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Ride-pooling (RP) service, as a form of shared mobility, enables multiple riders with similar itineraries to share the same vehicle and split the fee. This makes RP a promising on-demand feeder service for patrons with a common trip end in urban transportation. We propose the RP as Feeder (RPaF) services with tailored operational algorithms. Specifically, we have developed (i) a batch-based matching algorithm that pools a batch of requests within an optimized buffer distance to each RP vehicle; (ii) a dispatching algorithm that adaptively dispatches vehicles to pick up the matched requests for certain occupancy target; and (iii) a repositioning algorithm that relocates vehicles to unmatched requests based on their level of urgency. An agent-based microscopic simulation platform is designed to execute these operational algorithms (via the Operator module), generate spatially distributed random requests (Patron module), and account for traffic conditions (Vehicle module) in street networks. Extensive numerical experiments are conducted to showcase the effectiveness of RPaF services across various demand scenarios in typical morning rush hours. We compare RFaF with two on-demand feeder counterparts proposed in previous studies: Ride-Sharing as Feeder (RSaF) and Flexible-Route Feeder-Bus Transit (Flex-FBT). Comparisons reveal that given the same fleet size, RPaF generally outperforms RSaF in higher service rates (i.e., the percentage of requests served over all requests) and Flex-FBT in shorter average trip times for patrons. Lastly, we illustrate the implementation of RPaF in a real-world case study of the uptown Manhattan network (USA) using actual taxi trip data. The results demonstrate that RPaF effectively balances the level of service (service rate and patrons' average trip time) with operational costs (fleet size).

Previous Facial Beauty Prediction (FBP) methods generally model FB feature of an image as a point on the latent space, and learn a mapping from the point to a precise score. Although existing regression methods perform well on a single dataset, they are inclined to be sensitive to test data and have weak generalization ability. We think they underestimate two inconsistencies existing in the FBP problem: 1. inconsistency of FB standards among multiple datasets, and 2. inconsistency of human cognition on FB of an image. To address these issues, we propose a new Uncertainty-oriented Order Learning (UOL), where the order learning addresses the inconsistency of FB standards by learning the FB order relations among face images rather than a mapping, and the uncertainty modeling represents the inconsistency in human cognition. The key contribution of UOL is a designed distribution comparison module, which enables conventional order learning to learn the order of uncertain data. Extensive experiments on five datasets show that UOL outperforms the state-of-the-art methods on both accuracy and generalization ability.

Phase engineering strategies in two-dimensional transition metal dichalcogenides (2D-TMDs) have garnered significant attention due to their potential applications in electronics, optoelectronics, and energy storage. Various methods, including direct synthesis, pressure control, and chemical doping, have been employed to manipulate structural transitions in 2D-TMDs. Metal intercalation emerges as an effective technique to modulate phase transition dynamics by inserting external atoms or ions between the layers of 2D-TMDs, altering their electronic structure and physical properties. Here, we investigate the significant structural phase transitions in Pb(Ta1+xSe2)2 single crystals induced by Ta intercalation using a combination of Raman spectroscopy and first-principles calculations. The results highlight the pivotal role of Ta atoms in driving these transitions and elucidate the interplay between intercalation, phase transitions, and resulting electronic and vibrational properties in 2D-TMDs. By focusing on Pb(Ta1+xSe2)2 as an ideal case study and investigating like metal intercalation, this study advances understanding in the field and paves the way for the development of novel applications for 2D-TMDs, offering insights into the potential of these materials for future technological advancements.

In the realm of neural network models, the perpetual challenge remains in retaining task-relevant information while effectively discarding redundant data during propagation. In this paper, we introduce IB-AdCSCNet, a deep learning model grounded in information bottleneck theory. IB-AdCSCNet seamlessly integrates the information bottleneck trade-off strategy into deep networks by dynamically adjusting the trade-off hyperparameter $\lambda$ through gradient descent, updating it within the FISTA(Fast Iterative Shrinkage-Thresholding Algorithm ) framework. By optimizing the compressive excitation loss function induced by the information bottleneck principle, IB-AdCSCNet achieves an optimal balance between compression and fitting at a global level, approximating the globally optimal representation feature. This information bottleneck trade-off strategy driven by downstream tasks not only helps to learn effective features of the data, but also improves the generalization of the model. This study's contribution lies in presenting a model with consistent performance and offering a fresh perspective on merging deep learning with sparse representation theory, grounded in the information bottleneck concept. Experimental results on CIFAR-10 and CIFAR-100 datasets demonstrate that IB-AdCSCNet not only matches the performance of deep residual convolutional networks but also outperforms them when handling corrupted data. Through the inference of the IB trade-off, the model's robustness is notably enhanced.

Recommendation system (RS) plays significant roles in matching users information needs for Internet applications, and it usually utilizes the vanilla neural network as the backbone to handle embedding details. Recently, the large language model (LLM) has exhibited emergent abilities and achieved great breakthroughs both in the CV and NLP communities. Thus, it is logical to incorporate RS with LLM better, which has become an emerging research direction. Although some existing works have made their contributions to this issue, they mainly consider the single key situation (e.g. historical interactions), especially in sequential recommendation. The situation of multiple key-value data is simply neglected. This significant scenario is mainstream in real practical applications, where the information of users (e.g. age, occupation, etc) and items (e.g. title, category, etc) has more than one key. Therefore, we aim to implement sequential recommendations based on multiple key-value data by incorporating RS with LLM. In particular, we instruct tuning a prevalent open-source LLM (Llama 7B) in order to inject domain knowledge of RS into the pre-trained LLM. Since we adopt multiple key-value strategies, LLM is hard to learn well among these keys. Thus the general and innovative shuffle and mask strategies, as an innovative manner of data argument, are designed. To demonstrate the effectiveness of our approach, extensive experiments are conducted on the popular and suitable dataset MovieLens which contains multiple keys-value. The experimental results demonstrate that our approach can nicely and effectively complete this challenging issue., Comment: Accepted by CIKM2023 workshop at GenRec'23

The interplay among frustrated lattice geometry, nontrivial band topology and correlation yields rich quantum states of matter in Kagome systems. A series of recent members in this family, AV3Sb5 (A= K, Rb, Cs), exhibit a cascade of symmetry-breaking transitions, involving the 3Q chiral charge ordering, electronic nematicity, roton pair-density-wave and superconductivity. The nature of the superconducting order is yet to be resolved. Here, we report an indication of chiral superconducting domains with boundary supercurrents in intrinsic CsV3Sb5 flakes. Magnetic field-free superconducting diode effect is observed with polarity modulated by thermal histories, suggesting dynamical superconducting order domains in a spontaneous time-reversal symmetry breaking background. Strikingly, the critical current exhibits the double-slit superconducting interference patterns when subjected to an external magnetic field. Characteristics of the patterns are modulated by thermal cycling. These phenomena are proposed as a consequence of periodically modulated supercurrents flowing along certain domain boundaries constrained by fluxoid quantization. Our results imply a chiral superconducting order, opening a potential for exploring exotic physics, e.g. Majorana zero modes, in this intriguing topological Kagome system., Comment: 17 pages,13 figures

Charge density wave (CDW) in kagome materials with the geometric frustration is able to carry unconventional characteristics. Recently, a CDW has been observed below the antiferromagnetic order in kagome FeGe, in which magnetism and CDW are intertwined to form an emergent quantum ground state. However, the CDW is only short-ranged and the structural modulation originating from it has yet to be determined experimentally. Here we realize a long-range CDW order by post-annealing process, and resolve the structure model through single crystal x-ray diffraction. Occupational disorder of Ge resulting from short-range CDW correlations above $T_\mathrm{CDW}$ is identified from structure refinements. The partial dimerization of Ge along the $c$ axis is unveiled to be the dominant distortion for the CDW. Occupational disorder of Ge is also proved to exist in the CDW phase due to the random selection of partially dimerized Ge sites. Our work provides useful insights for understanding the unconventional nature of the CDW in FeGe., Comment: 4 figures and 1 table in the main text. Supplementary materials included. To be published in SCIENCE CHINA Physics, Mechanics & Astronomy (SCPMA)

The coexistence of electric dipoles and itinerant electrons in a solid was postulated decades ago, before being experimentally established in several 'polar metals' during the last decade. Here, we report a concentration-driven polar-to-nonpolar phase transition in electron-doped BaTiO_3. Comparing our case with other polar metals, we find a particular threshold concentration (n*) linked to the dipole density (n_d). The universal ratio n_d/n*=8(0.6) suggests a common mechanism across different polar systems, possibly explained by a dipolar Ruderman-Kittel-Kasuya-Yosida theory. Moreover, in BaTiO_3, we observe enhanced thermopower and upturn on resistivity at low temperatures near n*, resembling the Kondo effect. We argue that local electric dipoles act as two-level-systems, whose fluctuations couple with surrounding electron clouds, giving rise to a potential dipolar-counterpart of the Kondo effect. Our findings unveil a mostly uncharted territory for exploring emerging physics associated with electron-dipole correlations, encouraging further theoretical work on dipolar-RKKY and Kondo interactions., Comment: 8 pages, 4 figures