Bayesian hierarchical modelling for battery lifetime early prediction

Accurate prediction of battery health is essential for real-world system management and lab-based experiment design. However, building a life-prediction model from different cycling conditions is still a challenge. Large lifetime variability results from both cycling conditions and initial manufacturing variability, and this—along with the limited experimental resources usually available for each cycling condition—makes data-driven lifetime prediction challenging. Here, a hierarchical Bayesian linear model is proposed for battery life prediction, combining both individual cell features (reflecting manufacturing variability) with population-wide features (reflecting the impact of cycling conditions on the population average). The individual features were collected from the first 100 cycles of data, which is around 5-10% of lifetime. The model is able to predict end of life with a root mean square error of 3.2 days and mean absolute percentage error of 8.6%, measured through 5-fold cross-validation, overperforming the baseline (non-hierarchical) model by around 12-13%

DTBS: Dual-Teacher Bi-directional Self-training for Domain Adaptation in Nighttime Semantic Segmentation

Due to the poor illumination and the difficulty in annotating, nighttime conditions pose a significant challenge for autonomous vehicle perception systems. Unsupervised domain adaptation (UDA) has been widely applied to semantic segmentation on such images to adapt models from normal conditions to target nighttime-condition domains. Self-training (ST) is a paradigm in UDA, where a momentum teacher is utilized for pseudo-label prediction, but a confirmation bias issue exists. Because the one-directional knowledge transfer from a single teacher is insufficient to adapt to a large domain shift. To mitigate this issue, we propose to alleviate domain gap by incrementally considering style influence and illumination change. Therefore, we introduce a one-stage Dual-Teacher Bi-directional Self-training (DTBS) framework for smooth knowledge transfer and feedback. Based on two teacher models, we present a novel pipeline to respectively decouple style and illumination shift. In addition, we propose a new Re-weight exponential moving average (EMA) to merge the knowledge of style and illumination factors, and provide feedback to the student model. In this way, our method can be embedded in other UDA methods to enhance their performance. For example, the Cityscapes to ACDC night task yielded 53.8 mIoU (\%), which corresponds to an improvement of +5\% over the previous state-of-the-art. The code is available at \url{https://github.com/hf618/DTBS}

Hard Label Black Box Node Injection Attack on Graph Neural Networks

While graph neural networks have achieved state-of-the-art performances in many real-world tasks including graph classification and node classification, recent works have demonstrated they are also extremely vulnerable to adversarial attacks. Most previous works have focused on attacking node classification networks under impractical white-box scenarios. In this work, we will propose a non-targeted Hard Label Black Box Node Injection Attack on Graph Neural Networks, which to the best of our knowledge, is the first of its kind. Under this setting, more real world tasks can be studied because our attack assumes no prior knowledge about (1): the model architecture of the GNN we are attacking; (2): the model's gradients; (3): the output logits of the target GNN model. Our attack is based on an existing edge perturbation attack, from which we restrict the optimization process to formulate a node injection attack. In the work, we will evaluate the performance of the attack using three datasets, COIL-DEL, IMDB-BINARY, and NCI1

Scanning Tunneling Microscopy Studies of Metal Clusters Supported on Graphene and Silica Thin Film

The understanding of nucleation and growth of metals on a planar support at the atomic level is critical for both surface science research and heterogeneous catalysis studies. In this dissertation, two planar substrates, including graphene and ultra-thin silica film were employed for supported model catalysts studies. The structure and stability of several catalytically important metals supported on these two substrates were thoroughly investigated using scanning tunneling microscopy (STM) coupled with other traditional surface science techniques. In the study of the graphene/Ru(0001) system, the key factors that govern the growth and distribution of metals on the graphene have been studied based on different behaviors of five transition metals, namely Pt, Rh, Pd, Co, and Au supported on the template of a graphene moire pattern formed on Ru(0001). Both metal-carbon (M-C) bond strength and metal cohesive energies play significant roles in the cluster formation process and the M-C bond strength is the most important factor that affects the morphology of clusters at the initial stages of growth. Interestingly, Au exhibits two-dimensional (2-D) structures that span several moire unit cells. Preliminary data obtained by dosing molecular oxygen onto CO pre-covered Au islands suggest that the 2-D Au islands catalyze the oxidation of CO. Moreover, graphene/Ru(0001) system was modified by introducing transition metals, oxygen or carbon at the interface between the graphene and Ru(0001). Our STM results reveal that the geometric and/or electronic structure of graphene can be adjusted correspondingly. In the study of the silica thin film system, the structure of silica was carefully investigated and our STM images favor for the [SiO4] cluster model rather than the network structure. The nucleation and adsorption of three metals, namely Rh, Pt and Pd show that the bond strength between the metal atom and Si is the key factor that determines the nucleation sites at the initial stages of metal deposition. The annealing effect studies reveal that Rh and Pt atoms diffuse beneath the silica film and form the 2-D islands that are covered with a silica thin film. In contrast, the formation of Pd silicide was observed upon annealing to high temperatures

Rural area flooding digital prediction based on TELEMAC-2D

Hydrodynamic

Gr\"obner-Shirshov bases and linear bases for free multi-operated algebras over algebras with applications to differential Rota-Baxter algebras and integro-differential algebras

Quite much recent studies has been attracted to the operated algebra since it unifies various notions such as the differential algebra and the Rota-Baxter algebra. An $\Omega$-operated algebra is a an (associative) algebra equipped with a set $\Omega$ of linear operators which might satisfy certain operator identities such as the Leibniz rule. A free $\Omega$-operated algebra $B$ can be generated on an algebra $A$ similar to a free algebra generated on a set. If $A$ has a Gr\"{o}bner-Shirshov basis $G$ and if the linear operators $\Omega$ satisfy a set $\Phi$ of operator identities, it is natural to ask when the union $G\cup \Phi$ is a Gr\"{o}bner-Shirshov basis of $B$. A previous work answers this question affirmatively under a mild condition, and thereby obtains a canonical linear basis of $B$. In this paper, we answer this question in the general case of multiple linear operators. As applications we get operated Gr\"{o}bner-Shirshov bases for free differential Rota-Baxter algebras and free integro-differential algebras over algebras as well as their linear bases. One of the key technical difficulties is to introduce new monomial orders for the case of two operators, which might be of independent interest.Comment: 27 page