EqDrive: Efficient Equivariant Motion Forecasting with Multi-Modality for Autonomous Driving

Forecasting vehicular motions in autonomous driving requires a deep understanding of agent interactions and the preservation of motion equivariance under Euclidean geometric transformations. Traditional models often lack the sophistication needed to handle the intricate dynamics inherent to autonomous vehicles and the interaction relationships among agents in the scene. As a result, these models have a lower model capacity, which then leads to higher prediction errors and lower training efficiency. In our research, we employ EqMotion, a leading equivariant particle, and human prediction model that also accounts for invariant agent interactions, for the task of multi-agent vehicle motion forecasting. In addition, we use a multi-modal prediction mechanism to account for multiple possible future paths in a probabilistic manner. By leveraging EqMotion, our model achieves state-of-the-art (SOTA) performance with fewer parameters (1.2 million) and a significantly reduced training time (less than 2 hours).Comment: 6 pages, 7 figure

Equivariant Map and Agent Geometry for Autonomous Driving Motion Prediction

In autonomous driving, deep learning enabled motion prediction is a popular topic. A critical gap in traditional motion prediction methodologies lies in ensuring equivariance under Euclidean geometric transformations and maintaining invariant interaction relationships. This research introduces a groundbreaking solution by employing EqMotion, a theoretically geometric equivariant and interaction invariant motion prediction model for particles and humans, plus integrating agent-equivariant high-definition (HD) map features for context aware motion prediction in autonomous driving. The use of EqMotion as backbone marks a significant departure from existing methods by rigorously ensuring motion equivariance and interaction invariance. Equivariance here implies that an output motion must be equally transformed under the same Euclidean transformation as an input motion, while interaction invariance preserves the manner in which agents interact despite transformations. These properties make the network robust to arbitrary Euclidean transformations and contribute to more accurate prediction. In addition, we introduce an equivariant method to process the HD map to enrich the spatial understanding of the network while preserving the overall network equivariance property. By applying these technologies, our model is able to achieve high prediction accuracy while maintain a lightweight design and efficient data utilization

A Demand-side Evaluation of Web Assurance Services: An Empirical Study on AICPA/CICA WebTrust Services

WebTrust service uses an approach similar to financial statements attestation to provide assurance services to web hosts. Prior research generally supports CPAs’ qualifications and abilities in offering such services, but rarely explains the limited success of this endeavor. Based on a conceptual model, this study evaluates the demand side of web assurance services. Research questionnaires were used to gather information from consumers and business firms in Taiwan via the Internet. Our results show that consumers have fundamental understanding of web assurance seals and recognize the importance of web assurance services. However, only a portion of consumers are willing to pay additional costs for the assurance provided by web seals. In addition, CPAs have advantages in credibility and objectivity over other web assurance providers, and are more suitable in providing privacy assurance. An expectation gap exits, however, between consumers and web assurance providers. When offering such services, a provider may face the potential risk of lawsuit and should address the issue properly. The above results have implications for the WebTrust service providers in realigning their strategies in the web assurance market

Machine Learning Assisted Mechanical Metamaterial Design for Additive Manufacturing

Metamaterials, widely studied for its counterintuitive property such as negative Poisson’s ratio, negative refraction, negative thermal expansion, and employed in various fields, are recognised to provide foundation for superior multiscale structural designs. However, current mechanical metamaterial design methods usually relay on performing sizing optimisations on predefined topology or implementing time-consuming inverse homogenisation methods. Machine Learning (ML), as a powerful self-learning tool, is considered to have the potential of discovering metamaterial topology and extending its property bounds. This work considers the use of Neural Networks (NNs), (De-Convolutional Neural Networks) DCNNs and Generative Adversarial Networks (GANs) to speed up the generation of new topologies for metamaterials. NNs and DCNNs are trained to inversely generate metamaterial designs based on the input target effective macroscale properties, whilst the generator in GANs is expected to output diverse metamaterial microstructures with random noise inputs. This work highlights the potential of data-driven approaches in Design for Additive Manufacturing (DfAM) as an alternative to the time-consuming, conventional methods.Mechanical Engineerin

Performance Improvements of Selective Emitters by Laser Openings on Large-Area Multicrystalline Si Solar Cells

This study focuses on the laser opening technique used to form a selective emitter (SE) structure on multicrystalline silicon (mc-Si). This technique can be used in the large-area (156 × 156 mm2) solar cells. SE process of this investigation was performed using 3 samples SE1–SE3. Laser fluences can vary in range of 2–5 J/cm2. The optimal conversion efficiency of 15.95% is obtained with the SE3 (2 J/cm2 fluence) after laser opening with optimization of heavy and light dopant, which yields a gain of 0.48%abs compared with that of a reference cell (without fluence). In addition, this optimal SE3 cell displays improved characteristics compared with other cells with a higher average value of external quantum efficiency (EQEavg = 68.6%) and a lower average value of power loss (Ploss = 2.33 mW/cm2). For the fabrication of solar cells, the laser opening process comprises fewer steps than traditional photolithography does. Furthermore, the laser opening process decreases consumption of chemical materials; therefore, the laser opening process decreases both time and cost. Therefore, SE process is simple, cheap, and suitable for commercialization. Moreover, the prominent features of the process render it effective means to promote overall performance in the photovoltaic industry

Selective Excited-State Dynamics in a Unique Set of Rationally Designed Ni Porphyrins

In this work, we report the design and photophysical properties of a unique class of Ni porphyrins, in which the tert-butyl benzene substituents at the meso positions of the macrocycle were tethered by ethers with alkyl linkers. This not only results in the permanently locked ruf distortion of the macrocycle but also enables the engineering of the degree of distortion through varying the length of alkyl linkers, which addressed the complication of uncertainty in the specific structural distortions that has long plagued the porphyrin photophysical community. Using advanced time-resolved optical and X-ray absorption spectroscopy, we observed tunability in the excited-state relaxation pathway depending on the degree of distortion and characterized the associated transient intermediate structure. These findings provide a new avenue to afford accessibility to a wide range of excited-state properties in nonplanar porphyrins

\u3cem\u3eIn Situ\u3c/em\u3e Activated Co\u3csub\u3e3–x\u3c/sub\u3eNi\u3csub\u3ex\u3c/sub\u3eO\u3csub\u3e4\u3c/sub\u3e as a Highly Active and Ultrastable Electrocatalyst for Hydrogen Generation

The spinel Co3O4 has emerged as a promising alternative to noble-metal-based electrocatalysts for electrochemical water electrolysis in alkaline medium. However, pure Co3O4, despite having high activity in anodic water oxidation, remains inactive toward the hydrogen evolution reaction (HER). Here, a Ni-doped Co3O4(Co3–xNixO4) prepared by a simple method exhibits favorable HER activity and stability (\u3e300 h, whether in 1 M KOH or the realistic 30 wt % KOH solution) after in situ electrochemical activation, outperforming almost all of the oxide-based electrocatalysts. More importantly, using the combination of in situ Raman spectroscopy and multiple high-resolution electron microscopy techniques, it is identified that the surface of Co3–xNixO4 crystals is reduced into intertwined CoyNi1–yO nanoparticles with highly exposed {110} reactive planes. Density functional theory calculations further prove that the Ni-doped CoO component in CoyNi1–yO plays a major role during the alkaline HER, because the introduction of Ni atoms into Co–O octahedra can optimize the electrical conductivity and tailor the adsorption/desorption free energies of Had and OHad intermediates

Experimental and Numerical Investigations on Dynamic Mechanical Properties of TPMS Structures

Triply Periodic Minimal Surface (TPMS) lattice structures have been of increasing interest due to their light weighting, enhanced mechanical properties, and energy absorption characteristics for automotive and biomedical applications. With the advent of additive manufacturing and geometric modeling software, TPMS lattices with complex geometries can be realized. In this work, TPMS lattice structures were fabricated with PLA using fused filament fabrication (FFF) and their dynamic properties are characterized through drop tower experiments. Although lightweight TPMS lattices are beneficial for their impact absorption capability, most of the existing works are limited to quasi-static compression, and dynamic impact tests are rarely performed. The current study investigates the stress-strain and energy absorption characteristics of TPMS lattices through drop tower testing and numerical modeling. Finite element modeling for TPMS lattices is carried out to validate the experimental responses. The mechanical properties, deformation, and failure mechanisms of TPMS lattices under dynamic impact are summarized for potential future applications.Mechanical Engineerin

Asynchronous Photoexcited Electronic and Structural Relaxation in Lead-Free Perovskites

Vacancy-ordered lead-free perovskites with more-stable crystalline structures have been intensively explored as the alternatives for resolving the toxic and long-term stability issues of lead halide perovskites (LHPs). The dispersive energy bands produced by the closely packed halide octahedral sublattice in these perovskites are meanwhile anticipated to facility the mobility of charge carriers. However, these perovskites suffer from unexpectedly poor charge carrier transport. To tackle this issue, we have employed the ultrafast, elemental-specific X-ray transient absorption (XTA) spectroscopy to directly probe the photoexcited electronic and structural dynamics of a prototypical vacancy-ordered lead-free perovskite (Cs3Bi2Br9). We have discovered that the photogenerated holes quickly self-trapped at Br centers, simultaneously distorting the local lattice structure, likely forming small polarons in the configuration of Vk center (Br2– dimer). More significantly, we have found a surprisingly long-lived, structural distorted state with a lifetime of ∼59 μs, which is ∼3 orders of magnitude slower than that of the charge carrier recombination. Such long-lived structural distortion may produce a transient “background” under continuous light illumination, influencing the charge carrier transport along the lattice framework

Investigation of Low-Cost Surface Processing Techniques for Large-Size Multicrystalline Silicon Solar Cells

The subject of the present work is to develop a simple and effective method of enhancing conversion efficiency in large-size solar cells using multicrystalline silicon (mc-Si) wafer. In this work, industrial-type mc-Si solar cells with area of 125×125 mm2 were acid etched to produce simultaneously POCl3 emitters and silicon nitride deposition by plasma-enhanced chemical vapor deposited (PECVD). The study of surface morphology and reflectivity of different mc-Si etched surfaces has also been discussed in this research. Using our optimal acid etching solution ratio, we are able to fabricate mc-Si solar cells of 16.34% conversion efficiency with double layers silicon nitride (Si3N4) coating. From our experiment, we find that depositing double layers silicon nitride coating on mc-Si solar cells can get the optimal performance parameters. Open circuit (Voc) is 616 mV, short circuit current (Jsc) is 34.1 mA/cm2, and minority carrier diffusion length is 474.16 μm. The isotropic texturing and silicon nitride layers coating approach contribute to lowering cost and achieving high efficiency in mass production