Pre-intercalation: A valuable approach for the improvement of post-lithium battery materials

With the growing concern around the sustainability and supply of lithium, the need for alternative rechargeable energy storage technologies has become ever more pressing. Sodium-, potassium-, magnesium-, and zinc-ion batteries are fast becoming viable alternatives but are held back by capacity, rate and stability problems that have not developed comparably to lithium-ion batteries. To overcome these shortcomings and reduce the reliance on lithium, electrode materials used for these post-lithium batteries must be improved. Pre-intercalation of foreign species into the lattice of promising electrode materials can enhance their electrochemical performance in comparison to the un-pre-intercalated counterparts, closing the performance gap with lithium-ion batteries. This review article covers the common methods of pre-intercalating foreign species into electrode materials, the resulting structural effects and the improvements that are observed in the materials’ electrochemical performance for post-lithium batteries. Timely and impactful work reported previously are summarised as examples of these improvements, demonstrating the value and ever-growing importance of pre-intercalation in today’s battery landscape

Introduction and Suggestions on the Chinese Securities Credit Rating System from a Comparative Perspective

Credit rating is a burgeoning industry in China. However, ever since it was established by State Council in 1993, the development of the industry has faced various impediments. There are currently three major problems hindering its further development, as result of a lack of systematic statutory and judicial guidelines. These problems are: limited competition in the industry, rampant rating shopping and conflicts of interest, and limited remedy in a suit against false ratings. The rating industry in China has followed a different pattern. The statutory threshold requirement has proven too demanding for most rating agencies to comply with, barring many potential participants from the market. In addition, provisions prohibiting rating shopping and regulating conflicts of interest are narrowly drawn, targeting only direct conflicts of interest. While freedom of speech remains an invalid defense, judges\u27 reluctance to recognize substantial intangible harm combined with an insufficient judicial framework together make it hard for plaintiffs to prevail in lawsuits against false ratings

LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Network

Hierarchical multi-label text classification (HMTC) has been gaining popularity in recent years thanks to its applicability to a plethora of real-world applications. The existing HMTC algorithms largely focus on the design of classifiers, such as the local, global, or a combination of them. However, very few studies have focused on hierarchical feature extraction and explore the association between the hierarchical labels and the text. In this paper, we propose a Label-based Attention for Hierarchical Mutlti-label Text Classification Neural Network (LA-HCN), where the novel label-based attention module is designed to hierarchically extract important information from the text based on the labels from different hierarchy levels. Besides, hierarchical information is shared across levels while preserving the hierarchical label-based information. Separate local and global document embeddings are obtained and used to facilitate the respective local and global classifications. In our experiments, LA-HCN outperforms other state-of-the-art neural network-based HMTC algorithms on four public HMTC datasets. The ablation study also demonstrates the effectiveness of the proposed label-based attention module as well as the novel local and global embeddings and classifications. By visualizing the learned attention (words), we find that LA-HCN is able to extract meaningful information corresponding to the different labels which provides explainability that may be helpful for the human analyst.Comment: code is available at https://github.com/XinyiZ001/LA-HC

Software Sustainability: The Modern Tower of Babel

<p>The aim of this paper is to explore the emerging definitions of software sustainability from the field of software engineering in order to contribute to the question, what is software sustainability?</p

Influence of Personality-based Features for Dialogue Generation in Computational Narratives

In this paper, we present an approach for generating dialogues for characters within the context of computational narratives using personality-based features for deep neural networks. The approach integrates the requirements of both narrative genres and personality traits for the definition of character-based stylistic models. The modelling of characters’ features from existing datasets of complete stories permits the generation of personality-rich character dialogues. We present early results from an evaluation based on a sample of characters’ personality traits across different narrative genres, demonstrating variability in the resulting dialogue

App Parameter Energy Profiling: Optimizing App Energy Drain by Finding Tunable App Parameters

In this paper, we observe that modern mobile apps come with a large number of parameters that control the app behavior which indirectly affect the app energy drain, and using incorrect or non-optimal values for such app parameters can lead to app energy drain deficiency or even energy bugs. We argue conventional app energy optimization using an energy profiler which pinpoints energy hotspot code segments in the app source code may be ineffective in detecting such parameter-induced app energy deficiency. We propose app parameter energy profiling which identifies tunable app parameters that can reduce app energy drain without affecting app functions as a potentially more effective solution for debugging such app energy deficiency. We present the design and implementation of Medusa, an app parameter energy profiling framework. Medusa overcomes three key design challenges: how to filter out and narrow down candidate parameters, how to pick alternative parameter values, and how to perform reliable energy drain testing of app versions with mutated parameter values. We demonstrate the effectiveness of Medusa by applying it to a set of Android apps which successfully identifies tunable energy-reducing parameters

An Empirical Study on the Impact of Deep Parameters on Mobile App Energy Usage

Improving software performance through configuration parameter tuning is a common activity during software maintenance. Beyond traditional performance metrics like latency, mobile app developers are interested in reducing app energy usage. Some mobile apps have centralized locations for parameter tuning, similar to databases and operating systems, but it is common for mobile apps to have hundreds of parameters scattered around the source code. The correlation between these deep parameters and app energy usage is unclear. Researchers have studied the energy effects of deep parameters in specific modules, but we lack a systematic understanding of the energy impact of mobile deep parameters. In this paper we empirically investigate this topic, combining a developer survey with systematic energy measurements. Our motivational survey of 25 Android developers suggests that developers do not understand, and largely ignore, the energy impact of deep parameters. To assess the potential implications of this practice, we propose a deep parameter energy profiling framework that can analyze the energy impact of deep parameters in an app. Our framework identifies deep parameters, mutates them based on our parameter value selection scheme, and performs reliable energy impact analysis. Applying the framework to 16 popular Android apps, we discovered that deep parameter-induced energy inefficiency is rare. We found only 2 out of 1644 deep parameters for which a different value would significantly improve its app\u27s energy efficiency. A detailed analysis found that most deep parameters have either no energy impact, limited energy impact, or an energy impact only under extreme values. Our study suggests that it is generally safe for developers to ignore the energy impact when choosing deep parameter values in mobile apps

Direct reprogramming of induced neural progenitors: a new promising strategy for AD treatment.

Alzheimer\u27s disease (AD) is a prominent form of dementia, characterized by aggregation of the amyloid β-peptide (Aβ) plaques and neurofibrillary tangles, loss of synapses and neurons, and degeneration of cognitive functions. Currently, although a variety of medications can relieve some of the symptoms, there is no cure for AD. Recent breakthroughs in the stem cell field provide promising strategies for AD treatment. Stem cells including embryonic stem cells (ESCs), neural stem cells (NSCs), mesenchymal stem cells (MSCs), and induced pluripotent stem cells (iPSCs) are potentials for AD treatment. However, the limitation of cell sources, safety issues, and ethical issues restrict their applications in AD. Recently, the direct reprogramming of induced neural progenitor cells (iNPCs) has shed light on the treatment of AD. In this review, we will discuss the latest progress, challenges, and potential applications of direct reprogramming in AD treatment

LMRL Gym: Benchmarks for Multi-Turn Reinforcement Learning with Language Models

Large language models (LLMs) provide excellent text-generation capabilities, but standard prompting and generation methods generally do not lead to intentional or goal-directed agents and might necessitate considerable prompt tuning. This becomes particularly apparent in multi-turn conversations: even the best current LLMs rarely ask clarifying questions, engage in explicit information gathering, or take actions now that lead to better decisions after multiple turns. Reinforcement learning has the potential to leverage the powerful modeling capabilities of LLMs, as well as their internal representation of textual interactions, to create capable goal-directed language agents. This can enable intentional and temporally extended interactions, such as with humans, through coordinated persuasion and carefully crafted questions, or in goal-directed play through text games to bring about desired final outcomes. However, enabling this requires the community to develop stable and reliable reinforcement learning algorithms that can effectively train LLMs. Developing such algorithms requires tasks that can gauge progress on algorithm design, provide accessible and reproducible evaluations for multi-turn interactions, and cover a range of task properties and challenges in improving reinforcement learning algorithms. Our paper introduces the LMRL-Gym benchmark for evaluating multi-turn RL for LLMs, together with an open-source research framework containing a basic toolkit for getting started on multi-turn RL with offline value-based and policy-based RL methods. Our benchmark consists of 8 different language tasks, which require multiple rounds of language interaction and cover a range of tasks in open-ended dialogue and text games