Establishment of ground vibration prediction model for high-speed trains on embankments

A series of field measurement data is used to establish the prediction model of ground vibration induced by Taiwan high-speed trains on embankments. These measurements consist of various possible influence factors, such as train speed, ground shear wave velocity, and structure volume. The characteristics of near-field ground vibration, far-field vibration propagation, and vibration influence distance are then evaluated from these measurement data. The analyses reveal that the near-field ground vibration level mainly depends on train speed and ground shear wave velocity. The influence of structure volume on the vibration level is minor. The far-field vibration propagation is affected by ground shear wave velocity. The analysis results also show that the attenuation coefficient is different for each frequency range. In general, the measured ground vibration in the high frequency range has the highest attenuation coefficient and that in the low frequency range has the lowest. For the vibration influence distance, the rock can propagate the vibration to the farthest distance among all soil types while the sand/silt/clay soils show the shortest. Finally, a specific ground vibration prediction model is established using these characteristics

Platinum composite nanowires for ultrasensitive mass detection

Platinum (Pt) composite nanowires were grown on the tip of tungsten (W) microprobes by focused-electron-beam induced chemical vapor deposition (FEB-CVD). An electrical field was used to drive a transversal mechanical vibration of the nanowires. Such nanowire vibrations were found to display the first and second harmonic resonances with frequencies in the range of tens of MHz. The Young's modulus of the nanowires was estimated to be in the range of (1.4 ± 0.1) × 102 GPa to (4.7 ± 0.2) × 102 GPa, dependent on the wire size. A mass responsivity of 2.1 × 1021 Hz/kg Hz/kg was demonstrated with the minimum detectable mass of about 0.4 attogram. Our results indicated the potentials of FEB-CVD for the fabrication of nano-balances on any surface for ultra-sensitive mechanical applications

The Trickle-down Impact of Reward (In-)consistency on RLHF

Standard practice within Reinforcement Learning from Human Feedback (RLHF) involves optimizing against a Reward Model (RM), which itself is trained to reflect human preferences for desirable generations. A notable subject that is understudied is the (in-)consistency of RMs -- whether they can recognize the semantic changes to different prompts and appropriately adapt their reward assignments -- and their impact on the downstream RLHF model. In this paper, we visit a series of research questions relevant to RM inconsistency: (1) How can we measure the consistency of reward models? (2) How consistent are the existing RMs and how can we improve them? (3) In what ways does reward inconsistency influence the chatbots resulting from the RLHF model training? We propose Contrast Instructions -- a benchmarking strategy for the consistency of RM. Each example in Contrast Instructions features a pair of lexically similar instructions with different ground truth responses. A consistent RM is expected to rank the corresponding instruction and response higher than other combinations. We observe that current RMs trained with the standard ranking objective fail miserably on Contrast Instructions compared to average humans. To show that RM consistency can be improved efficiently without using extra training budget, we propose two techniques ConvexDA and RewardFusion, which enhance reward consistency through extrapolation during the RM training and inference stage, respectively. We show that RLHF models trained with a more consistent RM yield more useful responses, suggesting that reward inconsistency exhibits a trickle-down effect on the downstream RLHF process

Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations

Most video-and-language representation learning approaches employ contrastive learning, e.g., CLIP, to project the video and text features into a common latent space according to the semantic similarities of text-video pairs. However, such learned shared latent spaces are not often optimal, and the modality gap between visual and textual representation can not be fully eliminated. In this paper, we propose Expectation-Maximization Contrastive Learning (EMCL) to learn compact video-and-language representations. Specifically, we use the Expectation-Maximization algorithm to find a compact set of bases for the latent space, where the features could be concisely represented as the linear combinations of these bases. Such feature decomposition of video-and-language representations reduces the rank of the latent space, resulting in increased representing power for the semantics. Extensive experiments on three benchmark text-video retrieval datasets prove that our EMCL can learn more discriminative video-and-language representations than previous methods, and significantly outperform previous state-of-the-art methods across all metrics. More encouragingly, the proposed method can be applied to boost the performance of existing approaches either as a jointly training layer or an out-of-the-box inference module with no extra training, making it easy to be incorporated into any existing methods.Comment: Accepted to NeurIPS 202

What makes a good phorophyte? Predicting occupancy, species richness and abundance of vascular epiphytes in a lowland seasonal tropical forest

peer reviewedEpiphytes typically exhibit clustered distribution patterns, but predicting the spatial variation of their distribution at fine scales has long been a challenge. Taking advantage of a canopy crane giving access to 1.1 ha of lowland seasonal rainforest in Yunnan (China), we assess here which factors promote the probability that a given tree hosts epiphytes, and the variation of species richness and abundance of epiphytic spermatophytes and ferns among trees. Variation in epiphyte species richness as a function of host tree size, characteristics of its surrounding environment, topography and microclimatic conditions, were analyzed by Random Forest. Epiphytic spermatophytes and ferns occupied 2.3 and 10.8% of the available host trees, respectively. Significant models predicting which trees are more likely to host epiphytes than others were obtained, indicating that host tree characteristics and their local environment play a significant role in determining which host tree is most likely to be colonized. These models, as well as models for species richness and abundance, however, exhibited a moderate to low accuracy (r2 0.28 and 0.24 and of 0.12 and 0.14 for spermatophyte and fern richness and abundance, respectively). The best predictor of the presence of epiphytes on a tree, of its epiphytic species richness and abundance, was its DBH. In ferns, however, two peaks of species richness were observed, representing shade-loving ferns on small trees and sun-loving ferns on large trees. Microclimatic conditions and light intensity were the second best factor accounting for variation in species richness and abundance among trees. The contribution of liana infestation, host tree identity, and characteristics of neighboring trees were marginal. Our inclusion of a large number of host-tree characteristics and their local environment did not allow for an apparent improvement of model accuracy over studies with a more limited number of predictors, pointing to the role of chance upon tree colonization. Our results confirm the utmost importance of large trees with emergent canopies for the conservation of the epiphytic flora, but also indicate that epiphytic diversity assessments in tropical forests must also include small understorey trees, which should be further considered for conservation. The importance of the micro-climatic conditions that prevail at the level of each individual host tree further points to the necessity of maintaining a buffer zone around large host trees targeted for conservation

Research on the Compound Optimization Method of the Electrical and Thermal Properties of SiC/EP Composite Insulating Material.

In this paper, in order to improve the electrical and thermal properties of SiC/EP composites, the methods of compounding different crystalline SiC and micro-nano SiC particles are used to optimize them. Under different compound ratios, the thermal conductivity and breakdown voltage parameters of the composite material were investigated. It was found that for the SiC/EP composite materials of different crystal types of SiC, when the ratio of α and β silicon carbide is 1:1, the electrical performance of the composite material is the best, and the breakdown strength can be increased by more than 10% compared with the composite material filled with single crystal particles. For micro-nano compound SiC/EP composites, different total filling amounts of SiC correspond to different optimal ratios of micro/nano particles. At the optimal ratio, the introduction of nanoparticles can increase the breakdown strength of the composite material by more than 10%. Compared with the compound of different crystalline SiC, the advantage is that the introduction of a small amount of nanoparticles can play a strong role in enhancing the break-down field strength. For the filled composite materials, the thermal conductivity mainly depends on whether an effective heat conduction channel can be constructed. Through experiments and finite element simulation calculations, it is found that the filler shape and particle size have a greater impact on the thermal conductivity of the composite material, when the filler shape is rounder, the composite material can more effectively construct the heat conduction channel