results showed that processing of green algae to protein isolate enhances its potential nutritional and organoleptic acceptability as a diet component in a Controlled Ecological Life Support System

Potential for utilization of algal biomass for components of the diet in CELSS

The major nutritional components of the green algae (Scenedesmus obliquus) grown in a Constant Cell Density Apparatus were determined. Suitable methodology to prepare proteins from which three major undesirable components of these cells (i.e., cell walls, nucleic acids, and pigments) were either removed or substantially reduced was developed. Results showed that processing of green algae to protein isolate enhances is potential nutritional and organoleptic acceptability as a diet component in controlled Ecological Life Support System

Non-conventional approaches to food processing in CELSS, 1. Algal proteins: Characterization and process optimization

Protein isolate obtained from green algae cultivated under controlled conditions was characterized. Molecular weight determination of fractionated algal proteins using SDS-polyacrylamide gel electrophoresis revealed a wide spectrum of molecular weights ranging from 15,000 to 220,000. Isoelectric points of dissociated proteins were in the range of 3.95 to 6.20. Amino acid composition of protein isolate compared favorably with FAO standards. High content of essential amino acids leucine, valine, phenylalanine and lysine make algal protein isolate a high quality component of closed ecological life support system diets. To optimize the removal of algal lipids and pigments supercritical carbon dioxide extraction (with and without ethanol as a co-solvent) was used. Addition of ethanol to supercritical carbon dioxide resulted in more efficient removal of algal lipids and produced protein isolate with a good yield and protein recovery. The protein isolate extracted by the above mixture had an improved water solubility

Autonomous Traffic Engineering using Deep Reinforcement Learning

The evolution of communication technologies in the past few decades, has led to a huge increase in the complexity and the overall size of telecommunication networks. This phenomenon has increased the need for innovation in the field of Traffic Engineering (TE), as the already existing solutions are not flexible enough to adapt to these changes. With the appearance of 5G technologies, the urgency to revolutionize the field is higher than ever and the softwarization and virtualization of the infrastructure bring new possibilities for TE optimization, namely the possible use of Artificial Intelligence (AI) based methods for Traffic Management. The recent advances in AI have provided model-free optimization methods with algorithms like Deep Reinforcement Learning (DRL) that can be used to optimize traffic distributions in complex and hard to model Network scenarios. This thesis aims to provide a DRL-based solution for TE where an agent is capable of making routing decisions based on the current state of the network, with the goal of balancing the load between the network paths. A DRL agent is developed and trained in two different scenarios where the traffic that already exists in the network is generated randomly or according to a systematic pattern. A simulation environment was developed to train and evaluate the DRL agent.A evolução das tecnologias de comunicação nas útlimas décadas, tem dado origem a um grande aumento na complexidade e no tamanho das redes de telecomunicações. Este fenómeno tem aumentado a necessidade de inovação na área de Traffic Engineering (TE), visto que as soluções já existentes não são flexíveis o suficiente para se adaptarem a estas mudanças. Com a aproximação das tecnologias 5G, a urgência para revolucionar a área está cada vez maior e a softwarização e a virtualização das infraestruturas trazem novas possibilidades para otimizações de TE, nomeadamente o possível uso de métodos baseados em Inteligência Artificial (IA) para gerir o tráfego da rede. Os avanços recentes de IA têm criado métodos de otimização independentes de modelos (model-free), como Deep Reinforcement Learning (DRL) que pode ser usado para otimizar a distribuição de tráfego em cenários de redes complexas e difíceis de modelar. Esta dissertação tem como objetivo implementar uma solução à base de DRL, em que é desenhado um agente que é capaz de tomar decisões de encaminhamento, com o objetivo de gerir o tráfego pelos caminhos da rede. Um agente DRL é treinado em dois cenários diferentes onde o tráfego já existente na rede é gerado aleatóriamente ou de acordo com um padrão pré-definido. Foi criado um ambiente para treinar e para avaliar o agente DRL

Utilization of non-conventional systems for conversion of biomass to food components: Recovery optimization and characterizations of algal proteins and lipids

Protein isolate obtained from green algae (Scenedesmus obliquus) cultivated under controlled conditions was characterized. Molecular weight determination of fractionated algal proteins using SDS-polyacrylamide gel electrophoresis revealed a wide spectrum of molecular weights ranging from 15,000 to 220,000. Isoelectric points of dissociated proteins were in the range of 3.95 to 6.20. Amino acid composition of protein isolate compared favorably with FAO standards. High content of essential amino acids leucine, valine, phenylalanine and lysine makes algal protein isolate a high quality component of closed environment life support system (CELSS) diets. To optimize the removal of algal lipids and pigments supercritical carbon dioxide extraction (with and without ethanol as a co-solvent) was used. Addition of ethanol to supercritical CO2 resulted in more efficient removal of algal lipids and produced protein isolate with a good yield and protein recovery. The protein isolate extracted by the above mixture had an improved water solubility

Utilization of non-conventional systems for conversion of biomass to food components

Described here is work accomplished in investigating the potential use of micro-algae in yielding useful macronutrients for closed ecological life support systems in space habitats. Analysis of the chemical composition of the blue-green alga Synechoccus 6311 was done in the present work, and was compared to values found in previous work on the green algae Scenedesmus obliquus. Similar values were obtained for proteins, and lower values for nucleic acids and lipids. A second part of the work involved fabrication of food products containing various levels of incorporated algae (S. obliquus) proteins and/or lipids. Protein isolate was incorporated into a variety of food products such as bran muffins, fettuccine (spinach noodle imitation), and chocolate chip cookies. In the sensory analysis, the greenish color of the bran muffins and cookies was not found to be objectionable. The mild spinachy flavor was less detectable in chocolate chip cookies than in bran muffins. The color and taste of the algae noodles were found to be pleasant and compared well with commercially available spinach noodles

SQL-PaLM: Improved Large Language ModelAdaptation for Text-to-SQL

One impressive emergent capability of large language models (LLMs) is generation of code, including Structured Query Language (SQL) for databases. For the task of converting natural language text to SQL queries, Text-to-SQL, adaptation of LLMs is of paramount importance, both in in-context learning and fine-tuning settings, depending on the amount of adaptation data used. In this paper, we propose an LLM-based Text-to-SQL model SQL-PaLM, leveraging on PaLM-2, that pushes the state-of-the-art in both settings. Few-shot SQL-PaLM is based on an execution-based self-consistency prompting approach designed for Text-to-SQL, and achieves 77.3% in test-suite accuracy on Spider, which to our best knowledge is the first to outperform previous state-of-the-art with fine-tuning by a significant margin, 4%. Furthermore, we demonstrate that the fine-tuned SQL-PALM outperforms it further by another 1%. Towards applying SQL-PaLM to real-world scenarios we further evaluate its robustness on other challenging variants of Spider and demonstrate the superior generalization capability of SQL-PaLM. In addition, via extensive case studies, we demonstrate the impressive intelligent capabilities and various success enablers of LLM-based Text-to-SQL.Comment: 16 page

Universal Self-adaptive Prompting

A hallmark of modern large language models (LLMs) is their impressive general zero-shot and few-shot abilities, often elicited through prompt-based and/or in-context learning. However, while highly coveted and being the most general, zero-shot performances in LLMs are still typically weaker due to the lack of guidance and the difficulty of applying existing automatic prompt design methods in general tasks when ground-truth labels are unavailable. In this study, we address this by presenting Universal Self-adaptive Prompting (USP), an automatic prompt design approach specifically tailored for zero-shot learning (while compatible with few-shot). Requiring only a small amount of unlabeled data & an inference-only LLM, USP is highly versatile: to achieve universal prompting, USP categorizes a possible NLP task into one of the three possible task types, and then uses a corresponding selector to select the most suitable queries & zero-shot model-generated responses as pseudo-demonstrations, thereby generalizing ICL to the zero-shot setup in a fully automated way. We evaluate zero-shot USP with two PaLM models, and demonstrate performances that are considerably stronger than standard zero-shot baselines and are comparable to or even superior than few-shot baselines across more than 20 natural language understanding (NLU) and natural language generation (NLG) tasks.Comment: 10 pages, 3 figures, 4 tables (19 pages, 5 figures and 9 tables including references and appendices

Distilling Step-by-Step! Outperforming Larger Language Models with Less Training Data and Smaller Model Sizes

Deploying large language models (LLMs) is challenging because they are memory inefficient and compute-intensive for practical applications. In reaction, researchers train smaller task-specific models by either finetuning with human labels or distilling using LLM-generated labels. However, finetuning and distillation require large amounts of training data to achieve comparable performance to LLMs. We introduce Distilling step-by-step, a new mechanism that (a) trains smaller models that outperform LLMs, and (b) achieves so by leveraging less training data needed by finetuning or distillation. Our method extracts LLM rationales as additional supervision for training small models within a multi-task framework. We present three findings across 4 NLP benchmarks: First, compared to both finetuning and distillation, our mechanism achieves better performance with much fewer labeled/unlabeled training examples. Second, compared to few-shot prompted LLMs, we achieve better performance using substantially smaller model sizes. Third, we reduce both the model size and the amount of data required to outperform LLMs; our finetuned 770M T5 model outperforms the few-shot prompted 540B PaLM model using only 80% of available data on a benchmark, whereas standard finetuning the same T5 model struggles to match even by using 100% of the dataset. We release the code at: https://github.com/google-research/distilling-step-by-step .Comment: Accepted to Findings of ACL 202