INNOVATIVE SIMULATION AND OPTIMIZATION STUDIES ON GRID SYSTEM FOR TRANSSHIPMENT TERMINAL

Ph.DDOCTOR OF PHILOSOPH

Diverse Target and Contribution Scheduling for Domain Generalization

Generalization under the distribution shift has been a great challenge in computer vision. The prevailing practice of directly employing the one-hot labels as the training targets in domain generalization~(DG) can lead to gradient conflicts, making it insufficient for capturing the intrinsic class characteristics and hard to increase the intra-class variation. Besides, existing methods in DG mostly overlook the distinct contributions of source (seen) domains, resulting in uneven learning from these domains. To address these issues, we firstly present a theoretical and empirical analysis of the existence of gradient conflicts in DG, unveiling the previously unexplored relationship between distribution shifts and gradient conflicts during the optimization process. In this paper, we present a novel perspective of DG from the empirical source domain's risk and propose a new paradigm for DG called Diverse Target and Contribution Scheduling (DTCS). DTCS comprises two innovative modules: Diverse Target Supervision (DTS) and Diverse Contribution Balance (DCB), with the aim of addressing the limitations associated with the common utilization of one-hot labels and equal contributions for source domains in DG. In specific, DTS employs distinct soft labels as training targets to account for various feature distributions across domains and thereby mitigates the gradient conflicts, and DCB dynamically balances the contributions of source domains by ensuring a fair decline in losses of different source domains. Extensive experiments with analysis on four benchmark datasets show that the proposed method achieves a competitive performance in comparison with the state-of-the-art approaches, demonstrating the effectiveness and advantages of the proposed DTCS

Rethinking Domain Generalization: Discriminability and Generalizability

Domain generalization (DG) endeavors to develop robust models that possess strong generalizability while preserving excellent discriminability. Nonetheless, pivotal DG techniques tend to improve the feature generalizability by learning domain-invariant representations, inadvertently overlooking the feature discriminability. On the one hand, the simultaneous attainment of generalizability and discriminability of features presents a complex challenge, often entailing inherent contradictions. This challenge becomes particularly pronounced when domain-invariant features manifest reduced discriminability owing to the inclusion of unstable factors, \emph{i.e.,} spurious correlations. On the other hand, prevailing domain-invariant methods can be categorized as category-level alignment, susceptible to discarding indispensable features possessing substantial generalizability and narrowing intra-class variations. To surmount these obstacles, we rethink DG from a new perspective that concurrently imbues features with formidable discriminability and robust generalizability, and present a novel framework, namely, Discriminative Microscopic Distribution Alignment (DMDA). DMDA incorporates two core components: Selective Channel Pruning~(SCP) and Micro-level Distribution Alignment (MDA). Concretely, SCP attempts to curtail redundancy within neural networks, prioritizing stable attributes conducive to accurate classification. This approach alleviates the adverse effect of spurious domain invariance and amplifies the feature discriminability. Besides, MDA accentuates micro-level alignment within each class, going beyond mere category-level alignment. This strategy accommodates sufficient generalizable features and facilitates within-class variations. Extensive experiments on four benchmark datasets corroborate the efficacy of our method

Evolution of Interlayer Coupling in Twisted MoS2 Bilayers

Van der Waals (vdW) coupling is emerging as a powerful method to engineer and tailor physical properties of atomically thin two-dimensional (2D) materials. In graphene/graphene and graphene/boron-nitride structures it leads to interesting physical phenomena ranging from new van Hove singularities1-4 and Fermi velocity renormalization5, 6 to unconventional quantum Hall effects7 and Hofstadter's butterfly pattern8-12. 2D transition metal dichalcogenides (TMDCs), another system of predominantly vdW-coupled atomically thin layers13, 14, can also exhibit interesting but different coupling phenomena because TMDCs can be direct or indirect bandgap semiconductors15, 16. Here, we present the first study on the evolution of interlayer coupling with twist angles in as-grown MoS2 bilayers. We find that an indirect bandgap emerges in bilayers with any stacking configuration, but the bandgap size varies appreciably with the twist angle: it shows the largest redshift for AA- and AB-stacked bilayers, and a significantly smaller but constant redshift for all other twist angles. The vibration frequency of the out-of-plane phonon in MoS2 shows similar twist angle dependence. Our observations, together with ab initio calculations, reveal that this evolution of interlayer coupling originates from the repulsive steric effects, which leads to different interlayer separations between the two MoS2 layers in different stacking configurations

Language Models Can Improve Event Prediction by Few-Shot Abductive Reasoning

Large language models have shown astonishing performance on a wide range of reasoning tasks. In this paper, we investigate whether they could reason about real-world events and help improve the prediction performance of event sequence models. We design LAMP, a framework that integrates a large language model in event prediction. Particularly, the language model performs abductive reasoning to assist an event sequence model: the event model proposes predictions on future events given the past; instructed by a few expert-annotated demonstrations, the language model learns to suggest possible causes for each proposal; a search module finds out the previous events that match the causes; a scoring function learns to examine whether the retrieved events could actually cause the proposal. Through extensive experiments on several challenging real-world datasets, we demonstrate that our framework -- thanks to the reasoning capabilities of large language models -- could significantly outperform the state-of-the-art event sequence models.Comment: NeurIPS 2023 camera-read

Identification and validation of NAD+ metabolism-related biomarkers in patients with diabetic peripheral neuropathy

BackgroundThe mechanism of Nicotinamide Adenine Dinucleotide (NAD+) metabolism-related genes (NMRGs) in diabetic peripheral neuropathy (DPN) is unclear. This study aimed to find new NMRGs biomarkers in DPN.MethodsDPN related datasets GSE95849 and GSE185011 were acquired from the Gene Expression Omnibus (GEO) database. 51 NMRGs were collected from a previous article. To explore NMRGs expression in DPN and control samples, differential expression analysis was completed in GSE95849 to obtain differentially expressed genes (DEGs), and the intersection of DEGs and NMRGs was regarded as DE-NMRGs. Next, a protein-protein interaction (PPI) network based on DE-NMRGs was constructed and biomarkers were screened by eight algorithms. Additionally, Gene Set Enrichment Analysis (GSEA) enrichment analysis was completed, biomarker-based column line graphs were constructed, lncRNA-miRNA-mRNA and competing endogenouse (ce) RNA networks were constructed, and drug prediction was completed. Finally, biomarkers expression validation was completed in GSE95849 and GSE185011.Results5217 DEGs were obtained from GSE95849 and 21 overlapping genes of DEGs and NMRGs were DE-NMRGs. Functional enrichment analysis revealed that DE-NMRGs were associated with glycosyl compound metabolic process. The PPI network contained 93 protein-interaction pairs and 21 nodes, with strong interactions between NMNAT1 and NAMPT, NADK and NMNAT3, ENPP3 and NUDT12 as biomarkers based on 8 algorithms. Expression validation suggested that ENPP3 and NUDT12 were upregulated in DPN samples (P < 0.05). Moreover, an alignment diagram with good diagnostic efficacy based on ENPP3 and NUDT12 were identified was constructed. GSEA suggested that ENPP3 was enriched in Toll like receptor (TLR) pathway, NUDT12 was enriched in maturity onset diabetes of the young and insulin pathway. Furthermore, 18 potential miRNAs and 36 Transcription factors (TFs) were predicted and the miRNA-mRNA-TF networks were constructed, suggesting that ENPP3 might regulate hsa-miR-34a-5p by affecting MYNN. The ceRNA network suggested that XLOC_013024 might regulate hsa-let-7b-5p by affecting NUDT12. 15 drugs were predicted, with 8 drugs affecting NUDT12 such as resveratrol, and 13 drugs affecting ENPP3 such as troglitazone.ConclusionENPP3 and NUDT12 might play key roles in DPN, which provides reference for further research on DPN

Increased content and uniformity of enzyme-induced calcite precipitation realized by prehydrolysis and an accelerated injection rate

The utilization of enzyme-induced calcium carbonate precipitation (EICP) to consolidate aeolian sand has received significant attention in recent years. When EICP was used and cementing solution was injected in stages, the calcium carbonate content and uniformity were not improved simultaneously. A method is proposed to alleviate this problem by pre-reacting urea and urease before injecting the cementing solution and speeding up the injection rate. Experiments were designed to compare staged injections of EICP-cemented aeolian sand with and without the use of prehydrolysis and with different injection rates. The results show that 1) at the same injection rate, the content of calcium carbonate in the prehydrolysis samples after 12 injections was 66.1% higher than that in the samples without prehydrolysis. 2) When using prehydrolysis, the calcium carbonate content as a function of the injection rate decreased in the following order: 10 mL/min >15 mL/min >7.5 mL/min. The highest amount of calcium carbonate was generated at an injection rate of 10 mL/min and was mainly distributed on the surface. The calcium carbonate generated with an injection rate of 15 mL/min was uniformly distributed in the sand. These results indicate that the method could improve the efficiency of calcium carbonate generation and distribution uniformity, and could also be applied to form a hard crust on the surface of sandy soil or for reinforcing sandy soil by multiple injections

Inter-terminal transfer between port terminals. A continuous mathematical programming model to optimize scheduling and deployment of transport units

[EN] In most large port cities, the challenge of inter-terminal transfers (ITT) prevails due to the long distance between multiple terminals. The quantity of containers requiring movement between terminals as they connect from pre-carrier to on-carrier is increasing with the formation of the mega-alliances. The paper proposes a continuous time mathematical programming model to optimize the deployment and schedule of trucks and barges to minimize the number of operating transporters, their makespan, costs and the distance travelled by the containers by choosing the right combination of transporters and container movements while fulfilling time window restrictions imposed on reception of the containers. A multi-step routing problem is developed where transporters can travel from one terminal to another and/or load or unload containers from a specific batch at each step. The model proves successful in identifying the costless schedule and means of transportation. And a sensibility analysis over the parameters used is provided.Morales Fusco, P.; Pedrielli, G.; Zhou, C.; Lee, L.; Chew, E. (2016). Inter-terminal transfer between port terminals. A continuous mathematical programming model to optimize scheduling and deployment of transport units. En XII Congreso de ingeniería del transporte. 7, 8 y 9 de Junio, Valencia (España). Editorial Universitat Politècnica de València. 1471-1481. https://doi.org/10.4995/CIT2016.2015.4149OCS1471148

Electronic Structure, Surface Doping, and Optical Response in Epitaxial WSe2 Thin Films

High quality WSe2 films have been grown on bilayer graphene (BLG) with layer-by-layer control of thickness using molecular beam epitaxy (MBE). The combination of angle-resolved photoemission (ARPES), scanning tunneling microscopy/spectroscopy (STM/STS), and optical absorption measurements reveal the atomic and electronic structures evolution and optical response of WSe2/BLG. We observe that a bilayer of WSe2 is a direct bandgap semiconductor, when integrated in a BLG-based heterostructure, thus shifting the direct-indirect band gap crossover to trilayer WSe2. In the monolayer limit, WSe2 shows a spin-splitting of 475 meV in the valence band at the K point, the largest value observed among all the MX2 (M = Mo, W; X = S, Se) materials. The exciton binding energy of monolayer-WSe2/BLG is found to be 0.21 eV, a value that is orders of magnitude larger than that of conventional 3D semiconductors, yet small as compared to other 2D transition metal dichalcogennides (TMDCs) semiconductors. Finally, our finding regarding the overall modification of the electronic structure by an alkali metal surface electron doping opens a route to further control the electronic properties of TMDCs