Using Echo State Networks for Cryptography

Echo state networks are simple recurrent neural networks that are easy to implement and train. Despite their simplicity, they show a form of memory and can predict or regenerate sequences of data. We make use of this property to realize a novel neural cryptography scheme. The key idea is to assume that Alice and Bob share a copy of an echo state network. If Alice trains her copy to memorize a message, she can communicate the trained part of the network to Bob who plugs it into his copy to regenerate the message. Considering a byte-level representation of in- and output, the technique applies to arbitrary types of data (texts, images, audio files, etc.) and practical experiments reveal it to satisfy the fundamental cryptographic properties of diffusion and confusion.Comment: 8 pages, ICANN 201

Learning to Generate Better Than Your LLM

Reinforcement learning (RL) has emerged as a powerful paradigm for fine-tuning Large Language Models (LLMs) for text generation. In particular, recent LLMs such as ChatGPT and GPT-4 can engage in fluent conversations with users after finetuning with RL. Capitalizing on key properties of text generation, we seek to investigate RL algorithms beyond general purpose algorithms like Proximal Policy Optimization (PPO). In particular, we extend RL algorithms to allow them to interact with a dynamic black-box guide LLM and propose RL with guided feedback (RLGF), a suite of RL algorithms for LLM fine-tuning. We provide two ways for the guide LLM to interact with the LLM to be optimized for maximizing rewards. The guide LLM can generate text which serves as additional starting states for the RL optimization procedure. The guide LLM can also be used to complete the partial sentences generated by the LLM that is being optimized, treating the guide LLM as an expert to imitate and surpass eventually. We experiment on the IMDB positive sentiment, CommonGen, and TL;DR summarization tasks. We show that our RL algorithms achieve higher performance than supervised learning (SL) and the RL baseline PPO, demonstrating the benefit of interaction with the guide LLM. On both CommonGen and TL;DR, we not only outperform our SL baselines but also improve upon PPO across a variety of metrics beyond the one we optimized for. Our code can be found at https://github.com/Cornell-RL/tril.Comment: 23 pages, 5 figures, 7 tables, 4 algorithm

Proton coupled electron transfer reaction of phenols with excited state ruthenium(II) - polypyridyl complexes

The reaction of phenols with the excited state, *[Ru(bpy)3]2+ (E0 = 0.76V) and *[Ru(H2dcbpy)3]2+, (dcbpy = 4,4'-dicarboxy-2,2'-bipyridine) (E0 = 1.55 V vs. SCE) complexes in CH3CN has been studied by luminescence quenching technique and the quenching is dynamic. The formation of phenoxyl radical as a transient is confirmed by its characteristic absorption at 400 nm. The kq value is highly sensitive to the change of pH of the medium and ΔG0 of the reaction. Based on the treatment of kq data in terms of energetics of the reaction and pH of the medium, proton coupled electron transfer (PCET) mechanism has been proposed for the reaction

Is Reinforcement Learning (Not) for Natural Language Processing?: Benchmarks, Baselines, and Building Blocks for Natural Language Policy Optimization

We tackle the problem of aligning pre-trained large language models (LMs) with human preferences. If we view text generation as a sequential decision-making problem, reinforcement learning (RL) appears to be a natural conceptual framework. However, using RL for LM-based generation faces empirical challenges, including training instability due to the combinatorial action space, as well as a lack of open-source libraries and benchmarks customized for LM alignment. Thus, a question rises in the research community: is RL a practical paradigm for NLP? To help answer this, we first introduce an open-source modular library, RL4LMs (Reinforcement Learning for Language Models), for optimizing language generators with RL. The library consists of on-policy RL algorithms that can be used to train any encoder or encoder-decoder LM in the HuggingFace library (Wolf et al. 2020) with an arbitrary reward function. Next, we present the GRUE (General Reinforced-language Understanding Evaluation) benchmark, a set of 6 language generation tasks which are supervised not by target strings, but by reward functions which capture automated measures of human preference.GRUE is the first leaderboard-style evaluation of RL algorithms for NLP tasks. Finally, we introduce an easy-to-use, performant RL algorithm, NLPO (Natural Language Policy Optimization)} that learns to effectively reduce the combinatorial action space in language generation. We show 1) that RL techniques are generally better than supervised methods at aligning LMs to human preferences; and 2) that NLPO exhibits greater stability and performance than previous policy gradient methods (e.g., PPO (Schulman et al. 2017)), based on both automatic and human evaluation.Comment: Preprint. Under review. Code found at https://github.com/allenai/rl4lms and Project website at https://rl4lms.apps.allenai.org

Anti-Urolithiatic Activity of Melia Azedarach Linn Leaf Extract in Ethylene Glycol-Induced Urolithiasis in Male Albino Rats

Purpose: To investigate the anti-urolithiatic activity of the aqueous and alcoholic extracts of Melia azedarach Linn leaves in calcium oxalate urolithiasis in male albino rats.Methods: The effect of oral administration of aqueous and ethanol extracts of Melia azedarach Linn leaves on calcium oxalate urolithiasis has been investigated. Lithiasis was induced by oral adminstration of ethylene glycol (0.75 %v/v) in male albino rats for 28 days. Each of the extract (250 mg/kg) was administered orally day 0 as a prophylactic regimen and from day 15 as a curative regimen. Regular administration of ethylene glycol caused hyperoxaluria in ethylene glycol-fed animals, leading to increased renal retention and excretion of oxalate, calcium and phosphate. Histopathological study, urine microscopy, serum analysis and biochemical analysis of kidney homogenate were performed.Results: Oxalate and calcium excretion in urine increased (p < 0.01) to 3.68 ± 0.01 and 4.5 ± 0.01 mg/24 h, respectively, in lithiatic control animals compared to (0.37 ± 0.01 and 1.27 ± 0.12 mg/24 h) for the normal control group. Treatment with aqueous or ethanol extract (250 mg/kg, p.o.) significantly (p <0.01) reduced the elevated levels of calcium, oxalate and phosphate excretion in urine to 0.79 ± 0.01 and 1.09 ± 0.04 mg/24 h, respectively. Following treatment with the ethanol extract (250mg/kg), serum creatinine excretion was restored from 0.95 ± 0.01 mg/24 h to the normal level of 0.87 ± 0.01 mg/24 h. The results were comparable to those of the standard drug, allopurinol (50 mg/kg p.o.).Histopathological data for the kidney supported the foregoing results.Conclusions: The results demonstrate that the aqueous and ethanol extracts of Melia azedarach Linn leaves have potent antiurolithiatic activity against ethylene glycol-induced calcium oxalate urolithiasis in male albino rats.Keywords: Melia azedarach, Antiurolithiatic, Ethylene glycol, Urolithiasis, Excretion, Kidne

INVESTIGATION ON ANTIDIARRHOEAL ACTIVITY OF ARISTOLOCHIA INDICA LINN. ROOT EXTRACTS IN MICE

Background: The present study aimed at investigating the effect of ethanolic extract (EtAI), and aqueous extract (AqAI) of Aristolochia indica Linn roots on castor oil-induced diarrhoea and study on small intestinal transit. Phytochemical analysis of extracts was performed as per standard procedure. Materials and Methods: The oral toxicity study using Swiss albino mice was performed in accordance with OECD guidelines. The EtAI and AqAI extracts of Aristolochia indica Linn were studied for antidiarrhoeal property using castor oil-induced diarrhoeal model and charcoal-induced gastrointestinal motility test in Swiss albino mice. Results: Among the tested doses of 200 and 400 mg/kg body weight, the extracts reduced the frequency and severity of diarrhoea in test animals throughout the study period. At the same doses, the extract delayed the intestinal transit of charcoal meal in test animals as compared to the control and the results were statistically significant. Conclusion: Experimental findings showed that ethanol extract of Aristolochia indica Linn root possess significant antidiarrheal activity and may be a potent source of anti-diarrhoeal drug in future

Phytoremediation of heavy metal-contaminated sites: Eco-environmental concerns, field studies, sustainability issues and future prospects

Environmental contamination due to heavy metals (HMs) is of serious ecotoxicological concern worldwide because of their increasing use at industries. Due to non-biodegradable and persistent nature, HMs cause serious soil/water pollution and severe health hazards in living beings upon exposure. HMs can be genotoxic, carcinogenic, mutagenic, and teratogenic in nature even at low concentration. They may also act as endocrine disruptors and induce developmental as well as neurological disorders and thus, their removal from our natural environment is crucial for the rehabilitation of contaminated sites. To cope with HM pollution, phytoremediation has emerged as a low-cost and eco-sustainable solution to conventional physico-chemical cleanup methods that require high capital investment and labor alter soil properties and disturb soil microflora. Phytoremediation is a green technology wherein plants and associated microbes are used to remediate HM-contaminated sites to safeguard the environment and protect public health. Hence, in view of the above, the present paper aims to examine the feasibility of phytoremediation as a sustainable remediation technology for the management of metals-contaminated sites. Therefore, this paper provides an in-depth review on both the conventional and novel phytoremediation approaches, evaluate their efficacy to remove toxic metals from our natural environment, explore current scientific progresses, field experiences and sustainability issues and revise world over trends in phytoremediation research for its wider recognition and public acceptance as a sustainable remediation technology for the management of contaminated sites in 21st century

Experimental investigations on effects of tip clearance in mixed-flow compressor performance

Experimental investigations were carried out to study the effect of tip clearance (between impeller and stationary shroud) in a mixed-flow compressor stage. Two configurations, namely constant and variable clearance gaps, between impeller and stationary shroud were considered. For the purpose of the present investigations, a mixed-flow compressor stage was designed, fabricated, and experimentally evaluated. The flow investigations were carried out in a closed-circuit compressor rig. Detailed steady and unsteady flow measurements were carried out for three clearance gaps, namely 0.5 mm, 0.75 mm, and 0.9 mm. Through the experimental investigations, it was found that the constant tip-clearance configurations showed better performance in terms of pressure ratio and efficiency as compared to variable clearance configurations. For a given configuration, the pressure ratio and efficiency of the stage decrease with increase in the tip gap without indicating any optimum value. Tip-clearance flow had considerable effect on the flow through the diffuser