New Materials and Methods towards High-Energy Lithium Metal Batteries

The sluggish progress of battery technologies has drastically hindered the rapid development of electric vehicles and next-generation portable electronics. Improving the energy density requires breakthroughs in materials for both cathode and anode, and new characterization methods to accurately correlate the materials with their performances.For cathodes, lithium (Li) rich layered oxides exhibit high reversible specific capacities over 300 mAh g-1, attributing to the oxygen redox reaction. However, oxygen activity comes with instability in the form of oxygen loss, which is associated with irreversible voltage decay and capacity fading. Calculations suggest that incorporating 4d elements, such as Mo, enhances the structural stability by altering the local band structure and impeding oxygen vacancy formation. Driven by these findings, Mo is co-doped with Co into Li[Li0.2Ni0.2Mn0.6]O2, showing notably reduced voltage decay and capacity fading without sacrificing energy density and cycle life.The Li metal anode is critical to break the energy-density bottleneck of current Li-ion chemistry. Inactive Li formation is the immediate cause of capacity loss and catastrophic failure of Li metal batteries. However, its composition has not yet been quantitatively studied due to the lack of effective diagnosis tools that can accurately differentiate Li+ in solid electrolyte interphase (SEI) components and the electrically isolated unreacted metallic Li0, which together comprise the inactive Li. By establishing a new analytical method, Titration Gas Chromatography (TGC), we accurately quantify the contribution from unreacted metallic Li0 to the total amount of inactive Li. We identify the Li0, rather than the (electro)chemically formed Li+ in SEI, as the dominating cause for the inactive Li and capacity loss. Coupling the measurements of the unreacted metallic Li0 global content to the observations of its local micro- and nano-structure by cryogenic electron microscopies, we also reveal the formation mechanism of inactive Li in different types of electrolytes, and determine the true underlying cause of low CE in Li metal deposition and stripping. We ultimately propose strategies for highly efficient Li deposition and stripping to enable Li metal anode for next generation high-energy batteries

The Effectiveness of Highlighting Different Communication Orientations in Promoting Mobile Communication Technology at Work vs. at Home: Evidence from a Field Experiment

With the development of mobile communication technologies, people can now engage in seamless communications with family members and coworkers at both home and work. When promoting a new mobile communication technology (e.g., the 5G network), firms may be tempted to emphasize how the technology can strengthen communication both within and across the two domains with the hope of improving purchase rates. Yet research has suggested that people may perceive mobile communication differently depending on whether those they are communicating with others who belong to the same domain. Thus, the promotion of the technology to potential users should perhaps consider users’ location domain and their communication targets. Through a field experiment, we show that when promoting mobile communication technology in the home domain, highlighting prevention-focused communication promotes greater purchase rates. However, at work, when coworkers are the target of communication, highlighting promotion-focused communication works better. These findings can not only help practitioners design more effective promotional messages in promoting mobile communication technologies but also contribute to the understanding of nuanced differences in the nature of mobile communication that make it more appealing to users in different within- and cross-domain communication scenarios

BID: Boundary-Interior Decoding for Unsupervised Temporal Action Localization Pre-Trainin

Skeleton-based motion representations are robust for action localization and understanding for their invariance to perspective, lighting, and occlusion, compared with images. Yet, they are often ambiguous and incomplete when taken out of context, even for human annotators. As infants discern gestures before associating them with words, actions can be conceptualized before being grounded with labels. Therefore, we propose the first unsupervised pre-training framework, Boundary-Interior Decoding (BID), that partitions a skeleton-based motion sequence into discovered semantically meaningful pre-action segments. By fine-tuning our pre-training network with a small number of annotated data, we show results out-performing SOTA methods by a large margin.Comment: 18 pages, 8 figure

OPDAI at SemEval-2024 Task 6: Small LLMs can Accelerate Hallucination Detection with Weakly Supervised Data

This paper mainly describes a unified system for hallucination detection of LLMs, which wins the second prize in the model-agnostic track of the SemEval-2024 Task 6, and also achieves considerable results in the model-aware track. This task aims to detect hallucination with LLMs for three different text-generation tasks without labeled training data. We utilize prompt engineering and few-shot learning to verify the performance of different LLMs on the validation data. Then we select the LLMs with better performance to generate high-quality weakly supervised training data, which not only satisfies the consistency of different LLMs, but also satisfies the consistency of the optimal LLM with different sampling parameters. Furthermore, we finetune different LLMs by using the constructed training data, and finding that a relatively small LLM can achieve a competitive level of performance in hallucination detection, when compared to the large LLMs and the prompt-based approaches using GPT-4

Model-free False Data Injection Attack in Networked Control Systems: A Feedback Optimization Approach

Security issues have gathered growing interest within the control systems community, as physical components and communication networks are increasingly vulnerable to cyber attacks. In this context, recent literature has studied increasingly sophisticated \emph{false data injection} attacks, with the aim to design mitigative measures that improve the systems' security. Notably, data-driven attack strategies -- whereby the system dynamics is oblivious to the adversary -- have received increasing attention. However, many of the existing works on the topic rely on the implicit assumption of linear system dynamics, significantly limiting their scope. Contrary to that, in this work we design and analyze \emph{truly} model-free false data injection attack that applies to general linear and nonlinear systems. More specifically, we aim at designing an injected signal that steers the output of the system toward a (maliciously chosen) trajectory. We do so by designing a zeroth-order feedback optimization policy and jointly use probing signals for real-time measurements. We then characterize the quality of the proposed model-free attack through its optimality gap, which is affected by the dimensions of the attack signal, the number of iterations performed, and the convergence rate of the system. Finally, we extend the proposed attack scheme to the systems with internal noise. Extensive simulations show the effectiveness of the proposed attack scheme

Heuristic Learning for Co-Design Scheme of Optimal Sequential Attack

This paper considers a novel co-design problem of the optimal \textit{sequential} attack, whose attack strategy changes with the time series, and in which the \textit{sequential} attack selection strategy and \textit{sequential} attack signal are simultaneously designed. Different from the existing attack design works that separately focus on attack subsets or attack signals, the joint design of the attack strategy poses a huge challenge due to the deep coupling relation between the \textit{sequential} attack selection strategy and \textit{sequential} attack signal. In this manuscript, we decompose the sequential co-design problem into two equivalent sub-problems. Specifically, we first derive an analytical closed-form expression between the optimal attack signal and the sequential attack selection strategy. Furthermore, we prove the finite-time inverse convergence of the critical parameters in the injected optimal attack signal by discrete-time Lyapunov analysis, which enables the efficient off-line design of the attack signal and saves computing resources. Finally, we exploit its relationship to design a heuristic two-stage learning-based joint attack algorithm (HTL-JA), which can accelerate realization of the attack target compared to the one-stage proximal-policy-optimization-based (PPO) algorithm. Extensive simulations are conducted to show the effectiveness of the injected optimal sequential attack