Are shared electric scooters energy efficient?

Shared electric scooters (e-scooter) are booming across the world and widely regarded as a sustainable mobility service. An increasing number of studies have investigated the e-scooter trip patterns, safety risks, and environmental impacts, but few considered the energy efficiency of e-scooters. In this research, we collected the operational data of e-scooters from a major provider in Gothenburg to shed light on the energy efficiency performance of e-scooters in real cases. We first develop a multiple logarithmic regression model to examine the energy consumption of single trips and influencing factors. With the regression model, a Monte Carlo simulation framework is proposed to estimate the fleet energy consumption in various scenarios, taking into account both trip-related energy usage and energy loss in idle status. The results indicate that 40% of e-scooter battery energy was wasted in idle status in the current practice, mainly due to the relatively low usage rate (0.83) of e-scooters. If the average usage rate drops below 0.5, the wasted energy could reach up to 53%. In the end, we present a field example to showcase how to optimally integrate public transport with e-scooters from the perspective of energy efficiency. We hope the findings of this study could help understand and resolve the current and future challenges regarding the ever-growing e-scooter services

Weakly supervised deep learning for the detection of domain generation algorithms

Domain generation algorithms (DGAs) have become commonplace in malware that seeks to establish command and control communication between an infected machine and the botmaster. DGAs dynamically and consistently generate large volumes of malicious domain names, only a few of which are registered by the botmaster, within a short time window around their generation time, and subsequently resolved when the malware on the infected machine tries to access them. Deep neural networks that can classify domain names as benign or malicious are of great interest in the real-time defense against DGAs. In contrast with traditional machine learning models, deep networks do not rely on human engineered features. Instead, they can learn features automatically from data, provided that they are supplied with sufficiently large amounts of suitable training data. Obtaining cleanly labeled ground truth data is difficult and time consuming. Heuristically labeled data could potentially provide a source of training data for weakly supervised training of DGA detectors. We propose a set of heuristics for automatically labeling domain names monitored in real traffic, and then train and evaluate classifiers with the proposed heuristically labeled dataset. We show through experiments on a dataset with 50 million domain names that such heuristically labeled data is very useful in practice to improve the predictive accuracy of deep learning-based DGA classifiers, and that these deep neural networks significantly outperform a random forest classifier with human engineered features

Effects of welding displacement and energy director thickness on the ultrasonic welding of epoxy-to-polyetherimide based hybrid composite joints

This study aimed to develop robust thermoplastic-to-thermoset composite joints upon an ultrasonic welding process. The carbon fiber/epoxy composite was topped with a layer of polyetherimide (PEI) film by a co-curing process, making it “weldable” with the carbon fiber/PEI composite. The effects of welding displacement and thickness of the energy director (ED) on the welding process of the epoxy-to-PEI hybrid composite joints were investigated. The experimental results demonstrated that an optimal welding displacement existed for the best welding quality, whose value depended on the ED thickness. Given a certain ED thickness, the lap-shear strength (LSS) of the hybrid joints increased to a maximum value and then decreased as the welding displacement increased. By optimizing the displacement and ED thickness, a maximum LSS of 39.4 MPa was obtained for the hybrid joints. In which case, the level of the defects within the welding line was minimized, and the joints failed cohesively within the composite substrates

Safe RLHF: Safe Reinforcement Learning from Human Feedback

With the development of large language models (LLMs), striking a balance between the performance and safety of AI systems has never been more critical. However, the inherent tension between the objectives of helpfulness and harmlessness presents a significant challenge during LLM training. To address this issue, we propose Safe Reinforcement Learning from Human Feedback (Safe RLHF), a novel algorithm for human value alignment. Safe RLHF explicitly decouples human preferences regarding helpfulness and harmlessness, effectively avoiding the crowdworkers' confusion about the tension and allowing us to train separate reward and cost models. We formalize the safety concern of LLMs as an optimization task of maximizing the reward function while satisfying specified cost constraints. Leveraging the Lagrangian method to solve this constrained problem, Safe RLHF dynamically adjusts the balance between the two objectives during fine-tuning. Through a three-round fine-tuning using Safe RLHF, we demonstrate a superior ability to mitigate harmful responses while enhancing model performance compared to existing value-aligned algorithms. Experimentally, we fine-tuned the Alpaca-7B using Safe RLHF and aligned it with collected human preferences, significantly improving its helpfulness and harmlessness according to human evaluations

Constrained Update Projection Approach to Safe Policy Optimization

Safe reinforcement learning (RL) studies problems where an intelligent agent has to not only maximize reward but also avoid exploring unsafe areas. In this study, we propose CUP, a novel policy optimization method based on Constrained Update Projection framework that enjoys rigorous safety guarantee. Central to our CUP development is the newly proposed surrogate functions along with the performance bound. Compared to previous safe RL methods, CUP enjoys the benefits of 1) CUP generalizes the surrogate functions to generalized advantage estimator (GAE), leading to strong empirical performance. 2) CUP unifies performance bounds, providing a better understanding and interpretability for some existing algorithms; 3) CUP provides a non-convex implementation via only first-order optimizers, which does not require any strong approximation on the convexity of the objectives. To validate our CUP method, we compared CUP against a comprehensive list of safe RL baselines on a wide range of tasks. Experiments show the effectiveness of CUP both in terms of reward and safety constraint satisfaction. We have opened the source code of CUP at this link https://github.com/zmsn-2077/ CUP-safe-rl.Comment: Accepted by NeurIPS2022. arXiv admin note: substantial text overlap with arXiv:2202.0756

LLaMA-Adapter: Efficient Fine-tuning of Language Models with Zero-init Attention

We present LLaMA-Adapter, a lightweight adaption method to efficiently fine-tune LLaMA into an instruction-following model. Using 52K self-instruct demonstrations, LLaMA-Adapter only introduces 1.2M learnable parameters upon the frozen LLaMA 7B model, and costs less than one hour for fine-tuning on 8 A100 GPUs. Specifically, we adopt a set of learnable adaption prompts, and prepend them to the input text tokens at higher transformer layers. Then, a zero-init attention mechanism with zero gating is proposed, which adaptively injects the new instructional cues into LLaMA, while effectively preserves its pre-trained knowledge. With efficient training, LLaMA-Adapter generates high-quality responses, comparable to Alpaca with fully fine-tuned 7B parameters. Furthermore, our approach can be simply extended to multi-modal input, e.g., images, for image-conditioned LLaMA, which achieves superior reasoning capacity on ScienceQA. We release our code at https://github.com/ZrrSkywalker/LLaMA-Adapter.Comment: Work in Progress. Code is available at https://github.com/ZrrSkywalker/LLaMA-Adapte

RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision

3D occupancy prediction holds significant promise in the fields of robot perception and autonomous driving, which quantifies 3D scenes into grid cells with semantic labels. Recent works mainly utilize complete occupancy labels in 3D voxel space for supervision. However, the expensive annotation process and sometimes ambiguous labels have severely constrained the usability and scalability of 3D occupancy models. To address this, we present RenderOcc, a novel paradigm for training 3D occupancy models only using 2D labels. Specifically, we extract a NeRF-style 3D volume representation from multi-view images, and employ volume rendering techniques to establish 2D renderings, thus enabling direct 3D supervision from 2D semantics and depth labels. Additionally, we introduce an Auxiliary Ray method to tackle the issue of sparse viewpoints in autonomous driving scenarios, which leverages sequential frames to construct comprehensive 2D rendering for each object. To our best knowledge, RenderOcc is the first attempt to train multi-view 3D occupancy models only using 2D labels, reducing the dependence on costly 3D occupancy annotations. Extensive experiments demonstrate that RenderOcc achieves comparable performance to models fully supervised with 3D labels, underscoring the significance of this approach in real-world applications

Nitrogen Removal in a Horizontal Subsurface Flow Constructed Wetland Estimated Using the First-Order Kinetic Model

We monitored the water quality and hydrological conditions of a horizontal subsurface constructed wetland (HSSF-CW) in Beijing, China, for two years. We simulated the area-based constant and the temperature coefficient with the first-order kinetic model. We examined the relationships between the nitrogen (N) removal rate, N load, seasonal variations in the N removal rate, and environmental factors—such as the area-based constant, temperature, and dissolved oxygen (DO). The effluent ammonia (NH4 + -N) and nitrate (NO3 −-N) concentrations were significantly lower than the influent concentrations (p \u3c 0.01, n = 38). The NO3 −-N load was significantly correlated with the removal rate (R 2 = 0.96, p \u3c 0.01), but the NH4 + -N load was not correlated with the removal rate (R 2 = 0.02, p \u3e 0.01). The area-based constants of NO3 −-N and NH4 + -N at 20 ◦C were 27 ± 26 (mean ± SD) and 14 ± 10 m·year−1 , respectively. The temperature coefficients for NO3 −-N and NH4 + -N were estimated at 1.004 and 0.960, respectively. The area-based constants for NO3 −-N and NH4 + -N were not correlated with temperature (p \u3e 0.01). The NO3 −-N area-based constant was correlated with the corresponding load (R 2 = 0.96, p \u3c 0.01). The NH4 + -N area rate was correlated with DO (R 2 = 0.69, p \u3c 0.01), suggesting that the factors that influenced the N removal rate in this wetland met Liebig’s law of the minimum